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Conditional frequency spectra as a tool for studying selection on complex traits in biobanks
Roshni A Patel1, Clemens L Weiß2, Huisheng Zhu3
1Department of Genetics, Stanford University School of Medicine, Stanford, CA.
Biorxiv : the Preprint Server for Biology
|July 1, 2024
Summary
This study introduces a new method using conditional frequency spectra to analyze natural selection on complex traits, revealing evidence of stabilizing or purifying selection in human populations.
Area of Science:
- Population Genetics
- Human Genetics
- Evolutionary Biology
Background:
- Genome-wide association studies (GWAS) face challenges in studying natural selection on complex traits due to ascertainment bias.
- The relationship between variant effect size and allele frequency is constrained by selection, impacting GWAS power.
- Understanding these constraints is crucial for interpreting genetic associations.
Purpose of the Study:
- To develop a method accounting for GWAS ascertainment bias when studying natural selection.
- To investigate the impact of selection and demography on allele frequency dynamics.
- To analyze empirical data for evidence of selection on complex traits.
Main Methods:
- Characterized allele frequency dynamics under selection and non-equilibrium demography.
- Developed conditional frequency spectra to correct for GWAS ascertainment.
- Investigated empirical conditional frequency spectra for GWAS variants linked to 106 complex traits.
Main Results:
- Demonstrated the impact of selection and demography on allele frequency spectra.
- Showed the utility of conditional frequency spectra in realistic human demographic models.
- Found compelling evidence for stabilizing or purifying selection acting on GWAS variants for complex traits.
Conclusions:
- Conditional frequency spectra offer a powerful tool to study natural selection, overcoming GWAS ascertainment biases.
- The findings provide insights into the evolutionary forces shaping complex traits.
- Results have implications for polygenic score portability and understanding GWAS variant properties.
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