Metformin is a first-line oral agent used in the treatment of type 2 diabetes, but glycaemic response to this drug is highly variable. Understanding the genetic contribution to metformin response might increase the possibility of personalising metformin treatment. We aimed to establish the heritability of glycaemic response to metformin using the genome-wide complex trait analysis (GCTA) method.1c concentrations before and during metformin treatment from patients in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) study, which includes a cohort of patients with type 2 diabetes and is linked to comprehensive clinical databases and genome-wide association study data. We applied the GCTA method to estimate heritability for four definitions of glycaemic response to metformin: absolute reduction in HbA1c; proportional reduction in HbA1c; adjusted reduction in HbA1c; and whether or not the target on-treatment HbA1c of less than 7% (53 mmol/mol) was achieved, with adjustment for baseline HbA1c and known clinical covariates. Chromosome-wise heritability estimation was used to obtain further information about the genetic architecture.In this GCTA study, we obtained data about HbA1c, adjusted for pretreatment HbA1c. Chromosome-wise heritability estimates suggest that the genetic contribution is probably from individual variants scattered across the genome, which each have a small to moderate effect, rather than from a few loci that each have a large effect.5386 individuals were included in the final dataset, of whom 2085 had enough clinical data to define glycaemic response to metformin. The heritability of glycaemic response to metformin varied by response phenotype, with a heritability of 34%  for the absolute reduction in HbAGlycaemic response to metformin is heritable, thus glycaemic response to metformin is, in part, intrinsic to individual biological variation. Further genetic analysis might enable us to make better predictions for stratified medicine and to unravel new mechanisms of metformin action.Wellcome Trust. Metformin is the recommended first-line oral agent for the treatment of hyperglycaemia in patients with type 2 diabetes, with more than 100 million users worldwide. Despite its impressive safety record and efficacy at the population level, the exact mechanism of metformin action is still elusive and patients' glycaemic responses to metformin vary considerably.As with other complex traits, glycaemic response to metformin is probably determined by the interplay between genetic and environmental factors. Clinical variables such as BMI, drug adherence, and dosing only account for part of the variation.Historically, the heritability of drug response has rarely been established, largely because of the impracticality of applying the traditional twin and family study designs to drug-response phenotypes; assembling sufficient family members with the same diagnosis who have received the same medication and have been assessed using the same treatment outcome is all but impossible. Alternative methods using population-based genome-wide association study (GWAS) data for heritability estimation have been developed.In this GCTA study, we used a bioresource linked to electronic health record data (GoDARTS) rather than a specific cohort developed to assess metformin pharmacogenetics. As part of the Wellcome Trust UK type 2 diabetes case-control collection, patients with type 2 diabetes in Tayside, Scotland, have been invited to give written informed consent for DNA collection since October, 1997. So far, nearly 10 000 patients with type 2 diabetes have participated in the GoDARTS study.1c concentration, which is a routinely measured clinical test of glycaemic control in patients with diabetes, to establish glycaemic response to metformin (1c measurements of glycaemic control are not available in the GoDARTS study. Pretreatment (baseline) HbA1c was defined as the measurement closest to, and within 6 months of, the metformin start date (index date), whereas on-treatment HbA1c was defined as the minimum recorded HbA1c achieved within 18 months after the index date. We used four different response phenotypes: absolute reduction in HbA1c, which was the difference between baseline and on-treatment HbA1c; proportional reduction in HbA1c, which was the absolute reduction divided by baseline HbA1c; adjusted reduction in HbA1c, which was the residuals of absolute reduction adjusted by known clinical covariates such as baseline HbA1c, adherence, dose, creatinine clearance, and treatment group; and a dichotomous phenotype of whether or not the target on-treatment HbA1c of <7% (53 mmol/mol) was achieved, with adjustment for baseline HbA1c and known clinical covariates.We used HbAetformin ; fastingPatients who received metformin monotherapy used no other antidiabetic drugs in the 6 months before the index date or during the study period; sulfonylurea treatment was continued throughout the study period in patients who used metformin as an add-on therapy. The sulfonylurea dose was allowed to vary. Details about how the covariates are defined, and the response models, are outlined in the SUMMIT) study genotyped 3499 patients with the Illumina HumanOmniExpress microarray . Imputation to the HapMap3 panel—a database of common genetic variants that occur in human beings—and a stringent quality control pipeline were used to combine the two datasets and reduce the systematic discrepancy between the genotypes produced by the two microarrays and their corresponding calling algorithms (GWAS data in the GoDARTS cohort were available from two previous studies. The Wellcome Trust Case Control Consortium 2 study (WTCCC2)gorithms .We did two benchmark analyses with GCTA to validate the combined GWAS dataset. The first analysis showed that the heritability of human height was 46% (SE 6) in this cohort, which was consistent with previous estimates by studies applying the GCTA method.We used GCTA version 1.11 to calculate the pair-wise genetic relationship between individuals and create the genetic relationship matrix.1c concentrations and the heritability of on-treatment HbA1c concentrations. The most informative parameter estimated from this bivariate analysis was the genetic correlation (rg), which represents the proportion of variance shared between baseline HbA1c and on-treatment HbA1c concentrations that was contributed by common genetic determinants. The correlation between the residual variance is re, which represents, in part, contribution from environmental factors.Additionally, we used a bivariate analysis to jointly estimate the heritability of baseline HbAWe established statistical significance using the likelihood-ratio test of specific hypothesis. We report the asymptotic 95% CI, which was calculated as 1·96 times the SE. Because the SEs of the parameter estimates were derived from first-order Taylor series expansions about the likelihood in GCTA, they might be biased for moderate study sample sizes,The sponsor had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.The combined dataset included 1 150 943 autosomal SNPs from 6992 patients. After filtering for cryptic relatedness, 5386 independent individuals were included in the final dataset. Of these, 2085 patients had sufficient clinical data to define their glycaemic response to metformin phenotypes. h2) for baseline HbA1c was 29% , increasing to 42%  for on-treatment HbA1c . Of the four drug-response phenotypes, the model-adjusted reduction in HbA1c  and the ability to reach target HbA1c  were the most heritable. The heritability estimates for absolute reduction in HbA1c  and proportional reduction in HbA1c  were smaller, and were not statistically significant (Heritability (nt HbA1c . Baselinnificant .Table 2U1c attributable to each chromosome (chromosome-wise heritability) was regressed against the chromosome length, we noted a significant linear trend (p=0·037) for longer chromosomes to explain larger proportions of the variance. We also noted a similar trend (p=0·034) for achievement of target HbA1c.To assess whether the genetic contribution to variation in response to metformin is driven by a few loci with a large effect or many loci with small effect, we did univariate heritability estimations for each chromosome separately for the two glycaemic response phenotypes that were significantly heritable. The genetic contribution to variation in response is distributed across several chromosomes . When th1c concentrations estimated a moderate genetic correlation (rg) of 0·58 (95% CI 0·06–1·09) between these two traits  and Alzheimer's disease (h2=30% [SE 3]),1c response to metformin to a similar extent.PanelSystematic reviewWe searched PubMed on Feb 18, 2013, with the search terms “heritability” and “metformin”. We did not apply any publication date or language restrictions. We found no previous reports on the heritability of glycaemic response to metformin.InterpretationThis study is, to our knowledge, the first to establish that glycaemic response to metformin is likely to be moderately heritable. Enhanced GWAS studies will identify more variants, enabling better response predictions to be made, and will identify new mechanisms of metformin action in the reduction of hyperglycaemia in the treatment of type 2 diabetes.This study is, to our knowledge, the first to show that genetic differences contribute considerably to the variation noted in patients' glycaemic response to metformin . The her1c, could have had an undue effect on the reported trend.We did the chromosome-wise heritability estimation to provide information about the genetic architecture of glycaemic response to metformin. Clearly, several variants across different chromosomes contribute to the metformin response variation. The finding that the contribution by an individual chromosome is significantly correlated to its length suggests that on each chromosome might be many variants with a small to moderate effect size rather than a few variants with major effect. This hypothesis is also supported by results of the metformin response GWAS, which reported that no individual variant explained a large proportion of the variance.1c were greater than for the unadjusted models. Because baseline HbA1c has been well documented to have a major effect on the absolute reduction phenotype,1c measurements. These adjusted phenotypes probably best address the pharmacogenetics of metformin when considering what factors are associated with the greatest reduction in HbA1c for a given HbA1c concentration before metformin initiation. An adjusted phenotype was used to successfully identify variants near the ATM locus that affect on-treatment HbA1c but not baseline HbA1c.1c do capture some of the shared genetic component (rg) described above, and thus identified variants might reflect not only the response to metformin, but also the variance in HbA1c per se. When considering what the biological determinants of response to metformin are, a better phenotype might be the unadjusted absolute reduction in HbA1c because this measure does not capture any shared genetic contribution, only the variants with differential genetic effects between the HbA1c before and after initiation of metformin treatment.1c reduction model unadjusted for the baseline measure has been used in studies of statin pharmacogenetics.1c did not achieve statistical significance in our study of response to metformin treatment.In the univariate GCTA analysis we were able to assess whether different metformin response phenotypes are heritable. We do not have statistical power to conclude that one phenotype is more heritable than another, although the point estimates for the heritability of the response phenotypes that adjusted for the baseline HbA1c variation; a potentially different set of genetic and environmental determinants affect the on-treatment state . The shared variants that underlie the genetic correlation have the same effect on HbA1c variation in the two states, and their genetic contribution to HbA1c is not changed by metformin treatment. Thus an rg of 1 would imply that metformin intervention does not change the genetic determinants of HbA1c in the pretreatment and on-treatment state—ie, no pharmacogenetic effect occurs.rg would imply that the genetic determinants of HbA1c are largely different before and after metformin treatment, hence a strong pharmacogenetic effect. In our analysis, the point estimate for the shared genetic contribution was 0·58, suggesting that around half of the genetic determinants contributing to baseline HbA1c and on-treatment HbA1c concentrations were shared, with half the genetic determinants differing between the baseline and metformin treatment states; however, we do acknowledge that the 95% CI precludes a definitive conclusion of this bivariate analysis.We report a new application of the GCTA bivariate analysis in this drug-response study. This approach has advantages over univariate approaches because it does not make assumptions about the response model; rather, it uses a quantitative genetic approach to partition variance into genetic and environmental fractions that are shared and non-shared between two states or traits. The idea behind such an analysis is that intervention with metformin can change the physiological state of a patient. In the pretreatment state, a set of genetic and environmental factors determine the HbAnt state . The bivre) of 0·28 could be contributed by both environmental factors and shared rare genetic variants. Thus sequencing-based genomic studies with an emphasis on the rare drug-response variants are valid irrespective of the heritability estimates from GWAS SNPs.A key limitation of this study is the reasonably small sample size. However, the GoDARTS GWAS data used are from the largest metformin pharmacogenomic cohort done so far, including 2085 individuals who received metformin. Yet we still noted considerable 95% CIs for the heritability and genetic correlation estimates due to the limited sample size. Thus, despite having sufficient power to find that glycaemic response to metformin is a heritable trait, we do not have power to establish whether one drug response trait is more heritable than another. To do this, 4450 patients would be needed to statistically differentiate true heritability of 20% and 34%, which correspond to the two extremes of the estimated heritability of our four reported phenotypes.In summary, using GWAS data from 2085 patients with type 2 diabetes, our analysis showed that genetic variants contributed to the variation in glycaemic response to metformin, with the heritability of metformin response estimated at up to 34% . This result shows that a moderate proportion of the variance in glycaemic response is genetic, and represents underlying biological differences between individuals. The variants are likely to have a small-to-moderate effect and be scattered across the genome. So far, very little of the genetic contribution to metformin response has been identified; GWAS analyses with larger samples could find more genetic variants that enable better predictions to be made for personalised or stratified medicine, and unravel new mechanisms of metformin action in the reduction of hyperglycaemia.


N = 7,776), we show that most genetic variance lies in a small proportion of the genome and that previously identified linkage peaks have higher than expected regional variance.Despite considerable efforts, known genetic associations only explain a small fraction of predicted heritability. Regional associations combine information from multiple contiguous genetic variants and can improve variance explained at established association loci. However, regional associations are not easily amenable to estimation using summary association statistics because of sensitivity to linkage disequilibrium (LD). We now propose a novel method, LD Adjusted Regional Genetic Variance (LARGV), to estimate phenotypic variance explained by regional associations using summary statistics while accounting for LD. Our method is asymptotically equivalent to a multiple linear regression model when no interaction or haplotype effects are present. It has several applications, such as ranking of genetic regions according to variance explained or comparison of variance explained by two or more regions. Using height and BMI data from the Health Retirement Study ( Currently known genetic associations only explain a relatively small proportion of complex traits variance. In accordance with the widely accepted polygenic nature of complex traits, it has been proposed that weak, yet undetected, associations underlie complex trait heritability of a wide variety of phenotypes such as heightcis regulation of gene expression7Clustering of weak associations within defined chromosomal regions has been previously suggestedp-value (p < 0.01) and LD (r2 < 0.1)14Several methods have been described to estimate the overall contribution of common genetic variants to complex traits variance, but no method was specifically designed to estimate regional association using summary association statistics while accounting for LD . For insN = 7,776) for which individual-level genotypes were available. Using only summary association statistics for our method and corresponding individual-level data for variance component models, both methods provided consistent estimates of genome-wide genetic variance , highlighting the disadvantage of pruning instead of adjusting for LD. We obtained consistent results when using LD data from 1000G data instead of HRS  according to decreasing regional genetic variance by applying three different methods on Genetic Investigation of Anthropometric Traits consortium (GIANT) summary association statistics19d of HRS , changind of HRS , or chand of HRS .p-value for each block in both GIANT and HRS . On the other hand, the median minimum p-value was 1.9 × 10−3 for height in GIANT, with 9% of blocks having one or more genome-wide significant associations. The median minimum univariate p-values were 1.8 × 10−2 and 1.7 × 10−2 for BMI and height in HRS. No SNP reached genome-wide significance in HRS.A relatively small proportion of blocks contributed disproportionately to genetic variance. The top 25% SNP blocks explained 0.94 of BMI genetic variance (i.e. = 0.132/0.140) and 0.83 of height genetic variance when using LARGV on GIANT data to rank the genetic variance of each SNP block. These results could potentially be explained by the presence of one or more very strong associations in each of these top SNP blocks. To explore this possibility, we recorded the minimum univariate association SNP  and HRS . Median p = 0.002) enrichment in regional association within a distance of +/−7.5 Mb from the linkage marker, corresponding to an estimated excess regional genetic variance of 0.0044 over the genome-wide average  evidence of linkage in Europeans were identified, all for height average . Upon a p-values, suggesting the presence of multiple weak associations underlies their impact on phenotypic variance, especially for BMI. The concentration of genetic associations within these regions supports the existence of critical nodes in the genetic regulation of complex traits such as height and BMI, with implications not only for association testing but also for population genetics and natural selection. These results also suggest that a combination of strong genetic associations and regional associations contribute to complex traits variance, with the relative proportions varying across traits. For instance, a higher proportion of genetic variance was found in the top 25% blocks for BMI yet these blocks had less significant minimum univariate p-value than height.We propose a novel method to estimate regional genetic variance from summary association statistics. Using this method, we confirmed a major role of regional associations in complex trait heritability, whereby the aggregation of genetic associations contributes disproportionately to phenotypic variance. Selecting the top SNP blocks from the GIANT meta-analysis, we showed that 25% of the genome is responsible for up to 0.94 and 0.83 of BMI and height genetic variance. A large proportion of these blocks had unremarkable minimum univariate Our method can also be used to estimate the genetic variance explained by extended regions. We therefore tested the hypothesis that some of the previously identified linkage peaks are the result of regional associations. The only known linkage peak with LOD > 3.0 for height showed a marked and significant enrichment in regional association. This region had been previously identified in multiple linkage studies21Our proposed method has several advantages and is complementary to other methods. First, it provides results that are highly consistent with the widely used variance component models requiring individual-level data. Second, it is computationally straightforward and can be applied to large datasets. Third, it is agnostic and therefore complementary to functional annotations of the genome. Fourth, since SNPs are not pruned by either LD or significance, every SNP block or region can be evaluated.ηd, see Methods). While this information is currently not available, consistent results were observed when using LD data from either HRS or 1000G, demonstrating the robustness of our approach to LD misspecification. Third, we have only tested continuous traits. Nevertheless, our method can be easily adapted to other outcome types through the use of generalized linear models.A few limitations are worth mentioning. First, the assumption that SNPs contribute to genetic variance without any interaction or haplotype effects can lead to an underestimation of genetic variance. While our estimates were consistent with variance component models, there is a need for statistical models that better capture genetic variance when strong haplotype effects are expectedIn this report, we establish a novel method to estimate the regional contribution of common variants to complex traits variance using summary association statistics. Our method has several applications, such as ranking of genetic regions for genetic variance and identification of key regions contributing to genetic variance. Our method can also be used to perform network analysis using summary association statistics, or to combine summary association statistics with other types of genetic annotations as we have shown with linkage results.pareg@mcmaster.ca.We have previously shown that large-region joint association, where multiple genetic variants are included as independent variables in a linear model, is a simple and powerful way to test for regional associations when individual-level data are availabler2) between pairs of SNPs crossing block boundary is calculated. The cut-point that minimizes the maximal LD is chosen, thus defining the first block, and the procedure is repeated for each subsequent block until all SNPs on a chromosome arm have been assigned to a block. We empirically determined that SNP blocks of size 85 SNPs to 95 SNPs (median 90 SNPs) had a median physical size of 250 Kb.We first divided the genome into regions of contiguous SNPs varying in size (e.g. from 195 SNPs to 205 SNPs), herein referred to as SNP blocks and used as units for regional associations. To minimize inter-block LD and thus “spillage” associations, we devised a greedy algorithm optimizing the choice of block boundary sequentially from one end of a chromosome to the other. Briefly, using a user-defined minimum and maximum block size (in number of SNPs) and starting at one end of a chromosome arm, each possible “cut-point” between the first and second block are tested and maximal LD  SNP blocks. Furthermore, the distribution of adjusted R2 under the null hypothesis of no association has been previously describedR2.The use of adjusted Estimating the contribution of regional genetic associations from summary association statistics is challenging when the exact SNP linkage disequilibrium structure of source populations is unknown. While approaches have been described to perform joint or conditional associations23Y) standard normally distributed and genotypes normalized to have mean = 0 and standard deviation = 1 throughout. Given an n × m genotype matrix X representing genotypes at m SNPs in n individuals and the pairwise linkage disequilibrium (r2) between two SNPs k and l as r2k,l, for a SNP d, the following LD adjustment (ηd) can be defined as the summation of LD between the dth SNP and 100 SNPs upstream and downstream:Without loss of generalizability, we assume a quantitative trait . Only including SNPs with summary GWAS statistics in the sum, variance explained by each SNP bd denotes the univariate regression coefficient commonly reported in GWAS results (assuming genotypes have been standardized to have mean zero and SD = 1). Regional variance explained is then given by the sum of where D such that m by m symmetric matrix, where Dk is a m × 1 vector whose entries represent the kth column of D. We will make use of the following properties:To simplify the calculation, we define N is the total number of individuals in the sample.where β, whose individual components, i.e. the SNPs, i = 1, 2, …, m, have mean zero and variance σ2. The size of the variance σ2 is on the scale of M1−, where M is the number of genome-wide SNPs. The genetic model can be expressed as:Suppose the genotype matrix is fixed while the true genetic effect is a random vector ε is a vector of standard normal error with identity variance covariance matrix. Then, the vector of estimated multiple linear regression coefficients B is given by:where R2 can be written in terms of the true effect and the error term:The multivariate variance explained β has variance covariance matrix σ2I, the expected variance explained is simplified toand since the error term has identity variance covariance and the true effect and the variance can be calculated accordingly:b and directly obtained from GWAS summary statistics, are given byThe univariate regression coefficients, denoted by lower case The total variance explained over a region We can rewrite the expression by defining:Thus, we have (Equation 10):Since DΛD is a positive definite matrix, an upper bound for the variance can be expressed as (Equation 12):The variance of where m replaced by the “effective” number of genetic markers m). In other words, The expected values are equivalent between multiple linear regression model p < 1 × 10−6 were excluded, (5) individuals for whom the reported sex does not match their genetic sex were excluded, (6) SNPs with minor allele frequency lower than 0.02 were removed. The final dataset included 7,776 European participants genotyped for 740,748 SNPs. Height and BMI was adjusted for age and sex in all analyses. To mitigate the effect of outliers, we have removed values outside the 1st and 99th percentile range for each of height and BMI. All analyses are adjusted for the first 20 genetic principal components unless stated otherwise. All LD estimates used throughout the manuscript were derived from HRS genotypes. HRS was not part of the GIANT meta-analysis of height and BMI26We conducted large region joint association analysis for height using genome-wide data from the publicly available Health Retirement Study . HRS quality control criteria were used for filtering of both genotype and phenotype data, namely: (1) SNPs and individuals with missingness higher than 2% were excluded, (2) related individuals were excluded, (3) only participants with self-reported European ancestry genetically confirmed by principal component analysis were included, (4) SNPs with Hardy-Weinberg equilibrium How to cite this article: Pare, G. et al. A method to estimate the contribution of regional genetic associations to complex traits from summary association statistics. Sci. Rep. 6, 27644; doi: 10.1038/srep27644 (2016).


The current practice of using only a few strongly associated genetic markers in regression models results in generally low power in prediction or accounting for heritability of complex human traits.We illustrate here a Bayesian joint estimation of single nucleotide polymorphism (SNP) effects principle to improve prediction of phenotype status from pathway-focused sets of SNPs. Chronic fatigue syndrome (CFS), a complex disease of unknown etiology with no laboratory methods for diagnosis, was chosen to demonstrate the power of this Bayesian method. For CFS, such a genetic predictive model in combination with clinical evidence might lead to an earlier diagnosis than one based solely on clinical findings.One of our goals is to model disease status using Bayesian statistics which perform variable selection and parameter estimation simultaneously and which can induce the sparseness and smoothness of the SNP effects. Smoothness of the SNP effects is obtained by explicit modeling of the covariance structure of the SNP effects.The Bayesian model achieved perfect goodness of fit when tested within the sampled data. Tenfold cross-validation resulted in 80 % accuracy, one of the best so far for CFS in comparison to previous prediction models. Model reduction aspects were investigated in a computationally feasible manner. Additionally, genetic variation estimates provided by the model identified specific genetic markers for their biological role in the disease pathophysiology.This proof-of-principle study provides a powerful approach combining Bayesian methods, SNPs representing multiple pathways and rigorous case ascertainment for accurate genetic risk prediction modeling of complex diseases like CFS and other chronic diseases.The online version of this article (doi:10.1186/s40246-015-0030-6) contains supplementary material, which is available to authorized users. Human genetic studies generally focus on identifying genes associated with diseases and subsequently using the significant genetic information for phenotypic predictions. Statistical methods vary with each of these goals, with methods for gene identification becoming increasingly effective with genome-wide association studies (GWAS). However, modeling principles for phenotypic predictions of complex human diseases remain largely unexplored even in this era of GWAS , 2. PhenBayesian variable selection , 10 and This study adhered to human experimental guidelines of US Department of Health and Human Services and the Helsinki Declaration. The Centers for Disease Control Human Subjects committee approved the study protocol, and all subjects gave written informed consent. Subject recruitment, clinical evaluation, laboratory tests, and their classification were described previously . BrieflyHTR2C and MAOA). SNPs were selected from the SNP database (dbSNP) of National Center for Biotechnology Information database, Applied Biosystem’s SNPBrowser™ or from the literature. All SNP markers had a minor allele frequency ≥10 % with the exception of HTR2A rs6314 (6.2 %), a non-synonymous SNP. DNA extraction and genotyping were done as described earlier ).In conclusion, our results demonstrate, as a proof of principle, the power of using a combination of a Bayesian logistic mixture modeling principle, pathway-focused SNPs, and rigorous subject ascertainment for highly accurate prediction of complex phenotypes. WGV estimates provided by the model can also be useful to identify individual genetic markers/genes with potential biological functions. Future studies are warranted to expand this approach using multiple biological pathways and multiple populations  to deriv


HBA2 gene which, by the experimental design used in a malaria association study in Tanzania, shows a high missing data percentage and is weakly linked with the remaining genetic markers in the data set. We constructed different imputation models and studied their performance under different missing data conditions. Overall, MICE failed to accurately predict the true genotypes. However, using the best imputation model for the data, we obtained unbiased estimates for the genetic effects, and association signals of the HBA2 gene on malaria positivity. When the whole data set was analyzed with the same imputation model, the association signal increased from 0.80 to 2.70 before and after imputation, respectively. Conversely, postimputation estimates for the genetic effects remained the same in relation to the complete case analysis but showed increased precision. We argue that these postimputation estimates are reasonably unbiased, as a result of a good study design based on matching key socio-environmental factors.Multiple imputation based on chained equations (MICE) is an alternative missing genotype method that can use genetic and nongenetic auxiliary data to inform the imputation process. Previously, MICE was successfully tested on strongly linked genetic data. We have now tested it on data of the  Missing genotypes are common in genetic association studies but often discarded from the analysis. This popular practice typically decreases statistical efficiency and power in comparison to an analysis where missing data are taken into account conveniently. It may also introduce estimation bias, particularly when the missing data pattern is not completely random. Since the advent of the HapMap project and the decrease in costs of genome-wide association studies (GWAS), several imputation methods have been proposed to deal with missing genotypes of typed or untyped single-nucleotide polymorphisms  has proven to be a useful imputation technique in different statistical applications  in contrast to the low missing genotype percentage observed for the remaining SNPs in the data set (<10%). As a consequence, the subsequent statistical inferences will have different precision and power if only the complete case data are used. In this setting, MICE would appear to be the most promising tool to perform genotype imputation because one can inform the imputation process with genetic and nongenetic data. However, because of the high fraction of missing data on the α3.7-globin deletion, it is unclear whether this method could provide accurate results in terms of genetic association assessment, genetic effect estimation, and missing genotype prediction. The goal of this study was therefore to learn the limits of MICE in imputing missing genotypes in this nonstandard setting.This paper deals with genotype imputation in data from an association study  of malaria parasite positivity in Tanzania . We focus our study on the African αHBA2 gene was only attempted in a subset of individuals living in 13 villages from four altitude-based transects  in the Kilimanjaro and Tanga regions of Tanzania, as described elsewhere . In brief, genetic data refer to a total of 165 SNPs predominantly from malaria candidate genes , of which 110 passed our stringent quality control step . A complete list of the SNPs can be found elsewhere for a similar study in Mali . Experimentally, the number of α3.7-globin deletions per individual genotype was determined by polymerase chain reaction assays as described elsewhere  and samples with no DNA template as a negative control were included to each set of PCR reactions, as controls to assess the status of α3.7-globin deletion in the individuals. This was followed by gel electrophoresis in order to determine the number of α3.7-globin deletions  defining the genotype of an individual.We have also included data on the African αPlasmodium falciparum positivity (detected by microscopy inspection of blood smears on slides), and parasite density.For each individual in the study, we also have information on ethnicity, gender, age, hemoglobulin (Hb) levels, 3.7-globin deletions affects Plasmodium falciparum positivity . We first performed an unadjusted association analysis between the number of α3.7-globin deletions and parasite positivity using the Pearson's χ2 test for two-way contingency tables. We then used logistic regression to test the genetic association, adjusting for putative socio-environmental confounders, such as “ethnicity” , “age” (in years), “transect”  and “gender” . In this analysis, we compared two nested models, one including putative confounders only and another including both confounders and the genetic effects of the HBA2 locus. In the latter model, we considered two independent genetic effects of the HBA2 locus (λ1 and λ2 ), one associated with one deletion and another associated with two deletions. Model comparison was made by means of a likelihood ratio test, where a small p-value  is indicative of a statistical association between the HBA2 locus and malaria parasite positivity. Alternatively, one can use −log10 as a measure of association strength. Large values of this quantity suggest a strong association between the locus under analysis and the phenotype.The purpose of the genetic association analysis is to determine whether the number of the α3.7-globin genotyping was actually attempted, we tested whether missing genotypes were completely at random (MCAR) assuming a missing at random (MAR) mechanism as the saturated model for the data. This analysis was performed using the ACD package for the R software  generate random initial values for the missing genotypes, (2) estimate the above logistic regression model, (3) generate new random values for the original missing genotypes using the genotype probabilities predicted by the fitted model, (4) repeat steps 2 and 3 until convergence (of the chain), (5) estimate the corresponding genetic association model using the final iteration of the generated chain. To generate each imputed data set, we repeated the above algorithm with 25 or 100 iterations in order to have stable predictions for the missing data. For each  data set, we generated 25 or 100 imputed data sets to capture the whole uncertainty underlying the imputation process.We then used MICE to impute missing data under different scenarios and imputation models based on three-category logistic regression under a Multinomial sampling scheme . We generated 100 different random data sets with missing genotypes using three missing genotype proportions, 0.10, 0.25, and 0.50. Using the complete case data set again, we addressed a second situation where all genotypes were assumed missing from a given village.We carried out a simulation study in order to assess the performance of MICE on our data. We first adopted the standard approach of using the complete case data and treating a fraction of the existing genotypes as if they were missing, and attempted to impute them. We generated the missing genotypes independently of true α3.7-globin deletions and the genetic effects for the phenotype-genotype association, (ii) a “pseudo coverage” of the postimputation 95% confidence intervals for the genetic effects, and (iii) the genotype error rate and r2 statistic to assess genotype imputation accuracy. We defined a “pseudo estimation bias” as the average difference between postimputation estimates and those obtained from CCA, while “pseudo coverage” was determined by the proportion of postimputation 95% confidence intervals that contained the genetic effects estimated from CCA. Genotype error rate was determined by the percentage of imputed genotypes in disagreement with the true ones. To estimate the average number of α3.7-globin deletions per individual, we used the following formula0f, 1f, and 2f are the proportions of an individual having 0, 1, and 2 α3.7-globin deletions, respectively. In the case of the r2 statistic, we calculated the square of the correlation coefficient between the imputed and the true numbers of α3.7-globin deletions in the individuals with missing data, as done elsewhere  to those obtained after imputation. In particular, we calculated: (i) a pseudo estimation bias for the postimputation estimates of the average number of αAll statistical analyses  were carried out using the R software package (version 2.15). The corresponding scripts are available from the first author upon request.3.7-globin genotyping was actually attempted  in Mgome to 196 out of 378 individuals (51.9%) in Mokala. The HBA2 locus seems in Hardy-Weinberg equilibrium in most villages with the exception of Kadando, Tamota, Mgila, and Mgome where malaria tends to exert a higher selective pressure  = 5.71). However, after adjusting for putative confounders, the association signal dropped to 0.80 , we studied the performance of MICE in estimating different parameters of interest. Using the subset of 2826 individuals with complete information on α3.7-globin deletion status, we conducted a preliminary association analysis between that variable and the remaining data in order to identify the most informative covariates for the imputation process. In this analysis, we had a total of 110 SNPs that passed our stringent quality control step, five phenotypes measured in the individuals, and five socioenvironmental factors , rs3211938 , rs334 , and rs542998 . The association between HbS and α3.7-globin deletions was previously detected in the same data set by observing a higher frequency of heterozygous individuals in both genes than that expected under independent segregation ; (ii) Hb levels, mild anemia positivity, malaria parasite positivity, and transect (model IM2), and (iii) all covariates included in previous models (model IM3). We studied the performance of each model and compared them to the simple multiple imputation procedure based on the observed frequency of α3.7-globin deletions (model IM0).Motivated by the high frequency of missing data for the α0, and the amount of missing genotypes in the data set . Therefore, in the absence of SNPs in strong LD with the HBA2 gene, MICE has limited power in predicting the missing number of α3.7-globin deletions with great precision. Notwithstanding this limitation, all imputation models could provide unbiased estimates for the average number of α3.7-globin deletions. The range of these estimates increased with the missing data proportion.We first studied the performance of MICE simulating missing data under an MCAR mechanism. The estimated genotype error rates are ∼44% irrespective of the imputation model used, including IMet Table . In line2 and IM3 led to little estimation bias (0.11%–0.13%) of the maximum of the CCA log-likelihood function. Although producing genetic effect estimates with least bias, IM0 and IM1 led to the largest estimation bias (∼1%) of the maximum of the CCA log-likelihood function. Finally, every imputation model could generate confidence intervals that all included the genetic effect estimates obtained from CCA. This result indicates that there is a high degree of uncertainty underlying the genetic effect estimation.With respect to the association signals, the different imputation models led to an overall decrease in the maximum value of the log-likelihood, irrespective of the missing data proportion Table . This deHBA2 gene was not attempted in individuals from a given village. The genotype error rate varies with imputation models and villages with missing genotypes . Similar qualitative conclusion can be taken from the r2 statistic (Table S1). In these two models, the lowest and highest genotype errors were obtained from data of Mokala in the Kilimanjaro transect  and Mgome in the Tanga coast , respectively. Conversely, the highest value for the r2 statistic was found for Kileo data (0.015 – IM2 and 0.017 – IM3) with the exception of the IM2 for the Mokala data (r2 = 0.20). Curiously, each imputation model could perform better in missing data from villages in the Kilimanjaro transect than in the others. The reason for this is unclear but it might be related to a stronger association of the α3.7-globin deletions with the phenotypes and/or socio-environmental factors in this specific transect. On the other hand, all imputation models produced a very high genotype error rate for the missing data from Mgome (>55%). This result can be explained by a different selective pressure in this village due to a higher malaria exposure . On the other hand, irrespective of the imputation model used, little bias was obtained when missing data were from villages in the West Usambara transect. Imputing genotypes of Mgome, although leading to a high genotype error rate, did not lead to an extremely high estimation bias.With respect to the average number of α0) led to approximately unbiased association signals and genetic effect estimates, irrespective of the missing data under analysis . The corresponding genetic effect estimates are in most cases unbiased with a few exceptions. IM2 seems to lead to biased estimates for λ1 when missing data came from Mokala (Kilimanjaro), Mpinji (South Pare), Tamota (West Usambara), or Mgome (Tanga coast). Conversely, IM3 tends to produce biased estimates of λ2.The simple imputation procedure based on genotype proportions , (ii) imputation of missing data from the same 13 villages and an additional village where genotyping of the HBA2 gene was not attempted (scenario 2), and (iii) imputation of missing data from the 24 villages included in the original study design (scenario 3). According to our simulation study, we considered IM3 as the best imputation model for the data because it appears to produce a good compromise between estimation and association bias. The remaining results for IM0, IM1, and IM2 can be found in Table S3.We performed three different association analysis: (i) imputation of missing data from the 13 villages studied thus far . The mean association signals, ranging from 1.48 (Kilomeni) to 1.88 (Tewe), are inversely correlated with altitude across villages of the same transect. Genetic effect estimates range from −0.246 (Ubiri) to −0.216 (Handei) for 1λ and from 0.165 (Ubiri) to 0.208 (Handei) for 2λ, respectively.With respect to scenario 2, the overall missing genotype percentage varies from 37.2% (Ubiri) to 40.0% (Ngulu). The estimates of the average number of α3.7-globin deletions was estimated to be 0.336 after imputation . The post imputation association signals span more than six orders of magnitude (from 0.10 to 6.75) with an average of 2.70. The genetic effect estimates for 1λ and 2λ are −0.232  where missing genotypes is close to 62%, the average number of αon Table , a value3.7-globin deletion. We showed that, when missing genotypes were simulated under an MCAR assumption, MICE using model IM3 led to genotypic error rates of ∼44% and low r2 statistics but unbiased estimates for the different genetic parameters and association signals. We then conclude that MICE is useless in predicting the true status of the HBA2 gene of a particular individual but is rather useful in providing reliable genetic effect estimates and association signals. In theory, we expect that MICE would show better genotype prediction accuracy if we had in our data set any genetic marker in strong LD with the HBA2 gene. In line with this expectation is the study of Souverein et al.  mechanism. In this case, MICE using model IM3 can lead to biased or unbiased genetic effect estimates and association signals, depending on the villages where missing data comes from. The source of this bias is not totally clear but it may reflect the “strength” of an MNAR mechanism for the data. In fact, our results showed little bias while analyzing missing data from villages in intermediate altitudes because imputation can borrow information from other villages characterized by similar genetic and socio-environmental factors. Conversely, there is a significant bias when missing data comes from the coastal village of Mgome, which exhibits unique characteristics among all the 13 villages used in our simulation study. Thus, MICE under a putative MNAR mechanism should be used with caution, as also alerted by Souverein et al. . In this case, it is expected that missing genotypes are the result of a mixture between MCAR, MAR, and MNAR mechanisms. MAR or MCAR might arise from data of villages where genotyping of the HBA2 gene was actually attempted, while MNAR might occur in data from villages where genotyping of that gene was not attempted. In this overall analysis, the association signals of the α3.7-globin deletions became statistically significant, most likely due to an increased number of individuals under the analysis. If true, the use of MICE, besides increasing estimation precision, seems invaluable in detecting additional association signals from known and unknown genes with smaller effect that otherwise would remain undetected. This is particularly relevant in association studies of nonclinical malaria phenotypes where different genes with only moderate effects could be detected, as demonstrated by recent data from Mali (Maiga et al., The possibility of producing biased estimates when missing data comes from a specific village poses the question about the reliability and robustness of the results for the whole data set where there is no αWe believe that, under the realistic assumption of similar genetic pressure across transects, our results for the whole data set show no strong bias as a result of our well-balanced study design. The use of transects and their relationship with the high prevalence of specific ethnic groups was invaluable in controlling putative genetic substructures in the study area. A design using altitude-based transects has ensured the comparability of the villages in terms of malaria parasite exposure. The additional matching for gender and age has minimized further selective bias. The key to minimal estimation bias after data imputation therefore relies on having an adequate study design.3.7-globin deletions of a given individual, but seems to lead to the correct genetic effect estimates and association signals as long as the data are well-matched for key socio-environmental factors.In conclusion, MICE using an appropriate imputation model shows limitations in predicting the missing data of the α


SNP markers. We found evidence for inbreeding depression in 4‐month hindleg and weight, first‐year survival in males, and annual survival and breeding success in adults. Maternal inbreeding was found to depress both birthweight and 4‐month weight. We detected more instances of significant inbreeding depression using genomic estimators than the pedigree, which is partly explained through the increased sample sizes available. In conclusion, our results highlight that cross‐generational inbreeding effects warrant further exploration in species with parental care and that modern genomic tools can be used successfully instead of, or alongside, pedigrees in natural populations.There is ample evidence for inbreeding depression manifested as a reduction in fitness or fitness‐related traits in the focal individual. In many organisms, fitness is not only affected by genes carried by the individual, but also by genes carried by their parents, for example if receiving parental care. While maternal effects have been described in many systems, the extent to which inbreeding affects fitness directly through the focal individual, or indirectly through the inbreeding coefficients of its parents, has rarely been examined jointly. The Soay sheep study population is an excellent system in which to test for both effects, as lambs receive extended maternal care. Here, we tested for both maternal and individual inbreeding depression in three fitness‐related traits (birthweight and weight and hindleg length at 4 months of age) and three fitness components , using either pedigree‐derived inbreeding or genomic estimators calculated using ~37 000  Inbreeding depression, the reduction in fitness or fitness‐related trait values as a result of mating between related individuals, has intrigued evolutionary biologists for many decades. The genetic basis is not clearly understood, but is generally believed to be due to two phenomena which are driven by consanguinity: expression of (largely) recessive deleterious alleles or increased homozygosity at overdominant alleles. The relative importance of each is still a topic of debate, as few experiments are able to tease apart the two effects  requires pedigrees, which are laborious to obtain and even with genetic support, are often incomplete and contain errors, the net result of which is an underestimate of individual inbreeding coefficients , has been correlated with fitness or fitness‐related traits , heterozygosity was correlated with pedigree inbreeding coefficient and showed high identity disequilibrium, that is there was high covariance in heterozygosity among markers within individuals , suggesting that heterozygosity at thousands of markers can be used to detect inbreeding depression in the absence of a pedigree.Recent genomic advances have made it possible to screen individuals for tens of thousands of single nucleotide polymorphisms (SNP) relatively affordably. It has recently been shown using 13 198 SNP markers that, in a captive population of oldfield mice . Even with an extensive and complete pedigree, the realized proportion of the genome which is IBD will differ from this expectation due to Mendelian sampling and recombination. With genomic information, realized inbreeding coefficients can be estimated, enabling greater individual‐level precision than the pedigree  has been used since the allozyme era and is easy to calculate, as it does not require knowledge of population‐wide allele frequencies. However, the scale of this estimate is very different from pedigree inbreeding, and its relationship with pedigree‐derived inbreeding coefficients may be both population‐ and marker‐specific. In consequence, many estimators have been derived which account for allele frequencies  on fitness compared to fitness shown by outbred individuals (F = 0). However, except for Fhom, the genomic estimators here are fairly comparable to Fped. For the most common pedigree inbreeding classes, mean FROH and FGRM are relatively similar to the Fped estimates, even though there is substantial variance around the mean . For many purposes, however, the absolute values of F (or regressions of fitness on F) are not of great importance and a more pragmatic approach suffices, for example if we are interested in whether fitness‐related traits decrease significantly with estimates of F . This result is supported by the presence of significant Identity Disequilibrium measured by the estimator g2 in the r package InbreedR , which is exceedingly low compared to a meta‐analyses which obtained g2 estimates from 50 published HFC studies . The Soay sheep population, being a primitive remnant island population, has small Ne . However, inbreeding depression is of great concern for many endangered organisms and other species with small population sizes  recessive deleterious alleles, and thus in how powerful they are as estimators in the detection of inbreeding depression. Nonetheless, establishing which genomic estimator to use in the analysis of inbreeding depression is not an easy task, but can prove a crucial factor as to whether significant inbreeding depression is or is not detected. Based on simulation studies .Inverse pedigree relatedness matrix, pedigree and genomic inbreeding coefficients, fitness data, body size data, covariates and categorical variables included in the models are archived on Dryad  on August lamb body size.Appendix S2. Correlations in heterozygosity among loci and idenity disequilibrium.Fig. S4. The effect of number of SNPs on (a) the correlation between pedigree F and SNP‐by‐SNP based inbreeding estimators (Fhom and FGRM), and (b) the correlation in SNP based inbreeding estimators in one half of the markers with SNP based inbreeding estimators in the other half.Fig. S5. The effect of SNP marker number on the correlation of offspring and maternal genomic inbreeding estimators (Fhom and FGRM) with August weight in lambs.Click here for additional data file.


Feed efficiency traits were gain:feed, residual feed intake (RFI), residual feed intake adjusted for backfat thickness (RFIb) and for backfat and rump fat thickness (RFIsf), residual body weight gain (RG), residual intake and body weight gain (RIG), and residual intake and body weight gain using RFIb (RIGb) and RFIsf (RIGsf). The variance components were estimated by the restricted maximum likelihood method using a two-trait animal model. The heritability estimates (h2) were 0.14, 0.24, 0.20, 0.22, 0.19, 0.15, 0.11 and 0.11 for gain:feed, RFI, RFIb, RFIsf, RG, RIG, RIGb and RIGsf, respectively. All rph values between traits were close to zero, except for the correlation of feed efficiency traits with dry matter intake and average daily gain. High rg values were observed for the correlation of dry matter intake, average daily gain and metabolic weight with WS and hip height (>0.61) and low to medium values (0.15 to 0.48) with the carcass traits . Among the feed efficiency traits, RG showed the highest rg with WS and hip height (0.34 and 0.25) and the lowest rg with subcutaneous fat thickness (-0.17 to 0.18). The rg values of RFI, RFIb and RFIsf with WS , BF  and RF  were unfavorable. The rg values of gain:feed, RIG, RIGb and RIGsf with WS were low and favorable (0.07 to 0.22), while medium and unfavorable (-0.22 to -0.45) correlations were observed with fat thickness. The inclusion of subcutaneous fat thickness in the models used to calculate RFI did not reduce the rg between these traits. Selecting animals for higher feed efficiency will result in little or no genetic change in growth and will decrease subcutaneous fat thickness in the carcass.This study evaluated phenotypic (r The Brazilian meat industry is one of the most dynamic in the world. The average annual genetic trends for growth traits reported by Nellore breeding programs in Brazil ranges from 0.15 to 0.61% of the average/year for weaning weight and from 0.15 to 0.77% of the average/year for postweaning weights, while higher genetic trends are obtained for postweaning weight gain (0.11 to 1.02% of the average/year). As a correlated response, this selection has resulted in a genetic trend of 0.33% of the average/year in birth weight and of 0.35% of the average/year in mature cow weight 0.75.Dry matter intake was obtained as the mean of all valid days of feed intake during the test period. Average daily gain was estimated by linear regression of weights on days on test (DOT):0.75, and residual body weight gain (RG) was estimated as the residual of the regression equation of ADG on DMI and BW0.75  in males (yearling) born from 1996 to 2002  and in females (postweaning) born from 2004 to 2013. For the measurement of loin-eye area (LEA) and BF, the transducer was placed perpendicular to the spine in a transverse position over the longissimus dorsi muscle between the 12pin bone . The imaAnimals with more than one record outside the interval of ±3.5 standard deviations from the mean of the contemporary group were eliminated from the database. For the growth and carcass traits, the contemporary groups were defined as year of birth of the animal, selection line, and sex. For the feed efficiency traits, the contemporary group was defined as year of birth (2004 to 2013), test facility [3 facilities with at least 32 individual pens or one collective pen], and sex . 0.75, the model included random direct additive genetic effects, the fixed effects of contemporary group, and age of animal (linear effect) and age of dam (linear and quadratic effects) as covariates. For the other traits (growth and carcass), the model included random direct additive genetic and maternal permanent environmental effects (only for WS), in addition to the fixed effects of contemporary group and month of birth of the animal, age of animal (linear effect), and age of dam (linear and quadratic effects). A pedigree file containing 8,478 animals was used in all analyses.The (co)variance components were estimated by the restricted maximum likelihood method under a two-trait animal model using the WOMBAT program . For theThe general model can be written in matrix form as:α is the matrix of direct additive genetic (co)variances; Sc is the matrix of maternal permanent environmental variance; Se is the matrix of residual (co)variances; A is the numerator of the additive genetic relationship matrix; I is an identity matrix, and ⊕ is the direct product between matrices. It was also assumed that α, c and e are not correlated.In this study, it was assumed that:0.75 (R2 = 0.80) and ADGp = 0.099 (±0.006) x DMI + 0.002 (±0.001) x BW0.75 (R2 = 0.75), respectively. The mean RFI was -0.012 ± 0.712 kg/day (-2.314 to 4.964 kg/day) in males and -0.010 ± 0.375 kg/day (-1.176 to 1.287 kg/day) in females. The mean RG was 0.002 ± 0.136 kg/day (-0.707 to 0.332 kg/day) in males and 0.003 ± 0.097 kg/day (-0.283 to 0.329 kg/day) in females.The general equations of predicted DMI and ADG were: DMIp = 2.112 (±0.134) x ADG + 0.084 (±0.003) x BW0.75 was higher  than the coefficient of variation of RFI and RG (8.7% and 12%), which were calculated using mean DMI and ADG, respectively. Coefficients of variation of 4% to 8% for RFI and of 10% to 14% for RG were reported in studies on Bos taurus [Bos indicus) [b and RFIsf were similar.The coefficient of variation of DMI, ADG and BWindicus) , 4. Thes1 x BW0.75)/(β1 x BW0.75 x β2 x ADG), where β1 and β2 are the partial regression coefficients of DMI on ADG and BW0.75, respectively, and BW0.75 and ADG are the means shown in Bos taurus steers attending the Australian, Korean and Japanese markets required 67%, 72% and 61% of DMI for maintenance [Bos indicus animals have lower maintenance requirements than Bos taurus [Dry matter intake corresponded to 2.41% and 2.54% of the average body weight of males and females, respectively, during the test. A crude estimate of the percentage of feed consumed for maintenance, implicit in the RFI equation, can be obtained by , while the highest estimate was found for RFI. Pooled heritabilities higher than those estimated in the present study  were reported in a comprehensive meta-analysis [Bos taurus or composites. Also, a meta-analysis comprising 39 heritability estimates [The heritability estimates for the feed efficiency traits ranged from 0.11 to 0.24 . The lowstimates  found a 0.75. However, in the present study the heritability for RG (0.19 ± 0.06) was substantially lower than that estimated for ADG which was 0.41  the probability of a slow-growing animal to be highly ranked based on RIG [Although RG has been proposed concomitantly with RFI , geneticwas 0.41 , probablor RG 0.1 ± 0.06 wd on RIG .0.75), growth traits and ultrasound-measured carcass traits , rather than simply the separate measurement error of each trait as is the case for DMI, ADG, BF and RF [The heritabilities for the component traits of feed efficiency . The results are consistent with the findings of other studies [ studies , 12, 21. studies .The phenotypic relationships created by construction of the traits RFI, RG and RIG, i.e., zero correlations between RFI and ADG and between RG and DMI, a negative correlation between RIG and DMI and a positive correlation between RIG and ADG, do not necessarily imply similar genetic relationships . The genb and RFIsf with carcass fat thickness was slightly lower  lines selected for high and low growth rates compared to the unselected control line, with no evidence of the loss of feed efficiency after 15 years of selection for growth [Bos indicus), these results were confirmed by the similar RFI of animals from two lines selected for growth (high postweaning weight versus control) [Although selection for higher growth (WS) results in an increase in DMI and ADG , it seemr growth . In Nellcontrol) .In conclusion, feed efficiency, growth and carcass traits of Nellore cattle obtained postweaning show sufficient genetic variability to respond to selection; however, the genetic progress will be slower for the feed efficiency traits than for the other traits. In this breed, RFI is the most heritable feed efficiency trait. Additionally, there is evidence of low genetic antagonism between RFI and growth and of high genetic synergism between RFI and DMI. This fact makes the trait eligible for selection for higher feed efficiency. Selecting animals that convert feed more efficiently will imply a certain opposite correlated response in fat thickness, i.e., an antagonism of medium magnitude exists between the effect of genes for feed efficiency and the genes for carcass fat deposition in Nellore cattle.S1 File(DOCX)Click here for additional data file.S2 File(DOCX)Click here for additional data file.


Aegilops tauschii is the D-genome progenitor of hexaploid wheat (Triticum aestivum). It is considered to be an important source of genetic variation for wheat breeding, and its genome is an invaluable reference for wheat genomics. We conducted a genome-wide association study using 7,185 single nucleotide polymorphism (SNP) markers across 322 diverse accessions of Ae. tauschii that were systematically phenotyped for 29 morphological traits in order to identify marker-trait associations and candidate genes, assess genetic diversity, and classify the accessions based on phenotypic data and genotypic comparison. Using the general linear model and mixed linear model, we identified a total of 18 SNPs significantly associated with 10 morphological traits. Systematic search of the flanking sequences of trait-associated SNPs in public databases identified several genes that may be linked to variations in phenotypes. Cluster analysis using phenotypic data grouped accessions into four clusters, while accessions in the same cluster were not from the same Ae. tauschii subspecies or from the same area of origin. This work establishes a fundamental research platform for association studies in Ae. tauschii and also provides useful information for understanding the genetic mechanism of agronomic traits in wheat. Aegilops tauschii , the D-genome progenitor of hexaploid wheat (Triticum aestivum), is a diploid and self-pollinated plant. The D genome of Ae. tauschii and of the hexaploid wheat are closely related because of the recent origin of the latter by hybridisation of T. durum and Ae. tauschiiAe. tauschii is an important source of genetic variation for wheat breeding, and its genome is an invaluable reference for wheat genomics, as revealed by its utility for studying wheat gene space2T. aestivum was originated by hybridisation of T. turgidum (AABB) with Ae. tauschii (DD)Ae. tauschiiAe. tauschii may provide important insights for breeding elite wheat varieties. Additionally, the high genetic variability of morphological traits in Ae. tauschii may indicate the presence of several loci or alleles that still remain to be uncovered.Conventional linkage mapping is the most common approach to detect quantitative trait loci (QTLs), corresponding to complicated traits in plants. However, linkage mapping using bi-parental crosses is able to reveal information on two alleles at a given locus or few loci segregating in the study population. In addition, the resolution of the detected QTLs is poor, ranging from 10 to 30 cM, due to the limited number of recombination events that occur during the development of mapping populations6Arabidopsis1316171923The use of single nucleotide polymorphism (SNP) markers, in conjunction with statistical approaches for association mapping (AM), provides dense genome coverage, decreases genotypic errors, and allows the accurate identification of loci9Ae. tauschii accessions of diverse origin in order to: 1) investigate marker-trait associations for 29 morphological traits and 2) scan for candidate genes that control corresponding morphological traits. Furthermore, we aimed to provide a comprehensive overview on genetic diversity of morphological traits, as well as, subspecies classification based on phenotypic data and genotypic comparison. Overall, this study was designed to provide useful information for understanding the genetic mechanism of morphological traits in Ae. tauschii and further unlock the regulatory network of complicated morphological traits in this species.In the present study, we used the GWAS approach employing 7,185 SNP markers in a core collection of 322 P < 0.001) were observed between 2012 and 2013 for all traits, except for AL2 and GL2. Also, significant variation (P < 0.001) and significant differences (P < 0.001) in year × genotype interactions were observed for all traits.Analysis of variance (ANOVA) revealed significant variation among genotypes for all 29 morphological traits . The levP < 0.001) in 2013, while the rest 22 morphological traits including PH, FL, FW, LN, SPL, SPW, SPN, GL1, GW1, GW2, GT1, GT2, LL1, LL2, LW1, LW2, PL1, PL2, PW1, and PW2 were significantly decreased (P < 0.001) in 2013.Phenotypic variation among genotypes for each trait was confirmed by its range, mean, standard deviation, and coefficient of variation . The coeBroad-sense heritability estimates were calculated for all 29 morphological traits see . Among tP < 0.01) between the two years , while all other traits were moderately correlated. The best linear unbiased predictor (BLUP) was calculated from the fixed effects of phenotypic data to avail unbiased mean estimates and used in the correlation analysis among the study traits. The correlation coefficients of this combined analysis are presented in r = 0.989**, P < 0.01), while IL4 and SPN were highly negatively correlated . AL2, SPW, GT1, GT2, LW1, LW2, PL2, PW1, and PW2 were significantly correlated (P < 0.05) with all other traits. The remaining traits showed moderate to weak correlation among each other.Pearson’s correlation was used to investigate the relationship of traits between 2012 and 2013, and all of them were significantly correlated . The results were concordant with those of genotypic comparison  based on BLUP values was used to show the distance of four ison see  for 291 orrectly .Ae. tauschii ssp. tauschii_II. Cluster II included 78 accessions from 16 different areas of origin and 4 different subspecies; the most frequent subspecies was Ae. tauschii ssp. tauschii_I. Clusters III and IV comprised 44 and 87 accessions, respectively, that originated from 3 (Cluster III) and 13 (Cluster IV) different areas. Cluster III included 3 and Cluster IV included 4 different subspecies, whereas the most frequent subspecies was Ae. tauschii ssp. strangulata_II in Cluster III and Ae. tauschii ssp. strangulata_I in Cluster IV  was performed using the squared Euclidean distance matrix also based on BLUP values, and all accessions were divided into four clusters see  and S7. r IV see .Ae. tauschii accessions from different areas of origin were grouped in the same cluster, while accessions from the same area of origin were grouped into different clusters. For instance, all the accessions from Iran (65 accessions) were grouped into four clusters, suggesting the high levels of genetic diversity in each centre of origin. We also observed that Cluster I had a closer relationship with Cluster II, and the main subspecies in both clusters was Ae. tauschii ssp. tauschii. Cluster III had a closer relationship with Cluster IV, and the main subspecies in both clusters was Ae. tauschii ssp. strangulata  was used as a cut-off to identify marker-trait associations (MTAs). Using the mean phenotypic values from 2012 and 2013, 12,444 significant SNPs were detected by the GLM and 28 significant SNPs by the MLM . The average r2 values ranged from 2.74 to 7.09% . The average to 7.09% .r2 values ranged from 2.05 to 8.65%  were identified  were identified. In 2013, SNN was associated with the highest number of significant SNPs, and a total of five candidate genes  were identified. GT1 and GT2 were associated with the same number of significant SNPs, and the same candidate gene (CKX2.5) was identified. Similarly, GW1 and GW2 were also associated with the same number of significant SNPs and two candidate genes (116F2 and 115G1) were identified .Significant association of the same SNPs with multiple traits might be the result of pleiotropy. We observed that a SNP at 55.616 cM on chr 2D, a SNP at 132.198 cM on chr 4D, and a SNP at 151.266 cM on chr 5D were significantly associated with both GW1 and GW2. Also, a SNP at 113.167 cM on chr 5D was significantly associated with both GT1 and GT2 . These aGBQ4KXB02HJM7P_431, GDEEGVY02FLOCP_398, and GDRF1KQ02F8V30_278 were significantly associated with GW1 and GW2. SNP markers GBB4FNX02JQNSU_161 and GBUVHFX01CI5PL_126 were significantly associated with SNN and SNP markers contig15239_471, contig11810_520, GCE8AKX01ALM0H_152, and F5MV3MU01BU5XD_286 were significantly associated with SPN  originated by the hybridisation of T. turgidum (AABB) with Ae. tauschii (DD)4Ae. tauschii is along the southern shores of the Caspian Sea and in Azerbaijan, and this species has mainly spread eastwards from the centre of originAe. tauschii collections may help us in transferring desirable traits to common wheat.Hexaploid wheat (Ae. tauschii accessions showed significant (P < 0.001) levels of diversity, as revealed by ANOVA of all morphological traits, and the majority of traits were highly inheritable, showing a broad variation among the accessions. It is well-known that the genotype, environment, and their interaction play an important role in morphological traits. In the two different years, the means of some morphological traits were significantly higher or lower, suggesting that traits were probably affected much more strongly by environmental factors (i.e. year) than by genotype. Discriminant function analysis based on BLUP values was not congruent with the classification based on the genotypic comparison or the areas of originAe. tauschii ssp. tauschii and ssp. strangulata did not separate from each other entirely. Cluster I included 110 accessions from Ae. tauschii ssp. tauschii against 3 from ssp. Strangulate. Cluster II included 66 accessions from Ae. tauschii ssp. tauschii versus 12 from ssp. Strangulate. Cluster III and Cluster IV were 1 VS 43 and 27 VS 60 respectively. The intermediate forms and hybrids between the two subspecies reported by Kihara et al.Ae. tauschii ssp. tauschii and ssp. strangulata were not closely related to each other; however, it seems that intraspecies branching outs probably occurred at the same time with big changes in the genetic structure of Ae. tauschii collections.In this study, Ae. tauschii by employing the GWAS approach. It is known that linkage mapping can also detect QTLs using different segregating populations tested in different environments. Although, few QTLs have been identified in Ae. tauschii, a large number of QTLs related to agronomical traits have been identified in common wheat by the conventional mapping approach. Since the D-genome of Ae. tauschii and of common wheat are homologous, the identification of QTLs in Ae. tauschii by the GWAS approach may offer useful information for understanding the genetic mechanism of agronomic traits in wheat too.We identified many significant SNPs and related candidate genes associated with morphological traits in et al.Xgwm261 of chr 2DS. We identified four loci related to SPN on chr 2D, 3D, and 4D. These results were partially consistent with those of McCartney et al.Xbarc48 and Xgwm194 and with those of Rasheed et al.Ae. tauschii are yet to be finished, and therefore little information on chromosome locations can be provided. Hence, the loci identified herein as being associated with morphological traits cannot be directly compared with QTL reported by other researchers.In the present study, significant SNPs identified by the GLM or the MLM were distributed on chr 2D–7D. We identified a locus related to SL on chr 4D that was also reported by Sourdille Rht (reduced height) genes affect plant height; however, only the Rht-B1b (Rht1), Rht-D1b (Rht2), and Rht8c have been used extensively in agriculturePpd (response to photoperiod) genes, the Vrn  genes, and the Eps (earliness per se) genesHordeum vulgare and Zea mays and highly homologous to different species of Triticum or Aegilops. These genes included enzyme genes, such as Acc-2, CKX2.5, Ig1, TSAlike, Acc-1, BAM3, LR34, cytochrome P450, glutathione-S-transferase 2, and glutathione-S-transferase 1; hormone response genes, such as Aux/IAA gene family and ERF1; regulatory element genes, such as 1J9.1 and 1J9.2; and other genes, such as AXAH3, 116F2, 115G1, Rht-D1b, ZCCT2, ZCCT1, and SNF2P. Additionally, the semi-dwarfing gene Rht-D1b was identified to affect GW2 in our study, indicating that it may probably control more than one agronomic trait. Pleiotropic and multigenic effects were also observed in this study, such as SNPs GBQ4KXB02HJM7P_431 (at 55.616 cM on chr 2D), GDEEGVY02FLOCP_398 (at 132.198 cM on chr 4D),) and GDRF1KQ02F8V30_278 (at 151.266 cM on chr 5D) that are associated with both GW1 and GW2 traits; SNP GBB4FNX01BXV4Q_52 (at 113.167 cM on chr 5D) is associated with both GT1 and GT2 traits. Pleiotropic or closely linked genesAe. tauschii were complex and affected by polygenes.Previous genetic research has uncovered many genes that affect important agronomic traits, but only a few have been practically used in plant breeding. For instance, it is known that numerous Ae. tauschii, homologous pairing is expected between the donor and the recipient bread wheat chromosome (of the D genome) and no pairing induction is required. The most effective way is that skilful use of tetraploid wheat, which has AB genome, is hybridised with Ae. tauschii, and the chromosomes of the F1 hybrid are doubled using colchicine treatment. The product is a fertile synthetic hexaploid ABD genotype fully homologous to bread wheat. Such wheat–Ae. tauschii hybrids have a high level of sterility. Homologous chromosomes of the hexaploid wheat will readily recombine in a hybrid, and such synthetic lines can serve as a gene pool derived from Ae. tauschii that is ready for screening for any desired trait and allow for an easy transfer of responsible genesOur findings are a tool that can assist in genetic dissection of D genome of bread wheat. When the desired gene resides in D genome of Ae. tauschii accessions using 7,185 polymorphic SNP markers. Fifteen significant markers were detected by both GLM and MLM. At significant loci and flanking regions, we identified candidate genes for morphological traits including enzyme genes, hormone response genes, and other genes that may affect morphological traits. Additionally, discriminant function analysis and cluster analysis showed that there was no correlation between the morphological traits and the centres of origin and revealed high levels of genetic diversity among the tested populations. The identified SNPs and genes offer essential knowledge for cloning genes related to morphological traits in Ae. tauschii and wheat. These findings provide useful information for further unlocking of genetic mechanism of morphological traits in Ae. tauschii, followed by agronomic traits in wheat.In summary, we performed genome-wide association studies for morphological traits in a population containing 322 A. tauschii accessions used in this study were obtained from the Triticeae Research Institute of Sichuan Agricultural University (SAU). Detailed information for each accession is given in A total of 322 Ae. tauschii accessions were evaluated in the field at Wenjiang, Chengdu, China, during the growing season (April–June) in 2012 and 2013. All accessions were grown during the planting season (October 2011 and 2012). Every accession was planted in three rows, each row with five plants, the length of each row was 1.5 m, and spacing between plants and between rows was 0.3 m. Thus, each accession comprised 15 plants from three replications, and each replication contained five plants. Those 15 plants of each accession were selected to investigating morphological traits. A total of 29 morphological traits were investigated including plant height (PH), spike length (SL), internode length 1 (IL1), internode length 2 (IL2), internode length 3 (IL3), internode length 4 (IL4), flag leaf length (FL), flag leaf width (FW), leaf number (LN), stem node number (SNN), awn length 1 (AL1), awn length 2 (AL2), spikelet length (SPL), spikelet width (SPW), spikelet number (SPN), glume length 1 (GL1), glume length 2 (GL2), glume width 1 (GW1), glume width 2 (GW2), glume thickness 1 (GT1), glume thickness 2 (GT2), lemma length 1 (LL1), lemma length 2 (LL2), lemma width 1 (LW1), lemma width 2 (LW2), palea length 1 (PL1), palea length 2 (PL2), palea width 1 (PW1), and palea width 2 (PW2). Measurements were conducted using a straightedge and a Vernier calliper. A brief description of each trait is summarised in G/(VG + VE), where VG and VE are the estimates of genetic and environmental variance, respectivelyDescriptive statistics, ANOVA, correlation analysis, and heritability estimates of all traits were conducted in SAS 9.2  using 2-year data. Broad-sense heritability was defined as H = VAe. tauschii built from bacterial artificial chromosome clonesAe. tauschii accessions has been described in our previous studyA total of 7,185 polymorphic SNP markers in the array were uniquely mapped on the genetic map and the physical map of Ae. tauschiiK = 1–10 clusters were used to identify the optimal range of K. For each K, five independent runs were produced. The optimal value of K was determined using the delta K methodK = 4 was used, and the whole panel was divided into Subpopulation (Subp) 1 (Ae. tauschii ssp. taschii_I), Subp 2 (Ae. tauschii ssp. taschii_II), Subp 3 (Ae. tauschii ssp. strangulata_I), and Subp 4 (Ae. tauschii ssp. strangulata_I) based on our previous studyThe Bayesian inference program STRUCTURE 2.3.342–6 with a corresponding –log10(p) value of 3.84. Significant markers were demonstrated with a Manhattan plot generated in R 3.03 (http://www.r-project.org/). Significant p-values  were demonstrated with a Q–Q plot also generated in R.MTAs of 6,905 SNP markers with minor allele frequency (MAF) > 0.05 were evaluated based on the phenotypic mean data from 2012 and 2013, as well as BLUP values using Tassel 2.141http://www.ncbi.nlm.nih.gov/) GenBank non-redundant database, and the extending SNP marker sequence derived from a 5-kb increase around each SNP marker that was performed by BLAST in the IWGSC (http://www.wheatgenome.org/).Putative candidate genes were proposed for each significant MTA using the corresponding extending SNP marker sequence from the National Centre for Biotechnology Information .


Glycine max (L.) Merr.] might contribute to improving photosynthetic capacity and yield in regions with adverse environmental conditions. A collection of 332 diverse soybean genotypes were grown in 2 years (2009 and 2010) and chlorophyll a (eChl_A), chlorophyll b (eChl_B), and total chlorophyll (eChl_T) content as well as chlorophyll a/b ratio (eChl_R) in leaf tissues were determined by extraction and spectrometric determination. Total chlorophyll was also derived from canopy spectral reflectance measurements using a model of wavelet transformed spectra (tChl_T) as well as with a spectral reflectance index (iChl_T).Chlorophyll is a major component of chloroplasts and a better understanding of the genetic basis of chlorophyll in soybean [A genome-wide associating mapping approach was employed using 31,253 single nucleotide polymorphisms (SNPs) to identify loci associated with the extract based eChl_A, eChl_B, eChl_R and eChl_T measurements and the two canopy spectral reflectance-based methods (tChl_T and iChl_T). A total of 23 (14 loci), 15 (7 loci) and 14 SNPs (10 loci) showed significant association with eChl_A, eChl_B and eChl_R respectively. A total of 52 unique SNPs were significantly associated with total chlorophyll content based on at least one of the three approaches  and likely tagged 27 putative loci for total chlorophyll content, four of which were indicated by all three approaches.Results presented here show that markers for chlorophyll traits can be identified in soybean using both extract-based and canopy spectral reflectance-based phenotypes, and confirm that high-throughput phenotyping-amenable canopy spectral reflectance measurements can be used for association mapping.The online version of this article (doi:10.1186/s12870-016-0861-x) contains supplementary material, which is available to authorized users. Glycine max [L.] Merr.) is the world’s most widely grown legume crop and produces high quality grain which contains 35–55 % easily digestible protein, 17–27 % oil, and about 30 % carbohydrates, among others components . The leenthaler , expressTo match extract-based chlorophyll content determinations with chlorophyll assessments based on canopy spectral reflectance characteristics, reflectance measurements were conducted between 54 and 57 DAP in 2009 and 58 and 61 DAP in 2010 as described by Singh et al. . In brie840-870/∫R720-730] [Chlorophyll contents were calculated based on reflectance spectra from i) the ratio of the area under the curve in the 840–870 nm region and the 720–730 nm region [∫R720-730]  for iChl720-730]  for tChl720-730]  used the720-730] . Among t720-730] , one basAll descriptive statistics and Pearson correlation analyses were conducted for each variable  using PROC MEAN and PROC CORR procedures of SAS Version 9.3 . Variance components were determined using the PROC MIXED of SAS , 55 as dk (k = 8) was provided by joining the log probability of data [LnP(D)] from the STRUCTURE output and an ad hoc statistic Δk, determined by the value at which LnP(D) reached a plateau as described in [The genome-wide association mapping software TASSEL 5.2.3 was used to create a kinship matrix (K). All 31,253 polymorphic SNPs were used for generation of K based on the scaled Identity by State (IBS) similarity method as described . The sofribed in .http://www.soybase.org/snps/download.php) and were obtained for the 332 soybean accessions and used in this study. For genome-wide association mapping of eChl_A, eChl_B, eChl_R, eChl_T, tChl_T and iChl_T, 31,253 polymorphic SNPs with a minor allele frequency (MAF) ≥ 5 % across the 332 genotypes were used.Genotypic data from the SoySNP50K iSelect SNP Beadchip  are publGenome-wide association mapping was conducted based on the BLUP values using a mixed linear model with Q-matrix and K-matrix (MLM + Q + K). The Q and K matrices were used as corrections for population structure and/or genetic relatedness to help avoid false positives , 62, 63.P values ≥ 3.2, which is above the threshold used by others for soybean [Genome-wide association mapping based on the MLM + Q + K model was conducted with TASSEL 5 , 65. Mul soybean –70. Mark soybean , 72.-2) than in 2009 (20.50 MJ m-2). Precipitation totals for the months encompassing planting through collection of leaf disks and canopy spectral reflectance measurements  were the same or greater in 2009 and 2010 than the 30 year averages. Cumulative precipitation for May, June, and July was 418, 494, and 319 mm for 2009, 2010, and the 30-year average, respectively, and irrigation was therefore not necessary to avoid drought stress in either year.In general, environmental conditions for the period from planting through collection of leaf disks and canopy spectral reflectance measurements were similar between the 2 years and close to 30-year averages. Daily average temperatures between planting and leaf-disk sampling were somewhat higher in 2010 (24.73 °C) than in 2009 (22.88 °C). The observed differences in temperatures were mirrored by higher solar radiation in 2010 (21.77 MJ mP < 0.0001). However, except for the chlorophyll a/b ratio (eChl_R), no genotype by year interactions were observed. For all traits, mean and median values were larger in 2009 than 2010. The ranges in chlorophyll contents in 2009 were smaller than in 2010 for all traits except tChl_T. Across the 2 years, the range in chlorophyll content was largest for eChl_T (9.85 μg cm-2) followed by eChl_A (7.91 μg cm-2), tChl_T (6.06 μg cm-2), eChl_B (2.39 μg cm-2), and a considerably smaller range for iChl_T (0.51 μg cm-2). The eChl_R, the only trait for which a genotype by year interaction was observed, ranged from 2.94 to 4.26 (μg cm-2) across the 2 years. Correlation coefficients for each trait between the 2 years ranged from 0.35 for eChl_A to 0.45 for tChl_T and were highly significant (P ≤ 0.001), except for eChl_R which was 0.11 but nonetheless significant (P < 0.05).The 332 MG IV soybean genotypes varied widely for the different chlorophyll traits Fig. . Analysir = 0.90), eChl_A and eChl_T (r = 0.95), eChl_B and eChl_T (r = 0.94). The two canopy-based reflectance methods for total chlorophyll content were positively correlated with extract-based total chlorophyll content  and also showed significant positive correlations with extract-based chlorophyll a and chlorophyll b measurements  was conducted using an MLM + Q + K model using TASSEL 5.2.3 software. The K (kinship matrix) and Q (population structure) were used as corrections for genetic relatedness and population structure to help avoid false positives , 73. App2 for these loci ranged from 3.7 to 6.1 %  and 18 (iChl_T) candidate SNPs, representing 12 and 11 putative loci, respectively Fig. . The R2 Genome-wide association mapping for extract-based chlorophyll traits identified a total of 78 SNPs (23 + 15 + 14 + 26) with 43 unique putative candidate SNPs contributing to 14, 7, 10 and 15 putative loci for eChl_A and eChl_B, eChl_R and eChl_T, respectively . Of these 64 SNPs, five SNPs were identified based on all three methods, one SNP was in common between eChl_T and iChl_T only, and one SNP was in common between tChl_T and iChl_T only Figs.  and 4b. a/b ratio. Based on the 60 bp sequences flanking the 43 unique candidate SNPs for extract-based chlorophyll traits and 52 unique candidate SNPs for the three total chlorophyll content traits, a blast search was conducted with default parameters in Soybase (www.soybase.org) to identify putative candidate genes. The search for candidate genes found that, for extract-based chlorophyll traits, 12 SNPs were present in introns or coding regions of a gene, and that, for the three total chlorophyll content traits, 17 SNPs were present in introns, coding regions or 3’- untranslated regions (UTR) of a gene . These searches revealed 155 chlorophyll-related genes from Soybase and 12 chlorophyll-related genes from the KEGG pathway (data not shown). Of these chlorophyll-related genes, 28 were located within ± 3 Mb [All SNPs identified for eChl_A 23), eChl_B (15), eChl_R 14), eChl_T (26), tChl_T (20), and iChl_T (18) that satisfied the FDR < 0.05 were considered as the most promising candidate SNPs associated with chlorophyll contents or the Chl , eChl_B , eChl_T n ± 3 Mb , 74, 75 a/b ratios reported previously for soybean [a and chlorophyll b, the correlations of eChl_A and eChl_B with eChl_T were positive and very strong  and canopy-based spectral reflectance total chlorophyll content traits (tChl_T and iChl_T) was observed among the 332 soybean genotypes Fig. . The eCh soybean , 76. As a and Chl b are synthesized by the same pathway, can be interconverted by a Chl a—Chl b cycle, and sum to make up the total chlorophyll content [2+ into protoporphryin IX, which is the first committed step in chlorophyll biosynthesis (earlier steps are in common with the heme biosynthetic pathway) [Advances in high-throughput genotyping technologies have enabled genome-wide association analysis to be a powerful tool for detection and mapping of quantitative trait loci (QTLs) underlying complex traits in soybean. The MLM + Q + K model applied in this study resulted in the identification of between 14 and 26 significant SNPs for each of the investigated chlorophyll traits. The majority of the SNPs identified for eChl_A, eChl_B, and eChl_T, were common between at least two of these traits, and nine of them were common between all three traits. In fact, all SNPs that were identified for eChl_A were also identified for either eChl_B or eChl_T, or for all three traits Fig. . Specifi content , this wapathway) .The remaining 19 loci that were identified based on extract-based chlorophyll traits were marked by 34 SNPs, and a search for chlorophyll related genes identified 15 genes in their vicinity (±3 MB). Given how closely related the chlorophyll traits are, more confidence and greater importance can be given to loci that were identified based on more than one trait. These included two loci identified based on three chlorophyll traits  and eChl_A, eChl_T and eChl_R (Chr 8)), and 8 loci that were identified based on two chlorophyll traits. The remaining 9 loci were based on single extract-based chlorophyll traits Fig.   were the most prominent, followed by genes encoding proteins with magnesium chelatase activity (7 genes near 7 loci) Table . However2 values for total chlorophyll loci identified in this study were higher (3.7 to 6.1 %) than the R2 values (2.0 to 4.9 %) reported by Hao et al. (2012) [Total chlorophyll content was mapped based on leaf-level (eChl_T) and canopy-level estimates (tChl_T and iChl_T). In total, 64 SNPs, 52 of which were unique, were identified for total chlorophyll content based on these three phenotypes. These SNPs identify 27 putative loci in 16 chromosomal regions  . A searcFour putative loci, one each on chromosomes 8, 15, 19 and 20 were common for all three total chlorophyll phenotypes, thus imparting particular confidence in the validity of these loci  was observed among the 332 MG IV soybean genotypes examined in this study. Genome-wide association analysis identified putative loci associated with each of six chlorophyll traits examined. Twenty-four unique putative loci on 14 chromosomes were identified for extract-based chlorophyll traits. For total chlorophyll content, determined using three methods, association analyses identified 27 putative loci on 16 chromosomes. Several of the loci were identified by more than one chlorophyll trait and since the traits are closely related, more confidence and greater importance can be given to loci that were identified by more than one trait. While many of the putative loci identified were located near genes previously identified or annotated as related to chlorophyll traits, numerous SNPs marked chromosomal regions where no known chlorophyll-related genes were found. Putative chlorophyll-related loci identified based on high-throughput amenable canopy spectral reflectance characteristics indicate that canopy spectral reflectance can provide useful phenotypes for genome-wide association mapping.Significant variation in chlorophyll content (μg cma; eChl_B, extract-based chlorophyll b; eChl_R, extract-based chlorophyll a/b ratio; eChl_T, extract-based total chlorophyll; FDR, false discovery rate; GWAS, genome-wide association study; iChl_T, total chlorophyll based on spectral reflectance index; MLM, mixed linear model; MLR, multiple linear regression; QTLs, quantitative trait loci; SNPs, single nucleotide polymorphisms; tChl_T, total chlorophyll based on wavelet transformed spectraBLUP, best linear unbiased prediction; DAP, days after planting; eChl_A, extract-based chlorophyll


It is now clear that almost all complex traits have a highly polygenic component; that is, their genetic basis consists of relatively frequent risk alleles at a very large number of loci, each making a small contribution to variation, or disease susceptibility. This general conclusion appears to hold for intermediate phenotypes. Therefore, we should not expect these phenotypes to be associated with substantially larger effect sizes than conventional phenotypes. Instead, their usefulness is likely to lie in understanding the mechanism underpinning associations identified via genome-wide association studies of conventional phenotypes. Psychological and behavioral traits are under a considerable degree of genetic influence, with typical heritability statistics in the 0.40–0.60 range, but specific genetic variants associated with these traits have proved elusive. Since the first molecular genetic studies in the 1990s, there have been numerous reports of genetic association that have failed to consistently replicate. Early candidate gene studies selected variants for investigation on the basis of the known or presumed neurobiology of the trait of interest. However, it soon became clear that these studies were not delivering reliable associations—initial findings were typically followed by a collection of replications, partial replications, and nonreplications, so that over time the strength of evidence for any given association tended to decline.p < 5 × 10−8 was required. This necessitated very large sample sizes, often through international collaboration in the form of large, multistudy consortia. Replication of initial findings also became commonplace. Nevertheless, despite this success, variants identified to date via GWAS explain less than half the heritability of complex phenotypes estimated by twin and family studies. This has been described as the “missing heritability” problem  have been extremely successful in identifying genetic variants associated with a range of complex phenotypes. In less than a decade, several loci associated with various complex phenotypes have been identified, through large consortium-based efforts . This su problem .Throughout both the candidate gene and GWAS eras, a persistent topic of debate has been the extent to which the search for genetic loci might be facilitated by the use of intermediate phenotypes . These are phenotypes positioned somewhere between genetic variation and the downstream behavioral or psychological trait of interest. The implicit assumption of this approach is that, by focusing on phenotypes that are biologically closer to the genetic influence, genetic effects will be larger than for distal  phenotypes. This implies that the genetic architecture  of intermediate phenotypes should be different to that of behavioral phenotypes.One of the most important observations to emerge from GWAS is that almost all complex traits have a highly polygenic component; that is, their genetic basis consists of relatively frequent  risk alleles at a very large number of loci, each making a small contribution to variation, or disease susceptibility. We have previously argued that there is no strong evidence that genetic effects associated with intermediate phenotypes are substantially larger than those associated with behavioral phenotypes . The evihttp://enigma.ini.usc.edu). Critically, the loci identified “have comparable effect sizes to those observed in other genome-wide association studies of complex traits” . With increasing sample size will come an increasing number of variants identified, whose function will often be unclear. These variants will then become candidates for further interrogation, where we can be confident that there is a robust association with the downstream disease phenotype. In our opinion, it is here that intermediate phenotypes may prove valuable.Genome-wide association studies continue to grow in size, through the formation of large, international consortia (e.g., the Psychiatric Genomics Consortium: