| Application: | 11138, Genomic and evolutionary analyses of common disease in a large cohort |
| Title: | Identifying genetic variants that affect viability in large cohorts |
| Size: | 5.9 MB |
| Archived: | 26 Nov 2020 |
| Stability: | Complete |
| Personal: | No individual-level data |
Return 2883
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Notes
Our global understanding of adaptation in humans is limited to indirect statistical inferences from patterns of genetic variation, which are sensitive to past selection pressures. We introduced a method that allowed us to directly observe ongoing selection in humans by identifying genetic variants that affect survival to a given age (i.e., viability selection). We applied our approach to the GERA cohort and parents of the UK Biobank participants. We found viability effects of variants near the APOE and CHRNA3 genes, which are associated with the risk of Alzheimer disease and smoking behavior, respectively. We also tested for the joint effect of sets of genetic variants that influence quantitative traits. We uncovered an association between longer life span and genetic variants that delay puberty timing and age at first birth. We also detected detrimental effects of higher genetically predicted cholesterol levels, body mass index, risk of coronary artery disease (CAD), and risk of asthma on survival. Some of the observed effects differ between males and females, most notably those at the CHRNA3 gene and variants associated with risk of CAD and cholesterol levels. Beyond this application, our analysis shows how large biomedical data sets can be used to study natural selection in humans.Application 11138
Genomic and evolutionary analyses of common disease in a large cohort
Objectives of the proposed research: 1. To use genomic approaches to identify risk factors for a set of common diseases of particular importance in development and aging. In particular, we will focus on metabolic phenotypes (disease like type 2 diabetes, and phenotypes like BMI), reproductive phenotypes (like number of children and age at menarche), and common causes of death (cancer, heart disease, and neurological disease). 2. To determine the influence of natural selection (if any) on the frequencies of alleles influencing disease risk. The purpose of the UK Biobank is to improve the prevention, diagnosis, and treatment of illness. We aim to discover genetic and environmental risk factor that have causal influences on disease risk, and to measure the impact of these alleles on evolutionary fitness. Genomic research has identified thousands of variants that influence human traits like cholesterol levels, platelet counts, and blood pressure, but the influence of these traits on disease remains controversial. We will use novel statistical approaches to identify causal relationships between traits. We will also test whether these alleles influence overall mortality at different ages. The full cohort.
| Lead investigator: | Professor Molly Przeworski |
| Lead institution: | Columbia University |
1 related Return
| Return ID | App ID | Description | Archive Date |
|---|---|---|---|
| 702 | 11138 | Case-control association mapping by proxy using family history of disease | 16 Oct 2017 |
1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 2884 | Identifying genetic variants that affect viability in large cohorts | Hakhamanesh Mostafavi (+5) | 2017 | PLOS Biology |
Enabling scientific discoveries that improve human health