G). The G matrix can be constructed from thousands of markers spread across the genome. The strongest assumption of G and consequently of GBLUP is that all markers contribute equally to the genetic variance of a trait. This assumption is violated for traits that are controlled by a small number of quantitative trait loci (QTL) or individual QTL with large effects. In this paper, we investigate the performance of using a weighted genomic relationship matrix (wG) that takes into consideration the genetic architecture of the trait in order to improve predictive ability for a wide range of traits. Multiple methods were used to calculate weights for several economically relevant traits in US Holstein dairy cattle. Predictive performance was tested by k-means cross-validation.Genomic BLUP (GBLUP) can predict breeding values for non-phenotyped individuals based on the identity-by-state genomic relationship matrix . Furthermore, bias in prediction estimates was reduced compared to that resulting from the use of regular G. Even for traits with low heritability and lower general predictive performance , weighted G still yielded a gain in accuracy.Relaxing the GBLUP assumption of equal marker contribution by increasing the weight that is given to a specific marker in the construction of the trait-specific Genomic relationship matrices weighted by marker realized variance yielded more accurate and less biased predictions for traits regulated by few QTL. Genome-wide association analyses were used to derive marker weights for creating weighted genomic relationship matrices. However, this can be cumbersome and prone to low stability over generations because of erosion of linkage disequilibrium between markers and QTL. Future studies may include other sources of information, such as functional annotation and gene networks, to better exploit the genetic architecture of traits and produce more stable predictions.The online version of this article (doi:10.1186/s12711-015-0100-1) contains supplementary material, which is available to authorized users. Each method leans more or less on a priori assumptions about the genetic architecture of the trait and the linkage disequilibrium (LD) between markers and the quantitative trait loci (QTL) [Since the introduction of dense single nucleotide polymorphisms (SNP) chips, the statistical methodology used in genomic selection has undergone significant improvements. Prediction of genetic merit is most commonly based on linear regression models, in which the genomic breeding value (GEBV) for an individual is computed as the sum of the marker effects multiplied by the specific individual allelic variants across the entire genome . Since tci (QTL) .G) constructed from genotypic information [G matrix. This is opposed to other methods such as those of the Bayesian alphabet family, which often assume that the genetic variance is explained by a reduced number of markers, among which some have a small or no effect [Alternatively, genomic-BLUP (GBLUP) can take advantage of the genomic relationship matrix . De-regressed measures were derived from sires’ predicted transmitting ability values (PTA) from US national genetic evaluations for production, calving ease and type traits and used as pseudo-observations for subsequent analyses, given that these provide some advantages compared to other sources .The first group of traits analyzed included milk yield (MY), fat percentage (FP), and protein percentage (PP); the second group of traits included direct calving ease (DC) and maternal calving ease (MC); the third group of traits included body depth (BD), rump width (RW), stature (ST) and strength (SR). Heritabilities were obtained from VanRaden et al.  and are PTA were de-regressed prior to analysis following Garrick et al. . Sires wG matrix requires an estimate of marker effects [Weighting the  effects ,14,15. IwG matrix, as already proposed by de los Campos et al. [The first approach used for weighting was based on feature selection through single-marker regression. Only significant SNPs (p < 0.05) were used to construct the s et al. . The sinyij is the pseudo-phenotype for ith individual sired by the jth sire, bm are the mean and mth marker fixed effect, sj is sire additive genetic effect, where s ~ N, with A representing the additive relationship matrix derived from the pedigree, eijk is the residual, and wijk is the weight of the yijk dePTA, as proposed in Garrick et al. [xi is the ith row of the X incidence matrix reporting a vector of 1 s and the number of copies of the minor allele  for the ith individual, and zi is the ith row of the Z is the incidence matrix for the sire effect. The significance  of the F-test for the marker effect was used to select SNPs. Analyses were performed in R using the package ‘pedigreemm’ [where k et al. , xi is tigreemm’ .The other two methods involved fitting all markers simultaneously as random effects, modeled in a Bayesian framework . These mμ is the mean of the population, uj is effect of the jth marker, ai is the additive polygenic effect of the ith individual assuming the vector a ~ N, with A representing the additive relationship matrix derived from the pedigree, ei is the residual, wi is the weight of the yi dePTA, wij is the genotype of sire i at marker j, and zi is the ith row of the additive polygenic effect incidence matrix Z.where Two approaches were used to estimate marker effects: Bayesian Ridge-Regression (RR) and Bayesian LASSO (BL). These were chosen because they differ in the prior assumption on marker effects and their variances, as well as the penalization criteria applied to the estimation of these parameters ,23. RR wa were multivariate normal A is the pedigree-derived numerator relationship matrix, and priors for residual , where ν represents the degrees of freedom and S is the scale. In RR, the prior for marker effects was 2. While ν was arbitrarily equal to 5 for all models, S was chosen according to the expectation of variance for the specific trait. Therefore, given Vy, the variance for the pseudo-phenotypes, and Se = Vy(1 − h2)(ν − 2). Priors for the total genomic variance require that the variability of SNP allele frequency is known, which can be summarized as n is the number of individuals, m is the number of markers, xij is the number of copies of the minor allele, and xj is the average for marker j. According to this, the scale for genomic variance in RR was 2 regulating the amount of shrinkage. This value τ2 followed an exponential distribution Exp(τ2|λ), where λ is a regularization parameter for the shrinkage of marker effect estimates. In this study, λ was considered random and assigned a gamma distribution G, with given shape α1 and rate α2. While α1 was set equal to 1.01, we derived Prior specifications used here were defined following de los Campos et al. . For all package .W  was converted into M by subtracting 1, such that entries were −1, 0 and 1. Then, a vector t of length m was computed such that the entry for the ith marker was ti = 2(pi-0.5), where pi is the minor allele frequency. Matrix Z was obtained by subtracting t from Z. For ease of computation, instead of building G = ZZ’ directly, we constructed 39 004 single-locus genomic relationship matrices. For each zi column of Z we computed Gi = zizi’. The genomic relationship matrices were computed as a weighted sum of the single-marker genomic matrices for each of the methods, corrected for mean identity by state (IBS) relatedness. This method of constructing genomic matrices as a weighted average of several single-marker genomic matrices has already been proposed and used by Zhang et al. [i as the minor allele frequency and wi as weight for the ith marker, the following relationship matrices were built.For all genomic relationship matrices, we partially rearranged the procedure reported in . Marker g et al. . We defiGBASE the weight was calculated as:In The weighting applied to the base genomic matrix is a modification of the second method described by VanRaden , where mi in the weighted G matrix (GSM) was:When information on marker contribution was provided from different sources , it was possible to weight the contribution to the genomic matrix by the marker-specific realized variance. In the SM, the weight on marker i is an indicator with value ‘1’ assigned to markers for which P-value < 0.05, and ‘0’ otherwise.where SGBL for GRR were constructed with weights:Finally, from the two Bayesian approaches RR and BL, matrices ith marker.where GBASE, GSM, GRR, GBL) in turn. In addition, a prediction from a pedigree-derived relationship matrix (APED) was obtained in order to facilitate comparisons. MME were:For all traits, predictions were calculated using GBLUP, using different genomic matrices , 2 is the assumed heritability. Equations were solved by direct inversion and the vector of predicted values was obtained as y as the phenotype and ŷ as the vector of predicted values for the n masked observations in cross-validation, we used the value of λ that gave the solutions that minimized the difference where A, and were used as sources of information to separate individuals across medoids, i.e. clusters of individuals that were formed to maximize intra-group and minimize inter-group additive genetic relationships. Relative distances were computed as:To test the predictive ability of the models, a 4-fold k-means cross-validation was performed . Dissimidij is a measure of pedigree distance between individuals i and j, aij is the additive genetic relationship between the two individuals, aii and ajj are the diagonal elements of the relationship matrix for individuals i and j, respectively. Each medoid was used as a validation set using the remaining three as the training set. Predictions (ŷ) from each model were regressed on the weighted dePTA (y) to obtain measures of accuracy and bias. The former was assessed as the correlation between y and ŷ, while bias was measured as the regression coefficient from the linear model GSM, GRR, or GBL) and GBASE, divided by the accuracy of GBASE.where e medoid , as repomentclass2pt{minimOne of the major concerns of all weighting procedures stems from the difficulty to separate population stratification from actual QTL signals . In genoWe used two different empirical ways to assess the impact of spurious associations.First, results from genome-wide regression analysis were cross-referenced for consistency with associations reported in the literature. This was done in order to verify the validity of the associations obtained by the different methods. The full list of QTL for each of the traits analyzed was extracted from the Animal Genome QTL database  and mappVn is the variance for the nth window, including markers from the start position a to the stop position b, pi is the MAF for the ith marker and ith marker. In this case, the marker effects were derived using RR since this method allows fitting all markers simultaneously in the model (i.e. taking LD into account), with the same penalization across the genome (i.e. no marker-specific shrinkage). The values of t windows , were in turn declared significant and assigned a value of ‘1’, while the other non-significant windows were assigned a value of ‘0’. Once the different sets of significant markers were defined, the sensitivity to marker effect inflation was measured using false positives rate (FPR) computed as the proportion of windows that were declared significant but that did not contain a reported QTL over the total number of windows declared significant. This analysis is sensitive to the number of QTL reported in the database, therefore FPR was reported only for production traits, for which a large number of reported QTL are available. It should be noted that these three traits  are not completely representative of the genetic architecture of all traits under selection in dairy cattle but offer a framework for the interpretation of the overall results.where A, relationships among individuals are the expected identical by descent (IBD) probabilities under the Fisher model and depend essentially on the completeness of the pedigree [G relates individuals based on the number of alleles they share at each locus, regardless of ancestry (i.e. IBS). The weighted genomic relationship matrix wG is in turn expected to relate individuals based on the shared number of copies of a given allele at a QTL .Furthermore, we attempted to verify the possibility that family structure was still present in the weighted genomic matrices. In the pedigree-derived matrix pedigree . ConversA, GBASE, and wG. We speculate that if an association with a QTL was not found due to lack of statistical power, wG will be close to GBASE and both wG and GBASE will be roughly equidistant from A. A similar situation will arise by lack of association due the polygenic architecture of the trait. Conversely, if wG is still tracing population or family structure, its distance from GBASE will increase, while the distance from A should decrease, under the assumption that wG re-traces expected relationships. Finally, if the weighting procedure correctly pinpoints significant regions of the genome, wG should depart from GBASE, as well as from A. It should be noted that, within this framework, the lack of statistical power and the genetic architecture are not a concern since, in these cases, wG should essentially be equal to GBASE and the two procedures should produce the same results.The amount of inflation in prediction accuracy due to population stratification and still present in the weighted relationship matrix can be assessed empirically as the relative distance between the three matrices wG was built for each trait using the formulas reported above. In this case, and unlike in the cross-validation analysis, marker effects used were from RR estimates averaged over the four replicates. For each trait, the k-means clustering was repeated over the three matrices and individuals were assigned to the four different groups based on the genetic distance used to obtain the medoids. For each matrix, individual pair-wise comparisons were assigned the value ‘1’ to bins that appeared in the same medoid (regardless of the medoid label) and ‘0’ otherwise. The proportion of bins of wG that fell in the same medoid in comparison with either GBASE or A over the total number of pairs was then measured. This yielded an empirical relative distance between the three matrices for each trait.A measure of relative distance between matrices was obtained that assigned individuals to different groups. A specific PTA used in the analyses and their reliabilities are summarized in Table wG matrix were actually close to QTL for the traits of interest, while markers that were less heavily weighted still contributed to genomic relatedness, but with very low emphasis.Figure GBASE for all genomic matrices computed are reported in Table S1 [See Additional file wG obtained with RR to that obtained with A and GBASE is in Figure wG on the x-axis measures its similarity to A , while the respective position on the y-axis indicates its similarity to GBASE. Similarity between GBASE and A was equal to 0.82 . This means that 82% of the pairs of individuals that were in the same medoid in A were also in the same medoid in GBASE. We did not observe any trait for which wG appeared to be equivalent to GBASE or very similar to A.Mean, standard deviation and correlation with wG to A and GBASE were equal to 0.82 and 0.88, respectively. For these two traits, we can infer that the weighting procedure had a moderate impact, and that the weighing procedure was not affected by population stratification. MY, DC, MC, ST, and SR similarities of wG to GBASE were around 0.90 and those to A were around 0.85. The weighting procedure resulted in relatedness among individuals that weakly resembled the pedigree population stratification for these traits. For FP, the presence of a QTL of strong impact decreased the similarity of wG to GBASE (0.63), but did not affect its similarity to A, which remained at 0.82. For PP, wG resulted in a similarity to A that was very different (0.63) but it was still only moderately similar to GBASE (0.92).For BD and RW, similarities of wG was different from GBASE, although this dissimilarity was stronger for FP, which is the trait that presented the strongest deviation from the Fisherian assumptions.There was no evidence that the weighting procedure reintroduced population stratification in the weighted genomic matrices. For all traits, GBASE and GRR for FP is displayed in Figure GRR in the lower triangle, and GBASE in the upper triangle . Figure GBASE, which clearly represents the four medoids. In GRR, the same clustering appears rearranged.In all cases, regardless of heritability and putative architecture of the traits, the weighted genomic matrices appeared to incorporate some signal other than population structure. We believe that this signal represents true QTL effects, which could be represented as a third dimension in Figure The proportion of the variance absorbed by the top 10% of markers for each trait, as estimated with RR, is in Figure 2’ , medium ‘M-h2’ , and low ‘L-h2’ . This was done assuming that trait heritability was inversely proportional to the number of QTL. Although strong, this assumption facilitates the reporting and discussion of results, while verifying the assumption of better performance of wG models over GBASE models. It is expected that improvement in performance is directly proportional to heritability and inversely proportional to the number of QTL. Accuracy of prediction of the different methods is in Figure wG matrices relative to GBASE is in Figure For ease of comparison of results, traits were clustered by heritability as high ‘H-hAPED was obtained for comparison purposes . Only GBASE seemed to give relatively greater bias in predictions for MY (1.130 vs 0.981 for GBL) and SR (1.195 vs 1.126 for GSM) than wG. For BD, bias decreased from 1.305 for GBASE to 1.040 for GRR, and for ST, bias also decreased from 1.308 for GBASE to 1.194 for GBL. Surprisingly, the reversed situation was observed for MC, for which GRR and GBL resulted in a deflated variance of predictions , while GBASE and GSM were less biased predictors (0.807 and 0.803). Prediction appeared to be similar across models for DC, although with less bias (between 0.958 and 0.893 for GBASE and GBL).We found that bias of prediction was largely consistent with accuracy of prediction Figure . GBASE tIn this study, weighted GBLUP resulted in more accurate estimates of genomic values with lower bias in cross-validation for traits with high heritability and presence of QTL of even moderate effect. The largest decrease in bias from using weighted GBLUP was obtained for FP and PP, which presented the largest QTL, suggesting a greater departure of the genetic architecture from the Fisherian model.GBASE is that all markers contribute equally to genetic variation for the trait [G matrices can yield gains in accuracy when the heritability of the trait is higher and the number of QTL is smaller. Subsequently, Wang et al. [G was informed with weights derived from several sources, Bayes-B seemed to outperform RR, however their results were still based on simulated data. With real data, de los Campos et al. [G improved prediction for height in humans, which is a trait with high heritability and a large number of QTL [G was weighted with markers effects for any of the traits analyzed, which disagrees with our results.The assumption that underlies he trait . Based ohe trait . For suche trait , who perg et al.  demonstrg et al.  found ths et al.  found thr of QTL . Nonether of QTL  tested tr of QTL  compared2, any GBLUP outperformed APED: the genomic relationship matrix resulted in higher accuracies than the pedigree-derived relationship matrix, in particular for traits with high heritability and a small number of QTL. This is in agreement with findings from Nejati-Javaremi et al. [Except for L-hi et al.  and Hayei et al. , who comi et al. , becausewG. The least precise method for informing G appeared to include only markers with a P-value smaller than 0.05. The P-value was obtained from an F-test in a linear model in which the marker was considered as a fixed effect. Population stratification was corrected by comparing groups of paternal half-sibs (sire random additive genetic effect). In fact, this method, which was intentionally chosen as a ‘naive’ one, gave accuracies that were barely better than GBASE and predictions were still biased. Thus, we can infer that this method would not be appropriate with our data. Other methods involved multiple regression on markers, with (BL) or without penalization (RR), that both considered the animal additive genetic effect to correct for stratification. These methods are known to be suitable for different genetic architectures since BL is sensitive to the genetic architecture of the trait, while RR is not. In fact, in this study, when GBLUP included information on marker realized variance from RR and BL, these methods were identical in terms of performance, both for accuracy and bias reduction. However, when marker effects were used to inform G, as computed with these methods, bias decreased and accuracy increased proportional to how much each trait differed from the assumption of complete polygenic architecture. We confirmed that predictions were not inflated by population stratification or founder effects. This was verified with two empirical approaches, and both lead to the conclusion that wG accounted for real QTL effects.In this study, we tested different methods for deriving weights for G matrices that yielded the overall best accuracies were those informed with realized marker effects from Ridge Regression and Bayesian LASSO, while discriminating markers based on their P-value led to average or null improvement of predictive performance. The increase in predictive performance compared to the traditional GBLUP for other traits was moderate, but for traits with low heritability and low pedigree-based prediction accuracy, informing the genomic matrix with realized marker variances appeared to be advantageous. Regular genomic matrices lean on the assumption that each marker contributes equally to the additive genetic relationship. We demonstrated that this assumption can be violated, with increases in predictive ability of GBLUP for traits for which contributions to genetic variability are not evenly distributed across genomic regions. It is worth noting that predictions were not inflated by population stratification or founder effects.Our study aimed at testing different sources of information in order to weight marker contributions into the genomic relationship matrix and relax the assumption of equal contributions from all markers to additive genetic variability among individuals. The methods used to estimate marker weights were single-marker regression (markers are included if the F-test is significant), Bayesian Ridge Regression and Bayesian LASSO. Results showed that the informed genomic matrices can yield higher accuracy and lower bias than the regular genomic matrix and the pedigree-derived relationship matrix. Prediction performance generally increased when the number of QTL for a given trait was small, which was the case for fat percentage and protein percentage. The weighted Our results could be useful to increase the prediction accuracy of selection candidates in dairy cattle breeding schemes that use genomic information and BLUP methodology. They could also serve as background in the search for different sources of information to weight marker contributions in the genomic matrix, and markers could be weighted relying on functional information about the annotated genes that lie in their proximity.


Hanwoo (Korean cattle) is the native, taurine type of cattle breed of Korea and its history as a draft animal dates back to 5000 Years. In earlier times Hanwoo was used extensively for farming, transportation. Over the period of time, Hanwoo has changed to be meat type cattle. Full-scale production of Hanwoo as meat-type cattle has occurred since 1960s with the rapid growth of the Korean economy. Hanwoo is one of the most economically important species in Korea as it is a significant source of nutrition to the Korean people. Hanwoo beef is the most cherished food of Korea. One of the main goals of researchers is to increase the meat quality, quantity and taste of the beef. In this review we describe the origin, domestication of Hanwoo cattle and breeding program initiated from 1980’s. Moreover the advent of technological advancement had provided us a platform to perform genome wide selection on economic traits and its implementation into traditional breeding programs. Hanwoo (Korean cattle) is the native, taurine type, small sized cattle breed of Korea and its history as a draft animal dates back to 5000 Years. In earlier times Hanwoo was used extensively for farming, transportation and for religious sacrifice. Over the period of time Hanwoo cattle has evolved to be meat type cattle. Full-scale production of Hanwoo as meat-type cattle has occurred since 1960s with the rapid growth of the Korean economy. Despite its high price, beef from Hanwoo cattle is very popular among both Koreans and foreigners. Hanwoo beef is known for its marbled fat, tenderness, juiciness and characteristic flavor. There are four breeds of Hanwoo in Korea viz. brown Hanwoo, brindle Hanwoo, black Hanwoo and jeju black . When HThe major goal of researchers is to increase both the quality  and the quantity (Carcass weight) of the meat to benefit the Hanwoo beef industry. Therefore, the current selection index used in Korea Proven Bulls (KPN) program is based on 1) carcass weight on marbling score 2) the comparison on carcass weight. In order to support the beef industry along with the conventional selection program, the genomic selection is also being incorporated in selection and breeding programs. In the present review we discussed the origin, domestication, breeding strategies and genomic selection in Hanwoo cattle.Origin and domestication of not just Hanwoo but all north-east asian cattle is a topic of discussion among researchers. Researchers are of divided view when it comes to ancestry and domestication of Hanwoo. There are some studies on the origin and ancestry of this cattle breed. Han S. W.,  suggester2 statistic of SNPs up to 50 Mb apart across the genome was estimated. A mean value of r2 = 0.23 was observed in pairwise distances of <25 kb and dropped to 0.1 at 40 to 60 kb, which is similar to the average intermarker distance used in this study. The proportion of SNPs in useful LD (r2 ≥ 0.25) was 20% for the distance of 10 and 20 kb between SNPs. Analyses of past effective population size estimates based on direct estimates of recombination rates from SNP data demonstrated a decline in effective population size to Ne = 98.1 up to three generations ago  and effective population size (Ne) are important genetic parameters to understand population structure for optimal breeding program design. Lee et al.,  performeMarbling score (MS), carcass weight (CWT), eye muscle area (EMA) and back fat thickness (BF) are the traits to impact Korean beef industry the most. Therefore the selection index for Korea Proven Bulls uses eye muscle area on marbling score and their comparison on CWT.The first genetic breeding program called “Hanwoo-Gaeryang-Danji (HGD)” was initiated by Ministry of Agriculture and Forestry (MAF) in 1979. However, due to lack of awareness in the farmers to record the data and scarcity of the trained emulators, the program didn’t meet its objective. In 1999, a new program called “Hanwoo-Gaeryang-Nongga (HGN)” was introduced. Whereas HGD focused on a particular province HGN concentrated on individual farms. The cows from HGN program are used as major breeding stock for “Hanwoo Performance and Progeny Test (HPPT)” program . Prior Park et al.  carried The heritability and genetic correlation are important genetic parameters in breeding program. In Hanwoo population, heritability and genetic correlation were estimated using carcass data from National Hanwoo progeny tested population Figure A. A negaWe know conventional breeding methods rely on physical characteristics or phenotypes to calculate the breeding values (BV) of animals. The traditional methods are often not so accurate, inefficient and time consuming as most traits like meat quality are hard to measure and evident only when animal reaches maturity leading to a delay in verifying breeding results. With the advent of genome wide SNP panels we can overcome the drawbacks of the conventional breeding methods. Genome wide SNP panels will allow us to accurately and cost & time effectively determine the genomic estimated breeding values (GEBV) even in young animals and thus select animals at young age.Korean beef industry lay emphasis on meat yield and quality traits like carcass weight, marbling, intramuscular fat, eye muscle area. To identify the significant associations of SNPs with these traits can directly affect the selection of animals for a breeding program.BTB-01280026 (P = 4.02 × 10-11), located in the 25 Mb region on Bos taurus autosome 14 (BTA14). The most significant SNPs accounted for 6.73% to 10.55% of additive genetic variance, which is quite a large proportion of the total additive genetic variance. The most significant SNP  had 16.96 kg of allele substitution effect, and the second most significant SNP  had 18.06 kg of effect on carcass weight, which correspond to 44% and 47%, respectively, of the phenotypic standard deviation for carcass weight in Hanwoo cattle. Our results demonstrated that carcass weight was affected by a major Quantitative Trait Locus (QTL) with a large effect and by many SNPs with small effects that are normally distributed. As evident from manhattan plots of GWAS for four carcass traits shown in Figure Genome-wide association study (GWAS) is a process for inspection and screening of detectable common genetic variants (single-nucleotide polymorphisms) in individuals to identify the variant(s) associated with the trait under study. There are several GWAS studies that are successfully conducted in livestock species. A recent study on Hanwoo cattle conducted by lee et al. (2013) identified 6 highly significant SNPs on chromosome 14 to be associated with carcass weight in Hanwoo cattle  employs selection of an individual based on the genomic breeding value assessed through evaluating all the genetic markers located throughout the genome of that individual. Thus, the underlying principle of GS is to exploit the linkage disequilibrium of the QTL with one or more genetic marker(s) . MoleculApplication of genomic selection in Hanwoo preselection process such as performance test and then in progeny test in the breeding program might raise the genetic gain for marbling. A comparison of accuracy of GEBV and traditional EBV is tabulated in Table Recent advances in molecular biotechnology facilitate not only detection of genes that contribute to genetic variation of quantitative traits but also incorporation of genomic information into a conventional animal breeding program. The incorporated molecular information into GEBV may achieve an improvement of EBV and selection accuracy in cattle populations. In conclusion, we suggest incorporating molecular information into the conventional breeding programs to achieve better results in comparatively shorter time. Including molecular information will be a step further to achieve higher standards of Hanwoo beef.


Previous studies have shown that both single nucleotide polymorphisms (SNPs) and questionnaires-based method can be used for twin zygosity determination, but few validation studies have been conducted using Chinese populations. In the current study, we recruited 192 same sex Chinese adult twin pairs to evaluate the validity of using genetic markers-based method and questionnaire-based method in zygosity determination. We considered the relatedness analysis based on more than 0.6 million SNPs genotyping as the golden standards for zygosity determination. After quality control, qualified twins were left for relatedness analysis based on identical by descent calculation. Then those same sex twin pairs were included in the zygosity questionnaire validation analysis. Logistic regression model was applied to assess the discriminant ability of age, sex and the three questions in zygosity determination. Leave one out cross-validation was used as a measurement of internal validation. The results of zygosity determination based on 65 SNPs in 450k methylation array were all consistent with genotyping. Age, gender, questions of appearance confused by strangers and previously perceived zygosity consisted of the most predictable model with a consistency rate of 0.8698, cross validation predictive error of 0.1347. For twin studies with genotyping and\or 450k methylation array, there would be no need to conduct other zygosity testing for the sake of costs consideration. Zygosity determination, which was based on the description of genetic relationship between two individuals within a twin pair, is the foundation of all twin studies. In termSome studies showed that Infinium 450k methylation array is a powerful technique in terms of reagent costs, time of labor, sample throughput and coverage. It holds great promise for the better understanding of the epigenetic component in health and disease , 8. 450kWe have two objectives in the current study: 1) to examine the value of 450k methylation array in zygosity determination; 2) to validate the zygosity questionnaire in Chinese adult twins.The Chinese National Twin Registry (CNTR) is the first and also the largest population-based twin registry in China, where more than 30,000 twin pairs of all age and sex have been enrolled since 1999. Based o2 < 0.3). Two samples demonstrated signs of contamination , those two samples and their twin siblings were excluded. Finally, 180 pairs of twins with 695,406 SNPs remained after the quality control filters.A genome-wide genotyping scan among 192 pairs was carried out using Illumina HumanOmniZhongHua-8 BeadChip. High-quality genotyping was performed by laboratory specialized in Illumina SNP array genotyping following standard experimental procedures suggested by the manufacturer. A total of 894,956 SNPs were genotyped among 192 paired subjects. SNPs with minor allele frequency <0.05 , Hardy-Weinberg equilibrium P <0.0001, and SNPs call rate<97%  were excluded. Twins with a call rate less than 90% and their twin siblings were also ruled out for further zygosity analysis. Genome-wide heterozygosity of each individual was estimated to exclude cross contamination between samples. This test was performed using a subset of 155,588 SNPs pruned for linkage disequilibrium  according to the manufacturer’s recommendations. Briefly, samples were whole genome amplified followed by an enzymatic end-point fragmentation, precipitation, resuspension, hybridization and staining. Finally, the BeadChips were imaged using an Illumina iScan.By using the GenomeStudio Software 2010 Methylation module, DNA methylation level was displayed as beta-values ranging from 0 to 1. Beta-value was defined as the algorism M/(M +U+100), where M and U represent the methylated and unmethylated signal intensities respectively. For the methylation quality control , we usedOpposite sex twin pairs were considered to be DZ twins and were excluded in the questionnaire zygosity determination. We selected 192 same sex twin pairs to answer three questions about zygosity. The first one was about appearance similarity——“Could a stranger distinguish you and your sibling by yours current appearance?” with three options of Yes, No and Hard to say. The second one was about formal zygosity test——“Have you and your sibling taken a zygosity-diagnosing blood test?” and the answers were Yes, No and Do not know. The last one was about self previously perceived zygosity——“Which type of twins did you think that you and your sibling were?” with alternatives of MZ, DZ, and Do not know. The golden standard for zygosity determination among 192 twin pairs was based on the genotyping or methylation data.0, Z1 and Z2 respectively. Almost all of the alleles were same for MZ twins, so their Z2 was 1, while Z0, Z1 were both 0. DZ twins were more like full siblings; their Z0, Z1 and Z2 were 0.25, 0.5 and 0.25. Then we calculated the proportion IBD which was defined as the formula Z2+0.5Z1. The proportion IBD of MZ twins was 1; while for DZ twins was 0.5. The IBD analysis was conducted by PLINK genome command [0, Z1 was mapped to discriminate MZ and DZ twins.We calculated the probabilities of twin and his sibling if their alleles are identical-by-descent (IBD). Usually, there were three probabilities for paired individuals to have zero, one or two pairs of IBD alleles. Those probabilities were denoted as Z command . A scattZygosity determination by methylation array was conducted using two approaches. The first approach used IBD analysis as the genotyping data. The beta values of 65 SNPs were exported from GenomeStudio Methylation module. These beta values could be converted to SNPs genotypes because they would cluster, as much as they did in a standard genotyping theta graph , 24. WitWe built six models to identify the best-predicted components in questionnaire for zygosity determination. Zygosity based on genotyping or methylation data was binary dependent variable (MZ or DZ) in each model. The 6 models were as follows: 1) Model 1 only included the question about self previously perceived zygosity , noted as PPZ for short. 2) Model 2 only included the question about whether twins’ appearances confused by strangers , noted as CBS for short. 3) Model 3 was a complex strategy based on three zygosity question  curves were plotted to determine the best cut-offs in terms of sensitivity and specificity, and the areas under the curve (AUC) was used as a measure of overall performance. Leave one out cross-validation (LOOCV) was used on all models as a measure of internal validation. All the analysis was conducted using 180 pairs with 695,406 SNPs were available for relatedness check analysis. The proportion of IBD was divided into two categories: one class with proportion of IBD varied from 0.9923 to 1 indicating MZ twins, while the other varied from 0.4094 to 0.5723 indicating DZ twins . The var65 SNPs in the 450k methylation array were used to identify the zygosity among 188 pairs of twins. We conducted the same IBD analysis as using genotyping data to assess the discriminative ability for identifying MZ and DZ twins. The proportion of IBD with 65 SNPs had greater variation than the genotyping data. MZ twins had proportion of IBD either 0.9791 or 1, while DZ twins had a range of 0.2064 to 0.7662 . The scatter plot of 65 SNPs was different from genotyping . AlthougBy plotting the beta values of 65 SNPs in a scatter plot within a pair, we found representative samples (red dots) with an intra-pair correlation coefficient of 0.7478, scattered into seven possible spots . That grThe characteristics of 192 same-sex twin pairs were presented in In this study, we used 65 SNPs in the 450k methylation array to determine zygosity and we found that the results of 65 SNPs were consistent with genotyping. In the questionnaire-based zygosity determination analyses, age, gender, questions of appearance confused by strangers and previously perceived zygosity consisted of the most predictable model with an AUC of 89.03% (95% CI: 83.86%-94.19%). For twin studies with genotyping and\or 450k methylation array, there would be no need to conduct STRs for the sake of costs consideration.The 65 SNPs in the 450k methylation array could discriminate MZ from DZ twins, but the ability for discriminate DZ from other related individuals such as parents-offspring, half-siblings, cousins or unrelated ones was limited. As the results showed that DZ twins proportion of IBD ranged from 0.2064 to 1, we could not tell if there are any singletons or other related ones involved in the studies from methylation chips –27. HeplQuestionnaires with multiple questions plus age and gender would be better performed than a single question about zygosity determination. The major question about zygosity was based on twins’ current facial appearance, which might be varied across age or gender –31. Our The genotyping and 450k methylation array might bring benefits for both twin studies in developed and developing countries. For twin studies in developed countries, zygosity was based on questionnaire and DNA testing –38; reseThere were two limitations in this study. Firstly, the zygosity questionnaire was lack of the external validation samples that might not be successfully applied in other samples or populations. The second one was that the answers of “Do not know” in question of formal zygosity test and previous perceived zygosity were relatively high in current study. Probably because the majority of twins were recruited in rural areas of China, they might not be aware of the questions due to the low education level in those areas.In conclusion, 450k methylation array can be used in zygosity determination. Questionnaire-based zygosity assessment in Chinese adult twins can be regarded as a valid method in zygosity determination.S1 Table(XLS)Click here for additional data file.S2 Table(XLS)Click here for additional data file.


In this study, we analyze the Genetic Analysis Workshop 18 data to identify the genes and underlying single-nucleotide polymorphisms on 11 chromosomes that exhibit significant association with systolic blood pressure. We propose a novel family-based method for rare-variant association detection based on the hierarchical Bayesian framework. The method controls spurious associations caused by population stratification, and improves the statistical power to detect not only individual rare variants, but also genes with either continuous or binary outcomes. Our method utilizes nuclear family information, and takes into account the effects of all single-nucleotide polymorphisms in a gene, using a hierarchical model. When we apply this method to the genome-wide Genetic Analysis Workshop 18 data, several genes and single-nucleotide polymorphisms are identified as potentially related to systolic blood pressure. Current studies suggest that a large number of common variants identified in genome-wide association studies (GWAS) as being associated with various complex diseases can account for only a small portion of phenotype variation . With thThese methods, however, all assume that individuals are independently sampled and are, therefore, vulnerable to the influence of population stratification. Exploring marker transmission within a family avoids the issues of population stratification. More important, once anRV enters a family, it can segregate to other family members so that copies of the minor allele are enriched in the data. This could potentially increase the statistical power of family-based approaches.Here we propose a novel family-based Bayesian collapsing model (FBCM) capable of identifying associations of RVs and genes with quantitative phenotypes. The method builds on the hierarchical quantitative transmission disequilibrium test (HQTDT) . CompareSeveral statistical models based on the Bayesian framework, such as model selection  and multΚ diallelic loci  with MAF less than 1%. Given a set of i= 1,..., M nuclear families, each of which contains i nsiblings so that the total number of offspring is ijk Gfor the jth child in the ith family as the number of minor alleles at the kth locus. It is assumed that both parents of each child are available, and, correspondingly, the genotypic scores of the parents at the kth locus in the ith family are denoted by ik GMand ikGF, respectively, for the mother and father. Conditional on the parental genotypes, the expected score for the offspring in the ith family at the kth locus under mendelian law is jth child in the ith family at the kth locus, which is denoted by ijkD, is ijkG− ikGE. For technical reasons, we add a pseudolocus k = 0 and define GEi0 = Dij0 = 0. When at least 1of the parents carries the copies of minor alleles at a locus, it is then possible to observe deviation in offspring at this locus. However, given a moderate set of variants, it is very unlikely for an individual to harbor minor alleles at more than 2 causal variants. For instance, when MAF is 0.005 and there are 50 independent causal RVs, the probability of an individual having minor alleles at more than 2 loci is ij rand ijs, which are defined below.Consider a candidate gene that contains ij sare 0. Those with the smallest MAFs are selected if an individual has more than 2 loci with nonzero deviation. Thus, more emphasis is placed on those with smaller MAFs because deleterious functional variants tend to have low frequencies [This method dramatically shortens computational time by avoiding large-scale matrix computation in Gibbs sampling. If an individual has nonzero deviation at fewer than 2 loci, both or quencies . Given tquencies , for a mij ydenote the quantitative phenotype for the jth child in the ith family. The relationship between the phenotype and the set of RVs in the candidate gene can be expressed by a hierarchical modelLet µ is the global intercept and ij εis the random error. The genotypic score is decomposed into within-family and between-family components, and the construction of formula (1) guarantees the orthogonality of those 2 components. Inference based on β1 provides a stratification-resistant within-family test, while β2 estimates the genetic effect resulting from stratification. As a result of the limitation of the sample size for the inference and the fact that the variance components are not our major interest in this study, the family-level variable i φis modeled as a random effect. This enables us to capture the between-family variance that includes the influence of the family-specific environmental factor.where β1 and β2, respectively. ij rand ij sare individual-specific indices of which elements in jth child in the ith family. It is possible that some of the RVs are neutral, but may be associated with the phenotype through population stratification. Ignoring this possibility will not only inflate the type I error rate, but also will introduce noise after collapsing. By modeling these 2 situations using The vectors of variant-specific random effects Although it is less common to observe LD between RVs compared to common variants, only independent representative SNPs are selected and included in the analysis. Because 2 loci at most are taken into account for each individual, such selection improves the accuracy and efficiency of the model.β1 and β2 and Bern(0)--are selected to be independently sampled from Bernoulli distributions with hyper-parameters k pand k. βq1 and β2 are given a noninformative normal prior distribution with some variance The multiplicative relation in the 2 pairs  is the error function, defined as: where k, k ϵ 1,..., Κ. Note that if we treat αk as a model indicator, one way to quantify and summarize the posterior probabilities is to calculate the marginal BF, which is the ratio of the posterior odds to the prior odds of the same variable, defined as:where The model is implemented using WinBUGS with 50,000 iterations, and the convergence is checked by investigating the autocorrelations for all parameters. We also simulate several chains with different initial values simultaneously, and evaluate convergence with the Gelman-Rubin convergence diagnostic tool .r. Half the RVs are randomly selected to be neutral but are associated with the phenotype through population stratification, represented by an indicator vector s. The genotypic scores of the parents are independently sampled from kth RV, where G. G is divided into the expected genotypic score matrix E and the deviation matrix D for the offspring in a family. The phenotypes of the 2 sibs in each family are generated from N(β1 • (D  × r) + β2•(E  × s), Σ), where β1 is the effect size, β2= 0.5 reflects the effect of population stratification, and 4 and tuned the BF cutoff equal to 2 so that the type I error rate is controlled below 0.005. Figure β1 = 1 and 1.5.Unfortunately, for the Genetic Analysis Workshop (GAW)18 simulated data, only 275 trios can be incorporated in our analyses, owing to the large number of parents with missing genotype. Most causal SNPs with MAF less than 0.01 do not present minor alleles in these 275 trios, so they are not suitable data for testing our method. Consequently, we investigated the performance of FBCM by generating a variety of simulation scenarios involving different effect sizes and proportions of associated RVs. In particular, we consider the settings in which 20% to 100% of RVs are associated. The total number of families, the offspring in each family, and the total number of RVs are fixed at 300, 2, and 50, respectively. To generate genotypic data for each family, a proportion of the RVs are randomly selected to be causal, represented by an indicator vector To evaluate FBCM using real data, the association analyses are performed by fitting our method to the data that use the full pedigree structure provided, with the entry for each variant being the estimated number of minor alleles carried. Our aim is to identify the genes and underlying RVs related to systolic blood pressure (SBP) throughout those 11 chromosomes among the GAW18 type 2 diabetes families. To better reflect the association between predisposition to hypertension and the variants, the highest SBP measured at the 4 examination points is selected as the phenotype for each individual. Log-transformation of the phenotype is performed to fix the skewness of the phenotype distribution. The age corresponding to the highest measured SBP is included in our model as a covariate to account for the significant correlation between age and SBP. Individuals with any missing data or without parental information are excluded, leaving 275 trios remaining.http://www.ensembl.org/index.html), we investigated the genes on all 11 chromosomes. For each gene, only variants with MAF of less than 1% within the boundary of the gene are included in the analyses. The MAFs are estimated using 959 individuals in the dosage genotype data. The results are summarized using a Manhattan plot in Figure http://www.genenames.org/), and Ensembl gene IDs. The effect size conveys the estimated magnitude of the relationship between SBP and the transmission deviation. Our results show that most genes have BFs of far less than 1, and given only 275 trios in the analyses such results are not surprising. However, we still identify several genes with a BF larger than 2, which is the cut-off obtained from the simulation. The evidence of the association is substantial for those BFs between 3 and 10, based on Jeffery's grade of evidence [Using the gene information being obtained from Ensembl . The larger BFs favor the evidence against the null hypothesis and indicate that positive deviation from the expected number of transmitted minor alleles drives the effect of the gene, while the BFs much lower than 1 suggest the effect of the deviation in the opposite direction. For example, given the effect size of gene HNRNPA1P13 is 0.43, SNP at position 135765214 with BF 0.00368 indicates that more transmitted copies of a minor allele from parents are likely to have a negative impact on the phenotype.Next, we investigated the underlying SNPs among the most related gene, HNRNPA1P13. The most significant SNPs based on their BFs in formula (3) are listed at the bottom of Table The FBCM proposed here is a novel statistical method for analyzing the association of RVs in pedigree data. The new methodology accounts for potential nonassociated variants by introducing random effects in a multiplicative way to approximate and capture the variant effects. The FBCM also takes into account situations in which some pooled variants can be associated with a phenotype through population stratification. Although the between-family component in our model can be integrated into the family-level random effect n method , our modIn variant-level analysis, our method improves the power to detect RV effects. The improvement of statistical power can be achieved by accounting for the random effects of all variants in a candidate gene through Gibbs sampling. Moreover, in the GAW18 data, it has been shown that the occurrence of a RV tends to be more common if a family member carries a minor allele. Thus, the family-based analysis is expected to have more power than the independent population-based analysis. The results show that our family-based method is able to identify both genes and individual SNPs significantly related to the phenotype, even in RV situations.Our model can be further expanded in many ways. The appropriate link functions can be employed to handle other forms of phenotype, such as binary data. In this study, we focus on families without any missing data. However, for the trios with missing parental genotype information, the genetic score can be decomposed into between-family and within-family components by using only sibs genotypes. For the random effect distribution, the Bernoulli distribution is assigned as the prior distribution for the random effects of individual variants. For better modeling of the effects of individual variants, more sophisticated distributions can be employed.We have demonstrated that a novel FBCM can be applied to identify associations between RVs and quantitative traits for pedigree data. This method cannot only detect the gene effect, but can also pinpoint the underlying SNPs. Compared to other methods for handling RVs, our method based on family data improves statistical power by collapsing and accounting for all possible RV effects in a gene with population stratification controlled. Because the method allows for computational efficiency in obtaining the full posterior distribution, it is applicable to large-scale association tests. The results of our genome-wide analyses provide insights into the potential role of RVs in SBP.The authors declare that they have no competing interests.Both authors participated in designing the methods. He L, conducted the simulation, analyzed the data, interpreted the results and wrote the paper. Pitkaniemi co-designed the analysis and revised the paper. Both authors read and approved the final manuscript.


However, we discovered 31 major discrepancies between the InSiGHT classification and the CADD scores; these were explained in favor of the expert classification using population allele frequencies, cosegregation analyses, disease association studies, or a second‐tier test. Of 751 variants that could not be clinically classified by InSiGHT, CADD indicated that 47 variants were worth further study to confirm their putative pathogenicity. We demonstrate CADD is valuable in prioritizing variants in clinically relevant genes for further assessment by expert classification teams.Next‐generation sequencing in clinical diagnostics is providing valuable genomic variant data, which can be used to support healthcare decisions. In silico tools to predict pathogenicity are crucial to assess such variants and we have evaluated a new tool, Combined Annotation Dependent Depletion (CADD), and its classification of gene variants in Lynch syndrome by using a set of 2,210 DNA mismatch repair gene variants. These had already been classified by experts from InSiGHT's Variant Interpretation Committee. Overall, we found CADD scores do predict pathogenicity (Spearman's  Reliable estimation of gene variant pathogenicity, especially for missense variants and small in‐frame insertions/deletions (indels), is a major challenge in clinical genetics. This challenge is now being exacerbated by the introduction of next‐generation sequencing in clinical diagnostics, which is identifying large numbers of candidate disease‐causative variants, ranging from about 250 . The cumulative logits for all but the last class, j=1,...,J−1, are then defined as:Here, we considered a proportional odds model, using a logit link function: jθ represents the logit‐scaled cut‐off for class j, ix being the vector of explanatory variables for the ith observation, and β is the corresponding set of regression parameters. Note that xi⊤β does not contain an intercept. The parameters jθ act as a set of continuous “cut‐off points”, such that −∞<θ1<⋯<θJ−1<∞. To assess the probability that the ith observation falls within one of ordinal response classes j, we can write:This gives a regression for the cumulative logits:θ after convergence. Discrepancies were detected by analyzing the deviance of the observations. Deviance can be thought of as a measure of “surprise”, how likely a certain observation is under the fitted parameters of the model. Formally:iY being the observation and θ^ the parameters of the fitted model. Observations of θ corresponding to variants in the 95th percentile of the mean deviance—those with the highest deviance—were re‐examined.We used the CADD score as an explanatory variable for the ordinal response of InSiGHT. The parameters were estimated using JAGS, a program for analysis of Bayesian graphical models using Gibbs sampling Plummer, . Converghttp://molgenis.org/downloads/vdVelde_Kuiper_etal_2015/The data and scripts used in this paper can be downloaded from: http://chromium.liacs.nl/LOVD2/colon_cancer). A total of 534 variants had to be omitted, either because converting the complementary DNA HGVS nomenclature [den Dunnen and Paalman, T>R and A>Y substitutions), or because they had not yet been classified by the InSiGHT VIC . See Figure Methods and Materials for details. The 2,210 remaining variants fell within one of the five classes: class 1 (n = 151), class 2 (n = 84), class 3 (n = 751), class 4 (n = 181), or class 5 .We calculated the CADD scores for 2,744 MMR gene variants that were downloaded from the InSiGHT group LOVD . In Figure Overall, the CADD‐scaled C‐score distributions for each class correlate with the InSiGHT classification . Further analysis focused on the cases for which the nonpathogenic (class 1) and pathogenic (class 5) classifications were reversed, as these suggested major disagreements between CADD and the InSiGHT VIC verdict  cases for which a different class would be assigned . Re‐examination of the available data for these variants strongly supports the original InSiGHT classification based on the following evidence:Segregation data are inconsistent with the variant being a dominant, high‐risk, pathogenic sequence variant in pedigrees (likelihood ratio ≤0.01).Variant with reported frequency ≥1% in the general population (1000 Genomes Project), and no evidence that variant is a founder mutation.These are not high‐risk variants that are uniquely associated with Lynch syndrome .Variant leads to a known attenuated protein function, but this does not cause Lynch syndrome .We identified 12 variants  that were classified as nonpathogenic (class 1) by the InSiGHT VIC, but they were predicted to be pathogenic (class 5) according to the CADD‐based cumulative link model  by using population allele frequency as a filter or incorporating the use of patient pedigree analysis data; these are already common practices in many clinical laboratories. The remainder could be resolved by incorporating more in‐depth findings from validated protein functional assays or from risk estimates based on large, well‐designed, case‐control studies that consider cohort size, geography/ethnicity, and quality control measures [Thompson et al., We identified 19 cases  for which the cumulative link model predicted the respective variants to be class 1, whereas InSiGHT scored them as class 5. This indicates that the model might also underestimate effects. Similar to the approach to the false positives, outlined above, our re‐examination of these variants supported the original InSiGHT classification.CADD scores are developed for scoring any possible human SNVs or small indels [Kircher et al., Materials and Methods).Class 3 mainly contains variants for which insufficient clinical or molecular data are available, but also a limited number of variants that have discordant findings . Most of these variants can easily be assigned to another class as soon as more data become available. As expected, the distribution of the CADD scores for class 3 variants, as visualized in Figure σ = 1.04, 27 in MLH1, 10 in MSH2, four in MSH6, and two in PMS2). The remaining four were truncating mutations: two stop‐gain variants , and two frameshift variants (c.2252_2253del and c.2262del) with CADD scores of 39 and 40. These four variants are all located in the MLH1 gene; they were classified as class 3 by the InSiGHT VIC due to insufficient evidence, because the stop codons are introduced in the last exon (19) and are located outside any known functional domains.We performed this analysis and 47 variants  that the InSiGHT VIC classified as class 3 (uncertain significance) had CADD scores of ≥34, which fell in the >99% probability range for known class 5 (pathogenic) variants .We investigated the use of CADD scores for the prediction of clinical classifications by comparing them with a high‐quality clinical data set developed by the InSiGHT VIC, which is based on quantitative and qualitative interpretation of both clinical and molecular data. Generally, the CADD model predictions fitted the InSiGHT classification. Out of the 2210 variants we tested and classified by InSiGHT, we identified 12 (0.54%) nonpathogenic (class 1) variants that the CADD model predicted to be pathogenic (class 5), and 19 variants (0.86%) of class 5 that CADD predicted to be class 1. The difference could be explained by two considerations: the CADD model was not designed to classify large structural or splice‐site variants , and the clinical observations, population allele frequencies, and experimental molecular data sometimes convincingly suggested an alternative interpretation . CADD's main underestimation of pathogenicity was due to its inability to accurately predict the effects of whole exon deletions or duplications. In five such cases, the C‐score was in fact extremely high, but this was not translated into a high‐scaled C‐score. The use of a second‐tier test, in this case SnpEff, boosted the sensitivity of classifying via CADD by correcting 17 out of 19 of these underestimations.We showed that estimating the deleteriousness of whole exon deletions/duplications is a weakness of CADD and this needs to be addressed. The InSiGHT data show that such structural variation is often pathogenic, but this is not always recognized by CADD. To avoid incorrect results, and in line with the design limitations of CADD as acknowledged by its authors, we recommend CADD should not be used to judge the pathogenicity of large structural variation as part of an automated variant processing pipeline.We also investigated the 12 cases of pathogenicity overestimation by CADD, which showed that these false positives could be explained by data used for the InSiGHT classification that was not used for in silico prediction . These results underscore the importance of using clinical data in the diagnostic interpretation of variants.There are a few variants in the InSiGHT database with a known negative effect, such as attenuated protein function, that are classified as nonpathogenic. The InSiGHT VIC requires both concordant functional and clinical evidence to assign pathogenicity; they do not accept that attenuated function would necessarily be associated with Lynch Syndrome—or any phenotype for that matter. In our analysis, for example, CADD predicted a deleterious effect for MLH1:c.394G>C, which is indeed known to cause attenuated protein function [Lipkin et al., In general, there is limited added value in using CADD scores to assess truncating variants since they are already known to often be pathogenic for known disease genes. The field of in silico prediction benefits most from the power of CADD scores when they are applied to predict the pathogenicity of nonsynonymous SNVs. Here, we show that CADD performs well on this type of variant for Lynch syndrome, although a disease‐specific model performs better.We identified 47 variants that had been assigned by InSiGHT to class 3 (uncertain significance), which, according to the CADD model, had a high probability of being pathogenic. Of these, 24 missense variants were already strongly suspected of being pathogenic by a previous in silico study on MMR gene variant classification [Thompson et al., Taken together, we have shown that CADD scores are in high agreement with expert assessments of MMR gene variant pathogenicity that is based on multiple data sources for quantitative‐multifactorial and qualitative analysis. As expected, CADD scores are not yet suitable to interpret large structural variants such as deletions and duplications of exons. Other underestimation effects are rare and often detectable with a second‐tier test. Any overestimated variants could be excluded based on population frequency, cosegregation analyses, or evidence showing no association or causality.Calibrated in silico pathogenicity prediction models are not intended to replace functional wet‐laboratory studies, but are instead complementary methods to let clinics benefit from existing gold standard classifications, by accessing their expert knowledge and making it possible to assess and prioritize novel variants with reasonable confidence, without the need for often unfeasible amounts of laboratory work. We believe CADD fits this translatory role very well, particularly because of its generic and high‐throughput nature. Although CADD cannot replace clinical and molecular validation, it can, in a practical sense, assist in prioritizing variants for functional testing when an affected patient carries multiple poorly understood candidate variants, reducing waiting time for results.However, translating this knowledge into a clinical setting is not trivial. We constructed a model based on ordinal regression of known classifications to calibrate CADD scores as a predictor of pathogenicity for gene variants in the Lynch syndrome‐associated MMR genes. Similar efforts are required to unlock the potential of CADD scores as predictors for other disorders, leading to gene‐ or disease‐specific guidelines that can help clinicians translate CADD scores into clinical practice. The threshold for “what is pathogenic” is expected to be rather different to define depending on whether the disease is caused by dominantly or recessively acting mutations, whether the disease is Mendelian or complex/multigenic in origin, and so on. Although the fact that CADD scores are largely based on conservation indicates that it may not work as well for every gene, we believe that its overall usefulness is currently unmatched by other quantitative pathogenicity estimates.As a preliminary proof of principle, we compared the distributions of CADD scores of known pathogenic variants  for which class 5 (pathogenic) is the predicted class according to the CADD‐based modelClick here for additional data file.


To overcome these obstacles, we propose preprocessing single-nucleotide polymorphisms to adjust for linkage disequilibrium, and a novel Bayesian statistical model that exploits a hierarchical structure between single-nucleotide polymorphisms and genes. We obtain posterior samples using a hybrid Metropolis-within-Gibbs sampler, and further conduct inference on single-nucleotide polymorphism and gene associations using centroid estimation. Finally, we illustrate the proposed model and estimation procedure and discuss results obtained on the data provided for the Genetic Analysis Workshop 18.The primary goal of genome-wide association studies is to determine which genetic markers are associated with genetic traits, most commonly human diseases. As a result of the "large  We cast this problem as variable selection; however, because the number of observations in a GWAS data set,  problem . This pr problem . Models  problem , more co problem , and non problem . Bayesia problem  and refeX is known as observed data and to define the likelihood of the trait response y conditional on X. This can be problematic for inference because X depends on minor allele frequencies (MAFs), and elements of X can be highly correlated as a result of linkage disequilibrium (LD). It is possible to simulate genotypes by sampling and dichotomizing a random vector from a multivariate normal distribution with a zero mean vector and a covariance matrix that can be computed from the correlation between SNPs [X as though it was generated in this way; that is, we observe, in X, a correlated and categorized--by allele frequencies--version of the latent genotypes, which we denote Z. We model y using Z in place of X.It is usual to assume that the genotype data een SNPs . We propU, we compute C is the correlation matrix. For now, C is estimated using the sample correlation matrix.Instead of obtaining latent genotypes for each marker and individual, we settle with an approximation that allows us to fit a model with many SNPs. Denoting the continuous but correlated genotypes by y is quantitative and depends on Z and covariates V through a linear expectation:We assume that jth marker is associated with the trait and want to use the posterior distribution of each θ, we define a spike-and-slab prior on β, η, and integrate out β and η to obtain a simpler likelihood:We define  at zero . We use hierarchy: if g being active, then we give a positive or negative boost to the probability that a SNP is associated based on the number of active genes that cover it. We define random parameters We are also interested in possible effects on SNPs as a result of proximity to genes. These effects can be captured in our model by embedding a α is the prior probability of a gene being active. To sample θ and γ from their posterior distributions, we adopt a Gibbs sampling procedure with Metropolis-Hastings steps to sample from the posterior distributions of α. After checking for convergence, we use the centroid estimator to estimate the posterior probability of association (PPA) of the jth SNP, N samples from this procedure as g. By increasing the "boost" parameter where a posteriori (MAP) estimator, θ[An ubiquitous estimator in Bayesian inference is the maximum tion of θ.y, treated the 64,780 SNPs on chromosome 3 with MAF >0 as X, and an intercept term and sex as V. After eliminating individuals with missing data, 132 unrelated individuals remained. As only the real phenotypes were used, the analysis was performed without any knowledge of a simulating model. To run the model efficiently, we constructed 336 blocks such that the breaking points were positioned where adjacent SNPs had distance greater than 15,000 kilobases (kb). After a prior sensitivity analysis, we set α with stringent hyperparameters so as to concentrate the probability distribution about a low expected value of around 0.015, which corresponds to expecting 5 blocks out of 336 total to have 1 active gene. Table p values in Manhattan plots; a SNP with a high PPA is surrounded by SNPs with relatively higher PPA. The blue dots, on the other hand, do not show this pattern because the latent genotypes have been decorrelated. Moreover, we observe that 90.4% of the SNPs have a latent genotype PPA smaller than their raw genotype PPA. Figure α. The positive effect of using the latent genotypes as indicated by the smaller values of a priori, the SNPs have a lower PPA, and so gene effects are more cleanly observed. When using the raw genotypes, the SNP with the highest PPA is intronic to the CADPS gene. This gene interacts with the DRD2 gene, which is related to the negative regulation of blood pressure [FBLN2, that may also be involved in the regulation of blood pressure [Using the GWAS data set provided for Genetic Analysis Workshop 18 (GAW18), we modeled the first systolic blood pressure measurements as pressure . We obsepressure . The latα, that would favor more genes to be active.We presented a Bayesian variable selection approach that performs joint inference for quantitative trait association on collections of genetic markers while formally modeling gene effects through a hierarchical influence. In addition, we prescribe centroid estimators that are based on posterior probabilities of association and thus enable a direct interpretation of their values uniformly across studies without having to correct for multiple testing. We also proposed the novel use of latent genotypes as a way to account for SNP correlations caused by LD. We believe that this method offers a reasonably accurate and flexible assumption because genotypes are corrected directly in the model instead of considered in the estimation procedure, as, for example, as kernel weights in the sequence kernel association test (SKAT) . HoweverThe authors declare that they have no competing interests.IJ implemented the sampling algorithm in C++ and prepared the results; LEC proofread and approved the final manuscript.


MAP4 and FLNB, which included a common variant with a relatively large effect. For several variants in gene MAP4, single-marker analysis also provided high power. In contrast, collapsing only low-frequency variants performed much better for SCAP, DNASE1L3, and LOC152217, where rare variants in these genes had larger effect than common variants. However, for other causal variants, all approaches provided disappointingly poor performance. This poor performance appeared to occur because most of these causal variants explained a very small fraction of phenotypic variance. We also found that collapsing multiple variants did worse than single-marker analysis for several genes when they contained causal single-nucleotide polymorphisms (SNPs) with both positive and negative effects. Because half of causal SNPs were not found in the annotation file based on the 1000 Genomes Project, we found that power was also affected by our use of incomplete annotation information.Analysis of longitudinal family data is challenging because of 2 sources of correlations: correlations across longitudinal measurements and correlations among related individuals. We investigated whether analysis using long-term average  can enhance gene discovery compared with a single-visit analysis. We analyzed all 200 replicates of simulated systolic blood pressure (SBP) in Genetic Analysis Workshop 18 (GAW18) family data using both single-marker and collapsing methods. We considered 2 collapsing approaches: collapsing all variants and collapsing low-frequency variants. Analysis using long-term average performed slightly better than SBP measured at a single visit. Collapsing all variants performed much better than collapsing low-frequency variants at  Hypertension has been a difficult phenotype for gene discovery because it is a multifactorial complex phenotype. Although genome-wide association studies (GWAS) have successfully identified several genetic variants for many complex phenotypes, their reliance on common variants has become a barrier to further progress. Three recent GWAS consortia -3 identiAnalysis of longitudinal phenotypes in family data has been a challenge because of 2 sources of correlations: correlations across longitudinal measurements and correlations among related individuals within families. Analysis is often limited to a phenotype measured at a single visit (the first visit is most commonly used), ignoring the phenotype measured at other visits. Long-term average (LTA) of BP measurements has shown to be useful in several genetic epidemiology studies ,5. AnalyGenetic Analysis Workshop 18 (GAW18) provided whole genome sequencing (WGS) data in a pedigree-based sample and longitudinal phenotype data for BP and hypertension. We investigated whether analysis using the LTA can enhance gene discovery. Because GAW18 sequence data included mostly rare variants, in addition to single-marker analysis, we applied collapsing methods that assess the combined effect of multiple variants in each genomic region. Analyses were performed without knowledge of the underlying simulation model. However, we used the GAW18 answers in presenting the results.We used all 200 replicates of simulated systolic blood pressure (SBP) phenotype on 847 related individuals. GAW18 provided sequence data of 959 related individuals (either directly sequenced or imputed) of Mexican American heritage for more than 8 million variants. It provided 200 replicates of simulated BP and hypertension phenotypes for 849 individuals. Because the data included 2 monozygotic twin pairs, we excluded one from each twin, reducing the sample to 847.For each individual, we used an LTA of SBP measurements across all three visits. To evaluate the relative performance of using LTA of SBP, we also analyzed SBP measurement at visit 1 and visit 3 separately. For covariates of SBP at visit 1 (and 3), we used age, sex, smoking, body mass index, and medication use at visit 1 (and 3). For LTA, we used the average values of these covariates.Because of limited time, we restricted our analysis to chromosome 3 based on the results from the first replicate . Chromosome 3 contained 1,200,643 variants that were polymorphic in all 847 individuals. There were 134 causal variants in 22 genes influencing SBP on chromosome 3.http://www.sph.umich.edu/csg/abecasis/MACH/download/1000G-2010-06.html). The annotation file included 1103 genes on chromosome 3. Because this file did not include all variants observed in the GAW18 data, for all 22 causal genes on chromosome 3, we also manually checked the boundaries of these causal genes using PubMed. If a variant was contained in multiple genes, then the variant was used as part of each gene.To apply the collapsing methods, we used the annotation file that was constructed based on the 1000 Genomes Project  and type I error  rates at level 0.05. For each gene, power was computed by the proportion of replicates with Analysis using LTA consistently performed better than analysis using SBP measured at any single visit. SBP measured at visit 1 performed slightly better than analysis using SBP measured at visit 3. However, all three analyses performed poorly in terms of the overall power. The type I error was about 0.06 (0.057 for SBP1 and SBP3 and 0.059 for LTA), roughly keeping the level 0.05. Overall power across all 134 causal variants on chromosome 3 was about 0.12  across all 22 causal genes. Similar to single-marker analysis, collapsing methods using the LTA performed slightly better than analysis using SBP measured at any single visit. Type I errors were 0.07 and 0.06 when collapsing all variants and low-frequency variants, respectively, also roughly maintaining the nominal level 0.05. However, all analyses using collapsing methods performed poorly, similar to analysis using single-marker approach. The highest overall power (0.139) was achieved when collapsing all variants using LTA.Power at each causal gene varied greatly and depended on whether low-frequency variants were collapsed or not. Within each collapsing approach, results were consistent across three phenotypes SBP1, SBP3, and LTA), as shown in Table , and LTAMAP4 and FLNB, in which collapsing all variants performed much better than collapsing low-frequency variants. When collapsing all variants, the highest power was achieved for gene MAP4 (0.99). This is consistent with the results from single-marker approach, in which several variants in MAP4 had high power, as shown in Table FLNB (0.67), again consistent with single-marker analysis. Except for TUSC2, all genes in group 1 included a common variant with a relatively large effect.To understand how power at causal genes depended on collapsing, we focused on the analysis for LTA phenotype and carefully examined results from 2 collapsing approaches. Because these collapsing methods are developed for identifying rare variants, we also compared the results from single-marker analysis by choosing the best power across all rare SNPs within each gene. Table SCAP, DNASE1L3, and LOC152217; collapsing only low-frequency variants performed much better than collapsing all variants. Except for ZBTB38, rare variants in these genes had larger effect than common variants. The third group in Table CXCR6, in which single-marker analysis performed better than either collapsing approach. Gene CXCR6 contained one causal variant, and single-marker analysis performed better. All other genes in group 3 contained causal SNPs with both positive and negative effects, for which collapsing multiple variants did worse than single-marker analysis. The fourth group contained 4 genes, and all methods performed poorly. Although gene PAK2 did contain 4 causal SNPs in the Answers, none was used for collapsing approaches because they were outside the gene in the annotation file based on the 1000 Genomes Project.The second group in Table Analysis of longitudinal phenotypes in family data has been a challenge because of 2 sources of correlations: correlations across longitudinal measurements and correlations among related individuals within families. We applied association analysis using LTA approach to simulated SBP phenotype in 847 related individuals in GAW18 family data. Overall, analysis using the LTA performed slightly better than analysis using SBP measured at any single visit.MAP4 and FLNB, which included a common variant with a relatively large effect. For several variants in gene MAP4, single-marker analysis also provided high power. In contrast, collapsing only low-frequency variants performed much better for SCAP, DNASE1L3, and LOC152217, where rare variants in these genes had larger effect than common variants. However, for other causal variants, all approaches provided disappointingly poor performance. This poor performance appeared to occur because most of these causal variants explained very small fraction of phenotypic variance. We also found that collapsing multiple variants did worse than single-marker analysis for several genes when they contained causal SNPs with both positive and negative effects. Although our results are based on sequence data from chromosome 3, we expect that our findings would extend to the GAW18 WGS data.Collapsing all variants performed much better than collapsing low-frequency variants at We adapted Morris and Zeggini's collapsing (burden) test to account for family relationship and applied it to the GAW18 data set of all 847 related individuals. Many GAW18 investigators that analyzed only unrelated individuals (with sample size 142) have observed worse performance. For example, using the most commonly used nonburden test sequence kernel association test for unrelated individuals, overall power across all causal genes was about 0.05, which was almost identical to the type I error rate [To apply collapsing methods that assess the combined effect of multiple variants in a gene, it is necessary to know which SNPs are contained in the gene. GAW18 performed WGS of 1043 individuals of Mexican American heritage with an average 60x sequencing depth, with a goal of finding novel SNPs. However, this creates a problem for applying these rare variant approaches because available annotation information does not contain these novel variants. In particular, among 1458 causal SNPs in the Answers, only 731 SNPs were contained in the annotation file that was constructed based on the 1000 Genomes Project. We found that our results were affected by our use of incomplete annotation information. Although the results and issues that we presented in this paper were based on GAW18 data, they may be shared with other sequencing studies.The authors declare that they have no competing interests.YJS conceived of the study and drafted the manuscript. JB carried out statistical analysis and summarized the results. DCR participated in the design of the study and helped to draft the manuscript. All authors read and approved the final manuscript.


HTR2A) modulates the availability of serotonin, this study investigated to what extent HTR2A modulates individuals’ perspective taking ability and autistic-like traits. To examine the associations of the functional HTR2A polymorphism T102C (rs6313) with individuals’ perspective taking abilities and autistic-like traits, we differentiated individuals according to this polymorphism and measured empathic and autistic-like traits with Interpersonal Reactivity Index (IRI) and Autism-Spectrum Quotient (AQ) scale in 523 Chinese people. The results indicated that this polymorphism was significantly associated with the scores on Perspective Taking and Personal Distress subscales of IRI, and Communication subscale of AQ. Individuals with a greater number of the C alleles were less likely to spontaneously adopt the point of view of others, more likely to be anxious when observing the pain endured by others, and more likely to have communication problems. Moreover, the genotype effect on communication problems was mediated by individuals’ perspective taking ability. These findings provide evidence that the HTR2A T102C polymorphism is a predictor of individual differences in empathic and autistic-like traits and highlight the role of the gene in the connection between perspective taking and autistic-like traits.Previous studies have indicated that empathic traits, such as perspective taking, are associated with the levels of serotonin in the brain and with autism spectrum conditions. Inspired by the finding that the serotonin receptor 2A gene ( Empathy, in the broadest sense, is the lens through which we understand, experience, and respond to the internal states of others . It is cHTR2A) modulates empathic traits, particularly perspective taking ability.Empathic traits are strikingly variable among individuals. Twin studies showed that empathic traits are highly heritable, with a heritability of up to 67% for perspective taking and 34–47% for empathic response , implyinHTR2A is located at 13q14.2. The expression of HTR2A is regulated by several functional polymorphisms  were recruited from Henan University of Science and Technology, China. They were ethnic Han Chinese without any known ancestors of other ethnic origin. All of them were in the normal range of anxiety symptoms  as assessed by the Zung Self-Rating Anxiety Scale  and of dHTR2A gene was produced by polymerase chain reaction (PCR) with the upstream primer, 5′-AACTACGAACTCCCTAA-3′, and the downstream primer, 5′-GTATGTTTCCAGCAAT-3′. The PCR reaction was performed with an initial 5 min denaturation at 94°C, followed by 35 cycles of 94°C for 30 s, 60°C for 30 s, 72°C for 1 min, and a final extension period at 72°C for 5 min. The PCR product was incubated with the restriction enzyme MspI (Fermentas) at 37°C overnight in a 5 μL digestion system, containing 1.0 μL PCR products, 0.40 μL (10U/μL) MspI, 0.40 μL Tango buffer and 3.2 μL H2O. The incubated mixture was analyzed by using 8% polyacrylamide gel electrophoresis in 220 V for 2.5 h. The gels were stained with 1.0% silver nitrate solution, and the genotypes bands in gels were identified by the Bio-imaging Systems software. In the current sample of 523 individuals, the distribution of genotypes  did not deviate from Hardy-Weinberg Equilibrium . The genotype frequencies were similar to those found in other Chinese Han samples  as a single predictor . Because of multiple testing (n tests = 10), to control for the rate of false-positive findings by chance, we adjusted p values using Bonferroni correction. To estimate the probability of correctly rejecting the null hypothesis when it is false (1 – β), post hoc power analyses were carried out using the program G∗power 3.0 (2R > 1.48%) was required for a sample of 523 .To test the effects of the ower 3.0  with theN = 400) from the total sample 20,000 times and estimated the regression coefficient in each simulated subsample. Then we calculated the probability of the estimated regression coefficients reaching significance in the 20,000 subsamples. The size of the subsample was set at 200, 300, 400, or 500 (Table 1).To test the robustness of the results in the regression analyses, we randomly selected a subsample with a given size  in the carriers . To examTable 1, regression analyses revealed that the polymorphism was significantly associated with the total scores on the Perspective Taking subscale and Personal Distress subscale of IRI, and the Communication subscale of AQ both before and after Bonferroni correction. Individuals with a greater number of the C alleles, which is associated with lower activity of HTR2A, were less likely to spontaneously adopt the point of view of others, were more likely to be anxious when observing the pain endured by others, and were more likely to have communication problems . Additionally, results also showed that the polymorphism seemed to be associated with scores on the Attention to Detail subscale of AQ, with a greater number of the C alleles significantly associating with a decreased ability to notice or remember details; however, this result did not survive Bonferroni correction (Table 1).As shown in β = -0.125, t = -2.880, p = 0.004), when both genotype and Perspective Taking were included as predictors, the effect of genotype on Communication decreased, β = -0.098, t = -2.285, p = 0.023 . The total indirect effect accounted for 21.6% (1-0.098/0.125) of the genotype effect on Communication. Mediation analysis indicated a significant mediating effect of perspective taking ability on the relationship between HTR2A T102C polymorphism and communication problems, indirect effect estimate = -0.0075, SE = 0.0031, bias-corrected 95% confidence interval is .Considering that previous studies have demonstrated the causal link between individuals’ perspective taking ability and communication problems , we condDBH gene and empathic traits observed in our previous study .Given the association between a variant (-1021C/T) in the us study , we inclTable 4).Of note, 178 out of 523 participants completed the IRI previously in another study  and theyFigure 1 had increased probabilities of reaching significance as the subsample size increased (Table 1); the probability of reaching significance was 84–91% when the subsample size set at 400.Moreover, results from our simulated subsamples revealed that the effects shown in HTR2A T102C polymorphism modulates individuals’ empathic and autistic-like traits. As predicted, we found that the individuals with a greater number of the C alleles, which is associated with lower activity of HTR2A, were less likely to spontaneously adopt the point of view of others and more likely to be anxious when observing the pain endured by others. We also found that individuals with a greater number of the C alleles were more likely to have communication problems, an autistic-like trait. Further analysis showed that the genotype effect on communication is mediated by individuals’ perspective taking abilities.The current study investigated to what extent HTR2A, which regulates the serotonin levels in the brain, to a certain extent, is associated with individual differences in empathic traits. Individuals with a greater number of the C alleles of HTR2A T102C polymorphism, which is related to reduced excitation at post-synaptic neurons (5-HTT) on self-reported empathic traits  in the present study. As the effect size of a single polymorphism for a complex trait is relatively small (Several limitations of this study should be noted. First, we demonstrated small effects of a single polymorphism on empathic and autistic traits while these traits are likely to be influenced by multiple polymorphisms  and by aly small , this stly small . To subsHTR2A T102C and by measuring empathic and autistic-like traits with IRI and AQ scale, we demonstrated the impact of HTR2A gene on individuals’ empathic traits and social communication in a general population. Our findings highlight the role of the HTR2A gene in social functioning and the connection between perspective taking and autistic-like traits.By differentiating individuals according to the polymorphism of PG and JL contributed equally to this work. PG, JL, and XZ designed the study. PG, JL, PB, and XZ wrote the manuscript. PG, JL, and SL performed the experiment and analyzed the data.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.