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Genome-wide association studies for discrete traits.

ABSTRACT: Genome-wide association studies of discrete traits generally use simple methods of analysis based on chi(2) tests for contingency tables or logistic regression, at least for an initial scan of the entire genome. Nevertheless, more power might be obtained by using various methods that analyze multiple markers in combination. Methods based on sliding windows, wavelets, Bayesian shrinkage, or penalized likelihood methods, among others, were explored by various participants of Genetic Analysis Workshop 16 Group 1 to combine information across multiple markers within a region, while others used Bayesian variable selection methods for genome-wide multivariate analyses of all markers simultaneously. Imputation can be used to fill in missing markers on individual subjects within a study or in a meta-analysis of studies using different panels. Although multiple imputation theoretically should give more robust tests of association, one participant contribution found little difference between results of single and multiple imputation. Careful control of population stratification is essential, and two contributions found that previously reported associations with two genes disappeared after more precise control. Other issues considered by this group included subgroup analysis, gene-gene interactions, and the use of biomarkers.

SUBMITTER: Thomas DC

PROVIDER: S-EPMC2920891 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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Genome-wide association studies for discrete traits.

Thomas Duncan C DC

Genetic epidemiology 20090101

Genome-wide association studies of discrete traits generally use simple methods of analysis based on chi(2) tests for contingency tables or logistic regression, at least for an initial scan of the entire genome. Nevertheless, more power might be obtained by using various methods that analyze multiple markers in combination. Methods based on sliding windows, wavelets, Bayesian shrinkage, or penalized likelihood methods, among others, were explored by various participants of Genetic Analysis Works ...[more]

PMID: 19924710

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Project description:BackgroundGenome-wide association studies (GWAS) based on linkage disequilibrium (LD) provide a promising tool for the detection and fine mapping of quantitative trait loci (QTL) underlying complex agronomic traits. In this study we explored the genetic basis of variation for the traits heading date, plant height, thousand grain weight, starch content and crude protein content in a diverse collection of 224 spring barleys of worldwide origin. The whole panel was genotyped with a customized oligonucleotide pool assay containing 1536 SNPs using Illumina's GoldenGate technology resulting in 957 successful SNPs covering all chromosomes. The morphological trait "row type" (two-rowed spike vs. six-rowed spike) was used to confirm the high level of selectivity and sensitivity of the approach. This study describes the detection of QTL for the above mentioned agronomic traits by GWAS.ResultsPopulation structure in the panel was investigated by various methods and six subgroups that are mainly based on their spike morphology and region of origin. We explored the patterns of linkage disequilibrium (LD) among the whole panel for all seven barley chromosomes. Average LD was observed to decay below a critical level (r2-value 0.2) within a map distance of 5-10 cM. Phenotypic variation within the panel was reasonably large for all the traits. The heritabilities calculated for each trait over multi-environment experiments ranged between 0.90-0.95. Different statistical models were tested to control spurious LD caused by population structure and to calculate the P-value of marker-trait associations. Using a mixed linear model with kinship for controlling spurious LD effects, we found a total of 171 significant marker trait associations, which delineate into 107 QTL regions. Across all traits these can be grouped into 57 novel QTL and 50 QTL that are congruent with previously mapped QTL positions.ConclusionsOur results demonstrate that the described diverse barley panel can be efficiently used for GWAS of various quantitative traits, provided that population structure is appropriately taken into account. The observed significant marker trait associations provide a refined insight into the genetic architecture of important agronomic traits in barley. However, individual QTL account only for a small portion of phenotypic variation, which may be due to insufficient marker coverage and/or the elimination of rare alleles prior to analysis. The fact that the combined SNP effects fall short of explaining the complete phenotypic variance may support the hypothesis that the expression of a quantitative trait is caused by a large number of very small effects that escape detection. Notwithstanding these limitations, the integration of GWAS with biparental linkage mapping and an ever increasing body of genomic sequence information will facilitate the systematic isolation of agronomically important genes and subsequent analysis of their allelic diversity.

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Genome-wide association studies for discrete traits.

Publications

Genome-wide association studies for discrete traits.

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