HERC2[OCA2] P = 2.3×10−62, SLC24A5 P = 9.6×10−9) that account for both blue versus brown eye color and varying intensities of brown eye color. We identify four major loci  for skin color that together account for 35% of the total variance, but the genetic component with the largest effect (∼44%) is average genomic ancestry. Our results suggest that adjacent cis-acting regulatory loci for OCA2 explain the relationship between skin and eye color, and point to an underlying genetic architecture in which several genes of moderate effect act together with many genes of small effect to explain ∼70% of the estimated heritability.Variation in human skin and eye color is substantial and especially apparent in admixed populations, yet the underlying genetic architecture is poorly understood because most genome-wide studies are based on individuals of European ancestry. We study pigmentary variation in 699 individuals from Cape Verde, where extensive West African/European admixture has given rise to a broad range in trait values and genomic ancestry proportions. We develop and apply a new approach for measuring eye color, and identify two major loci ( Differences in skin and eye color are some of the most obvious traits that underlie human diversity, yet most of our knowledge regarding the genetic basis for these traits is based on the limited range of variation represented by individuals of European ancestry. We have studied a unique population in Cape Verde, an archipelago located off the West African coast, in which extensive mixing between individuals of Portuguese and West African ancestry has given rise to a broad range of phenotypes and ancestral genome proportions. Our results help to explain how genes work together to control the full range of pigmentary phenotypic diversity, provide new insight into the evolution of these traits, and provide a model for understanding other types of quantitative variation in admixed populations. Differences in human skin color have been shaped principally and dramatically by selection th Century, and subsequently prospered during the transatlantic slave trade. In this population, extensive genetic admixture between the African and European ancestral populations during the last several centuries Cape Verde is an archipelago of ten islands located 300 miles off the coast of Western Africa. The previously uninhabited islands were discovered and colonized by the Portuguese in the 15Pigmentary variation in Cape Verde also provides an opportunity to investigate and disentangle the effects of locus-specific vs. genome-wide ancestry effects, a problem that is especially relevant to admixed populations, which are poorly represented in existing GWAS efforts, and for whom health disparities often correlate with genome-wide admixture proportions. For example, in previous studies of African-American cohorts, darker skin color (used as a proxy for genome-wide ancestry) correlates with higher blood pressure and with lower socioeconomic status, but the extent to which genetic factors contribute to these correlations is unclear Previous studies of African-European skin color variation have focused mostly on candidate genes that exhibit large allele frequency differences between ancestral populations, and have led to the view that a small number of loci account for most of the phenotypic variation In an admixed population, the effects of individual loci on a quantitative trait can be detected either by a correlation between genotype and phenotype, or by a correlation between local ancestry and phenotype. Genotype-based approaches are expected to be more powerful for traits where the causative allele exists at similar frequencies in ancestral populations, while ancestry-based approaches should be more powerful for traits where the causative allele exhibits large frequency differences in ancestral populations. Here we apply and compare genotype-based and ancestry-based association approaches for skin and eye color in 699 Cape Verdean individuals; both approaches identify two major loci for eye color, and four major loci for skin color. Surprisingly, the genetic component with the greatest effect on skin color is not a single locus but average genomic ancestry, which, together with these four major loci, accounts for most of the estimated heritable variation. Our results indicate that Cape Verdean pigmentary variation is the result of variation in a different set of genes from those determining variation within Europe, suggest that long-range regulatory effects help to explain the relationship between skin and eye color, and highlight the potential and the pitfalls of using allele distribution patterns and signatures of selection as indicators of phenotypic differences.We first investigated the pattern and distribution of continental ancestry in 699 Cape Verdeans for whom detailed pigmentary phenotype and high density genotype information was available, the latter obtained with the Illumina 1 M platform as described below. Data from CEU and YRI HapMap individuals frappeThe program The pattern of African genomic ancestry in Cape Verde is less skewed than in many other African-European populations. For example, in a randomly sampled African-American cohort from the Family Blood Pressure Study (FBPP) The broad range of genomic ancestry makes the Cape Verde population particularly well suited for understanding the genetic basis of Afro-European phenotypic diversity; at the same time, careful adjustment for population stratification is critical in an association analysis. The genome-wide association results described below are based on a linear regression adjusting for the first three principal components (PC) but the same model adjusting for up to 10 PCs does not qualitatively alter our results. Additionally, we have taken two complementary approaches: (1) a linear regression adjusting for African genomic ancestry and self-reported island of birth, and (2) a mixed effect model that considers relatedness and stratification, implemented in the program, EMMAX For skin color, we used reflectance spectroscopy on the upper inner arm to calculate a modified melanin (MM) index, which appears normally distributed with a mean of 7.39 and standard deviation of 0.85 for the Cape Verde cohort. By comparison, MM in individuals of European ancestry is smaller and much more narrowly distributed, with a mean of 5.38 and standard deviation of 0.27 For eye color, we developed a new measure based on automated analysis of digital photographs that captures the full range of African-European variation. We observed that RGB reflectance values project onto an empirical curve that begins and ends, respectively, with very light blue, and very dark brown eyes . We descThe correlation between skin color and African genomic ancestry (R∧2 = 0.44) is apparent both across and within  islands P<5.7×10−8 (SLC24A5 for skin color and HERC2 (OCA2) for eye color; P value distributions when genotypes at SLC24A5 and HERC2 (OCA2) are considered as covariates. No additional loci were found in conditional analyses that adjust for the most significant SNPs in the known loci (four for skin and two for eye color). After removing SNPs surrounding these six loci, the genomic control parameters were 1.02 and 1.00 for skin color and eye color, respectively. We also examined the six loci identified in the combined sample in each island separately  and eye (n = 625) color. For genotype-based association, four skin color loci  and two eye color loci (15q13.1 and 15q21.1) met genome-wide criteria for significance, 5.7×10−8 . In anal5.7×10−8  and 15q15.7×10−8 , dominatparately ; the dirparately . Taken tparately , these rFor ancestry-based mapping, we used SABER+, an extension of a Markov-Hidden Markov Model method SLC24A5 that affects skin and eye color similarly, but the 15q13.1 region harbors two distinct peaks of regulatory sequence variation that act separately on skin and eye color.To help evaluate potential causative loci and molecular variants for skin and eye color, we imputed SNP information from HapMap phase II SLC45A2 L374F  GRM5 (rs10831496) in this sample, and in analyses that consider TYR and GRM5 SNPs as covariates, both remain significant . Thus, the effect of the 11q14.3 locus on skin color could be mediated by a combination of regulatory and coding variation.The two remaining skin color loci at 11q14.3 and 15q13.1 exhibit the strongest association signals within intronic regions of GRM5  and APBA5 ; conversely, APBA2 does not affect eye color if skin color is also included as a covariate . Thus, the effects of HERC2 and APBA2 are genetically and statistically separable. Still, there is strong experimental evidence that the HERC2 region represents regulatory variation that acts via OCA2HERC2 (OCA2) and APBA2 (OCA2) to indicate both their proximity to a well-known pigmentation gene, and a likely molecular mechanism.We considered whether the skin color GWAS signal for distinct , and linHERC2 (OCA2), there is a second eye color locus at 15q21.1 whose position corresponds to the SLC24A5 A111T allele , indicating that variation at these loci affects different shades of brown eye color.In addition to the major eye color locus at T allele . The assex >0.15 , both loSLC45A2 and APBA2 (OCA2) GWAS signals coincide with sharp CMS signals (SLC24A5 and HERC2 (OCA2) (GRM5-TYR region (SLC24A5 >> SLC45A2≈GRM5-TYR > APBA2 (OCA2), see below), but, as expected, does correlate with African – European allelic divergence as estimated by HapMap YRI – CEU comparisons, for which the most highly associated SNP at each of the four skin color loci (SLC24A5), 0.80 (APBA2 [OCA2]), 0.74 (SLC45A2), and 0.56 (GRM5-TYR).Population genetic signatures of positive selection have been used as ancillary evidence to identify and/or refine pigmentation genes  signals ; broader2 (OCA2) . By contR region . This dilor loci  exhibitsASIP and KITLG, reported previously to affect skin color in populations with African-European admixture IRF4, MC1R, SLC24A4, TYRP1, reported previously to affect skin color in populations of European ancestry SLC24A5, in which the ability to detect a candidate locus at different levels of significance was evaluated as a function of effect size. For a genome-wide significance level (P<5×10−8), the results of our simulations reveal >90% power to detect an effect size of 0.21 MM , incomplete correction for genomic ancestry with small panels of ancestry informative markers in the earlier studies , or real effects of these loci that are too small to detect in our study.As shown in APBA2 (OCA2), lies close to but has effects that are genetically separable from, the major eye color locus, HERC2 (OCA2). Selective pressure for fair skin in European ancestors is thought to reflect the need for sunlight-induced vitamin D production HERC2 (OCA2) allele may have hitchhiked on a derivative APBA2-bearing chromosome, or could have undergone independent selection for reasons that are distinct from those affecting APBA2 (OCA2).As indicated above, one of the major skin color loci, APBA2 (OCA2) allele is present at low frequencies in most populations of African ancestry, and at high frequencies in most populations of Asian and European ancestry. By contrast, the derived HERC2 (OCA2) allele is absent from African and East Asian populations, and appears at high frequency only in Western and Northern Europe (APBA2 (OCA2) mutation conferring light skin arose before the spread of humans out of Africa, and that a HERC2 (OCA2) mutation conferring pale eye color arose much later.To explore these ideas, we first examined worldwide allele frequency distributions for the most strongly associated SNP at each locus, using information from the Human Genome Diversity Project (HGDP) n Europe . These rHERC2 (OCA2) and >90 kb in length for APBA2 [OCA2]), whereas the ancestral alleles are found on multiple, shorter, haplotypes. However, measures of linkage disequilibrium between the two loci are not significant , extended haplotypes for the derived alleles are separated by ∼800 kb, and approximately half of the European chromosomes we examined carry an ancestral HERC2 (OCA2) haplotype together with a derivative APBA2 (OCA2) haplotype, or vice versa (HERC2 (OCA2) and APBA2 (OCA2) regions  allele is mostly dominant over the derived allele, consistent with the near recessive mode of inheritance of blue eye phenotype in Europeans , and the sum of effect sizes for homozygous substitution at all four loci would shift, in an individual, an amount about the same as ∼1/3 of the total range of skin color we observed .GRM5-TYR, APBA2 (OCA2), and SLC45A2, and about 7% for SLC24A5. However, these estimates, based on conventional regression analyses in which individual genomic ancestry is considered as a covariate, fail to consider admixture stratification, wherein admixture proportions vary widely due to recent admixing, and genotypes at unlinked loci remain correlated. For example, even though SLC24A5 and SLC45A2 lie on different chromosomes, their genotypes in the Cape Verde population are correlated with genomic ancestry and, therefore, with each other. Thus, in any particular individual, genomic ancestry has predictive value for genotype at each of the four major skin color loci, and vice versa.From the second perspective, the proportions of phenotypic variance attributed solely to genotype at each of the four major skin color loci are quite small, about 2% each for SLC24A5, GRM5-TYR, APBA2 (OCA2), and SLC45A2 are 18%, 7%, 9% and 7%, respectively, considerably less than the proportion of variance attributed to individual genomic ancestry, 44%  eye color locus. Neither functional nor expression studies suggest a role for the APBA2 protein in pigmentation ABPA2 (OCA2) is analogous to that of HERC2 (OCA2), with distinct cis-acting regulatory regions that can affect eye and skin color independently. Critical evaluation of this hypothesis will require functional studies similar to those described by Visser et al. HERC2 and APBA2 contains a cluster of segmental duplications that serves as a frequent deletion breakpoint (BP3) involved in Prader-Willi and Angelman syndromes OCA2APBA2. If, however, a chromatin loop does form between APBA2 and OCA2, the BP3 region must be contained within that loop.Our work confirms the very strong effect of rs12913832 on eye color but provides little evidence for an effect on skin color, since neither the transcribed region of OCA2 can be modulated and selected separately in specific pigment cell compartments could help to explain the correlation and evolutionary history of skin and eye color. In particular, loss-of-function for OCA2 can cause blindness due to loss of melanin from retinal pigment epithelial cells APBA2 region in Europeans represents cell type-specific regulation that promotes fair skin while preserving visual function. Finally, our population genetic analysis of HERC2 and APBA2, together with similar work on the HERC2 - OCA2 interval from Donnelly et al. Furthermore, the notion that expression of SLC24A5 and KITLG have been reported to have effect sizes that correspond to 25%–38% and 20%–25% of the European-West African difference in skin color, respectively SLC24A5 corresponds to 13% of the range of skin color variation in Cape Verde, and we did not detect KITLG as a skin color locus. For SLC24A5, this apparent discrepancy stems from whether effect sizes are described as a fraction of the “average pigmentation difference between European-Americans and African Americans of about 30 melanin units” SLC24A5 allele in Cape Verde that corresponds to 4.9 melanin units, compared to 4.8 melanin units estimated by Lamason et al. Our analysis of skin color genetic architecture generally assigns smaller contributions to individual genes than in previous work. In particular, KITLG. Like APBA2 (OCA2), SLC24A5, and SLC45A2, SNPs in KITLG exhibit large CEU-YRI allele frequency differences of 0.7–0.99. In the Cape Verde sample, we used dense genotype data  to correct for genomic ancestry, and only the first three loci exhibited a significant association with skin color. By contrast, the observations of Miller et al. on the effects of KITLG in an African-American sample used a relatively small set of admixture informative markers to correct for genomic ancestry, which may have led to an overestimation of effect size. Our results do not exclude the possibility that variation in KITLG affects skin color—indeed, like other genes in which mutations cause strong pigmentary effects in model organisms  and SLC24A5, individual genomic ancestry has relatively little effect on eye color. By contrast, the “rest of the genome” (beyond APBA2 (OCA2), GRM5-TYR, and SLC24A5) has a very strong influence on skin color, nearly twice that of all four single loci combined. What is the biological basis for the large effect of individual ancestry on skin color? Theoretical explanations include non-genetic components that correlate with ancestry, or many genes of small effect whose contributions were not strong enough to be detected in our sample. We favor the latter alternative, which supports a view of skin color genetic architecture that is multimodal, in which many genes of small effect cumulatively explain the correlation between skin color and individual ancestry, and together with several genes of moderate effect , SLC45A2, and SLC24A5), account for high overall heritability. These observations also have important implications for potential forensic applications, and argue that efforts to predict pigmentary phenotype from genotype should be based on dense genotype and/or whole genome sequence information rather than small panels of SNPs.Although eye color and skin color are correlated, their underlying genetic architecture in Cape Verde is very different. Beyond Human history has given rise to group-level differences in both phenotype and genotype, and recently admixed populations such as Cape Verde offer the potential to unravel the causal relationships. But relying primarily on differences in population-based allele frequencies or haplotype architecture to infer causes of morphologic variation is a form of genetic stereotyping that can lead to erroneous conclusions. An accurate understanding of human diversity requires that we measure phenotype as well as genotype.Cape Verde individuals were recruited from six islands of the archipelago . Each paGenotyping was performed using the Illumina Infinium HD Human1M-Duo Beadarray , according to the manufacturer's recommendations. A total of 1,016,423 SNP markers were assayed. SNPs with genotyping call rate <95%, SNPs showing replication errors across 20 replicated samples, SNPs in which the apparent heterozygosity was equal to one, and SNPs for which only a single allele was called in the Cape Verde dataset were removed from the analyses. Hardy-Weinberg equilibrium was not used as a quality filter due to the admixed nature of the subjects. After applying all quality control filters, 903,837 autosomal SNPs were available for analysis, of which 24,478 had minor allele frequency (MAF) <0.01.Two samples with call rate below 95% were removed. Identity-by-states (IBS), estimated using all autosomal SNPs in PLINK Quantitative measures of skin pigmentation were taken on the upper inner arms using a handheld spectrophotometer . The instrument reports a melanin index (M index), which equals to 100× log (1/% melanin reflectance at 650 nM). For each subject, three consecutive measurements were taken on each arm. We calculated the mean of the six measurements using the Huber M-estimator  values of full-eye digital photographs, taken at a fixed exposure setting. Briefly, we used a common landmark in the pupil to identify an area that corresponds to the left eye. We next delineated the inner and outer boundaries of the iris by seeking circular edges that mark sharp changes in RGB values. The extracted irises were manually inspected and found sufficiently accurate. The median values of the red, blue and green channels, over all pixels in the reconstructed iris region in an individual, were normalized to sum to one. We processed data from 1272 Cape Verdeans (of whom 625 were genotyped) with this approach.We supplemented this dataset with manually curated RGB data from 1278 Europeans  with the first principal component computed on Cape Verdean genotypes alone without using any ancestral individuals. Thus, while the CEU and YRI are imperfect ancestral populations, they do not cause a large bias in either genome-wide or local ancestry estimates.Although CEU and YRI are approximations of the true ancestral populations of Cape Verde, in previous work on admixed populations from Mexico frappe and SABER+ analyses included 537,895 SNP markers that are in common between the Cape Verdean and the HapMap samples.Locus-specific ancestry was estimated with SABER+, using the haplotypes from the HapMap project to approximate the ancestral populations. SABER+ extends a previously described approach, SABER, by implementing a new Autoregressive Hidden Markov Model (ARHMM), in which the haplotype structure within each ancestral population is adaptively learned through constructing a binary decision tree frappe (r = 0.99).Principal Component analysis (PCA) was performed using EIGENSTRAT P values reported in Association between each SNP and a phenotype (MM index for skin and T index for eye pigmentation) was assessed using an additive model, coding genotypes as 0, 1, and 2. Sex was adjusted as a covariate; age was found not correlated with the phenotypes (P>0.5 for both skin and eye colors), and hence was not included as covariate. Assessment and control for population stratification is described in −8 were considered genome-wide significant. Conditional analyses were performed using a linear model that included the genotype at a major locus: SLC24A5 for skin and HERC2 (OCA2) for eye. To evaluate potential secondary signals, we also carried out an association scan conditioning at all index SNPs, and found no evidence for secondary signals except in the GRM5-TYR region  as described in the conditional analysis section of the We restricted the association scans to the 879,359 autosomal SNPs with MAF>0.01; SNPs achieving a P<5.7×10−6 for this ancestry-based mapping approach For ancestry mapping, which seeks statistical association between locus-specific ancestry and a phenotype, we used a linear regression model similar to that used in the genotype-based association, except substituting genotype with the posterior estimates of ancestry at a SNP, estimated using SABER+; again, sex and the first three PCs were used as covariates. Based on a combination of simulation and theory, we have previously established a genome-wide significant criterion of p<7×10SLC24A5 genotypes, and skin color phenotypes. Specifically, local ancestry was first simulated from the known distribution of genome-wide ancestry, and the genotype at a candidate locus was then simulated using local ancestry and the estimated ancestral allele frequencies . Phenotype for each individual was then calculated from a linear model in which genome-wide ancestry, genotype at SLC24A5 rs1426654, and genotype at the candidate locus were used as covariates together with a random error term whose variance was chosen so that the phenotypic variance of the simulated dataset matched the variance actually observed in the Cape Verde sample. This approach preserves a realistic level of correlation structure between phenotype, genome-wide ancestry proportions and genotypes, and also takes into account the two strongest predictors of phenotype: genome-wide ancestry and genotype at SLC24A5. The linear model for calculating phenotype used regression coefficients of −4.247 for genome-wide European ancestry and −0.3459 per copy of SLC24A5 rs1426654 derived allele; for the candidate locus, we varied the regression coefficient to evaluate power for different effect sizes.Simulated datasets were based on the observed distributions of genome-wide ancestry, APBA2 (0.155) GRM5-TYR (0.193), SLC24A5 (0.334), and SLC45A2 (0.166) are from a linear regression of the Cape Verde skin color data in which genomic ancestry and genotypes at the four major loci are included as covariates. Effect sizes shown in KITLG (0.19 MM–0.26 MM) are estimated from Miller et al. ASIP (0.24 MM) is estimated from 3.5 melanin units based on data in Bonilla et al. A linear regression model was then used to test the association between phenotype and genotype, and power calculated as the proportion of 1000 simulations in which the association exceeded a pre-specified alpha-level. Effect sizes shown in KITLG allele frequencies of 0.056 and 0.827.The power values shown in Imputation was performed for an ∼1–2.5 Mb window chosen to extend +/−500 kb of all genes identified in the association study. Imputed genotypes were inferred with the MaCH 1.0.16 package We directly compared association results for imputed genotypes with the composite of multiple signal (CMS) scores for positive selection developed by Sabeti and colleagues Haplotype analysis of the 15q13.1 loci in Europeans was based on HapMap Population-attributable variance refers to the proportion of phenotypic variation that can be accounted for by genotype of one or more loci, and is analogous to the epidemiologic concept of population-attributable risk. In the presence of admixture stratification, population-attributable variance for multiple loci sums to more than unity, because genotypes at unlinked loci are correlated with each other and with genome-wide ancestry.k , the fractional reduction in phenotype Y[−k] = Y−βkGk, where Y is the observed phenotype, and Gk ε  is the genotype, calculated for each individual in the population. Population-attributable variance is then computed as PAVk = [var(Y)−var(Y[−k])]/var(Y). Analogously, the population-attributable variance for a set of loci is determined by setting the genotype of each locus to a common baseline simultaneously. Since population-attributable variance is computed only on variances of Y[−k], the choice of the baseline genotype is irrelevant.We estimated population-attributable variance for the Cape Verde population by determining the fractional reduction in phenotypic variance that would occur if genotypes at a locus of interest were set to a common baseline in all individuals, holding all other covariates and genotypes unchanged. Specifically, for locus k and β1 and G2, and let the true phenotype, Y, be To estimate the proportion of phenotypic variation for a single locus that cannot otherwise be accounted for by genotype at unlinked loci, we compare the residual variance in a full regression model  to residual variance in a reduced model that does not include the locus of interest. Finally, variance accounted for by genome-wide ancestry is simply the R-squared statistic from a regression model that includes ancestry. We note that the PAV is non-additive; in other words, variance accounted by two loci is not the sum of the PAV of the two loci individually. To see this, consider a simpler situation with just two loci, with genotype GThe numerator of PAV for locus 1 isSimilarly, we have for locus 2,On the other hand, the numerator of PAV for loci 1 and 2 together is1 and G2 to represent the derived (pigmentation lightening) alleles at the two loci, then cov>0 due to admixture linkage disequilibrium, γ1γ2>0, and we have V12<(V1+V2), as is indicated by the overlapping areas in In our situation, if we code G2 = 0.44) is much higher than expected for a trait determined by the action of a small number of genes, we performed simulations using the observed genotype and ancestry information in the Cape Verde individuals. The phenotypes were simulated assuming a genetic architecture with three major loci, each of which has the same effect as SLC24A5 and is perfectly ancestrally informative. The residual non-genetic variance was assumed to be independent of any genotype or ancestry, such that the total variance in each simulated phenotype matches that observed for skin pigmentation in Cape Verde. In 1000 simulations, the correlation between genome-wide ancestry and the simulated phenotype has a mean of 0.27 with a standard deviation of 0.037.To support the statement that correlation between skin color and African genomic ancestry  for the genotyped SNPs. Plots are ordered by chromosomal position. (a) Genotype association scan results for skin color. (b) Genotype association scan results for eye color. Dashed red lines correspond to the genome-wide significance threshold.EMMAX results for skin and eye color in the total Cape Verdean cohort. (TIF)Click here for additional data file.Figure S2P values for Kolmogorov-Smirnov tests are 0.01538 and 0.00944 for the comparison to selection-based candidate genes and random SNPs, respectively). The distribution of the SNPs from the selection-based candidate genes is not significantly different from the random SNPs (P = 0.125).Distribution of allele frequencies for the two sets of candidate gene P values, and a control set of random SNPs (as depicted in (TIF)Click here for additional data file.Figure S3Effect of HERC2 (OCA2) and SLC24A5 genotype on eye color. Quantitative assessment of eye color with the T-index as described in the text and (TIF)Click here for additional data file.Table S1GWAS results and derived allele frequencies stratified by island cluster.(DOCX)Click here for additional data file.Table S2Major loci for skin and eye color (without conditioning).(DOCX)Click here for additional data file.Table S3Frequency of the derived allele for the eye and skin color loci at 15q13 in the Old World populations of HGDP and HapMap phase III datasets.(DOCX)Click here for additional data file.Table S4Candidate loci chosen based on function.(DOCX)Click here for additional data file.Table S5Candidate loci chosen based on selective signatures.(DOCX)Click here for additional data file.


Twin studies of behavior problems in childhood point to substantial genetic influence. It is now possible to estimate genetic influence using DNA alone in samples of unrelated individuals, not relying on family-based designs such as twins. A linear mixed model, which incorporates DNA microarray data, has confirmed twin results by showing substantial genetic influence for diverse traits in adults. Here we present direct comparisons between twin and DNA heritability estimates for childhood behavior problems as rated by parents, teachers, and children themselves.Behavior problem data from 2,500 UK-representative 12-year-old twin pairs were used in twin analyses; DNA analyses were based on 1 member of the twin pair with genotype data for 1.7 million DNA markers. Diverse behavior problems were assessed, including autistic, depressive, and hyperactive symptoms. Genetic influence from DNA was estimated using genome-wide complex trait analysis (GCTA), and the twin estimates of heritability were based on standard twin model fitting.Behavior problems in childhood—whether rated by parents, teachers, or children themselves—show no significant genetic influence using GCTA, even though twin study estimates of heritability are substantial in the same sample, and even though both GCTA and twin study estimates of genetic influence are substantial for cognitive and anthropometric traits.We suggest that this new type of “missing heritability,” that is, the gap between GCTA and twin study estimates for behavior problems in childhood, is due to nonadditive genetic influence, which will make it more difficult to identify genes responsible for heritability. Behavior problems in childhood, such as anxiety, depression, autistic symptoms, hyperactivity, and conduct problems, are common, with a cumulative incidence during childhood of 12% for 1 or more disorders.Furthermore, their heritability is surprisingly high: for example, twin studies using parent ratings typically report heritabilities in the range of 40% for anxiety and depression to 60% for autistic symptoms and hyperactivity.It is now possible to test the validity of these heritability estimates using DNA from unrelated individuals, a method called genome-wide complex trait analysis (GCTA).For the first time, we report GCTA estimates for childhood behavior problems as rated by parents, teachers, and the children themselves. To gauge the true breadth of the missing heritability gap, we compared GCTA estimates to twin heritability estimates for the same measures in the same sample. We also compared these results for behavior problems to results for height and weight, and cognitive traits in the same sample.The sample was drawn from the Twins Early Development Study (TEDS), which is a multivariate longitudinal study that recruited more than 11,000 twin pairs born in England and Wales in 1994, 1995, and 1996.The present analyses were limited to children for whom DNA, genome-wide genotyping, and behavior problem and cognitive data were available. Moreover, the twin analyses were based only on twins included in the GCTA analyses, to provide a more precise comparison between GCTA and twin study results.2 > 0.2).p < .05, which were used as covariates in GCTA analyses.DNA was available for 3,747 children 11 and 12 years of age  whose first language was English and who had no major medical or psychiatric problems. From that sample, DNA samples of 3,665 individuals (only 1 member of a twin pair) were successfully hybridized to Affymetrix GeneChip 6.0 SNP genotyping arrays using standard experimental protocols as part of the WTCCC2 project. In addition to nearly 700,000 genotyped SNPs, more than 1 million other SNPs were imputed from HapMap 2, HapMap 3, and WTCCC controls, using IMPUTE v.2 software.Of these 3,152 children, the present analyses were limited to those for whom behavior problem and cognitive data were available. Twin zygosity was diagnosed on the basis of physical similarity, and questionable cases were verified with analysis of DNA markers.All of the measures have been reported in previous TEDS publications, which can be consulted for greater detail.The behavior problem measures described in this section have been widely used in the literature. As is the case in the literature, these measures are modestly correlated, 0.33 on average for the scores described below. Given the modest correlation among the measures and the focus of the present paper on comparisons between GCTA and twin estimates for diverse behavior problems, we present results separately for the behavior problem scales rather than conducting multivariate analyses.DSM-IV–based ADHD scale from the Conners' Parent Rating Scale–Revised (CPRS-R). The questionnaire consisted of 2 scales: inattentiveness and hyperactivity-impulsivity.ADHD symptoms were assessed via parent-rated questionnaire, which was the Antisocial behavior was assessed using parent and teacher ratings on the Antisocial Process Screening Device. The questionnaire included 3 scales: callous-unemotional, impulsivity, and narcissism.The Childhood Asperger Syndrome Test questionnaire was rated by parents and teachers and includes 3 scales: communication, nonsocial, and social, from which a composite was also formed.The Moods and Feelings Questionnaire was rated by the children and their parents.The Strengths and Difficulties Questionnaire was assessed by the children and their parents. The questionnaire includes 4 behavior problem scales  from which a composite was created. The SDQ also includes a positive prosocial scale that was not included in these analyses of behavior problems.Cognitive data were collected online via the Internet using adaptive branching, which enabled measurement of the full range of ability using a relatively small number of items.Four measures of reading were used. Two measures assessed reading comprehension: the reading comprehension subtest of the Peabody Individual Achievement Test (PIAT) and the Global Online Assessment for Learning (GOAL) Formative Assessment in Literacy for Key Stage 3. Reading fluency was assessed by an adaptation of the Woodcock-Johnson III Reading Fluency Test (WJRF) and by the Test of Word Reading Efficiency (TOWRE), which was administered by telephone.Assessment of mathematics targeted 3 components of mathematics: understanding numbers, non-numerical processes, and computation and knowledge. The items for these 3 scales were based on the National Foundation of Educational Research 5–14 Mathematics Series.Three components of language were assessed: syntax, semantics, and pragmatics. Syntax was measured using the Listening Grammar subtest of the Test of Adolescent and Adult Language. Semantics was assessed using Level 2 of the Figurative Language subtest of the Test of Language Competence. Pragmatics was assessed using Level 2 of the Making Inferences subtest of the Test of Language Competence.The verbal tests were Wechsler Intelligence Scale for Children as a Processing Instrument (WISC-III-PI) Multiple Choice Information  and Vocabulary Multiple Choice subtest. The 2 nonverbal reasoning tests were Wechsler Intelligence Scale for Children–III (WISC-III-UK) Picture Completion and Raven's Standard and Advanced Progressive Matrices. General cognitive ability was indexed as a composite of the 4 verbal and nonverbal tests.Height and weight were assessed at age 12 years via self-report.www.r-project.orgTo create composite scores for the measures, standardized residuals were derived for each scale regressed on sex and age. Outliers above or below 3 SD from the mean were excluded and the scale was quantile normalized.GCTA software was used to conduct these analyses.et al.Conceptually, when performing GCTA analysis, we compare a matrix of pairwise genomic similarity to a matrix of pairwise phenotypic similarity using a random-effects mixed linear model.In contrast to GCTA, the twin method models variance/covariance for pairs of related individuals: monozygotic (MZ) twins who are genetically 100% identical, and dizygotic (DZ) twins who share on average 50% of their segregating alleles. Differences in within-pair correlations for MZ and DZ twins are then used to partition the variance into genetic and environmental effects. Variance is attributed to additive genetic influence to the extent that MZ correlations are higher than those for DZ twins. The twin method, unlike GCTA, partitions environmental influence into 2 components: shared or common (C) environmental influences, which is residual MZ twin resemblance not explained by genetics, and nonshared or unique environmental influences (E), which is the extent to which MZ twins differ and includes error of measurement. Detailed description of this A (additive genetics) C (common environment) and E  model and the discussion of related issues can be found elsewhere.The twin data were modeled using Cholesky decomposition of the variance within structural equation modeling software OpenMx.Why do GCTA estimates show no significant genetic influence for diverse childhood behavior problems as rated by parents, teachers, or children themselves, even though twin study estimates of heritability are significant and substantial in the same sample using the same measures, and even though GCTA estimates for cognitive traits are significant and substantial? One broad category of explanations involves mechanisms by which GCTA underestimates twin heritability, more so for behavior problems than for cognitive traits. As mentioned earlier, GCTA underestimates twin heritability because it captures only additive genetic effects tagged by the common SNPs used on GWA arrays. Gene–gene interactions, gene–environment interactions, and rare alleles will widen the gap between GCTA and twin estimates of heritability. However, it is not clear why this gap would be greater for behavior problems than for cognitive traits.Nonetheless, we can test 1 of these hypotheses in our study, namely, that nonadditive genetic variance is greater for behavior problems than for cognitive traits. Because MZ twins are identical genetically, they are identical even for interactions among many genes (epistasis), whereas such epistatic effects, on average, scarcely contribute to similarity for DZ twins.Another, nonmutually exclusive, explanation of why GCTA underestimates twin heritability for behavior problems more than for cognitive abilities is that additive genetic variance might be greater for cognitive traits than for behavior problems. Not previously considered in this context is assortative mating, which increases additive genetic variance for all loci associated with the trait for which spouse correlate. Assortative mating, indexed by the correlation between spouses, is often greater for cognitive traits than for behavior problems: spouse correlations are about 0.40 for cognitive ability.A second broad category of explanations, and again not mutually exclusive, is that twin studies overestimate heritability for behavior problems more than for cognitive traits. One reason to take this seriously is that twin studies yield higher estimates of heritability than do adoption studies for personality traits, which are related to behavior problems in that personality includes traits such as emotionality, impulsivity, and activity level.Another methodological possible explanation of the low GCTA heritability estimates could be the skewed distributions, which are often found for measures of behavior problems. As shown in Overall, greater nonadditive genetic influence for behavior problems than for cognitive traits emerges as the leading candidate to explain the greater GCTA/twin heritability gap for behavior problems. The only problem with this explanation is that our twin results for behavior problems in children do not indicate nonadditive genetic effects, even though other twin studies of behavior problems in children and twin studies of adult personality point to some nonadditive genetic effects. In the absence of a more parsimonious explanation, we suggest that nonadditive genetic effects for behavior problems in childhood are masked by a general inflation of twin similarity for both MZ and DZ twins. One possibility is that this general twin inflation could be due to experiences that are shared by members of both MZ and DZ twin pairs. However, this possibility seems less implausible, because such shared experiences would seem likely to affect cognitive traits at least as much as behavior problems. A general inflation of twin similarity due to rating bias is another, more promising, possibility: a major difference between behavior problems and cognitive traits is that behavior problems are rated on questionnaires whereas cognitive traits are measured by tests, and ratings are inherently more prone to bias than tests. Such a general inflation of MZ and DZ twin correlations would mask nonadditive genetic variance because it would reduce the difference between MZ and DZ twin correlations. For example, let us suppose that the “true” MZ and DZ twin correlations were 0.3 and 0.1, respectively, suggesting some nonadditive genetic variance. Inflating both twin correlations by 0.1 would result in MZ and DZ correlations of 0.4 and 0.2, respectively, suggesting only additive genetic influence.In summary, we propose that a combination of 3 factors is responsible for the greater GCTA/twin heritability gap for behavior problems as compared to cognitive traits. First, nonadditive genetic variance is greater for behavior problems than for cognitive traits, reducing GCTA estimates for behavior problems relative to cognitive traits. Second, greater assortative mating for cognitive traits as compared to behavior problems produces more additive genetic variance for cognitive traits, which results in greater GCTA estimates and thus lowers the GCTA/twin heritability gap for cognitive traits but not for behavior problems. Third, the reason that our twin data do not indicate nonadditive genetic effects for behavior problems in childhood is that these nonadditive genetic effects are masked by inflated correlations for both MZ and DZ twins for ratings of behavior problems. Although this set of hypotheses is speculative, if true, it would suggest that GWA studies will find it more difficult to identify SNPs associated with behavior problems than with cognitive traits. It also suggests that nonadditive genetic variance might contribute importantly to the genetic architecture of behavior problems in childhood, and perhaps in adult personality and psychopathology as well.


Several platforms for the analysis of genome-wide association data are available. However, these platforms focus on the evaluation of the genotype inherited by affected (i.e. case) individuals, whereas for some conditions (e.g. birth defects) the genotype of the mothers of affected individuals may also contribute to risk. For such conditions, it is critical to evaluate associations with both the maternal and the inherited (i.e. case) genotype. When genotype data are available for case-parent triads, a likelihood-based approach using log-linear modeling can be used to assess both the maternal and inherited genotypes. However, available software packages for log-linear analyses are not well suited to the analysis of typical genome-wide association data (e.g. including missing data).(MI-GWAS) for log-linear analyses of maternal and inherited genetic effects in large, genome-wide datasets, is described. MI-GWAS uses SAS and LEM software in combination to appropriately format data, perform the log-linear analyses and summarize the results. This platform was evaluated using existing genome-wide data and was shown to perform accurately and relatively efficiently.An integrated platform, Maternal and Inherited Analyses for Genome-wide Association Studies http://www.sph.uth.tmc.edu/sbrr/mi-gwas.htm.The MI-GWAS platform provides a valuable tool for the analysis of association of a phenotype or condition with maternal and inherited genotypes using genome-wide data from case-parent triads. The source code for this platform is freely available at  Howevironment . Howeverironment -17, nearironment , which cThe most common study designs used in GWA studies include the case-control and case-parent triad designs. Though the case-control approach has been used more frequently, distinguishing between maternal and inherited genetic effects using this study design requires the addition of samples from mothers of both cases and controls, resulting in increased genotyping costs . Moreove®, SAS Institute Inc.), but, when data from incomplete triads are included, require programming of the EM, which is cumbersome. The specialized program, LEM [The most commonly used method for assessing maternal and inherited genetic effects using case-parent triad data is a log-linear, likelihood-based approach ,22. In aram, LEM ,26, can To address the need for an efficient approach for analyzing maternal and inherited genetic effects using GWA data from case-parent triad studies, a novel computational platform was created. This platform uses SAS to prepare the data in an LEM-compatible format, calls LEM to evaluate each SNP, and extracts and summarizes relevant data from the LEM output files. The performance of this platform was evaluated using existing, case-parent triad GWA data.The theoretical distribution (assuming Mendelian inheritance and mating symmetry) of case-parent triad genotype data  can be fitted to the observed triad counts by maximum likelihood using the following log-linear model:nF,M,C) correspond to the expected count of each genotype combination . The γj term stratifies on parental mating type , and I is an indicator variable that equals 0, 1, or 2, corresponding to the number of high-risk alleles present in the child's or mother's genotype (C and M respectively). The offset, wF,M,C, accounts for the heterozygous genotype being twice as likely as either homozygous genotype in offspring of double heterozygous matings (assuming Mendelian transmission). Under this model, the risk corresponding to a genotype with one copy of the "high-risk" allele compared to no copies can be estimated by exp(β1) for the inherited genotype and exp(α1) for the maternal genotype, and the risk corresponding to a genotype with two copies compared to no copies can be estimated by exp(β2) for the inherited genotype and exp(α2) for the maternal genotype. The significance of the inherited genetic effect can be evaluated using a two degree of freedom likelihood ratio test (LRT) to compare the log-likelihood of the full model (i.e. Model 1) to that of a reduced model in which β1 = β2 = 0. The null hypothesis for this test is that conditional on parental mating type, the distribution of affected offspring agrees with Mendelian expectation. Similar to the TDT, this LRT provides a test of linkage in the presence of linkage disequilibrium that is not vulnerable to confounding due to population stratification [1 = α2 = 0. The null hypothesis for this test is that reciprocal parental mating types (e.g. Aa × AA and AA × Aa) occur at the same frequency among parents of affected individuals [The values of E. However, in some circumstances power can be increased by imposing constraints on the general model. For example, a linear constraint can be imposed upon the full model for the genetic effect being tested, leaving the terms for the other genetic effect unconstrained. This allows for a one degree of freedom LRT. The use of a linear constraint has been shown to perform well under a variety of circumstances [Model 1 is a general (i.e. unrestricted) model, in which no constraints are placed on the relationships between alleles  http://www.sph.uth.tmc.edu/sbrr/mi-gwas.htm.SAS version 9.2 was used to develop a platform for the efficient analysis of maternal and inherited genetic effects in GWA data, using LEM to apply the log-linear modeling likelihood-based approach. This platform is called Maternal and Inherited Analyses for Genome-wide Association Studies (MI-GWAS) and is freely available at Briefly, MI-GWAS:1. Reads data from a single PLINK ped file that contains a separate row of data for each triad member. Each row contains all genotype data for a single individual, and each genotype is coded as two alleles (i.e. two columns of data per genotype). By default, MI-GWAS treats the allele coded as "2" under the Illumina 1/2 allele coding system as the high-risk allele.2. Converts the PLINK data into one LEM data file per SNP, with a single row of genotype data for each triad, with genotypes in the order of mother, father, and child. These genotypes are coded as 0, 1, or 2, for number of high-risk alleles present (i.e. one column of data per genotype).3. Creates LEM program files for each SNP - one program file per model.4. Calls LEM to run the relevant analyses (i.e. full and reduced models) for each SNP.5. Extracts relevant output  from LEM.6. Calculates relevant LRTs and associated p-values.7. Computes 95% confidence intervals (CIs) for all relative risks.8. Merges results with the PLINK marker map file, which includes chromosome number and rs number for each SNP.9. Sorts results by the LRT p-value, separately for the inherited and maternal genotype, and outputs two corresponding data files.10. Sorts results by relative risk estimates, separately for the inherited and maternal genotype, and outputs two corresponding data files.To increase computation efficiency, repeating sections of code (i.e. macros) are used to split these steps into consecutive blocks that process subsets of SNPs . Each consecutive subset of SNPs is processed separately, and after processing of the last subset is complete, all results are merged. A visual overview of the structure of these steps of the platform is provided in Figure The use of SAS, a popular statistical software package, allows for flexibility in the MI-GWAS platform with simple programming changes. For example, the user can specify a range of successive SNPs to analyze rather than running all SNPs at once. This allows the user to split the analysis between multiple runs or between multiple computers. The program also includes code that can be modified to allow the user to specify specific individuals, triads, or SNPs to be excluded from the analysis. Further, the log-linear models can be modified to accommodate other family-based association designs (e.g. study designs that incorporate grandparents' genotypes ), other To validate the MI-GWAS platform and evaluate its performance, it was used to analyze a large, unpublished GWA study dataset derived from case-parent triads ascertained through cases with a conotruncal heart defect. The subject recruitment methods for this study have been previously described . BrieflyTo verify MI-GWAS results for inherited genetic effects using the CHOP dataset, selected SNPs were evaluated for complete triads using both PLINK and MI-GWAS and the chi-square values for the TDT and LRT, respectively, were compared.  To verify MI-GWAS results for maternal genetic effects using this dataset, selected SNPs were evaluated using both MI-GWAS and LEM run outside of MI-GWAS, and the resulting LRT p-values were compared. In addition, MI-GWAS was used to replicate the findings of a candidate gene study conducted in this study population, using LEM run outside of MI-GWAS . To be cThe evaluation dataset included data on 530,350 SNPs from 837 case-parent triads (497 complete triads and 340 triads with one or two members missing). To confirm MI-GWAS results for the test of inherited genetic effect, MI-GWAS was used to analyze all SNPs in the subset of complete triads, and chi-square values for the LRT were compared to TDT chi-square values obtained using PLINK to analyze the same data. The chi-square values for the LRTs of inherited genetic effects generated by MI-GWAS were quite similar to the TDT chi-square values generated using PLINK Table . There wAs expected, MI-GWAS running times were somewhat faster on computers with better specifications. For the same 60,000 SNPs, running times ranged from 11 hours 35 minutes to 22 hours 48 minutes for four computers with differing specifications  and reading a common GWA data input format (i.e. PLINK format). Further, unlike the TDT approach under PLINK, analysis under MI-GWAS uses a log-linear approach that provides estimates of effect size, allows use of data from incomplete triads -23, and,For some conditions, maternal genetic effects may influence the risk of disease in offspring via an effect on the in utero environment. However, maternal genetic effects have not been widely evaluated in GWA studies, at least partially due to lack of a platform designed to analyze maternal genetic effects using GWA data from case-parent triads. The application of the MI-GWAS platform to GWA analyses expands the types of genetic effects that can be evaluated with triad GWA data, which may lead to new insights regarding the etiology of common diseases. Future developments of the MI-GWAS platform will involve improving the efficiency of the platform, and incorporating analyses of additional types of effects .Project name: MI-GWAS http://www.sph.uth.tmc.edu/sbrr/mi-gwas.htmProject home page: Operating system(s): Platform independent®Programming language: SAS® release 9.2, LEM release 1.0 (freely available at http://spitswww.uvt.nl/~vermunt/#Software)Other requirements: SASLicence: None for MI-GWASAny restrictions to use by non-academics: None for MI-GWASAJA developed and tested the MI-GWAS platform, and wrote the manuscript. LEM conceived the study, provided input on the design and testing of the MI-GWAS platform and helped to draft and edit the manuscript. Both authors read and approved the final manuscript.


Despite considerable success of genome wide association (GWA) studies in identifying causal variants for many human diseases, their success in unraveling the genetic basis to complex diseases has been more mitigated. Pathogen population structure may impact upon the infectious phenotype, especially with the intense short-term selective pressure that drug treatment exerts on pathogens. Rigorous analysis that accounts for repeated measures and disentangles the influence of genetic and environmental factors must be performed. Attempts should be made to consider whether pathogen diversity will impact upon host genetic responses to infection.Plasmodium falciparum phenotypes, the number of clinical malaria episodes (PFA) and the proportion of these episodes positive for gametocytes (Pfgam), in a family-based cohort followed for 19 years, during which time there were four successive drug treatment regimes, with documented appearance of drug resistance. Repeated measures and variance components analyses were performed with fixed environmental, additive genetic, intra-individual and maternal effects for each drug period. Whilst there was a significant additive genetic effect underlying PFA during the first drug period of study, this was lost in subsequent periods. There was no additive genetic effect for Pfgam. The intra-individual effect increased significantly in the chloroquine period.We analyzed the heritability of two The loss of an additive genetic effect following novel drug treatment may result in significant loss of power to detect genes in a GWA study. Prior genetic analysis must be a pre-requisite for more detailed GWA studies. The temporal changes in the individual genetic and the intra-individual estimates are consistent with those expected if there were specific host-parasite interactions. The complex basis to the human response to malaria parasite infection likely includes dominance/epistatic genetic effects encompassed within the intra-individual variance component. Evaluating their role in influencing the outcome of infection through host genotype by parasite genotype interactions warrants research effort. The genomics era has heralded a plethora of Genome Wide Association (GWA) studies that have successfully identified genetic determinants of many medical disorders The application of GWA studies to infectious diseases has only more recently developed Plasmodium falciparum malaria In recent years, particular attention has been paid to addressing the human genetic susceptibility and resistance to To date genetic analyses have implicitly assumed that any variation brought about by parasite diversity will only have a minor impact, especially with very broad binary phenotypes such as severe versus mild malaria. This has been to some extent confirmed in animal models, but significant host-by-parasite interactions have been observed P. falciparum-related phenotypes: the number of clinical malaria episodes (PFA) Pfgam) PFA will be strongly influenced by variation in transmission intensity, whereas Pfgam will more strongly reflect the host-parasite interaction. In addition to calculating the heritability, we estimate the shared environment (here house) and intra-individual  effects, including maternal effects.Here we address the extent to which malaria phenotypes in a longitudinal family-based epidemiological study are influenced by the changes in anti-malarial drug treatment and in transmission intensity from 1990 to 2008. We estimate the heritability of two The project protocol and objectives were carefully explained to the assembled village population and informed consent was individually obtained from all subjects either by signature or by thumbprint on a voluntary consent form written in both French and in Wolof, the local language. Consent was obtained in the presence of the school director, an independent witness. For very young children, parents or designated tutors signed on their behalf. The protocol was approved by the Ethical Committee of the Institut Pasteur de Dakar and the Ministère de la Santé et de la Prévention of Senegal. An agreement between Institut Pasteur de Dakar, Institut de Recherche pour le Développement and the Ministère de la Santé et de la Prévention of Senegal defines all research activities in the study cohorts. Each year, the project was re-examined by the Conseil de Perfectionnement of the Institut Pasteur de Dakar and the assembled village population; informed consent was individually renewed from all subjects.The study was conducted in the malaria research project carried out since 1990 in a family-based cohort in Senegal, which has perennial holoendemic transmission (high force of infection). This site is managed by a tripartite agreement between the Institut Pasteur de Dakar, the Institut de Recherche pour le Développement and the Ministère de la Santé et de la Prévention of Senegal. A field research station with a dispensary run by nurses was constructed for the program and the health care is free-of-charge for the volunteers. All participants were asked to come to a study clinic for all their healthcare needs. Every person satisfying adhesion conditions could become a volunteer and every volunteer could leave the study at any time, therefore forming a dynamic open cohort. Further details of the study sites and adhesion criteria are previously described The family structure (pedigree) was available after a demographic census performed for every volunteer at his adhesion in the project. A verbal interview of mothers or key representatives of the household was used to obtain information on genetic relationships between studied individuals, their children, their parents, and to identify genetic links among the population. The total pedigree comprised 828 individuals, including absent or dead relatives, composed of 206 nuclear families (father – mother couples with at least one child) with an average of 3.6 children each. In addition, previous typing with microsatellites has enabled the construction of a pedigree based on Identity-by-Descent (IBD) using MERLIN P. falciparum clinical episodes per trimester (PFA) and (ii) the proportion of clinical episodes that were positive for gametocytes, parasite stages transmissible to mosquitoes (Pfgam). A malaria episode is defined as a clinical presentation with measured fever (axillary temperature >37.5°C) or fever-related symptoms  and with a blood smear positive for P. falciparum at a parasite/leukocyte ratio higher than the age-dependent pyrogenic threshold previously defined by Rogier et al. PFA, we first excluded any observations of each trimester for which the individual concerned was not present for at least 30 days ( = 1/3 of the trimester). Individuals satisfying presence conditions without any P. falciparum clinical episode in a trimester were classified as PFA = 0; individuals satisfying presence conditions with 1 or more malaria clinical episodes in a trimester correspond to person-trimester with PFA = {1, 2, 3, 4, or 5}. Repeated clinical presentations within 15 consecutive days were not considered to be independent and were excluded from the analyses, unless there was a parasite negative blood smear between two clinical episodes. In all cases parasite positivity was established as follows. Thick and thin blood films were prepared and stained by 3% Giemsa stain. Blood films were examined under an oil immersion objective at ×1000 magnification by the trained laboratory technicians and 200 thick film fields were examined to count the number of asexual and gametocyte parasite stages. The proportion of clinical episodes carrying gametocytes excluded any repeated clinical presentations within 15 days of previous treatment.The parasite phenotypes analyzed were: (i) the number of st chloroquine period, 4 categories: 2000 to 2003 to the 2nd chloroquine period, 3 categories: 2004 to 2006 for fansidar period, 3 categories: 2006 to 2008 for ACT period) and logarithm of number of days present in each trimester. For Pfgam, we additionally considered the presence of other Plasmodium spp. parasites  and time since last treatment. For Pfgam, age was found to be best described as a continuous variable in each drug period. By contrast, age classes <5 years, [5–15[, [15–35[ and ≥35 years best described the effect of age on PFA. Only individuals for whom there was pedigree information were included in the analysis.The following covariates were considered: sex, house, season  nested within year, year  from mid-2006 to 2008. The chloroquine drug period was divided into before (CQ1) and after (CQ2) 1999. This was done both to reduce the chloroquine period data set size and to examine the chloroquine periods prior to and during the observed emergence of parasite resistance to this drug From 1990 to 2008, four different drug regimens were implemented: P. falciparum clinical episodes per trimester (PFA) or (ii) a Binomial distribution when the studied phenotype was the proportion of clinical episodes that were positive for gametocytes (Pfgam). Genetic covariance, or relationship among all pairs of individuals in the study and among their parents or more distant ancestors, were stored in a squared matrix, the Pedigree-based genetic relatedness matrix, of dimension K×K where K is the total number of individuals in the pedigree including those with missing phenotypes. Genetic covariance between two individuals was computed using the pedigree information as described below:We implemented Generalized Linear Mixed Models (GLMM) using SAS 9.1.3  procedures GLIMMIX, MIXED and INBREED r = 2× coancestry where the coancestry between A and B is calculated using the method presented in Falconer and Mackay (1996) coancestry = Σp(1/2)n(p)×(1 + I Common Ancestor) where p is the number of paths in the pedigree linking A and B, n(p) the number of individuals (including A and B) for each path p and IX is the inbreeding coefficient of an individual X, which is equal to the coancestry between the two parents of X. IX is set to 0 if X is a founder. This matrix was built using INBREED procedure of SAS and then integrated into the models For A and B, a given pair in a pedigree, the genetic covariance is computed as The objective of the model used for the analysis was to estimate and separate different sources of variation underlying the total variation observed for the phenotype: the relative contributions of human genetics (additive genetic variance), intra-individual variance, maternal effects, house effects and unexplained residual variation. The repeated measurements design allows us to separate additive genetic variance from intra-individual variance. The occurrence of related individuals living in different houses allows separation of additive genetic variance from that due to shared household. Therefore, the random part of the mixed models included (i) the house identification variable as random effect assuming independence between houses to capture variance due to houses, (ii) the individual identification variable twice: a first time to capture the additive genetic variance by assuming non-independence between individuals and using the subpart of the Pedigree-based genetic relatedness matrix concerning individuals for which the phenotype was observed as covariance matrix between all pairs and a second time to capture other individual variances  assuming independence between individuals and (iii) the mother identification variable to capture maternal effects, assuming non-independence between mothers and offspring, using the subpart of the Pedigree-based genetic relatedness matrix concerning mothers of individuals for which the phenotype was observed. The unexplained residual variation was then deduced.PFA was analyzed using a Poisson regression model, which explicitly takes into account the non-negative integer-valued aspect of the dependent count variable. Therefore a GLMM with a Poisson distribution was fitted using SAS proc GLIMMIX and log as the link function between E(PFA | covariates) and a predictor that is linear. Initially a maximal model with all covariates was fitted and a minimal adequate model including only significant covariates was obtained. The effect of each covariate on the outcome variable was estimated taking into account both inbreeding, via the genetic relatedness matrix integrated in the SAS Proc GLIMMIX using the LDATA option, and repeated measures, as well as house effects.g is the additive genetic effect, m is the maternal effect, b is the intra-individual effect, c is the house effect and ε is a random residual; g2σ , m2σ , b2σ , c2σ , ε2σ are the additive genetic, maternal, intra-individual, house and residual variances, respectively. AN represents the matrix of additive genetic relationships between individuals, with dimension N×N, AM represents the matrix of additive genetic relationships of mothers to offspring, with dimension M×M, IN is an identity matrix with dimension N×N, IH is an identity matrix with dimension H×H, and In is an identity matrix with dimension n×n; and n = Σini where in is the number of measure for individual i, N is the number of individuals for which the phenotype was observed and M the number of their mothers.The vector of random effects was assumed to follow a multivariate normal distribution:g2σ/(g2σ + m2σ + b2σ + c2σ + ε2σ)The heritability is defined by For each variance component, an estimate was also generated for each individual contributing to the overall component. Thus, for the additive genetic and intra-individual effects, an estimate was established for each person. Similarly for house and maternal effects, estimates were established for each house and mother.Pfgam was analyzed by fitting a GLMM with a Binomial distribution, using SAS proc GLIMMIX PFA in the first analysis.P. falciparum clinical episodes per person per trimester (PFA). Over the 19-year study period, 713 individuals were present from between one and 75 complete trimesters generating 22,169 person-trimesters of presence. There were a total of 5,680 clinical P. falciparum episodes. The maximum number of PFA per person-trimester was five and the median was one. 485 individuals had at least one PFA positive trimester during the study period. The maximum number of clinical episodes per person per drug period was 40 and the median was two. The first composite phenotype considered was the number of P. falciparum clinical episodes that were positive for gametocytes, the parasite stage transmissible to mosquitoes. The prevalence of gametocytes at clinical presentation increased from 37% in the quinine period to 48% in both the chloroquine periods before decreasing to 17% and 12% in the Fansidar and ACT periods respectively , and the two chloroquine periods (P<0.001). Yearly variation had a significant impact in all periods except ACT. An increasing number of days of individual presence increased gametocyte carriage in the CQ1 period (P = 0.02) and increasing time since last drug treatment increased gametocyte carriage in the Fansidar period (P = 0.02).The second composite phenotype considered was the number of ectively . The perPFA was estimated by drug period. During the quinine period there was significant heritability, estimated at 46%, but which decreased and became non-significant in the subsequent drug treatment periods . There were only individual significant estimates for the additive genetic effect during the quinine period. Nineteen individuals had significant estimates during the quinine period; fourteen of these were present during more than one drug period but none had significant estimates subsequent to the quinine period. By contrast, five of them had significant estimates for the intra-individual effect in periods subsequent to the quinine period. Overall, individual estimates of genetic effects were highly correlated with intra-individual effects by drug period when non-zero  have so in only single drug periods. Interestingly, the degree to which the significant points separate from the rest appears to increase with time (i.e. from CQ1 through ACT); the blue triangles (Fansidar) and yellow circles (ACT) are more clearly separated from the rest in their respective Y-axis rows. This increase in the intra-individual variance component as displayed though individual estimates over time is reflected in the summarized intra-individual variance component in Of the 210 individuals present throughout the 19 year period, 69 had significant intra-individual estimates: fifty individuals in only one treatment period and the remainder in two (n = 15) or three (n = 4) different periods. As can be seen in per se had no effect (P = 0.31). Thus, there was no difference in time between infections in the quinine and 2 chloroquine periods, suggesting that treatment failure was not causing this significant increase in the intra-individual variance component.In the knowledge that resistance to chloroquine and then Fansidar emerged during the respective drug treatment periods, a potentially confounding factor would clearly be repetitive presentation of a single infection because of treatment failure. To evaluate whether the observed increases in the intra-individual variance was a result of drug treatment failure, we examined whether individuals with significant individual intra-individual estimates had a shorter time since previous treatment in the quinine and chloroquine periods, when incidence rate remained high and stable. Although the time since previous treatment for those individuals having significant intra-individual estimates at any time was shorter than for those never having significant estimates (P<0.001), drug period Heritability for the prevalence of gametocytes during clinical presentation only approached significance during the Fansidar period (P = 0.057) . By contPFA: there was significant correlation between estimates in consecutive drug periods, both with respect to estimates of individual intra-individual and additive genetic effects, but no correlation between more distantly related periods . There is thus no indication that previous drug treatment is causing this intra-individual effect. No obvious factor, such as age or sickle cell trait, was shared by such individuals. Five of these individuals had significant intra-individual estimates for PFA. Only one individual had a significant intra-individual estimate in the CQ1 period and was not significant in the CQ2 period.The strongly significant intra-individual variance component in CQ2 was due to 12 individuals, eight of whom repeatedly had gametocytes and four who rarely presented with gametocytes. Although the time since previous drug treatment was shorter in these significant individuals, there was no difference between those frequently carrying gametocytes and those rarely doing so . This suggests that the implementation of the new drug in some way interfered with the human genetic contribution to the outcome of infection. In direct contrast, the intra-individual variance component increased following the implementation of chloroquine.Firstly, it was notable that for P. falciparum clinical episodes in our cohort at any time period. The very high correlation of the individual genetic and intra-individual estimates within each drug period suggests that the two effects are highly confounded. This might be a result of insufficient resolution of the relatedness matrix within each drug period – i.e. either not enough relative-pairs were present within each period and/or the IBD matrix was not sufficiently resolved. This would lead to confounding between shared environmental, additive and non-additive genetic effects Intra-individual variance encompasses effects specific to each individual, classically including maternal effects and dominance (non-additive) genetic effects The most evident change in the parasite population during the study was the development of resistance first to chloroquine and then to Fansidar PFA, variability in gametocyte production in an infection will reflect the influence of the host-parasite interaction. Both parasite and host genetics can influence gametocyte production PFA and the two phenotypes were not correlated. Moreover, as for PFA, there was good correlation in estimates across only consecutive periods. These comparable effects to PFA were particularly notable during the period when transmission intensity was stable. Subsequently, the decrease in intensity in the Fansidar and ACT periods was accompanied by an even more significant decrease in gametocyte prevalence, resulting in perilously small sample sizes for reliable analysis.In contrast to the immeasurable effect of very fine scale spatial heterogeneity in exposure to infection that will impact on PFA. This would support the hypothesis that changes in the parasite population diversity are contributing to the observed phenotype.Here, the period of drug treatment strongly influenced this phenotype. Such an influence has been well documented following treatment. Chloroquine increases gametocyte production Estimation of heritability in its broad sense in natural populations is not possible and hence narrow sense heritability, which estimates the additive genetic contribution, is calculated. Actual values of heritability are specific for a study population at a particular time and thus strict comparison is not informative, although broad trends can be inferred. The size of heritability provides an indication of the power to detect the effect of individual genes when performing GWA studies. Here it is clear that for several reasons, the choice of the study period for GWA study analysis will affect the quality of the signal. The requirement for large longitudinal data sets to generate sufficient power must therefore be offset by the ever-increasing noise that accompanies long-term data sets – more time means more variance The peculiarity of the variance component analyses in this study was the replacement of an additive genetic component by an intra-individual component over time. Classical components of the intra-individual component, such as maternal effects, were not found to be the root cause of this and spatial heterogeneity in exposure seems an insufficient explanation, especially during the quinine and chloroquine periods. Insufficient resolution and power of the pedigree matrix may have led to confounding between additive and non-additive genetic components, but again this seems an inadequate explanation given the mean genetic relatedness of the individuals implicated. Observed patterns of individual estimates were consistent with there being specific host-parasite interactions. Although relatives might be expected to respond similarly to an identical parasite, this might not be detectable as an additive genetic component. To what extent changes in the parasite population can impact upon genetic studies is important to understand, both on a practical level of study sampling strategy and at a fundamental level to ask whether candidate genes should be expected to have an effect under whatever circumstances. In the hypothetical case of population fixation of a protective gene, heritability will be zero. What will be the expected heritability in a diverse human population if parasite diversity approaches zero? Will certain genes only be protective against a sub-set of parasites?In this study we have found suggestive evidence that the parasite population may impact upon estimates of heritability. Whereas a review of theory and data have led to the suggestion that additive genetic variance will represent the majority of genetic variance in complex traits


Genome-wide association studies have been used successfully to detect associations between common genetic variants and complex diseases, but common single-nucleotide polymorphisms (SNPs) detected by these studies explain only 5–10% of disease heritability. Alternatively, the common disease/rare variants hypothesis suggests that complex diseases are often caused by multiple rare variants with moderate to high effects. Under this hypothesis, the analysis of the cumulative effect of rare variants may thus help us discover the missing genetic variations. Collapsing all rare variants across a functional region is currently a popular method to find rare variants that may have a causal effect on certain diseases. However, the power of tests based on collapsing methods is often impaired by misclassification of functional variants. We develop a data-adaptive forward selection procedure that selectively chooses only variants that improve the association signal between functional regions and the disease risk. We apply our strategy to the Genetic Analysis Workshop 17 unrelated individuals data with quantitative traits. The type I error rate and the power of different collapsing functions are evaluated. The substantially higher power of the proposed strategy was demonstrated. The new method provides a useful strategy for the association study of sequencing data by taking advantage of the selection of rare variants. Genome-wide association studies (GWAS) have been used successfully to detect associations between common genetic variants and complex diseases. However, common single-nucleotide polymorphisms (SNPs) detected by current GWAS explain only at most 5–10% of disease heritability -4. GWAS With the development of next-generation sequencing technologies, more rare variants can be genotyped, so the analysis of associations between rare variants and diseases becomes possible. It is well known that traditional GWAS lack power for detecting rare variants. More powerful tests are needed to analyze resequencing genetic data. Recently, Li and Leal  proposedSeveral tests based on different collapsing strategies for case-control studies have been proposed. One is the cohort allelic sum test (CAST) , in whicHowever, the collapsing methods may not be robust and could be seriously impaired by misclassification of collapsing regions . RegionsWe analyze the GAW17 unrelated individuals data set, with 200 replicates of simulated phenotypes Q1 and Q2, to compare the power of different tests. There are 697 samples consisting of 209 case subjects and 488 control subjects. The data set contains 24,487 SNPs within 3,205 genes generated using real sequence data from the 1000 Genomes Project. We define rare variants as SNPs with a minor allele frequency less than 0.01 and perform collapsing within each gene. Because collapsing methods do not work well if the region includes too few SNPs, we first filter the genes according to a criterion of having at least 10 variants with one or more common SNPs. After filtering, we have 553 genes for analysis. The analysis is performed with the knowledge of the underlying simulating model.p-value greater than 0.05 are selected as covariates for the adjustment. The top five eigenvectors from Eigenstrat  for the 549 nonfunctional genes as the type I error rate of the test. Because different tests should adapt to different types of genes, power is calculated by (number of tests rejected at the null hypothesis)/200 for each functional gene.We analyzed 553 filtered genes on the 200 replicates for phenotypes Q1 and Q2. The type I error rate of the test and the power of the test are defined as follows. Take Q1 as an example; there are 4 functional and 549 nonfunctional genes. At a given significance level We found that the type I error of tests using the weighted-sum function was inflated for Q1. To have a fair comparison, we selected a different significance level for each test to have the type I error rates of all tests at the same level. Some permutation tests cannot have exact 5% type I error because there were only 1,000 permutations. Therefore we chose a significance level so that all tests had a well-controlled false-positive rate of about 6% for Q1 and 5% for Q2  or common variants (Tcom). All forward-selection-based tests were ranked at the top for KDR, which demonstrates the potential power of the forward selection strategy. For Q2, all tests became underpowered. Three genes  were detected by at least one test with power greater than 5%. FSws achieved the highest power (8%) in detecting SIRT1.For Q1, We have proposed a data-adaptive forward selection strategy for genetic association studies with multiple common and rare variants. The proposed test is aimed at selecting rare variants for collapsing that best amplify the association signal between functional regions and phenotypes. Traditional collapsing methods do not have the option of selectively collapsing only functional rare variants with the same effects on the risk of disease; thus they may be underpowered by misclassification in collapsing regions. The major advantage of our method is that it can selectively collapse rare variants with the same genetic effect as the common variant in the functional region, thus leading to a more significant association signal. To correct the bias resulting from selection and to alleviate the computational burden, we performed a genome-wide permutation test. We evaluated the power of our method using different collapsing functions, based on the same type I error. The results show that the proposed method has substantially higher power across different collapsing strategies. The way to select rare variants for each functional region also suggests that our method may have higher power to detect functional regions with an either positive (damage) or negative (protective) effect on the disease traits, even if no common variant is associated with the disease, so long as enough rare variants collectively affect the disease. The forward selection strategy could also be a powerful tool by adding different weights based on allele frequencies in order to lower the effect of common variants.We developed a data-adaptive forward selection procedure by collapsing a common variant with selected rare variants. The validity and substantially higher power of the proposed strategy were demonstrated using the GAW17 data. The method provides a useful strategy for association studies of sequencing data by taking advantage of the selection of rare variants.The authors declare that they have no competing interests.YD contributed in the development of the statistical test, provided data analysis strategies, and drafted the manuscript. LG carried out part of the programing work. RJ and JD both supervised the whole process and participated in drafting the manuscript. All authors read and approved the manuscript.


Avena sativa L.). Until recently, the molecular marker technology in oat has not allowed for many marker-trait association studies to determine the genetic mechanisms for tolerance. A genome-wide association study (GWAS) was performed on 428 spring oat lines using a recently developed high-density oat single nucleotide polymorphism (SNP) array as well as a SNP-based consensus map. Marker-trait associations were performed using a Q-K mixed model approach to control for population structure and relatedness. Six significant SNP-trait associations representing two QTL were found on chromosomes 3C (Mrg17) and 18D (Mrg04). This is the first report of BYDV tolerance QTL on chromosome 3C (Mrg17) and 18D (Mrg04). Haplotypes using the two QTL were evaluated and distinct classes for tolerance were identified based on the number of favorable alleles. A large number of lines carrying both favorable alleles were observed in the panel.Barley yellow dwarf viruses (BYDVs) are responsible for the disease barley yellow dwarf (BYD) and affect many cereals including oat ( Avena sativa L.) is an important cereal crop grown worldwide with nutritional benefits for both livestock and humans [Oat , MAV (Sitobion avenae) [Schizaphis graminum) make up the genus Luteovirus. Cereal yellow dwarf virus RPV [Rhopalosiphum padi; formally known as barley yellow dwarf virus RPV) and Maize yellow dwarf virus RMV  are in the genus Polerovirus. Viruses in the genera Luteovirus have replication-related protein related to members of Tombusviridae, while viruses in the genus Polerovirus have replication-related proteins similar to those of Sobemoviruses [Rhopalosiphum, however, Sitobion avenae and Schizaphis graminum are commonly found in specific regions [Rhopalosiphum that vector BYDV, Rhopalosiphum maidis and Rhopalosiphum padi. Rhopalosiphum padi is the primary vector for BYDV in the Illinois, transmitting the PAV strain [There are several virus strains and at least 25 aphid species that transmit BYDV. Specific strains are classified into two genera based on genome structure. BYDVs PAV ( avenae) , and SGVirus RPV   would allow for more efficient selection of tolerant lines by aiding in the introgression of multiple genes controlling the trait. Until recently, the only available marker platforms in oat were restriction fragment length polymorphism (RFLP) , amplifiBreeding for host-plant tolerance to BYD is the most effective method used to combat the destructive nature of the disease. It is believed that two to four genes are responsible for BYDV tolerance in oats . Four QTTriticum aestivum L.), and barley (Hordeum vulgare L.). In wheat, two genes have been identified for tolerance, Bdv1 and Bdv2 [Ryd1 [Ryd2 [Ryd3 [Ryd4 [Ryd2 gene on chromosome 3HL [Ryd2 [BYDV tolerance has been examined in other crops such as wheat  provide a powerful approach to determine the genetic structure of complex traits in crops, and to date, GWAS have not been conducted for BYDV tolerance in oat. GWAS detect marker-trait associations by exploiting linkage disequilibrium between a marker allele and the causative QTL allele; however, population structure within the panel of lines and genetic relationships within the population can lead to false positive associations and therefore need to be taken into consideration , 37. GWAThe association panel was developed by the Collaborative Oat Research Enterprise (CORE). The CORE is made up of a group of research scientists from North America and several other countries worldwide. Lines submitted to the panel by oat breeders had a high level of diversity and originated from six countries . A total of 428 spring oat lines were included in the final panel and phenotyped for BYDV tolerance.Rhopalosiphum padi (L.) carrying the Illinois isolate BYDV-PAV. This isolate is maintained at the University of Illinois at Urbana-Champaign by continuous aphid cultures grown on barley plants. One month prior to field inoculation, aphids were distributed onto a larger number of barley plants in order to increase aphid numbers to a level satisfactory for the number of hills to be inoculated. The aphids are then mixed with cornmeal in a 50/50 ratio to prevent aphids from sticking together or to the inoculation device while inoculating. Each 15 seed hill is then hand inoculated using a device containing a loading cell that equally measures out the same volume of aphid/cornmeal mixture (roughly 70 aphids per inoculation). The aphid/cornmeal mixture then falls down onto the hill via a plastic tube. The plants were then sprayed with insecticide (Cygon 2E™) after one week to kill the aphids. BYDV tolerance was evaluated after stem elongation was completed. Each hill is then scored using a scale from 0 to 9 with 0 being assigned to the most tolerant plants and 9 indicating the most sensitive [Tolerance for BYDV was evaluated at the University of Illinois Crop Sciences Research and Education Center, Urbana, IL in 2010 and 2011. Two replications of hills were planted in a randomized complete block design with 15 seeds per hill in the BYDV nursery. When the seedlings were in the three leaf stage (approximately 20 days after planting), the hills were inoculated with viruliferous ensitive , 41.Yijk is the observed BYDV phenotype, μ is the overall mean, yeari is the random effect of the ith year, blockj is the random effect of the jth block within the ith year, linek is the random effect of the kth line, year x lineik is the random effect of the interaction between the ith year and the kth line, and εijk is the random error term. Broad-sense heritabilities on an entry-mean basis (H2) were calculated for BYDV tolerance using the variance components from the mixed model above  for BYDV tolerance were calculated using a mixed model:el above  and equael above :H2=σg2σ2) higher than 0.8 were binned and a representative SNP from each bin was used [A total of 428 spring oat lines were included in the final panel and were genotyped using a high density oat SNP array containing 6000 SNPs. Genotyping was performed at the USDA-ARS Small Grains Genotyping Lab in Fargo, ND using the Infinium assay developed by Illumina . A totalA previously developed SNP-based physically anchored consensus map was used for marker locations . The mapX = 0,1,2 correspond to genotype BB, AB, AA at marker l, and p and q are the allele frequencies for allele A and B. The measure is averaged over all loci. The mixed model procedure was performed using the Q-K mixed model function with a false discovery rate α = 0.05 for multiple testing correction.Principal component analysis (PCA) was performed in order to examine the level of genetic structure in the panel (Q-matrix) via the PCA for Population Stratification function. The amount of relatedness  was performed via the Relationship Matrix function. The above functions, as well as the marker-trait associations were performed in JMP Genomics 7 . A Q-K mixed model was used for the marker-trait associations using the PCA values to form the Q-matrix, treated as fixed effect and the identical-by-descent (IBD) values for the K-matrix, treated as random effect. IBD values were calculated using equation ;IBDi,j=A wide range of phenotypic variation was observed for BYDV tolerance . This waThe level of population structure was examined to gain understanding of the possible effect on the association analysis. Principal component analysis (PCA) using eigenvalues on marker data showed that PCA 1 accounted for 8.7% of the variation in the data. The first five PCAs accounted for a total of 23.3% of the variation in the data showing there may be some slight population structure in the panel of lines. Five principle components were determined to be sufficient and used in the final model. Relatedness was estimated using IBD statistics.A total of six SNPs on chromosome 3C (Mrg17) and chromosome 18D (Mrg04) were significantly associated with BYDV tolerance . Note thUsing the most significant marker on chromosome 3C  and 18D , haplotypes were compared using contrasts . For easTwo different sets of near-isogenic lines (NILs) were included in the panel from the University of Illinois at Urbana-Champaign . Each seThe haplotypes using SNP3C and SNP18D exhibited similar patterns in both families. In family IL2250, the two tolerant lines (IL2250-18 and IL2250-14) both had BYDV BLUPs of 3.14 and 3.37 respectively and both lines had a “+ +” haplotype. The moderately tolerant line IL2250-3 had BLUP of 4.95 with a haplotype of “- +” and the sensitive line IL2250-15 with a BLUP of 6.98 had the unfavorable allele at both SNPs (“- -“).In the second family, the two tolerant lines IL2294-3 and IL2294-8 had BLUPs of 3.37 and 4.73. Both lines had the favorable allele at both SNPs (“+ +”). IL2294-1 had a BLUP of 6.31 and had the favorable allele at SNP3C and the unfavorable allele at SNP18D (“+ -“). IL2294-2 was the most sensitive line in the family and had a BLUP of 6.98. As expected this line had the unfavorable allele at both SNP markers (“- -“).Barley yellow dwarf is one of the most destructive diseases of cereal crops worldwide. New breeding strategies such as marker-assisted selection and genomic selection along with high-throughput genotyping platforms can help to provide breeders with the tools necessary to introgress tolerance into elite cultivars. The identification of QTL associated with BYDV tolerance is an important first step to understanding the genetics of the tolerance mechanisms. In this study, genome-wide association mapping was performed on a panel of 428 spring oat cultivars using a 6K oat SNP array. This panel of lines includes a diverse amount of oat germplasm from several countries and breeding programs. A total of 1402 SNPs were used for genome-wide association for BYDV tolerance. A broad and continuous distribution was observed for BYDV tolerance across the panel and is in agreement with previous work that shows host plant tolerance for BYDV is multigenic –26, 46. Population structure can result in false associations between markers and traits and therefore should be evaluated for proper analysis . PrincipPrevious studies have identified QTL for BYDV tolerance; however, many were performed with a very low number of markers, and the new SNP-based consensus maps did not use any of the markers reported before making it difficult to compare the linkage maps and the consensus map. Two bi-parental recombinant inbred lines populations using a previous version of the consensus map and the same 6K high density oat SNP array identified two large effect QTL that mapped to chromosomes 3C (Mrg17) and 19A (Mrg04) . In thisSeveral consensus map versions have been published to date, with the most recent in 2016. With the new map, nomenclature and some marker locations have changed from previous versions. Because of this, the new nomenclature has been added to the chromosome nomenclature of this study and previous studies. SNP GMI_ES05_c3073_282, representing the most significant QTL on chromosome 18D (Mrg04), did in fact agree with the QTL identified on chromosome 19A (Mrg04) from previously mentioned bi-parental studies . For theThe six significant marker sequences were blasted to various databases, including NCBI BLAST, BARLEX and the Wheat Genome Database. A 65% agreement was observed for the sequence with SNP3C for a mosaic virus helicase domain binding protein in wheat. Observing this was interesting since the protein was viral related and therefore could show conserved regions of viral tolerance/resistance genes. However, none of the identified BYDV genes/QTL in wheat or barley was identified in the searches.Examining the potential haplotypes for the identified QTL showed that having the favorable alleles at both loci (“++”) leads to improved levels of tolerance (lower BYDV scores) . AlthougFrom a breeding perspective, it is important to identify the tolerant lines that were submitted, as well as where they came from. The complete list of lines is included in the supplementary material as well as a breakdown of the haplotypes by breeding programs . OverallThe two families of NIL lines that were included in this study were both developed using phenotypic selection during the 1990s . It is nHost plant tolerance for BYD is the most effective mechanism for controlling the barley yellow dwarf virus. In this study, two QTL were identified on chromosomes 3C (Mrg17) and 18D (Mrg04) using a 6K high density oat SNP array and oat consensus map. This study also identified three main levels of BYDV tolerance based on the number of favorable alleles at the two loci. The largest group with over half the lines in the panel formed the “tolerant” class and had favorable alleles at both 3C (Mrg17) and 18D (Mrg04). Highly tolerant lines were observed from every program that made submissions to the panel, except from programs from Europe. The QTL and associated SNPs identified in this study can be used in marker-assisted selection or genomic selection programs to better improve host plant tolerance in oat for BYD.S1 Table(XLSX)Click here for additional data file.