An exponential increase in QTL detection with an increased sample size

Author:

Chitre Apurva S1,Polesskaya Oksana1,Munro Daniel12,Cheng Riyan1,Mohammadi Pejman23,Holl Katie4,Gao Jianjun1,Bimschleger Hannah1,Martinez Angel Garcia5,George Anthony M6,Gileta Alexander F17,Han Wenyan5,Horvath Aidan8,Hughson Alesa8,Ishiwari Keita69,King Christopher P10,Lamparelli Alexander10,Versaggi Cassandra L10,Martin Connor D69,St. Pierre Celine L11,Tripi Jordan A10,Richards Jerry B69,Wang Tengfei5,Chen Hao5,Flagel Shelly B812,Meyer Paul10,Robinson Terry E13,Solberg Woods Leah C14,Palmer Abraham A115ORCID

Affiliation:

1. Department of Psychiatry, University of California San Diego , La Jolla, CA 92093 , USA

2. Department of Integrative Structural and Computational Biology, The Scripps Research Institute , La Jolla, CA 92037 , USA

3. Scripps Research Translational Institute, The Scripps Research Institute , La Jolla, CA 92037 , USA

4. Department of Physiology, Medical College of Wisconsin , Milwaukee, WI 53226 , USA

5. Department of Pharmacology, University of Tennessee Health Science Center , Memphis, TN 38163 , USA

6. Clinical and Research Institute on Addictions, State University of New York at Buffalo , Buffalo, NY 14203 , USA

7. Department of Human Genetics, University of Chicago , Chicago, IL 60637 , USA

8. Department of Psychiatry, University of Michigan , Ann Arbor, MI 48109 , USA

9. Department of Pharmacology and Toxicology, State University of New York at Buffalo , Buffalo, NY 14203 , USA

10. Department of Psychology, State University of New York at Buffalo , Buffalo, NY 14260 , USA

11. Department of Genetics, Washington University in St Louis , St Louis, MO 63110 , USA

12. Michigan Neuroscience Institute, University of Michigan , Ann Arbor, MI 48109 , USA

13. Department of Psychology, University of Michigan , Ann Arbor, MI 48109 , USA

14. Department of Internal Medicine, Wake Forest School of Medicine , Winston-Salem, NC 27101 , USA

15. Institute for Genomic Medicine, University of California San Diego , La Jolla, CA 92093 , USA

Abstract

Abstract Power analyses are often used to determine the number of animals required for a genome-wide association study (GWAS). These analyses are typically intended to estimate the sample size needed for at least 1 locus to exceed a genome-wide significance threshold. A related question that is less commonly considered is the number of significant loci that will be discovered with a given sample size. We used simulations based on a real data set that consisted of 3,173 male and female adult N/NIH heterogeneous stock rats to explore the relationship between sample size and the number of significant loci discovered. Our simulations examined the number of loci identified in subsamples of the full data set. The subsampling analysis was conducted for 4 traits with low (0.15 ± 0.03), medium (0.31 ± 0.03 and 0.36 ± 0.03), and high (0.46 ± 0.03) SNP-based heritabilities. For each trait, we subsampled the data 100 times at different sample sizes (500, 1,000, 1,500, 2,000, and 2,500). We observed an exponential increase in the number of significant loci with larger sample sizes. Our results are consistent with similar observations in human GWAS and imply that future rodent GWAS should use sample sizes that are significantly larger than those needed to obtain a single significant result.

Funder

National Institute on Drug Abuse

National Institute of Diabetes and Digestive and Kidney Diseases

Publisher

Oxford University Press (OUP)

Subject

Genetics

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