Dataset Information

Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology.

ABSTRACT: Genome-wide association studies (GWAS) have been widely used in genetic dissection of complex traits. However, common methods are all based on a fixed-SNP-effect mixed linear model (MLM) and single marker analysis, such as efficient mixed model analysis (EMMA). These methods require Bonferroni correction for multiple tests, which often is too conservative when the number of markers is extremely large. To address this concern, we proposed a random-SNP-effect MLM (RMLM) and a multi-locus RMLM (MRMLM) for GWAS. The RMLM simply treats the SNP-effect as random, but it allows a modified Bonferroni correction to be used to calculate the threshold p value for significance tests. The MRMLM is a multi-locus model including markers selected from the RMLM method with a less stringent selection criterion. Due to the multi-locus nature, no multiple test correction is needed. Simulation studies show that the MRMLM is more powerful in QTN detection and more accurate in QTN effect estimation than the RMLM, which in turn is more powerful and accurate than the EMMA. To demonstrate the new methods, we analyzed six flowering time related traits in Arabidopsis thaliana and detected more genes than previous reported using the EMMA. Therefore, the MRMLM provides an alternative for multi-locus GWAS.

SUBMITTER: Wang SB

PROVIDER: S-EPMC4726296 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology.

Wang Shi-Bo SB Feng Jian-Ying JY Ren Wen-Long WL Huang Bo B Zhou Ling L Wen Yang-Jun YJ Zhang Jin J Dunwell Jim M JM Xu Shizhong S Zhang Yuan-Ming YM

Scientific reports 20160120

Genome-wide association studies (GWAS) have been widely used in genetic dissection of complex traits. However, common methods are all based on a fixed-SNP-effect mixed linear model (MLM) and single marker analysis, such as efficient mixed model analysis (EMMA). These methods require Bonferroni correction for multiple tests, which often is too conservative when the number of markers is extremely large. To address this concern, we proposed a random-SNP-effect MLM (RMLM) and a multi-locus RMLM (MRM ...[more]

PMID: 26787347

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Project description:BackgroundDiseases caused by Pseudomonas syringae (Ps) are recognized as the most damaging factors in fruit trees with a significant economic and sanitary impact on crops. Among them, bacterial canker of apricot is exceedingly difficult to control due to a lack of efficient prophylactic measures. Several sources of partial resistance have been identified among genetic resources but the underlying genetic pattern has not been elucidated thus far. In this study, we phenotyped bacterial canker susceptibility in an apricot core-collection of 73 accessions over 4 years by measuring canker and superficial browning lengths issued from artificial inoculations in the orchard. In order to investigate the genetic architecture of partial resistance, we performed a genome-wide association study using best linear unbiased predictors on genetic (G) and genetic x year (G × Y) interaction effects extracted from linear mixed models. Using a set of 63,236 single-nucleotide polymorphism markers genotyped in the germplasm over the whole genome, multi-locus and multi-variate mixed models aimed at mapping the resistance while controlling for relatedness between individuals.ResultsWe detected 11 significant associations over 7 candidate loci linked to disease resistance under the two most severe years. Colocalizations between G and G × Y terms indicated a modulation on allelic effect depending on environmental conditions. Among the candidate loci, two loci on chromosomes 5 and 6 had a high impact on both canker length and superficial browning, explaining 41 and 26% of the total phenotypic variance, respectively. We found unexpected long-range linkage disequilibrium (LD) between these two markers revealing an inter-chromosomal LD block linking the two underlying genes. This result supports the hypothesis of a co-adaptation effect due to selection through population demography. Candidate genes annotations suggest a functional pathway involving abscisic acid, a hormone mainly known for mediating abiotic stress responses but also reported as a potential factor in plant-pathogen interactions.ConclusionsOur study contributed to the first detailed characterization of the genetic determinants of partial resistance to bacterial canker in a Rosaceae species. It provided tools for fruit tree breeding by identifying progenitors with favorable haplotypes and by providing major-effect markers for a marker-assisted selection strategy.

Dataset Information

Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology.

Publications

Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology.

Similar Datasets

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