Genetic diseases are a wide group of diseases in which the etiopathogenesis is caused by or related to genetic factors. The role of genetics in the disease development can be more or less relevant depending on the specific characteristics of the disease, and a wide spectrum of complexity exists.Monogenic diseases, for example, are directly caused by defects in a specific gene whereas complex and polygenic diseases are generally caused by the interactions between multiple genes or between genetic and environmental factors. To the last category belong many forms of cancer, an uncontrolled growth of cells with alterations of the genetic materials.In the last decade, a large amount of experimental data has become available, so the identification of strategies to process and, most importantly, interpret them is crucial. The massive volume of data, both in terms of quantity and of dimensionality, and their heterogeneity and low signal-to-noise ratio are just some of the most obvious challenges that they present. To give an example, single nucleotide DNA mutations are one of the most common factors analysed in relation to the development of a genetic disease. However, this sometimes translates into dealing with millions of variants measured across thousands of individuals, where only a handful are informative. In fact, other more complex factors, such as gene expression, could play a significant role.The aim of this special issue is to review the recent advances in computational methods concerned with genetic diseases.The issue received sixteen submissions; each one was referred by at least two international reviewers that we warmly thank for their time. Six papers have been accepted for the publication.“A New Approach for Mining Order-Preserving Submatrices Based on all Common Subsequences” by Y. Xue et al. proposes, in the context of gene expression data, a pattern-based subspace clustering or OPSM (order-preserving submatrix model), based on frequent sequential pattern. The approach has been experimentally proven to be able to discover the biological significant OPSMs and deep OPSMs exhaustively.“Evolutionary Influenced Interaction Pattern as Indicator for the Investigation of Natural Variants Causing Nephrogenic Diabetes Insipidus” by S. Grunert and D. Labudde is devoted to the application of a high-throughput analysis method based on motif conservation among proteins of the same protein family for analysis of interacting sequences. This investigation can help to analyze the pathogenic impact of mutations causing alterations in interacting regions of a protein. This analysis has been applied on membrane proteins, in particular to the aquaporin 2 whose mutants are involved in nephrogenic diabetes insipidus.“Unified Modeling of Familial Mediterranean Fever and Cryopyrin Associated Periodic Syndromes” by Y. Bozkurt et al. describes a unifying dynamical model for Familial Mediterranean Fever (FMF) and Cryopyrin Associated Periodic Syndromes (CAPS) in the form of coupled nonlinear ordinary differential equations. The authors perform a comprehensive bifurcation analysis of the model and show that it exhibits three modes, capturing the healthy, FMF, and CAPS cases. They present extensive simulation results for the model that match clinical observations.“Enhancing the Lasso Approach for Developing a Survival Prediction Model Based on Gene Expression Data” by S. Kaneko et al. presents a novel improvement to the lasso approach, one of the most widely used method to correlate gene expression data with cancer patients' survival. This new algorithm significantly increases the ability to identify “true positives” and its validity is shown on both simulated and real data.“Statistical and Computational Methods for Genetic Diseases: An Overview” by Francesco Camastra, Maria Donata Di Taranto, and Antonino Staiano gives a survey of statistical and computational methods used to analyse the pathogenic role of sequence variants as well as to identify genetic markers of complex diseases by association studies, meta-analysis, and expression studies.“Optimization and Corroboration of the Regulatory Pathway of p42.3 Protein in the Pathogenesis of Gastric Carcinoma” by Y. Hao et al. provides important research directions for exploring the mechanism of action of p42.3 protein in gastric cancer. Through a Bayesian network model, the potential important role of p42.3 is verified by both theoretical analysis and preliminary test.We hope that the readers of this journal will find in the issue interesting papers and that this can encourage and foster further research on computational methods for genetic diseases.Francesco CamastraFrancesco CamastraRoberto AmatoRoberto AmatoMaria Donata Di TarantoMaria Donata Di TarantoAntonino StaianoAntonino Staiano


Chlamydotis undulata) with an exhaustive recorded pedigree, we investigated the genetics of aging in one female reproductive trait (egg production) and three male reproductive traits , that display senescence at the phenotypic level. Animal models revealed an increase in additive genetic variance with age for courtship display rate and egg production but an unexpected absence of increased additive genetic variance for ejaculate size and no additive genetic variance for sperm viability. Our results suggest that the mechanisms behind the senescence of some traits are linked with a change in genetic expression, whereas for some other traits, aging may result from the constraints associated with physiological wear and tear on the organism throughout the life of the individual.Do all traits within an organism age for the same reason? Evolutionary theories of aging share a common assumption: the strength of natural selection declines with age. A corollary is that additive genetic variance should increase with age. However, not all senescent traits display such increases suggesting that other mechanisms may be at play. Using longitudinal data collected from more than 5400 houbara bustards ( Aging is defined as a progressive decline in the age-specific fitness components of an organism due to internal physiological degeneration ,2. From Drosophila melanogaster, [Mus musculus [Arabidopsis thaliana [Cygnus olor [Cervus elaphus [Ovis aries [Ovis Canadensis [Silene latifolia [Drosophila, the additive genetic variance is known to be stable with age for egg production [Empirical studies have provided mixed results regarding changes in additive genetic variance at older ages. Historically, the genetic theories of aging have been largely supported in diverse laboratory model species, including invertebrates, vertebrates and plants . More rnus olor ,15, red  elaphus ,17, Soayis aries , Bighornnadensis  and Sileatifolia ). Howeveoduction , whereasoduction –25. Howeoduction ,27, and oduction . Hence, Senescence patterns have usually been measured on single traits, but recent studies have highlighted that within the same organism, different traits can vary in their onsets and rates of phenotypic senescence –31. The Chlamydotis undulata), a non-domestic and non-laboratory bird species in which phenotypic patterns of senescence have been found in both male and female reproductive parameters. A recent study [In this study, we explored the genetic basis of senescence in male and female reproductive traits of the houbara bustard , one maChlamydotis undulata undulata is a medium-sized endangered bird that inhabits semi-arid areas in North Africa and in the Canary Islands. The sharp decline of the populations, mainly due to over-hunting and habitat degradation, [ad libitum and cared for by a team of qualified veterinarians, and standards from sanitary authorities are regularly met.The north-African houbara bustard adation,  led to tadation, . The datThe founder birds of the captive population were born from eggs collected in the wild. All other birds were produced through a carefully planned breeding program aiming to equalize representation of founders and minimize inbreeding . ControlDuring the breeding season, captive males exhibit a complex courtship display combining conspicuous visual and acoustic components similar to those observed in the wild . These data points from individuals older than 15 years represent less than 10 males that were genetically unrelated and less than 30 females that were weakly genetically related, so no genetic information could be determined from these age classes . The cut2 (in years) as fixed effects to allow for non-linear variation of the traits with age .Phenotypic senescence in all traits was investigated using generalized mixed models with a Poisson distribution. Each model included individual identity as a random effect to account for repeated measurements as well as Age (in years) and AgeThe genetic architecture of aging was explored using “animal models” ,47. AnimSperm viability data were transformed using the arc-tangent function and analyzed assuming a Gaussian distribution. Ejaculate size, courtship display rate and egg production were Poisson distributed count data. In linear model analyses, the common way to address skewed Poisson distributed data is to log-transform data to obtain a Gaussian distribution. Nevertheless, transformation is not recommended for count data . AlthougThe convergence of the Bayesian models was assessed by (i) graphically checking the posterior estimates and (ii) ensuring that the autocorrelation of all parameter estimates along the MCMC chain was lower than 0.05. Low autocorrelation was achieved using a minimum of 1 200 000 iterations with a burning in of 200 000 and a thinning of 1000, but the number of iterations and thinning were increased in case of autocorrelation. We used parameter expanded priors in all analyses to avoid information from the prior, with one exception for the random regression animal model of courtship display rate, for which we used a prior designed as V = diag(n)*(Vp/3) and nu = n . However, when comparing results from models run with a parameter expanded prior or a slightly informative prior for other traits, the results were similar (results not shown).th group, observations were pooled into one group for 9-to-15-year-old individuals . For ejaculate size and sperm viability, 8-year-old individuals were also pooled into a last age-class because senescence starts early and we needed to ensure sufficient power. This resulted in a sample of at least 100 individuals in each age class  model estimating variances and covariances between all age-classes [The ideal model would be a multivariate , we included a permanent environment effect and age fitted as a fixed effect because measurements were repeated for individuals.For ejaculate size and sperm viability, several measures were available for the same individual each year, so we fitted an identity random effect to account for these repeated measurements. To account for the potential impact of the frequency of collection, we used the number of days elapsed since the last ejaculate as a fixed effect. Moreover, seasonal variation in ejaculate size was taken into account with the number of days since the first ejaculate of the year fitted as a fixed effect. Note thatconsidering quadratic and cubic effects did not change the results. As year of collection and age were confounded with higher ages in our data set, year of collection was not fitted in our animal models.For those models using a Poisson distribution, DIC cannot be used to assess the best model see .Because Va was estimated from models that assumed a Poisson distribution, the estimate of heritability should account for the link between mean and variance. We calculated Poisson heritability  asses  of a trait among age classes. This matrix was estimated by running bivariate animal models on discrete age-classes as used in univariate models. In bivariate models, the fixed effects remained the same as in the corresponding univariate models, but the vectors of random effects were replaced by the matrices of variance-covariance  . The use2 distribution with degrees of freedom equaling the difference in the number of parameters between the two models.To select the best RRAM model, we followed a two-step selection strategy as in Brommer et al.  using loy is the vector of phenotypic observations on all individuals, μ is the mean phenotype, X is the design matrix relating individuals to fixed effects β and id is the individual effect. For all traits, age was fitted as a factorial fixed effect to control for differences in phenotypic values across ages. For ejaculate size, the frequency of collection and seasonal variation defined above were also fitted as fixed effects. Legendre polynomial functions (f) were used to model individual variance, f, where i was the order of Legendre polynomial and ageST was the age parameter scaled between -1 and 1  to meet the Legendre polynomial conditions of application; e is the residual variance covariance matrix. We allowed for heterogeneity in residual variance by fitting age-specific residual variances , and the residual correlations between age classes were constrained to zero.A random regression model was used, i.e.,best, the order of Legendre polynomial that best described individual variation across ages.We started by comparing the fit of a model assuming first-order polynomial variation between individuals i = 1 in  to the fid), and the polynomial order for a, breeding values, was sequentially increased starting from 0. Finally, the best animal model was also run with a Poisson distribution using the MCMCglmm package.From this best model, we partitioned the individual variance into a function of additive genetic variance and permanent environment variance as,G) was obtained from back-transformation of the Legendre polynomial function f. The variance estimates are provided on the liability scale  was found in all traits across the entire data set and within age classes . For speFor ejaculate size, the posterior estimates of additive genetic variance were greater than 0 in all age classes and Va was lower for older birds . In contFor courtship display rate, the posterior estimates of additive genetic variance were clearly greater than 0 for all age classes except for the 8-year-old age class . The paiFor the number of eggs produced, additive genetic variance increased significantly across ages . This inBivariate animal models provided no evidence for negative genetic correlations between the first age class (1 year old) and the oldest age class in any of the traits. For ejaculate size and courtship display rates, genetic correlations between young birds (1 year old) and the oldest birds were low and not significantly different from zero. For the number of eggs produced, these genetic correlations remained significantly positive between the young and old birds Tables –8.When genetic correlations between young birds and birds of other age classes were excluded, genetic correlations varied between 0.24–0.92 for courtship display rate and 0.52–0.98 for egg production. For both traits, genetic correlations decreased as age differences increased. For ejaculate size, genetic correlations ranged from 0.79–0.99, and the decrease of these correlations as age differences increased seemed moderate in comparison with those of other traits Tables –8.Because of the extremely low additive genetic variance of sperm viability across age classes, the genetic correlations between age-classes were not calculated for this trait Tables –8.Protopiophila litigata [Homo sapiens: [Hirundo rustica: [Gallus gallus domesticus: [Gallus gallus domesticus: [All reproductive traits of the houbara bustard investigated here showed senescence at the phenotypic level corroborating previous findings on this species . These slitigata ), sperm sapiens: ; Hirundorustica: ; Gallus esticus: ) and eggesticus: ); see [6esticus:  for reviBoth courtship display rate and egg production showed a terminal increase in additive genetic variance, consistent with genetic theories of senescence ,9. AlthoIn turn, the pattern of additive genetic variance was more complex for courtship display rate. Although high additive genetic variance in young and older age classes and low genetic variance in mature individuals is apparent is some species ,16, addiFor both courtship display rates and number of eggs produced, positive genetic correlations among early and late expression of the traits do not support within trait antagonistic pleiotropy. One reason could be that in the animal model, the effects of all loci are averaged together so that if AP occurs among some loci, it may not be detected . Hence, In contrast with courtship display rates and number of eggs, no increase in additive genetic variance was detected for sperm traits. Altogether, the stability of genetic variance in ejaculate size and the positive genetic correlations between the early and late expressions did not support either the MA or AP theories .Nauphoeta cinerea) [For sperm viability, the amount of additive genetic variance was extremely low in the houbara bustard (cinerea) . Howevercinerea)  showed acinerea) , we did cinerea)  for revicinerea) . The perThree methodological issues may explain the lack of age-related genetic patterns of both sperm traits, i.e. ejaculate size and viability. First, by reducing the genotype differences among older birds, selective disappearance can reduce genetic variance at old ages compared to that at younger ages. To assess the importance of selective disappearance in shaping these patterns, all univariate animal models were re-run after the removal of the animals that had died during the time period covered by our study. A few birds died during the period of the study, so it was not possible to run a model with longevity as a covariate because the data were right-censored. The removal of dead animals from the analyses allowed us to test whether an increase in Va was masked by a negative correlation between the longevity of individuals and their poor reproductive performance. The results provided in Drosophila have found results that challenge predictions of both MA and AP genetic theories of senescence  [Larus canus) [How can the existence of senescence be explained in the absence of genetic changes over the lifespan? Evolutionary biologists interested in the evolution of the mortality senescence of e e.g., –25). In . In Drosicollis)  and comms canus)  also fouThe first explanation relies on the assumptions that were used by theoretical models to predict age-related patterns of genetic variance components of MA and AP ,9. AlthoAn alternative evolutionary theory of aging, the reliability theory of aging ,72,73, iWe show contrasting age-related genetic variance patterns among reproductive traits. Although the increase in additive genetic variance for courtship display rates and number of eggs produced fit the mutation accumulation and antagonistic pleiotropy theories of aging, no such genetic signal was observed in sperm performance despite marked phenotypic senescence. To our knowledge, this study provides the first quantitative genetic assessment of aging for sperm performance traits.The decrease in the force of selection should apply to all traits related to fitness, although the magnitudes of these effects depend on how each particular trait actually covaries with fitness. Together with the difficulty of detecting small ranges of variation using quantitative genetics models, it may explain why we did not detect an increase in the additive genetic variance of sperm traits. Rose et al.  argued tThis diversity of mechanisms might imply that at the population or species level, the senescence of some traits is directly related to additive genetic variance, which may or may not involve trade-offs between ages. Consequently, investigating aging phenotypes as a whole using multivariate analyses rather than a single trait is thus important in shaping an accurate picture of the mechanisms of aging .S1 FigTo account for selective disappearance, all birds which died during the time of study were removed from the analyses. We used univariate animal models described in material and method.(DOCX)Click here for additional data file.S2 FigData were log(x+1) transformed and Gaussian link functions were used into the models.(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.S2 Text(DOCX)Click here for additional data file.S3 TextThe deviance information criterion (DIC), back-transformation of the Legendre polynomial function into Va and age-related variation of the additive genetic variance plotted from the best random regression animal model found in Tables (DOCX)Click here for additional data file.


The action of effect is hypothesized to be a decrease of shared environmental  influences on educational attainment, giving more room for genetic differences between individuals to impact on the variation of the trait. However, this hypothesis has not yet found consistent evidence. Support for this effect relies mainly on comparisons between countries adopting different educational systems or between different time periods within a country reflecting changes in general policy. Using a population-based sample of 1271 pairs of adult twins, we analyzed the effect of the introduction of a specific educational policy in Spain in 1970. The shared-environmental variance decreased, leading to an increase in heritability in the post-reform cohort (44 vs. 67%) for males. Unstandardized estimates of genetic variance were of a similar magnitude (.56 vs. .57) between cohorts, while shared environmental variance decreased from .56 to .04. Heritability remained in the same range for women (40 vs. 34%). Our results support the role of educational policy in affecting the relative weight of genetic and environmental factors on educational attainment, such that increasing equality in educational opportunities increases heritability estimates by reducing variation of non-genetic familial origin.Secular variation in the heritability of educational attainment are proposed to be due to the implementation of more egalitarian educational policies leading to increased equality in educational opportunities in the second part of the 20 Changes in the heritability of educational attainment (EA) depending on situational conditions have long been a subject of interest to researchers from different disciplines. The analysis of such variations as related to educational policy may provide valuable information about the effects of the school system on individual differences in EA. It has been suggested that increasing equality in educational opportunities reduces variation of environmental origin and, consequently, leads to an increase in genetic variation (and thus the heritability) of this phenotype , 2. ThatThe idea that more egalitarian educational policies increase heritability by means of a reduction in shared environmental variance has been pursued by different research groups. Heath et al.  found thAlthough family, twin and adoption studies, and even molecular genetics research, have provided consistent evidence for genetic variation in EA –18, the In spite of the interest of this question a definitive answer remains elusive and the role of educational policy on heritability estimations still generates controversy . Part ofThe first students under this reform were those born in 1961. This cut-point can be used to differentiate two cohorts drawn from the Murcia Twin Registry (MTR), which comprises twins born in the southeast of Spain between 1940 and 1966 . AlthougA population-based sample of adult male, female and opposite-sex twins of the Murcia Twin Registry (MTR)  providedThe sample selected for this study consisted of 2326 subjects . The mean age was 53.41 (SD = 7.1) for males and 54.25 (SD = 7.4) for females. The sample was distributed in 289 males from monozygotic twin pairs (MZM), 361 males from dizygotic same-sex pairs (DZM), 491 females from monozygotic pairs (MZF), 503 females from dizygotic same-sex pairs (DZF), as well as 327 males and 355 females from dizygotic opposite sex pairs (DOS). In total, the sample comprised 1271 pairs of twins (1055 complete and 216 incomplete). Zygosity was determined by questionnaire and DNA testing.The data analyzed in this report were collected, by personal and telephone interview, in three different waves. Self-report data (years of education assessed by highest degree attained) was obtained. As participants were over 40 when they were interviewed, it was assumed that the reported EA should be definitive for nearly all cases; additionally, we presumed that no differences should be expected according to the moment of data collection.low , medium  and high .EA ranged from Illiterate to University-High degree levels, following the guidelines of the Spanish National Statistics Institute. Achievement was categorized in: Participants were divided into two groups: pre-reform cohort, consisting of those twins born before 1961 (65.4%) and the post-reform cohort, with those born in 1961 and after (34.6%).All the participants in the MTR were previously contacted via an invitation letter containing the objectives of the registry and study information. Later on, they are contacted by telephone and oral informed consent is obtained prior to any data collection. When participants are involved in face-to-face interviews written informed consent is obtained. The MTR procedures, including informed consent, followed in this study have been approved by the Murcia University Ethical Committee and it follows national regulations regarding personal data protection. Applicable institutional and governmental regulations concerning the ethical use of human volunteers were followed during this research.Data preparation and descriptive and preliminary analyses were performed in SPSS v.19. Assumptions of the twin design  were checked by comparing a model with the applied constraints to a fully saturated model. Further details of the twin design, including assumptions, can be found elsewhere , 26.The data were analysed using Structural Equation Modelling (SEM), which allows one to test specific theoretical models in a multiple group approach. We applied the classical twin design , 28 to eLiability threshold models were fitted, assuming that the ordered EA categories reflect an imprecise measurement of an underlying normal distribution (with mean of 0 and variance of 1) and one or more estimated thresholds to discriminate between the categories . This li2 -distribution with degrees of freedom (df) equal to the difference in df between the two models. The best-fitting model was chosen in each case by deducting the residual deviance of the compared models and by comparing Akaike’s Information Criterion (AIC).To be able to use all available data Full Information Maximum Likelihood estimation of raw data was used. In this method, twice the negative log-Likelihood (-2LL) of the data for each family is calculated, and parameters are estimated so that the likelihood of the data is maximized. Means, variances, and twin correlations were estimated in a saturated model. Equality in parameters across groups were assessed by likelihood ratio tests (LRT), obtained by subtracting -2LL of a restricted model from that of a full model. The resulting test statistic has a χThe distribution of our categorized EA variable according to sex and cohort is presented in 2 = 3.18; df = 1; p > .05; Post-reform: χ2 = 0; df = 1; p > .05).Preliminary analyses comparing different models confirmed that means and variances were significantly different between cohorts for males and females separately, since equating means or variances between cohorts produced a significant loss of goodness-of-fit (p < .05) in all cases. Equating male and female means within cohorts also produced a similar worsening of fit (p < .05). However, variances for males and females within cohorts could be equated without significant loss of fit , indicating a significant change in the distribution of variance sources. Proportions of A, C and E remain more stable in women and the parameters could be equated across cohorts without loss of fit in the model .These observations were confirmed by model-fitting analyses. The proportions of variance explained by additive genetic influences (A), common (C) and unique environment (E) are shown in 2 = 4.08; df = 1; p = .04), while equating A or E estimates across cohort did not result in a significant loss of fit. In fact, the unstandardized estimates of genetic variance were almost the same across educational systems, indicating that absolute impact of genetics on individual differences did not vary . We found a significantly higher heritability in post-reform compared to pre-reform male cohorts, which appears to be the result of a significantly lower impact of shared environmental factors in the cohort exposed to the more egalitarian policy. The absolute impact of genetics on EA remained the same independently of the educational conditions, but the influence of those environmental factors common to members of the same family on the advance of the individual within the educational system was much lower for the younger male cohort, thus increasing the relative weight of genetics on EA variability after reform implementation. That is, the underlying genetically determined characteristics that affect the number of years that an individual stays in the educational system, such as cognitive or personality traits, exerted the same influence before and after the educational reform. However, the environmental factors that make members of the same family similar, such as socioeconomic status, lost impact after the reform. These changes in heritability took place in the context of a generalized increase in the mean levels of EA see  which isThese results expand on those obtained previously , 11, proIn interpreting these results it must be taken into account that the 1970 reform in Spain did take place in an evolving society and other socioeconomic changes may have collaborated to the observed changes. In fact, the early 1960’s witnessed the beginning of a period of substantial economic development  However The changes in the heritability of EA are only significant for males, while the relative impact of genetic and environmental influences in females is not significantly different across cohorts. This difference cannot be explained by total variance disparity in EA between genders, since the reduction of such disparity between cohorts was of a similar magnitude, as illustrated by national data presented in When interpreting our results, we should take into account our limitation in the capacity to explain sex differences, which adds up to the one already mentioned about the impossibility for this kind of studies to isolate the effects of a specific policy within an evolving society. Additionally, we should also point out that the sample size in this study limits the power to detect the differences in the sources of variance for AE according to sex and cohort, as seen by the large confidence intervals around some of the twin correlations and parameter estimates; and that the post-reform cohort comprises a limited amount of time. A larger sample size, incorporating younger subjects, would be desirable to confirm our findings.In summary, these results should be interpreted with caution. The very nature and characteristics of the research question, and the methodological boundaries mentioned preclude definitive conclusions. None the less, the distinctive sample of this study confers a unique opportunity to get a first glance on the effects of a specific educational reform on the heritability of EA. The hypothesis stating that more egalitarian educational policies should bear an increase in heritability by means of a reduction in shared environmental variance, has received some support previously. Our results provide further evidence for this argument and contribute to the analysis of the role of social policies on human complex behavior.


Genome-wide association studies have successfully identified thousands of loci for a range of human complex traits and diseases. The proportion of phenotypic variance explained by significant associations is, however, limited. Given the same dense SNP panels, mixed model analyses capture a greater proportion of phenotypic variance than single SNP analyses but the total is generally still less than the genetic variance estimated from pedigree studies. Combining information from pedigree relationships and SNPs, we examined 16 complex anthropometric and cardiometabolic traits in a Scottish family-based cohort comprising up to 20,000 individuals genotyped for ~520,000 common autosomal SNPs. The inclusion of related individuals provides the opportunity to also estimate the genetic variance associated with pedigree as well as the effects of common family environment. Trait variation was partitioned into SNP-associated and pedigree-associated genetic variation, shared nuclear family environment, shared couple (partner) environment and shared full-sibling environment. Results demonstrate that trait heritabilities vary widely but, on average across traits, SNP-associated and pedigree-associated genetic effects each explain around half the genetic variance. For most traits the recently-shared environment of couples is also significant, accounting for ~11% of the phenotypic variance on average. On the other hand, the environment shared largely in the past by members of a nuclear family or by full-siblings, has a more limited impact. Our findings point to appropriate models to use in future studies as pedigree-associated genetic effects and couple environmental effects have seldom been taken into account in genotype-based analyses. Appropriate description of the trait variation could help understand causes of intra-individual variation and in the detection of contributing loci and environmental factors. Unravelling overall trait architecture of complex traits and diseases is important for phenotype prediction and disease prevention and correct modelling of the trait will further aid discovery of causative loci. Here we take advantage of genome-wide data and a large family-based study to examine the role of common genetic variants, pedigree-associated genetic variants, shared family environment, shared couple environment and shared sibling environment on 16 anthropometric and cardiometabolic traits. By analysing up to ~20,000 Scottish individuals, we find that common genetic variants, pedigree-associated genetic variants and recently-shared environment of couples are the most important contributors to variation in these traits, while past family and sibling environment have a limited impact. Further studies on the pedigree-associated genetic variation and the shared couple environment effect are needed, as little research has been devoted to them so far. The proportion of phenotypic variance contributed by genetic variation is termed the heritability h2) . The her . The heh2) . As a coh2) . Therefoh2. Genome-wide association studies (GWAS), on the contrary, identify causal loci through their association with recorded genetic markers and then aggregate the proportion of variance explained by statistically-significant variants [A classic approach to gauging the heritability in humans is by comparing the observed phenotypic similarity to the expected genetic resemblance between relatives inferred from family pedigrees . This me studies ,7. Note variants ,9, whichanalysis –12. Althanalysis ,12. Thisanalysis ,13,14. Aability” ,15.et al. [Recently, Yang et al. ,17 have et al. , to esti or GRM) ,17. The e latter ,12. As aelatives .et al. [Recent studies have started dissecting the heritable component of variation and other components shared among relatives by studying more complex populations made-up of both unrelated individuals and extended pedigrees ,12,19. FIn this study, we use data from a single homogeneous cohort consisting of approximately 20,000 adults with varying degrees of relationships sampled from Scotland. The individuals have data on over 520,000 SNPs distributed across the autosomes. The dense marker information together with extended genealogical information allows us to partition the phenotypic variance and explore the genetic and environmental effects shared among related individuals .We analyse eight anthropometric traits, comprising height, weight, fat, body mass index (BMI), hips, waist, waist-to-hips ratio (WHR) and a body shape index (ABSI)  and eighIn our work, we implement alternative models to estimate effects that might contribute to the variation in the 16 traits analysed. Results show that, with these data, we can separate total genetic variation into SNP-associated and pedigree-associated genetic influences. We also observe that past family environment and shared full-sibling environment generally have a limited impact on trait variation, whereas the effect in couples of living in the current (shared) environment is always important in our data.We conducted variance component analyses to dissect the phenotypic variation for traits recorded in the Generation Scotland: Scottish Family Health Study (GS:SFHS) cohort  into genet al. [GRMg, GRMkin, ERMFamily, ERMSib and ERMCouple respectively, where GRM refers to genomic relationship matrices and ERM refers to environmental relationship matrices.We first explored the extent to which estimates of a couple . The totn et al.  and compG’ for GRMg, ‘K’ for GRMkin, ‘F’ for ERMFamily, ‘S’ for ERMSib and ‘C’ for ERMCouple–e.g. model ‘GKC’ = GRMg + GRMkin + ERMCouple. All models included a residual matrix .For brevity, we named different alternative models using abbreviations according to first subscript letter of the effects examined. We coded ‘We identified the most appropriate model for each trait by a stepwise model selection process via removing non-significant components from the full model based on a Wald test of their estimated effect and a likelihood ratio test (LRT), and we estimated the effects of significant factors using the selected models in GS10K. We repeated the model selection and corresponding variance component analyses in GS20K to identify differences resulting from analysing a more complex population structure, encompassing a larger proportion of close relationships.More details about traits, matrices and models are given in Material and Methods and Model robustness and the effectiveness of the model selection were tested using simulated data based on GS10K.G’, ‘K’, ‘F’, ‘S’, ‘C’, ‘GK’, ‘GF’ and ‘GKFSC’) and those representing the commonest conclusions of model selection in analyses of the real GS10K data . We conducted a simulation study using real genotype and pedigree information from GS10K to evaluate the robustness of our models. To make computation feasible, we mainly focused on data simulated under the simplest and most complex models  when simulated phenotypes were only contributed by one of the five corresponding effects plus residual variation. Under these models .In the first scenario, we examined the performance of our models  when the simulated phenotypes were determined by SNP-associated genetic effects and one of the familial effects (either pedigree-associated genetics or nuclear family environment) plus residual variation. Results . Results  and nuclear family environment (ERMFamily) or SNP and pedigree genetics (GRMkin) and couple environment (ERMCouple) or SNP and pedigree genetics and nuclear family and couple environment accordingly, since they were the major factors in each corresponding scenario.As results demonstrated, in all situations our model selection procedure generally (≥80%) selected the appropriate model which contains all major components of phenotypic variation. The remaining times in the first two of these scenarios, pedigree-associated genetic effects or those plus shared couple environment were selected instead of nuclear family environmental effects or vice versa, and in the remaining two replicates in the third of these scenarios we missed pedigree-associated genetic effects. In addition, our model selection never fully detected all minor contributions to the phenotype in the first two of these scenarios when the minor effects were too small (e.g. effects contribute to ≤5% of the phenotypic variance).Both issues identified above  are likely to have been due to limitations in the data structure of GS10K, which provides too few of the appropriate relationships for corresponding effects  to resolve correlations between parameters and detect minor effects. These limitations have been greatly ameliorated in the GS20K data.We also conducted variance component analyses using the final selected model for each replicate . For tho‘G’ in the presence of relatives, compared to the estimates of In the first analyses of the real data, we looked for evidence of familial effects (either pedigree-associated genetics or nuclear family environment) in our cohort. As shown by simulation , if therr), which was from 0.5i-0.5 to 0.5i+0.5 for ith degree relatives .st degree relatives, around 450 pairs of 2nd and 500 pairs of 3rd degree relatives, but the majority of pairs of individuals (over 99.9%) were genetically unrelated . In total, there were around 6,600 unrelated individuals (defined using the criteria described above) in GS10K.With these criteria, GS10K consisted of more than 3,500 pairs of 1G’ for subpopulations of GS10K made-up of individuals with different degrees of relatedness (using the upper bound of the expected relatedness of each category as GRM cut-off points in GCTA). We estimated st degree relatives that resulted in inflation of the estimates  were retained in the final models for all 16 traits, indicating that all traits examined here are heritable. Regarding variation associated with families, pedigree-associated genetic effects (represented by GRMkin) and nuclear family environmental effects (represented by ERMFamily) were retained in the final models for 10 and 4 out of 16 traits respectively. However, in GS10K, the data structure did not allow for both familial effects to be retained together in the final models for any trait. Additionally, the final models for glucose and DBP included neither GRMkin nor ERMFamily, which is consistent with the previous conclusion derived from As shown in ERMCouple) were retained in the final models for 12 out of 16 traits and sibling environmental effects (represented by ERMSib) only remained for creatinine and TC.The additional environmental influences of couple environmental effects (represented by GKC’ was most often selected (9 out of 16 traits) in the model selection procedure in GS10K. Therefore, this suggests that the common environment shared by couples, SNP-associated and pedigree-associated genetic effects are important for the control of a large proportion of the human complex traits we examined, while the shared family and full-sibling environment have a more limited impactAlthough the final model varied between traits, the model ‘GRMg) in the final models provided estimates of SNP-associated genetic effects (GRMkin) in the final models, the estimates of GRMg and GRMkin in the final models, the estimates of For the 10 traits that retained pedigree-associated genetic effects  was kept in the final models, and Creatinine and TC were the only two traits for which the common sibling environment , For those 12 traits that demonstrated evidence of couple effects , although the estimated effects were usually small. Moreover, due to an increased number and higher proportion of 2in GS10K , there wGKSC’  in the final models were of 0.20, 0.23, 0.05 and 0.11 respectively  comprising samples from up to 20,000 individuals.The aim of this study was to better understand the architecture of human complex traits by dissecting phenotypic variation into SNP-associated additive genetic variation , the signal of couple effects was quite strong. We did observe significant phenotypic correlation between couple pairs for almost all traits in our data  resulting in models containing all major phenotype components . HoweverTo sum up, we provide evidence that for the traits we have analysed, heritabilities are divided approximately evenly between pedigree-associated and SNP-associated genetic effects. This is the case even when, as here, we have taken care to consider various models of environmental covariation of first-degree relatives (including couples). It appears that confounding factors like dominance, shared full-sibling environment and the past rearing environment seem to have relatively small contribution to phenotypic variation for these traits in our population. We find that current shared environment of couples is able to account for another ~11% on average of the phenotypic variation of human complex traits. This has been seldom mentioned in previous heritability studies but we note that as an effect that inflates the covariance between nominally unrelated individuals, it should not substantially bias or inflate Overall, our work shows that SNP-associated genetic effects, pedigree-associated genetic effects and current shared couple environmental effects are three fundamental components of phenotypic variation for traits related to anthropometrics and cardiometabolism and current shared environmental effects have more impact than past shared environmental effects. This also has implications for models to be used in further studies of the architecture of complex traits including utilising the appropriate models for GWAS and related analyses and for personalised disease risk prediction.The data were obtained from the Generation Scotland: Scottish Family Health Study (GS:SFHS). Ethical approval for the study was given by the NHS Tayside committee on research ethics (reference 05/s1401/89) and participants provided written consent. Governance of the study, including public engagement, protocol development and access arrangements, was overseen by an independent advisory board, established by the Scottish governmenthttp://www.generationscotland.org), which was collected by a cross-disciplinary collaboration of Scottish medical schools and the National Health Service (NHS) from Feb 2006 to Mar 2011 [Our dataset came from the Generation Scotland Scottish Family Health Study (GS:SFHS) project  to missing.Data for 16 complex traits were used. These were 8 anthropometric traits: height, weight, fat, body mass index (ight2/3)  and 8 caData also contained the information of sex, age, clinics where the phenotypes were measured and Scottish Index of Multiple Deprivation . A desc−6 and a missingness > 2% were excluded. Duplicate samples, gender discrepancies and individuals with more than 5% missingness were also removed. After the quality control we kept 9,863 individuals genotyped for 550,796 common SNPs over the 22 autosomes.The first set of analyses presented in the manuscript are based on a data set of nearly 10,000 individuals from GS:SFHS (GS10K). These have multiple degrees of kinships, including 5,061 family members from 1,612 nuclear or extended families, and were genotyped with the Illumina OMNiExpress chip . We conducted data quality control in Plink v1.07  and GenAAn extended dataset (GS20K) was used to validate the results obtained with GS10K and evaluate the effect of including further close relationships in our data. The extra 10,000 individuals were genotyped with the same chip and quality control was performed using the same criteria as in the GS10K. After quality control, GS20K consisted of 20,032 individuals, 18,293 of whom came from 6,578 nuclear or extended families, and 519,729 common SNPs across the 22 autosomes.A comparison of the difference in relationships between GS10K and GS20K can be seen in GRMg, GRMkin, ERMFamily, ERMSib and ERMCouple.Our model allows trait variation to be influenced by the genetic effects associated with SNPs (s (es2), . To estiet al. [A genomic relationship matrix (GRM) contains estimated genomic relatedness between pairs of individuals calculated from identity-by-state marker relationships as in Yang et al. ,17.pi is the minor allele frequency (MAF) for SNP i, xji or xki is the allelic dose for individual j or k at locus i  and N is the total number of SNPs.Each off-diagonal entry in the GRM represents the realised genomic relationship between a pair of individuals:Each entry on the diagonal represents the inbreeding coefficient calculated as:GRMg and obtained GRMkin by modification of GRMg in R [KIBS and KIBS>t in Zaitlen et al. [We used GCTA  to generRMg in R . Their dn et al.  respectiGRMg: a GRM estimated using all common SNPs, and designed to capture the additive genetic variance explained by common SNPs in the population sample.GRMkin: a modified GRM calculated as in Zaitlen et al. [GRMkin was created by setting to 0 all entries in GRMg smaller than 0.025.n et al.  designedThe number of entries different from 0 in each of the matrices is shown in An environmental relationship matrix (ERM) is a covariance matrix designed to capture the variance due to common environmental effects shared among a specified group of individuals.The ERM coefficient for each pair of individuals is 1 in if they share a particular environment, e.g., living in the same area or coming from the same family; otherwise, it is 0. Each entry on the diagonal is 1.ERMCouple, ERMSib and ERMFamily.We generated 3 different ERMs in R : ERMCoupERMCouple: ERMCouple was designed to capture the common environmental effects shared between a couple. The ERM coefficient of two individuals was 1 if they were identified as a couple, defined as a pair of individuals with at least one offspring within GS:SFHS. Each entry on the diagonal was 1.ERMSib: ERMSib was designed to capture the common environmental effects shared between full-siblings. The ERM coefficient of two individuals was 1 if they were identified as full-siblings. Each diagonal entry was 1.ERMFamily: ERMFamily was designed to capture the common environmental effects shared within each nuclear family comprising parents and offspring. The ERM coefficient of two individuals was 1 if they were identified as a parent-offspring pair, full-siblings or a couple. The ERM coefficient of two individuals was 1 if they were identified as nuclear family members, including parent-offspring, couple and full-sibling relationships. Each diagonal entry was 1.The number of entries different from 0 in each of the environmental matrices is shown in y is an n × 1 vector of observed phenotypes with n being the sample size , and V the total phenotypic variance matrix, β is an m × 1 vector of fixed effects with m being the total level of covariates and X its design matrix with dimension n × m, gg is an n × 1 vector of the total additive genetic effects of the individuals captured by genotyped SNPs with gkin is an n × 1 vector of the extra genetic effects associated with the pedigree for relatives with ef, es and ec are n × 1 vectors representing the common environmental effects shared by nuclear family members, full-siblings and couples with ε is an n × 1 vector of residuals. We fitted a range of models including different combinations of effects, and named them using abbreviations according to the effects used. We used the codes ‘G’ for GRMg, ‘K’ for GRMkin, ‘F’ for ERMFamily, ‘S’ for ERMSib and ‘C’ for ERMCouple –e.g. ‘GKC’ = GRMg + GRMkin + ERMCouple, and the proportion of total phenotypic variance captured by each matrix was termed We used the genomic and environmental matrices described above to partition the phenotypic variance observed for the traits using a mixed model in a restricted maximum likelihood (REML) framework. The analyses were implemented in GCTA . The equThere were 31 different models from all the possible combinations of the five matrices. The abbreviations for each model and the formulae to estimate each term in each model are listed in 2, sex-by-age interaction, clinic, standardised SIMD and SIMD2 and the first 20 eigenvectors of GRMg (to ameliorate problems associated with data structure).In addition to the matrices described , we always included the fixed effects of sex, age, ageWe conducted a stepwise model selection to find the most appropriate genetic and environmental model for each trait and dissect the phenotypic variation into its components .GKFSC’, where all matrices were fitted together. We performed a Wald test and a log-likelihood ratio test  for eacIn order to evaluate the robustness of our models and the performance of our stepwise model selection, we conducted a simulation study. We simulated, based on the real genotypic information and the real pedigree, different sets of phenotypes for each of the 9,863 individuals in GS10K.et al. [GRMg and GRMkin. Each locus was assigned an effect size driven from exponential distribution as in Fisher [For simulating the genetic effects, we used a similar approach to Zaitlen et al.  by dividn Fisher  and the For environmental factors, we simulated a sibling environmental effect, a couple environmental effect and two nuclear family environmental effects (youth and adulthood environments) for each individual. The corresponding effect sizes for sibling, couple and nuclear family environmental effects were derived from In addition, we simulated a random residual effect for each individual, the residuals were derived from We evaluated the robustness of our models under situations where phenotypes were contributed by i) one of the five effects, ii) SNP-associated genetic effects and one of the familial effects  and iii) SNP-associated genetic effects, familial effects and other environmental effects. All scenarios included residuals and 50 to 100 replicates were analysed for each scenario. The results of simulations were evaluated using a Z-test, which tested whether the mean estimate for each parameter deviated significantly from its simulated value. Note, it was too time consuming to explore all the possible combinations of models and simulated phenotypes, therefore, we mainly focused on the models that were selected in model selection procedure for the real phenotypes in GS10K  as well ERMFamily posited a relationship between siblings, parents-offspring and couples is somewhat confounded with the addition of GRMkin and ERMCouple, making separation and estimation of these effects Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.


Additional 27 autosomal STR loci were analyzed using two domestic kits AGCU 21+1 and STRtyper-10G. The combined maternity index (CMI) was calculated to be 3.3 × 1013, but the putative mother denied that she had given birth to the child. In order to reach an accurate conclusion, further testing of 20 X-chromosomal short tandem repeats (X-STRs), 40 single nucleotide polymorphism (SNP) loci, and mitochondrial DNA (mtDNA) was carried out.For solving a maternity case, 19 autosomal short tandem repeats (STRs) were amplified using the AmpFℓSTRThe putative mother and the boy shared at least one allele at all 46 tested autosomal STR loci. But, according to the profile data of 20 X-STR and 40 SNP markers, different genotypes at 13 X-STR loci and five SNP loci excluded maternity. Mitochondrial profiles also clearly excluded the mother as a parent of the son because they have multiple differences. It was finally found that the putative mother is the sister of the biological father.Different kinds of genetic markers needfully supplement the use of autosomal STR loci in case where the putative parent is suspected to be related to the true parent. Profiles derived from polymorphic short tandem repeats (STRs) are used worldwide in paternity testing and individual identification. In complex cases of kinship analysis, autosomal single nucleotide polymorphism (SNP), X-chromosomal short tandem repeat (X-STRs), and mitochondrial DNA (mtDNA) could be used to complement autosomal STR typing.Genotypes of STRs and X-STRs are routinely determined using commercial PCR-based amplification kits with subsequent fragment length determination using capillary electrophoresis with laser-induced fluorescence of labeled primers.Several years ago, SNP and mtDNA analysis using a PCR and electrospray ionization mass spectrometry-based methods , 2 have In this paper we describe an interesting case. A couple went to the Public Security Bureau to declare account for a boy, they claimed the child was abandoned two years ago shortly after birth, and they picked him up and brought him up to date. In order to prove that the boy was really not their child, parentage testing was performed as officially requested. The putative mother (M) shared at least an allele with the boy (B) at each of the 46 autosomal STRs detected, but the putative father was excluded as the biological father with 18 inconsistent loci out of the 46 markers. The mother was adamant that she had not given birth to the child. Below, we emphatically describe the use of additional non-STR genetic markers  to interrogate the potential maternal association between the putative mother and child.Blood samples from the putative mother and the child were collected with informed consent under protocols approved by the IFS ethics committee at the Institute of Forensic Science, Ministry of Justice, China. DNA extraction was performed using a Chelex-100 and proteinase K protocol . The quaCapillary electrophoresis-based STR and X-STR typing was performed on an Applied Biosystems 3130XL genetic analyzer following PCR on an Applied Biosystems GeneAmp 9700 thermalcycler. Data was analyzed using Applied Biosystem’s GeneMapper software V3.2. Commercial autosomal STR panels employed in this work include Applied Biosystem’s 16-marker AmpFℓSTR Sinofiler and Promega’s 16-marker PowerPlex 16. Twenty seven additional autosomal STRs were interrogated using the domestic 21+1 kit . The comMass spectrometry-based SNP and mtDNA typing was performed using Ibis Biosciences’ PLEX-ID platform . A set o13 by no means excludes the putative mother from being the biological mother.As shown in Table The putative mother refused the conclusion that she was the real mother. In order to get at the facts, the likelihoods of the genotype profiles given various identity-by-descent (IBD) distributions were then calculated by J Ge and B Budowle using MPKin , 13. AccTable 6 for a given SNP locus, there would be approximately a 1 in a million probability for a child to have an allele that is inconsistent with the mother’s genotype at a single locus and less than a 1 in 1030 probability of having five loci inconsistent with the biological mother’s genotype. These data clearly exclude the putative mother from being the biological mother of the child.In this study, the full 40 SNP panel was run on DNA derived from the putative mother and the child. As illustrated in Table C43 T22 to A44 G13 C46 T22. Note also that the putative mother has 45 A’s and 24 T’s over these coordinates and the child, regardless of C-length variation, consistently has 44 A’s and 22 T’s. These differing base compositions represent multiple clear and unique differences between the mitochondrial profiles of the child and the putative mother over the coordinates spanned by a single primer pair. Perhaps more importantly, there are clear and distinct base composition differences in 9 of the 24 primer pairs, clearly and unambiguously inconsistent with a profile shared between child and biological mother.In this study, mitochondrial profiles were derived from the putative mother and child and compared. As illustrated in Table Forty-six autosomal STRs, 20 X-STRs, 40 SNPs, and mtDNA were typed for the resolved case. Calculated on the basis of population genetics data –10, 14, The data presented in Table ® Identifiler® kit, which amplifies 15 autosomal STRs simultaneously. Therefore, it is clearly important to increase the number of investigated loci or include a typing of sex chromosome specific STRs to further ascertain the results. It is particularly worth mentioning that X-STRs would have been a quick way to exclude relationships and very powerful in some deficiency cases (as well as incest cases), even though the power of discrimination of the X-STRs is less than the autosomal STRs [This case warns that there will be instances when strong DNA evidence will lead to an incorrect conclusion, especially in cases with an unknown family background. von Wurmb-Schwark once reported the possible pitfalls in deficiency cases . Accordimal STRs . As show−15. As these markers are robust in terms of stability of inheritance, it serves as a useful tool, orthogonal to STRs. As for mtDNA, due to maternal inheritance, the marker is valuable for testing of relationships between maternal individuals. Although the PLEX-ID platform for analyzing of SNP and mtDNA in this case is now no longer available, the comparative analysis of the PLEX-ID technology and the traditional capillary electrophoretic system for typing of amplified DNA fragments has demonstrated the potential advantages of the mass-spectrometric technique [Besides, autosomal SNPs and mtDNA could also be used to complement autosomal STR typing if there is a possibility of the putative mother being genetically related to the biological parents of the child. The 40-locus binary markers detected in the case were originally selected by Kidd and co-workers . This 40echnique .


Dagginess  and host nematode parasite resistance are important animal welfare traits in New Zealand sheep. Genomic prediction (GP) estimates the genetic merit, as a molecular breeding value (mBV), for each trait based on many SNPs. The additional information the mBV provides (as determined by its accuracy) has led to its incorporation into breeding schemes. Some GP methods give SNP effects, which provide additional information to identify genome-wide associations (GWAS) for a trait of interest. Here we report results from a GP and GWAS study for dagginess and host nematode parasite resistance in a New Zealand sheep industry resource.P < 1 × 10−6) on OAR15, the same two SNPs for each of the parasite traits (OAR26) and none for DAG3. A notable peak was also observed on OAR7 for all the parasite traits, however, it did not reach the Bonferroni significance threshold.Genomic prediction analysis was performed using 50K SNP chip data and parent average-removed, de-regressed BVs for five traits, from a resource of 8705 pedigree recorded animals. The five traits were dag score at three and eight months  and nematode faecal egg count in summer (FEC1), autumn (FEC2) and as an adult (AFEC). The resource consisted of Romney, Coopworth, Perendale, Texel and various breed crosses . The pure breeds, apart from Texel, plus CompRCP were used to develop the GP. The resulting SNP effects were used to identify genetic regions associated with dagginess and parasite resistance. Accuracies of the weighted correlation between mBV and true BV ranged between −0.07 (Texel) and 0.56 (Coopworth) for DAG3 and DAG8. For FEC1, FEC2 and AFEC accuracies ranged between −0.22 (CompRCPT) and 0.69 (Coopworth). The weighted average individual accuracy  ranges were 0.13 (Texel) to 0.52 (Coopworth) and 0.11 (Texel) to 0.55 (Coopworth) respectively, for dagginess and parasite traits. There was one SNP for DAG8 that reached Bonferroni significance threshold  contains supplementary material, which is available to authorized users. Dagginess  and internal nematode parasites, are major animal health traits with ethical and welfare implications of interest to the New Zealand sheep industry. Firstly, dagginess has a known association with flystrike (genetic correlations range between 0.34 ± 0.13 and 0.81 ± 0.15) , with thNematodirus egg count (NEM), have been shown to be moderately heritable traits G1), as dy, for each trait as follows: y = Xβ + Zu + e where X is a matrix of the first six principal components of the G1  as a quantile - quantile (QQ) plot.The quantiles were calculated to check whether the distribution of the observed -loghttp://www.ensembl.org/Ovis_aries). A further literature search and Online Mendelian Inheritance in Man (OMIM) were used to identify candidate genes.For peaks that reached the Bonferroni threshold, the genomic region was explored by inspecting a 100kbp window surrounding the location of the significant SNP using ovine genome v 3.1 (available at Ensembl The data sets supporting the results of this article are included within the article and its additional files.


Most currently available family based association tests are designed to account only for nuclear families with complete genotypes for parents as well as offspring. Due to the availability of increasingly less expensive generation of whole genome sequencing information, genetic studies are able to collect data for more families and from large family cohorts with the goal of improving statistical power. However, due to missing genotypes, many families are not included in the family based association tests, negating the benefits of large scale sequencing data. Here, we present the CIFBAT method to use incomplete families in Family Based Association Test (FBAT) to evaluate robustness against missing data. CIFBAT uses quantile intervals of the FBAT statistic by randomly choosing valid completions of incomplete family genotypes based on Mendelian inheritance rules. By considering all valid completions equally likely and computing quantile intervals over many randomized iterations, CIFBAT avoids assumption of a homogeneous population structure or any particular missingness pattern in the data. Using simulated data, we show that the quantile intervals computed by CIFBAT are useful in validating robustness of the FBAT statistic against missing data and in identifying genomic markers with higher precision. We also propose a novel set of candidate genomic markers for uterine related abnormalities from analysis of familial whole genome sequences, and provide validation for a previously established set of candidate markers for Type 1 diabetes. We have provided a software package that incorporates TDT, robustTDT, FBAT, and CIFBAT. The data format proposed for the software uses half the memory space that the standard FBAT format (PED) files use, making it efficient for large scale genome wide association studies. A wide variety of genetic association studies have been performed with the aim of discovering genomic markers for a given phenotype of interest. While many of these studies are population based, there is a renewed interest in family-based studies due to the inability of population based studies to account for much of the heritability of most common phenotypes , robustTDT . We present analysis of simulated genotype data to demonstrate applicability of CIFBAT in detecting bias in the FBAT statistic due to missing data and in identifying genomic markers with higher precision. We also present results from analysis of familial whole genome sequencing data set for maternal uterine anomalies and from a candidate marker data set for type 1 diabetes.TDT-type tests are known to inflate the type I error rate where there is missing parental genotype information or undetected genotype errors or both  under consideration. For example, for additive model, Xi counts the number of non-reference alleles observed in the offspring, and can take a value of 0, 1, or 2 for a bi-allelic genomic marker . When μ > 0, affected trait Ti > 0 and unaffected trait Ti < 0, so both affected and unaffected trios are used in the test. For the analyses presented in this paper, we used μ = 0.5 in order to assign equal but opposite weights to affected and unaffected trios.μ∈X − E[X] and Variance(X) shown in Figure Figure X − E[X] and Variance(X). For the trio type shown in Figure Here we describe an example to explain computation of the statistics Trio types 6 and 7 for autosomal chromosomes Figure  are not Xi − E[Xi], Variance(Xi), and Ti are computed for each trio, Uc and Variance(Uc) are computed by summation over all the trios and, finally, the FBAT statistic Zc is computed as ratio of Uc and standard deviation of Uc.Once Zc is essentially a z-score measuring deviation from the null hypothesis of no linkage and no association. When evaluating bi-allelic markers, a positive Zc indicates that the allele being tested was over-transmitted to the affected offspring, whereas a negative Zc indicates under-transmission to affected offspring. P-values are computed considering this as a two-sided test.The FBAT test described above does not account for incomplete trios in the study cohort. This might lead to undetected bias in the test statistic. We have implemented CIFBAT to detect potential bias in the FBAT statistic in presence of incomplete trios and to identify significant genomic markers with higher precision. CIFBAT considers all the valid completions of each incomplete trio equally likely and computes QIs of the FBAT statistic over many iterations of randomized completions.X − E[X] and Variance(X) for all valid completions of these incomplete trios under additive, dominant, and recessive models respectively. Table X − E[X] and Variance(X) are equal to 0.Figure c” denotes a complete trio or a statistic related to complete trios, subscript “m” denotes missing (incomplete) trio or a statistic related to incomplete trios, and subscript “r” denotes a random variable.We will now describe how CIFBAT computes QIs of the FBAT statistic. In the following explanation, subscript “k complete trios and d incomplete trios. The total Uc and Variance(Uc) for all the complete trios are computed as:X' denote the offspring's genotype for a randomly chosen completion. The total contribution of incomplete trios is a random variable Umr computed as summation of contributions based on their random completions by CIFBAT:U statistic and the variance are computed as the sum of the statistics from complete and incomplete trios.Z statistic is computed as:Umr and the corresponding Zr, each time with a randomly selected set of completions for all incomplete trios. QIs of Zr (α/2 and 100- α/2) and the corresponding p-values are then computed for a pre-defined confidence level (α = 0.05 by default).Suppose for a genomic marker under evaluation, our data set consists of In order to explore the statistical response of CIFBAT under various scenarios of missing data, we simulated family genotype data under no association and a genetic model of disease explained in detail in Data Sheet When data is “missing completely at random” (MCAR), the probability of an observation being missing does not depend on observed or unobserved measurements. This scenario was simulated by introducing missing genotypes for randomly chosen samples in the data. This type of missing data was use in the study of markers with no association.Data can also be “missing at random” (MAR), which means that the missingness mechanism depends only on an observed measurement, and not on any unobserved measurements. This scenario was simulated with respect to the following observed variables separately: gender, subpopulation, and disease status.Lastly, data can be “missing not at random” (MNAR) when the missingness mechanism depends on unobserved measurements. This can be difficult to detect and can lead to invalid inference if the missing data is ignored or incorrectly modeled. We simulated this scenario by concentrating missing data on heterozygous or homozygous samples, i.e., samples that were heterozygous or homozygous for the non-reference allele were more likely to be missing.ij, is defined as:ij is the initial log probability of disease for offspring j in family i. α represents environmental or other hidden factors that contribute to disease. For each causative SNP m, β is a log ratio of penetrance values comparing having one or two alleles to none. X(G) is a vector of dummy variables indicating a count of alternative alleles. Disease status was probabilistically determined using πij; offspring within a family have correlated probabilities for disease, but are independent across families.The simulated binary phenotype is either uniformly random or based on a bi-allelic additive genetic model closely following the work of Yang et al. , where eIn each simulated scenario, 4000 pedigrees were generated and the mean population prevalence was 12.5%. An equal number of cases and controls were used, and missing data was generated at rates of 0, 1, 5, and 10% at the sample level  was also used to identify significant CIFBAT QIs. An interval was considered significant if the p-value spread was below the p-value threshold.A false discovery rate (FDR) cut-off of 10% was used to identify significant results. The equivalent In each simulation, the causative SNPs are known. This allows computation of performance metrics useful for comparing CIFBAT and FBAT. Among the metrics computed are sensitivity , the proportion of true positives called, specificity, the proportion of true negatives called, precision, proportion of predicted trues that are actually true, and the F-measure which is the harmonic mean of recall and precision.p-value cut-off of 5.71e-05, was used to identify significant hits from FBAT. The p-value cut-off from FBAT results was also used to identify significant 95% QIs computed with CIFBAT. A QI was considered significant if the entire spread was below the p-value cut-off. A significant FBAT p-value together with a significant CIFBAT quantile interval indicated robustness against missing data. On the other hand, a significant FBAT statistic and a non-significant CIFBAT quantile interval hinted at potential bias in the FBAT statistic due to missing data.As more and more association studies employ whole genome sequences, it becomes important that novel association methods are evaluated for applicability and performance in genome wide association analyses. With this purpose, we used CIFBAT to analyze whole genome sequences of 784 nuclear families for uterine anomalies in mothers, represented as a binary phenotype. Samples of peripheral blood were collected from fathers, mothers and newborns at the Inova Fairfax Medical Center in Falls Church, Virginia, and sequencing was done at >40X depth using Complete Genomics' whole genome sequencing platform. Fifty-two (52) out of the 784 mothers were diagnosed with various uterine anomalies including endometriosis, bicornuate uterus, didelphic uterus, etc. (complete list in Text S1). These were used as cases in our study. Various filters were applied to the genomic markers to reduce noise in the data and to ensure applicability of family based association tests (Text S1). Altogether, we tested 3,808,482 autosomal markers under both FBAT as well as CIFBAT. A false discovery rate (FDR) cut-off of 10%, which translated to a p-value cut-off of 1.10e-02 was used to indicate significant results based on CIFBAT QIs. Again, a significant FBAT statistic with a significant CIFBAT quantile interval implied robustness against missing data, whereas a significant FBAT statistic with a non-significant CIFBAT quantile interval implied potential bias due to missing data.To demonstrate the use of CIFBAT in refining candidate sets of markers, we analyzed Type 1 diabetes data that consisted of a dichotomous phenotype variable and 351 markers within 22 candidate genes that have been previously identified as contributing to the risk of Type 1 diabetes. The data were collected by the Type 1 Diabetes Genetics Consortium (T1DGC). There were 2313 pedigrees consisting of 2345 nuclear families with one or more offspring. To ensure independence of nuclear families, we excluded one randomly selected family from every pair of families that had at least one common parent. We also excluded offspring whose type 1 diabetes affectation status was unknown as well as founders that had no sequenced offspring in the data. Finally, we were left with 2314 nuclear families with one or more offspring which we then analyzed using CIFBAT. An FDR cut-off of 10% was used to indicate significant results from FBAT. The equivalent b and c under all three models. Tables In addition to developing the CIFBAT method, we have extended capabilities of the original TDT and robustTDT implementations as follows. PLINK . The simulated scenarios of missing data are not meant to be comprehensive, but rather illustrative of the effects on algorithms that use missing data in making inferences. Comparisons were primarily based on recall and precision.p-values remained uniformly distributed, resulting in 10% false positives at p-value threshold of 0.1 (with no FDR correction). CIFBAT Zr score QIs, based on the interval median, were normally distributed around zero , zero simulated markers are found to be significant at a 10% FDR level, as desired.When phenotypes were uniformly random, and data was missing completely at random, the FBAT Across all scenarios, for both FBAT and CIFBAT, the smallest effect on the recall occurred when missing data was concentrated on the controls, and the largest effect occurred when missing data was concentrated on the cases. In general, the cases will have more occurrences of a causative variant, so when missing data is concentrated on the cases, it weakens the power of the tests to detect the overabundance of causative alleles in cases, leading to false negative results. On the other hand, when missing data is concentrated on controls, it does not affect the signal to the same extent. Figure The simulations showed that overall, in comparison to FBAT, CIFBAT tended to trade lower recall for higher precision; meaning that while fewer variants were called significant, they were more likely to be true positives Figure . For exaUltimately, the levels of recall and precision are influenced by the disease model parameters. For example, changes in recall and precision can be seen in Figures Overall, the F-measure, which is the harmonic mean of precision and recall, was comparable between FBAT and CIFBAT, with FBAT having a median F-measure of 0.67 and CIFBAT having a median F-measure of 0.64 over various simulation scenarios Table .The simulation also generated results where the CIFBAT QIs were significant but the FBAT statistic was not, although these were few in number (5/300) and no clear pattern emerged to explain their occurrence. Figures We analyzed familial whole genome sequencing data to identify potential genomic markers for maternal uterine anomalies. Further, we utilize this section to explain interpretation and usage of CIFBAT QIs in validating robustness of FBAT results in presence of missing genotype data.TCRBV9S1A1T. This marker was significant under FBAT with p-value less than machine epsilon (2.22e-16), as well as under CIFBAT with a 95% QI . Figure We compared the counts of significant markers exclusively and jointly identified by FBAT and CIFBAT. Figure FAM189A1. Figure The remaining 309 (~56%) markers, significant under FBAT and not under CIFBAT, indicates that these FBAT results might have been affected by missing data. In such cases, CIFBAT is useful in identifying potential false positives and refining the feature set for further downstream analyses. Example of one such marker is chr15:29443416  which is an intronic variant in gene OR8I2. Figure Additionally, there were 38 markers that were exclusively significant under CIFBAT Table . While iOverall, CIFBAT can be a useful test to evaluate robustness of FBAT statistic against missing data. Markers that were significant under FBAT and further validated by CIFBAT after inclusion of incomplete families provide a refined set of candidate markers for downstream analysis. In contrast to refining the set of candidate markers by ~50%, CIFBAT exclusively identified only a small fraction (6.8%) of significant markers collectively identified (589) by FBAT and CIFBAT. These can be potentially interesting markers that warrant further investigation. This feature of CIFBAT is particularly useful in genome wide association studies where the initial set of markers to be evaluated for candidacy is invariably very large. CIFBAT can be used in these studies to refine the set of markers for downstream analyses. Moreover, often the underlying genetic model or the pattern of missing data is unknown; in such cases, CIFBAT can be a great advantage in evaluating the effects of missing data without any assumptions about the underlying model.p-values from FBAT were corrected for multiple testing using Benjamini-Hochberg method  gene were identified as significant by FBAT based on complete trios. Of these, 11 markers also showed significant QIs under CIFBAT when incomplete trios were included in the test  markers in the insulin  markers in the Two (2) markers in the CTLA4 gene were significant under FBAT, but inclusion of incomplete trios under CIFBAT produced insignificant QIs for both the markers.IFIH1 gene were significant under FBAT and were further validated by CIFBAT after inclusion of incomplete trios in the test.Four (4) markers in the interleukin  family of genes, and 1 marker in the Overall, CIFBAT provided further validation of 20 (55.55%) out of 36 markers that were significant based on FBAT, indicating that these results were not biased by missing data. The remaining 16 markers were not significant anymore when incomplete trios were included under CIFBAT, suggesting that the FBAT test might have been biased due to missing data.Missing data in genetic association studies pose several challenges. They result in loss of statistical power if samples with missing data are excluded from the study. On the other hand, if missing data is imputed without consideration for the underlying data model, it might lead to often undetectable biases in the results. Here, we have implemented CIFBAT to detect potential bias in FBAT due to the presence of missing data and to identify significant genomic markers with higher precision. Unlike likelihood based methods that use sufficient statistics to impute missing genotypes, CIFBAT considers all valid completions of an incomplete trio equally likely and computes QIs of the FBAT statistic over many randomized iterations. In doing so, CIFBAT does not assume a homogeneous population and retains robustness to population stratification which is a crucial feature of family based association tests.Using simulated data, we have shown that CIFBAT is useful in validating the robustness of FBAT statistic against missing data and identifying candidate markers with higher precision. We have also demonstrated the applicability of CIFBAT in genome wide association studies, and its usefulness in refining sets of candidate markers for more targeted downstream analyses.https://github.com/IlyaLab/FamSuite) that researchers can use to analyze their data and compare results from TDT, robustTDT, FBAT, and CIFBAT.CIFBAT uses a memory efficient data format, making it apt for analyzing whole genome sequencing. We also extended TDT to handle dominant and recessive genetic models in addition to the default additive model, and extended robustTDT to handle the X chromosome in addition to autosomal chromosomes. We have provided a comprehensive software package called FamSuite (VD conceived the study, wrote the software and conducted analysis of familial whole genome sequences, DG conducted simulation study, TK contributed significantly in method development and analysis guidance, RK contributed toward method development, BB provided critical reviews toward strengthening method and analysis, JV, JN, and IS provided supervision, interpretation of the results and contributed to the writing of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


CACNA2D3, identified by both longitudinal and baseline approaches, had a stronger signal in the longitudinal analysis (p = 2.65 × 10−7) compared to that in the baseline analysis (p = 2.48 × 10−5). The effect size of the longitudinal mixed-effects model and mean trait were higher compared to the 2-stage approach. The longitudinal results provided stable results different from that using 1 observation at baseline and generally had lower p values.In recent years, longitudinal family-based studies have had success in identifying genetic variants that influence complex traits in genome-wide association studies. In this paper, we suggest that longitudinal analyses may contain valuable information that can enable identification of additional associations compared to baseline analyses. Using Genetic Analysis Workshop 18 data, consisting of whole genome sequence data in a pedigree-based sample, we compared 3 methods for the genetic analysis of longitudinal data to an analysis that used baseline data only. These longitudinal methods were (a) longitudinal mixed-effects model; (b) analysis of the mean trait over time; and (c) a 2-stage analysis, with estimation of a random intercept in the first stage and regression of the random intercept on a single-nucleotide polymorphism at the second stage. All methods accounted for the familial correlation among subjects within a pedigree. The analyses considered common variants with minor allele frequency above 5% on chromosome 3. Analyses were performed without knowledge of the simulation model. The 3 longitudinal methods showed consistent results, which were generally different from those found by using only the baseline observation. The gene  Longitudinal data analyses are widely used in genome-wide association studies to assess genetic and environmental risk factors and their association with phenotypes of interest -3. They In this paper, our main object is to compare existing methods of longitudinal data analyses with those that use only 1 baseline measure in association studies. We explore the following longitudinal methods: (a) a longitudinal mixed-effects model; (b) analysis of the mean trait over time; and (c) a 2-stage analysis, with estimation of a random intercept in the first stage and regression of the random intercept on a single-nucleotide polymorphism (SNP) in the second stage. These longitudinal methods use statistics that capture the level of a trait, such as a mean, to detect genetic associations as opposed to methods that focus on the change in the trait over time, such as a slope. Despite the strengths and integrated approach of a longitudinal mixed model, its implementation is very computer-intensive because of its complex structure. Therefore, the main motivation for trying some "simpler" alternative longitudinal models, such as analysis of the mean trait over time and a 2-stage analysis, is to see if they can serve as good substitutes with equally good performance.p <0.05) with SBP or DBP were selected. For SBP, we adjusted for age, sex, and tobacco smoking. For DBP, we adjusted for age, sex, tobacco smoking, and centered age squared.We used real phenotype data collected in the San Antonio Family Heart Study, including sex, age, year of examination, systolic and diastolic blood pressure, use of antihypertensive medications, and tobacco smoking at up to 4 time points for 939 subjects in 20 pedigrees. Of the 939 participants, 244 attended only 1 exam; for the remaining subjects, the median follow-up time was 11 years with a median gap time between assessments of 5 years. We analyzed 2 continuous traits: systolic blood pressure (SBP) and diastolic blood pressure (DBP). For participants on medication, we imputed both SBP and DBP to mimic what their unmedicated values would be. If a subject was on medications at an exam, we imputed the blood pressure at this exam to be the average blood pressure of all observations with higher values among those of the same gender and ± 10 years of the age of the subject. We performed a preliminary analysis to select covariates for both SBP and DBP. Variables significantly associated  consisted of whole genome sequence data in a pedigree-based sample with longitudinal phenotype data for hypertension and related traits. A total of 26.8 million SNPs were identified in the 483 individuals. After eliminating 19 outlier individuals who failed to meet SNP quality control criteria such as fractions and ratio of homogeneous and heterogeneous sites and fraction of novel SNPs, 24 million SNPs passed support vector machine and indel proximity filters. Genotype calls cleaned of mendelian errors and dosages were provided for 959 individuals (464 directly sequenced and the rest imputed) for 8,348,674 locations in the genome. A majority of the SNPs were rare variants; 51% had a minor allele frequency (MAF) below 1%. As suggested by GAW18 leaders, all analyses for this current paper were based on 402,985 common variants (MAF ≥5%) of chromosome 3 only, accounting for around one-third of the total number of variants on the chromosome.i denotes the ith pedigree, and j denotes the jth individual in the ith pedigree. For this individual, m covariates, and i,j)th person. Within each pedigree, the For comparison with the methods that used the longitudinal data, we applied a baseline association analysis that considered only the first observation (baseline) for each person. In addition to adjusting for covariates, we incorporated a familial correlation structure (kinship coefficient matrix) into the model as kinship" , which eThe notations of To compare with the baseline approach, we considered 3 approaches for longitudinal analyses of these data: (a) longitudinal mixed-effects association analysis, (b) mean measure in longitudinal association analysis, and (c) 2-stage longitudinal association analysis.We used a random-intercept mixed effects model with familial correlation structure . The modi denotes the ith pedigree, and j denotes the jth individual in the ith pedigree. For this individual, Here igreemm" , which uWe also considered the mean across all time points as the trait and its corresponding averaged covariates as one alternative for longitudinal association analysis. This model is:i denotes the ith pedigree, and j denotes the jth individual in the ith pedigree. For this individual, Here This model was implemented using the function lmekin in R (version 2.9.2) package "kinship" , using mAnother longitudinal approach employs a 2-stage strategy . In the i denotes the ith pedigree, and j denotes the jth individual in the ith pedigree. For this individual, Here In the second stage, random intercept Here Gauderman et al  pointed MAP4, the top variant influencing simulated SBP and DBP, as the functional variant for power calculations. To determine power, we tested the null hypothesis that the trait SBP was not associated with the functional variant, versus the alternative hypothesis that it is associated. Therefore, results would be considered statistically significant if the p value obtained using the analysis methods fell below a predetermined threshold. Here we divided the significance level 0.05 by the approximate number  of independent SNPs on chromosome 3 to adjust for multiple testing. We used PLINK (http://pngu.mgh.harvard.edu/~purcell/plink/) [We conducted power calculations for all 4 methods and evaluated type I error by means of the genomic control value. We chose the variant (chromosome 3: 47956424) on gene /plink/)  to prunep value of common variants on chromosome 3.For each of the 4 methods, genomic control value was used to assess the extent of the inflation of type I error, based on the FGF12 and FHIT. The mean measure and 2-stage methods yielded similar results. For DBP, the 3 longitudinal methods yielded consistent results , with SNPs nearly reaching a Bonferroni significance threshold. This gene was also found using the baseline method but was less significant (rank = 2, p = 2.76E-05 in Table For SBP, there were no shared results in the top 10 hits between the baseline approach and the other 3 longitudinal methods (Table Power was computed to assess the baseline method and the 3 longitudinal methods (Table For both traits, the genes identified by the 3 longitudinal methods were consistent, but different from those found with the baseline approach. From the perspective of computational time, the mean measure and 2-stage methods were more computer efficient than the LME method. Furthermore, these 2 longitudinal methods were more powerful than the LME method. These 2 methods can act as efficient and powerful "substitutes" for LME. The mean measure method worked as well as the 2-stage method, identifying the same genes. The signals found with the 2-stage method (third row of Manhattan plot in Figure The authors declare that they have no competing interests.SW, WG, JN, CA, CTL, LAC designed the overall study, SW, WG, JN, CA conducted statistical analyses and SW, WG, JN drafted the manuscript. All authors read and approved the final manuscript.