PT  - JOURNAL ARTICLE
AU  - Daniel J. M. Crouch
AU  - Jamie R.J. Inshaw
AU  - Catherine C. Robertson
AU  - Jia-Yuan Zhang
AU  - Wei-Min Chen
AU  - Suna Onengut-Gumuscu
AU  - Antony J. Cutler
AU  - Carlo Sidore
AU  - Francesco Cucca
AU  - Flemming Pociot
AU  - Patrick Concannon
AU  - Stephen S. Rich
AU  - John A. Todd
TI  - Enhanced genetic analysis of type 1 diabetes by selecting variants on both effect size and significance, and by integration with autoimmune thyroid disease
AID  - 10.1101/2021.02.05.429962
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2021.02.05.429962
4099  - http://biorxiv.org/content/early/2022/04/20/2021.02.05.429962.short
4100  - http://biorxiv.org/content/early/2022/04/20/2021.02.05.429962.full
AB  - For polygenic traits, associations with genetic variants can be detected over many chromosome regions, owing to the availability oflarge sample sizes. Most variants, however, have small effects on disease risk and, therefore, unravelling the causal variants, target genes, and biology of these variants is challenging. Here, we define the Bigger or False Discovery Rate (BFDR) as the probability that either a variant is a false-positive or a randomly drawn, true-positive association exceeds it in effect size. Using the BFDR, we identified 302 previously unreported signals with larger effect associations with type 1 diabetes and autoimmune thyroid disease. Out of 239 genome-wide significant signals in both diseases, only 66 (28%) show evidence for having a large effect using the BFDR, further demonstrating how using a combination of effect size and significance, rather than significance alone, is important in identifying SNPs and candidate genes for further investigation.Competing Interest StatementJ.A.T. is a member of a Human Genetics Advisory Board of GSK. All other authors declare no competing interests.