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

Heli Chen,Yunfeng Zhao,Runqing Yang,Zhiyu Hao

doi:10.1186/s12864-020-6552-x

Abstract

BackgroundHaplotypes 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.ResultsBased 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.ConclusionsTwo 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.

Highlights

Haplotypes combine the effects of several single nucleotide polymorphisms (SNPs) with high linkage disequilibrium, which benefit the genome-wide association analysis (GWAS)
To speed up genome-wide regression scans, we introduce the fastLmPure function in the R/RcppArmadillo package to infer the effect of tested genetic units
Haplotype construction Haplotype blocks of the genomic dataset were constructed using the Four Gamete Test method (FGT) [21], which is implemented in the Haploview software [22]

Summary

Introduction

Haplotypes combine the effects of several single nucleotide polymorphisms (SNPs) with high linkage disequilibrium, which benefit the genome-wide association analysis (GWAS). The high computing intensity of LMM has motivated the development of simpler algorithms [10,11,12,13,14,15,16,17] to reduce the computational burden, allowing LMM to become a widely used and powerful approach in genome-wide association studies (GWAS). These simplified methods work by reducing the LMM or replacing the restricted maximum likelihood (REML) [18] with spectral decomposition.

Methods

Results

Conclusion