TY - JOUR T1 - Enhanced genetic analysis of type 1 diabetes by selecting variants on both effect size and significance, and by integration with autoimmune thyroid disease JF - bioRxiv DO - 10.1101/2021.02.05.429962 SP - 2021.02.05.429962 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 Y1 - 2021/01/01 UR - http://biorxiv.org/content/early/2021/02/09/2021.02.05.429962.abstract N2 - 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. ER -