| 1. |
- Okada, Yukinori, et al.
(author)
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Genetics of rheumatoid arthritis contributes to biology and drug discovery
- 2014
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In: Nature. - : Nature Publishing Group. - 0028-0836 .- 1476-4687. ; 506:7488, s. 376-381
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Journal article (peer-reviewed)abstract
- A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA)(1). Here we performed a genome-wide association study meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating similar to 10 million single-nucleotide polymorphisms. We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 101 (refs 2-4). We devised an in silico pipeline using established bioinformatics methods based on functional annotation(5), cis-acting expression quantitative trait loci(6) and pathway analyses(7-9)-as well as novel methods based on genetic overlap with human primary immunodeficiency, haematological cancer somatic mutations and knockout mouse phenotypes-to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.
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| 2. |
- Han, Buhm, et al.
(author)
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A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases
- 2016
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In: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 48:7, s. 803-
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Journal article (peer-reviewed)abstract
- There is growing evidence of shared risk alleles for complex traits (pleiotropy), including autoimmune and neuropsychiatric diseases. This might be due to sharing among all individuals (whole-group pleiotropy) or a subset of individuals in a genetically heterogeneous cohort (subgroup heterogeneity). Here we describe the use of a well-powered statistic, BUHMBOX, to distinguish between those two situations using genotype data. We observed a shared genetic basis for 11 autoimmune diseases and type 1 diabetes (T1D; P < 1 x 10(-4)) and for 11 autoimmune diseases and rheumatoid arthritis (RA; P < 1 x 10(-3)). This sharing was not explained by subgroup heterogeneity (corrected P-BUHMBOX > 0.2; 6,670 T1D cases and 7,279 RA cases). Genetic sharing between seronegative and seropostive RA (P < 1 x 10(-9)) had significant evidence of subgroup heterogeneity, suggesting a subgroup of seropositive-like cases within seronegative cases (P-BUHMBOX = 0.008; 2,406 seronegative RA cases). We also observed a shared genetic basis for major depressive disorder (MDD) and schizophrenia (P < 1 x 10(-4)) that was not explained by subgroup heterogeneity (P-BUHMBOX = 0.28; 9,238 MDD cases).
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| 3. |
- Ishigaki, Kazuyoshi, et al.
(author)
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Multi-ancestry genome-wide association analyses identify novel genetic mechanisms in rheumatoid arthritis
- 2022
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In: Nature Genetics. - : Springer Nature. - 1061-4036 .- 1546-1718. ; 54:11, s. 1640-1651
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Journal article (peer-reviewed)abstract
- Rheumatoid arthritis (RA) is a highly heritable complex disease with unknown etiology. Multi-ancestry genetic research of RA promises to improve power to detect genetic signals, fine-mapping resolution and performances of polygenic risk scores (PRS). Here, we present a large-scale genome-wide association study (GWAS) of RA, which includes 276,020 samples from five ancestral groups. We conducted a multi-ancestry meta-analysis and identified 124 loci (P < 5 × 10−8), of which 34 are novel. Candidate genes at the novel loci suggest essential roles of the immune system (for example, TNIP2 and TNFRSF11A) and joint tissues (for example, WISP1) in RA etiology. Multi-ancestry fine-mapping identified putatively causal variants with biological insights (for example, LEF1). Moreover, PRS based on multi-ancestry GWAS outperformed PRS based on single-ancestry GWAS and had comparable performance between populations of European and East Asian ancestries. Our study provides several insights into the etiology of RA and improves the genetic predictability of RA.
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| 4. |
- Lenz, Tobias L., et al.
(author)
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Widespread non-additive and interaction effects within HLA loci modulate the risk of autoimmune diseases
- 2015
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In: Nature Genetics. - : Macmillan Publishers Ltd.. - 1061-4036 .- 1546-1718. ; 47:9, s. 1085-1090
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Journal article (peer-reviewed)abstract
- Human leukocyte antigen (HLA) genes confer substantial risk for autoimmune diseases on a log-additive scale. Here we speculated that differences in autoantigen-binding repertoires between a heterozygote's two expressed HLA variants might result in additional non-additive risk effects. We tested the non-additive disease contributions of classical HLA alleles in patients and matched controls for five common autoimmune diseases: rheumatoid arthritis (n(cases) = 5,337), type 1 diabetes (T1D; n(cases) = 5,567), psoriasis vulgaris (n(cases) = 3,089), idiopathic achalasia (n(cases) = 727) and celiac disease (ncases = 11,115). In four of the five diseases, we observed highly significant, non-additive dominance effects (rheumatoid arthritis, P = 2.5 x 10(-12); T1D, P = 2.4 x 10(-10); psoriasis, P = 5.9 x 10(-6); celiac disease, P = 1.2 x 10(-87)). In three of these diseases, the non-additive dominance effects were explained by interactions between specific classical HLA alleles (rheumatoid arthritis, P = 1.8 x 10(-3); T1D, P = 8.6 x 10(-27); celiac disease, P = 6.0 x 10(-100)). These interactions generally increased disease risk and explained moderate but significant fractions of phenotypic variance (rheumatoid arthritis, 1.4%; T1D, 4.0%; celiac disease, 4.1%) beyond a simple additive model.
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| 5. |
- Westra, Harm-Jan, et al.
(author)
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Fine-mapping and functional studies highlight potential causal variants for rheumatoid arthritis and type 1 diabetes
- 2018
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In: Nature Genetics. - : Nature Publishing Group. - 1061-4036 .- 1546-1718. ; 50:10, s. 1366-
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Journal article (peer-reviewed)abstract
- To define potentially causal variants for autoimmune disease, we fine-mapped(1,2) 76 rheumatoid arthritis (11,475 cases,15,870 controls)(3) and type 1 diabetes loci (9,334 cases, 11,111 controls)(4). 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.
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