Population Structure In Genome-wide Association Studies Research Articles

Identification of novel putative alleles related to important agronomic traits of wheat using robust strategies in GWAS

Principal component analysis (PCA) is widely used in various genetics studies. In this study, the role of classical PCA (cPCA) and robust PCA (rPCA) was evaluated explicitly in genome-wide association studies (GWAS). We evaluated 294 wheat genotypes under well-watered and rain-fed, focusing on spike traits. First, we showed that some phenotypic and genotypic observations could be outliers based on cPCA and different rPCA algorithms (Proj, Grid, Hubert, and Locantore). Hubert’s method provided a better approach to identifying outliers, which helped to understand the nature of these samples. These outliers led to the deviation of the heritability of traits from the actual value. Then, we performed GWAS with 36,000 single nucleotide polymorphisms (SNPs) based on the traditional approach and two robust strategies. In the conventional approach and using the first three components of cPCA as population structure, 184 and 139 marker-trait associations (MTAs) were identified for five traits in well-watered and rain-fed environments, respectively. In the first robust strategy and when rPCA was used as population structure in GWAS, we observed that the Hubert and Grid methods identified new MTAs, especially for yield and spike weight on chromosomes 7A and 6B. In the second strategy, we followed the classical and robust principal component-based GWAS, where the first two PCs obtained from phenotypic variables were used instead of traits. In the recent strategy, despite the similarity between the methods, some new MTAs were identified that can be considered pleiotropic. Hubert's method provided a better linear combination of traits because it had the most MTAs in common with the traditional approach. Newly identified SNPs, including rs19833 (5B) and rs48316 (2B), were annotated with important genes with vital biological processes and molecular functions. The approaches presented in this study can reduce the misleading GWAS results caused by the adverse effect of outlier observations.

Genome-wide associations and detection of potential candidate genes for direct genetic and maternal genetic effects influencing dairy cattle body weight at different ages

BackgroundBody weight (BW) at different ages are of increasing importance in dairy cattle breeding schemes, because of their strong correlation with energy efficiency traits, and their impact on cow health, longevity and farm economy. In total, 15,921 dairy cattle from 56 large-scale test-herds with BW records were genotyped for 45,613 single nucleotide polymorphisms (SNPs). This dataset was used for genome-wide association studies (GWAS), in order to localize potential candidate genes for direct and maternal genetic effects on BW recorded at birth (BW0), at 2 to 3 months of age (BW23), and at 13 to 14 months of age (BW1314).ResultsThe first 20 principal components (PC) of the genomic relationship matrix ({mathbf{G}}) grouped the genotyped cattle into three clusters. In the statistical models used for GWAS, correction for population structure was done by including polygenic effects with various genetic similarity matrices, such as the pedigree-based relationship matrix ({mathbf{A}}), the {mathbf{G}}-matrix, the reduced {mathbf{G}}-matrix LOCO (i.e. exclusion of the chromosome on which the candidate SNP is located), and LOCO plus chromosome-wide PC. Inflation factors for direct genetic effects using {mathbf{A}} and LOCO were larger than 1.17. For {mathbf{G}} and LOCO plus chromosome-wide PC, inflation factors were very close to 1.0. According to Bonferroni correction, ten, two and seven significant SNPs were detected for the direct genetic effect on BW0, BW23, and BW1314, respectively. Seventy-six candidate genes contributed to direct genetic effects on BW with four involved in growth and developmental processes: FGF6, FGF23, TNNT3, and OMD. For maternal genetic effects on BW0, only three significant SNPs (according to Bonferroni correction), and four potential candidate genes, were identified. The most significant SNP on chromosome 19 explained only 0.14% of the maternal de-regressed proof variance for BW0.ConclusionsFor correction of population structure in GWAS, we suggest a statistical model that considers LOCO plus chromosome-wide PC. Regarding direct genetic effects, several SNPs had a significant effect on BW at different ages, and only two SNPs on chromosome 5 had a significant effect on all three BW traits. Thus, different potential candidate genes regulate BW at different ages. Maternal genetic effects followed an infinitesimal model.

The Effect of Genomic Inversions on Estimation of Population Genetic Parameters from SNP Data

In recent years it has emerged that structural variants have a substantial impact on genomic variation. Inversion polymorphisms represent a significant class of structural variant, and despite the challenges in their detection, data on inversions in the human genome are increasing rapidly. Statistical methods for inferring parameters such as the recombination rate and the selection coefficient have generally been developed without accounting for the presence of inversions. Here we exploit new software for simulating inversions in population genetic data, invertFREGENE, to assess the potential impact of inversions on such methods. Using data simulated by invertFREGENE, as well as real data from several sources, we test whether large inversions have a disruptive effect on widely applied population genetics methods for inferring recombination rates, for detecting selection, and for controlling for population structure in genome-wide association studies (GWAS). We find that recombination rates estimated by LDhat are biased downward at inversion loci relative to the true contemporary recombination rates at the loci but that recombination hotspots are not falsely inferred at inversion breakpoints as may have been expected. We find that the integrated haplotype score (iHS) method for detecting selection appears robust to the presence of inversions. Finally, we observe a strong bias in the genome-wide results of principal components analysis (PCA), used to control for population structure in GWAS, in the presence of even a single large inversion, confirming the necessity to thin SNPs by linkage disequilibrium at large physical distances to obtain unbiased results.

A mixed-model approach for genome-wide association studies of correlated traits in structured populations

Genome-wide association studies (GWAS) are a standard approach for studying the genetics of natural variation. A major concern in GWAS is the need to account for the complicated dependence-structure of the data both between loci as well as between individuals. Mixed models have emerged as a general and flexible approach for correcting for population structure in GWAS. Here we extend this linear mixed model approach to carry out GWAS of correlated phenotypes, deriving a fully parameterized multi-trait mixed model (MTMM) that considers both the within-trait and between-trait variance components simultaneously for multiple traits. We apply this to human cohort data for correlated blood lipid traits from the Northern Finland Birth Cohort 1966, and demonstrate greatly increased power to detect pleiotropic loci that affect more than one blood lipid trait. We also apply this to an Arabidopsis dataset for flowering measurements in two different locations, identifying loci whose effect depends on the environment.

The utility of mitochondrial and y chromosome phylogenetic data to improve correction for population stratification.

Genome-wide association (GWA) studies have become a standard approach for discovering and validating genomic polymorphisms putatively associated with phenotypes of interest. Accounting for population structure in GWA studies is critical to attain unbiased parameter measurements and control Type I error. One common approach to accounting for population structure is to include several principal components derived from the entire autosomal dataset, which reflects population structure signal. However, knowing which components to include is subjective and generally not conclusive. We examined how phylogenetic signal from mitochondrial DNA (mtDNA) and chromosome Y (chr:Y) markers is concordant with principal component data based on autosomal markers to determine whether mtDNA and chr:Y phylogenetic data can help guide principal component selection. Using HAPMAP and other original data from individuals of multiple ancestries, we examined the relationships of mtDNA and chr:Y phylogenetic signal with the autosomal PCA using best subset logistic regression. We show that while the two approaches agree at times, this is independent of the component order and not completely represented in the Eigen values. Additionally, we use simulations to demonstrate that our approach leads to a slightly reduced Type I error rate compared to the standard approach. This approach provides preliminary evidence to support the theoretical concept that mtDNA and chr:Y data can be informative in locating the PCs that are most associated with evolutionary history of populations that are being studied, although the utility of such information will depend on the specific situation.