Mixed linear model approach adapted for genome-wide association studies

Abstract Background Heilongjiang Province is a high-quality japonica rice cultivation area in China. One in ten bowls of Chinese rice is produced here. Increasing yield is one of the main aims of rice production in this area. However, yield is a complex quantitative trait composed of many factors. The purpose of this study was to determine how many genetic loci are associated with yield-related traits. Genome-wide association studies (GWAS) were performed on 450 accessions collected from northeast Asia, including Russia, Korea, Japan and Heilongjiang Province of China. These accessions consist of elite varieties and landraces introduced into Heilongjiang Province decade ago. Results After resequencing of the 450 accessions, 189,019 single nucleotide polymorphisms (SNPs) were used for association studies by two different models, a general linear model (GLM) and a mixed linear model (MLM), examining four traits: days to heading (DH), plant height (PH), panicle weight (PW) and tiller number (TI). Over 25 SNPs were found to be associated with each trait. Among them, 22 SNPs were selected to identify candidate genes, and 2, 8, 1 and 11 SNPs were found to be located in 3′ UTR region, intron region, coding region and intergenic region, respectively. Conclusions All SNPs detected in this research may become candidates for further fine mapping and may be used in the molecular breeding of high-latitude rice.

Download Full-text

A Mixed Model Approach to Genome-Wide Association Studies for Selection Signatures, with Application to Mice Bred for Voluntary Exercise Behavior

Genetics ◽

10.1534/genetics.117.300102 ◽

2017 ◽

pp. genetics.300102.2017 ◽

Cited By ~ 5

Author(s):

Shizhong Xu ◽

Theodore Garland

Keyword(s):

Mixed Model ◽

Association Studies ◽

Exercise Behavior ◽

Voluntary Exercise ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Selection Signatures ◽

Mixed Model Approach ◽

Genome Wide ◽

Model Approach

Download Full-text

Integrative functional linear model for genome-wide association studies with multiple traits

Biostatistics ◽

10.1093/biostatistics/kxaa043 ◽

2020 ◽

Author(s):

Yang Li ◽

Fan Wang ◽

Mengyun Wu ◽

Shuangge Ma

Keyword(s):

Linear Model ◽

Association Studies ◽

Genome Wide Association ◽

High Dimensionality ◽

Sufficient Information ◽

Genome Wide Association Studies ◽

Multiple Traits ◽

Functional Linear Model ◽

Genome Wide ◽

Good Prediction Accuracy

Summary In recent biomedical research, genome-wide association studies (GWAS) have demonstrated great success in investigating the genetic architecture of human diseases. For many complex diseases, multiple correlated traits have been collected. However, most of the existing GWAS are still limited because they analyze each trait separately without considering their correlations and suffer from a lack of sufficient information. Moreover, the high dimensionality of single nucleotide polymorphism (SNP) data still poses tremendous challenges to statistical methods, in both theoretical and practical aspects. In this article, we innovatively propose an integrative functional linear model for GWAS with multiple traits. This study is the first to approximate SNPs as functional objects in a joint model of multiple traits with penalization techniques. It effectively accommodates the high dimensionality of SNPs and correlations among multiple traits to facilitate information borrowing. Our extensive simulation studies demonstrate the satisfactory performance of the proposed method in the identification and estimation of disease-associated genetic variants, compared to four alternatives. The analysis of type 2 diabetes data leads to biologically meaningful findings with good prediction accuracy and selection stability.

Download Full-text

A mixed-model approach for genome-wide association studies of correlated traits in structured populations

Nature Genetics ◽

10.1038/ng.2376 ◽

2012 ◽

Vol 44 (9) ◽

pp. 1066-1071 ◽

Cited By ~ 235

Author(s):

Arthur Korte ◽

Bjarni J Vilhjálmsson ◽

Vincent Segura ◽

Alexander Platt ◽

Quan Long ◽

...

Keyword(s):

Mixed Model ◽

Association Studies ◽

Structured Populations ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Correlated Traits ◽

Mixed Model Approach ◽

Genome Wide ◽

Model Approach

Download Full-text

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

10.1101/077404 ◽

2016 ◽

Author(s):

Yang-Jun Wen ◽

Hanwen Zhang ◽

Jin Zhang ◽

Jian-Ying Feng ◽

Bo Huang ◽

...

Keyword(s):

Mixed Model ◽

Genetic Model ◽

Association Studies ◽

Selection Criterion ◽

Real Data ◽

Mixed Linear Model ◽

Genome Wide Association ◽

Residual Variance ◽

Genome Wide Association Studies ◽

Genome Wide

AbstractAlthough the mixed linear model (MLM) such as efficient mixed model association (EMMA), has been widely used in genome-wide association studies (GWAS), relatively little is known about fast and efficient algorithms to implement multi-locus GWAS. To address this issue, we report a fast multi-locus random-SNP-effect EMMA (FASTmrEMMA). In this method, a new matrix transformation was constructed to obtain a new genetic model that includes only quantitative trait nucleotide (QTN) variation and normal residual error; letting the number of nonzero eigenvalues be one and fixing the polygenic-to-residual variance ratio was used to increase computing speed. All the putative QTNs with the ≤0.005 P-values in the first step of the new method were included in one multi-locus model for true QTN detection. Owing to the multi-locus feature, the Bonferroni correction is replaced by a less stringent selection criterion. Results from analyses of both simulated and real data showed that FASTmrEMMA is more powerful in QTN detection, model fit and robustness, has less bias in QTN effect estimation, and requires less running time than the current single- and multi-locus methodologies for GWAS, such as E-BAYES, SUPER, EMMA, CMLM and ECMLM. Therefore, FASTmrEMMA provides an alternative for multi-locus GWAS.

Download Full-text

Is it reasonable to account for population structure in genome-wide association studies?

10.1101/647768 ◽

2019 ◽

Author(s):

Bongsong Kim

Keyword(s):

Population Structure ◽

Linear Model ◽

Association Studies ◽

Significant Snps ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Continuous Variables ◽

Genome Wide ◽

Manhattan Plot

AbstractPopulation structure is widely perceived as a noise factor that undermines the quality of association between an SNP variable and a phenotypic variable in genome-wide association studies (GWAS). The linear model for GWAS generally accounts for population-structure variables to obtain the adjusted phenotype which has less noise. Its result is known to amplify the contrast between significant SNPs and insignificant SNPs in a resultant Manhattan plot. In fact, however, conventional GWAS practice often implements the linear model in an unusual way in that the population-structure variables are incorporated into the linear model in the form of continuous variables rather than factor variables. If the coefficients for population-structure variables change across all SNPs, then each SNP variable will be regressed against a differently adjusted phenotypic variable, making the GWAS process unreliable. Focusing on this concern, this study investigated whether accounting for population-structure variables in the linear model for GWAS can assure the adjusted phenotypes to be consistent across all SNPs. The result showed that the adjusted phenotypes resulting across all SNPs were not consistent, which is alarming considering conventional GWAS practice that accounts for population structure.

Download Full-text