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Leveraging primate-specific genomic information for genetic studies of complex diseases.

ABSTRACT: Genomic changes specific to higher primates are regarded as primate-specific genomic information (PSI). Using PSI to inform genetic studies is highly desirable but hampered by three factors: heterogeneity among PSI studies, lack of integrated profiles of the identified PSI elements and dearth of relevant functional information. We report a database of 19,767 PSI elements collated from nine types of brain-related studies, which form 19,473 non-overlapping PSI regions that distribute unevenly but jointly cover only 0.81% of the genome. About 2.5% of the PSI regions colocalized with variants identified in genome-wide association studies, with disease loci more likely colocalized than quantitative trait loci (p = 1.6 × 10^-5), particularly in regions without obvious regulatory roles. We further showed an LRP4 exemplar region with PSI elements orchestrated with common and rare disease variants and other functional elements. Our results render PSI elements as a valuable source to inform genetic studies of complex diseases.

SUBMITTER: Wei WH

PROVIDER: S-EPMC10086602 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Leveraging primate-specific genomic information for genetic studies of complex diseases.

Wei Wen-Hua WH Guo Hui H

Frontiers in bioinformatics 20230328

Genomic changes specific to higher primates are regarded as primate-specific genomic information (PSI). Using PSI to inform genetic studies is highly desirable but hampered by three factors: heterogeneity among PSI studies, lack of integrated profiles of the identified PSI elements and dearth of relevant functional information. We report a database of 19,767 PSI elements collated from nine types of brain-related studies, which form 19,473 non-overlapping PSI regions that distribute unevenly but ...[more]

PMID: 37056664

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Project description:Genetic researchers often collect disease related quantitative traits in addition to disease status because they are interested in understanding the pathophysiology of disease processes. In genome-wide association (GWA) studies, these quantitative phenotypes may be relevant to disease development and serve as intermediate phenotypes or they could be behavioral or other risk factors that predict disease risk. Statistical tests combining both disease status and quantitative risk factors should be more powerful than case-control studies, as the former incorporates more information about the disease. In this paper, we proposed a modified inverse-variance weighted meta-analysis method to combine disease status and quantitative intermediate phenotype information. The simulation results showed that when an intermediate phenotype was available, the inverse-variance weighted method had more power than did a case-control study of complex diseases, especially in identifying susceptibility loci having minor effects. We further applied this modified meta-analysis to a study of imputed lung cancer genotypes with smoking data in 1154 cases and 1137 matched controls. The most significant SNPs came from the CHRNA3-CHRNA5-CHRNB4 region on chromosome 15q24-25.1, which has been replicated in many other studies. Our results confirm that this CHRNA region is associated with both lung cancer development and smoking behavior. We also detected three significant SNPs--rs1800469, rs1982072, and rs2241714--in the promoter region of the TGFB1 gene on chromosome 19 (p?=?1.46×10(-5), 1.18×10(-5), and 6.57×10(-6), respectively). The SNP rs1800469 is reported to be associated with chronic obstructive pulmonary disease and lung cancer in cigarette smokers. The present study is the first GWA study to replicate this result. Signals in the 3q26 region were also identified in the meta-analysis. We demonstrate the intermediate phenotype can potentially enhance the power of complex disease association analysis and the modified meta-analysis method is robust to incorporate intermediate phenotype or other quantitative risk factor in the analysis.

Dataset Information

Leveraging primate-specific genomic information for genetic studies of complex diseases.

Publications

Leveraging primate-specific genomic information for genetic studies of complex diseases.

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