Person:

Hu, Xinli

Summary: We created a fast, robust and general C++ implementation of a single-nucleotide polymorphism (SNP) set enrichment algorithm to identify cell types, tissues and pathways affected by risk loci. It tests trait-associated genomic loci for enrichment of specificity to conditions (cell types, tissues and pathways). We use a non-parametric statistical approach to compute empirical P-values by comparison with null SNP sets. As a proof of concept, we present novel applications of our method to four sets of genome-wide significant SNPs associated with red blood cell count, multiple sclerosis, celiac disease and HDL cholesterol. Availability and implementation: http://broadinstitute.org/mpg/snpsea Contact: soumya@broadinstitute.org Supplementary information: Supplementary data are available at Bioinformatics online.

Genome-wide association studies (GWAS) and subsequent dense-genotyping of associated loci identified over a hundred single-nucleotide polymorphism (SNP) variants associated with the risk of rheumatoid arthritis (RA), type 1 diabetes (T1D), and celiac disease (CeD). Immunological and genetic studies suggest a role for CD4-positive effector memory T (CD+ TEM) cells in the pathogenesis of these diseases. To elucidate mechanisms of autoimmune disease alleles, we investigated molecular phenotypes in CD4+ effector memory T cells potentially affected by these variants. In a cohort of genotyped healthy individuals, we isolated high purity CD4+ TEM cells from peripheral blood, then assayed relative abundance, proliferation upon T cell receptor (TCR) stimulation, and the transcription of 215 genes within disease loci before and after stimulation. We identified 46 genes regulated by cis-acting expression quantitative trait loci (eQTL), the majority of which we detected in stimulated cells. Eleven of the 46 genes with eQTLs were previously undetected in peripheral blood mononuclear cells. Of 96 risk alleles of RA, T1D, and/or CeD in densely genotyped loci, eleven overlapped cis-eQTLs, of which five alleles completely explained the respective signals. A non-coding variant, rs389862A, increased proliferative response (p = 4.75×10−8). In addition, baseline expression of seventeen genes in resting cells reliably predicted proliferative response after TCR stimulation. Strikingly, however, there was no evidence that risk alleles modulated CD4+ TEM abundance or proliferation. Our study underscores the power of examining molecular phenotypes in relevant cells and conditions for understanding pathogenic mechanisms of disease variants.

Summary Using the Immunochip custom single nucleotide polymorphism (SNP) array, designed for dense genotyping of 186 genome wide association study (GWAS) confirmed loci we analysed 11,475 rheumatoid arthritis cases of European ancestry and 15,870 controls for 129,464 markers. The data were combined in meta-analysis with GWAS data from additional independent cases (n=2,363) and controls (n=17,872). We identified fourteen novel loci; nine were associated with rheumatoid arthritis overall and 5 specifically in anti-citrillunated peptide antibody positive disease, bringing the number of confirmed European ancestry rheumatoid arthritis loci to 46. We refined the peak of association to a single gene for 19 loci, identified secondary independent effects at six loci and association to low frequency variants (minor allele frequency <0.05) at 4 loci. Bioinformatic analysis of the data generated strong hypotheses for the causal SNP at seven loci. This study illustrates the advantages of dense SNP mapping analysis to inform subsequent functional investigations.

Crohn’s disease (CD) and ulcerative colitis (UC), the two common forms of inflammatory bowel disease (IBD), affect over 2.5 million people of European ancestry with rising prevalence in other populations1. Genome-wide association studies (GWAS) and subsequent meta-analyses of CD and UC2,3 as separate phenotypes implicated previously unsuspected mechanisms, such as autophagy4, in pathogenesis and showed that some IBD loci are shared with other inflammatory diseases5. Here we expand knowledge of relevant pathways by undertaking a meta-analysis of CD and UC genome-wide association scans, with validation of significant findings in more than 75,000 cases and controls. We identify 71 new associations, for a total of 163 IBD loci that meet genome-wide significance thresholds. Most loci contribute to both phenotypes, and both directional and balancing selection effects are evident. Many IBD loci are also implicated in other immune-mediated disorders, most notably with ankylosing spondylitis and psoriasis. We also observe striking overlap between susceptibility loci for IBD and mycobacterial infection. Gene co-expression network analysis emphasizes this relationship, with pathways shared between host responses to mycobacteria and those predisposing to IBD.

Human leukocyte antigen (HLA) genes confer strong 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 may result in additional non-additive risk effects. We tested non-additive disease contributions of classical HLA alleles in patients and matched controls for five common autoimmune diseases: rheumatoid arthritis (RA, Ncases=5,337), type 1 diabetes (T1D, Ncases=5,567), psoriasis vulgaris (Ncases=3,089), idiopathic achalasia (Ncases=727), and celiac disease (Ncases=11,115). In four out of five diseases, we observed highly significant non-additive dominance effects (RA: P=2.5×1012; T1D: P=2.4×10−10; psoriasis: P=5.9×10−6; celiac disease: P=1.2×10−87). In three of these diseases, the dominance effects were explained by interactions between specific classical HLA alleles (RA: P=1.8×10−3; T1D: P=8.6×1027; celiac disease: P=6.0×10−100). These interactions generally increased disease risk and explained moderate but significant fractions of phenotypic variance (RA: 1.4%, T1D: 4.0%, and celiac disease: 4.1%, beyond a simple additive model).

Autoimmune diseases are chronic and debilitating conditions arising from abnormal immune responses directed against normal body tissues; they collectively affect the lives of 5-10% of the world population. These diseases often show familial clustering, suggesting strong genetic heritability. For many of autoimmune diseases, variation in the human leukocyte antigen (HLA) genes is the primary modulator of genetic risk. Recently, genome-wide association studies (GWAS) identified hundreds of genomic regions outside the HLA that harbor additional risk-conferring variants. The ultimate goal is to identify the precise causal variants and understand the mechanisms by which they lead to autoimmunity, which is challenged by complexities of the genome and the immune system. In this work, my colleagues and I developed and applied experimental and computational tools to reveal critical clues from multiple genetic and biological data types. First, we devised a statistical algorithm to identify the critical cell types involved in different autoimmune diseases. Two strongly heritable and common diseases, rheumatoid arthritis (RA) and type 1 diabetes (T1D), both involve the adaptive immune system, specifically the CD4+ T cells. We then conducted focused studies in CD4+ T cells using high-throughput genomic and proteomic technologies, and showed that immunological phenotypes and functions varied with genetic differences across individuals. To facilitate this study, we developed an automated computational tool to efficiently and reliably analyze the large-scale data. Finally, the HLA genes, which encode a family of highly variable antigen-recognition proteins, are the longest-known and strongest modulators of genetic risk in T1D. However, the extraordinary level of polymorphism and complex structure in the HLA region largely hindered precise localization and functional investigation of the causal mutations. We used dense-genotyping and robust statistical analyses to pinpoint the amino acid residue changes at a few key amino acid positions that explained the majority of disease risk within the HLA. The work presented in this dissertation revealed the specific immune cell populations, genetic variants, and cellular functions that affect RA, T1D, and other autoimmune diseases. Furthermore, it offers a rational framework, as well as powerful open-source computational tools, that can be applied in future functional genomic studies.