N = 15 661, with replication N = 75 821), we identified 11 loci (9 novel) explaining 3.8% of the variance in mean 2D:4D ratio. We also found weak evidence for association  between 2D:4D ratio and sensitivity to testosterone [length of the CAG microsatellite repeat in the androgen receptor (AR) gene] in females only. Furthermore, genetic variants associated with (adult) testosterone levels and/or sex hormone-binding globulin were not associated with 2D:4D ratio in our sample. Although we were unable to find strong evidence from our genetic study to support the hypothesis that 2D:4D ratio is a direct biomarker of prenatal exposure to androgens in healthy individuals, our findings do not explicitly exclude this possibility, and pathways involving testosterone may become apparent as the size of the discovery sample increases further. Our findings provide new insight into the underlying biology shaping 2D:4D variation in the general population.The ratio of the length of the index finger to that of the ring finger (2D:4D) is sexually dimorphic and is commonly used as a non-invasive biomarker of prenatal androgen exposure. Most association studies of 2D:4D ratio with a diverse range of sex-specific traits have typically involved small sample sizes and have been difficult to replicate, raising questions around the utility and precise meaning of the measure. In the largest genome-wide association meta-analysis of 2D:4D ratio to date ( It has long been hypothesized that prenatal sex steroids, particularly testosterone, permanently modify the developing nervous system during critical periods of development, which in turn influences behavior in later life . Whilst h2: 50–80%) , a syndrome that commonly includes abnormal digits  . Two rel 50–80%) ,30. The  females  and incr females . The minl digits . Additiondrogens ,37 and dndrogens , suggestit ratio .AR) gene, located on the X chromosome, which codes for a receptor protein that facilitates physiological responses to androgens such as testosterone and dihydrotestosterone  exhibit more feminine 2D:4D ratios, consistent with an effect of reduced prenatal testosterone exposure on digit ratio . These iosterone . In vitrcription . Therefopulation .N = 15 661, with replication N = 75 821) to date. In addition, we aim to leverage genetics to scrutinize the evidence surrounding the hypothesis that 2D:4D ratio reflects prenatal androgen exposure. Specifically, we investigated whether there was association between repeat number in the androgen receptor (XAR) and 2D:4D ratio and we used our GWAS results to examine: (1) whether there was any association between 2D:4D ratio and genetic variants in pathways known to be linked with androgens; and (2) whether there was any relationship between 2D:4D ratio and genetic markers known to be related to (adult) levels of testosterone and/or sex hormone binding globulin (SHBG). We hypothesize that if 2D:4D ratio is truly influenced by levels of prenatal testosterone in utero, then it is reasonable to expect that genetic variants related to androgen sensitivity (XAR) and/or serum levels of testosterone/SHBG might also show association with 2D:4D ratio.The aim of this study was to investigate the genetic determinants of 2D:4D ratio by performing the largest GWAS meta-analysis . The genetic correlation between the left and right hand ratios was not different from 1 , indicating that the vast majority of SNPs contributing to variation in the ratio influence both hands. Therefore, in the main text we present the results from the left  hand ratio, due to the larger sample size achieved by including the Generation R Study, and include the right hand and mean ratio results in the Univariate genetic restricted maximum likelihood (GREML) analysis in ALSPAC revealed that common genome-wide variation explained a substantial proportion of the variance in 2D:4D ratio . Further, no evidence of heterogeneity was detected between the discovery cohorts (P < 5 × 10−8) in the discovery meta-analysis of 15 661 individuals for 2D:4D ratio .As a secondary analysis, we also conducted sex-stratified analyses in each of the discovery cohorts and combined the results using a fixed-effects meta-analysis. The majority of the loci reaching genome-wide significance in the male or female only analyses were identified in the combined analysis . Consistent with this, only one locus on chromosome 9 reached genome-wide significance for difference in the magnitude of the regression coefficients between males and females for the average of both hands 2D:4D ratio , OLA1, LIN28B, GLI3, FLI1, SMOC1, SALL1, TOX3 and SALL3. At the 2q31.1 locus, DEPICT prioritized two genes, HODX11 and HOXD12, whilst at the 1q22 locus, five genes were prioritized: EFNA1, DPM3, EFNA3, KRTCAP2 and SLC50A1. We will subsequently refer to this locus as EFNA1, which is the nearest gene to the top association signal.We used Data-driven Expression-Prioritized Integration for Complex Traits (DEPICT)  to identP < 0.01, which mapped to the MSX1 PPI sub-network , in addition to two nearest genes, which showed high expression in the brain (OLA1 and SALL3).When using the meta-analysis results from the average 2D:4D ratio of both hands, one gene set reached a false discovery rate (FDR) -network . The tisP < 0.01 . Based oAR gene, a proxy of testosterone sensitivity, in a meta-analysis of the ALSPAC and QIMR cohorts. We found nominal evidence for a weak positive association between the number of CAG repeats in the AR gene on the X chromosome and mean 2D:4D ratio in females , but not in males  was associated with increased 2D:4D ratio  and increased SHBG, the T allele at rs1573036  was associated with decreased 2D:4D ratio  and increased SHBG and the T allele at rs72829446  was associated with increased 2D:4D ratio  and increased testosterone (P = 0.10). Additionally, there was no difference in the male and female effect sizes from the sex-stratified analyses across the 18 SNPs (Fisher’s combined probability test for the heterogeneity P = 0.23). Only one SNP, rs3779195, showed heterogeneity between males and females with the SHGB increasing T allele negatively associated with 2D:4D ratio in males and positively associated with 2D:4D ratio in females .Given the putative relationship between prenatal testosterone levels and 2D:4D ratio, we also examined the association between five published SNPs  shown to influence testosterone levels , 48 and osterone  .Given the previous observational epidemiological associations between 2D:4D ratio and sex-dependent behaviors, we used publicly available GWAS summary results for a variety of traits to estimate genetic correlations with 2D:4D ratio using linkage-disequilibrium (LD) score regression . We preset al.  regardinLIN28B and SMOC1. These 11 loci explained 3.8% of the variance in mean 2D:4D ratio. After assessing association between 2D:4D ratio and a range of testosterone-related traits, we found no conclusive evidence of the 2D:4D ratio constituting a marker of prenatal androgen exposure, although it is possible that pathways involving testosterone may become apparent as the size of our GWAS increases in the future. Yet, associations at distinct novel loci provide additional insight into the underlying biology shaping 2D:4D ratio variation.The 2D:4D ratio, a sexually dimorphic trait, has been extensively used in adults as a biomarker for prenatal androgen exposure. In the largest genetic association study of 2D:4D ratio to date, we identified nine novel loci for 2D:4D ratio, in addition to replicating two previously identified loci, GLIS1, which is expressed across several organs in the reproductive system, including the prostate, vagina, testis and cervix. Glis1, the protein encoded by GLIS1, is a Kruppel-like zinc finger protein that appears to have a critical role in controlling gene expression during specific stages of embryogenesis ; SNPs in LDAH have also been associated with prostate cancer risk ]. We did, however, find an association involving a variant in HOXD12 . The exact role of HOXD12 has not yet been determined; however, the homeobox family of genes plays an important role in morphogenesis and is particularly relevant in the development of the limbs and genitals  and/or SNPs robustly associated with androgen levels/levels of SHBG, should also be related to 2D:4D ratio. Whilst we did detect some evidence of a positive association between the number of CAG repeats in AR and 2D:4D ratio in females, we note that the small effect size would not be significant after adjusting for the multiple statistical tests we performed. Power calculations suggest that our combined sample of N = 5826 individuals in the XAR meta-analysis was well powered (∼78%) to detect a locus responsible for 0.001% of the phenotypic variance in 2D:4D ratio (one-tailed α = 0.05). In comparison, all of our genome-wide significant SNPs explained >0.001% of the variance in 2D:4D ratio (most explained much more variance than this). This suggests that if genetic variation in XAR does contribute to variation in 2D:4D ratio through, for example, sensitivity to testosterone, its effect is likely to be small relative to other sources of genetic variation. Two recent smaller meta-analyses also failed to find an association between length of the repeat in XAR and 2D:4D ratio , the Generation R Study, the Rotterdam Study, the Western Australian Pregnancy Cohort (Raine) Study, TwinsUK and the Queensland Institute of Medical Research (QIMR) sample, which was drawn from the Brisbane Adolescent Twin Study . The 23andMe cohort was used for replication of the genome-wide significant findings. Details of each of these studies, including how the 2D:4D ratio was measured and the genotyping methods, are provided in the We drew on data from six cohorts for the discovery genome-wide meta-analysis and association with the CAG repeat in All cohorts in the discovery meta-analysis or replication obtained ethical approval from their local ethics review boards; ALSAPC from the ALSPAC Law and Ethics Committee and the Local Research Ethics Committees, The Generation R Study from the Medical Ethics Committee of the Erasmus Medical Center in Rotterdam, QIMR from the QIMR Human Research Ethics Committee, the Raine study from the King Edward Memorial Hospital and Princess Margaret Hospital for Children Human Research Ethics Committees, the Rotterdam Study from the Medical Ethics Committee of the Erasmus Medical Center in Rotterdam, TwinsUK from the Guy’s and St Thomas’ (GSTT) Ethics Committee and research participants from 23andMe provided informed consent and participated in the research online, under a protocol approved by the external AAHRPP-accredited IRB, Ethical & Independent Review Services (E&I Review).The 2D:4D ratio was calculated as the length of the second digit divided by the length of the fourth digit, multiplied by 100 so as to avoid computational difficulties due to the low variance of the trait. In all studies, the measure was normally distributed, so no further transformation was applied.To estimate the proportion of additive genetic variance in 2D:4D ratio explained by directly genotyped SNPs, we conducted a univariate GREML analysis using the GCTA software  in >4900Genome-wide association analysis using imputed dosages to account for uncertainty in the imputation was performed using linear regression in each cohort, adjusting for sex. In addition, the QIMR and TwinsUK cohorts accounted for zygosity and relatedness. The Generation R Study (European subset), the Rotterdam Study and Raine adjusted for four principal components for population stratification. A sensitivity analysis to maximize power was conducted by including all individuals of the Generation R Study with adjustment for 20 principal components to account for the multi-ethnic sample as performed earlier . ResultsWe also tested whether the regression coefficients differed between males and females. We carried out genome-wide association analysis in males and females separately in each of the discovery cohorts and, as with the main analysis, we excluded variants with a MAF <1% and poor imputation quality . We performed a fixed-effects inverse-variance weighted meta-analysis of each sex in METAL , adjustiWe performed approximate conditional and joint SNP association analysis using the GCTA software , which uP < 5 × 10−8 for the left, right or average 2D:4D ratio) in the discovery meta-analysis were replicated in the 23andMe dataset. If the imputed SNP passed quality control or the genotyped SNP was unavailable, then the imputed SNP was used for analysis, otherwise the genotyped SNP was used. Analysis of each SNP was performed using linear regression, adjusting for sex, age, the first five principal components for population stratification and genotyping platform. Results were adjusted for a genomic control inflation factor of λ = 1.074.SNPs that reached genome-wide significance  and the variance of the 2D:4D ratio (Var(Y)) using the following formula:The variance explained by each SNP was calculated using the effect size from the discovery meta-analysis , the minor and major allele frequencies  with P-values below the genome-wide significant threshold (P < 5 × 10−8). Gene-set enrichment was further grouped into ‘meta gene sets’ by similarity clustering, as described previously ; we filtered the summary statistics to the subset of HapMap3 SNPs, as advised by the developers, to ensure that no bias was introduced due to poor imputation quality. Where the sample size for each SNP was included in the results file was flagged using –N-col; if no sample size was available then the maximum sample size reported in the reference for the GWAS meta-analysis was used (i.e. the summary statistics for each SNP was assumed to have been estimated using the same sample size). SNPs were excluded if the minor allele frequency was <0.01, the strand was ambiguous, the rs number was duplicated or they had a sample size less than 60% of the total sample size available. Once all the files were reformatted, we used the ldsc.py python script, also on the developer’s website, to calculate the genetic correlation between 2D:4D ratio and each of the traits and diseases. The European LD Score files that were calculated from the 1000 Genomes reference panel and provided by the developers were used for the analysis.Summary statistics from the GWAS meta-analysis for traits and diseases of interest were downloaded from the relevant consortium website (see HMG online.Supplementary FiguresClick here for additional data file.Supplementary TablesClick here for additional data file.


Sclerotinia sclerotiorum (Lib.) de Bary, the causal agent of white mold (WM). A diverse panel of 465 soybean plant introduction accessions was phenotyped for WM resistance in replicated field and greenhouse tests. All plant accessions were previously genotyped using the SoySNP50K BeadChip. The predictive ability of six GP models were compared, and the impact of marker density and training population size on the predictive ability was investigated. Cross-prediction among environments was tested to determine the effectiveness of the prediction models. GP models had similar prediction accuracies for all experiments. Predictive ability did not improve significantly by using more than 5k SNPs, or by increasing the training population size . The GP model effectively predicted WM resistance across field and greenhouse experiments when each was used as either the training or validation population. The GP model was able to identify WM-resistant accessions in the USDA soybean germplasm collection that had previously been reported and were not included in the study panel. This study demonstrated the applicability of GP to identify useful genetic sources of WM resistance for soybean breeding. Further research will confirm the applicability of the proposed approach to other complex disease resistance traits and in other crops.The objective of this study was to explore the potential of genomic prediction (GP) for soybean resistance against  Sclerotinia sclerotiorum (Lib.) de Bary, is a devastating disease of many economically important crops including soybean  observed that predicted accuracies did not improve significantly with the increase of the training population size when unrelated populations from different breeding programs were merged to create a new population. Similarly, the lack of significant increase in predictive ability with an increased number of individuals in the training population could therefore be explained by the high diversity of soybean lines in this study  concluded that strategies that maximize the phenotypic variance, by selecting individuals from the two-tail distribution, are preferred to those using genotypes with the largest or lowest phenotypic deviation [On the other hand, Boligon eviation . Thus, ceviation . Indeed The main objective in GP analysis is to use one generation of data to train the model which would then be used to predict the data of the next generation . Models Field and greenhouse experiments differed for inoculation and phenotypic evaluation. Field2014 and Field2015 experiments were inoculated when plots that had reached crop growth stage R1, while GH2014 and GH2015 were inoculated when the third trifoliate was fully expanded, approximately four and six weeks after transplanting respectively. Moreover Field2014 and Field2015 was rated when plants reached the growth stage R5, about 30 days after inoculation (DAI), while GH2014 and GH2015 were rated 14 DAI. Additionally, genetic mechanism of WM resistance is likely not common between the field and greenhouse screening tests. These differences may explain why prediction ability in field was twice than greenhouse.The models derived from field data had prediction accuracies twice that of those derived from greenhouse data . This maet al. (2015) compared models with and without the G x E interaction to estimate prediction accuracy for yield in wheat [Good predictive ability was observed when the same type of environment was used. The phenotypic correlation between environments ranged from 0.15 (GH2014 and GH2015) to 0.46 (Field2014 and Field2015) . Lopez-Cin wheat . They coin wheat .Twenty-nine previously reported resistant PI lines were compared with our results. Those resistant PI lines were: PI391589B, PI507352, PI561345, PI196157, PI398637, PI358318A, PI189919, PI189861, PI437527, PI549066, PI567157A, PI416776, PI561331, PI437764, PI507353, PI548312, PI504502, PI437072, PI194634, PI281850, PI423941, PI423949, PI424242, PI458520, PI503336, PI593972, PI593973, PI603148 and PI243547 [Most resistant accessions from the USDA soybean germplasm collection have different countries of origin  and beloTherefore, we concluded that for WM GP, results from the different GP models did not vary significantly, and minimal response was observed for changes in TPS and marker numbers. GP approaches were shown to be useful to the breeder to assist in selecting among the USDA soybean germplasm collection. The results from this research will assist the establishment of GP approaches for germplasm enhancement using plant introductions and gene back accessions to help maximize the useful genetic diversity for disease resistance breeding.Glycine max core collection of 1,685 soybean genotypes that represented the genetic diversity of the entire USDA soybean germplasm collection as determined through pedigree and marker allele analysis [This study included 465 PIs from the USDA soybean germplasm collection and consisted of 144, 168, and 153 accessions of maturity groups (MG) I, II, and III, respectively. This panel was a subgroup of a large analysis . Accessiet al. (2014). S. sclerotiorum cultures were started from surface-sterilized sclerotia obtained from a field in Northeast Iowa in 2013 [Each soybean accession was planted in a one-row plot, 1.50 m long arranged in a randomized complete block design with two replications with 15 seeds/m and 24 seeds/m in 2014 and 2015 respectively. Planting was done with a customized ALMACO Cone Plot Planter on 23 May 2014 (experiment referred as Field2014) and 01 June 2015 (experiment referred as Field2015). WM inoculum preparation, inoculation, and rating were modified from the cotton pad method described by Bastien  in 2013 . Inocula in 2013  by placi in 2013 . The disNo specific permissions were required for field locations/activities, as experiment sites are managed by the Iowa State University and university researchers are permitted to perform experiments as described in this study. Field studies did not involve endangered or protected species.In 2014, the greenhouse experiment was planted using a randomized complete block design with two replications (GH2014). An experimental unit consisted of one plant. Plants were inoculated at the V3 growth stage using a cut-petiole method, which is preferred for indoor screening . In 2015http://soybase.org). SNPs with a missing rate greater than 10% were excluded from further analyses and the remaining missing data were imputed using BEAGLE version 3.3.1 with default parameter settings [All 465 plant accessions had been previously genotyped on an Illumina Infinium SoySNP50K BeadChip , and thesettings ,58. The yij is the phenotypic value for the ith genotype in the jth environment; μ is the overall mean; Gi is the random effect of the ith genotype; Ej is the random effect of the jth environment; GEij is the GenotypexEnvironment effect assigned as random; and ϵijk is the residual error.A completely random genotype x environment model were used to calculate variance components of individual factors (lmer function in R package lme4 [H) was calculated across environments for DSI in field environments. The equation used was:G is genotype and E is environment, e is the number of environments and r is the number of replicates.Variance components were calculated using the age lme4 . Broad-sclmm function , Rj is the effect of the jth replication, Ak is the effect of the kth accession, RAjk is replication × accession interaction, Dl is the effect of the lth inoculation date (inoculation was done per plot at the R1 growth stage therefore inoculations were made on multiple dates in the entire nursery), Sm is the effect of the mth row, and DSlm is inoculation date × row interaction. The term Yjklm represents a vector of ratings and πijklm is the probability that Yjklm will be rated at or below the ith response category. Replication was assumed to be a fixed effect, and all other terms were assumed to be random effects.All experiments and years were analyzed separately. When an experiment was replicated and the disease response was ordinal, a logistic mixed model analysis was used to obtain BLUP for each accession. A mixed model approach was chosen to accommodate the unbalanced sample size among cultivars due to emergence rates. The analysis was performed using the package; ) executepackage; . The modSimilarly, the model for GH2014 and GH2015 wasDue to lack of replication in GH2015 experiments, phenotypic data for each accession consisted of the average response of the two plants.Principal components analysis (PCA) was performed to estimate the diversity among soybean accessions. PCA was calculated based on all SNPs. MANOVA was used to estimate correlations between experiments and years based on GEBV estimated by GP models. PCA and MANOVA were performed using the stats package in R software.Six GP methods were used to analyze greenhouse and field experiments: ridge regression best linear unbiased prediction (RR-BLUP), Bayes A, Bayes B, Bayes Cπ, Bayesian LASSO (BLASSO), and Reproducing Kernel Hilbert Spaces (RKHS) regression. The genetic value estimated by the logistic regression was used as input in all GP models.G/M, where VG is the genetic variance, and M is the number of markers. In Bayes A, each marker effect is drawn from a normal distribution with its own variance: N [RR-BLUP, Bayes A, and Bayes B were described by in a previous study . RR-BLUPl. (2001).Bayes Cπ assumes common marker variances and allows for some markers to have no effect . Additioλ2 is assigned a gamma prior with rate (δ) and shape (r) which, in this study, was the default in the Bayesian Generalized Linear Regression (BGLR) package in R software. Finally, inverse chi-square priors were assigned to the variance parameters, and the scale and degree of freedom parameters were set to Su = Se = 1 and d:f:e = d:f:u = 4, respectively.In BLASSO , marker gi= ui+ fi, where ui is the mean, and fi is a Gaussian process with a (co)variance function proportional to the evaluations of a reproducing kernel, K, evaluated in marker genotypes. Here, xi and xj are vectors of marker genotype codes for the ith and jth individuals, respectively. All hyperparameters were assumed following [In RKHS regression, genetic values are viewed as a Gaussian process. When markers and a pedigree are available, genetic values are modeled as the sum of two components: ollowing . The genThe ten-fold cross-validation (CV) was performed to avoid an inflated estimate of the predictive ability of GP. In ten-fold CV, the genotypes set was divided into ten equally-sized subgroups. Of these, nine subgroups were used as the training population to fit each prediction model while the remaining subgroup was used as the validation population to assess the correlation between the observed and predicted trait values. This process was repeated ten times, with each subgroup being the validation population exactly once. This process was repeated fifty times by randomizing the genotypes and re-forming the folds. The mean of the 50 correlation coefficients was reported.Predictive ability was assessed using the Pearson correlation of the predicted GEBV and the genetic value estimated by the logistic regression in the validation population .The effect of marker numbers on GP predictive ability was determined through ten-fold cross-validation by including random samples of 100, 200, 500, 1k, 5k, 10k, 15k, 20k, 25k, and 30k SNPs from the full marker set. These samples were compared with the model which used 36,105 markers. For this analysis, the marker file was divided in 20 sets, where each set was composed with all SNPs in each chromosome. Then the same number of SNPs was picked in each set, i.e., each chromosome. The SNPs were picked randomly within each set. All codes were run in R software.The impact of TPS on predictive ability was evaluated using training sets of variable sizes . All marker subsets described above were used to evaluate these training population sizes. The predictive ability was estimated for each combination of marker number and TPS.To verify the prediction capacity of the GP model to predict WM in unrelated experiments, each experiment (two field and GH experiments each) was used as the training population to validate the other three experiments. The RR-BLUP method was fitted using all markers, and 352  genotypes were used in the training population.In order to test the ability of the GP model to identify WM resistant genotypes from the USDA soybean germplasm collection, twenty-nine plant-introduction (PI) soybean accessions, previously reported to have WM resistance , were chSNPs effect estimated in the training population by RR-BLUP using the average between Field2014 and Field2015 was used to predict the entire USDA soybean germplasm collection . The average between Field2014 and Field2015 was chosen to perform this analysis because the field experiment provided the greatest precision based on the estimated broad-sense heritability and GWS accuracy. The 5% (982 accessions) most resistant accessions were selected and compared with the 5% and 10% most resistant accessions selected in our study.All analyses were executed in R software . RR-BLUPA total of 20,000 burn-ins  and 40,000 saved iterations, obtained from the Markov chain Monte Carlo (MCMC) method, was used in all Bayesian methods. The convergence of Bayesian models was checked by inspecting trace plots of variance parameters.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 FigThe range in predictive ability is among the 50 replicates of the cross validation experimentBA–Bayes A; BB–Bayes B; BC–Bayes Cπ; BL–Bayesian LASSO; GB–Genomic Best Linear Unbiased Prediction; RK–Reproducing Kernel Hilbert Space Regression; BR–Best Linear Unbiased Prediction.(TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.S6 Fig(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.


Picea mariana [Mill.] BSP) for growth and wood quality traits, and concurrently examined factors affecting GS model accuracy.Genomic selection (GS) uses information from genomic signatures consisting of thousands of genetic markers to predict complex traits. As such, GS represents a promising approach to accelerate tree breeding, which is especially relevant for the genetic improvement of boreal conifers characterized by long breeding cycles. In the present study, we tested GS in an advanced-breeding population of the boreal black spruce (The study relied on 734 25-year-old trees belonging to 34 full-sib families derived from 27 parents and that were established on two contrasting sites. Genomic profiles were obtained from 4993 Single Nucleotide Polymorphisms (SNPs) representative of as many gene loci distributed among the 12 linkage groups common to spruce. GS models were obtained for four growth and wood traits. Validation using independent sets of trees showed that GS model accuracy was high, related to trait heritability and equivalent to that of conventional pedigree-based models. In forward selection, gains per unit of time were three times higher with the GS approach than with conventional selection. In addition, models were also accurate across sites, indicating little genotype-by-environment interaction in the area investigated. Using information from half-sibs instead of full-sibs led to a significant reduction in model accuracy, indicating that the inclusion of relatedness in the model contributed to its higher accuracies. About 500 to 1000 markers were sufficient to obtain GS model accuracy almost equivalent to that obtained with all markers, whether they were well spread across the genome or from a single linkage group, further confirming the implication of relatedness and potential long-range linkage disequilibrium (LD) in the high accuracy estimates obtained. Only slightly higher model accuracy was obtained when using marker subsets that were identified to carry large effects, indicating a minor role for short-range LD in this population.This study supports the integration of GS models in advanced-generation tree breeding programs, given that high genomic prediction accuracy was obtained with a relatively small number of markers due to high relatedness and family structure in the population. In boreal spruce breeding programs and similar ones with long breeding cycles, much larger gain per unit of time can be obtained from genomic selection at an early age than by the conventional approach. GS thus appears highly profitable, especially in the context of forward selection in species which are amenable to mass vegetative propagation of selected stock, such as spruces. Genomics of forest trees is rapidly gaining momentum as it promises to unravel the genetic control of adaptive and economically important traits, in part to satisfy the increasing demand for high quality wood fibre worldwide but also, to cope with the increasing challenges imposed by changing climates and environments , 2. UsesTo overcome the limitations of MAS, genomic selection (GS) uses dense genomic marker information  of individuals, as well as their parents when available, to predict their breeding value . ContrarMost economically important traits are of quantitative nature and controlled by many genes . ConsequPinus taeda L.)  B.S.P.) is one of the most abundantly distributed trees throughout the transcontinental boreal forest of Canada and Alaska. It is also one of the most reforested species in Canada, with 65 million seedlings being planted per year in the province of Québec alone, and has been the subject of advanced-generation breeding programs [Black spruce (programs .The objectives of this study were to (1) estimate accuracies and gains per unit of time derived from GS for wood quality and growth traits in an advanced-breeding population of the black spruce; (2) assess variation in accuracy from building site-specific or multiple-site models; (3) explore the roles of relatedness, short- and long-range linkage disequilibrium in genomic prediction accuracy by simulating different family structures and testing different subsets of markers; and (4) investigate the effect of sample sizes on model accuracy.To meet these objectives, we used trait data obtained from 25-year-old black spruces grown in a genetic test replicated on two environmentally contrasted sites in Québec Canada. The genetic test consisted of full-sib families generated using a partial diallel mating design. Trees were genotyped for 4993 SNP markers representative of as many distinct gene loci, and originating from a black spruce high-confidence SNP catalogue previously assembled and validated .Phenotypic and genetic data were obtained for 734 25-year-old progeny trees belonging to 34 controlled-pollinated families derived from a partial diallel mating design that consisted of 27 parents originating from 9 provenances from the Canadian provinces of Ontario, New Brunswick and Manitoba, and one from Maine in the northeastern United States. The parental trees had previously been identified for their superior growth and stem form in a range-wide provenance trial established on 4 sites in Québec . ProgenyTree height and diameter at breast height  were recorded in 2013 at the age of 25 years, and a wood increment core was extracted from the south facing side of each tree. Cores were stored in a freezer, conditioned to 7% moisture and cut to 1.68 mm thickness prior to X-ray densitometry analyses . Wood density was calculated as a ring area weighted mean from recorded pith to bark wood density profiles. Microfibril angle (MFA) was estimated using X-Ray diffraction, on a Bruker D8 Discovery unit equipped with an area array detector, as per Ukrainetz et al. .Picea abies (L.) Karst.), including draft genome sequences [GenTrain  quality score ≥0.25, a call rate ≥85%, a fixation index |FIS| ≤ 0.50, and a minor allele frequency (MAF) ≥0.0055. Furthermore, SNPs with minor frequency alleles that were present in less than 10 individuals were not considered. Of the retained markers, only 2% had a minimum allele frequency (MAF) < 5%, indicating a small number of rare allele markers.In recent years, significant genomic resources have been developed for white spruce and Norway spruce  and e is the error term with ~ N. X, T and S are incidence matrices, and A is the numerator of the relationship matrix describing the additive relationship among individuals and I is the identity matrix. All the trees sampled were used to estimate these reference breeding values.For each trait, an individual tree  model was fit using the GS3 software  in ordery is the vector of standardized phenotypes, β is a vector of fixed effects including an overall mean, α is the random marker effect, e the random error, and X and Z are incidence matrices. The latter was built from the number of alleles observed for each individual and SNP, and coded as 0, 1 and 2 representing the number of copies of the minor allele. Approximately 1.8% of the total number of genotypes was missing and imputed as the mean of the respective marker rounded to the next genotype value. Given that only biallelic markers were retained for the analyses, values of +0.5aj and −0.5aj were arbitrarily assigned to alleles 1 and 2 respectively, which follows conventional parametrization where the difference between the two homozygotes equals two aj [N, where I is the identity matrix. The model hence assumes common variance and all marker coefficients are minimized to the same extent, which is commonly called ridge regression. The approach was deemed appropriate for similar traits evaluated in white spruce and assumingly controlled by many genes of small effect [Z’ij is the indicator co-variate  for the ith tree at the jth locus and âj is the estimated effect at the jth locus.In the first set of analyses, phenotypes from both sites were combined and GS models were fit using a simple Bayesian framework in the GS3 software , 56, con two aj . Marker l effect , and wasThe GS3 software uses the Gauss-Seidel algorithm with residual update for best linear unbiased prediction (BLUP), and for best linear unbiased estimation of random and fixed effects respectively . The algTenfold cross-validation was performed for pedigree-based and marker-based models in order to obtain predicted breeding values for both approaches. For each round of cross-validation, 10% of the trees were randomly drawn from each family and set aside for validation, the remainder being used for model training. Each individual was thus included in one validation set. Model quality was evaluated by the accuracy, r, which is defined here as the correlation between the cross-validated genomic-predicted breeding values and the “true” or reference breeding values previously calculated using pedigree information and all available sampled trees. The predictive ability, r, was similarly evaluated as the correlation between predicted and actual phenotypes.Different GS scenarios were also tested to investigate the influence of site on model quality: the basic model contained all 734 trees from the two sites combined (see above); another set of models were trained and validated with only data from a single site, and the last models were trained with data from one site and validated with data from the second site.i.e. different trees, but originating from the same full-sib families. For comparison purposes, a genomic selection scenario with half-sib families was also tested where the training and validation sets shared female progenitors, but form different families, hence implicating different males, or vice versa. Estimates of model accuracy were also obtained by using completely unrelated trees from families and provenances excluded from model training.In order to address questions related to the influence of relatedness and long-range linkage disequilibrium (LD) on genomic selection accuracy, we investigated the effect of family structure. Training and validation sets were genetically related in the basic models, To investigate the effect of LD, and that of various subsets of markers on GS model accuracy, different subsets were delineated and used to build models. Hence, genomic selection models were constructed with subsets of 1,000, 500 and 250 markers randomly drawn from the 4,993 SNPs available, as well as with a set of 250 markers having the largest absolute effects and a set containing the 4,743 remaining markers. Accuracy and predictive ability of these reduced models were then compared with those of the basic model. GS models were also constructed separately with markers identified for each of the 12 black spruce linkage groups, in order to investigate the contribution of various genomic regions to the genetic control of the different traits and the influence of LD on GS model accuracy. Given the very high synteny and collinearity between the white spruce and black spruce genomes , we usedRandom subsets of individuals of various sizes were used to examine the minimum number of trees needed to build GS models without significantly loosing accuracy. Starting with the complete sample set of 4,993 SNPs and 734 trees, about one third of the trees was iteratively removed, creating subsets of 490, 330, 224, 147 and 106 trees that were subsequently analysed with all the 4,993 SNPs.Estimates of genetic gain from conventional selection or from GS were obtained by using predicted breeding values for each trait and by considering a selection intensity of 5%. Gains per year were obtained by assuming a spruce breeding cycle of a minimum of 28 years for conventional selection, and a shortened cycle of 9 years for GS, with 4 years for crosses and production of seedlots that are full-siblings to the training population, followed by 1 year for selection of individuals using markers and genomic selection models, and 4 years for vegetative propagation of selected individuals for seedling production. This last scenario thus assumes GS under a forward selection scheme, which is possible in black spruce [Genotypic and phenotypic information was gathered from a total of 734 25-year-old black spruce trees planted in two different forest environments, and representing an advanced-generation breeding population of full-sib families from crosses involving parents from nine Canadian provenances and one from Maine in the U.S.A. Population structure was found to be weak, in congruence with previous genetic studies relying on molecular markers and implicating populations from eastern Canada , 53, 57.h2 = 0.74) and height growth (h2 = 0.68), and somewhat weaker values observed for DBH and wood density .When the two test sites were simultaneously considered, with all markers and independent trees for validation, the accuracy of the GS models was high for all traits assessed. Moreover, these accuracy estimates were similar to those of the polygenic models using pedigree and phenotypic information Table . DiffereIn terms of genetic gains, the model containing trees from both sites resulted in the largest gain predictions Table . The gaiIn order to assess the extent of the genotype-by-environment interaction, and to evaluate if GS model accuracy is affected by sites, we constructed and validated GS models for each of the two different sites Table . AccuracThe relatively good overall model accuracies obtained by applying models developed on one site and applied to the second one does not mask important within-site environmental differences. Field records identified more trait variation and vegetation competition on site 1  compared with site 2 , which led to lower heritability estimates for site 1  than the average MAF of remaining 4,743 markers of lower effects. Markers with the largest effects most likely retraced family linkages better, given their higher MAF and thus, higher information value. Modelling the remaining 4,743 lower-effect markers led to accuracies marginally lower than estimates for the full model.The modelling of 250 markers that had previously been identified to contribute the largest absolute effects in the basic model, led to accuracy estimates comparable to those of the basic model including all 4,993 markers Fig. . These mIn a further analysis, we investigated the effect of the genomic location of markers of GS models for the different traits. Therefore, we used markers of genes for which a set of 2928 homologs have been recently mapped to the white spruce genome , which hThe effect of sample size used for GS model training was investigated. Starting with the full set of 734 sampled trees from both sites, we randomly removed trees and evaluated model quality for various subsets. As expected, accuracy of the GS models from the combined-site analysis generally decreased when fewer trees were used  was observed for markers with largest effects. Such markers could track family linkages more effectively than random markers, especially when small numbers of markers are used. Also, the marginally higher accuracy achieved with markers located on a specific chromosome compared to same number of markers but spread over the entire genome indicate that a better tracking of family linkages is achieved when a small number of markers are located on the same chromosome. Thus, this factor could also be potentially useful to reduce genotyping and GS costs in highly structured populations when the genome location of markers is known.The fact that slightly higher GS model accuracy was obtained with models using only markers with largest effects , compared to models estimated with all markers, could imply that part of the short-range LD can be picked-up by the GS models when a reduced numbers of large-effect markers are employed. However, there could be confounding factors, such as the The results of the present study on black spruce indicate that, at least in the short term, GS holds substantial promises for efficient application in populations of small effective size, such as advanced-breeding populations. With the scale of marker densities usually employed (at a rate of a few markers per cM), the overall short-range LD between markers and causal loci is likely not sufficiently well captured to make GS efficient in largely unstructured natural populations of conifers or between unrelated breeding populations. In small and structured populations, we show that prediction accuracy is null when relatedness between training and testing data sets is absent. Marker densities of one or several orders of magnitude higher would likely be necessary to improve accuracy in such conditions. Thus, high relatedness between individuals appears to be a prerequisite to obtain highly accurate GEBVs.From an applied point of view, lowering marker densities may be feasible without major loss in accuracy in order to reduce genotyping costs. Information relative to relatedness would be more precise when markers are located on a single chromosome instead of spreading an equivalent number of markers over the whole genome, and with using markers with highest MAF. These approaches may be considered when a reduced genotyping assay is needed.Based on the present results, the GS application with highest potential for spruce breeders would be to select with high accuracy superior individuals within a group of full-sib families. This would effectively increase the relatedness as well as the size of unrecombined chromosome blocks generated by controlled crossing in a small breeding population. One obvious beneficial implementation of GS in such a context would be to repeat the controlled crosses that were used to build the GS models to generate much larger full-sib families and thus, apply higher selection intensities and obtain larger genetic gains. Future studies also need to evaluate to which extent genomic selection models developed for the present generation could be applied to the next generation of progeny, as recombination should break up some of the established linkage . HoweverFor species amenable to vegetative propagation and for organizations having access to somatic embryogenesis and/or rooted cutting facilities, as it is the case in eastern Canada for spruces, individuals identified with GS at the early seedling stage could also be propagated and mass-produced for reforestation programs within only a few years . With su


High-throughput sequencing methods provide a cost-effective approach for genotyping and are commonly used in population genetics studies. A drawback of these methods, however, is that sequencing and genotyping errors can arise... High-throughput sequencing methods that multiplex a large number of individuals have provided a cost-effective approach for discovering genome-wide genetic variation in large populations. These sequencing methods are increasingly being utilized in population genetic studies across a diverse range of species. Two side-effects of these methods, however, are (1) sequencing errors and (2) heterozygous genotypes called as homozygous due to only one allele at a particular locus being sequenced, which occurs when the sequencing depth is insufficient. Both of these errors have a profound effect on the estimation of linkage disequilibrium (LD) and, if not taken into account, lead to inaccurate estimates. We developed a new likelihood method, GUS-LD, to estimate pairwise linkage disequilibrium using low coverage sequencing data that accounts for undercalled heterozygous genotypes and sequencing errors. Our findings show that accurate estimates were obtained using GUS-LD, whereas underestimation of LD results if no adjustment is made for the errors. LINKAGE disequilibrium (LD) is the term given to the nonrandom association of alleles located at different loci in a population. Quantifying the level of LD, or estimating the pairwise LD between all loci in a population, is of interest to many researchers as it has many important applications. For example, in association mapping studies, LD is used to identify candidate regions of the genome associated with a particular trait or disease, and can provide finer resolution in mapping compared to linkage-based studies . LD is ae.g., Today, many species are being sequenced using high-throughput sequencing methods that multiplex a large number of individuals. Some of the most popular sequencing methods are whole genome sequencing, and reduced representation approaches such as genotyping-by-sequencing , a heterozygous genotype may be called as homozygous  where most of the reads do not cover both sites. Alternatively, it restricts the analysis to loci that are very close, which may not be that useful. One way of removing genotyping errors resulting from low sequencing depth is to set genotype calls with an associated read depth below some threshold value to missing. However, such filtering results in fewer individuals and SNPs for a given sequencing cost , fastPHAWe present a new method for estimating pairwise LD using low coverage sequencing data, without requiring haplotype phasing, a known chromosomal order or filtering with regard to read depth. In essence, our method is based on the likelihood method by j, respectively, and let j, respectively. The LD coefficient is defined as , then the log-likelihood for the number of reference alleles is,n is taken as the number of individuals with a nonzero read depth at both loci.The number of reads for the reference allele, imate is ,E(D^)=2nD depends on the allele frequencies, comparing levels of LD between markers can be difficult using the disequilibrium coefficient. Consequently, many alternative measures of LD have been proposed in the literature; see guzzled).Since the range of ; see D′  and r2  while the sires were predominantly Wapiti , but included some red deer. The animals were managed in accordance with the provisions of the New Zealand Animal Welfare Act 1999, and the Codes of Welfare developed under sections 68−79 of the Act. Tissue samples were collected in the form of ear tissue punches and DNA extracted according to PstI and variations of the standard laboratory methodology as outlined in GUS-LD was also compared to the standard likelihood approach using a dataset consisting of 666 farmed deer and 38 of their sires. The dams were unrecorded red deer ], D was not near the upper or lower bound of its parameter space. This bias is due to poor sampling properties of For the first set of simulations, the bias of the LD estimates for the various LD measures are given in D was close to, or on, the lower or upper bound of its parameter space [Equation (2)], GUS-LD tended to yield smaller SE than the standard approach.The bias and SE of the LD estimates for alternative combinations of allele frequencies are given in Figure S1  of the LD estimates for the various pairwise LD measures are given in D was larger as the true value of D increased when D was on its upper boundary , and miscalled bases leads to underestimation of LD when these errors are not taken into account. This is important, as biased estimates of LD can have a profound effect on downstream analyses. For example, in case-control association studies, it has been shown using simulations that the presence of genotyping errors leads to reduced power in detecting an association between a locus and phenotype  lying outside the parameter space of D, whereas maximization is performed with respect to the constraint of Equation (2). When genotype calls are accurate and without error, this bias, in estimating D when its true value is near its upper or lower bound, is absent.With low coverage sequencing data, there are issues with estimating LD when the true parameter value lies near or on the upper or lower bound of its parameter space [Equation (2)]. Specifically, the bias in the LD estimates increases as e.g., e.g., There are many potential applications of using pairwise LD estimates from GUS-LD. For example, they could be used for quantifying the extent of LD decay in populations relative to physical distance from an assembly or genetic distance computed from a linkage analysis. This should prove a popular application since there are numerous studies already using sequencing data for this purpose in a number of species . In this case, the proportion of heterozygotes seen as homozygotes will be larger than expected under the model, which would result in some bias in the LD estimates at low sequencing depth. If additional information is available, then the probabilities in Equation (5) can be adjusted to reflect alternative sampling models. Lastly, it is assumed that sequencing errors occur independently between reads. In reality, this assumption may not hold, although it has been found to be reasonable in some scenarios (For the methodology developed in this paper, a number of assumptions have been made. First, genotype calls observed in the sequencing data are assumed to be conditionally independent between loci given the true genotype call. This assumption is reasonable provided that loci are not located on the same sequencing read across individuals. Estimation of LD is unaffected by the presence of genotyping errors resulting from low read depth when the loci are located on the same read as the true underlying haplotypes in the individuals are preserved. Depending on their settings, many variant callers allow for multiple SNPs to be called on the same sequencing read. However, it is more practical to only retain a single SNP from a given read as the loss of information is minimal and is outweighed by the reduced computational time. Other assumptions include that missing genotypes resulting from read depths of zero occur randomly, and that the alleles of the true genotypes are sampled randomly in the sequencing process. If the latter assumption does not hold, one allele will be sampled more frequently than the other (The main contributions of this paper are twofold. First, we have demonstrated that there can be significant bias in LD estimates from sequencing data when the read depth is low and the associated errors are not taken into account. This highlights the need for practitioners to either remove these errors by filtering or adjust their methodology to account for these errors. This is particularly important as some LD analyses give no explicit mention of a minimum cut-off with respect to read depth being used. Second, we have proposed GUS-LD as a new method to estimate LD using low-coverage sequencing data. GUS-LD will prove valuable to researchers seeking to undertake population studies when cost constraints prohibit the production of high-coverage sequencing data or other types of genetic data. In fact, our simulation results suggest that it is more cost-efficient to use low coverage data, as it allows more individuals to be sequenced for the same cost and results in smaller mean square errors for the LD estimates. From our results, the optimal sequencing depth was between 2 and 5, which was similar to the optimal read depth observed by


It has been repeatedly stressed that family-based samples suffer less from genetic heterogeneity and that association analyses with family-based samples are expected to be powerful for detecting susceptibility loci for rare disease. Various approaches for rare-variant analysis with family-based samples have been proposed.In this report, performances of the existing methods were compared with the simulated data set provided as part of Genetic Analysis Workshop 19 (GAW19). We considered the rare variant transmission disequilibrium test (RV-TDT), generalized estimating equations-based kernel association (GEE-KM) test, an extended combined multivariate and collapsing test for pedigree data (known as Pedigree Combined Multivariate and Collapsing [PedCMC]), gene-level kernel and burden association tests with disease status for pedigree data (PedGene), and the family-based rare variant association test (FARVAT).The results show that PedGene and FARVAT are usually the most efficient, and the optimal test statistic provided by FARVAT is robust under different disease models. Furthermore, FARVAT was implemented with C++, which is more computationally faster than other methods.Considering both statistical and computational efficiency, we conclude that FARVAT is a good choice for rare-variant analysis with extended families. It has been reported that rare variants may be functionally more related to diseases than common variants –3. HowevIn spite of these successful findings, the analysis with population-based samples suffers from genetic heterogeneity. The common-disease rare-variant hypothesis assumes that there are multiple rare causal variants, and it has often been expected that rare causal variants may not be shared between affected individuals. Consequently, rare variant association analysis with population-based samples might suffer from the genetic heterogeneity between affected individuals, and various analysis strategies, such as selecting individuals with extreme phenotypes, have been proposed to minimize genetic heterogeneity , 6. In pIn this report, we compare the performance of existing rare variant association methods for family-based samples using Genetic Analysis Workshop 19 (GAW19) simulated data. We considered 5 different methods for dichotomous phenotypes: the rare variant transmission disequilibrium test (RV-TDT) , generalRV-TDT  is an exWang et al.  extendedPedCMC  was propSchaid et al.  proposedChoi et al.  proposedWe focused on the sequencing data in a pedigree-based sample from GAW19. Individuals with systolic blood pressure (SBP) less than 140 or diastolic blood pressure (DBP) greater than 90 were assigned to be affected by hypertension. Genotypes for 959 individuals imputed from 464 sequenced subjects were used in our analysis, and we considered rare variants whose minor allele frequencies were less than 0.05. Rare variants were annotated with high-, moderate-, and low-risk effect by using SnpEff software , and thop values were utilized to calculate the empirical sizes. Table For the evaluation of statistical validity, the empirical type I error estimates for all methods were calculated at various significance levels with 200 replicates. We used Q1 as the phenotype and converted it to binary phenotype with prevalence 22.6 %. There were 7210 genes in each replicate, and thus 71,442,000 Genes with the top 6 largest effects on both simulated SBP and DBP were selected to evaluate the empirical powers for all methods. Rare variants in the selected genes with causal effects on SBP and DBP are all included for each gene-set file, and a certain number of rare variants with no effect in each gene were randomly selected to make the proportion of causal variants 10, 25, and 50 %. We found that the results for 25 % are similar to those for 10 and 50 %, and we only presented results for 10 and 50 % in Table Furthermore, we compared other features of each method, such as computational time, and the summary is provided in Table In this report, we evaluated several FBATs for detecting rare variants using GAW19 data. We found that FARVAT and PedGene usually provide similar statistical efficiency, and recommend the SKAT-O–type statistic provided by FARVAT because its power has been robust under various disease models. In addition, FARVAT was implemented with C++, and we found that it was computationally fast. Furthermore, it can load various input file formats, and provides additional information about minor allele counts. Therefore, we can conclude that FARVAT is a good strategy for rare-variant analysis with extended families in terms of both computational and statistical efficiency.


LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways.Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits . The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci:  Past genome-wide associate studies have identified hundreds of genetic loci that influence body size and shape when examined one trait at a time. Here, Jeff and colleagues develop an aggregate score of various body traits, and use meta-analysis to find new loci linked to body shape. MC4R and near POMC/ADCY3 are each associated with BMI and height. However, the BMI-increasing allele of the near-MC4R locus is associated with increased height, whereas the BMI-increasing allele of the near-POMC/ADCY3 locus is associated with reduced height1Large-scale meta-analyses of genome-wide association studies (GWAS) have identified numerous loci for anthropometric traits, including more than 600 loci for height1247810124In recent years, several approaches have been developed to examine whether single-nucleotide polymorphisms (SNPs) influence multiple correlated traits associated with disease1315Therefore, the purpose of our study was twofold. First, we aimed to capture body shape in its multi-dimensional structure using PCs from several commonly available anthropometric traits. To allow the meta-analysis of summary statistics across a large number of cohorts, we developed an approach that calculates averaged PCs (AvPCs) that robustly represent body shape across a wide range of studies. Second, using this approach, we aimed to identify genetic loci associated with body shape based on the AvPCs in 65 studies of the GIANT Consortium, including >170,000 individuals.As basis for our analysis of body shape we used six anthropometric traits: BMI, WHR, height, weight, hip and waist circumference. First, we performed separate PCA in a subset of 20 large population-based studies , we estimated that AvPC2 has the highest heritability (75–80%), consistent with the fact that height is the main contributing trait to this AvPC with a strong genetic componentConsistent with the individual anthropometric traits, the four AvPCs that describe body shape are also heritable. Using data from four isolate populations  for at least one of the four AvPCs  . In total, 183 loci had already been established for BMI, WHRadjBMI or height . Of the 29 previously established loci, 24 have been associated with BMI only13n>339,000; P<7.2 × 10−3; LEMD2 has been reported to be associated with expression of LEMD2 in liver (P=1.66 × 10−9) 20LEMD2  was previously reported for its promising association  with energy intake at dinner in a small GWAS of 815 Hispanic childrenCD47  of the 124 loci associated with AvPC2 had previously been identified for heightFor AvPC2, we identified no novel loci. Almost all n=22 of theRPS6KA5 (>20 kb) and C14orf159 (>30 kb), failed to reach genome-wide significance in previous WHRadjBMI GWAS  and was nominally associated with extreme obesity risk  GANAB, rs7949030, showed some evidence of association with WHRadjBMI in the latest GIANT GWAS  and was reported to be an eQTL for several other genesMIR3654, EEF1G, EML3, BSCL2, HNRNPUL2-BSCL2, LRRN4CL was found29BSCL2 is of interest, as it is a known candidate gene for the most severe lipodystrophy phenotypeHNRNPUL2-BSCL2, AHNAK, LRRN4CL and INTS5  in ARL15 was moderately associated with higher BMI , increased adiponectin levels  was previously found to be associated with increased risk of childhood obesity in European and African Americansr2EUR=0.065) with the duplication (represented by rs16992296), located 168 kb upstream. The lead SNP (rs7855432) of the second locus, ANP32B, was moderately associated with height  r2=0.95, D′= 1.0) was reported to be promisingly associated with coronary heart disease risk Seven loci were identified for AvPC4, of which five had been previously reported; one for BMI and height, one for WHR and height, one for height only and two for WHR only134.87 refs , 40), hi.87 refs , 42) and1.0 ref. ). A duplWe developed a PCA-based approach to capture variation across multiple traits simultaneously in a uniform way across multiple studies. Resulting AvPCs are a robust cross-phenotype representation allowing their use in large-scale meta-analyses. We assessed this approach to capture body shape based on six individual anthropometric traits and identified six novel loci that were not identified before in much larger GWAS-meta-analyses for BMI, WHRadjBMI and height12The AvPCs are combinations of different anthropometric traits and therefore capture more complex body shape phenotypes than the single traits. AvPC1, representing overall adiposity, and AvPC2, representing height with respect to WHR, are the most important contributors to body shape, explaining on average more than 80% of the variation. More specific body shape types were captured by AvPC3 and AvPC4 and were defined by impact of height and WHR (AvPC3) or BMI, waist and hip (AvPC4). Our initial analyses demonstrated that the loadings are stable across studies, study designs and between men and women. Moreover, we have shown that the AvPCs are heritable traits and correlated with cardiometabolic traits and risk factors.To further demonstrate the strength of this approach, we compared total variance explained of single traits and AvPCs by SNPs previously identified in single-trait GWAS . We subsequently use these AvPCs to calculate PCs in all participating studies. This approach ensures that PCs are calculated in a uniform manner across all studies, thus facilitating subsequent meta-analyses. This approach could be applied to capture genetic variation across related traits that is currently not captured by single-traits GWAS .Consistent with published anthropometric traits101112r2>0.8), lack of power due to sample size for SNPs of modest effects, or perhaps the AvPCs introduces noise to purely single traits such as height. Consistent with this finding, we also observe that some single traits also explain more of the variance of body shape compared with AvPCs. Our comparison of the variance explained between previous single-traits meta-GWAS and our AvPCs support this evidence for overlapping associated variants. Since AvPC2 represents largely a single trait, height, with large height loadings we were unable to explain more of the variance. In fact we explained less of the variance, which is likely due to noise introduced using this composite AvPCs phenotype. This observation is also evident for variance in body shape explained by height compared with AvPC3 and AvPC4, but is in contrast to BMI, a complex trait comprised of multiple anthropometric measurements, which explains less variance in body shape compared with AvPC3 and AvPC4. It is important to emphasize our approach is most informative for complex traits such as BMI that are derived from a series of other traits. We believe that using PC space to define complex traits is useful for the detection of loci involved in multiple pathways that might go undetected in a single trait setting.Even though our approach has several advantages, it is not meant to replace single trait GWAS analyses. A number of loci that were identified in the latest single-trait GWAS were not identified in our body shape GWAS; that is, we identified 124 loci (or 14.2%) of the 837 loci recently reported in the GIANT single-trait meta-analyses . This maWe have developed a new strategy that applies a PCA approach in a meta-analysis setting to combine composite phenotypes in a harmonized way across multiple studies. We successfully applied this approach to anthropometric traits to capture body shape. The derived combined anthropometric traits (AvPCs) were shown to be heritable and correlated to cardio-metabolic traits. Large-scale GWAS meta-analyses of the AvPCs identified six new loci that were not identified by previous single-trait GWAS that were twice as large in samples size. This PCA approach could maximize gene discovery for other correlated traits, such as cancers, immune disease, hematologic traits and so on. and may identify genes that point towards shared physiological pathways.In the first stage analyses, 43 studies participated  that had HapMap 2 imputed genome-wide data available. A subset of 20 studies with unrelated individuals was used for calculation of average loadings. Second stage analyses were performed in 10 studies  with genome-wide data that became available after the first stage and 12 studies  with Cardio-MetaboChip (by Illumina) data . This average correlation matrix is then used as basis for a PCA. The loadings that result from this PCA are called average loadings  and Suppn=909), CROATIA-Korcula (n=842), CROATIA-Split (n=499) and ORCADES (n=866) using the ‘polygenic' function of the GenABEL package for R Heritability of the avPCs was calculated within four population isolates, CROATIA-Vis (http://www.r-project.org/) that calculated the AvPCs as linear combination of residuals of the study phenotypes with the use of the average loadings. This was done for men and women separately and additionally for combined in studies with relatedness structure. As the first four PCs explain on average more than 99% of the variance >10−6, imputation quality, minor allele count (MAC) >3). The meta analyses of GWAS results for the first four AvPCs we combined sex-stratified results for studies with unrelated individuals and unstratified GWAS results for studies with relatedness individuals. Meta analyses were performed with METAL P values were corrected by the genomic control inflation factor λ =1.29, λ(PC2)= 1.407, λ(PC3)= 1.236, λ(PC4)= 1.136). Results were limited to SNPs that are in HapMap 2 and had results for more than 30,000 individuals. Heterogeneity analysis was performed with METAL. Each AvPC all SNPs with a promising P value  were identified in combined analyses. To identify promising loci clustering  with PLINKGWAS on the first four AvPCs were calculated for men and women separately in studies of unrelated samples and combined for studies with related samples with an adjustment for study site when necessary. All studies of the first stage analyses used HapMap 2 imputed genome-wide data. GWAS results underwent extensive quality control and study-wise filtering =8.8 × 10−152), rs13296358 =5.4 × 10−67)). For both SNPs the effect was driven only by a single study and no other SNP in high LD had a promising P value. Therefore, these two SNPs were removed from further analyses.Two SNPs that were promising for the first principal component had very low heterogeneity As mentioned above for second stage analyses a mixture of studies with genome-wide SNP data and MetaboChip genotypes was available. Some of the leading SNPs of the first stage analyses were not genotyped on the MetaboChip. To increase the power for all promising SNPs of each AvPC proxies were defined that were all SNPs close to promising SNPs (distance <500 kb), in high LD (LD>0.9) and available in more than 70% of the individuals of the second stage. Results of the second stage analyses underwent the same quality control as first stage results.P value in the combined analysis were named genome-wide significant loci and the best SNP of the combined analyses (largest absolute beta) was reported as topSNP of this locus.The combined analyses of all first and second stage GWAS was performed with METALP value should stay promising  in both conditional analyses. Second, we checked by look-ups if those genome-wide significant SNPs that are independent from the previously reported topSNPs were not genome-wide significant  in GIANT analyses12Two analyses were performed to distinguish between genome-wide significant body shape loci that are known from previous GWAS on BMI, height and WHR and novel body shape loci. First, conditional analyses were performed. We used the 226 reported topSNPs  of published GIANT analyses on BMI, height and WHR121512novel SNPs if they fulfil the following conditions:Genome-wide significant SNPs are named P value of conditioned analyses on topSNPs reported by previous GIANT analyses  remained promising .P value in previous GIANT analyses  was not genome-wide significant .For identification of potential pleiotropic effects several look-ups in various large-scale consortia on different phenotypes were performed, including GIANT, DIAGRAM and MAGIC, all references are given in the results table of the look-ups . For comhttp://www.r-project.org/) if not stated otherwise. Apart from the GCTA analyses, which uses LD structure of KORA F4, all LD analyses were performed in PLINK based on HapMap 2 (CEU) genotypes. For comparison of findings between loci from different AvPCs two loci are assumed to be identical if the topSNPs are in high LD (LD>0.8).PCA, further analyses and plots were generated with R .Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary Figures 1-7, Supplementary Tables 1-5Association results for 189 GWAS-significant loci.Transferability of novel loci in other anthropometric GWASPhenotype summary statistics of all studies in 1st and 2nd stage analyses.Genotyping description for all studies in 1st and 2nd stage analyses.


In 1994, the publication of Herrnstein’s and Murray’s The Bell Curve resulted in a social science maelstrom of responses. In the present study, we argue that Herrnstein’s and Murray’s assertions were made prematurely, on their own terms, given the lack of data available to test the role of genotype in the dynamics of achievement and attainment in U.S. society. Today, however, the scientific community has access to at least one dataset that is nationally representative and has genome-wide molecular markers. We deploy those data from the Health and Retirement Study in order to test the core series of propositions offered by Herrnstein and Murray in 1994. First, we ask whether the effect of genotype is increasing in predictive power across birth cohorts in the middle twentieth century. Second, we ask whether assortative mating on relevant genotypes is increasing across the same time period. Finally, we ask whether educational genotypes are increasingly predictive of fertility (number ever born [NEB]) in tandem with the rising (negative) association of educational outcomes and NEB. The answers to these questions are mostly no; while molecular genetic markers can predict educational attainment, we find little evidence for the proposition that we are becoming increasingly genetically stratified. American Sociological Review and on through the landmark studies by More than two decades ago, the psychologist Richard Herrnstein and the policy scholar Charles Murray (hereafter H&M) penned a best-selling book that purported to explain the shifting process of economic attainment in American society . Their nMany critics pointed out the flaws of their provocative argument at the time, including sociologists , economidecreasingly affected by the social environment, which by extension indicates that they must be increasingly affected by genes. (P. 109–110)High cognitive ability as of the 1990s means, more than ever before, that the chances of success in life are good and getting better all the time, and these are Add to this phenomenon known as assortative mating. Likes attract when it comes to marriage, and intelligence is one of the most important of those likes. When this propensity to mate by IQ is combined with increasingly efficient educational and occupational stratification, assortative mating by IQ has more powerful effects on the next generation than it had on the previous one. This process too seems to be getting stronger, part of the brew creating an American class system. (P. 91–92)The professional consensus is that the United States has experienced dysgenic pressures throughout either most of the century (the optimists) or all of the century (the pessimists). Women of all races and ethnic groups follow this pattern in similar fashion. There is some evidence that blacks and Latinos are experiencing even more severe dysgenic pressures than whites, which could lead to further divergence between whites and other groups in future generations. (P. 341)H&M also make arguments about the role of genetic differences in explaining ongoing racial inequalities in U.S. society. We do not deal directly with those claims because the approach we deploy (polygenic score construction—see below) does not allow for ascertainment bias–free estimation across groups with different continental ancestries.Until recently, the study of human genetic variation (including the research on which Herrnstein and Murray based their conclusions) has consisted mainly of behavior genetics studies, in which twin and adoption designs were used to identify heritable, or genetic, variation in various traits  of the variance–covariance matrix of the genotypic data. The PCs are included to guard against the well-known problem of population stratification: the tendency for allele frequencies to covary with unobserved environmental confounds  to measure genetic propensities for a large number of outcomes . A GWAS onfounds .i is a weighted average across the number of SNPs (n) of the number of reference alleles x  at that SNP multiplied by the score for that SNP (β): Using results from a GWAS, researchers can compile a polygenic score for a particular outcome that aggregates thousands of SNPs across the genome and weights them by the strength of their association. In essence, a polygenic score is a weighted average or composite score that takes into account information across an individual’s entire genome to measure their genetic predisposition or risk to a particular outcome. Or, a polygenic score (PGS) for individual Polygenic scores have several attractive features. First, unlike candidate genes, they are "hypothesis-free" measures—i.e., ex ante knowledge about the biological processes involved is not needed to estimate a score for a particular measure. Rather, a polygenic scores casts a wide net across an individual’s entire genome to yield a single quantitative measure of genetic risk, allowing researchers to explore how genes operate within environments where the biological mechanisms are not yet fully understood . For ot.Recently, a GWAS was conducted for educational attainment . Using t2 values are largely the result of classical errors-in-variables–induced attenuation bias because of the fact that the common genotyping platforms are picking up SNPs that are in incomplete linkage  with the true causal SNPs of genetic effects. That is, X is measured with error, leading to inconsistent estimates that converge toward zero. Denser genotyping platforms and better imputation to nonmeasured SNPs, thanks to the availability of the 1000 Genomes database, to which researchers can now impute , may reduce this form of regression dilution to some extent. Ultimately, full genome sequencing may, of course, obviate the need for imputation and drastically reduce this form of measurement error in the Xs. A second source of imprecision is in sampling error; this reduces accuracy and is solved by increasing sample sizes . In model 2 of panel A, we control for birth year, as those with higher scores are more likely to live longer, given the well-documented negative association between education and mortality (< 0.001). In model 3 of panel A, we keep the main effect for birth year and add the interaction between the PGS and birth year. The effect for the interaction term is negative , suggesting that the PGS is less, rather than more, predictive of educational attainment in later birth cohorts. .We do not believe these results are obtained because of measurement error; when we correct for measurement error, the same pattern of results remains see . Another3 = –0.009 versus b = –0.003 for women). The opening of higher education to men took place earlier in the twentieth century than it did for women, thanks to the GI Bill and other policies , we find that it increases as we move from high school completion to the transition to and completion of college , panel A< 0.001), a finding consistent with earlier work showing general spousal assortment on genotypes , such that an additional standard deviation in educational PGS results in 1/15th fewer children on average. More importantly, however, model 4 of panel A shows that the interaction between birth year and PGS—the direct test of whether such dynamics are shifting over time as H&M suggest—is insignificant. Therefore, as a society, we do not seem to be experiencing increasing dysgenic dynamics, despite the fears of H&M, among others  endowment —a survey of men and women born in the years 1957–1964. This is hardly a wide enough swath of birth cohorts (particularly because everyone was born postwar) to test their grand theory. By contrast, we test their hypotheses using molecular data across a much wider  birth cohort distribution.With this in mind, the present article examined three empirical phenomena that flow from this argument and fail to find much corroborating evidence: (1) the correlation between education genotype and years of schooling is not increasing across birth cohorts in the twentieth century (only for the transition from college to graduate school does genetics appear to have increased in importance); (2) assortative mating on the underlying genetic architecture for educational attainment is flat, not increasing as phenotypic educational assortment seems to be; and (3), there is no change in the relative fertility rate by education genotype across birth cohorts—i.e. we do not appear to have entered a period of "dysgenics." We do not believe our results are driven by ascertainment bias or random measurement error see  or by se1 Deploying this educational stage analysis, we also tested a genetic version of maximally maintained inequality theory (MMI)  see . A prediIn conclusion, though we are less than two decades away from the 2033 date at which Young had predicted the final "revolt" against an entrenched meritocratic system (in the United Kingdom), it seems—if the present results are to be believed—we are still a long way away from that dystopia he painted over a half century ago.Supplemental Material


MAOA) on extraversion and neuroticism from adolescence to adulthood, using modern latent variable methods. A sample of 1,160 male and 1,180 female participants with complete genotyping data was drawn from a British national birth cohort, the MRC National Survey of Health and Development (NSHD). The predictor variable was based on a latent variable representing genetic variations of the MAOA gene measured by three SNPs . Latent phenotype variables were constructed using psychometric methods to represent cross-sectional and longitudinal phenotypes of extraversion and neuroticism measured at ages 16 and 26. In males, the MAOA genetic latent variable (AAG) was associated with lower extraversion score at age 16 , as well as greater increase in extraversion score from 16 to 26 years . No genetic association was found for neuroticism after adjustment for multiple testing. Although, we did not find statistically significant associations after multiple testing correction in females, this result needs to be interpreted with caution due to issues related to x-inactivation in females. The latent variable method is an effective way of modeling phenotype- and genetic-based variances and may therefore improve the methodology of molecular genetic studies of complex psychological traits.Very few molecular genetic studies of personality traits have used longitudinal phenotypic data, therefore molecular basis for developmental change and stability of personality remains to be explored. We examined the role of the monoamine oxidase A gene ( Personality traits, such as neuroticism and extraversion, are relatively stable during adulthood  in various psychopathologies in adults and children, including antisocial behavior  provides the strong rationale for studying MAOA gene in personality and other complex psychological traits.Recent molecular genetic studies have provided multiple lines of evidence supporting the role of the monoamine oxidase A gene  affected by methylation lead to changes in the expression pattern.Studies using MAOA common genetic variants  through early adulthood (age 26), using data from the MRC National Survey of Health and Development (NSHD), also known as the British 1946 birth cohort. We hypothesized that the effects of the MAOA gene on personality traits would change through maturation, and may differ between males and females.In the present study, we investigated the effects of the MAOA genetic variation and personality traits, a latent genetic variable was created based on three MAOA SNPs: rs3788862, rs5906957, and rs979606.Genetic associations are typically estimated from univariate analysis based on one SNP or several SNPs within a gene region that are analyzed separately. It has been proposed that summary latent genetic variables can better capture genetic variance  and the age-related changes from 16 to 26 in the phenotypes . The utilization of psychometric latent variable methods is advantageous in that personality phenotypes constructed this way are corrected for measurement errors, thus providing more accurate estimations of genetic effects , and there is evidence for incomplete inactivation of X-chromosome in females  were, in most respects, representative of the national population of that age born in Britain  provided a blood sample. Ethical approval for this research was obtained from the North Thames Multi-Centre Research Ethics Committee and from relevant local research ethics committees in the survey areas. Informed consent was given by all the respondents. All analyses were performed using Mplus 7.4, with the WLSMV estimator and the theta parameterisation . These SNPs were selected using a Tagger implementation of the Haploview programme 9 , or other SNPs mentioned earlier that are in high/complete LD with them, have been previously reported to be associated with MAOA functional activity  according to the manufacturer's protocol. The three 06 Table , were tyFor SNPs rs3788862 and rs5906957, in males A allele  was coded as 0 and G allele as 1; in females genotype AA was coded as 0, AG as 1, and GG as 2. For SNP rs979606, in males G allele  was coded as 0 and A allele as 1; in females genotype GG was coded as 0, AG as 1, and AA as 2. The three SNPs were used as binary  or ordinal  indicator variables for psychometrically modeling the latent genetic variable . For the cross-sectional analysis, MAOA latent genetic variable was specified to be a predictor of the latent variables representing personality traits . The MAOA latent variable was included in all subsequent analyses  with lower extraversion scores at age 16 in males  showed larger effect sizes for both the association with age 16 extraversion , and the change in extraversion from age 16 to age 26 .In an additional sensitivity analysis for the male participants, To assess the statistical power of the analyses conducted in the male sample, Monte Carlo simulation analysis was performed both for our latent genetic approach and the traditional single SNP approach Table . The lata priori power analysis with similar parameterization as the cross-sectional association model for extraversion at 16 in males, for effect sizes with phenotypic variances explained at 1%, 2%, and 3%. For 1% phenotypic variance explained, the resulted power was 0.55 based on latent genetic approach and 0.41 for the corresponding single SNP approach (with an effect size of 0.7%). For 2% phenotypic variance explained, the resulted power was 0.84 based on latent genetic approach and 0.71 for the corresponding single SNP approach (with an effect size of 1.2%). For 3% phenotypic variance explained, the resulted power was 0.95 based on latent genetic approach and 0.87 for the corresponding single SNP approach (with an effect size of 1.6%).We additionally conducted Both the post hoc and a priori power analysis have shown higher power in latent genetic approach compared to those found from single SNP approach, which will be even smaller once multiple testing corrections are applied for all three SNPs.MAOA gene based on a latent genetic variable of three SNPs was investigated in relation to cross-sectional and longitudinal psychometric phenotypes of neuroticism and extraversion in a population-based study. The results revealed age-specific effects in males. In males, the MAOA latent genetic factor was associated with extraversion at age 16 and with the change scores in extraversion from 16 to 26 years.The association of the MAOA uVNTR is associated with Harm Avoidance  are associated with ADHD, ASD, and antisocial behavior, which have higher prevalence in men than in women  than the currently implemented latent variable method, because in single SNP analysis the number of tests depends on the number of SNPs in the gene-set of interest. Therefore, the multiple testing correction burden is greater. On the contrary, the latent variable method summarizes the genetic information from the SNPs via a single latent variable, therefore only a single test is implemented irrespective of the number of SNPs considered.Therefore, the methodological strength of the present study lies in the use of the latent variable approach which has several appealing features over other commonly applied methods. In particular, the analysis for each SNP separately is less powerful  and easy to implement in practice, the polygenic score has two important limitations. First, it requires a priori knowledge of the separate SNP effects. Second, for its computation the sampling variability  of the separate SNP effects is ignored. This implies that the effects of the polygenic score will be inevitably attenuated . This phenomenon has been thoroughly studied in the measurement error literature , although the power (0.84) is sufficient for detecting variances explained at 2%, for effect size of 1% variance explained, the power drops to 0.41. Therefore, it is important to ensure the study is of sufficient sample size to detect expected effect sizes with sufficient power.Furthermore, as shown in the additional simulation power analysis performed for the current study, the latent genetic approach demonstrated consistently higher statistical power compared to single SNP approach, even before applying corrections for multiple testing. Moreover, when analysing all the SNPs simultaneously in the same model, we explicitly took advantage of the potential correlations among SNPs. Both positive and negative correlations between the SNPs are allowed, while this correlation is totally ignored in the separate SNP tests. Although MAOA gene in personality traits. Furthermore, replications based on large studies are needed to confirm the effects observed in the male sample of the present study. As such, future GWAS studies could include genes on X chromosome both in male samples and in female samples for putative genetic associations.In terms of limitations, we did not formally test for population stratification, however all participants in the current study were of white Caucasian background, so the population stratification is unlikely. We did not take into account environmental exposures, whereas genetic factors and known to interact with environmental factors in their influences on complex traits. Similarly, in the current study, we did not have functional indicators  to test for effect modification that could correspond with developmental stages. Moreover, as already mentioned previously, our results in females need to be interpreted with caution. It is known that many X-chromosome genes may express differently in males and females, due to incomplete inactivation of one of two X chromosomes in females, as well as other mechanisms (as discussed above). In our study, information on the status of X-chromosome inactivation was not available. Future studies which incorporate such information would be very helpful in order to further clarify and determine the role of MAOA genetic variation affects personality traits in age-specific manner in males. Our study highlights the importance of applying a life course developmental approach to behavioral genetics (Scerif and Karmiloff-Smith, MAOA, which is widely thought to interact with environmental factors to influence behavioral outcomes across developmental stages (Kim-Cohen et al., MAOA gene plays in the crafting of personality.In conclusion, the present study confirms that MX, DG, and PJ designed the study, MX conducted analysis, MX and DG wrote up the manuscript; RT and JH contributed to the design of genotypic modeling; AM and TC contributed to design of phenotypic modeling. All authors contributed to the formulation of the study in its current form and improved iterative versions of the manuscript. All authors agree to be accountable for all aspects of the study and give final approval of the version to be published.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.