Joint association analysis method to dissect complex genetic architecture of multiple genetically related traits

Genome-wide association study(GWAS)has been a standard approach to discover the genetic determinants underlying complex traits.It is a major challenge in GWAS how to improve analysis power,uncover complex genetic correlation,and reveal gene-gene and gene-environment interactions through integrated analysis of multiple genetically related traits.To combat these challenges,we proposed a mixed linear model-based joint association analysis method for multiple traits,which include epistasis and geneenvironment interaction in the mapping model and utilize within-trait variance and between-trait covariance simultaneously;A F-statistics based on Wilks statistics is used to test the significance of each SNP and paired interacted SNPs,each genetic effects of QTS are estimated and tested by the MCMC method based on a QTS full model.Simulations showed that the multi-trait GWAS method could provide increased power in detecting pleiotropic loci affecting more than one trait,and can unbiasedly estimate effects of QTS.To demonstrate the performance of the proposed method,we analyzed four blood lipid traits in Multi-Ethnic Study of Atherosclerosis(MESA)Cohort and two yield-related traits in a rice immortalized F2 dataset.A software package was developed for the proposed method.

Genomic insights into the genetic basis of cotton breeding in China

The excellent Upland cotton(Gossypium hirsutum)cultivars developed since 1949 have made a huge contribution to cotton production in China,the world's largest producer and consumer of cotton.However,the genetic and genomic basis for the improvements of these cotton cultivars remains largely unclear.In this study,we selected 16 Upland cotton cultivars with important historical status in Chinese cotton breeding and constructed a multiparent,advanced generation,intercross(MAGiC)population comprising 920 recombinant inbred lines.A genome-wide association study using the MAGIC population identified 54 genomic loci associated with lint yield and fiber quality.Of them,25(46.30%)pleiotropic genomic loci cause simultaneous changes of lint yield and/or fiber quality traits,revealing complex trade-offs and linkage drags in Upland cotton agronomic traits.Deep sequencing data of 11 introduced ancestor cultivars and publicly available resequencing datasets of 839 cultivars developed in China during the past 70 years were integrated to explore the historical distribution and origin of the elite or selected alleles.Interestingly,85%oftheseelitealleles were selectedandfixed fromdifferent Americanancestors,consistentwithcotton breeding practices in China.However,seven elite alleles of native origin that are responsible for Fusarium wilt resistance,early maturing,good-quality fiber,and other characteristics were not found in American an-cestors but have greatly contributed to Chinese cotton breeding and wide cultivation.Taken together,these results provide a genetic basis for further improving cotton cultivars and reveal that the genetic composition of Chinese cotton cultivars is narrow and mainly derived from early introduced American varieties.