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Statistical Power to Detect Genetic (Co)Variance of Complex Traits Using SNP Data in Unrelated Samples

Author

Listed:
  • Peter M Visscher
  • Gibran Hemani
  • Anna A E Vinkhuyzen
  • Guo-Bo Chen
  • Sang Hong Lee
  • Naomi R Wray
  • Michael E Goddard
  • Jian Yang

Abstract

We have recently developed analysis methods (GREML) to estimate the genetic variance of a complex trait/disease and the genetic correlation between two complex traits/diseases using genome-wide single nucleotide polymorphism (SNP) data in unrelated individuals. Here we use analytical derivations and simulations to quantify the sampling variance of the estimate of the proportion of phenotypic variance captured by all SNPs for quantitative traits and case-control studies. We also derive the approximate sampling variance of the estimate of a genetic correlation in a bivariate analysis, when two complex traits are either measured on the same or different individuals. We show that the sampling variance is inversely proportional to the number of pairwise contrasts in the analysis and to the variance in SNP-derived genetic relationships. For bivariate analysis, the sampling variance of the genetic correlation additionally depends on the harmonic mean of the proportion of variance explained by the SNPs for the two traits and the genetic correlation between the traits, and depends on the phenotypic correlation when the traits are measured on the same individuals. We provide an online tool for calculating the power of detecting genetic (co)variation using genome-wide SNP data. The new theory and online tool will be helpful to plan experimental designs to estimate the missing heritability that has not yet been fully revealed through genome-wide association studies, and to estimate the genetic overlap between complex traits (diseases) in particular when the traits (diseases) are not measured on the same samples.Author Summary: Genome-wide association studies (GWAS) have identified thousands of genetic variants for hundreds of traits and diseases. However, the genetic variants discovered from GWAS only explained a small fraction of the heritability, resulting in the question of “missing heritability”. We have recently developed approaches (called GREML) to estimate the overall contribution of all SNPs to the phenotypic variance of a trait (disease) and the proportion of genetic overlap between traits (diseases). A frequently asked question is that how many samples are required to estimate the proportion of variance attributable to all SNPs and the proportion of genetic overlap with useful precision. In this study, we derive the standard errors of the estimated parameters from theory and find that they are highly consistent with those observed values from published results and those obtained from simulation. The theory together with an online application tool will be helpful to plan experimental design to quantify the missing heritability, and to estimate the genetic overlap between traits (diseases) especially when it is unfeasible to have the traits (diseases) measured on the same individuals.

Suggested Citation

  • Peter M Visscher & Gibran Hemani & Anna A E Vinkhuyzen & Guo-Bo Chen & Sang Hong Lee & Naomi R Wray & Michael E Goddard & Jian Yang, 2014. "Statistical Power to Detect Genetic (Co)Variance of Complex Traits Using SNP Data in Unrelated Samples," PLOS Genetics, Public Library of Science, vol. 10(4), pages 1-10, April.
    • Handle: RePEc:plo:pgen00:1004269
    DOI: 10.1371/journal.pgen.1004269
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    References listed on IDEAS

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    1. Frank Dudbridge, 2013. "Power and Predictive Accuracy of Polygenic Risk Scores," PLOS Genetics, Public Library of Science, vol. 9(3), pages 1-17, March.
    2. Ian J. Deary & Jian Yang & Gail Davies & Sarah E. Harris & Albert Tenesa & David Liewald & Michelle Luciano & Lorna M. Lopez & Alan J. Gow & Janie Corley & Paul Redmond & Helen C. Fox & Suzanne J. Row, 2012. "Genetic contributions to stability and change in intelligence from childhood to old age," Nature, Nature, vol. 482(7384), pages 212-215, February.
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    2. Liat Shenhav & Ori Furman & Leah Briscoe & Mike Thompson & Justin D Silverman & Itzhak Mizrahi & Eran Halperin, 2019. "Modeling the temporal dynamics of the gut microbial community in adults and infants," PLOS Computational Biology, Public Library of Science, vol. 15(6), pages 1-21, June.
    3. Hyeokmoon Kweon & Casper A.P. Burik & Richard Karlsson Linner & Ronald de Vlaming & Aysu Okbay & Daphne Martschenko & Kathryn Paige Harden & Thomas A. DiPrete & Philipp D. Koellinger, 2020. "Genetic Fortune: Winning or Losing Education, Income, and Health," Tinbergen Institute Discussion Papers 20-053/V, Tinbergen Institute, revised 01 Dec 2020.
    4. Jue-Sheng Ong & Mathias Seviiri & Jean Claude Dusingize & Yeda Wu & Xikun Han & Jianxin Shi & Catherine M. Olsen & Rachel E. Neale & John F. Thompson & Robyn P. M. Saw & Kerwin F. Shannon & Graham J. , 2023. "Uncovering the complex relationship between balding, testosterone and skin cancers in men," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    5. Heather M. Burrow & Raphael Mrode & Ally Okeyo Mwai & Mike P. Coffey & Ben J. Hayes, 2021. "Challenges and Opportunities in Applying Genomic Selection to Ruminants Owned by Smallholder Farmers," Agriculture, MDPI, vol. 11(11), pages 1-15, November.
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