Selection and explosive growth alter genetic architecture and hamper the detection of causal rare variants
- Lawrence H. Uricchio1,2,6,
- Noah A. Zaitlen1,3,4,
- Chun Jimmie Ye1,3,5,
- John S. Witte3,5 and
- Ryan D. Hernandez1,3,4
- 1Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California 94143, USA;
- 2Graduate Program in Bioinformatics, University of California, San Francisco, San Francisco, California 94143, USA;
- 3Institute for Human Genetics, University of California, San Francisco, San Francisco, California 94143, USA;
- 4Institute for Quantitative Biosciences (QB3), University of California, San Francisco, San Francisco, California 94143, USA;
- 5Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California 94143, USA
- Corresponding authors: uricchio{at}stanford.edu, ryan.hernandez{at}ucsf.edu
Abstract
The role of rare alleles in complex phenotypes has been hotly debated, but most rare variant association tests (RVATs) do not account for the evolutionary forces that affect genetic architecture. Here, we use simulation and numerical algorithms to show that explosive population growth, as experienced by human populations, can dramatically increase the impact of very rare alleles on trait variance. We then assess the ability of RVATs to detect causal loci using simulations and human RNA-seq data. Surprisingly, we find that statistical performance is worst for phenotypes in which genetic variance is due mainly to rare alleles, and explosive population growth decreases power. Although many studies have attempted to identify causal rare variants, few have reported novel associations. This has sometimes been interpreted to mean that rare variants make negligible contributions to complex trait heritability. Our work shows that RVATs are not robust to realistic human evolutionary forces, so general conclusions about the impact of rare variants on complex traits may be premature.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.202440.115.
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Freely available online through the Genome Research Open Access option.
- Received November 25, 2015.
- Accepted May 16, 2016.
This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
