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Dissecting polygenic signals from genome-wide association studies on human behaviour

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  • Abdel Abdellaoui

    (University of Amsterdam)

  • Karin J. H. Verweij

    (University of Amsterdam)

Abstract

Genome-wide association studies on human behavioural traits are producing large amounts of polygenic signals with significant predictive power and potentially useful biological clues. Behavioural traits are more distal and are less directly under biological control compared with physical characteristics, which makes the associated genetic effects harder to interpret. The results of genome-wide association studies for human behaviour are likely made up of a composite of signals from different sources. While sample sizes continue to increase, we outline additional steps that need to be taken to better delineate the origin of the increasingly stronger polygenic signals. In addition to genetic effects on the traits themselves, the major sources of polygenic signals are those that are associated with correlated traits, environmental effects and ascertainment bias. Advances in statistical approaches that disentangle polygenic effects from different traits as well as extending data collection to families and social circles with better geographical coverage will probably contribute to filling the gap of knowledge between genetic effects and behavioural outcomes.

Suggested Citation

  • Abdel Abdellaoui & Karin J. H. Verweij, 2021. "Dissecting polygenic signals from genome-wide association studies on human behaviour," Nature Human Behaviour, Nature, vol. 5(6), pages 686-694, June.
  • Handle: RePEc:nat:nathum:v:5:y:2021:i:6:d:10.1038_s41562-021-01110-y
    DOI: 10.1038/s41562-021-01110-y
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    Cited by:

    1. Tabea Schoeler & Doug Speed & Eleonora Porcu & Nicola Pirastu & Jean-Baptiste Pingault & Zoltán Kutalik, 2023. "Participation bias in the UK Biobank distorts genetic associations and downstream analyses," Nature Human Behaviour, Nature, vol. 7(7), pages 1216-1227, July.
    2. Clara Albiñana & Zhihong Zhu & Andrew J. Schork & Andrés Ingason & Hugues Aschard & Isabell Brikell & Cynthia M. Bulik & Liselotte V. Petersen & Esben Agerbo & Jakob Grove & Merete Nordentoft & David , 2023. "Multi-PGS enhances polygenic prediction by combining 937 polygenic scores," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    3. Bertoni, Marco & Marin-Lopez, Blas A. & Sanz-de-Galdeano, Anna, 2023. "Subjective Gender-Based Patterns in ADHD Diagnosis," IZA Discussion Papers 16634, Institute of Labor Economics (IZA).
    4. Maria Niarchou & Daniel E. Gustavson & J. Fah Sathirapongsasuti & Manuel Anglada-Tort & Else Eising & Eamonn Bell & Evonne McArthur & Peter Straub & J. Devin McAuley & John A. Capra & Fredrik Ullén & , 2022. "Genome-wide association study of musical beat synchronization demonstrates high polygenicity," Nature Human Behaviour, Nature, vol. 6(9), pages 1292-1309, September.
    5. Tuomo Hartonen & Bradley Jermy & Hanna Sõnajalg & Pekka Vartiainen & Kristi Krebs & Andrius Vabalas & Tuija Leino & Hanna Nohynek & Jonas Sivelä & Reedik Mägi & Mark Daly & Hanna M. Ollila & Lili Mila, 2023. "Nationwide health, socio-economic and genetic predictors of COVID-19 vaccination status in Finland," Nature Human Behaviour, Nature, vol. 7(7), pages 1069-1083, July.
    6. Bertoni, M.; & Marin-Lopez, B.A.; & Sanz-de-Galdeano, A.;, 2023. "Subjective Gender-Based Patterns in ADHD Diagnosis," Health, Econometrics and Data Group (HEDG) Working Papers 23/17, HEDG, c/o Department of Economics, University of York.

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