TY - JOUR
T1 - An exponential increase in QTL detection with an increased sample size
AU - Chitre, Apurva S.
AU - Polesskaya, Oksana
AU - Munro, Daniel
AU - Cheng, Riyan
AU - Mohammadi, Pejman
AU - Holl, Katie
AU - Gao, Jianjun
AU - Bimschleger, Hannah
AU - Martinez, Angel Garcia
AU - George, Anthony M.
AU - Gileta, Alexander F.
AU - Han, Wenyan
AU - Horvath, Aidan
AU - Hughson, Alesa
AU - Ishiwari, Keita
AU - King, Christopher P.
AU - Lamparelli, Alexander
AU - Versaggi, Cassandra L.
AU - Martin, Connor D.
AU - Pierre, Celine L.St
AU - Tripi, Jordan A.
AU - Richards, Jerry B.
AU - Wang, Tengfei
AU - Chen, Hao
AU - Flagel, Shelly B.
AU - Meyer, Paul
AU - Robinson, Terry E.
AU - Solberg Woods, Leah C.
AU - Palmer, Abraham A.
N1 - Publisher Copyright:
© The Author(s) 2023. Published by Oxford University Press on behalf of The Genetics Society of America.
PY - 2023/6
Y1 - 2023/6
N2 - 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.
AB - 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.
KW - GWAS
KW - QTL
KW - heterogeneous stock (HS) rats
KW - sample size
UR - https://www.scopus.com/pages/publications/85160458532
U2 - 10.1093/genetics/iyad054
DO - 10.1093/genetics/iyad054
M3 - Article
C2 - 36974931
AN - SCOPUS:85160458532
SN - 0016-6731
VL - 224
JO - Genetics
JF - Genetics
IS - 2
M1 - iyad054
ER -