Genetic and Epigenetic Fine-Mapping of Causal Autoimmune Disease Variants

Genome-wide association studies (GWAS), have become the most powerful tool to search the numerous potential risk genetic loci for the susceptibility of many complicated diseases in recent years. However, despite of the more comprehensive analysis of GWAS, there are some limitations for the method. First, it is difficult to identify true causal variants due to the haplotype construction based on the linkage disequilibrium. Besides, most causal variants identified by GWAS were non-coding variants. Although it has been suggested non-coding causal variants may contribute to epigenetic regulation, such as histone acetylation, methylation or DNA methylation, mRNA splicing and the regulation of RNA transcription. The mechanisms of action, and the cellular states and processes in which they function were largely unknown. In a recent study, Farh et al . developed a fine-mapping algorithm to identify candidate causal genetic variants in 21 autoimmune diseases from 39 GWAS studies (1). Through integrated predictions with transcription and cis-regulatory map for several kinds of immune and non-immune cell types, including resting and stimulated CD4+ T cell, regulatory cell, B cell and monocytes, etc., they had provided the unique information about the distributions and features of causal variants in the susceptibility of autoimmune diseases. Accordingly, more than 90% susceptible variants are reside in non-coding and around 60% variants were located in immune-cell transcription factor binding sites (enhancer), which contribute to activating or modulating T or B cell immune response. However, only 10–20% risk variants appear to act directly classical recognizable transcription factor binding sites to regulate gene expression while the 80–90% of non-coding genetic variants functions directly by modifying the non-classical regulatory sequence. In addition, most non-coding risk variants, including those that alter gene expression, affect non-canonical sequence determinants not well-explained by current gene regulatory models.

Identifying noncoding risk variants remains a challenging task. Because noncoding variants exert their effects in the context of a gene regulatory network (GRN), we hypothesize that explicit use of disease-relevant GRNs can significantly improve the inference accuracy of noncoding risk variants. We describe Annotation of Regulatory Variants using Integrated Networks (ARVIN), a general computational framework for predicting causal noncoding variants. It employs a set of novel regulatory network-based features, combined with sequence-based features to infer noncoding risk variants. Using known causal variants in gene promoters and enhancers in a number of diseases, we show ARVIN outperforms state-of-the-art methods that use sequence-based features alone. Additional experimental validation using reporter assay further demonstrates the accuracy of ARVIN. Application of ARVIN to seven autoimmune diseases provides a holistic view of the gene subnetwork perturbed by the combinatorial action of the entire set of risk noncoding mutations.

A Flexible Bayesian Framework for Modeling Haplotype Association with Disease, Allowing for Dominance Effects of the Underlying Causative Variants

Functional annotation of noncoding causal variants in autoimmune diseases

Blood cell traits’ GWAS loci colocalization with variation in PU.1 genomic occupancy prioritizes causal noncoding regulatory variants

Human genetic variants are associated with many traits through largely unknown mechanisms. Here, combining approximately 260,000 Japanese study participants, a Japanese-specific genotype reference panel and statistical fine-mapping, we identified 4,423 significant loci across 63 quantitative traits, among which 601 were new, and 9,406 putatively causal variants. New associations included Japanese-specific coding, splicing and noncoding variants, exemplified by a damaging missense variant rs730881101 in TNNT2 associated with lower heart function and increased risk for heart failure (P = 1.4 × 10−15 and odds ratio = 4.5, 95% confidence interval = 3.1–6.5). Putative causal noncoding variants were supported by state-of-art in silico functional assays and had comparable effect sizes to coding variants. A plausible example of new mechanisms of causal variants is an enrichment of causal variants in 3′ untranslated regions (UTRs), including the Japanese-specific rs13306436 in IL6 associated with pro-inflammatory traits and protection against tuberculosis. We experimentally showed that transcripts with rs13306436 are resistant to mRNA degradation by regnase-1, an RNA-binding protein. Our study provides a list of fine-mapped causal variants to be tested for functionality and underscores the importance of sequencing, genotyping and association efforts in diverse populations.

To define potentially causal variants for autoimmune disease, we fine-mapped1,2 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.

A unifying statistical framework to discover disease genes from GWASs

Genome-wide association studies (GWAS) have emerged as one of the most powerful tools to define DNA sequence variants associated with common diseases or phenotypes. Massive phenotyping efforts coupled with inexpensive genotyping arrays have resulted in GWAS data on millions of individuals and an

Sequence variation in regulatory DNA alters gene expression and shapes genetically complex traits. However, the identification of individual, causal regulatory variants is challenging. Here, we used a massively parallel reporter assay to measure the cis-regulatory consequences of 5832 natural DNA variants in the promoters of 2503 genes in the yeast Saccharomyces cerevisiae. We identified 451 causal variants, which underlie genetic loci known to affect gene expression. Several promoters harbored multiple causal variants. In five promoters, pairs of variants showed non-additive, epistatic interactions. Causal variants were enriched at conserved nucleotides, tended to have low derived allele frequency, and were depleted from promoters of essential genes, which is consistent with the action of negative selection. Causal variants were also enriched for alterations in transcription factor binding sites. Models integrating these features provided modest, but statistically significant, ability to predict causal variants. This work revealed a complex molecular basis for cis-acting regulatory variation.

Sequence variation in regulatory DNA alters gene expression and shapes genetically complex traits. However, the identification of individual, causal regulatory variants is challenging. Here, we used a massively parallel reporter assay to measure the cis-regulatory consequences of 5832 natural DNA variants in the promoters of 2503 genes in the yeast Saccharomyces cerevisiae. We identified 451 causal variants, which underlie genetic loci known to affect gene expression. Several promoters harbored multiple causal variants. In five promoters, pairs of variants showed non-additive, epistatic interactions. Causal variants were enriched at conserved nucleotides, tended to have low derived allele frequency, and were depleted from promoters of essential genes, which is consistent with the action of negative selection. Causal variants were also enriched for alterations in transcription factor binding sites. Models integrating these features provided modest, but statistically significant, ability to predict causal variants. This work revealed a complex molecular basis for cis-acting regulatory variation.

Major progress in disease genetics has been made through genome-wide association studies (GWASs). One of the key tasks for post-GWAS analyses is to identify causal noncoding variants with regulatory function. Here, on the basis of >2000 functional features, we developed a convolutional neural network framework for combinatorial, nonlinear modeling of complex patterns shared by risk variants scattered among multiple associated loci. When applied for major psychiatric disorders and autoimmune diseases, neural and immune features, respectively, exhibited high explanatory power while reflecting the pathophysiology of the relevant disease. The predicted causal variants were concentrated in active regulatory regions of relevant cell types and tended to be in physical contact with transcription factors while residing in evolutionarily conserved regions and resulting in expression changes of genes related to the given disease. We demonstrate some examples of novel candidate causal variants and associated genes. Our method is expected to contribute to the identification and functional interpretation of potential causal noncoding variants in post-GWAS analyses.

DNA variants that alter gene expression contribute to variation in many phenotypic traits. In particular, trans-acting variants, which are often located on different chromosomes from the genes they affect, are an important source of heritable gene expression variation. However, our knowledge about the identity and mechanism of causal trans-acting variants remains limited. Here, we developed a fine-mapping strategy called CRISPR-Swap and dissected three expression quantitative trait locus (eQTL) hotspots known to alter the expression of numerous genes in trans in the yeast Saccharomyces cerevisiae. Causal variants were identified by engineering recombinant alleles and quantifying the effects of these alleles on the expression of a green fluorescent protein-tagged gene affected by the given locus in trans. We validated the effect of each variant on the expression of multiple genes by RNA-sequencing. The three variants differed in their molecular mechanism, the type of genes they reside in, and their distribution in natural populations. While a missense leucine-to-serine variant at position 63 in the transcription factor Oaf1 (L63S) was almost exclusively present in the reference laboratory strain, the two other variants were frequent among S. cerevisiae isolates. A causal missense variant in the glucose receptor Rgt2 (V539I) occurred at a poorly conserved amino acid residue and its effect was strongly dependent on the concentration of glucose in the culture medium. A noncoding variant in the conserved fatty acid regulated (FAR) element of the OLE1 promoter influenced the expression of the fatty acid desaturase Ole1 in cis and, by modulating the level of this essential enzyme, other genes in trans. The OAF1 and OLE1 variants showed a non-additive genetic interaction, and affected cellular lipid metabolism. These results demonstrate that the molecular basis of trans-regulatory variation is diverse, highlighting the challenges in predicting which natural genetic variants affect gene expression.

DNA variants that alter gene expression contribute to variation in many phenotypic traits. In particular, trans-acting variants, which are often located on different chromosomes from the genes they affect, are an important source of heritable gene expression variation. However, our knowledge about the identity and mechanism of causal trans-acting variants remains limited. Here, we developed a fine-mapping strategy called CRISPR-Swap and dissected three expression quantitative trait locus (eQTL) hotspots known to alter the expression of numerous genes in trans in the yeast Saccharomyces cerevisiae. Causal variants were identified by engineering recombinant alleles and quantifying the effects of these alleles on the expression of a green fluorescent protein-tagged gene affected by the given locus in trans. We validated the effect of each variant on the expression of multiple genes by RNA-sequencing. The three variants differed in their molecular mechanism, the type of genes they reside in, and their distribution in natural populations. While a missense leucine-to-serine variant at position 63 in the transcription factor Oaf1 (L63S) was almost exclusively present in the reference laboratory strain, the two other variants were frequent among S. cerevisiae isolates. A causal missense variant in the glucose receptor Rgt2 (V539I) occurred at a poorly conserved amino acid residue and its effect was strongly dependent on the concentration of glucose in the culture medium. A noncoding variant in the conserved fatty acid regulated (FAR) element of the OLE1 promoter influenced the expression of the fatty acid desaturase Ole1 in cis and, by modulating the level of this essential enzyme, other genes in trans. The OAF1 and OLE1 variants showed a non-additive genetic interaction, and affected cellular lipid metabolism. These results demonstrate that the molecular basis of trans-regulatory variation is diverse, highlighting the challenges in predicting which natural genetic variants affect gene expression.

The vast majority of risk loci (over 90%) associated with autoimmune diseases are located in the noncoding regions of the genome and have not been fine-mapped down to individual causal variants. Prioritizing these causal variants is complicated by linkage disequilibrium and the unknown function of the noncoding genetic variation. In this study, we prioritized causal variants within 64 loci associated with psoriasis risk. We first employed the SUSIE method to estimate the conditional (a posteriori) causal probability of each candidate polymorphism within a given risk locus. We then narrowed down potential functional variants by colocalizing candidate SNPs with eQTLs that affected gene expression in immune cells. Specifically, we ran colocalization analysis with eQTL markers from 23 immune cell types and 48 human tissues from the Genotype-Tissue Expression (GTEx) Consortium. A careful selection of eQTL markers in immune cells related to pathogenesis enabled us to predict the molecular function of putative causal variants at two risk loci. These causal variants function dynamically in the activated states of two immune cell types and were absent in the GTEx human tissue eQTL marker sets. Finally, we cross-checked genomic sites for the inferred regulatory variants by comparing them with chromatin modifications associated with gene expression regulation. This paper presents a detailed analysis and discussion of a regulatory variant affecting TNFAIP3 gene expression in antigen-presenting monocytes activated by bacterial ligands. The dynamic role of the causal variant at the risk locus in response to bacterial ligands aligns with the well-known triggering effect of bacterial infection in the onset and exacerbation of psoriasis. Thus, our findings shed light on the molecular mechanisms underlying genetic–environmental interactions involved in the pathogenesis of psoriasis.