Mapping small-effect and linked quantitative trait loci for complex traits in backcross or DH populations via a multi-locus GWAS methodology

Abstract

Composite interval mapping (CIM) is the most widely-used method in linkage analysis. Its main feature is the ability to control genomic background effects via inclusion of co-factors in its genetic model. However, the result often depends on how the co-factors are selected, especially for small-effect and linked quantitative trait loci (QTL). To address this issue, here we proposed a new method under the framework of genome-wide association studies (GWAS). First, a single-locus random-SNP-effect mixed linear model method for GWAS was used to scan each putative QTL on the genome in backcross or doubled haploid populations. Here, controlling background via selecting markers in the CIM was replaced by estimating polygenic variance. Then, all the peaks in the negative logarithm P-value curve were selected as the positions of multiple putative QTL to be included in a multi-locus genetic model, and true QTL were automatically identified by empirical Bayes. This called genome-wide CIM (GCIM). A series of simulated and real datasets was used to validate the new method. As a result, the new method had higher power in QTL detection, greater accuracy in QTL effect estimation, and stronger robustness under various backgrounds as compared with the CIM and empirical Bayes methods.

QTL Mapping of Kernel Traits and Validation of a Major QTL for Kernel Length-Width Ratio Using SNP and Bulked Segregant Analysis in Wheat

“…A 6% denaturing polyacrylamide gel electrophoresis (PAGE) was used to separate PCR conducts. Using the Biparental Populations (BIP) module of the inclusive composite interval mapping (ICIM) in the software QTL IciMapping 4.1 (http://www.isbreeding.net) for QTL analysis 42 . The critical LOD scores for a significant QTL were set at 3.0.…”

Metabolome-wide association studies for agronomic traits of rice

“…Simple linear regression is similar to Pearson's correlation analysis where only one metabolite is considered at a time. An advantage of using the mixed model in MWAS is to provide an efficient control for spurious positives by adding polymetabolomic effects (random effect), which is analogous to polygenic effects in GWAS (Wang et al 2016a(Wang et al , 2016bYu et al 2006;Zhang et al 2005). In Bayes B, association analysis is actually translated to a cluster analysis in which trait-associated metabolites and trait-irrelevant metabolites are assigned into different groups (Pérez and de Los Campos 2014).…”

A fast multi-locus random-SNP-effect EMMA for genome-wide association studies

“…Because the random model approach will shrink the estimated SNP effects toward zero when the simulated QTN effects are small, leading to maximum correlation between observed and predicted phenotypic values [25,35]. Meanwhile, the power in the detection of QTN with random effect is higher than that with fixed effect [36].…”

“… W 12 ( , , , ) c w w w is an nc  matrix of covariates (fixed effects) including a column vector of 1, population structure [2] or principle component [39] may be incorporated into W , and α is a 1 c vector of fixed effects including the intercept; X is an 1 n vector of marker genotypes, In the current methods, including EMMA [3], CMLM/P3D [18], ECMLM [19], EMMAX [20], FaST-LMM [21], FaST-LMM-Select [22], SUPER [24], GEMMA [4], and GRAMMA-Gamma [23],  is treated as a fixed effect, from which it is relatively easy to estimate 2 g  and 2 e  . In this study we treat  as random in order to make the model more realistic [25,35,36]. In this case, three variance components need to be estimated under the assumption that QTN variance is zero, because most SNPs are not associated with the trait of interest.…”