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  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.02.05.429962
4099  - http://biorxiv.org/content/early/2021/02/09/2021.02.05.429962.short
4100  - http://biorxiv.org/content/early/2021/02/09/2021.02.05.429962.full
AB  - For polygenic traits, associations with genetic variants can be detected over many chromosome regions, owing to the availability of large sample sizes. The majority of variants, however, have small effects on disease risk and, therefore, unraveling 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 identify new variants with larger effect associations with type 1 diabetes and autoimmune thyroid disease.Competing Interest StatementThe authors have declared no competing interest.