Identification of 38 novel loci for systemic lupus erythematosus and genetic heterogeneity between ancestral groups.
Yong-Fei WangYan ZhangZhiming LinHuoru ZhangTing-You WangYujie CaoDavid L MorrisYujun ShengXianyong YinShi-Long ZhongXiaoqiong GuYao LeiJing HeQi WuJiangshan Jane ShenJing YangTai-Hing LamJia-Huang LinJim Zhiming MaiMengbiao GuoYuanjia TangYanhui ChenQin SongBo BanChi Chiu MokYong CuiLiangjing LuNan ShenPak-Chung ShamWallace Chak Sing LauDavid K SmithTimothy J VyseXuejun ZhangYu Lung LauWanling YangPublished in: Nature communications (2021)
Systemic lupus erythematosus (SLE), a worldwide autoimmune disease with high heritability, shows differences in prevalence, severity and age of onset among different ancestral groups. Previous genetic studies have focused more on European populations, which appear to be the least affected. Consequently, the genetic variations that underlie the commonalities, differences and treatment options in SLE among ancestral groups have not been well elucidated. To address this, we undertake a genome-wide association study, increasing the sample size of Chinese populations to the level of existing European studies. Thirty-eight novel SLE-associated loci and incomplete sharing of genetic architecture are identified. In addition to the human leukocyte antigen (HLA) region, nine disease loci show clear ancestral differences and implicate antibody production as a potential mechanism for differences in disease manifestation. Polygenic risk scores perform significantly better when trained on ancestry-matched data sets. These analyses help to reveal the genetic basis for disparities in SLE among ancestral groups.
Keyphrases
- systemic lupus erythematosus
- genome wide
- genome wide association study
- disease activity
- dna methylation
- copy number
- endothelial cells
- gene expression
- rheumatoid arthritis
- multiple sclerosis
- single cell
- healthcare
- risk factors
- electronic health record
- machine learning
- genome wide association
- risk assessment
- deep learning
- body composition
- artificial intelligence
- climate change