Abstract Genome wide association studies (GWAS) can play an essential role in understanding genetic basis of complex traits in plants and animals. Conventional SNP-based linear mixed models (LMM) used in many GWAS that marginally test single nucleotide polymorphisms (SNPs) have successfully identified many loci with major and minor effects. In plants, the relatively small population size in GWAS and the high genetic diversity found many plant species can impede mapping efforts on complex traits. Here we present a novel haplotype-based trait fine-mapping framework, HapFM, to supplement current GWAS methods. HapFM uses genotype data to partition the genome into haplotype blocks, identifies haplotype clusters within each block, and then performs genome-wide haplotype fine-mapping to infer the causal haplotype blocks of trait. We benchmarked HapFM, GEMMA, BSLMM, and GMMAT in both simulation and real plant GWAS datasets. HapFM consistently resulted in higher mapping power than the other GWAS methods in simulations with high polygenicity. Moreover, it resulted in higher mapping resolution, especially in regions of high LD, by identifying small causal blocks in the larger haplotype block. In the Arabidopsis flowering time (FT10) datasets, HapFM identified four novel loci compared to GEMMA’s results, and its average mapping interval of HapFM was 9.6 times smaller than that of GEMMA. In conclusion, HapFM is tailored for plant GWAS to result in high mapping power on complex traits and improved mapping resolution to facilitate crop improvement.

Causal Haplotype Block Identification in Plant Genome-Wide Association Studies