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Physical Sciences and Mathematics
University of Nebraska - Lincoln
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Next-Generation Sequencing Data-Based Association Testing Of A Group Of Genetic Markers For Complex Responses Using A Generalized Linear Model Framework, Zheng Xu, Song Yan, Cong Wu, Qing Duan, Sixia Chen, Yun Li
Next-Generation Sequencing Data-Based Association Testing Of A Group Of Genetic Markers For Complex Responses Using A Generalized Linear Model Framework, Zheng Xu, Song Yan, Cong Wu, Qing Duan, Sixia Chen, Yun Li
School of Computing: Faculty Publications
To study the relationship between genetic variants and phenotypes, association testing is adopted; however, most association studies are conducted by genotype-based testing. Testing methods based on next-generation sequencing (NGS) data without genotype calling demonstrate an advantage over testing methods based on genotypes in the scenarios when genotype estimation is not accurate. Our objective was to develop NGS data-based methods for association studies to fill the gap in the literature. Single-variant testing methods based on NGS data have been proposed, including our previously proposed single-variant NGS data-based testing method, i.e., UNC combo method. The NGS data-based group testing method has been …
Next-Generation Sequencing Data-Based Association Testing Of A Group Of Genetic Markers For Complex Responses Using A Generalized Linear Model Framework, Zheng Xu, Song Yan, Cong Wu, Qing Duan, Sixia Chen, Yun Li
Next-Generation Sequencing Data-Based Association Testing Of A Group Of Genetic Markers For Complex Responses Using A Generalized Linear Model Framework, Zheng Xu, Song Yan, Cong Wu, Qing Duan, Sixia Chen, Yun Li
School of Computing: Faculty Publications
To study the relationship between genetic variants and phenotypes, association testing is adopted; however, most association studies are conducted by genotype-based testing. Testing methods based on next-generation sequencing (NGS) data without genotype calling demonstrate an advantage over testing methods based on genotypes in the scenarios when genotype estimation is not accurate. Our objective was to develop NGS data-based methods for association studies to fill the gap in the literature. Single-variant testing methods based on NGS data have been proposed, including our previously proposed single-variant NGS data-based testing method, i.e., UNC combo method. The NGS data-based group testing method has been …
Efficient Two-Stage Analysis For Complex Trait Association With Arbitrary Depth Sequencing Data, Zheng Xu, Song Yan, Shuai Yuan, Cong Wu, Sixia Chen, Zifang Guo
Efficient Two-Stage Analysis For Complex Trait Association With Arbitrary Depth Sequencing Data, Zheng Xu, Song Yan, Shuai Yuan, Cong Wu, Sixia Chen, Zifang Guo
School of Computing: Faculty Publications
Sequencing-based genetic association analysis is typically performed by first generating genotype calls from sequence data and then performing association tests on the called genotypes. Standard approaches require accurate genotype calling (GC), which can be achieved either with high sequencing depth (typically available in a small number of individuals) or via computationally intensive multi-sample linkage disequilibrium (LD)-aware methods. We propose a computationally efficient two-stage combination approach for association analysis, in which single-nucleotide polymorphisms (SNPs) are screened in the first stage via a rapid maximum likelihood (ML)-based method on sequence data directly (without first calling genotypes), and then the selected SNPs are …