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<p>Within-family genome-wide association studies (GWAS) can separate direct genetic effects from non-direct genetic biases introduced by analyses based on unrelated individuals, yet evidence regarding metabolic phenotypes remains sparse. Here, we aim to uncover non-direct genetic effects for metabolic traits and the role of diet in the non-direct genetic mechanism. We conducted family-based GWAS studies on six metabolic traits using data from full siblings (<i>N</i> = 777) and parent–offspring trios (<i>N</i> = 386). We calculated and compared within-family and population-based polygenic score (PGS) associations to identify non-direct genetic effects. Additionally, we assessed the parental indirect genetic effects of diet on offspring's metabolic traits. Within-sibship GWAS analyses were also conducted to evaluate the impact of non-direct genetic effects at the individual variant level. On average, the magnitudes of within-family PGS associations for metabolic traits showed a 35.2% reduction compared to population-based estimates, suggesting the presence of non-direct genetic effects. This discrepancy diminished after accounting for dietary score, indicating that diet is a major source of non-direct genetic effects. Additionally, parental indirect genetic effects of diet were revealed in parent–offspring models. For instance, PGS of parental fat consumption was positively related to the child’s blood glucose levels (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\beta \)</EquationSource> </InlineEquation>: 0.44, 95% CI 0.21–0.67). After excluding non-direct genetic effects, within-sibship GWAS models are more effective at identifying functional genes associated with metabolic traits. Our study showed significant contributions of non-direct genetic effects on metabolic traits and also identified diet as a major source of non-direct genetic effects. These findings underlined the importance of family-based GWAS data in disentangling the genetic effects and gene-environment correlations underlying metabolic traits.</p>

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Diet as a source of the non-direct genetic effects in metabolic traits: evidence from a family-based GWAS study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月13日

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  • Han Xiao,
  • Zechen Zhou,
  • Yujia Ma,
  • Xiaoyi Li,
  • Kexin Ding,
  • Yiqun Wu,
  • Tao Wu,
  • Yonghua Hu,
  • Dafang Chen

摘要

Within-family genome-wide association studies (GWAS) can separate direct genetic effects from non-direct genetic biases introduced by analyses based on unrelated individuals, yet evidence regarding metabolic phenotypes remains sparse. Here, we aim to uncover non-direct genetic effects for metabolic traits and the role of diet in the non-direct genetic mechanism. We conducted family-based GWAS studies on six metabolic traits using data from full siblings (N = 777) and parent–offspring trios (N = 386). We calculated and compared within-family and population-based polygenic score (PGS) associations to identify non-direct genetic effects. Additionally, we assessed the parental indirect genetic effects of diet on offspring's metabolic traits. Within-sibship GWAS analyses were also conducted to evaluate the impact of non-direct genetic effects at the individual variant level. On average, the magnitudes of within-family PGS associations for metabolic traits showed a 35.2% reduction compared to population-based estimates, suggesting the presence of non-direct genetic effects. This discrepancy diminished after accounting for dietary score, indicating that diet is a major source of non-direct genetic effects. Additionally, parental indirect genetic effects of diet were revealed in parent–offspring models. For instance, PGS of parental fat consumption was positively related to the child’s blood glucose levels ( \(\beta \) : 0.44, 95% CI 0.21–0.67). After excluding non-direct genetic effects, within-sibship GWAS models are more effective at identifying functional genes associated with metabolic traits. Our study showed significant contributions of non-direct genetic effects on metabolic traits and also identified diet as a major source of non-direct genetic effects. These findings underlined the importance of family-based GWAS data in disentangling the genetic effects and gene-environment correlations underlying metabolic traits.