Fine-mapping and functional studies highlight potential causal variants for rheumatoid arthritis and type 1 diabetes

Abstract

To define potentially causal variants for autoimmune disease, we fine-mapped 76 rheumatoid arthritis (11,475 cases, 15,870 controls) and type 1 diabetes loci (9,334 cases, 11,111 controls). After sequencing 799 1-kilobase regulatory (H3K4me3) regions within these loci in 568 individuals, we observed accurate imputation for 89% of common variants. We defined credible sets of ≤5 causal variants at 5 rheumatoid arthritis and 10 type 1 diabetes loci. We identified potentially causal missense variants at DNASE1L3, PTPN22, SH2B3, and TYK2, and noncoding variants at MEG3, CD28-CTLA4, and IL2RA. We also identified potential candidate causal variants at SIRPG and TNFAIP3. Using functional assays, we confirmed allele-specific protein binding and differential enhancer activity for three variants: the CD28-CTLA4 rs117701653 SNP, MEG3 rs34552516 indel, and TNFAIP3 rs35926684 indel.

Functionally-informed fine-mapping and polygenic localization of complex trait heritability

“…Genome-wide association studies of complex traits have been extremely successful in identifying loci harboring causal variants but less successful in fine-mapping the underlying causal variants, making the development of fine-mapping methods a key priority 1,2 . Fine-mapping methods aim to pinpoint causal variants by accounting for linkage disequilibrium (LD) between variants [3][4][5][6][7][8][9][10][11][12] , but have limited power in the presence of strong LD. One way to increase fine-mapping power is to prioritize variants in functional annotations that are enriched for complex trait heritability 7,8,10,[13][14][15][16][17] .…”

Advances in genetics toward identifying pathogenic cell states of rheumatoid arthritis

“…Loci harboring causal variants also contain significantly associated variants which are statistical artifacts of the causal association due to strong LD. Here, we will discuss genetic fine mapping studies that have elucidated the biological mechanisms underpinning RA: Eyre et al who preceded a GWAS meta analysis with dense fine mapping to improve imputation accuracy and power, Farh et al who0 implements a Bayesian statistical framework, and Westra et al who uses genetic fine mapping and further interrogates putatively causal SNPs with experimental tests of functionality.…”

“…To better understand causal variation in RA and type 1 diabetes (T1D), our group aimed to fine map causal variants across 76 loci . We first imputed variant genotypes with the 1000 Genomes European cohort which greatly increased the number of interrogated variants as well as the power to detect putatively causal variants; this two‐step approach combining imputation with fine‐mapping had not previously been done.…”

“…loci. 49 We first imputed variant genotypes with the 1000 Genomes European cohort which greatly increased the number of interrogated variants as well as the power to detect putatively causal variants;…”

Immune disease variants modulate gene expression in regulatory CD4+ T cells and inform drug targets

“…The majority of chromQTLs were actQTLs, where we detected 5,798 regions under genetic control ( Figure 1B ). Of all analyzed genetic variants (4,640,249) only a small fraction mapped in at least one chromatin feature and was either linked to chromQTLs (38,928; 0.84%), eQTLs (22,768; 0.49%) or both (12,936; 0.28%) ( Figure 1D ). For 25% (1,039) of all eQTL genes we observed that at least one eQTL variant was a chromQTL and was also physically located in a chromatin peak ( Figure 1E ), and for an additional 2,028 eQTL genes we were able to link an eQTL variant to a chromatin peak however we did not detect a QTL effect on any chromatin feature.…”