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<p>Estimating variance components of phenotypes provides a fundamental basis for understanding complex traits. However, most existing methods require genotype data, which is costly to obtain and often unavailable, limiting their scalability. To address this limitation, we developed BIGFAM, a genotype-free framework that estimates variance components by genetic, shared environmental, and X chromosome effects using only phenotype data from relative pairs. We analyze variance components in Generation Scotland and UK Biobank datasets and demonstrate that BIGFAM’s estimates show high correlation with genotype-based methods (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41467_2025_60502_Article_IEq1.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="11" /> </InlineMediaObject> <EquationSource Format="TEX">\(r\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>r</mi> </math></EquationSource> </InlineEquation> = 0.85 for heritability and 0.64 for X chromosome components). We identify strong nuclear-family-specific shared environmental effects in dietary-related phenotypes. These results establish a new approach for analyzing variance components across diverse populations without the need for genetic data.</p>

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BIGFAM - variance components analysis from relatives without genotype

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月1日

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  • Jaeeun Jerry Lee,
  • Buhm Han

摘要

Estimating variance components of phenotypes provides a fundamental basis for understanding complex traits. However, most existing methods require genotype data, which is costly to obtain and often unavailable, limiting their scalability. To address this limitation, we developed BIGFAM, a genotype-free framework that estimates variance components by genetic, shared environmental, and X chromosome effects using only phenotype data from relative pairs. We analyze variance components in Generation Scotland and UK Biobank datasets and demonstrate that BIGFAM’s estimates show high correlation with genotype-based methods ( \(r\) r  = 0.85 for heritability and 0.64 for X chromosome components). We identify strong nuclear-family-specific shared environmental effects in dietary-related phenotypes. These results establish a new approach for analyzing variance components across diverse populations without the need for genetic data.