Salmo trutta from two populations. Fish from the assumedly resident population showed clearly shorter migration distances than the fish from the assumed migratory population at the ages of 2 and 3 years. By using two alternative analytical pipelines with 22186 and 18264 SNPs obtained through RAD-sequencing, we searched for associations between individual migration distance, and both called genotypes and genotype probabilities. None of the SNPs showed statistically significant individual effects on migration after correction for multiple testing. By choosing a less stringent threshold, defined as an overlap of the top 0.1% SNPs identified by the analytical pipelines, GAPIT and Angsd, we identified eight candidate genes that are potentially linked to individual migration distance. While our results demonstrate large individual and population level differences in migration distances, the detected genetic associations were weak suggesting that migration traits likely have multigenic control.A better understanding of the environmental and genetic contribution to migratory behavior and the evolution of traits linked to migration is crucial for fish conservation and fisheries management. Up to date, a few genes with unequivocal influence on the adoption of alternative migration strategies have been identified in salmonids. Here, we used a common garden set-up to measure individual migration distances of generally highly polymorphic brown trout  Originally developed for case-control comparisons in medical sciences . During recent years, the genetic components underlying the dichotomy between resident and migratory forms have been increasingly studied, particularly in the genus Onchorhynchus  was used to genotype 116 individuals from the tails of the estimated distribution of migration distances . We used two complementary association analyses to identify candidate genes for migration tendency in data corrected for population differences. Since brown trout migratory behavior has been recently found to associate with several outlier markers , mass 520.3 g ± 126.0 g; males: n = 9, length 400.4 mm ± 82.3 mm, mass 795.8 g ± 412.0 g) and hatchery  fish were crossed in fully factorial 3 × 3 matrices in three replicates (within and between population crosses). The eggs were pooled within strains  and incubated in four replicates per strain. After hatching, the progeny was raised in the hatchery in 3.2 m2 fiberglass tanks  according to standard methods and fed ad libitum with size-adjusted commercial dry feeds  until tagging  and tagged with a 12 mm half duplex passive integrated transponder (HDX-PIT) tags under benzocaine anesthesia (40 mg L−1). The fish  were randomized evenly into eight circular migration channels  .We used two strains of brown trout originating from a single river system discharging to Lake Oulujärvi, Northern Central Finland, for this study: 1) hatchery-bred migratory fish originating from rivers Varisjoki and Kongasjoki (referred as OUV), and 2) wild resident fish collected via electrofishing from a small headwater tributary . The water input (∼55 l s-1) was adjacent to the water outlet to create unidirectional flow . The channels were equipped with four stationary PIT-tag antennae (reading rate 9 s-1) at equal intervals. An antenna was constructed of five coil inductor loops made of PVC-coated multithread copper wire (ø = 4 mm) , with each antenna connected to one of four computers configured to run and save  ASCII data ).The experimental migration channels  were built in 75 mn = 4) were transferred to the experimental channels supplied only with natural food on 5th November 2015 (n = 4) were transferred into a 50 m2 concrete rearing pond in order to be fed with commercial fish feeds ad libitum until April 2016. The feed-augmented fish  were recovered on 14th April 2016 and transferred to the four experimental channels the next day. Continuous PIT-telemetry in all eight streams  were returned back to the smolt migration ponds where they were maintained with natural food until the end of the experiment in following summer 2017. The rest of the fish were transferred to a 50 m2 concrete pond to be maintained with commercial dry food until the spring release in 2017 (see below).Because smolt migration is expected to be influenced by food availability  were held in a 50 m2 concrete rearing ponds with additional fish from the same cohort  over winter. The fish were recovered and measured for length  and body mass  under anesthesia on 14th March 2017. These fish were then transferred into four migration channel ponds after one night’s recovery in four 3.2 m2 indoor fiberglass tanks on 15.3.2017 at 10:00 . The migration experiment was ended at two occasions: one random pond per feeding regime was emptied on 7th June 2017 to collect lethal physiological samples from killed fish and the rest of the ponds were emptied on 21th June 2017. All recovered fish were measured for length and body mass , 211.7 g ± 139.7 g; VAA: 231.6 mm ± 39.8 mm, 123.2 g ± 60.8 g) under anesthesia, and fish in good condition were transferred back to the rearing ponds under standard rearing and feeding protocols. The fish sampled on 7th June 2017 were analyzed for their migration behavior from 16th March 2017 at midnight to 6th June 2017 at midnight. All the other fish were analyzed for their migration behavior from 16th March 2017 at midnight to 20th June 2017 at midnight.Brown trout augmented with commercial feed tested in 2016 (http://pitdata.net/). Based on the order of detections, the movement distances were calculated for each individual as quarter rounds up- and downstream. The hourly data were further analyzed by calculating the total individual distance moved downstream using custom codes in AV Bio-Statistics 5.2 (written by A.V.). The recording computers had to be restarted on a weekly basis, and when calculating migratory distances, the fish were assumed to have maintained their previous position during these maintenance breaks (< 30 min).PIT detection data were transformed to individual movements per hour by counting the PIT-specific detections at each antenna using application-specific software  and prioritized the presence of data from both years. This selection procedure resulted a subset of 116 individuals  that is not representative of all individuals in the experiment but provided extreme phenotypic values for the GWAS. Because individual migration distances could have been influenced by the test channel (which was confounded with diet treatment), rearing tank, year, sex (see below for the determination method) and feeding treatment, we removed their effects using a linear mixed effects (LME) model including sex, year , test pond and year × pond –interaction as fixed factors and rearing tanks / test pond during the previous year as a random factor. Prior to the analysis, migration distances were ln-transformed to meet normality. The model was fitted in SPSS 23.0.0.2 (IBM Corp.) using restricted maximum likelihood with random terms based on variance components and temporal covariance matrix based on diagonal structure with heterogenous variance. The residuals from this model were used to represent individual tendency to migrate . The values were comparable between the channels, sexes and years, but included the effect of strain and fish size as these variables could mechanistically explain the originally genetic effect on migration. For the GWAS, the residual values for the two years were averaged; when migration distance was available for one year only , that value was used. This was justified as the inter-year averages were zero and migration residuals showed high individual repeatability between years  in each channel  that delivered the sequences in FastQ format for bioinformatics analyses. Process_radtags function in Stacks v1.40 software  was used for a SNP to be processed. In addition, a minimum mapping quality of ten, minimum base score of 20, a minor allele frequency of 0.05  using the hw.test function of adegenet  were excluded. A compressed mixed linear model (MLM) was fitted using the GAPIT package in R environment  is based on a linear regression while the second (Stacks) relies on a linear mixed model. The linear regression association method takes the uncertainty of the genotypes into account while performing the association and is thus a powerful method than can still control for false positive  and ignoring more subtle, but real, associations (Type II error) due to low power and multiple testing burden . As a coAngsd and GAPIT, even if these SNPs did not pass significance in multiple testing. Some of these markers can still represent true genotype-phenotype associations particularly in combination with other markers with small effects. As studied markers likely do not represent causative SNPs affecting migration, we identified the closest genes to the observed SNPs based on the Atlantic salmon reference genome.In order to correct statistical significances for multiple testing, we used a false discovery rate correction  separatei.e., phenotypic scores) between the populations. Figure S3 is a comparison between SNPs obtained for each pipeline. Raw sequence data are deposited on NCBI (PRJNA552287). Movement data and all codes used in this study are available upon request. Supplemental material available at FigShare: https://doi.org/10.25387/g3.7660295.Supplementary materials are available on Figshare. These include filtered genotypes for both pathways, individual phenotypes, population structure and association results of both analyses. Figure S1 is a picture of the circular channels. Figure S2 is a boxplot of the residuals , indicating the presence of genetic or epigenetic component affecting migration. The type III fixed effect tests indicated that smolt migration was affected by year  and testing pond  but not by sex of the fish  or the testing pond × year interaction . Because the used fish represented a subset of fish in the smolt migration experiment, these results are technical for the purpose of this work, but representative with respect to population differences due to stratified sampling within each test pond.According to the LME added with the strain effect, the assumed migratory OUV strain moved significantly farther downstream than the resident VAA strain .The Q-Q plot showed that there was no inflation in the dataset and the correction for population stratification was adequate . 24430 SAngsd dataset of 22186 SNPs. The top 0.1% percent of markers corresponded to 22 SNPs.None of the individual SNPs showed statistically significant association with migration scores after the multiple test correction (FDR) in the final Stacks dataset showed a conservative pattern with slightly right-skewed distribution indicating that the statistical framework used for correcting for various factors was slightly too conservative . The top 0.1% percent of markers corresponded to 18 SNPs.Q-Q plots with the ervative . In totaStacks were found among the 24330 SNPs identified by Angsd. The overlap was consistent across chromosomes . Despite of different analytical approaches, the estimated P-values for Stacks and Angsd showed very high correlation  and clustered together in relatively narrow genomic regions . Depending on data structure and method assumptions different approaches can perform differently  this is hardly surprising, as these studies analyzed to large extent non-overlapping parts of brown trout genome. Yet, the lack of statistically significant individual SNPs after FDR correction is not valid counterevidence for migration being genetically influenced, because SNPs identified using RADSeq cover only a small proportion of the whole genome. Thus, it is possible that many causative variants affecting migration were not captured, given the low level of linkage disequilibrium in brown trout  or whole-genome sequencing would be necessary.Compared to previous comparative work (Our study shows how brown trout individuals and populations differing in their migration strategies could bear genetic signatures associated with their life-history. This alone is a valuable result for management and conservation purposes, as it indicates that ecotypes should be managed differently in order to maintain the life-history diversity . These rTo conclude, our study demonstrates that migration in brown trout has a genetic or epigenetic component but does not fully resolve the mechanistic and causal pathways for variation in migration tendency. By linking telemetry in common garden with genomic data, we identified two promising genomic regions and eight candidate genes potentially associated with migratory behavior. However, additional testing using higher number of SNPs and analysis of inter-population hybrids is still needed for validation of the putative association signals and for better understanding of the molecular function and adaptive significance of the identified candidate genes.


Datasets comprising twins and their children can be a useful tool for understanding the nature of intergenerational associations between parent and offspring phenotypes. In the present article we explore structural equation models previously used to analyse Children-of-Twins data, highlighting some limitations and considerations. We then present new variants of these models, showing that extending the models to include multiple offspring per parent addresses several of the limitations discussed. Accompanying the updated models, we provide power calculations and demonstrate with application to simulated data. We then apply to intergenerational analyses of height and weight, using a sub-study of the Norwegian Mother and Child Cohort (MoBa); the Intergenerational Transmission of Risk (IToR) project, wherein all kinships in the MoBa data have been identified (a children-of-twins-and-siblings study). Finally, we consider how to interpret the findings of these models and discuss future directions.The online version of this article (10.1007/s10519-018-9912-4) contains supplementary material, which is available to authorized users. Studying samples of twin pairs with children can provide valuable insight into the nature of intergenerational associations. Using samples of monozygotic (MZ) and dizygotic (DZ) twins with children , it is possible to evaluate hypotheses regarding the nature of intergenerational transmission and ask whether associations between parent and child remain after accounting for their genetic relatedness.The use of samples of twins with children to examine intergenerational associations has been discussed in depth elsewhere  comparing avuncular relationships in MZ and DZ families, siblings, half-sibling and cousins can be used. Children share 25, 12.5 and 6.25% of their genetic variance with their parent’s sibling, half-sibling and cousin respectively. These differences in relatedness can be used to estimate the role of genetic factors in explaining intergenerational associations in the same way that twin data is used  and/or children-of-sibling (CoS) data and investigate the nature of intergenerational associations. While various multilevel regression models have been applied to such data, in keeping with behavioural genetic tradition, structural equation models (SEMs) have been most widely used, and it is these models that we focus on in this article. Biometric SEMs are advantageous in that they allow researchers to quantify the relative importance of genetic and environmental influences in explaining variance on a trait and covariance between traits. Our intention in this article is to describe the SEMs thus far applied to CoT data, discuss their limitations, and suggest extensions to the models that help to overcome these limitations. We then apply these extended models, first demonstrating their power using simulated data, and then using real data originating from the Norwegian Intergenerational Transmission of Risk (IToR) project, a sub study of the Norwegian Mother and Child Cohort Study (MoBa) . Second, many on-going twin studies have participants who are in, or are entering, adulthood. When these participants begin having children, such datasets will have the potential to teach us a great deal about the nature of intergenerational transmission. It is therefore worthwhile exploring how best to model these associations, and how best to collect data for this purpose.Several CoT SEMs have previously been published. In Fig. The model in Fig. A key limitation of the models presented in Fig. In Fig. invariant across children . However, many parental phenotypes have the potential to vary between children within the same family . As such, the MCoT model in Fig. variant across offspring.The MCoT model presented in Fig. In extending the MCoT model for use with parent phenotypes that vary between offspring, it is important to consider how to specify the ‘within-person’ correlation that occurs between, for example, Twin 1’s parenting of their first and second child. In some CoT models  from genetic transmission is dependent upon the power to detect each of these pathways. For phenotypic transmission, this means the p path. For genetic transmission, this involves genetic effects in the parent generation (a1) and the transmission of these effects to the next generation (a1′). Of all these paths , there is least power to detect a1′. Power to detect the a1′ pathway is dependent upon many factors, including the magnitude of the parent-offspring correlation; the proportion of the correlation that is attributable to genetic overlap; the size of the avuncular relatedness coefficients (0.50 and 0.25 for families in which parents are MZ and DZ twins respectively); the size of the sample; the size of the families within the sample, the MZ:DZ ratio; and the heritability of the phenotypes. In the following power analyses we use simulated data to first demonstrate how the power to detect a1′ is increased by increasing the number of offspring included per parent. We then introduce shared environmental effects into parent and child phenotypes, and explore models including significant extended family effects (c1′) as these are often omitted from CoT models.In Table In all power analyses we simulated data comprising a sample of 1000 twin pairs (40% MZ) with children. We have tried to simulate a scenario that we believe to be representative of a real-world situation—two moderately heritable traits with an intergenerational correlation of 0.35. In the first few power analyses  in Table In Table Previously it has been noted that some CoT models have difficulty converging on a correct solution when parental shared environment is non-zero  to be 40% attributable to genetic transmission, 40% attributable to extended family effects, and 20% attributable to phenotypic transmission. As with all of our analyses, results showed that MCoT-var had more power than MCoT-inv, which had more power than CoT. The inclusion of c1′ further reduced power to detect a1′ in all models. In order to reach 80% power to detect a1′, the CoT, MCoT-inv and MCoT-var models required 2697, 1452, and 1218 families respectively. Power to detect c1′ was far greater than was power to detect a1′, with ~ half or fewer families required to achieve 80% power. Models 1d, 2d, and 3d  confirm that in the absence of a1′, power to detect c1′ remained very high and considerably higher than a1′ of a similar magnitude.In the supplementary materials we explore the consequences of not modelling c1′ where it does in fact play a role in intergenerational associations (Table S1). We also explore the consequences of unmodelled dominance effects (Table S2). These analyses are discussed towards the end of this article.To further demonstrate the power gained by including multiple children per family in CoT/CoS SEMs, we applied two of the above models to data from the Intergenerational Transmission of Risk (ITOR) project. The ITOR project is based on the Norwegian Mother and Child Cohort Study (MoBa)  CoT model to the data. We then fitted MCoT models to the data, allowing for two children per mother. We chose the MCoT-inv model for the analysis of height because maternal height did not differ by offspring. To the weight data we fitted an MCoT-var model, because maternal weight did differ by offspring.The sample for analysis comprised 10,610 mothers on whom we had data. These included MZ and DZ twin pairs, siblings, and half-siblings . These individual mothers constituted 4875 complete sibling pairs, and 860 incomplete pairs (i.e. singletons). We had data on 8281 children. In the first analysis (one child per mother), 7090 of these children were included. In the second analysis (up to two children per mother), all were included. In the second analysis there were 1191 complete sibling pairs in the offspring generation. The sibling pairs in the offspring generation also included MZ twin pairs (35), and half-sibling pairs (9).Parameter estimates from all analyses are displayed in Table For height, the results of model fitting  revealed that it was possible to drop A1′, C1′ or the p path from the CoT model, indicating that it was not possible to distinguish between the different routes of intergenerational transmission in this analysis. Conversely, in the MCoT model, it was not possible to drop the A1′ path or the C1′ path, indicating that both of these routes were significant. Examination of the MCoT model confirms the importance of genetic factors, with 87% of the mother–child covariance being attributable to genetic transmission. These results would strongly suggest that genetic transmission is the primary reason for correlations between maternal height and the height of their 18-month-old children.For weight, model fitting results led to the same conclusion for the CoT and MCoT analysis, whereby genetic transmission explained the covariance between maternal pre-pregnancy weight and that of their child at age 18 months. The results of model fitting and confidence intervals highlights the greater power of the MCoT model to detect a1′. While the participation rate of 41% in the MoBa study gives some cause for caution in interpreting these findings, we are not aware of any reason to expect the mechanisms underlying the correlation between mother and child height and weight to vary by participation.In this article we have demonstrated how to expand CoT models to include multiple children per twin, and how doing so can increase power and solve some of the shortcomings of previous models. Using real data, we have shown that the inclusion of more than one offspring per parent can have a substantive impact on results, even when only a small proportion of the overall sample has more than one child in the study. For the association between mother and child height, the (one child per parent) CoT model could not distinguish genetic from phenotypic transmission. However, once we included complete sib-ships in the offspring generation, our MCoT model clearly demonstrated that this association could best be understood as primarily genetic in nature.We believe that our extensions to existing CoT models, and findings regarding the associated increases in statistical power, constitute an important step forward and will be informative to researchers as they make use of available CoT and family data. It is also our desire that this article be of use to researchers interested in collecting new data on twins and their children. Our findings emphasise the benefits of collecting data on multiple children per parent. It is also worth considering other ways in which researchers might get the most out of CoT data. For example, the inclusion and retention of childless twins and incomplete pairs in CoT databases is advisable. In typical twin studies (i.e. twins without offspring), the inclusion of incomplete pairs means that twins without a co-twin in the sample can still contribute to the estimation of means and variances, even if they do not contribute information to the estimation of twin covariance and aetiological decomposition. In a CoT study, if only one member of a twin pair has a child, including data on the childless co-twin will also allow this family to contribute to avuncular correlations. Thus, such families can also inform on the nature of intergenerational associations. This is preferable to only including families in which both twins have offspring, and will increase the generalizability of the sample .It is also worth highlighting that the models we present are not only suitable for use with twin data but can easily be adapted for use with population or family databases, where comparisons between MZ and DZ twin families can be accompanied or replaced by comparisons between siblings, half-siblings, cousins, and half-cousins (e.g. as in the ITOR sample in this article). That is, comparisons between any groups of differentially related families can be used in this way. Power will be lower where relatedness coefficients are smaller, but the potential for larger sample sizes will often mitigate this.Below we attempt to expand and clarify upon the interpretation of the CoT models we have presented, discuss some nuances, and highlight future directions:above and beyond effects attributable to genetic relatedness and extended family effects. (2) Offspring phenotype causally influences parent phenotype above and beyond effects attributable to genetic relatedness and extended family effects. (3) Parent and offspring phenotype are each associated with a third variable, not included in the model and not captured by shared genetic or extended family effects, that has a causal influence on each, thus creating/inflating their statistical association. Note that these interpretations do not entirely preclude genetic effects from playing a role in parent–child associations—parent and child phenotype can each be under genetic influence, so e.g. the genetically influenced behaviour of a child could have an influence on their parent’s behaviour (and/or vice versa). Such effects would constitute an evocative rGE and in the models presented would load onto the phenotypic pathway. The phenotypic pathway suggests that there may be a role for exposure—that is, after accounting for genetic relatedness, the parent phenotype may influence child phenotype, and/or vice versa. The p path has previously been described using several different terms including ‘non-genetic’, ‘environmental’, and ‘social’. We have chosen to use the term ‘phenotypic’, as this carries fewer implications about the origin of the association.The path that we have labelled ‘p’ in Figs. In terms of the way that the SEMs in Figs. The A1′ pathway in the CoT models presented captures covariance between the parent and child phenotypes that is attributable to their genetic relatedness. That is, this pathway is intended to estimate intergenerational genetic transmission. It is worth noting here that in recent years several papers have included reports of non-significant A1′ pathways where some genetic transmission might have been expected. For example, if depression is heritable, why is none of the intergenerational association between parent and child depression attributable to genetic transmission  , and children reporting otherwise . In CaT studies the heritability of parenting is calculated based on how similarly individual parents parent their MZ vs. DZ twin children. In contrast, CoT studies involve comparing how similar MZ vs. DZ twins are in parenting their own children. As such, estimates of the heritability of parenting derived from CoT samples are less susceptible to reporter effects.Previously, when presenting findings from CoT studies we have encountered questions from some reviewers and researchers along the lines of “if children-as-twin studies report that the association between, for example, parenting and behavioural problems is partially genetic . We have also included a c1′ path, via which extended family effects can be transmitted that can explain some or all of the correlation between parent and child phenotypes. Other possible shared environmental effects are possible, and several have been modelled in Swedish population registry data using intergenerational models similar to those we present here  was non-zero, power to detect A1′ reduced substantially, even when the shared environment played no role in explaining the intergenerational association. This finding aligns with reports by Narusyte et al. , that thWe also explored models designed for those situations in which the shared environment may play a role in explaining intergenerational associations via the pathway c1′. Where genetic and extended family environment effects explained equal portions of the intergenerational association, power to detect c1′ was higher than power to detect a1′. The presence of c1′ also led to a slight reduction in power to detect a1′ relative to models in which c1′ was held at zero. Importantly, failing to allow for a possible role of c1′ when it did in fact play a role in explaining intergenerational associations led to an overestimation of a1′ and underestimation of p. This is an important finding given that many CoT models to date have not allowed for the possibility of an effect of the extended family environment. To ensure accurate results we would encourage researchers to include c1′ paths (or similar) in their models. If clearly non-significant then these paths can be dropped from models in order to maximise power to detect a1′.genetic dominance. In twin models, latent factors can be added to capture effects attributable to interactions between alleles at a locus . Such interactions increase the similarity between siblings whenever they share the same alleles at a given locus. That is, when siblings inherit the same genes as one another from both their mother and their father. MZ twins are genetically identical, so they share all dominance genetic effects with one another. Full siblings inherit the same genes from both their mother and their father at a given locus 25% of the time on average. Thus, for MZ twins the correlation for D (rD) is 1.00, whereas for DZ twins and full siblings rD = 0.25. For other family dyads  rD = 0.00 because they do not share both parents. In this paper we have focussed on the use of CoT/CoS data in understanding the nature of intergenerational associations. We have therefore omitted dominance effects from our models because such effects are not directly transmitted from one generation to the next. This is because children only inherit one copy of each gene from each parent. It is also generally not possible using twin data to estimate the effects of dominance and the shared environment at the same time. However, in the scripts provided in the supplementary materials we do allow for parent (D1) and child (D2) dominance effects for use with phenotypes in which dominance is present. In simulations included in the supplementary materials we explored the consequences of omitting significant parental dominance effects from our models. Generally, the models we present appear to deal with unmodelled dominance well, and conclusions regarding the nature of intergenerational associations should not be biased except for instances in which the extended family environment also plays a role .In the models presented in Figs. As with all models, CoT and MCoT models have limitations that should be taken into consideration. Many of these limitations have been discussed elsewhere. For example, a CoT-specific version of the equal environments assumption (EEA) is that the children of MZ twins are not exposed to their parent’s co-twin any more than are the children of DZ twins. Although, unlike the EEA in standard twin studies, in CoT studies it is quite easy to account for this by measuring the amount of avuncular contact and including in the model . It would also be possible to regress the effects of age out of phenotypes before entering into SEMs, or to include age as a covariate. While we have not explored the possibility of modelling sex differences in our models, it would be possible to do so, although we would anticipate that power considerations would mean that very large samples are required to accurately identify any differences.In the present article we have followed others , they are pleiotropic (if the phenotypes are distinct), they persist across time (i.e. they associate with the same phenotype in cohorts born in different eras), and they explain a portion of familial similarity. Where well-powered studies indicate that genetic transmission does not play a role, then it will be important to consider what processes might explain associations. And while it is not the case that associations under genetic influence are not amenable to intervention, when effects do remain after accounting for relatedness, it will be possible to guide intervention efforts to focus on those familial risk factors that constitute true environmental influences on child development.Below is the link to the electronic supplementary material.Supplementary material 1 (PDF 73 KB)Supplementary material 2 (R 61 KB)


Humoral immune responses to infectious agents or vaccination vary substantially among individuals, and many of the factors responsible for this variability remain to be defined. Current evidence suggests that human genetic variation influences (i) serum immunoglobulin levels, (ii) seroconversion rates, and (iii) intensity of antigen-specific immune responses. Here, we evaluated the impact of intrinsic (age and sex), environmental, and genetic factors on the variability of humoral response to common pathogens and vaccines.Milieu Intérieur cohort). Using clinical-grade serological assays, we measured total IgA, IgE, IgG, and IgM levels, as well as qualitative (serostatus) and quantitative IgG responses to cytomegalovirus, Epstein-Barr virus, herpes simplex virus 1 and 2, varicella zoster virus, Helicobacter pylori, Toxoplasma gondii, influenza A virus, measles, mumps, rubella, and hepatitis B virus. Following genome-wide genotyping of single nucleotide polymorphisms and imputation, we examined associations between ~ 5 million genetic variants and antibody responses using single marker and gene burden tests.We characterized the serological response to 15 antigens from common human pathogens or vaccines, in an age- and sex-stratified cohort of 1000 healthy individuals  class II region on chromosome 6 and anti-EBV and anti-rubella IgG levels. We used HLA imputation to fine map these associations to amino acid variants in the peptide-binding groove of HLA-DRβ1 and HLA-DPβ1, respectively. We also observed significant associations for total IgA levels with two loci on chromosome 2 and with specific KIR-HLA combinations.Using extensive serological testing and genome-wide association analyses in a well-characterized cohort of healthy individuals, we demonstrated that age, sex, and specific human genetic variants contribute to inter-individual variability in humoral immunity. By highlighting genes and pathways implicated in the normal antibody response to frequently encountered antigens, these findings provide a basis to better understand disease pathogenesis.ClinicalTrials.gov, NCT01699893The online version of this article (10.1186/s13073-018-0568-8) contains supplementary material, which is available to authorized users. Humans are regularly exposed to infectious agents, including common viruses such as cytomegalovirus (CMV), Epstein-Barr virus (EBV), or herpes simplex virus-1 (HSV-1) that have the ability to persist as latent infections throughout life—with possible reactivation events depending on extrinsic and intrinsic factors –4, the gSeveral genetic variants have been identified that account for inter-individual differences in susceptibility to pathogens –9 and inMilieu Intérieur cohort consists of 1000 healthy individuals that were recruited by BioTrial . The cohort is stratified by sex  and age . Donors were selected based on stringent inclusion and exclusion criteria, previously described [ClinicalTrials.gov (study# NCT01699893).The escribed . BrieflyToxoplasma gondi, and anti-CMV IgGs were measured using the BioPlex 2200 ToRC IgG kit on the BioPlex 2200 analyzer (Bio-Rad). Anti-HSV1 and anti-HSV2 IgGs were measured using the BioPlex 2200 HSV-1 and HSV-2 IgG kit on the BioPlex 2200 analyzer (Bio-Rad). IgGs against Helicobacter Pylori were measured by EIA using the PLATELIA H. pylori IgG kit (BioRad) on the VIDAS automate (Biomérieux). Anti-influenza A IgGs were measured by ELISA using the NovaLisa IgG kit from NovaTec (Biomérieux) that explores responses to grade 2 H3N2 Texas 1/77 strain. In all cases, the criteria for serostatus definition  were established by the manufacturer and are indicated in Additional file Total IgG, IgM, IgE, and IgA levels were measured using clinical grade turbidimetric test on AU 400 Olympus at the BioTrial . Antigen-specific serological tests were performed using clinical-grade assays measuring IgG levels, according to the manufacturer’s instructions. A list and description of the assays is provided in Additional file A large number of demographical and clinical variables are available in the Milieu Intérieur cohort as a description of the environment of the healthy donors . These iUsing serostatus variables as the response, and non-genetic variables as treatment variables, we fitted a logistic regression model for each response and treatment variable pair. A total of 14 × 52 = 742 models where therefore fitted. Age and sex where included as controls for all models, except if that variable was the treatment variable. We tested the impact of the clinical and demographical variables using a likelihood ratio test. All 742 tests where considered a multiple testing family with the false discovery rate (FDR) as error rate.y~Age + I(Age^2) + Sex, family = binomial)). Scaling was achieved by centering age variable at the mean age. When indicated, we used a second model that includes age, sex as well as an interaction term for age and sex ). All continuous traits  were log10-transformed in donors assigned as positive using the clinical cutoff suggested by the manufacturer. We used false discovery rate (FDR) correction for the number of serologies tested (associations with P < 0.05 were considered significant).To examine the impact of age and sex, we performed logistic and linear regression analyses for serostatus and IgG levels, respectively. For logistic regression, we included both scaled linear and quadratic terms for the age variable (model = glm(P < 10−7). These quality-control filters yielded a total of 661,332 and 87,960 variants for the HumanOmniExpress and HumanExome BeadChips, respectively. Average concordance rate for the 16,753 SNPs shared between the two genotyping platforms was 99.9925%, and individual concordance rates ranged from 99.8 to 100%.Blood was collected in 5-mL sodium EDTA tubes and was kept at room temperature (18°–25°) until processing. DNA was extracted from human whole blood and genotyped at 719,665 single nucleotide polymorphisms (SNPs) using the HumanOmniExpress-24 BeadChip (Illumina). The SNP call rate was higher than 97% in all donors. To increase coverage of rare and potentially functional variation, 966 of the 1000 donors were also genotyped at 245,766 exonic variants using the HumanExome-12 BeadChip. The HumanExome variant call rate was lower than 97% in 11 donors, which were thus removed from this dataset. We filtered out from both datasets genetic variants that (i) were unmapped on dbSNP138, (ii) were duplicated, (iii) had a low genotype clustering quality (GenTrain score < 0.35), (iv) had a call rate < 99%, (v) were monomorphic, (vi) were on sex chromosomes, or (vii) diverged significantly from Hardy-Weinberg equilibrium . We obtained a total of 12,058,650 genetic variants to be used in association analyses.We used positional Burrows-Wheeler transform for genotype imputation, starting with the 661,332 quality-controlled SNPs genotyped on the HumanOmniExpress array. Phasing was performed using EAGLE2 (v2.0.5) . As refeWe used SNP2HLA (v1.03)  to imputWe used KIR*IMP  to imputN = 5,699,237). We used PLINK (v1.9) [Pthreshold < 2.6 × 10−9) corrected for the number of antigens and immunoglobulin classes tested (N = 19). For specific HLA analyses, we used PLINK (v1.07) [For single-variant association analyses, we only considered SNPs with a MAF of > 5% (K v1.9)  to perfoK (v1.9)  and stabK (v1.9)  as detaiK (v1.9) , were in.9  to pe (v1.07)  to perfoN is the sample size. We used genome-wide Bonferroni correction for multiple testing, accounting for the number of phenotypes tested (Pthreshold < 2.6 × 10−9).We used SnpEff (v4.3g)  to annotMilieu Interieur (MI) cohort. After log10 transformation, total IgG, IgM, IgA, and IgE levels showed normal distributions, with a median ± sd of 1.02 ± 0.08 g/l, 0.01 ± 0.2 g/l, 0.31 ± 0.18 g/l, and 1.51 ± 0.62 UI/ml, respectively , and one parasite: Toxoplasma gondii (T. gondii); (ii) one recurrent virus: influenza A virus (IAV); and (iii) four viruses for which most donors received vaccination: measles, mumps, rubella, and HBV (HBs and HBc antigens). The distributions of log10-transformed antigen-specific IgG levels in the 1000 donors for the 15 serologies are shown in Additional file We then evaluated specific IgG responses to multiple antigens from the following infections and vaccines: (i) seven common persistent pathogens, including five viruses: CMV, EBV , herpes simplex virus 1 and 2 (HSV-1 & 2), varicella zoster virus (VZV), one bacterium: T. gondii (56%). By contrast, fewer individuals were seropositive for CMV (35%), HSV-2 (21%), and H. pylori (18%)  were positive for anti-HBc IgG, indicative of previous HBV infection . For rubella, measles, and mumps, seropositivity rates were 94, 91, and 89%, respectively. For the majority of the donors, this likely reflects vaccination with a trivalent vaccine, which was integrated in 1984 as part of national recommendations in France, but for some, in particular the > 40-year-old individuals of the cohort, it may reflect acquired immunity due to natural infection.The vast majority of the 1000 healthy donors were chronically infected with EBV  and VZV (93%). Many also showed high-titer antibodies specific for IAV (77%), HSV-1 (65%), and Milieu Interieur cohort were surveyed for a large number of variables related to infection and vaccination history, childhood diseases, health-related habits, and socio-demographical variables (http://www.milieuinterieur.fr/en/research-activities/cohort/crf-data). Of these, 53 where chosen for subsequent analysis of their impact on serostatus. This selection is based on the one done in [Subjects included in the  done in , with a T. gondii). We observed in this case either (i) a linear increase in seropositivity rates over the five decades of age for CMV  and T. gondii ; or (ii) a non-linear increase in seropositivity rates for HSV-1, with a steeper slope before age 40   were more likely to have detectable specific IgG, with a mean beta estimate of 0.04 for linear associations Fig. . ProfileP = 0.02, Additional file We also observed a significant association between sex and serostatus for 8 of the 15 antigens, with a mean beta estimate of 0.07 , P = 3.7 × 10−6, Additional file We further examined the impact of age and sex on the levels of total IgG, IgM, IgA, and IgE detected in the serum of the patients, as well as on the levels of antigen-specific IgGs in seropositive individuals. We observed a low impact of age and sex with total immunoglobulin levels Fig. . Age alsPthreshold = 2.6 × 10−9, for which we did not observe any significant association. In particular, we did not replicate the previously reported associations with H. pylori serostatus on chromosomes 1  and 4  [H. pylori antibody distribution to the rest of the study population: no association was found . The quantile-quantile (QQ) plots and lambda values of all genome-wide logistic regressions are available in Additional file To test if human genetic factors influence the rate of seroconversion upon exposure, we performed genome-wide association studies. Specifically, we searched for associations between 5.7 million common polymorphisms (MAF > 5%) and the 15 serostatus in the 1000 healthy donors. Based on our results regarding age and sex, we included both as covariates in all models. After correcting for the number of antibodies considered, the threshold for genome-wide significance was  = 0.97) . We veriMI cohort was found with residues at position 31 of the HLA-DRβ1 subunit  = 0.8 ) and phenylalanine  = 0.81 ), are consistent in direction and in almost perfect linkage disequilibrium (LD) with the glutamic acid residue at position 96 in HLA-DRβ1 that was identified in the previous study  of a SNP rs11186609 on chromosome 10 with total IgA levels . The closest gene for this signal is SH2D4B.We found no SNPs associated with total IgG, IgM, IgE, and IgA levels. Additional file P = 3 × 10−14, beta = 0.29 for the A allele)   [We next explored associations between human genetic variants and antigen-specific IgG levels in seropositive donors. Information on possible inflation of false positive rates of these linear regressions is available in Additional file  = 0.76) .Fig. 3AsHLA-DQB1*03:01 showed the lowest P value for association with EBV EBNA (P = 1.3 × 10−7), and HLA-DPB1*03:01 was the top signal for rubella (P = 3.8 × 10−6). At the level of amino acid positions, position 58 of the HLA-DRβ1 protein associated with anti-EBV (EBNA antigen) IgG levels (P = 2.5 × 10−11). This is consistent with the results of previous studies linking genetic variations in HLA-DRβ1 with levels of anti-EBV EBNA-specific IgGs [P = 1.1 × 10−9, Table To fine map the associations observed in the HLA region, we tested four-digit HLA alleles and variable amino positions in HLA proteins. At the level of HLA alleles, fic IgGs , 39, 40 Pthreshold < 5.4 × 10−7), we observed significant associations between total IgA levels and the two following HLA-KIR combinations: HLA-B*14:02/KIR3DL1 and HLA-C*08:02/KIR2DS4 , which we collapsed by gene and tested together using the kernel-regression-based association test SKAT [ACADL (P = 3.4 × 10−11) and TMEM131 (P = 7.8 × 10−11)  genes have been associated with diseases as diverse as autoimmunity, viral infections, reproductive failure, and cancer [. Also, given the novelty of KIR imputation method and the lack of possibility of benchmarking its reliability in the MI cohort, further replication of these results will be needed. Yet these findings support the concept that variations in the sequence of HLA class II molecules, or specific KIRs/HLA class I interactions play a critical role in shaping humoral immune responses in humans. In particular, our findings confirm that small differences in the capacity of HLA class II molecules to bind specific viral peptides can have a measurable impact on downstream antibody production. As such, our study emphasizes the importance of considering HLA diversity in disease association studies where associations between IgG levels and autoimmune diseases are being explored.Using a fine-mapping approach, we replicated the previously reported associations of variation in the HLA-DRβ1 protein with influenza A serostatus and anti-EBV IgG titers , 12, impd cancer . To dateSH2D4B. By collapsing the rare variants present in our dataset into gene sets and testing them for association with the immunoglobulin phenotypes, we identified two additional loci that participate to natural variation in IgA levels. These associations mapped to the genes ACADL and TMEM131. ACADL encodes an enzyme with long-chain acyl-CoA dehydrogenase activity, and polymorphisms have been associated with pulmonary surfactant dysfunction [ACADL could play a role in regulating the balance between mucosal and circulating IgA. Further studies will be needed to test this hypothesis, as well as the potential impact of our findings in other IgA-related diseases.We identified nominal significance for some but not all of the previously reported associations with levels of total IgG, IgM, and IgA, as well as a suggestive association of total IgA levels with an intergenic region on chromosome 10—closest gene being function . As the TLR1 and FCGR2A locus with serostatus for H. pylori [Milieu Interieur cohort, versus 56% in the previous ones, reducing the likelihood of replication due to decreased statistical power.We were not able to replicate previous associations of . pylori . We beliT. gondii during adulthood. In our cohort, the likelihood of being seropositive for one of these infections is comparable at age 20 and 40. This observation raises interesting questions about the factors that could prevent some individuals from becoming seropositive upon late-life exposure, considering the high likelihood of being in contact with the pathogens because of their high prevalence in humans (CMV and HSV-1) or because of frequent interactions with an animal reservoir (toxoplasmosis). Second, both age and sex have a strong correlation with serostatus, i.e., older and female donors were more likely to be seropositive. Although increased seropositivity with age probably reflects continuous exposure, the sex effect is intriguing. Indeed, our study considered humoral immunity to microbial agents that differ significantly in terms of physiopathology and that do not necessarily have a childhood reservoir. Also, our analysis shows that associations persist after removal of potential confounding factors such as marital status and/or number of kids. As such, we believe that our results may highlight a general impact of sex on humoral immune response variability, i.e., a tendency for women to be more likely to seroconvert after exposure, as compared to men of same age. Gender-specific differences in humoral responses have been previously observed for a large number of viral and bacterial vaccines including influenza, hepatitis A and B, rubella, measles, rabies, yellow fever, meningococcus, pneumococcus, diphtheria, tetanus, and Brucella [In addition to genetics findings, our study re-examined the impact of age and sex, as well as non-genetic variables, on humoral immune responses. Although this question has been previously addressed, our well-stratified cohort brings interesting additional insights. One interesting finding is the high rate of seroconversion for CMV, HSV-1, and Brucella , 44. AloBrucella , 45, andBrucella  as well Brucella . This coBrucella . FinallyIn sum, our study provides evidence that age, sex, and host genetics contribute to natural variation in humoral immunity in humans. The identified associations have the potential to help improve vaccination strategies and/or dissect pathogenic mechanisms implicated in human diseases related to immunoglobulin production such as autoimmunity.Additional file 1:Table S1. Assay details for serologies. Table S2. Cutoffs and seroprevalence for serologies. Table S3. List of covariates used for each phenotype. Table S4. Associations of environmental variables with serostatus. Table S5. Association of serologies with age and sex. Table S6. Associations of amino acid positions in HLA proteins with Influenza A serology. Table S7. LD between residues in HLA-DRβ1 at position 13 and 96. Table S8. Replication of SNPs associated with levels of total IgM, IgA and IgG. Table S9. LD between residues in HLA-DRβ1 at position 15 and 11. (XLSX 65 kb)Additional file 2:Figure S1. Principal Component Analysis. Figure S2. Distribution of serological variables, and clinical thresholds. Figure S3. Seroprevalence data. Figure S4. Impact of non-genetic factors on serostatus. Figure S5. Evolution of serostatus with age and sex. Figure S6. Correlations between age and IgG specific to Rubella and T. gondii. Figure S7. QQ plots for logistic regressions preformed in the study. Figure S8. QQ plots for linear regressions preformed on total Ig levels. Figure S9. QQ plots for linear regressions preformed for pathogen-specific IgG levels. Figure S10. QQ plots for burden testing analyses preformed for all binary phenotypes. Figure S11. QQ plots for burden testing analyses preformed for total Ig levels. Figure S12. QQ plots for burden testing analyses preformed for pathogen-specific IgG levels. (DOCX 89996 kb)


In genomic prediction, single-step method has been demonstrated to outperform multi-step methods. This study investigated the efficiency of genomic prediction for seven body measurement traits in Yorkshire population and simulated data using single-step method. For Yorkshire population, in total, 592 individuals were genotyped with Illumina PorcineSNP80 marker panel. We compared the prediction accuracy obtained from a traditional pedigree-based method (BLUP), a genomic BLUP (GBLUP) and a single-step genomic BLUP (ssGBLUP) through 20 replicates of 5-fold cross-validation (CV). In addition, we also compared the performance of two-trait ssGBLUP and single-trait ssGBLUP for the traits with different gradients of genetic correlation. Our results indicated the GBLUP method generally provided lower accuracies of prediction than BLUP and ssGBLUP methods, and the average standard deviation of unbiasedness was as large as 0.278. For single-step methods, the accuracies of ssGBLUP for seven body measurement traits ranged from 0.543 to 0.785, and the unbiasedness of ssGBLUP ranged from 0.834 to 1.026, respectively. ssGBLUP generally generated 1% on average higher prediction accuracy than traditional BLUP, the improvement of ssGBLUP and the performance of GBLUP was lower than expected mainly due to the small number of genotyped animals, it was further demonstrated by our simulation study. We simulated two traits with heritabilities 0.1, 0.3, and with high genetic correlation 0.7, our results also showed that the prediction accuracies were low for GBLUP compared with other three methods with different genotyped reference population sizes and the accuracies were improved with increasing the genotyped reference population size. However, the increase was small for ssGBLUP compared with BLUP when the genotyped reference population size was <500. Our results also demonstrated that the accuracies of genomic prediction can be further improved by implementing two-trait ssGBLUP model, the maximum gain on accuracy was 2 and 2.6% for trait of chest width compared to single-trait ssGBLUP and traditional BLUP, while the gain was decreased with the weakness of genetic correlation. Two-trait ssGBLUP even performed worse than single trait analysis in the scenario of low genetic correlation. The purpose of animal breeding is to genetically improve the performance of a population, which is usually achieved by selecting the best animals among the current generation to be served as parents of the next generation. In pig breeding, the main objective traits are growth and reproduction traits, such as days to 100 kg, backfat thickness and total number of piglets born. Body measurement traits are usually not concerned because of the difficulty of phenotypic data collection and its economic importance. However, the genetic evaluation of body measurement traits is becoming more important recently. The estimation of live weight by body measurements have been applied to different animal species  has been developed by constructing H matrix using marker genotypes and pedigree jointly instead of G matrix or pedigree-based relationship matrix alone  estimate genetic parameters of seven body measurement traits; (2) assess the accuracy of genomic selection using single-step strategy in the scenario of small genotyped animals; (3) improve the accuracy of genomic prediction using two-trait ssGBLUP with different gradients of genetic correlation; (4) investigate the impact of different genotyped reference population sizes on the accuracy of genomic prediction using simulated data.The whole procedure for collecting pig blood samples was carried out in strict accordance with the protocol approved by the Animal Care and Use Committee of China Agricultural University (Permit Number: DK996).−3 and 2.5 × 10−3, respectively. Mimicking the pig breeding, phenotypic values were generated by adding simulated phenotypes and fixed effect values, and the fixed effects include sex-generation which were generated through uniform distribution from 0 to 1. We randomly selected 1,000 individuals from generation 10 as validation population, genotyped reference populations of different sizes of 50, 100, 300, 500, 1,000, 2,000, and 3,000 were randomly sampled from generation 8, 30,519 animals were traced back to construct pedigree relationship matrix.The multiple-trait genomic simulation software GPOPSIM . Genotyping was performed using a PorcineSNP80 BeadChip  which includes 68,528 SNPs across the entire pig genome. In total, 592 pigs with body measurement traits records were genotyped.P-value < 10−7 were excluded, in addition, the individuals with reliability of EBV <0.35 for 7 body measurement traits were removed. After quality control, all genotyped individuals remained and 55,759 SNPs were finally used.Missing genotypes of SNPs with known chromosomal positions were imputed by Beagle  and two-trait ssGBLUP methods with combined relationship matrix constructed from marker and pedigree information, were used to predict breeding values.The traditional animal model with a pedigree-based relationship matrix was applied to predict breeding values for each trait separately. The model was defined as:y is the vector of phenotypic values, b is the vector of fixed effects including herd-year-season-sex, X is the incidence matrix associating b with y, W is the covariate of body weight, γ is the regression coefficient, g is the vector of additive genetic effects, following a normal distribution of A is the matrix of additive genetic relationships, Z is the incidence matrix associating g with y; e is the vector of random residuals with distribution of N, and subscripts 1 and 2 refer to non-genotyped and genotyped animals, respectively. Compared with the H matrix, the inverse of H was simple which used to solve the mixed model equations, the inverse of H was:in which Gw= 0.95Ga+0. 05A22 , the G matrix was adjusted according to Christensen et al. , where M = Z1 and Z2 are the incidence matrix associating g1 and g2 with y1 and y2, N, where I is the identity matrix and R = where In this study, the accuracy and unbiasedness of prediction were obtained through 5-fold cross-validation (CV) for real data. The genotyped individuals were randomly partitioned into five nearly equal sized subpopulations. Of the five subpopulations, a single subpopulation was retained as the validation population and the remaining four subpopulations were used as reference population. The cross-validation process was then repeated five times, with each of the five subpopulations used exactly once as the validation population. For all scenarios, the 5-fold CV was replicated 20 times, resulting in 20 averaged accuracies of genomic prediction. The two-trait ssGBLUP with CV was also implemented to predict GEBV for the traits with different gradients of genetic correlation. For BLUP, GBLUP and ssGBLUP, the validation set was same in each replicate of 5-fold CV.r for BLUP and ssGBLUP methods and r for GBLUP method, the correlation between predicted breeding values (PBV) and EBV or yc in the validation population. In addition, b, the regression of EBV or yc on PBV was also calculated to assess the possible inflation or unbiasedness of predictions. It should be noted here that EBV and yc were calculated based on a 7-trait animal model as described before, while PBV was calculated for validation population by traditional single-trait BLUP, GBLUP, or ssGBLUP assuming the phenotypic values of validation population were missing. For simulated data, the true breeding values (TBV) of all individuals were available, r and b in the validation population were used to assess the accuracy and unbiasedness.For real data, the accuracy of genomic prediction was evaluated as Number of records, birth year of animals, the descriptive statistics, estimated heritabilities and genetic correlations of the 7 body measurement traits and two simulated traits were presented in Tables Table The accuracy and unbiasedness of traditional BLUP with the same reference and validation population as ssGBLUP was also demonstrated in Table In order to evaluate the efficiency of multi-trait model on the improvement of genomic prediction, two-trait ssGBLUP model was compared with single-trait ssGBLUP model. Meanwhile, genetic correlations of three different gradients  were taken in account in two-trait ssGBLUP to evaluate the impact of genetic correlations on the genomic prediction e.g., high, medium and low genetic correlations of CW with RW 0.84), CG (0.25) and TG (0.01), and three gradients genetic correlations of 0.75 (CG), 0.20 (RW) and 0.06 (BH) with AG. Compared to single-trait model, as shown in Table , CG 0.2 to 0.703Similarly, the superiority of two-trait model was also demonstrated on AG. However, only tiny improvement on accuracy was obtained, the accuracies of genomic prediction in three gradient genetic correlations were nearly same and only slightly higher than the those in single-trait model. Likewise, there was no improvement on unbiasedness of genomic prediction from two-trait model as the regression coefficients with 1.002 from single-trait model was already very close to 1.0.Table The same trend was shown in Figure As shown in Figure Figure In this study, the estimated heritabilities of 7 body measurement traits were medium to high ranging from 0.27 to 0.50. The estimates of heritability of body length (BL) and body height (BH) were consistent with the study by Do et al.  where thThe genetic correlations of 7 body measurement traits ranged from −0.28 to 0.84, these traits were highly, medium or low correlated. Usually, the medium or highly correlated traits was more reasonable for multiple-trait BLUP model, while in the estimation of breeding values in this study, two or three highly correlated trait model performed as similar as the 7-trait model (data not shown). The genetic correlation between CW and RW was lower than the value of 0.56 reported by Kutwana et al. . Howeverw was set to 0.95 in each analysis as other studies  The genotyped reference was not large enough to improve the genomic predictive ability. In real data, about 400 genotyped reference animals could not probably provide more extra information compared to the pedigree information consisting of 5,000 individuals. Similarly, it also deduced the lower accuracy of GBLUP than BLUP in this study. In simulation study, GBLUP also produced lower accuracy than BLUP even the genotyped reference population size reached 3,000, it was still very small compared to non-genotyped individuals of 26,000 utilized by BLUP. Meanwhile, the improvement was also tiny for ssGBLUP compared to BLUP when the genotyped reference population size was small. Lourenco et al.  also repGenetic correlation between traits has been used to improve the statistical power to detect QTL controlling traits of interest , the accuracies of genomic prediction were lower than that of separate single trait analysis, which was also reported by Jia and Jannink  and WangIn this study, we used different single-step strategies and GBLUP method to investigate the efficiency of genomic prediction on body measurement traits in Yorkshire and simulated data. We compared the prediction accuracy of a traditional pedigree-based method (BLUP) with the GBLUP and single-step genomic BLUP (ssGBLUP) through 20 replicates of 5-fold cross-validation (CV) in pig data. Meanwhile, we investigated the impact of genotyped reference population size on the accuracy of genomic prediction through simulation study. In addition, we also compared the performance of two-trait ssGBLUP and single-trait ssGBLUP for the traits with different gradients of genetic correlation. Our results indicated that ssGBLUP generally generated higher prediction accuracy than traditional BLUP, the improvement was lower than expected mainly due to the small number of genotyped animals. Our results also demonstrated that the accuracies of genomic prediction can be further improved by implementing two-trait ssGBLUP model.https://figshare.com/articles/single-step_strategies/7434203.The simulated data and 7 body measurement traits data of Yorkshire supporting the conclusions of this article are available from the Figshare: XD and QZ designed and supervised the study. HS conducted the statistical analyses and interpretation of results. JZ conceived the experimental traits and sample preparation for DNA genotyping. HS simulated the data and wrote the manuscript, which was critically remarks by XD and QZ. All authors read and approved 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.


Fhb1 (for Type-II resistance), Fhb2 (Type-II), and Fhb5 (Type-I plus some Type-II), which originate from Sumai 3, are among the most important that confer resistance in hexaploid wheat. Near-isogenic lines (NILs), in the CDC Alsask  and CDC Go  backgrounds, carrying these genes in all possible combinations were developed using flanking microsatellite markers and evaluated for their response to FHB and deoxynivalenol (DON) accumulation in eight environments. NILs were haplotyped with wheat 90 K iSelect assay to elucidate the genomic composition and confirm alleles’ presence. Other than evaluating the effects of three major genes in common genetic background, the study elucidated the epistatic gene interactions as they influence FHB measurements; identified loci other than Fhb1, Fhb2, and Fhb5, in both recurrent and donor parents and examined annotated proteins in gene intervals.Fusarium head blight resistance genes, Fhb5 was as good as Fhb1 in conferring field resistance in both populations. In addition to Fhb1, Fhb2, and Fhb5, four to five resistance improving alleles in both populations were identified and three of five in CDC Go were contributed by the susceptible parent. The introgressed chromosome regions carried genes encoding disease resistance proteins, protein kinases, nucleotide-binding and leucine rich repeats’ domains. Complex epistatic gene-gene interactions among marker loci  explained > 20% of the phenotypic variation in FHB measurements.Genotyping using 81,857 single nucleotide polymorphism (SNP) markers revealed polymorphism on all chromosomes and that the NILs carried < 3% of alleles from the resistant donor. Significant improvement in field resistance (Type-I + Type-II) resulted only among the CDC Alsask NILs, not the CDC Go NILs. The phenotypic response of NILs carrying combinations of Sumai 3 derived genes suggested non-additive responses and Fhb1, Fhb2, Fhb5. Results verified that marker-assisted selection is possible for the introgression of exotic FHB resistance genes, however, the genetic background of the recipient line and epistatic interactions can have a strong influence on expression and penetrance of any given gene.Immediate Sumai 3 derivatives carry a number of resistance improving minor effect alleles, other than The online version of this article (10.1186/s12870-019-1782-2) contains supplementary material, which is available to authorized users. Triticum spp. L.) is one of the most important field crops worldwide as it serves as staple food for a large proportion of the global population. Wheat production is challenged by several constraints and Fusarium head blight is one of the major biotic limitations. There are several Fusarium spp. that cause head blight or scab; Fusarium graimnearum Schwabe (syn. Gibberella zeae Schw. [Petch]) is the main culprit in North America, and it is also hosted by maize (Zea mays L.) and barley (Hordeum vulgare L.)  were assessed for FHB symptoms in 2010 in a greenhouse (GH) equipped with incandescent lamps, 16 h photoperiod and 22/16 °C day/night temperatures. Two isolates of ulations . At 50% ulations .F. graminearum macro-conidia prepared with the isolates M9–07-1 (3-ADON), M7–07-1 (3-ADON), M1–07-1 (15-ADON) and M3–07-2 (15-ADON). The isolates used were originally provided by Dr. Jeannie Gilbert at the CRC-AAFC. Isolates were cultured in Spezieller Nährstoffarmer Agar (SNA) for seven days and then incubated in Carboxymethyl Cellulose (CMC) media for another seven to ten days. The number of spores was counted to calculate their concentration. Prior to field application, the suspension of the four isolates was mixed in equal proportions (based on macro-conidia concentration) to provide a total concentration of 50,000 macro-conidia spores/ml. The field application was achieved using a CO2 backpack sprayer and directed to the wheat spikes at flowering (anthesis) stage. A second application was performed to the same rows three days later. After each inoculation, plots were mist irrigated overnight. Visual assessments of disease incidence (% of infected spikes in the plot) and severity (% of spikelets infected on the infected spikes) were made on each plot 18–21 days after the first inoculation. Fusarium head blight index for each plot was calculated as follows: (disease incidence x disease severity)/100. At Morden, MB and Saskatoon, SK, irrigated nurseries were inoculated with air-dried corn spawn (colonized by F. graminearum) at 50% anthesis. Each plot was assessed using an FHB index (%incidence x %severity/ 100) (FLD_IND) based on disease incidence (%) (FLD_INC) and severity (%) (FLD_SEV) at 21 to 23 dpi. Cultivars CDC Teal (S), AC Barrie , and ND2710  were included as checks in Morden and Saskatoon. Cultivars AC Vista, and CDC Teal were used as S check, AC Cora as I check, 5602HR as MR, and FHB37 as R check in Carman nursery. Up to 50 spikes of each NIL were harvested by hand and retained for DON quantification (FLD_DON).The same NILs, parents and check cultivars evaluated in the greenhouse were also assessed in the field nursery at Carman, MB from 2010 to 2013 and 2016, Saskatoon, SK in 2016, and at Morden, MB in 2015. In 2010, the CDC Alsask population was not evaluated in the field because the seed under multiplication in the greenhouse was not ready for field planting. The field trial was set up as a randomized complete block design with two replicates in Carman and four replicates in Morden and Saskatoon. Plots at Morden and Carman consisted of single 1.5 m and 1 m rows, respectively, and in the Saskatoon nursery in hills. Sowing density was approximately 80 seeds per row and 30 seeds per hill. At Carman, every plot in the nursery was artificially inoculated with a suspension of The spikes of each NIL were harvested from two replicates at the fully ripe stage  and drihttps://wheat-urgi.versailles.inra.fr/Seq-Repository/Assemblies) using an in-house BLAST portal. The best hits, based on sequence similarity and cumulative alignment length percentage of matches, were considered. For annotation, the wheat genome scaffolds carrying the marker were retrieved from the BLAST searches and used to find genes expressed on the scaffolds using POTAGE (PopSeq Ordered Triticum aestivum Gene Expression) . The SNP markers flanking the Fhb1 region of Carberry were provided by Dr. Ron Knox  and were also mapped in ND2710 by Zhao et al. [Fhb1, Fhb2, and Fhb5) were tested as well as all other markers regardless of significance. A linear regression model was used to calculate P-values for pairwise as well as multiple marker-marker interactions using an in-house designed script in the R environment [For haplotype analyses and to assign each NIL entry to a QTL class, SNP markers tightly linked to SSR markers or mapped to o et al. . To analironment . A falseironment  and modey is the n × 1 vector for phenotypic observation, μ is the population mean, Z1 is a vector (Zl1 … Znl)T, for the genotype indicators of locus l, Zil takes one of two values  depending on which parental allele was passed on to line i, for locus l, γl is the additive (main) effect of locus l, l and l’, and e is the residual error vector.Where: P = 0.001.From each chromosome, one marker from each group of redundant/co-segregating markers was chosen for the epistatic interaction analyses. Any marker-marker interaction for a given phenotype was declared significant at F-values were calculated using the procedure MIXED in SAS ver. 9.4 with the ‘ddfm = kenwardroger’ option to approximate degrees of freedom. Mean separation was conducted using the least significant difference (LSD) test (Fisher’s least significant difference). All tests used a nominal alpha level of 0.05. Broad-sense heritability (H2) was calculated as described in [The phenotypic data collected from field and greenhouse evaluations was subjected to correlation and analysis of variance (ANOVA). Before conducting ANOVA, assumptions of independence, normal distribution and homogeneity of residuals for all class variables were verified using Shapiro-Wilk and Levene’s tests implemented in procedure UNIVARIATE in SAS  ver. 9.4 . Heterogeneous variances, if any, were modeled using the ‘repeated/group = effect’ statement in procedure MIXED . Variancribed in . Pearsonribed in  in the Rribed in . For bipAdditional file 1:Protocol #1.Procedure for Neogen enzyme linked immune-sorbent aasay (ELISA) for deoxynivalenol (DON) quantification in Fusarium head blight infected grains. Table S1. Proportions of the recurrent parent (RP) and donor parent (DP) genomes in the near-isogenic lines for CDC Go and CDC Alsask streams based on 81,587 SNP markers from 90 K iSelect assay. Here: A, B, H, U represent recurrent parent, donor parent, heterozygous, and unknown alleles, respectively. Figure S1. Polymorphism in CDC Go and CDC Alsask near-isogenic lines (NILs). Figure S2. Polymorphism in CDC Alsask near-isogenic lines on Chromosomes other than 3B, 5A, 6B. Figure S3. Polymorphism in CDC Go near-isogenic lines on Chromosomes other than 3B, 5A, 6B. Figure S4. GGE Biplots for CDC Go and CDC Alsask near-isogenic lines (NILs). (DOCX 1474 kb)Additional file 2:List of annotated genes in the intervals of FHB resistance loci. (XLSX 429 kb)


The growth of DNA biobanks linked to data from electronic health records (EHRs) has enabled the discovery of numerous associations between genomic variants and clinical phenotypes. Nonetheless, although clinical data are generally longitudinal, standard approaches for detecting genotype-phenotype associations in such linked data, notably logistic regression, do not naturally account for variation in the period of follow-up or the time at which an event occurs. Here we explored the advantages of quantifying associations using Cox proportional hazards regression, which can account for the age at which a patient first visited the healthcare system (left truncation) and the age at which a patient either last visited the healthcare system or acquired a particular phenotype (right censoring).In comprehensive simulations, we found that, compared to logistic regression, Cox regression had greater power at equivalent Type I error. We then scanned for genotype-phenotype associations using logistic regression and Cox regression on 50 phenotypes derived from the EHRs of 49,792 genotyped individuals. Consistent with the findings from our simulations, Cox regression had approximately 10% greater relative sensitivity for detecting known associations from the NHGRI-EBI GWAS Catalog. In terms of effect sizes, the hazard ratios estimated by Cox regression were strongly correlated with the odds ratios estimated by logistic regression.As longitudinal health-related data continue to grow, Cox regression may improve our ability to identify the genetic basis for a wide range of human phenotypes. The growth of DNA biobanks linked to data from electronic health records (EHRs) has enabled the discovery of numerous associations between genomic variants and clinical phenotypes . Two salStatistical modeling of time-to-event data has been well studied and frequently applied to the clinical domain . One sucHere we sought to compare the performance of Cox regression and logistic regression for identifying genotype-phenotype associations in genetic data linked to EHR data. Using both simulated and empirical data, we found that Cox regression shows a modest but consistent improvement in statistical power over logistic regression.p-value cutoffs, the true positive rate tended to be higher for Cox regression than for logistic regression  ≥ 5, however, the p-value from Cox regression tended to be moderately lower than the p-value from logistic regression .We next compared the two methods using genetic data linked to electronic health records. We selected a cohort of 49,792 individuals of European ancestry, genotyped using the Illumina MEGA platform. We defined 50 phenotypes from the EHR, with the number of cases per phenotype ranging from 104 to 7972 . In our analysis of EHR data, the time to event corresponded to the age at which a person either received a particular diagnosis code for the second time or was censored. Although acquisition of a diagnosis code is only an approximation for onset of a phenotype, the Kaplan-Meier curves for multiple phenotypes suggest that this approximation is valid –10.P ≤ 10− 4) is likely to catch all significant associations without appreciably increasing computational cost.To account for the fact that most individuals in our data are not observed from birth, we used the age of each individual’s first visit. This formulation of Cox regression, with left truncation and right censoring, corresponds to a counting process  and is nOur use of the GWAS Catalog has multiple limitations. First, both methods showed low sensitivity, likely because for half of the 50 phenotypes, the number of EHR-derived cases was in the hundreds, whereas the number of cases from GWAS Catalog studies for these phenotypes was in the thousands. Thus, our analyses were underpowered for many SNP-phenotype associations. Second, the majority of studies in the GWAS Catalog followed a case-control design and quantified associations using either logistic or linear regression, not Cox regression. Thus, although the GWAS Catalog is the closest we have to a gold standard, it was important that our analyses of simulated data and empirical data gave consistent results.All of Us Research Program [Here we used Cox regression to model the time to a single event, i.e., diagnosis of a particular phenotype. In the future, more sophisticated models may be able to account for subsequent response to treatment or semi-continuous traits such as lab values. We are especially interested in the potential of models that relax the proportional hazards assumption , 15 and  Program , 19, if We compared logistic regression and Cox regression in comprehensive simulations. As the effect sizes estimated by the two methods are not equivalent , we evaluated the methods in terms of average power and type I error calculated from true and false associations in each simulation.The simulations and the analyses were designed to approximately mimic the empirical study on EHR data. In each simulation, we sampled minor allele counts for 800,000 SNPs in 50,000 individuals from a binomial distribution, with each minor allele’s probability independently simulated from the distribution of minor allele frequencies in the empirical genotype data. For simplicity, we simulated a haploid genome, i.e., each individual had only one allele at each SNP. Of the 800,000 minor alleles, 100 were declared as true risk alleles and the remaining 799,900 minor alleles were declared as false risk alleles by setting their coefficients to 0. We simulated data from both a Cox model and a logistic model. Due to computational burden, for each simulation model, we used 1000 simulations to assess true positive rates and 125 simulations to assess false positive rates.To simulate data from a Cox model, the true event time was simulated from a multivariable Cox regression with baseline hazard generated from Exponential(λ) with λ = 10,000 and the parametric component including all SNPs. The coefficients of the 100 true alleles sampled from Unif, i.e., a uniform distribution between 0.3 and 0.5, and coefficients of the remaining minor alleles were zeros. The censoring time was simulated from Gamma and set at an upper bound of 2, which was designed to represent administrative censoring. The Gamma distribution is non-informative and allows non-uniform censoring . The rigTo simulate data from a logistic model, age (a surrogate of the true event time) was simulated from a normal distribution with mean 60 and standard deviation 5. The event indicator was simulated from a logistic regression model with all SNPs and age. The coefficients were sampled from Unif for the 100 true alleles, zero for the remaining null minor alleles, and 0.001 for age. The censoring time was simulated from Unif , leadingStatistical significance was based on Bonferroni correction with an overall type I error rate of 0.01, 0.05, and 0.1.p-value of Hardy-Weinberg equilibrium ≥0.001, and p-value of association with batch ≥10− 5. To calculate the principal components (PCs) of genetic ancestry, we followed the recommended procedure of the SNPRelate R package v1.16.0 [Our empirical data came from the Vanderbilt Synthetic Derivative  and BioVU (a DNA biobank linked to the Synthetic Derivative) . We used v1.16.0 . Specifi v1.16.0 .To compare the ability of Cox and logistic regression to detect known associations, we selected 50 phenotypes that could be studied with EHR data and which also had known associations from the NHGRI-EBI GWAS Catalog v1.0.2 r2018-08-30  and the first four principal components of genetic ancestry  . Cox regP ≤ 5·10− 8. For each phenotype, we then used LDlink [2 ≥ 0.8). For each associated SNP for each phenotype, we then determined which SNPs on the MEGA platform were in LD with that SNP (r2 ≥ 0.8), and assigned those SNPs to the corresponding phenotype and LD block. Using the EHR-based GWAS results, we then calculated the sensitivity of Cox regression and logistic regression based on the number of phenotype-LD block pairs for which at least one SNP in that LD block had a p-value less than a given p-value cutoff (across a range of cutoffs).For each mapped study from the GWAS Catalog, we only considered SNPs having an association d LDlink  to groupAdditional file 1: Table S1. Information for each of the 50 phenotypes.Additional file 2: Figs. S1-S3. Supplemental figures for principal components of genetic ancestry and GWAS results using Cox and logistic regression.Additional file 3: Table S2. Mapping between phecodes and GWAS Catalog study accessions.