Dataset Information

Methodological implementation of mixed linear models in multi-locus genome-wide association studies.

ABSTRACT: The mixed linear model has been widely used in genome-wide association studies (GWAS), but its application to multi-locus GWAS analysis has not been explored and assessed. Here, we implemented a fast multi-locus random-SNP-effect EMMA (FASTmrEMMA) model for GWAS. The model is built on random single nucleotide polymorphism (SNP) effects and a new algorithm. This algorithm whitens the covariance matrix of the polygenic matrix K and environmental noise, and specifies the number of nonzero eigenvalues as one. The model first chooses all putative quantitative trait nucleotides (QTNs) with???0.005 P-values and then includes them in a multi-locus model for true QTN detection. Owing to the multi-locus feature, the Bonferroni correction is replaced by a less stringent selection criterion. Results from analyses of both simulated and real data showed that FASTmrEMMA is more powerful in QTN detection and model fit, has less bias in QTN effect estimation and requires a less running time than existing single- and multi-locus methods, such as empirical Bayes, settlement of mixed linear model under progressively exclusive relationship (SUPER), efficient mixed model association (EMMA), compressed MLM (CMLM) and enriched CMLM (ECMLM). FASTmrEMMA provides an alternative for multi-locus GWAS.

SUBMITTER: Wen YJ

PROVIDER: S-EPMC6054291 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Methodological implementation of mixed linear models in multi-locus genome-wide association studies.

Wen Yang-Jun YJ Zhang Hanwen H Ni Yuan-Li YL Huang Bo B Zhang Jin J Feng Jian-Ying JY Wang Shi-Bo SB Dunwell Jim M JM Zhang Yuan-Ming YM Wu Rongling R

Briefings in bioinformatics 20180701 4

The mixed linear model has been widely used in genome-wide association studies (GWAS), but its application to multi-locus GWAS analysis has not been explored and assessed. Here, we implemented a fast multi-locus random-SNP-effect EMMA (FASTmrEMMA) model for GWAS. The model is built on random single nucleotide polymorphism (SNP) effects and a new algorithm. This algorithm whitens the covariance matrix of the polygenic matrix K and environmental noise, and specifies the number of nonzero eigenvalu ...[more]

PMID: 28158525

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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

Methodological implementation of mixed linear models in multi-locus genome-wide association studies.

Publications

Methodological implementation of mixed linear models in multi-locus genome-wide association studies.

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