G3 Genes Genomes GeneticsGeneticsPeer-reviewed journal article
Effects of kinship correction on inflation of genetic interaction statistics in commonly used mouse populations
Anna L. TyleriD, Bahá El KassabyiD, Georgi Kolishovski, Jake EmersoniD, Ann WellsiD, J. Matthew MahoneyiD, Gregory W. CarteriD
Abstract
It is well understood that variation in relatedness among individuals, or kinship, can lead to false genetic associations. Multiple methods have been developed to adjust for kinship while maintaining power to detect true associations. However, relatively unstudied, are the effects of kinship on genetic interaction test statistics. Here we performed a survey of kinship effects on studies of six commonly used mouse populations. We measured inflation of main effect test statistics, genetic interaction test statistics, and interaction test statistics reparametrized by the Combined Analysis of Pleiotropy and Epistasis (CAPE). We also performed linear mixed model (LMM) kinship corrections using two types of kinship matrix: an overall kinship matrix calculated from the full set of genotyped markers, and a reduced kinship matrix, which left out markers on the chromosome(s) being tested. We found that test statistic inflation varied across populations and was driven largely by linkage disequilibrium. In contrast, there was no observable inflation in the genetic interaction test statistics. CAPE statistics were inflated at a level in between that of the main effects and the interaction effects. The overall kinship matrix overcorrected the inflation of main effect statistics relative to the reduced kinship matrix. The two types of kinship matrices had similar effects on the interaction statistics and CAPE statistics, although the overall kinship matrix trended toward a more severe correction. In conclusion, we recommend using a LMM kinship correction for both main effects and genetic interactions and further recommend that the kinship matrix be calculated from a reduced set of markers in which the chromosomes being tested are omitted from the calculation. This is particularly important in populations with substantial population structure, such as recombinant inbred lines in which genomic replicates are used.
- organism: mouse
- assay: whole-genome sequencing
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 5
- FWCI
- 0.42
- Field percentile
- top 45%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Genetic association models are robust to common population kinship estimation biases
Zhuoran Hou, Alejandro Ochoa·GeneticsPeer-reviewed·Feb 27, 2023
- Correcting statistical bias in correlation-based kinship estimators
Wei Jiang, Xiangyu Zhang, … Hongyu Zhao·bioRxiv·Jan 13, 2021
- Estimation of inbreeding and kinship coefficients via latent identity-by-descent states
Yongtao Guan, Daniel A Levy·BioinformaticsPeer-reviewed·Feb 1, 2024
- Mitigating the Effects of Population Stratification in Gene-Gene Interaction Studies
Neelotpal Das, Masao Ueki, The Alzheimer's Disease Neuroimaging Initiative·bioRxiv·Aug 21, 2026
- Required marker properties for unbiased estimates of the genetic correlation between populations
Yvonne C. J. Wientjes, M.P.L. Calus, … Piter Bijma·Genetics Selection EvolutionPeer-reviewed·Dec 1, 2018
- Why are education, socioeconomic position and intelligence genetically correlated?
Tim Morris, Neil M Davies, … George Davey Smith·bioRxiv·Dec 6, 2019