This study assessed whether selection for high growth in a challenging environment of medium salinity produces tilapia genotypes that perform well across different production environments. We estimated the genetic correlations between trait expressions in saline and freshwater using a strain of Nile tilapia selected for fast growth under salinity water of 15–20 ppt. We also estimated the heritability and genetic correlations for new traits of commercial importance  in a full pedigree comprising 36,145 fish. The genetic correlations for the novel characters between the two environments were 0.78–0.99, suggesting that the effect of genotype by environment interaction was not biologically important. Across the environments, the heritability for body weight was moderate to high (0.32–0.62), indicating that this population will continue responding to future selection. The estimates of heritability for sexual maturity and survival were low but significant. The additive genetic components also exist for FCR, gill condition and deformity. Genetic correlations of harvest body weight with sexual maturity were positive and those between harvest body weight with FCR were negative. Our results indicate that the genetic line selected under a moderate saline water environment can be cultured successfully in freshwater systems. Understanding the interactions between genotype and environment (G × E) is of utmost importance in the design of selective breeding programs to develop improved strains that express optimal growth and adaptability in a wide range of environments. In a review of 24 studies in both farmed animals and model species, Falconervs. plant based protein diets78vs. low stocking density)and between diverse environmental parametersSeveral studies have examined the effect of G × E interaction by testing full- and half-sib families in varying conditions, such as between culture systems34While many studies have investigated the traits of growth and survival, and their respective G × E interactions, as it relates to profit and income for aquatic species, less well understood are the traits of feed conversion ratio1317192022The principal objective of this study was to investigate whether selection for high growth rate in a challenging environment of medium salinity produces tilapia genotypes that can perform well in freshwater systems. In resolving this, we (i) examined the effect of genotype by environment interaction for new traits  in a Nile tilapia population undergoing five generations of selection for high growth in moderate saline environment (15–20 ppt); ii) estimated the genetic parameters for these traits together with gill related diseases and body weight; and (iii) deciphered phenotypic and genetic associations among traits recorded during the selection process (2007 to 2014).The fish used in this study originated from a genetic line which has undergone five generations of selection 2008–2012) for increased harvest body weight in moderately saline water (15–20 ppt) in Vietnam. During this period, a total number of 922 breeders  were used to produce progeny with the average selected proportion of 4.43% in females and 3.48% in males each generation. Within- and between-family selection was practised based on estimated breeding values (EBVs) for body weight. The EBVs were estimated for all individuals in the pedigree using a statistical model that included the fixed effects of generation, sex and age and the random additive genetic effect of individual animal008–2012 Earthen ponds of various sizes (e. g. 50 × 40 × 1.2 m) were used for conditioning, breeding and nursing and were prepared following a standard protocol2 low saline water (5–10 ppt) pond. The fry of each family were reared in three replicate hapas at an initial stocking density of 200 fry per hapa (or about 55 fry per square meter of surface water). For the first nursing period from hatching to 20 d, a commercial powder feed  was provided four times daily with a feeding rate at 10% of the total body mass in hapas. The fry were transferred to a larger hapa (3 × 2 × 1.5 m) for further rearing from 21 d post hatching to tagging at 62 d post hatching. The feeding rate was reduced to 5% of total body weight and fry were fed twice a day (8.00 and 16.00) with a commercial pellet (35% protein). When the fingerlings reached 5–8 g, about 100 individuals were randomly sampled from each family (30–35 fingerlings per nursing hapa) and physically tagged with passive integrated transponder tags (PIT). The identification number, body weight (BW) and standard length (L) were recorded. The tagged fingerlings were then conditioned for 4–5 d in fibreglass tanks without feeding. During the conditioning period, fish that lost their tags or died were replaced by other individuals from the same family.Fry rearing was conducted in nursery hapas (3 × 1.2 × 1.5 m with 2 mm mesh size) in a 1000 m2 30 × 50 × 1.5 m) located at Quy Kim Station in Hai Phong, 120 km from Hanoi. The salinity level in experimental ponds was controlled by adding freshwater (0 ppt) and sea water (30 ppt). The temperature and dissolved oxygen ranged from 17.3–34.5 °C and 3.5–6.4 mg/l in freshwater environment and from 17.8–34.7 °C and 3.7–6.5 mg/l in the moderately saline water environment. In both culture systems, the initial stocking density was 2.5 fish per square meter of surface water. During the grow-out period, the fish were fed a commercial dry pellet with 35% protein content twice a day (8.00 and 16.00) at the average feeding rate of 5% of the total body mass. The feeding rate was adjusted monthly according to the growth rate of the fish, the latter being determined from a random sample of 100 animals per environment. The same feeding, culturing and management practices were applied in both environments.After conditioning, the tagged fingerlings from 77 families were split into two groups  and were randomly assigned to freshwater or moderate saline water (15–20 ppt) earthen ponds ; Ni is the average number of fish during the grow-out period in environment ith. Wtgainj is weight gain in kilogram unit of fish jth.At the end of the grow-out period of approximately 120 d, all the experimental fish were harvested to measure body traits, sexual maturity, morphological deformity, gill condition and survival. Individual fish were weighted using a digital scale (nearest to 0.1 g). Standard length was also measured with a ruler (with precision of 1 mm). Body depth and width was measured at the highest vertical distance and thickness position, respectively, with a calliper (±0.1 mm). Survival was recorded as a binary trait and also computed from the difference between the number of fish at stocking and at harvest. Fish alive at harvest were coded as 1, otherwise as 0 for those that was dead (missing) at harvest. Maturity status of all individual fish was recorded as either: (i) immature and not ready to spawn with white/clear and flat genital papilla and normal abdomen (=0); and (ii) mature and ready to spawn with pink to red and protruding genital papilla, fully opened genital pore, and distended abdomen (=1). Morphological deformities commonly found in tilapia include: scale disorientation, gill, eyes, skeletal, soft tissue, and split fins deformityK), which is the most common measurement to describe body shape of several fish species, was derived from measurements of body weight and standard lengthKi is Fulton condition factor of fish ith; Wti is the harvest weight in gram unit of fish ith; Lthi is the harvest length in centimetre unit of fish ith.In addition, body condition or Fulton condition factor  were undertaken using PROC UNIVARIATE in SAS 9.3y is the vector of observations for body traits, survival rate, maturity, FCR, deformity and gill condition, b is the vector of the fixed effects of generation (six generations and a base population pedigree), sex  and testing environments . Linear covariates of age from birth to harvest and stocking weight were fitted within sex and generation in the model. Vector a is the random animal additive genetic effects ~  relationship matrix among the animals, c is the vector of dam effects  ~  = G = Aσa2. The remaining effects are assumed to be distributed as var(e) = R = Ic) = W = II is an identity matrix. The expectations of all random effects are zero, cov  = 0 and cov  = 0 and thus var (y) = ZGZ’σa2 + WIW’ + R.After all significant fixed effects were identified ; the final model fitted for all traits included the systematic effects of generation, testing environment, sex and their two-way interaction. Stocking weight and age from birth to harvest within sex and generation were fitted as linear covariates for body traits. The random effects are the additive genetics effects of individual fish and the maternal and common environment of dam. In matrix notation the mixed model was written as:b and the best linear unbiased prediction of a and c are:1 = 2 = The mixed model equation for the best linear unbiased estimator (BLUE) of estimable functions of Heritabilities, phenotypic and genetic correlations: For estimation of heritability and genetic correlations, the ASReml software version 3.0(A). Since the traits were measured on animals in different environments, there is no environmental covariance between them. The phenotypic correlations do not exist because any individual fish expressed the trait in only one environment.First, measurements in both environments were treated as one trait, and the model included all the significant fixed and random effects as described in Equation Heritability for weight, body shape, FCR, survival and maturity were calculated as x/(1 + ex)) was used where p is the probability of fish survival (maturity or deformity) recorded at harvest. The fixed effects (Fi) are the same as defined in Equation ak) and dam (dl) for survival and maturity  on the liability scales (logit) can be transformed to observed (0/1) scale using the formula of Robertson and Lerneret al.The maternal and common environmental variance xi and xj are the estimates of heritability, maternal effects, or genetic correlations for the two traits, and σiand σj are their respective standard errors. Both xj and σj were set to zero or one when we tested whether an estimate was significantly different from zero or one, respectively. Resulting z-scores were then tested against a large sample normal distribution.To test for significant differences in genetic parameter estimates between fresh and saline environments, we used the z-score as an approximate method for assessing whether heritability and correlation estimates were significantly different from each other or from zero, and whether genetic correlations between the two environments were significantly different from oneAll animal care and experimental procedures were performed in accordance with guidelines and regulations approved by the ethical committee of University of the Sunshine Coast, Queensland, Australia .vs. 226.9 g, P < 0.05). Basic statistics for all traits are given in The main difference between the two environments was the level of salinity .The fish were reared at a starting weight of 4.5 g and a steady linear increase in growth over three sampling periods was recorded in both fresh and medium salinity environments (results not presented). At the final harvest (7270 fish measured at 196 d), the mean body weight of tilapia in moderate saline water was greater than those in freshwater (256.9 P < 0.001). The difference in deformity rate between the two testing environments was not significant (P > 0.05).Least squares means of traits studied  obtained from a mixed model that included all possible systematic fixed effects  and random terms of individual fish and common full-sibs are shown in 2) and common environmental effects (c2) for traits studied  were estimated separately for each testing environment and across the two environments, using both animal and animal and dam models (P < 0.05). In general, heritability and common environmental effects for body traits was higher in saline water than in freshwater, but lower for body condition and survival rate. However, the differences in heritability for all traits studied between the two environments were not significant (P > 0.05). Moderate to high heritabilities for body condition and FCR were estimated by the animal model, whereas they were low and associated with high standard errors when the animal and dam model was used. Similar results were found for heritability of sexual maturity across the models. The heritability for survival was low to moderate (0.03–0.27) across the two environments. In addition, a very low heritability (0.01–0.07) was estimated for gill condition and deformity. For all traits except gill condition and deformity, the c2 effect ranged from 0.01 to 0.25 in both saline and fresh water. The large range of c2 indicates that by omission of this effect, the heritability for traits studied were overestimated.Heritability . The genetic correlation estimate for morphological deformity trait had a high standard error (0.99 ± 0.62). Similarly, common environmental correlations estimated for body weight, FCR and maturity were very high (0.87–0.99) (results not tabulated). The close to one genetic correlations for traits studied indicated that genotype by environment interaction was likely to be unimportant for many of these characteristics in the present population.Oncorhynchus mykiss) shown by Kause, et al.et al.Salmo salar). Across species, there are no published information on the G × E effect on feed conversion ratio (FCR) in aquaculture to compare with our current study.The principal aim of our study was to understand if the hypothesis proposed by Falconer22rg = 0.92 ± 0.04) and for survival (rg = 0.77 ± 0.03). By contrast, a significant G × E interaction occurred for harvest body weight when the selection environment in the nucleus differed greatly from production systems, such as freshwater vs. moderate saline water in Nile tilapiaSalvelinus alpinus)Solea solea)et al.rg = 0.72) and brackish water (rg = 0.58). These results suggest that selection under favourable well-controlled environments in the nucleus not only result in re-ranking of breeding candidates but also a fraction of genetic gain achieved from the selection program can be captured in production systems. This contrasts with our findings showing that the present selected line under moderate saline water can capture about 77% and 92% genetic gain in survival and body weight when its offspring were grown under freshwater ponds. Our breeding program to improve growth performance under moderate saline water provides tilapia seed for both freshwater and saline water production systems. Conducting separate breeding programs may not be justified when the genetic correlation of the trait expressions between the two environments is greater than 0.7–0.8In a comparison with the present study, in which the experimental fish were selected in a sub-optimal culture environment , other researchers used genetic lines selected under favourable environmental conditions and then conducted performance testing under culture systems that are generally close to the selection environment used in the nucleus. The literature3433342 = 0.32–0.4). This suggests that sexual maturity can respond to selection as reported by Wild, et al.et al.Dicentrarchus labraxSeriola lalandiIn addition to the genetic correlations, we estimated heritability for a range of traits in saline- and fresh-water, separately. The non-significant difference in magnitude of heritability for these traits indicates that the G × E interaction was mainly due to scaling effect, i.e. there was no re-ranking of selected candidates between saline- and fresh-water. We also aimed to estimate heritability for new traits in tilapia, including deformity, sexual maturity, gill condition and FCR. Our first estimates of genetic parameters for fitness related traits show that there is an additive genetic component especially for sexual maturity . Using animal mixed model, Henryon, et al.et al.Coregonus lavaretusImproving efficiency of food utilisation is crucial to reduce production costs of aquaculture enterprises. In aquatic species, it is costly and practically infeasible to record the feed intake of individual animal. Our calculation of feed conversion ratio (FCR) can be used as an approximate measure although it does not reflect a ‘real’ variation in feed efficiency among animals. However, this measure of FCR is practical under the conditions of commercial production. The present estimate of heritability indicates that FCR is heritable  between body weight and sexual maturity. This suggests that selection for increased growth rate may lead to early maturation in Nile tilapia. Our results are consistent with those reported for other species, including Rainbow trout542353Another important observation from our study is a high and positive genetic correlation (rg = −0.82 ± 0.06) between survival and deformity is expected because deformed fish usually have lower survival abilities than healthy animal. Consistent with other studiesrg between body weight and K factor = 0.19 ± 0.04). This is in contrast with the non-significant estimates between the two traits in other studiesThis is the first time, that genetic and phenotypic correlations between survival and deformity are reported in Nile tilapia. The high and negative genetic and phenotypic correlation (P < 0.001). The FCR estimate in our research (1.08–1.89) was within the range from 1.5 to 2.5 reported under practical commercial grow-out conditions in freshwater environments when using pelleted Nile tilapia feedsThe fish cultured in saline water showed better growth performance and efficiency of feed utilisation than those grown in freshwater  tested in both saline-and fresh-water systems. The moderate to high heritability for traits studied, especially body weight, indicates that our improved strain will continue responding to future selection. The close to one genetic correlations between trait expressions in the two testing environments indicate that our selected line under moderate salinity water can perform well in both saline-and fresh-water production environments, and that conducting two separate breeding programs for saline-and fresh- water is not justified for this population of Nile tilapia. Objective measurements of gonadal development of tilapia females and gill condition of live animals also merit further studies.How to cite this article: Thoa, N. P. et al. Does selection in a challenging environment produce Nile tilapia genotypes that can thrive in a range of production systems? Sci. Rep.6, 21486; doi: 10.1038/srep21486 (2016).


N ≤ 7,366 twin pairs) showed that peer problems in childhood are heritable  but genetically heterogeneous from age to age . GCTA  provided furthermore little support for the contribution of measured common genetic variants during childhood  ≤ 0.11) though these influences become stronger in adolescence  ≤ 0.27). A subsequent cross-sectional genome-wide screen in ALSPAC  focussed on peer problems with the highest GCTA-heritability . Single variant signals (P ≤ 10−5) were followed up in TEDS  and, in search for autism quantitative trait loci, explored within two autism samples . There was, however, no evidence for association in TEDS and little evidence for an overlap with the autistic continuum. In summary, our findings suggest that problematic peer relationships are heritable but genetically complex and heterogeneous from age to age, with an increase in common measurable genetic variation during adolescence.Peer behaviour plays an important role in the development of social adjustment, though little is known about its genetic architecture. We conducted a twin study combined with a genome-wide complex trait analysis (GCTA) and a genome-wide screen to characterise genetic influences on problematic peer behaviour during childhood and adolescence. This included a series of longitudinal measures (parent-reported Strengths-and-Difficulties Questionnaire) from a UK population-based birth-cohort , and a UK twin sample . Longitudinal twin analysis (TEDS;  One of the most important developmental tasks during childhood and adolescence is the acquisition of social skills that permit the formation of successful peer relationships  though little is known about how representative these findings are within a longitudinal context including whether genetic factors are stable over time. Given variability in friendship networks during development , a severe childhood neurodevelopmental condition  from a representative population sample in the UK , and a representative UK twin sample , this study aimed to investigate the genetic architecture of problematic peer behaviour from a developmental perspective. Specifically, we studied evidence for additive genetic effects using a longitudinal twin design and performed multiple cross-sectional GCTA-heritability analyses. This was followed by GWAS focussing on the phenotypic measures with the highest evidence for measurable common genetic influences. We finally explored the strongest population-based single genetic association signals also in two autism samples.Here, we report evidence for the contribution of additive genetic influences to problematic peer relationships during development. These genetic factors are genetically heterogeneous and show an increase in common measurable genetic variation during adolescence. We found, however, no evidence for single SNP association at the genome-wide significance level at any age, and little support for an overlap with the autistic continuum.http://www.bris.ac.uk/alspac/researchers/data-access/data-dictionary).GCTA and subsequent genome-wide analysis were conducted in children from ALSPAC, a UK population-based longitudinal pregnancy-ascertained birth-cohort (estimated birth date: April 1991–December 1992) . TEDS began when multiple births were identified from birth records and the families were invited to take part in the study; 16,810 pairs of twins were originally enrolled in TEDS. More than 10,000 of these twin pairs remain enrolled in the study to date. DNA has been collected for more than 7,000 pairs, and genome-wide genotyping data for two million DNA markers are available for around 3,500 individuals. Information is available on the twins using a combination of parent, teacher, and child rated questionnaire measures, home visits, linkage of records and online tests of cognition and behaviour. The TEDS families have taken part in studies roughly once every 2 years since the twins were 18 months of age. Ethical approval for each stage of TEDS has been obtained from the Institute of Psychiatry Ethics Committee, and informed consent was collected from the parents for each assessment. Further details about the composition and representativeness of the sample, and an overview of the measures collected are available elsewhere , short behavioural screening instrument applicable to children and adolescents ranging from 4 to 16 years (Goodman α = 0.57) . The SD Goodman . The SDQ Goodman , the rel Goodman . The val Goodman , and hig Goodman . Differe Goodman .ρ): 0.22 < ρ < 0.57; Supplementary Table S1) and TEDS . As expected, assessments closer in age were more strongly correlated than those that spanned the entire developmental period (Supplementary Tables S1 and S2).The peer problem subscale includes the items: (I) “Rather solitary, tends to play alone”; (II) “Has at least one good friend”; (III) “Generally liked by other children”; (IV) “Picked on or bullied by other children”; and (V) “Gets on better with adults than with other children”. Each item was rated as “not true” (0), “somewhat true” 1) or “certainly true” (2) and items (II) and (III) were reverse-coded Goodman . All ite or “certTwin analyses were used to estimate the relative contribution of genetic and environmental influences to individual differences in quantitative peer problem scores. Twin intraclass correlations were calculated  twin analyses were used to go beyond estimating the cross-sectional importance of genetic and environmental factors and to consider the degree to which genes and environments important at one age are also important at later ages  were excluded. Individual participant samples were removed on the basis of sex mismatches, minimal or excessive heterozygosity, disproportionate levels of individual missingness, cryptic relatedness, insufficient sample replication and non-European ancestry. Using 464,311 directly genotyped SNPs, genotypes for 8,365 independent individuals (irrespective of available phenotypic data) were imputed to HapMapCEU (Utah residents with Northern and Western European ancestry from the Centre d’Etude du PolymorphismeHumain collection) individuals (Rel 22) using MACH . SNPs with greater probability of a null call were down-weighted in the analysis, thresholding at 0.9. Imputation was carried out using the IMPUTE2 software on clean genotype data by a two-stage approach with both a haploid reference panel (HapMap2 and HapMap3 SNP data on the 120 unrelated HapMap CEU trios (Rel 22)) and a diploid reference panel  as previously described .NSNPs = 2,449,665, MAF ≥ 0.01, imputation accuracy MACH-R2 > 0.3; TEDS: NSNPs = 1,588,650 (MAF ≥ 0.01 and INFO > 0.7 score). For sensitivity analysis, GCTA was also performed using peer problem scores adjusted for age, sex and the two most significant principal components, in addition to adjusted and subsequently rank-transformed scores. GCTA estimates from ALSPAC and TEDS were combined using fixed-effects inverse-variance meta-analysis, and evidence for overall heterogeneity was tested using Cochran’s Q-test.Using GCTA  common imputed and genotyped SNPs  within ALSPAC. Association analyses were performed using a quasi-Poisson regression model, which can accommodate overdispersion (Faraway R ʻstats’ library). Specifically, counts of peer problems were regressed on allele dosage as well as age, sex and the two most significant ancestry-informative principal components  thus represent changes in log-counts of peer problems per effect allele, based on SNP dosage scores. All single time-point findings were subjected to genomic control (GC)-correction , we conducted three single time-point GWASs on ~2.45 million ( Faraway  (R ʻstatP < 0.001) and MAF <1 %) as well as the exclusion of individuals . The final data set included 3,299 individuals (793 pedigrees) and 513,312 SNPs. Genotypes were imputed to HapMap CEU (release 22) using MaCH, excluding all imputed genotypes with a per-genotype posterior probability <0.9. Selected population-based signals were investigated with FBAT, a family-based association test  pedigrees and the Autism Case–Control (ACC) cohort in an exploratory search for autism QTL. Within the AGRE pedigrees, there are three diagnostic categories based on the Autism Diagnostic Interview–Revised (ADI–R)  or two (SNP × age and SNP × age2) interaction effects. In the presence of SNP–age interaction effects, we modelled the SNP effect at different ages spanning early childhood (4 years) and later adolescence (17 years), by centering age at the respective age. We considered a SNP signal of 5 × 10−8 at any age (including combined effects from main and interaction effects) within the longitudinal modelling framework as genome-wide significant.All population-based signals in ALSPAC with tentative support for autism QTL were furthermore modelled longitudinally. For this, we used a mixed Poisson regression framework (R:‘lme4’ library), where overdispersion can be modelled through the random error part (Gelman and Hill h2 ≤ 0.71), with negligible shared environmental effects (0.02 < c2 ≤ 0.09) and moderate non-shared/residual influences . Twin modelling furthermore provided evidence for considerable genetic heterogeneity during development with a genetic correlation of just 0.32 between ages 4 and 11 , suggesting increased stability in genetic influences into early adolescence (Supplementary Table S3). Non-shared environmental overlap  and children from a national twin sample . Longitudinal twin analysis in TEDS  ≤ 0.11, Pmin = 0.04; Fig. h2(ALSPAC) ≤ 0.27, 0.0002 < P ≤ 0.01; Fig. rg = 0.53 (SE = 0.24), P = 0.05), irrespective of phenotype adjustment  and phenotype transformation (Supplementary Table S6). GCTA-based genetic correlations for peer problem measures during childhood are not reported because of estimation problems due to low GCTA-h2.GCTA based on samples of independent children from ALSPAC and TEDS  revealed that measured common additive genetic effects explained only a modest amount of variance during early to late childhood  ≤ 0.27, 0.0002 < GCTA-P ≤ 0.03). During the discovery stage, three genome-wide screens were carried out within children from the general population (ALSPAC), thus allowing for genetic heterogeneity during development through a cross-sectional design. At these ages, association signals showed no genome-wide significant deviation from the null hypothesis and there was little evidence for population stratification . The strongest genetic associations with problematic peer relationships were identified on chromosome 12p12.1 within ST8SIA1 (10 years: rs722248 GC-corrected P = 2.1 × 10−6), on chromosome 10p15.1 near KLF6 (13 years: rs7898258 GC-corrected P = 2.4 × 10−7) and on chromosome 2p22.2 near CRIM1 (17 years: rs3770890 GC-corrected P = 5.3 × 10−7). None of the signals showed consistent evidence for association (P < 10−5) throughout development. However, independent SNPs within CRIM1 (Linkage disequilibrium (LD) r2 < 0.3) were associated with problematic peer relationships during different stages of development  from the discovery stage was carried out in a sample of unrelated children from TEDS with peer problem measures at 9 and 11 years of age (as parent-rated 5-item SDQ peer problem scores at later ages are not available). This follow-up analysis in TEDS (Supplementary Table S7) provided no evidence for association, assuming the same direction of genetic effect as observed in the discovery cohort.During the second stage of the genome-wide screen, a follow-up of independent association signals  showed, however, that two common population-based signals, rs7873232 at 9p24.2 and rs6451614 at 5p13.1  interactions. So far, we found no indication for non-additive genetic effects in our twin analyses, which would be suggested if the non-identical twin correlations are less than half the identical twin correlations. The twin method, however, is not a very powerful method for separating non-additive from additive genetic effects , which could explain the genetic heterogeneity across development. Evidence suggests that peers are a crucial influence on young people’s outcomes, especially in adolescence when young people have more opportunities to select their own peer groups. The increased genetic stability with age accompanies this increased control in selecting social networks in adolescence compared to earlier in childhood. This interplay between genetic and environmental features may represent a form of social calibration of genetic influences as the young people discover and settle into their personality and peer group behaviour during this developmental stage.Further investigations are, however, required to explain the observed change in GCTA-heritability in more detail, with, for example, accurate assessments of pubertal status via sex hormone measures.N = 8,455) there was more than 80 % power to detect genetic variation with MAF of 0.02 explaining as little as 0.5 % of the phenotypic variance at the genome-wide significance level . Second, within TEDS, there was no evidence for measurable common genetic effects contributing to peer problems during early, middle and late childhood as captured by GCTA-h2, including the phenotypic measures selected for GWAS follow-up. Finally, assuming that genetic heterogeneity persists throughout development, common genetic signals underlying peer problems in ALSPAC children, especially during later adolescence, may be different to genetic influences contributing to problematic peer behaviour in TEDS children during middle and late childhood.In this study, we have focussed our subsequent genome-wide screen on the phenotypes with the highest GCTA-heritability . This genome-wide scan did not identify any genetic variation at the genome-wide significant level. However, given the observed genetic heterogeneity during development, which is likely to extend into adolescence, and the increase in GCTA-heritability with progressing age, the lack of genome-wide findings needs to be placed in perspective. First, the population-based follow-up sample (TEDS) was smaller and thus less powerful than the discovery sample (ALSPAC) though in the combined ALSPAC and TEDS sample  on chromosome 9p24.2  and 5p13.1  within the AGRE but not the ACC sample, assuming the same direction of effect. These findings are consistent with the hypothesis of underlying autism QTL, but also with chance. Closer investigation of these signals in ALSPAC showed that SNP effects vary over time. However, variability in genetic effects during childhood and adolescence is likely to be a common phenomenon in typically developing children. As such, these SNP signals are consistent with our findings from twin analysis and GCTA suggesting genetic heterogeneity during development, while autistic traits are typically characterised by high genetic stability (Holmboe et al. In summary, our study showed that peer problems are highly heritable throughout development. Their genetic architecture is, however, complex and involves an increase in measurable common genetic effects during later adolescence as well as genetic heterogeneity.


Akkermansia) and a variant near PLD1, a gene previously associated with body mass index. Moreover, we replicate a previously reported association from a quantitative trait locus (QTL) mapping study of fecal microbiome abundance in mice . Finally, based on the significance distribution of the associated microbiome QTLs in our study with respect to chromatin accessibility profiles, we identified tissues in which host genetic variation may be acting to influence bacterial abundance in the gut.The bacterial composition of the human fecal microbiome is influenced by many lifestyle factors, notably diet. It is less clear, however, what role host genetics plays in dictating the composition of bacteria living in the gut. In this study, we examined the association of ~200K host genotypes with the relative abundance of fecal bacterial taxa in a founder population, the Hutterites, during two seasons . These individuals live and eat communally, minimizing variation due to environmental exposures, including diet, which could potentially mask small genetic effects. Using a GWAS approach that takes into account the relatedness between subjects, we identified at least 8 bacterial taxa whose abundances were associated with single nucleotide polymorphisms in the host genome in each season (at genome-wide FDR of 20%). For example, we identified an association between a taxon known to affect obesity (genus  Humans have complex interactions with the bacteria that live in and on their bodies, referred to as the microbiota. AlteratSeveral groups have investigated the heritability of the gut microbiome in humans and model organisms; however, estimates of genetic contribution to bacterial abundance vary between studies. One early study used temperature gradient gel electrophoresis (TGGE) to examine the similarity of the bacterial 16S rRNA genes from gut bacteria between individuals with varying degrees of relatedness. Their findings showed TGGE profiles were increasingly similar as the relatedness between pairs of individuals also increased. Althouget al. identified 18 quantitative trait loci (QTL) associated with various bacterial taxa in the gut using advanced intercrossed mouse lines. This study demonstrates the utility of genome-wide approaches, however, no such study has been reported in humans.While evidence for host genetics influencing the gut microbiota is gaining traction, we still lack an understanding of what genes or genetic variants in the human genome might potentially influence bacterial profiles. Many studies have focused on candidate genes, where either natural variation segregating in humans–26 or geBifidobacterium), at least four bacterial taxa in each season were differentially abundant by sex. In addition, we identified at least eight bacterial taxa in each season that are associated with at least one single nucleotide polymorphism (SNP) at a genome-wide significance level. Finally, we identified pathways and candidate tissues where host genetic variation may be acting to influence bacterial abundance in the gut.To address this gap, we examined the fecal microbiome from the Hutterites, a religious isolate living in North America. Importantly, members of this population live and eat on large communal farms, called colonies, limiting inter-individual variation in environmental exposures that might mask genetic effects on microbiome composition. In particular, meals are prepared and eaten in a communal kitchen and dining room, respectively. Previous work in this population examined temporal differences between winter and summer gut microbiomes. Here, wThe protocol was approved by the University of Chicago IRB (protocol 10-416-B). Written informed consent was obtained from all adult participants and the parents of minors. In addition, written assent was obtained from minor participants.The data for the 16S rRNA amplicon sequencing, genotypes, GWAS results, and metadata have been deposited in dbGaP under accession numbers phs000680 and phs000185.Stool collection, DNA extraction, 16S rRNA gene sequencing, and classification were performed as described previously. Brieflyhttp://media.affymetrix.com/support/technical/whitepapers/brlmm_whitepaper.pdf) and genotypes for the Genome-Wide Human SNP Array 5.0 and 6.0 were called using Birdseed[P-values ≥ 0.001, and fewer than 5 Mendelian errors across all individuals. 271,365 SNPs that were on all three platforms remained after quality control filtering and re-annotation to the human genome version 19 .Genotyping in 1415 Hutterite individuals  was performed using either the Affymetrix 500k Array Set, the Genome-Wide Human SNP Array 5.0, or the Genome-Wide Human SNP Array 6.0 as part of a long-term research program studying the genetic basis of complex phenotypes in the Hutterites–35. Geno Birdseed. SNPs weThe following bacterial data processing was done separately for samples collected in the winter and in the summer. Because there would be limited power to detect associations in rare members of the microbiota, we first eliminated from subsequent analyses bacterial taxa that did not have at least one read in at least 75% of individuals . Next, taxon data was fit to a standard normal distribution across individuals by quantile normalization using the R function qqnorm. FinallyOur previous work revealed broad, temporal differences in gut bacterial abundance between winter and summer in this population. HoweverCorrelations of individual bacterial taxa to age and sex were performed using linear mixed models that corrected for the relatedness between individuals, as implemented in GEMMA (v0.94). First, All bacterial taxa present at the genus level in each sample were used to calculate alpha diversity metrics , without quantile normalization applied to relative abundances and no taxa eliminated due to rarity. Diversity metrics were calculated using the vegan package in R.“Chip heritability”  were regressed from each bacterial taxon within each seasonal analysis. P-value threshold and less conservative q-value thresholds (0.2 and 0.1) were considered to correct for multiple testing within each genome-wide association study[Prior to association testing, age, sex and date of collection were regressed from normalized bacterial taxon relative abundance data. GEMMA (v0.94) was used to perform GWAS on the residuals, including the relatedness matrices as described above to account for inter-individual relatedness. SNPs were eliminated if their minor allele frequency was lower than 10% in the individuals tested. A total of 211,319 SNPs in the winter analysis, 210,924 SNPs in the summer analysis, and 212,153 SNPs in the “seasons combined” analysis were examined. Both a conservative Bonferroni corrected ion study.P-value was calculated by dividing the number of overlaps equal to or greater than the observed number of overlaps for that season by the number of permutations  in the null distribution.The number of bacterial taxa within each season that had both non-zero “chip heritability” and at least one genome-wide significant association were examined. To determine the significance of this overlap within each season, “chip heritability” estimates were permuted across bacterial taxa and the overlap of taxa that had both non-zero permuted “chip heritability” and at least one genome-wide significant SNP association was calculated, generating a null distribution of the number of overlaps expected by chance. An empirical et al.[It is unclear in what tissues genetic variation might be acting in the human body to influence bacterial abundance in the gut. To investigate this, for each bacterial taxon that either had a genome-wide significant association through GWAS or non-zero “chip heritability”, we examined the enrichment of GWAS SNPs in DNase hypersensitivity (DHS) peaks for 16 tissues. In the human genome, DHS peaks mark areas of accessible chromatin, which is assumed to typically participate in gene regulatory functions. Many of these accessible regulatory regions of the human genome are cell-type or temporally specific. Therefoet al. demonstret al.[http://www.uwencode.org/proj/Science_Maurano_Humbert_et_al/ on February 12th, 2014. The 349 available cell lines were clustered into 16 tissue classifications by calculating Euclidean distances for DNase peaks between all pairs of lines  in DHS peaks from each of 16 tissues, compared to the genome-wide distribution of all tested SNPs located within DHS peaks of the same tissue. The enrichment of SNPs in DHS peaks per tissue  were calculated as described in Maurano et al., with the exception that the following P-value thresholds were examined for each bacterial abundance GWAS only if at least 50 SNPs had P-values at or below the cutoff: 1.0, 0.5, 0.1, 0.05, 0.01, 0.005, 0.001, 0.0005, 0.0001, 0.00005, 0.00001, 0.000005  for each tissue  at least one SNP met suggestive genome-wide significance, or ii) there was non-zero “chip heritability” for that taxon . For increasingly significant 0.000005 . Signifih tissue . Note thP-value was calculated using the function gene2p (method = SpD) in postgwas by taking into account the dependency structure between SNPs. Gene ontology enrichment analyses were performed using the gwasGOenrich function with the following parameters: ontologies for cellular components, molecular function, and biological process were examined , pruneTermsBySize = 8, pkgname.GO = “org.Hs.eg.db”, and topGOalgorithm = “classic”. Correction for multiple testing was accomplished using q-values.GSEA was performed for any bacterial abundance GWAS with at least one significantly associated SNP using the R package postgwas. Each teet al.[16S rRNA amplicon data from Yatsunenko et al. was downet al.. SimilarP < 0.01)[Bifidobacterium, Bifidobacterium have previously been shown to decrease in abundance with age in the gut[In a previous study of these individuals, we examined the relationship of microbiome alpha diversity to age and sex. While a significant association between diversity and sex was not observed in either winter or summer, diversity was significantly inversely correlated with age during the winter months (P < 0.01). Here, wn the gut, 48.In contrast, many bacterial taxa showed sex specific abundance patterns , is the maximum amount of the variance in a phenotype that can be explained by the genetic variation interrogated in a GWAS framework. This meWe calculated PVE for each bacterial taxon using ~200k SNPs, as implemented in GEMMA see . ApproxiIn addition to bacterial relative abundance, we examined the “chip heritability” for three alpha diversity metrics. Diversity measures were calculated at the genus level  and included all classified bacterial genera see . There wP-value threshold at 7.0 x 10−10. Given our sample size , only associations with very large effect sizes would be expected to pass this significance threshold. Unsurprisingly, no variants passed this threshold in any of our analyses; therefore, we consider the individual SNP/taxon associations identified in these studies to be suggestive and requiring further replication.To determine if specific variants in the human genome are associated with microbial abundance in the gut, we employed a classic GWAS approach for quantitative trait mapping. A large number of SNP-taxon associations were tested , which sets a study-wise Bonferroni corrected within each bacterial abundance GWAS individually, either by Bonferroni correction or by q-value (considering significance thresholds at both q ≤ 0.1 and q ≤ 0.2 cutoffs). Limiting the correction for multiple testing to the number of tests done within an individual GWAS is similar to the typical correction applied to GWAS with human disease .At least one bacterial taxon per season was significantly associated with at least one SNP (at a Bonferroni significance threshold), and the abundances of at least eight bacterial taxa are associated with at least one SNP at q-values less than or equal to 0.2 , Fig 3. P < 0.01), were slightly enriched for significant associations in the analysis of data from both seasons (P = 0.07), but were not enriched for genome-wide associations in the summer microbiome data . However, for several bacterial GWAS , GSEA resulted in significant enrichments for biological processes one might expect to be interacting with the microbiome (q ≤ 0.2). For example, GSEA revealed enrichments of genes categorized under immune processes (summer: genus Sporacetigenium) and metabolic processes , as well as in pathways that generate ribosomal components  and act in multi-organism process and communication (“seasons combined”: genus Anaerostipes). Finally, enrichment in olfactory receptor pathways was observed for a number of taxa . One caveat to note is that olfactory receptor genes tend to be clustered in the genome, which can lead to spurious enrichment in gene ontology tests.In order to gain insight into the type of host pathways and functions that might influence bacterial abundance in the gut, we performed gene set enrichment analysis (GSEA) on all GWAS with at least one genome-wide significant association in our study . For moset al.[Our GWAS revealed a number of candidate variants that potentially influence gut microbiome composition; however, we lack an understanding of the relevant tissues in which these variants act. We sought to provide insight into this question by intersecting our GWAS results with DNase hypersensitivity (DHS) data, following the approach of Maurano et al.. Cell tyWe performed this candidate tissue identification analysis for each of our GWAS of bacterial abundance when either i) at least one SNP met suggestive genome-wide significance, or ii) there was non-zero “chip heritability” for that taxon . When considering all bacterial taxa that met our inclusion criteria from either the winter or summer data sets, we do not observe more candidate tissues identified at nominal significance than we would expect by chance . In contAkkermansia and muscle tissues for genus Dolosigranulum. Both intestine and stomach tissues were identified as potential candidates for genus Faecalibacterium. Family Neisseriaceae and family Pasteurellaceae both have a large number of tissues identified as candidates . Endothelial tissues were identified for genus ndidates , S6 Fig.In this study, we examined the role of host genetics in determining gut microbiome composition in an isolated, communally living population: the Hutterites. We first explored non-genetic effects of age and sex, and demonstrated that at least four bacterial taxa show sex specific patterns of abundance in each season. We further demonstrated that at least 13 bacterial taxa show evidence of heritability in each season and ultimately identified at least eight bacterial taxa in each season whose abundances were significantly associated with genetic variation in the human genome. Finally, we examined host tissue types in which genetic variation may be acting to influence gut microbial composition.Lactococcus showed strong evidence of association (LOD score = 8) with markers on mouse chromosome 10. In our study, genus Lactococcus also showed evidence of genetic association in the summer . Interestingly, the associated variant in the Hutterites is located in the syntenic linkage interval observed in the mouse QTL study, providing evidence of replication of this association across species. The size of the linkage interval in Benson et al. spans tens of megabases of the mouse genome, however, and finer mapping is needed to confirm replication.Although no human replication cohorts are published, we replicated a previously reported association from a quantitative trait locus (QTL) mapping study of microbial abundance in the gut of advanced intercross mouse strains. In thatAkkermansia ; but BMI was measured on these individuals 3–5 years prior to microbiome sampling. Further investigation is needed to validate this intriguing hypothesis by confirming the relationship of Akkermansia to PLD1 and obesity, ideally within a set of samples where microbiome and obesity measures are collected concurrently.One of the more biologically interesting results of our study was the identification of SNPs that were associated with abundance levels of genus ermansia . These Safficking–59. Thisafficking. In addiy in mice. These cll types . EndotheBifidobacterium, order Rhizobiales; combined: genus Anaerofilum, genus Faecalibacterium). It has already been demonstrated that olfactory receptors form an interface between the host and the gut microbiota. In mice, an olfactory receptor expressed in the kidneys responds to metabolites produced by gut bacteria, and this process aids in regulating blood pressure systemically via renin production[In addition to examining human variation that met statistical thresholds at a genome-wide significance level, gene-set enrichment analysis revealed a number of human cellular pathways that might be an important interface between the host and the microbiome. In particular, olfactory receptor activity was significant in GSEA for five taxon GWAS  or indibundance . F. prauacterized. This baacterized and is kacterized. High lee colitis, Crohn’se colitis–77, and e colitis. Given tScardovia, genus Gordonibacter, genus Anaerotruncus, and phylum Proteobacteria. These abundance differences are directionally consistent across season and there are a number of hypotheses for these observations. One potential explaination is that inherent biological differences between the sexes (for instance hormone levels) could drive the observed bacterial abundance differences. Alternatively, the division of labor could drive sex specific differences between men and women in Hutterite society[In addition to examining the role of host genetics in determining gut microbiome composition, we also identified bacterial taxa that are differentially abundant by sex. In the Hutterites, at least four bacterial taxa differ in abundance between the sexes each season, including genus e society. For exae society. The autet al.[To further investigate these two hypotheses, we examined whether sex is associated with bacterial abundances in additional populations, including individuals from the USA and Venezuela, using the data produced by Yatsunenko et al. . For many GWAS of common diseases, the sample sizes necessary to detect significant associations are thousands to tens of thousands of individuals. We proposed that the relatively uniform environment that the Hutterites are exposed to, in particular their communal diet, would reduce variation due to non-genetic factors and increase our power to detect genetic associations. While this may be true, it is clear that much larger sample sizes will be needed to ensure sufficient power to detect association with the high multiple testing burden and for narrowing the confidence intervals on heritability estimates. A second limitation is that published replication cohorts are not currently available for these traits in humans and we are unable to confirm any associations we detect in independent human samples.Akkermansia, in particular, to host genetics, including genome-wide significant GWAS hits .PCA was performed on all individuals (genus level data) after combining data that had been normalized within season first. Quantile normalization within each season separately before combining data eliminates seasonal differences along the top 10 principal components Click here for additional data file.S2 FigClostridium, B) genus Collinsella, C) genus Gordonibacter, D) genus Mitsuokella, E) genus Scardovia, F) order Burkholderiales, G) phylum TM7.Each of these bacterial taxa shows differential abundance by sex in at least two seasons examined. For most bacterial taxa, the direction of association stays constant between males and females regardless of season, pointing to consistent variation of bacterial abundance between the sexes. Significance: q ≤ 0.05 = *, q ≤ 0.01 = **, q ≤ 0.001 = ***, ns = not significant). Bacterial taxa include A) genus (TIFF)Click here for additional data file.S3 Fig“Chip heritability”  along with the standard error around the estimate (gray bars) is shown for the bacterial taxa observed in all three seasonal analyses. Bacterial taxa are ordered by the PVE in the “seasons combined” analysis. Filled circles indicate taxa that had at least one genome-wide significant SNP association in that season (either q ≤ 0.2 or Bonferroni corrected). Open circles indicate taxa that did not have any SNPs associated with their abundance in that season after correcting for multiple tests genome-wide.(TIFF)Click here for additional data file.S4 FigThe number of overlaps between bacterial taxa showing heritability (from “chip heritability” estimation) and bacterial taxa with at least one genome-wide significant association from GWAS is shown for each season with the colored, dashed line. A null distribution of overlaps expected by chance given the number of bacterial taxa showing heritability and showing a GWAS hit was calculated by permuting which bacterial taxa were labeled as heritable 1000 times. An empirical p-value was calculated by dividing the number of overlaps observed in the permutations greater than the actual overlap by the total number of permutations (1000). This was done for each season: A) winter, B) summer, and C) “seasons combined”.(TIFF)Click here for additional data file.S5 FigP-values were calculated via permutation for each tissue for each bacterial taxon that had either non-zero “chip heritability” or at least one genome-wide significant SNP. The number of expected associations with a P ≤ 0.05 is indicated with the red line on each plot. As can be seen from the histograms, most tissues do not show enrichment for low GWAS p-value SNPs in DHS peaks (large excess around P = 1). For winter (A) and summer (B), there are not more associations at P ≤ 0.05 than we would expect by chance. However, in the “seasons combined” analysis (C), there is a slight excess of P-values ≤ 0.05, indicating significant associations exist in this analysis . Therefore, candidate tissue analysis for winter and summer were not considered further.(TIFF)Click here for additional data file.S6 FigP-values (x-axis in (A), (C), and (E)) are enriched (y-axis) in DNase Hypersensitivity Sites (DHS) for various tissues, compared to the genome-wide distribution of GWAS SNPs falling in DHS peaks for that tissue. Identified tissues are listed in the top left corner for each bacterial taxon examined . Any tissue not listed  did not have significant enrichment in the lowest P-value bin. B, D, and F) Boxplots (gray) indicate the null distribution of enrichments of SNPs in the lowest GWAS P-value bin in DHS peaks for the given tissues calculated from GWAS permutations from (A), (C), and (E), respectively. The actual enrichment of SNPs in the lowest GWAS P-value bin overlapping DHS peaks for each tissue is indicated by the colored star. All enrichments shown have a P ≤ 0.05. A & B) family Neisseriaceae, C & D) family Pasteurellaceae, and E & F) genus Dolosigranulum.A, C, and E) GWAS variants with increasingly low (TIFF)Click here for additional data file.S7 FigP-values for bacterial taxa correlated with sex in the USA (A) and Venezuelan (C) populations. B and D) Quantile-quantile plots (Q-Q plots) of the -log10 for sex correlation testing in the USA (B) and Venezuelan (D) populations. In the USA, there is an excess of low P-values, indicating many bacterial taxa show differential abundance by sex.A and C) Histograms of the (TIFF)Click here for additional data file.S1 FileThis file directory contains three folders, one for each seasonal analysis . Within each folder there is one text file per bacteria tested. Each text file contains 17 rows and variable numbers of columns. Each row represents one tissue type as determined by clustering analysis of DHS peaks . Each column represents a GWAS p-value threshold examined. The minimum threshold chosen for each bacterium contains at least 50 SNPs. Entries are the enrichment of GWAS SNPs under that threshold compared to genome-wide for that tissue type.(ZIP)Click here for additional data file.S2 FileThis folder contains GSEA results for all that had at least one genome-wide significant hit (either after Bonferroni correction or q < 0.2). It contains three folders . Each folder contains three files per bacterial taxa tested . Only GO categories with a q < 0.2 were examined. If a file contains only header information, there were no significant enrichments.(ZIP)Click here for additional data file.S1 TableThis table contains the list of bacteria that were trimmed due to high correlation with another bacterium for the "winter", "summer", and "seasons combined" analyses.(XLSX)Click here for additional data file.S2 TableThis table contains proportion of variance explained (PVE) estimates ("chip" heritability) for the "winter", "summer", and "seasons combined" analyses and reported by GEMMA.(XLSX)Click here for additional data file.S3 Tableet al. and the tissue it was classified as using the clustering procedure described in the methods.This table lists the DNase Hypersensitivity Site (DHS) sample names from Maurano (XLSX)Click here for additional data file.S4 TableP-values representing the significance of enrichment of GWAS SNPs overlapping DHS peaks in each of the 16 tissues examined in the lowest GWAS p-value bin tested for each bacterial taxon.This table contains (XLSX)Click here for additional data file.S5 TableP-values and q-values for age correlations done in GEMMA for the "winter", "summer", and "seasons combined" analyses.This table contains tables of (XLSX)Click here for additional data file.S6 TableP-values and q-values for sex correlations done in GEMMA for the "winter", "summer", and "seasons combined" analyses.This table contains tables of (XLSX)Click here for additional data file.S7 TableP-value after Bonferroni correction or with a q-value < = 0.2.This table contains all SNPs that met a genome-wide significance threshold either with a (XLSX)Click here for additional data file.S8 TableP-values and q-values for the sex associations Click here for additional data file.S9 TableThis table lists the number of bacterial taxa that showed evidence of being differentially abundant by sex in all three Hutterite seasons examined  and two of the Yatsunenko populations (USA and Venezuela). Only the taxa identified as common in both populations examined were considered. The significance of the overlap in bacterial taxa identified in each population is listed see .(XLSX)Click here for additional data file.


Newcastle disease (ND) and avian influenza (AI) are the most feared diseases in the poultry industry worldwide. They can cause flock mortality up to 100%, resulting in a catastrophic economic loss. This is the first study to investigate the feasibility of genomic selection for antibody response to Newcastle disease virus (Ab-NDV) and antibody response to Avian Influenza virus (Ab-AIV) in chickens. The data were collected from a crossbred population. Breeding values for Ab-NDV and Ab-AIV were estimated using a pedigree-based best linear unbiased prediction model (BLUP) and a genomic best linear unbiased prediction model (GBLUP). Single-trait and multiple-trait analyses were implemented. According to the analysis using the pedigree-based model, the heritability for Ab-NDV estimated from the single-trait and multiple-trait models was 0.478 and 0.487, respectively. The heritability for Ab-AIV estimated from the two models was 0.301 and 0.291, respectively. The estimated genetic correlation between the two traits was 0.438. A four-fold cross-validation was used to assess the accuracy of the estimated breeding values (EBV) in the two validation scenarios. In the family sample scenario each half-sib family is randomly allocated to one of four subsets and in the random sample scenario the individuals are randomly divided into four subsets. In the family sample scenario, compared with the pedigree-based model, the accuracy of the genomic prediction increased from 0.086 to 0.237 for Ab-NDV and from 0.080 to 0.347 for Ab-AIV. In the random sample scenario, the accuracy was improved from 0.389 to 0.427 for Ab-NDV and from 0.281 to 0.367 for Ab-AIV. The multiple-trait GBLUP model led to a slightly higher accuracy of genomic prediction for both traits. These results indicate that genomic selection for antibody response to ND and AI in chickens is promising. Newcastle disease (ND) and avian influenza (AI) are regarded as two of the most important diseases of poultry worldwide and can lead to a flock mortality up to 100% Previous genomic studies mainly focused on single-trait analysis; however, several traits of economic importance, which may be genetically related, were usually selected for animal breeding. Using the information from correlated traits, multiple-trait analysis could improve the accuracy of the estimated breeding value This study was approved by the Animal Care Committee of the Institute of Animal Science, Guangdong Academy of Agricultural Sciences , the Approval No. is GAAS-IAS-2009-73. Blood samples of birds were collected from the brachial vein by standard venipuncture procedure. The chickens were treated humanely, and none of them were sacrificed for this study.The chicken population in this study originated from a cross between two divergently selected lines, i.e., the “High Quality chicken Line A” (HQLA) and the Huiyang Beard chicken (HB), as described in Sheng et al Chickens were vaccinated with commercial vaccines in accordance with the instructions. At 25 days of age, the chickens were vaccinated against Newcastle disease with a commercially available LaSota strain  using the eye drop technique. After 25 days, a second vaccination against Newcastle disease was implemented. At 40 days of age, the chickens were vaccinated with a commercially Avian Influenza Inactivated H9 strain Vaccine using the eye drop technique. At 91 days of age, blood samples were collected. NDV and AIV antibody levels in the blood samples were measured using indirect ELISA and were expressed as the S/P value of the corresponding dilutions, according to the instructions for the commercial ELISA kit . A set of λ was investigated. Finally, parameter λ was set as 0.05 and 0.5 for Ab-NDV and Ab-AIV, respectively. After transformation, the two traits met normal distribution and the analysis was based on the transformed data.The birds were genotyped using the Illumina Chicken 60K SNP Beadchip ST or GBLUPST, and multiple-trait analysis as BLUPMT or GBLUPMT, respectively.In this study, breeding values were estimated using two models, i.e., a pedigree-based best linear unbiased prediction model (BLUP) and a genomic best linear unbiased prediction model (GBLUP). Both single-trait and multiple-trait analysis were implemented. Hereafter single-trait analysis is denoted as BLUPy is the vector of the Box-Cox translation data of Ab-NDV and Ab-AIV, b is the vector of the fixed effect including sex and hatch,a is a vector of additive genetic effects, e is the vector of random residuals, X and Z are incidence matrices. It is assumed that A is the additive genetic relationship matrix built using pedigree information in which unknown parents of birds in the two parental lines are treated as two different genetic groups, aV is the covariance matrix of additive genetic effects, and 0R is residual covariance matrix. aV and 0R is a scalar in single-trait analysis and has a dimension of two in multiple-trait analysis.The pedigree-based BLUP model y, b, X and e are the same as those in the BLUP model, g is a vector of genomic breeding values to be estimated, Z is the incidence matrices of g. It is assumed that G the additive genetic relationship matrix based on SNP markersgV is the covariance matrix of genomic breeding values.The GBLUP model based on genomic information is:The variance and covariance components were estimated using Average Information Restricted Maximum Likelihood (AIREML) A four-fold cross validation was used in this study. Two scenarios were considered with regard to training and validation sets. The first cross validation scenario is random family sampling (CVF), in which all of the data for 8 half-sib families were randomly divided into four subsets, i.e., each subset has 2 half-sib families. The second cross validation scenario is random individual sampling (CVR), in which all of the data (511 birds) were randomly split into four subsets. In each fold of validation, one data set was used as the test data set and the other three data sets as training datasets. In the family sample scenario, the test birds did not have any sibs as training birds, and thus had a distant relationship to the training birds. Conversely, in the random sample scenario, the test birds had many sibs as training birds, and thus had a close relationship with the training birds. To account for population structure and sampling variation, splitting was repeated 10 times in CVF and 50 times in CVR. The numbers of birds in the test and training data sets for each fold of validation are shown in c), where yc was calculated as the phenotypic value corrected for fixed sex and batch effects. A paired t test was implemented to test the differences among the correlations obtained from these prediction models. The paired t test treated a fold of validation as a subject and took a pair of correlation coefficients for the fold from two models as a matched pair of observations.In this study the accuracy of prediction was defined as the correlation between prediction and corrected phenotypic value  had a low predictive ability. As expected, the genomic models (GBLUPST and GBLUPMT) performed better than the conventional BLUP models. The accuracies between the conventional model and genomic model were significantly different. In CVR, the gain in predictive ability by using marker information was not as large as in CVF, but the difference between the conventional model and the genomic model was still significant.ST for Ab-NDV and Ab-AIV increased by 0.137 and 0.238 in CVF, and 0.033 and 0.080 in CVR, respectively. In multiple-trait analysis, in contrast to conventional EBV, the accuracies of the genomic predictions from GBLUPMT for Ab-NDV and Ab-AIV increased by 0.150 and 0.267 in CVF, and 0.038 and 0.086 in CVR, respectively. For both traits, the multiple-trait model (GBLUPMT) led to an increase in accuracy of genomic prediction.In single-trait analysis, compared with conventional EBV, the accuracies of the genomic predictions from GBLUPCross-breeding is widely applied in beef, pigs and chickens In addition, the present study showed a favorable moderate genetic correlation between Ab-NDV and Ab-AIV, which indicates that selection for one trait will led to a correlated favorable response for the other trait.Genomic prediction based on genome-wide dense markers captures not only the information about linkage disequilibrium between markers and QTL but also the additive genetic relationship between individuals The Bayesian model is another type of model that was widely implemented in genomic selection. Several previous studies have reported differences in the accuracy of genomic prediction between GBLUP and Bayesian models. Su et al. In this study, the data were split into training and test data sets in two different ways, and cross-validation was carried out based on these data sets to evaluate the accuracy of genomic predictions. The accuracies in CVR were higher than those in CVF. This is because, in contrast with CVF, the test birds had sibs in the training data in CVR. Therefore, the genetic ties between test and training birds are much stronger in CVR, which confirms that the accuracies were higher with more close genetic relationship between test and training animals, as reported by previous studies MT) gave better genomic prediction than the single-trait analysis (GBLUPST), which was more pronounced in CVF. This suggests that the extra information from the correlated trait would be relatively more important when the information from relatives is weaker.Comparisons between single-trait and multiple-trait models for genomic prediction have been reported in previous studies. Christensen et al. 2 = 0.05) the reliability of GEBV using multiple-trait model increased up to 0.07 compared with the reliability using single-trait model. In contrast, for the higher heritability (h2 = 0.30) the reliability of GEBV did not improve. Jia et al. Genetic correlations among traits impacts the accuracy of the multiple-trait model. Using simulation data, Calus et al. This is the first study on genomic prediction for Ab-NDV and Ab-AIV. It was found that Ab-NDV and Ab-AIV were moderately heritable. Genomic prediction can greatly improve the accuracy of estimated breeding values. The genomic prediction using the multiple-trait model was more accurate than prediction using the single-trait model. The results indicate that genomic selection for Ab-NDV and Ab-AIV is promising.


DGAT1 for yield traits for both breeds; those SNPs also showed the largest dominance effects for fat yield (both breeds) as well as for Holstein milk yield.Dominance may be an important source of non-additive genetic variance for many traits of dairy cattle. However, nearly all prediction models for dairy cattle have included only additive effects because of the limited number of cows with both genotypes and phenotypes. The role of dominance in the Holstein and Jersey breeds was investigated for eight traits: milk, fat, and protein yields; productive life; daughter pregnancy rate; somatic cell score; fat percent and protein percent. Additive and dominance variance components were estimated and then used to estimate additive and dominance effects of single nucleotide polymorphisms (SNPs). The predictive abilities of three models with both additive and dominance effects and a model with additive effects only were assessed using ten-fold cross-validation. One procedure estimated dominance values, and another estimated dominance deviations; calculation of the dominance relationship matrix was different for the two methods. The third approach enlarged the dataset by including cows with genotype probabilities derived using genotyped ancestors. For yield traits, dominance variance accounted for 5 and 7% of total variance for Holsteins and Jerseys, respectively; using dominance deviations resulted in smaller dominance and larger additive variance estimates. For non-yield traits, dominance variances were very small for both breeds. For yield traits, including additive and dominance effects fit the data better than including only additive effects; average correlations between estimated genetic effects and phenotypes showed that prediction accuracy increased when both effects rather than just additive effects were included. No corresponding gains in prediction ability were found for non-yield traits. Including cows with derived genotype probabilities from genotyped ancestors did not improve prediction accuracy. The largest additive effects were located on chromosome 14 near  Simulations and validation studies using real data have indicated that genomic selection can provide remarkably high accuracy of predicted breeding values (BV) of individuals without their own records or without progeny records Few studies have attempted to generalize and apply genomic selection models that include non-additive genetic effects with large data sets Although many cows with phenotypes do not have genotypes, their sires and dams or their sires and maternal grandsires (MGS) have genotypes.. The expected genotype probabilities for those cows based can be calculated using genotypes of the ancestors and the allele frequencies in the population. Boysen et al. Many statistical models and algorithms have been proposed to predict BV using genome-wide dense markers, which differ in the assumption of distributions of SNP effects This study had four goals. First, additive and dominance variance components were estimated using Holstein and Jersey data for eight traits. Second, predictive ability of models that included additive and dominance effects was compared with that of a model that included only additive effects. Third, predictions obtained using different dominance coefficients were compared. Fourth, model prediction was tested by expanding the data set to include cows with genotype probabilities derived based on ancestor genotypes.Genotypes were available from the Council on Dairy Cattle Breeding  for Holsteins and Jerseys. Genotypes were from six different SNP arrays: the Bovine3K, BovineLD, BovineSNP50, and BovineHD , and the GeneSeek Genomic Profiler and GeneSeek Genomic Profiler HD . All genotypes were imputed to a BovineSNP50 basis using findhap.f90 software Phenotypic data were yield deviations for milk, fat, and protein; productive life (PL); daughter pregnancy rate (DPR); somatic cell score (SCS), fat percent (fat%) and protein percent (protein%) for first parity. Yield deviations for fat% and protein% were obtained indirectly as (yield deviation of fat% = ((fat mean for base cows+fat yield deviation)/(milk mean for base cows+milk yield deviation) - fat mean for base cows/milk mean for base cows) *100; and a corresponding formula for protein%). The values of trait mean for base cows were 11,839, 432 and 396Kg for Holstein milk, fat and protein, respectively, and corresponding values were 8379, 384 and 298Kg for Jersey breed. DPR is defined as percentage of non-pregnant cows that become pregnant during each 21-day period; a DPR of 1 implies that cows are 1% more likely to become pregnant during that estrus cycle than cows with an evaluation of 0. PL is defined as time in the milking herd before removal by voluntary culling, involuntary culling, or death; credits for each month in milk are obtained from standard lactation curves and then summed across all lactations; diminishing credits within lactation give cows more credit for beginning a new lactation than for continuing to milk in previous lactation; cows get 8 months credit for 305-day first-lactation records, 10 months credit for second lactations, 10.2 months credit for third and later lactations, partial credits for shorter records, and extra credits for longer records.C). The second included cows with phenotypes, but genotype probabilities were calculated from genotyped sire and dam (DATAS-D). The third included cows with phenotypes but genotype probabilities were calculated from genotyped sire and MGS , the probabilities of possible genotypes  for cows were computed assire), P(Adam), and P(AMGS) are the probabilities that allele A was transmitted to offspring from sire, dam and MGS, respectively; P(Bsire), P(Bdam), and P(BMGS) are the probabilities that allele B was transmitted to offspring from sire, dam and MGS, respectively; and population allele frequencies were q for A and p for B. Thenandsire or Bdam or BMGS).The same approach was used to calculate P(BC data set was used to estimate variance components and SNP effects (additive and dominance) and to perform ten-fold cross-validation for prediction. Variance estimation and validation were also conducted using the combined data sets (DATAC+DATAS-D+DATAS-MGS). The same testing data sets were used when cross-validation was performed on DATAC only or on the combined data sets.The DATAVariance components for each trait were estimated using the GBLUP method by including additive or additive and dominance genetic effects; the single-trait linear mixed models used were:y is a vector of management group deviations for each trait; u, uS-D, and uS-MGS are the intercepts; a, a1, a2, and a3 are vectors of additive effects for animals; d1, d2, and d3 are vectors of dominance effects; e, e1, e2, and e3 are the vectors of random residuals for animals; 1 is a vector with elements of 1, and 1S-D and 1S-MGS are vectors with elements of 1 for DATAS-D and DATAS-MGS, respectively, and 0 for other records. Each animal had a single record; therefore, G and D1 (or D2) are additive and dominance genomic relationship matrices, respectively; R is the coefficient matrix for error variance:NS-MGS is the number of daughters for each sire-MGS pair. The G,D1, and D2 were constructed based on information from genome-wide markers k is the total number of SNPs; Z is a centered genotype matrix with each z is a genotype code  minus 2ip; ip is the frequency of the second of two alleles at locus i; iq is the frequency of the first allele at locus i; the elements of H equal 0−2iqip for homozygous alleles and 1−2iqip for heterozygous alleles; and the elements of M equal iqip, and Then, Variance components were estimated using average-information restricted maximum likelihood (AI-REML) The additive and dominance effects for each SNP were estimated using the SNP-BLUP method with the variance components described previously. Using the MAD model as an example, the mixed model equation for estimating each SNP effect was.SNP areSNP, MAD2SNP, and MAD3SNP, respectively.SNP, MADSNP, and MAD2SNP models used data only from DATAC, and equations were solved by the inversion method. However, the MAD3SNP model used data from all three data sets . Because some cow genotypes were probabilities and required >1 character for storage, calculations for SNP for big data. A blend of first- and second-order Jacobi iteration was implemented with two relaxation factors Solutions for small populations can be obtained directly by building the mixed model equations shown in (1) and inverting the left-hand side. The MAGoodness-of-fit for each model was evaluated using likelihoods based on the whole data set as well as correlations between predicted BV and phenotypes in the training data. The superiority of model MAD and MAD2 over MA was tested using a likelihood ratio test. Cross-validation was used to measure prediction accuracy, with the data set randomly divided into ten approximately equal portions. Nine of the portions were used in turn for training the models to estimate SNP effects, and the remaining portion was used for testing prediction accuracy. The predictive ability of the model was evaluated by comparing predictions and phenotypes of animals in the testing data set and was measured as the correlation between predicted genetic values and phenotypes. Predictions of additive genetic effect (BV) and total genetic value (defined as the sum of additive and dominance effects in the model) were both evaluated. Paired two-sample t-tests were used to test correlations for differences.P<0.0001) better than did MA. For PL, DPR, and SCS, the −2 log likelihoods were similar for MA, MAD, and MAD2. The model including dominance also fit the data better than MA for protein% (both breed) and fat% of Holstein. The number of animals in MAD3 was different from that for MA, MAD, and MAD2; therefore, the likelihood for MAD3 was not comparable with that for other models.Measures of goodness-of-fit based on likelihood ratio tests are in Average correlations between estimated genetic effects and phenotypes in training data for ten-fold cross-validation  also indP<0.005) and Jersey yield traits (P<0.001). However, for Jersey PL, DPR, and SCS as well as Holstein PL and DPR, correlations from MA, MAD, and MAD2 from testing data were almost the same and did not differ statistically (P>0.2). Jersey correlations from testing data were lower than Holstein correlations except for PL. By enlarging the data set, MAD3 did not provide better prediction for either Holsteins or Jerseys. The standard deviation of correlations from ten-fold cross-validation ranged from 0.017 to 0.024 on different traits for Holstein, and from 0.018 to 0.043 for Jersey; yield traits had lower standard deviation than other traits.Predictive ability for Holstein and Jersey yield traits was better for MAD and MAD2 than for MA based on correlations from testing data used in the ten-fold cross-validation . For MADDGAT1Based on additive and dominance SNP effects from MAD, Manhattan plots for eight traits were constructed, and the ten SNP with largest effect were characterized. Bos taurus genome For yield traits, The magnitude of dominance variance relative to phenotypic variance for different traits varied widely for genotyped Holstein and Jerseys cows in the United States. Dominance variances were larger for MAD than for MAD2. Dominance heritability from MAD for milk yield was 5% for Holsteins and 7% for Jerseys, which was slightly higher than the results reported by Sun et al. Additive and non-additive variances usually have been estimated using models with pedigree-based relationship matrices. Van Tassell et al. + cells in mice; however, added epistasis did not contribute to accuracy. Su et al. The likelihood ratio test showed that a model with a dominance effect had better goodness of fit for yield traits than did a model with only an additive effect. Therefore, non-additive genetic variance is important for complex traits, and a model with non-additive genetic effects is expected to increase prediction accuracy. In this study, MAD was approximately 2% better than MA for predicting phenotypes in testing data sets. Lee et al. The difference between MAD and MAD2 was how the dominance relationship matrix was calculated. In this study, estimates for dominance variance were larger and additive variances smaller for MAD compared with MAD2. Vitezica et al. C, DATAS-D, and DATAS-MGS), MAD3 underestimated additive heritability. A better model might treat the three groups as correlated phenotypes to account for differences in genotype accuracy and phenotype distributions between them. The cows with imputed genotype probabilities were offspring of genetically superior (elite) animals, and pre-selection may have affected the results and caused bias. Another issue that may need to be addressed is if including all of the genotyped females is optimal. Some elite cows were genomically tested after their phenotypes showed them to be superior and may represent only a small fraction of a herd . Such cows are highly selected, and predictions may become more accurate by limiting their data.The MAD3 model was expected to increase predictive ability even more than MAD and MAD2 because it included sire-dam and sire-MGS groups to increase the available data; however, it did not. Perhaps because of the more complex model needed to deal with combined data  on chromosome 14 that affects yield traits P ≤ 0.01) dominance effects for fat yield on chromosome 14 within the DGAT1 region. The current study and Boysen et al. P ≤ 0.01) dominance effects for SCS. A QTL that affects yield traits have been identified on chromosome 6 using granddaughter designs in U.S. The Dominance variance accounted for about 5 and 7% of total variance for yield traits for Holsteins and Jerseys, respectively, based on the MAD model. For PL, DPR, SCS, fat% and protein% dominance variances were very small, especially for Holsteins. The MAD model had smaller additive and larger dominance variance estimates compared with MAD2. The likelihood ratio test showed that a model with dominance effects included had better goodness of fit than an additive-only model for yield traits. Based on ten-fold cross-validation, the MAD and MAD2 models can increase prediction ability for Holstein and Jersey yield traits; improvements from the two models were similar. Prediction accuracy did not improve by including cows with derived genotypes. The largest additive effects were located on chromosome 14 for all three yield traits for both breeds, and those SNP also had the largest dominance effects for fat yield for Holsteins and Jerseys as well as Holstein milk yield. Dominance effects should be considered for inclusion in routine genomic evaluation models to improve prediction accuracy and exploit specific combining ability.Text S1(DOCX)Click here for additional data file.


Sedentary behavior (SB) expression and its underlying causal factors have been progressively studied, as it is a major determinant of decreased health quality. In the present study we applied Genotype x Age (GxAge) and Genotype x Sex (GxSex) interaction methods to determine if the phenotypic expression of different SB traits is influenced by an interaction between genetic architecture and both age and sex. A total of 1345 subjects, comprising 249 fathers, 327 mothers, 334 sons and 325 daughters, from 339 families of The Portuguese Healthy Family Study were included in the analysis. SB traits were assessed by means of a 3-d physical activity recall, the Baecke and IPAQ questionnaires. GxAge and GxSex interactions were analyzed using SOLAR 4.0 software. Sedentary behaviour heritability estimates were not always statistically significant (p>0.05) and ranged from 3% to 27%. The GxSex and GxAge interaction models were significantly better than the single polygenic models for TV (min/day), EEsed , personal computer (PC) usage and physical activty (PA) tertiles. The GxAge model is also significantly better than the polygenic model for Sed (min/day). For EEsed, PA tertiles, PC and Sed, the GxAge interaction was significant because the genetic correlation between SB environments was significantly different from 1. Further, PC and Sed variance heterogeneity among distinct ages were observed. The GxSex interaction was significant for EEsed due to genetic variance heterogeneity between genders and for PC due to a genetic correlation less than 1 across both sexes. Our results suggest that SB expression may be influenced by the interactions between genotype with both sex and age. Further, different sedentary behaviors seem to have distinct genetic architectures and are differentially affected by age and sex. Sedentary behaviors, i.e. sitting or reclining, have been increasingly linked with poor health status, and are associated with chronic diseases such as type 2 diabetes, and overall mortality In a review of the genetic basis of physical activity and sedentary behavior, Santos et al. The main purposes of the present study, using a nuclear family design, are to estimate the magnitude of genetic factors responsible for the variation in distinct sedentary behavior phenotypes, and to study potential Genotype-by-Sex and Genotype-by-Age interactions influencing these sedentary behaviors. We hypothesize that SB is genetically driven and that GxSex and GxAge interactions will have a significant influence in SB phenotype expression.The Healthy Family Study, from the Portuguese Famílias Saudáveis (FAMS), investigates the relationship among metabolic syndrome indicators, physical activity, physical fitness and body composition in nuclear families. Children and adolescents aged ≤ 18 years were recruited in schools from Azores and Madeira archipelagos, and the northern and central regions of mainland Portugal, and were approached to freely participate in the study with their siblings and parents. School officers provided family lists, and families with at least two siblings were initially invited. However, given that families with 3 or more children are scarce in the Portuguese population The standardized procedures of Lohman et al. Using a 3-d physical activity diary (B3DAR) The Baecke questionnaire is a self-administered questionnaire aiming to analyze 3 different dimensions of habitual physical activity: (i) sports physical activity (SPA), (ii) leisure physical activity (LPA) and (iii) work/school physical activity (WPA). It is a Likert scale-based questionnaire that refers to the past 12 months, and includes 16 questions divided into 3 distinct sections: questions 1-8 assess work or school physical activity; questions 9–12 assess sports activities and exercise programs; and questions 13–16 assess leisure and locomotive activities. The sums of the specific scores of each section constitute the physical activity level for WPA, SPA and LPA, respectively. The sum of these 3 scores constitutes the habitual or total physical activity level (TPA). Sports were subdivided into three levels of energy expenditure according to Durnin and Passmore [12]; the low level for sports such as, billiards, sailing, bowling, golf ; the middle level for sports such as, badminton, cycling, dancing, swimming and tennis ; and the high level for sports such as boxing, football, basketball, rugby and rowing . A question about the total time per day the subject used a personal computer (PC) was applied to further classify sedentary behavior. Also, subjects were considered sedentary, light active and high active, depending upon if they were in the first, second, or third tertile of TPA.The International Physical Activity Questionnaire (IPAQ) Descriptive analyses were performed in PEDSTATS v0.6.12 and group comparisons were made in PEPI 4.0.y define the covariance of the basic polygenic model as:i and j index individuals, i and j are the same, and 0 otherwise. This model is used to estimate heritability, which is given as: 2) were computed using a maximum likelihood approach to estimate variance components under the standard polygenic model as implemented in SOLAR v.4.3.1 software Univariate quantitative genetic procedures as implemented in SOLAR v4.1 To test for GxSex and GxAge interactions, basic initial hypotheses were formulated regarding the variance/covariance relationship of a SB indicator between family members with different gender or age. For the GxSex model we treated sex as a discrete environment , and for the GxAge model we treated the age continuum as a continuous environment. As regards Genotype by Environment (GxE) interaction in general, the fundamental null hypothesis is that the expression of a polygenotype  is independent of the environment. It can be shown from first principles that if there is no GxSex or GxAge interaction, the same SB trait measured in subjects with different genders or ages will have a genetic correlation of 1.0 and the genetic variance will be homogeneous across both genders or across all ages f and m index females and males, respectively, and for an across-sex genetic correlation denoted by The basic polygenic model can be extended to account for GxSex interaction by allowing for sex-specific additive genetic and environmental variances denoted by i-th individual standardized against the sample mean,. An additional justification for the exponential function is suggested by the alternative name of this approach, namely the log-linear model of the variance: Under the GxAge interaction model, variance and correlation are modeled as continuous functions of age. For the genetic variance function  variance is modeled using an exponential function to ensure positivity, which is required since any variance is a squared term i-th and j-th individuals: That is, on taking the natural logarithm of the variance modeled as an exponential function, we have the equation of a line. In this form, the variance homogeneity null hypothesis obviously holds for a slope-term equal to 0: For reasons detailed in Diego et al. We also performed a post-hoc power analysis to determine the power in our sample to detect GxSex and GxAge interaction effects. Power is defined as the probability of correctly rejecting the null hypothesis, and is computed as the probability integral from the point on the alternative distribution corresponding to the nominal significance level or alpha (on the null distribution) to the upper limit of the alternative distribution at positive infinity. Since the total probability of any distribution is 1, power can be conveniently computed as:2 = 0.401, p = 0.05), sons and daughters , and between parents and offspring . As for the IPAQ phenotypes, offspring spend more time watching TV (p<0.01) and sitting (p<0.001) than parents. Mothers spend less time watching TV (p<0.01) and no significant differences were observed between fathers and mothers sitting time. Parents' energy expenditure in SB is higher than offsprings' (p<0.001), even though time spent in sedentary behaviors as assessed by the B3DAR was higher in offspring (p<0.001).As assessed by the Baecke questionnaire, no differences were observed for PC usage between fathers and mothers  by means of a log-likelihood ratio test see . The GxSIn order to assess if there is GxAge and/or GxSex interactions, each of the full models were compared to its constrained alternatives (i.e. setting g) equals 1 and variance homogeneity, were significantly rejected. g = −0.85; p = 0.009).The power analysis results for the GxSex model are presented in The power analysis results for the GxAge model are presented in The novelty of these results presented in this study is the estimation of heritability for a set of distinct SB markers in nuclear families as well as the application of genotype x environment interaction models to better understand how SB is influenced by age and sex. It should be emphasized that the heritabilities varied from 3% (sitting) to 27% (Sed), which means that these phenotypes have distinct genetic contributions. This is quite relevant to understanding the mechanisms that lead to distinct sedentary behaviors and suggests that even though some behaviors are under some genetic regulation, others seem to be just an adaptation to the contemporary way of living. The only result that could be compared with other studies is from the 3-d diary, that yielded an heritability estimate of 27% for sedentary behavior which is in agreement with previous data from the Quebec Family Study Viva La Familia study Some studies have used accelerometry to quantify sedentary behavior 2 = 755.56; P<0.05), highlighting the presence of sex differences in the expression of SB. However, in 2010, another study with the NTR found that irrespective of age, sedentarism is more prevalent in females when using a distinct marker of SB . Once again, this seems to be quite difficult to understand, as even within the same sample and the same research group, SB results can be so diverse. This is why SB may not be a one-dimensional phenotype per se. Rather, it is a multi-dimensional behavior, characterized by low energy expenditure that is quite diverse in their structure, levels and patterns. Our results raise new questions regarding daily activities and/or routines, because evidence shows that males tend to be more active than females The growing interest in the association between SB and health has led researchers to examine the influence of age and sex in the expression of distinct sedentary behaviors. Specifically, Genotype x Sex and Genotype x Age interaction analyses can be very helpful to disentangle these issues and are a sophisticated way to analyze the association of age and gender with SB. We found that the GxSex interaction model was a significantly better predictor of TV, EEsed, PC and PA tertiles than the polygenic model, meaning that there is variability in the expression of these behaviors that may be explained by differences between males and females in the genetic regulation of SB. Given the consistently higher additive genetic variance in males, our interpretation of these results is that the genetic regulation of energy expenditure in sedentary behaviors is stronger in males than in females. Once again, we were not able to find any study comparable to ours, namely because our model does not include any ‘family/shared’ factors as a source of environmental variation. However, previous work In regard to the influence of age on SB, the GxAge interaction model was a significantly better predictor of TV viewing, EEsed, PC, Sed and PA tertiles than the polygenic model, meaning that the expression of SB could be age-specific. In this case, the age effect is not simply the accumulation of habits and experiences as people get older, but mainly the way by which our genetic background probably deals with those routines. To the best of our knowledge, this kind of analysis has never been performed on SB and is of utmost importance because it can help to explain why some people tend to become sedentary as age increases. Typically, it is expected that people become more sedentary as they move from childhood to adolescence, adulthood and finally late adulthood. This has been attributed to changes in lifestyle between these life stages. For instance, adolescent development is known for being highly dependent upon peer approval, as engaging in sports or leisure's activities is dependent upon being part of a team or club Some limitations of the present study must be addressed: (i) our sample is not representative of the general Portuguese population, (ii) the relatively young and healthy sample of families may limit the generalizability of the results to older individuals, (iii) the usage of self-reporting SB is doomed to diminished reliability when compared to more objective measures which may influence heritability estimates, and (iv) the lack of information about resting metabolic rate may influence the EEsed and Sed results, although previous work In conclusion, the present results highlight that, in our sample, 3 to 27% of the variation in sedentary behaviors is due to a genetic component adding to the known fact that sedentarism is a multifaceted and variable construct that is highly dependent upon measurement strategy. Further, our results suggest that SB expression may be influenced by the interactions between genes with both sex and age, which helps to understand some of the observed variation in these traits' expression. However, it must be acknowledged that different sedentary behaviors have distinct genetic foundations and are affected differently by age and sex, which should motivate efforts to develop better assessment tools that can be more easily comparable.