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Background <p>The phenotypic outcomes of de novo variants (DNVs) in autism spectrum disorder (ASD) exhibit wide variability. To date, no study has comprehensively estimated DNV effects accounting for familial phenotypic background.</p> Methods <p>To evaluate DNV effects in a family-relative context, we defined within-family standardized deviations (WFSD) by subtracting phenotype scores of unaffected family members and standardizing the result. We applied this approach to 78,685 individuals from 21,735 families from ASD cohorts of diverse ancestries. We compared the distribution, associations with disruptive DNVs, and gene discovery results between WFSD and raw phenotype scores. We further performed outlier analysis based on WFSDs per gene to detect genes with high variability between families.</p> Results <p>We observed that ASD probands with disruptive DNVs exhibited greater behavioral symptoms and lower adaptive functioning relative to their within-family unaffected members. Compared to raw phenotype scores, WFSD provided clearer associations with DNVs and enabled greater yield in DNV-enriched gene discovery, including 18 novel ASD-associated genes. Outlier analysis identified 11 genes with high intrafamilial variability in phenotypic effects, influenced by mutation sites within functional domains or exons.</p> Conclusions <p>Familial DNV analysis provides accurate effect estimates, a reliable basis for predicting clinical outcomes, and precise support while minimizing confounding from family background. This approach improves the identification of ASD-associated genes with true phenotypic effects by reducing variability, as well as genes with genuine phenotypic heterogeneity across families driven by mutation site. These findings enhance our understanding of ASD phenotype variability and inform potential targets for intervention.</p>

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Evaluation of familial phenotype deviation to measure the impact of de novo mutations in autism

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月20日

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  • Soo-Whee Kim,
  • Hyeji Lee,
  • Da Yea Song,
  • Gang-Hee Lee,
  • Jae Hyun Han,
  • Jee Won Lee,
  • Hee Jung Byun,
  • Ji Hyun Son,
  • Ye Rim Kim,
  • Yoojeong Lee,
  • Eunjoon Kim,
  • Donna M. Werling,
  • So Hyun Kim,
  • Stephan J. Sanders,
  • Hee Jeong Yoo,
  • Joon-Yong An

摘要

Background

The phenotypic outcomes of de novo variants (DNVs) in autism spectrum disorder (ASD) exhibit wide variability. To date, no study has comprehensively estimated DNV effects accounting for familial phenotypic background.

Methods

To evaluate DNV effects in a family-relative context, we defined within-family standardized deviations (WFSD) by subtracting phenotype scores of unaffected family members and standardizing the result. We applied this approach to 78,685 individuals from 21,735 families from ASD cohorts of diverse ancestries. We compared the distribution, associations with disruptive DNVs, and gene discovery results between WFSD and raw phenotype scores. We further performed outlier analysis based on WFSDs per gene to detect genes with high variability between families.

Results

We observed that ASD probands with disruptive DNVs exhibited greater behavioral symptoms and lower adaptive functioning relative to their within-family unaffected members. Compared to raw phenotype scores, WFSD provided clearer associations with DNVs and enabled greater yield in DNV-enriched gene discovery, including 18 novel ASD-associated genes. Outlier analysis identified 11 genes with high intrafamilial variability in phenotypic effects, influenced by mutation sites within functional domains or exons.

Conclusions

Familial DNV analysis provides accurate effect estimates, a reliable basis for predicting clinical outcomes, and precise support while minimizing confounding from family background. This approach improves the identification of ASD-associated genes with true phenotypic effects by reducing variability, as well as genes with genuine phenotypic heterogeneity across families driven by mutation site. These findings enhance our understanding of ASD phenotype variability and inform potential targets for intervention.