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Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects.

Laurence J Howe1,2, Michel G Nivard3, Tim T Morris4,5

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Summary
This summary is machine-generated.

Family-based genome-wide association studies (GWAS) reveal genetic effects distinct from population estimates. Analyzing sibling data helps disentangle direct genetic influences from environmental and familial factors for complex traits.

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Area of Science:

  • Human genetics
  • Complex trait analysis
  • Population genetics

Background:

  • Genome-wide association studies (GWAS) typically use unrelated individuals, capturing direct genetic effects, demography, and indirect genetic effects from relatives.
  • Family-based GWAS designs can better control for demographic and indirect genetic effects, but large-scale family datasets have been limited.

Purpose of the Study:

  • To leverage large-scale sibling data to differentiate between population-level and within-family genetic effects for various human phenotypes.
  • To assess the impact of family-based designs on estimates of heritability, genetic correlations, and causal inference using Mendelian randomization.

Main Methods:

  • Combined data from 178,086 siblings across 19 cohorts to perform both between-family (population) and within-family (within-sibship) GWAS for 25 phenotypes.
  • Compared GWAS estimates, SNP heritability, genetic correlations, and Mendelian randomization results between population and within-sibship analyses.
  • Investigated evidence of polygenic adaptation using within-sibship estimates.

Main Results:

  • Within-sibship GWAS estimates were smaller than population estimates for complex traits like height, educational attainment, and cognitive ability, indicating substantial influence of non-inherited factors.
  • Differences were observed in downstream analyses, including attenuation of the genetic correlation between educational attainment and BMI towards zero in within-sibship analyses.
  • Molecular phenotypes, such as low-density lipoprotein cholesterol, showed consistent results between population and within-sibship analyses, suggesting stronger direct genetic influence.

Conclusions:

  • Family-based GWAS designs are crucial for accurately estimating direct genetic effects, particularly for phenotypes influenced by demographic factors and indirect genetic effects from relatives.
  • Within-sibship analyses revealed smaller genetic effects for many complex human traits, highlighting the importance of accounting for shared environmental and familial influences.
  • Within-sibship data provided evidence for polygenic adaptation, demonstrating the utility of family designs in evolutionary genetic studies.