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Genomic convergence of genome-wide investigations for complex traits.

ABSTRACT: Genome-wide investigations for identifying the genes for complex traits are considered to be agnostic in terms of prior assumptions for the responsible DNA alterations. The agreement of genome-wide association studies (GWAS) and genome-wide linkage scans (GWLS) has not been explored to date. In this study, a genomic convergence approach of GWAS and GWLS was implemented for the first time in order to identify genomic loci supported by both methods. A database with 376 GWLS and 102 GWAS for 19 complex traits was created. Data regarding the location and statistical significance for each genetic marker were extracted from articles or web-based databases. Convergence was quantified as the proportion of significant GWAS markers located within linked regions. Convergence was variable (0-73.3%) and was found to be significantly higher than expected by chance only for two of the 19 phenotypes. Seventy five loci of interest were identified, which being supported by independent lines of evidence, could merit prioritization in future investigations. Although convergence is supportive of genuine effects, lack of agreement between GWLS and GWAS is also indicative that these studies are designed to answer different questions and are not equally well suited for deciphering the genetics of complex traits.

SUBMITTER: Kitsios GD

PROVIDER: S-EPMC2971667 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature

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Genomic convergence of genome-wide investigations for complex traits.

Kitsios Georgios D GD Zintzaras Elias E

Annals of human genetics 20090709 Pt 5

Genome-wide investigations for identifying the genes for complex traits are considered to be agnostic in terms of prior assumptions for the responsible DNA alterations. The agreement of genome-wide association studies (GWAS) and genome-wide linkage scans (GWLS) has not been explored to date. In this study, a genomic convergence approach of GWAS and GWLS was implemented for the first time in order to identify genomic loci supported by both methods. A database with 376 GWLS and 102 GWAS for 19 com ...[more]

PMID: 19604225

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Project description:The objectives of this study were to identify informative genomic regions that affect the exterior traits of purebred Korean Yorkshire pigs and to investigate and compare the accuracy of genomic prediction for response variables. Phenotypic data on body height (BH), body length (BL), and total teat number (TTN) from 2,432 Yorkshire pigs were used to obtain breeding values including as response variable the estimated breeding value (EBV) and 2 types of deregressed EBVs-one including the parent average (DEBVincPA) and the other excluding it (DEBVexcPA). A final genotype panel comprising 46,199 SNP markers was retained for analysis after quality control for common SNPs. The BayesB and BayesC methods-with various ? and weighted response variables (EBV, DEBVincPA, or DEBVexcPA)-were used to estimate SNP effects, through the genome-wide association study. The significance of genomic windows (1 Mb) was obtained at 1.0% additive genetic variance and was subsequently used to identify informative genomic regions. Furthermore, SNPs with a high model frequency (?0.90) were considered informative. The accuracy of genomic prediction was estimated using a 5-fold cross-validation with the K-means clustering method. Genomic accuracy was measured as the genomic correlation between the molecular breeding value and the individual weighted response variables (EBV, DEBVincPA, or DEBVexcPA). The number of identified informative windows (1 Mb) for BH, BL, and TTN was 4, 3, and 4, respectively. The number of significant SNPs for BH, BL, and TTN was 6, 4, and 5, respectively. Diversity ? did not influence the accuracy of genomic prediction. The BayesB method showed slightly higher genomic accuracy for exterior traits than BayesC method in this study. In addition, the genomic accuracy using DEBVincPA as response variable was higher than that using other response variables. Therefore, the genomic accuracy using BayesB (? = 0.90) with DEBVinPA as a response variable was the most effective in this study. The genomic accuracy values for BH, BL, and TTN were calculated to be 0.52, 0.60, and 0.51, respectively.

Project description:Genetic progress for forage quality has been poor in alfalfa (Medicago sativa L.), the most-grown forage legume worldwide. This study aimed at exploring opportunities for marker-assisted selection (MAS) and genomic selection of forage quality traits based on breeding values of parent plants. Some 154 genotypes from a broadly-based reference population were genotyped by genotyping-by-sequencing (GBS), and phenotyped for leaf-to-stem ratio, leaf and stem contents of protein, neutral detergent fiber (NDF) and acid detergent lignin (ADL), and leaf and stem NDF digestibility after 24 hours (NDFD), of their dense-planted half-sib progenies in three growing conditions (summer harvest, full irrigation; summer harvest, suspended irrigation; autumn harvest). Trait-marker analyses were performed on progeny values averaged over conditions, owing to modest germplasm × condition interaction. Genomic selection exploited 11,450 polymorphic SNP markers, whereas a subset of 8,494 M. truncatula-aligned markers were used for a genome-wide association study (GWAS). GWAS confirmed the polygenic control of quality traits and, in agreement with phenotypic correlations, indicated substantially different genetic control of a given trait in stems and leaves. It detected several SNPs in different annotated genes that were highly linked to stem protein content. Also, it identified a small genomic region on chromosome 8 with high concentration of annotated genes associated with leaf ADL, including one gene probably involved in the lignin pathway. Three genomic selection models, i.e., Ridge-regression BLUP, Bayes B and Bayesian Lasso, displayed similar prediction accuracy, whereas SVR-lin was less accurate. Accuracy values were moderate (0.3-0.4) for stem NDFD and leaf protein content, modest for leaf ADL and NDFD, and low to very low for the other traits. Along with previous results for the same germplasm set, this study indicates that GBS data can be exploited to improve both quality traits (by genomic selection or MAS) and forage yield.

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Genomic convergence of genome-wide investigations for complex traits.

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Genomic convergence of genome-wide investigations for complex traits.

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