A fast-linear mixed model for genome-wide haplotype association analysis: application to agronomic traits in maize.

Abstract:

BACKGROUND:Haplotypes combine the effects of several single nucleotide polymorphisms (SNPs) with high linkage disequilibrium, which benefit the genome-wide association analysis (GWAS). In the haplotype association analysis, both haplotype alleles and blocks are tested. Haplotype alleles can be inferred with the same statistics as SNPs in the linear mixed model, while blocks require the formulation of unified statistics to fit different genetic units, such as SNPs, haplotypes, and copy number variations. RESULTS:Based on the FaST-LMM, the fastLmPure function in the R/RcppArmadillo package has been introduced to speed up genome-wide regression scans by a re-weighted least square estimation. When large or highly significant blocks are tested based on EMMAX, the genome-wide haplotype association analysis takes only one to two rounds of genome-wide regression scans. With a genomic dataset of 541,595 SNPs from 513 maize inbred lines, 90,770 haplotype blocks were constructed across the whole genome, and three types of markers (SNPs, haplotype alleles, and haplotype blocks) were genome-widely associated with 17 agronomic traits in maize using the software developed here. CONCLUSIONS:Two SNPs were identified for LNAE, four haplotype alleles for TMAL, LNAE, CD, and DTH, and only three blocks reached the significant level for TMAL, CD, and KNPR. Compared to the R/lm function, the computational time was reduced by ~ 10-15 times.

journal_name

BMC Genomics

journal_title

BMC genomics

authors

Chen H,Hao Z,Zhao Y,Yang R

doi

10.1186/s12864-020-6552-x

subject

Has Abstract

pub_date

2020-02-11 00:00:00

pages

151

issue

1

issn

1471-2164

pii

10.1186/s12864-020-6552-x

journal_volume

21

pub_type

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