scholarly journals GRAMMAR-Lambda: An Extreme Simplification for Genome-wide Mixed Model Association Analysis

2021 ◽  
Author(s):  
Runqing Yang ◽  
Jin Gao ◽  
Yuxin Song ◽  
Zhiyu Hao ◽  
Pao Xu
Keyword(s):  
Association Analysis ◽  
Statistical Power ◽  
Large Scale ◽  
Mixed Model ◽  
Genome Wide Association ◽  
Genomic Heritability ◽  
Naive Method ◽  
Genome Wide ◽  
Complex Population ◽  
Association Method

AbstractA highly efficient genome-wide association method, GRAMMAR-Lambda is proposed to make simple genomic control for the test statistics deflated by GRAMMAR, producing statistical power as high as exact mixed model association method. Using the simulated and real phenotypes, we show that at a moderate or above genomic heritability, polygenic effects can be estimated using a small number of randomly selected markers, which extremely simplify genome-wide association analysis with an approximate computational complexity to naïve method in large-scale complex population. Upon a test at once, joint association analysis offers significant increase in statistical power over existing methods.

scholarly journals Efficient Genomic Control for Mixed Model Associations in Large-scale Population

2021 ◽  
Author(s):  
Zhiyu Hao ◽  
Jin Gao ◽  
Yuxin Song ◽  
Runqing Yang ◽  
Di Liu
Keyword(s):  
Association Analysis ◽  
Statistical Power ◽  
Large Scale ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
False Negative ◽  
False Negative Rate ◽  
Genomic Control ◽  
Genomic Heritability ◽  
Complex Population

AbstractAmong linear mixed model-based association methods, GRAMMAR has the lowest computing complexity for association tests, but it produces a high false-negative rate due to the deflation of test statistics for complex population structure. Here, we present an optimized GRAMMAR method by efficient genomic control, Optim-GRAMMAR, that estimates the phenotype residuals by regulating downward genomic heritability in the genomic best linear unbiased prediction. Even though using the fewer sampling markers to evaluate genomic relationship matrices and genomic controls, Optim-GRAMMAR retains a similar statistical power to the exact mixed model association analysis, which infers an extremely efficient approach to handle large-scale data. Moreover, joint association analysis significantly improved statistical power over existing methods.

scholarly journals Generalized Hierarchical Mixed Model Association Analysis

2021 ◽  
Author(s):  
Runqing Yang ◽  
Yuxin Song ◽  
Zhiyu Hao ◽  
Zhonghua Liu
Keyword(s):  
Association Analysis ◽  
Statistical Power ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Least Square ◽  
Linear Unbiased Prediction ◽  
Genomic Breeding ◽  
Genome Wide ◽  
Complex Population ◽  
Best Linear Unbiased

AbstractIn genome-wide association analysis for complex diseases, we partitioned the genomic generalized linear mixed model (GLMM) into two hierarchies—the GLMM regarding genomic breeding values (GBVs) and a generalized linear regression of the GBVs to the tested marker effects. In the first hierarchy, the GBVs were predicted by solving for the genomic best linear unbiased prediction for GLMM, and in the second hierarchy, association tests were performed using the generalized least square (GLS) method. The so-called Hi-GLMM method exhibited advantages over existing methods in terms of both genomic control for complex population structure and statistical power to detect quantitative trait nucleotides (QTNs), especially when the GBVs were estimated precisely, and using joint association analysis for QTN candidates obtained from a test at once.

Identify known and novel candidate genes associated with backfat thickness in Duroc pigs by large-scale genome-wide association analysis

2022 ◽  
Author(s):  
Rongrong Ding ◽  
Zhanwei Zhuang ◽  
Yibin Qiu ◽  
Donglin Ruan ◽  
Jie Wu ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Association Analysis ◽  
Large Scale ◽  
Major Gene ◽  
Gene Set Enrichment Analysis ◽  
Genome Wide Association ◽  
Backfat Thickness ◽  
Lean Meat ◽  
Genome Wide Association Analysis ◽  
Genome Wide

Abstract Backfat thickness (BFT) is complex and economically important traits in the pig industry, since it reflects fat deposition and can be used to measure the carcass lean meat percentage in pigs. In this study, all 6,550 pigs were genotyped using the Geneseek Porcine 50K SNP Chip to identify SNPs related to BFT and to search for candidate genes through genome-wide association analysis in two Duroc populations. In total, 80 SNPs, including 39 significant and 41 suggestive SNPs, and 6 QTLs were identified significantly associated with the BFT. In addition, 9 candidate genes, including a proven major gene MC4R, 3 important candidate genes (RYR1, HMGA1 and NUDT3) which were previously described as related to BFT, and 5 novel candidate genes (SIRT2, NKAIN2, AMH, SORCS1 and SORCS3) were found based on their potential functional roles in BFT. The functions of candidate genes and gene set enrichment analysis indicate that most important pathways are related to energy homeostasis and adipogenesis. Finally, our data suggests that most of the candidate genes can be directly used for genetic improvement through molecular markers, except that the MC4R gene has an antagonistic effect on growth rate and carcass lean meat percentage in breeding. Our results will advance our understanding of the complex genetic architecture of BFT traits, and laid the foundation for additional genetic studies to increase carcass lean meat percentage of pig through marker-assisted selection and/or genomic selection.

scholarly journals Hybrid of Restricted and Penalized Maximum Likelihood Method for Efficient Genome-Wide Association Study

Genes ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1286
Author(s):  
Wenlong Ren ◽  
Zhikai Liang ◽  
Shu He ◽  
Jing Xiao
Keyword(s):  
Maximum Likelihood ◽  
Variance Components ◽  
Statistical Power ◽  
Mixed Model ◽  
Single Locus ◽  
Genome Wide Association ◽  
Likelihood Method ◽  
Penalized Maximum Likelihood ◽  
Genome Wide ◽  
Estimation Of Variance

In genome-wide association studies, linear mixed models (LMMs) have been widely used to explore the molecular mechanism of complex traits. However, typical association approaches suffer from several important drawbacks: estimation of variance components in LMMs with large scale individuals is computationally slow; single-locus model is unsatisfactory to handle complex confounding and causes loss of statistical power. To address these issues, we propose an efficient two-stage method based on hybrid of restricted and penalized maximum likelihood, named HRePML. Firstly, we performed restricted maximum likelihood (REML) on single-locus LMM to remove unrelated markers, where spectral decomposition on covariance matrix was used to fast estimate variance components. Secondly, we carried out penalized maximum likelihood (PML) on multi-locus LMM for markers with reasonably large effects. To validate the effectiveness of HRePML, we conducted a series of simulation studies and real data analyses. As a result, our method always had the highest average statistical power compared with multi-locus mixed-model (MLMM), fixed and random model circulating probability unification (FarmCPU), and genome-wide efficient mixed model association (GEMMA). More importantly, HRePML can provide higher accuracy estimation of marker effects. HRePML also identifies 41 previous reported genes associated with development traits in Arabidopsis, which is more than was detected by the other methods.

scholarly journals FVGWAS: Fast voxelwise genome wide association analysis of large-scale imaging genetic data

NeuroImage ◽  
2015 ◽  
Vol 118 ◽  
pp. 613-627 ◽  
Author(s):  
Meiyan Huang ◽  
Thomas Nichols ◽  
Chao Huang ◽  
Yang Yu ◽  
Zhaohua Lu ◽  
...  
Keyword(s):  
Association Analysis ◽  
Large Scale ◽  
Genetic Data ◽  
Genome Wide Association ◽  
Genome Wide Association Analysis ◽  
Genome Wide

scholarly journals Optimal Genomic Control in Large-scale Genetic Associations for Binary Diseases

2021 ◽  
Author(s):  
Runqing Yang ◽  
Yuxin Song ◽  
Li Jiang ◽  
Zhiyu Hao ◽  
Runqing Yang
Keyword(s):  
Multiple Testing ◽  
Statistical Power ◽  
Large Scale ◽  
Association Studies ◽  
Joint Analysis ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Genomic Heritability ◽  
Large Scale Data ◽  
Genome Wide

Abstract Complex computation and approximate solution hinder the application of generalized linear mixed models (GLMM) into genome-wide association studies. We extended GRAMMAR to handle binary diseases by considering genomic breeding values (GBVs) estimated in advance as a known predictor in genomic logit regression, and then controlled polygenic effects by regulating downward genomic heritability. Using simulations and case analyses, we showed in optimizing GRAMMAR, polygenic effects and genomic controls could be evaluated using the fewer sampling markers, which extremely simplified GLMM-based association analysis in large-scale data. In addition, joint analysis for quantitative trait nucleotide (QTN) candidates chosen by multiple testing offered significant improved statistical power to detect QTNs over existing methods.

scholarly journals Genome-wide Hierarchical Mixed Model Association Analysis

2021 ◽  
Author(s):  
Runqing Yang ◽  
Di Liu ◽  
Zhiyu Hao ◽  
Yuxin Song ◽  
Runqing Yang ◽  
...  
Keyword(s):  
Association Analysis ◽  
Statistical Power ◽  
Mixed Model ◽  
False Negative ◽  
Best Linear Unbiased Prediction ◽  
Least Square ◽  
Linear Unbiased Prediction ◽  
Genomic Breeding ◽  
Genome Wide ◽  
Best Linear Unbiased

Abstract We partitioned the genomic mixed model into two hierarchies to firstly estimate genomic breeding values (GBVs) using the genomic best linear unbiased prediction and then statistically infer the association of GBVs with each SNP using the generalized least square. The genome-wide hierarchical mixed model association study (named Hi-LMM) can correct effectively confounders with polygenic effects as residuals in association tests, preventing potential false negative errors produced with GRAMMAR or EMMAX. The Hi-LMM performs the same statistical power as the exact FaST-LMM with the same computing efficiency as EMMAX. When the GBVs have been estimated precisely, Hi-LMM outperforms existing methods in statistical power, especially through joint association analysis.

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