Quantitative multidimensional phenotypes improve genetic analysis of laterality traits

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作者
Judith Schmitz
Mo Zheng
Kelvin F. H. Lui
Catherine McBride
Connie S.-H. Ho
Silvia Paracchini
机构
[1] University of St Andrews,School of Medicine
[2] The Chinese University of Hong Kong,Department of Psychology
[3] Lingnan University,Wofoo Joseph Lee Consulting and Counselling Psychology Research Centre
[4] The University of Hong Kong,Psychology Department
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Translational Psychiatry | / 12卷
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摘要
Handedness is the most commonly investigated lateralised phenotype and is usually measured as a binary left/right category. Its links with psychiatric and neurodevelopmental disorders prompted studies aimed at understanding the underlying genetics, while other measures and side preferences have been less studied. We investigated the heritability of hand, as well as foot, and eye preference by assessing parental effects (n ≤ 5028 family trios) and SNP-based heritability (SNP-h2, n ≤ 5931 children) in the Avon Longitudinal Study of Parents and Children (ALSPAC). An independent twin cohort from Hong Kong (n = 358) was used to replicate results from structural equation modelling (SEM). Parental left-side preference increased the chance of an individual to be left-sided for the same trait, with stronger maternal than paternal effects for footedness. By regressing out the effects of sex, age, and ancestry, we transformed laterality categories into quantitative measures. The SNP-h2 for quantitative handedness and footedness was 0.21 and 0.23, respectively, which is higher than the SNP-h2 reported in larger genetic studies using binary handedness measures. The heritability of the quantitative measure of handedness increased (0.45) compared to a binary measure for writing hand (0.27) in the Hong Kong twins. Genomic and behavioural SEM identified a shared genetic factor contributing to handedness, footedness, and eyedness, but no independent effects on individual phenotypes. Our analysis demonstrates how quantitative multidimensional laterality phenotypes are better suited to capture the underlying genetics than binary traits.
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