Presence of multiple independent effects in risk loci of common complex human diseases

Simultaneous selection of multiple important single nucleotide polymorphisms in familial genome wide association studies data

We propose a resampling-based fast variable selection technique for detecting relevant single nucleotide polymorphisms (SNP) in a multi-marker mixed effect model. Due to computational complexity, current practice primarily involves testing the effect of one SNP at a time, commonly termed as 'single SNP association analysis'. Joint modeling of genetic variants within a gene or pathway may have better power to detect associated genetic variants, especially the ones with weak effects. In this paper, we propose a computationally efficient model selection approach-based on the e-values framework-for single SNP detection in families while utilizing information on multiple SNPs simultaneously. To overcome computational bottleneck of traditional model selection methods, our method trains one single model, and utilizes a fast and scalable bootstrap procedure. We illustrate through numerical studies that our proposed method is more effective in detecting SNPs associated with a trait than either single-marker analysis using family data or model selection methods that ignore the familial dependency structure. Further, we perform gene-level analysis in Minnesota Center for Twin and Family Research (MCTFR) dataset using our method to detect several SNPs using this that have been implicated to be associated with alcohol consumption.

A major mechanism for immunomodulation: Dietary fibres and acid metabolites

Diet and the gut microbiota have a profound influence on physiology and health, however, mechanisms are still emerging. Here we outline several pathways that gut microbiota products, particularly short-chain fatty acids (SCFAs), use to maintain gut and immune homeostasis. Dietary fibre is fermented by the gut microbiota in the colon, and large quantities of SCFAs such as acetate, propionate, and butyrate are produced. Dietary fibre and SCFAs enhance epithelial integrity and thereby limit systemic endotoxemia. Moreover, SCFAs inhibit histone deacetylases (HDAC), and thereby affect gene transcription. SCFAs also bind to 'metabolite-sensing' G-protein coupled receptors (GPCRs) such as GPR43, which promotes immune homeostasis. The enormous amounts of SCFAs produced in the colon are sufficient to lower pH, which affects the function of proton sensors such as GPR65 expressed on the gut epithelium and immune cells. GPR65 is an anti-inflammatory Gα_s-coupled receptor, which leads to the inhibition of inflammatory cytokines. The importance of GPR65 in inflammatory diseases is underscored by genetics associated with the missense variant I231L (rs3742704), which is associated with human inflammatory bowel disease, atopic dermatitis, and asthma. There is enormous scope to manipulate these pathways using specialized diets that release very high amounts of specific SCFAs in the gut, and we believe that therapies that rely on chemically modified foods is a promising approach. Such an approach includes high SCFA-producing diets, which we have shown to decrease numerous inflammatory western diseases in mouse models. These diets operate at many levels - increased gut integrity, changes to the gut microbiome, and promotion of immune homeostasis, which represents a new and highly promising way to prevent or treat human disease.

GPR65 as a potential immune checkpoint regulates the immune microenvironment according to pan-cancer analysis

It has been reported that inhibition of GPR65 may be effective for the treatment of certain cancers. Nevertheless, the role of GPR65 in various cancers remains unknown. We conducted an exhaustive pan-cancer analysis of GPR65 using multiple databases, including TCGA, GTEx, BioGPS, HPA, cBioPortal, and GeneCards. GPR65 was found to be differentially expressed in various cancers and linked to tumor mutational burden (TMB), microsatellite instability (MSI), and Ploidy, playing a key function in the tumor microenvironment (TME). It is closely linked to the development of Th17 cells as well as Th1 and Th2 cells in certain cancers. Our findings indicate that the expression of GPR65 is highly linked with clinical prognosis, mutations, and immune cell infiltration. It was revealed as an indicator of patient prognosis as well as a possible immunomodulatory role. As a possible new immunological checkpoint, GPR65 could be a target for tumor immunotherapy.

Identification of arsenic-tolerant varieties and candidate genes of tolerance in spring wheat (Triticum aestivum L.)

Despite the growing concerns about arsenic toxicity, information on tolerance and responsible genetic factors in wheat remains elusive. To address that, the present study aimed to screen the wheat varieties against arsenic based on growth parameters, yield, grain accumulation, and associated genes. A total of 110 wheat varieties were grown in arsenic-contaminated regions to record physio-morphological traits. The wheat 90K Infinium iSelect SNP array was used for the genome-wide association model to identify genomic regions. Wheat varieties such as Punjab-81, AARI-11, and Daman showed arsenic concentrations >45 μg/kg in similar conditions as well as the impact on grain yield, chlorophyll, Thousand Kernel Weight, and plant height. Contrastingly, varieties like Kohistan-97, As-2002, Barani-70, and Pari-73 showed grain concentrations <5 μg/kg grown under highly contaminated conditions. Three significant loci associated with arsenic accumulation in grain were identified on chromosomes 6A (qASG1-6A) and 6B (qASG3-6B and qASG4-6B). Annotation at these loci identified 39 wheat genes among which several were important for growth and tolerance against stress. The candidate gene (TraesCS6B02G429400) responsible for Glutathione-S-transferase was identified in the present study and must be investigated further using a transcriptomic approach. The present study provided background information for breeding prospects to improve wheat yield and tolerance against arsenic.

Genetic Factors and Their Role in the Pathogenesis of Biliary Atresia

Biliary Atresia, a common basis for neonatal cholestasis and primary indication for Liver Transplantation, accounts for 60% of pediatric Liver Transplantations. While the pathogenesis of Biliary Atresia remains obscure, abnormalities within bile ducts and the liver, inflammation, fibrosis and cilia defects are thought to comprise the pathological basis for this condition. The findings of genetic variants in Biliary Atresia, such as Copy Number Variations and Single Nucleotide Polymorphism, are considered as essential factors in the development of this condition. In this review, we summarize and analyze these Biliary Atresia variants from a perspective of their pathological characteristics. In conclusion, such analyses may offer novel insights into the pathogenesis of Biliary Atresia and provide a foundation for future studies directed toward a better understanding and treatment of Biliary Atresia.

Discordant associations of educational attainment with ASD and ADHD implicate a polygenic form of pleiotropy

Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) are complex co-occurring neurodevelopmental conditions. Their genetic architectures reveal striking similarities but also differences, including strong, discordant polygenic associations with educational attainment (EA). To study genetic mechanisms that present as ASD-related positive and ADHD-related negative genetic correlations with EA, we carry out multivariable regression analyses using genome-wide summary statistics (N = 10,610-766,345). Our results show that EA-related genetic variation is shared across ASD and ADHD architectures, involving identical marker alleles. However, the polygenic association profile with EA, across shared marker alleles, is discordant for ASD versus ADHD risk, indicating independent effects. At the single-variant level, our results suggest either biological pleiotropy or co-localisation of different risk variants, implicating MIR19A/19B microRNA mechanisms. At the polygenic level, they point to a polygenic form of pleiotropy that contributes to the detectable genome-wide correlation between ASD and ADHD and is consistent with effect cancellation across EA-related regions.

pH and Proton Sensor GPR65 Determine Susceptibility to Atopic Dermatitis

pH sensing by GPR65 regulates various inflammatory conditions, but its role in skin remains unknown. In this study, we performed a phenome-wide association study and report that the T allele of GPR65-intronic single-nucleotide polymorphism rs8005161, which reduces GPR65 signaling, showed a significant association with atopic dermatitis, in addition to inflammatory bowel diseases and asthma, as previously reported. Consistent with this genetic association in humans, we show that deficiency of GPR65 in mice resulted in markedly exacerbated disease in the MC903 experimental model of atopic dermatitis. Deficiency of GPR65 also increased neutrophil migration in vitro. Moreover, GPR65 deficiency in mice resulted in higher expression of the inflammatory cytokine TNF-α by T cells. In humans, CD4⁺ T cells from rs8005161 heterozygous individuals expressed higher levels of TNF-α after PMA/ionomycin stimulation, particularly under pH 6 conditions. pH sensing by GPR65 appears to be important for regulating the pathogenesis of atopic dermatitis.

Anti-Inflammatory and Immunomodulatory Effects of Probiotics in Gut Inflammation: A Door to the Body

Hosting millions of microorganisms, the digestive tract is the primary and most important part of bacterial colonization. On one side, in cases of opportunistic invasion, the abundant bacterial population inside intestinal tissues may face potential health problems such as inflammation and infections. Therefore, the immune system has evolved to sustain the host-microbiota symbiotic relationship. On the other hand, to maintain host immune homeostasis, the intestinal microflora often exerts an immunoregulatory function that cannot be ignored. A field of great interest is the association of either microbiota or probiotics with the immune system concerning clinical uses. This microbial community regulates some of the host's metabolic and physiological functions and drives early-life immune system maturation, contributing to their homeostasis throughout life. Changes in gut microbiota can occur through modification in function, composition (dysbiosis), or microbiota-host interplays. Studies on animals and humans show that probiotics can have a pivotal effect on the modulation of immune and inflammatory mechanisms; however, the precise mechanisms have not yet been well defined. Diet, age, BMI (body mass index), medications, and stress may confound the benefits of probiotic intake. In addition to host gut functions (permeability and physiology), all these agents have profound implications for the gut microbiome composition. The use of probiotics could improve the gut microbial population, increase mucus-secretion, and prevent the destruction of tight junction proteins by decreasing the number of lipopolysaccharides (LPSs). When LPS binds endothelial cells to toll-like receptors (TLR 2, 4), dendritic cells and macrophage cells are activated, and inflammatory markers are increased. Furthermore, a decrease in gut dysbiosis and intestinal leakage after probiotic therapy may minimize the development of inflammatory biomarkers and blunt unnecessary activation of the immune system. In turn, probiotics improve the differentiation of T-cells against Th2 and development of Th2 cytokines such as IL-4 and IL-10. The present narrative review explores the interactions between gut microflora/probiotics and the immune system starting from the general perspective of a biological plausibility to get to the in vitro and in vivo demonstrations of a probiotic-based approach up to the possible uses for novel therapeutic strategies.

The Role of Prebiotics and Probiotics in Prevention of Allergic Diseases in Infants

Allergic diseases have been linked to genetic and/or environmental factors, such as antibiotic use, westernized high fat and low fiber diet, which lead to early intestinal dysbiosis, and account for the rise in allergy prevalence, especially in western countries. Allergic diseases have shown reduced microbial diversity, including fewer lactobacilli and bifidobacteria, within the neonatal microbiota, before the onset of atopic diseases. Raised interest in microbiota manipulating strategies to restore the microbial balance for atopic disease prevention, through prebiotics, probiotics, or synbiotics supplementation, has been reported. We reviewed and discussed the role of prebiotics and/or probiotics supplementation for allergy prevention in infants. We searched PubMed and the Cochrane Database using keywords relating to "allergy" OR "allergic disorders," "prevention" AND "prebiotics" OR "probiotics" OR "synbiotics." We limited our evaluation to papers of English language including children aged 0-2 years old. Different products or strains used, different period of intervention, duration of supplementation, has hampered the draw of definitive conclusions on the clinical impact of probiotics and/or prebiotics for prevention of allergic diseases in infants, except for atopic dermatitis in infants at high-risk. This preventive effect on eczema in high-risk infants is supported by clear evidence for probiotics but only moderate evidence for prebiotic supplementation. However, the optimal prebiotic or strain of probiotic, dose, duration, and timing of intervention remains uncertain. Particularly, a combined pre- and post-natal intervention appeared of stronger benefit, although the definition of the optimal intervention starting time during gestation, the timing, and duration in the post-natal period, as well as the best target population, are still an unmet need.

Metabolite-Sensing G Protein-Coupled Receptors Connect the Diet-Microbiota-Metabolites Axis to Inflammatory Bowel Disease

Increasing evidence has indicated that diet and metabolites, including bacteria- and host-derived metabolites, orchestrate host pathophysiology by regulating metabolism, immune system and inflammation. Indeed, autoimmune diseases such as inflammatory bowel disease (IBD) are associated with the modulation of host response to diets. One crucial mechanism by which the microbiota affects the host is signaling through G protein-coupled receptors (GPCRs) termed metabolite-sensing GPCRs. In the gut, both immune and nonimmune cells express GPCRs and their activation generally provide anti-inflammatory signals through regulation of both the immune system functions and the epithelial integrity. Members of GPCR family serve as a link between microbiota, immune system and intestinal epithelium by which all these components crucially participate to maintain the gut homeostasis. Conversely, impaired GPCR signaling is associated with IBD and other diseases, including hepatic steatosis, diabetes, cardiovascular disease, and asthma. In this review, we first outline the signaling, function, expression and the physiological role of several groups of metabolite-sensing GPCRs. We then discuss recent findings on their role in the regulation of the inflammation, their existing endogenous and synthetic ligands and innovative approaches to therapeutically target inflammatory bowel disease.

Large-scale replication study identified multiple independent SNPs in RET synergistically associated with Hirschsprung disease in Southern Chinese population

Hischsprung disease (HSCR) is an intestinal disorder with strong genetic components. RET was considered as the strongest contributor. Multiple single nucleotide polymorphisms (SNP) were demonstrated as associated with HSCR in different populations. However, whether the associations of reported SNPs derived from one causal variants or congregations of multiple variants were still not clear. In this study, we successfully genotyped 16 SNPs in RET with a largest case-control study to date, totaling 1470 HSCR and 1473 control subjects in South Chinese population. Multiple independent contributors were identified through pairwise and stepwise logistic regression. The intragenic synergistic effect among these SNPs were further explored and cross validated by logistic regression and multifactor dimensionality reduction (MDR). Noteworthy, in further subclinical manifestation analysis, the six potential independent contributors in RET were more essential for the patients with short-segment aganglionosis (S-HSCR). Although functional evaluations are required, our comprehensive analysis for RET gene integrating detailed disease subphenotypes might facilitate improved understanding for the genetic understanding of HSCR etiology.

Metabolite-Sensing G Protein-Coupled Receptors-Facilitators of Diet-Related Immune Regulation

Nutrition and the gut microbiome regulate many systems, including the immune, metabolic, and nervous systems. We propose that the host responds to deficiency (or sufficiency) of dietary and bacterial metabolites in a dynamic way, to optimize responses and survival. A family of G protein-coupled receptors (GPCRs) termed the metabolite-sensing GPCRs bind to various metabolites and transmit signals that are important for proper immune and metabolic functions. Members of this family include GPR43, GPR41, GPR109A, GPR120, GPR40, GPR84, GPR35, and GPR91. In addition, bile acid receptors such as GPR131 (TGR5) and proton-sensing receptors such as GPR65 show similar features. A consistent feature of this family of GPCRs is that they provide anti-inflammatory signals; many also regulate metabolism and gut homeostasis. These receptors represent one of the main mechanisms whereby the gut microbiome affects vertebrate physiology, and they also provide a link between the immune and metabolic systems. Insufficient signaling through one or more of these metabolite-sensing GPCRs likely contributes to human diseases such as asthma, food allergies, type 1 and type 2 diabetes, hepatic steatosis, cardiovascular disease, and inflammatory bowel diseases.

Dense Genotyping of Immune-Related Regions Identifies Loci for Rheumatoid Arthritis Risk and Damage in African Americans

Over 100 risk loci for rheumatoid arthritis (RA) have been identified in individuals of European and Asian descent, but the genetic basis for RA in African Americans is less well understood. We genotyped 610 African Americans with autoantibody positive RA and 933 African American controls on the ImmunoChip (iChip) array. Using multivariable regression we evaluated the association between iChip markers and the risk of RA and radiographic severity. The single nucleotide polymorphism (SNP) rs1964995 (OR = 1.97, p = 1.28 × 10^-15) near HLA-DRB1 was the most strongly associated risk SNP for RA susceptibility; SNPs in AFF3, TNFSF11, and TNFSF18 loci were suggestively associated (10^-4 < p < 3.1 × 10^-6). Trans-ethnic fine mapping of AFF3 identified a 90% credible set containing previously studied variants including rs9653442, rs7608424, and rs6712515 as well as the novel candidate variant rs11681966; several of these likely influence AFF3 gene expression level. Variants in TNFRSF9, CTLA4, IL2RA, C5/TRAF1, and ETS1 - but no variants within the major histocompatibility complex - were associated with RA radiographic severity. Conditional regression and pairwise linkage disequilibrium (LD) analyses suggest that additional pathogenic variants may be found in ETS1 and IL2RA beyond those found in other ethnicities. In summary, we use the dense genotyping of the iChip array and unique LD structure of African Americans to validate known risk loci for RA susceptibility and radiographic severity, and to better characterize the associations of AFF3, ETS1, and IL2RA.

Gene-based analysis of the fibroblast growth factor receptor signaling pathway in relation to breast cancer in African American women: the AMBER consortium

We conducted gene-based analysis in 26 genes in the FGFR signaling pathway to identify genes carrying genetic variation affecting risk of breast cancer and the specific estrogen receptor (ER) subtypes. Tagging single-nucleotide polymorphisms (SNPs) for each gene were selected and genotyped on a customized Illumina Exome Array. Imputation was carried out using 1000 Genomes haplotypes. The analysis included 3237 SNPs in 3663 breast cancer cases (including 1983 ER-positive, and 1098 ER-negative) and 4687 controls from the African American Breast Cancer Epidemiology and Risk consortium, a collaborative project of four large studies of breast cancer in African American women (Carolina Breast Cancer Study, Black Women's Health Study, Women's Circle of Health Study, and Multiethnic Cohort). We used a multi-locus adaptive joint (AdaJoint) test to determine the association of each gene in the FGFR signaling pathway with overall breast cancer and ER subtypes. The FGF1 gene was significantly associated with risk of ER-negative breast cancer (P = 0.001). The FGFR2 gene was associated with risk of overall breast cancer (P = 0.002) and ER-positive breast cancer (P = 0.002). The FGF1 gene affects risk of ER-negative breast cancer in African American women. We confirmed the association of the FGFR2 gene with risk of overall and ER-positive breast cancer. These results highlight the importance of the FGFR signaling pathway in the pathogenesis of breast cancer, and suggest that different genes in the same pathway may be associated with different ER breast cancer subtypes.

The human microbiome, asthma, and allergy

The human microbiome can be defined as the microorganisms that reside within and on our bodies and how they interact with the environment. Recent research suggests that numerous mutually beneficial interactions occur between a human and their microbiome, including those that are essential for good health. Modern microbiological detection techniques have contributed to new knowledge about microorganisms in their human environment. These findings reveal that the microbiomes of the lung and gut contribute to the pathogenesis of asthma and allergy. For example, evidence indicates that the microbiome of the gut regulates the activities of helper T cell subsets (Th1 and Th2) that affect the development of immune tolerance. Moreover, recent studies demonstrate differences between the lung microbiomes of healthy and asthmatic subjects. The hygiene and biodiversity hypotheses explain how exposure to microorganisms is associated with asthma and allergy. Although those living in developed countries are exposed to fewer and less diverse microorganisms compared with the inhabitants of developing countries, they are experiencing an increase in the incidence of asthma and allergies. Detailed analyses of the human microbiome, as are being conducted under the auspices of the Human Microbiome Project initiated in 2007, promise to contribute insights into the mechanisms and factors that cause asthma and allergy that may lead to the development of strategies to prevent and treat these diseases.

Solving the genetic puzzle of systemic lupus erythematosus

In recent years, genome-wide association studies on systemic lupus erythematosus (SLE) have significantly improved our understanding of the genetic architecture of this prototypic autoimmune disease. However, there is still a long way to go before we can fully understand the genetic factors for this very heterogeneous disease and the interplays between environmental factors and genetic predisposition that lead to the pathogenesis of SLE. Here we summarize the recent advances in our understanding of the genetics of SLE and discuss the future directions towards fully elucidating the mechanisms of this disease.

An introductory review of parallel independent component analysis (p-ICA) and a guide to applying p-ICA to genetic data and imaging phenotypes to identify disease-associated biological pathways and systems in common complex disorders

Complex inherited phenotypes, including those for many common medical and psychiatric diseases, are most likely underpinned by multiple genes contributing to interlocking molecular biological processes, along with environmental factors (Owen et al., 2010). Despite this, genotyping strategies for complex, inherited, disease-related phenotypes mostly employ univariate analyses, e.g., genome wide association. Such procedures most often identify isolated risk-related SNPs or loci, not the underlying biological pathways necessary to help guide the development of novel treatment approaches. This article focuses on the multivariate analysis strategy of parallel (i.e., simultaneous combination of SNP and neuroimage information) independent component analysis (p-ICA), which typically yields large clusters of functionally related SNPs statistically correlated with phenotype components, whose overall molecular biologic relevance is inferred subsequently using annotation software suites. Because this is a novel approach, whose details are relatively new to the field we summarize its underlying principles and address conceptual questions regarding interpretation of resulting data and provide practical illustrations of the method.

Contribution of large region joint associations to complex traits genetics

A polygenic model of inheritance, whereby hundreds or thousands of weakly associated variants contribute to a trait’s heritability, has been proposed to underlie the genetic architecture of complex traits. However, relatively few genetic variants have been positively identified so far and they collectively explain only a small fraction of the predicted heritability. We hypothesized that joint association of multiple weakly associated variants over large chromosomal regions contributes to complex traits variance. Confirmation of such regional associations can help identify new loci and lead to a better understanding of known ones. To test this hypothesis, we first characterized the ability of commonly used genetic association models to identify large region joint associations. Through theoretical derivation and simulation, we showed that multivariate linear models where multiple SNPs are included as independent predictors have the most favorable association profile. Based on these results, we tested for large region association with height in 3,740 European participants from the Health and Retirement Study (HRS) study. Adjusting for SNPs with known association with height, we demonstrated clustering of weak associations (p = 2x10^-4) in regions extending up to 433.0 Kb from known height loci. The contribution of regional associations to phenotypic variance was estimated at 0.172 (95% CI 0.063-0.279; p < 0.001), which compared favorably to 0.129 explained by known height variants. Conversely, we showed that suggestively associated regions are enriched for known height loci. To extend our findings to other traits, we also tested BMI, HDLc and CRP for large region associations, with consistent results for CRP. Our results demonstrate the presence of large region joint associations and suggest these can be used to pinpoint weakly associated SNPs.

It is widely accepted that genetics influences a broad range of human traits and diseases, yet only a few genetic variants are known to determine these traits and their impact is modest. In this report, we made the hypothesis that combining information from a large number of genetic variants would help better explain how they together contribute to traits such as height. To do so, we first had to select a proper method to integrate large numbers of genetic variants in a single test, here named “large region joint association”. Next, we tested our method on height in 3,740 European participants from the Health and Retirement Study. We showed that the contribution of regional associations to variation in height was 17.2%, as compared to the 12.9% explained by known genetic determinants of height. In other words, the joint effect of multiple genetic variants integrated together contributed to a substantial fraction of the genetics of height. These results are significant because they can help identify new genes or genetic regions associated with human traits or diseases. Conversely, these results can be used to better understand genes that we already know are associated. Furthermore, our results provide insights on how traits are genetically determined.

The gut microbiota and inflammatory noncommunicable diseases: associations and potentials for gut microbiota therapies

Rapid environmental transition and modern lifestyles are likely driving changes in the biodiversity of the human gut microbiota. With clear effects on physiologic, immunologic, and metabolic processes in human health, aberrations in the gut microbiome and intestinal homeostasis have the capacity for multisystem effects. Changes in microbial composition are implicated in the increasing propensity for a broad range of inflammatory diseases, such as allergic disease, asthma, inflammatory bowel disease (IBD), obesity, and associated noncommunicable diseases (NCDs). There are also suggestive implications for neurodevelopment and mental health. These diverse multisystem influences have sparked interest in strategies that might favorably modulate the gut microbiota to reduce the risk of many NCDs. For example, specific prebiotics promote favorable intestinal colonization, and their fermented products have anti-inflammatory properties. Specific probiotics also have immunomodulatory and metabolic effects. However, when evaluated in clinical trials, the effects are variable, preliminary, or limited in magnitude. Fecal microbiota transplantation is another emerging therapy that regulates inflammation in experimental models. In human subjects it has been successfully used in cases of Clostridium difficile infection and IBD, although controlled trials are lacking for IBD. Here we discuss relationships between gut colonization and inflammatory NCDs and gut microbiota modulation strategies for their treatment and prevention.

Identification of allelic heterogeneity at type-2 diabetes loci and impact on prediction

Although over 60 single nucleotide polymorphisms (SNPs) have been identified by meta-analysis of genome-wide association studies for type-2 diabetes (T2D) among individuals of European descent, much of the genetic variation remains unexplained. There are likely many more SNPs that contribute to variation in T2D risk, some of which may lie in the regions surrounding established SNPs - a phenomenon often referred to as allelic heterogeneity. Here, we use the summary statistics from the DIAGRAM consortium meta-analysis of T2D genome-wide association studies along with linkage disequilibrium patterns inferred from a large reference sample to identify novel SNPs associated with T2D surrounding each of the previously established risk loci. We then examine the extent to which the use of these additional SNPs improves prediction of T2D risk in an independent validation dataset. Our results suggest that multiple SNPs at each of 3 loci contribute to T2D susceptibility (TCF7L2, CDKN2A/B, and KCNQ1; p<5×10⁻⁸). Using a less stringent threshold (p<5×10⁻⁴), we identify 34 additional loci with multiple associated SNPs. The addition of these SNPs slightly improves T2D prediction compared to the use of only the respective lead SNPs, when assessed using an independent validation cohort. Our findings suggest that some currently established T2D risk loci likely harbor multiple polymorphisms which contribute independently and collectively to T2D risk. This opens a promising avenue for improving prediction of T2D, and for a better understanding of the genetic architecture of T2D.

Resequencing the susceptibility gene, ITGAM, identifies two functionally deleterious rare variants in systemic lupus erythematosus cases

Introduction

The majority of the genetic variance of systemic lupus erythematosus (SLE) remains unexplained by the common disease-common variant hypothesis. Rare variants, which are not detectable by genome-wide association studies because of their low frequencies, are predicted to explain part of this ”missing heritability.” However, recent studies identifying rare variants within known disease-susceptibility loci have failed to show genetic associations because of their extremely low frequencies, leading to the questioning of the contribution of rare variants to disease susceptibility. A common (minor allele frequency = 17.4% in cases) nonsynonymous coding variant rs1143679 (R77H) in ITGAM (CD11b), which forms half of the heterodimeric integrin receptor, complement receptor 3 (CR3), is robustly associated with SLE and has been shown to impair CR3-mediated phagocytosis.

Methods

We resequenced ITGAM in 73 SLE cases and identified two previously unidentified, case-specific nonsynonymous variants, F941V and G1145S. Both variants were genotyped in 2,107 and 949 additional SLE cases, respectively, to estimate their frequencies in a disease population. An in vitro model was used to assess the impact of F941V and G1145S, together with two nonsynonymous ITGAM polymorphisms, A858V (rs1143683) and M441T (rs11861251), on CR3-mediated phagocytosis. A paired two-tailed t test was used to compare the phagocytic capabilities of each variant with that of wild-type CR3.

Results

Both rare variants, F941V and G1145S, significantly impair CR3-mediated phagocytosis in an in vitro model (61% reduction, P = 0.006; 26% reduction, P = 0.0232). However, neither of the common variants, M441T and A858V, had an effect on phagocytosis. Neither rare variant was observed again in the genotyping of additional SLE cases, suggesting that there frequencies are extremely low.

Conclusions

Our results add further evidence to the functional importance of ITGAM in SLE pathogenesis through impaired phagocytosis. Additionally, this study provides a new example of the identification of rare variants in common-allele-associated loci, which, because of their extremely low frequencies, are not statistically associated. However, the demonstration of their functional effects adds support to their contribution to disease risk, and questions the current notion of dismissing the contribution of very rare variants on purely statistical analyses.

The effects of a MAP2K5 microRNA target site SNP on risk for anxiety and depressive disorders

Functional variants that contribute to genomewide association study (GWAS) signals are difficult to identify. MicroRNAs could contribute to some of these gene-trait relationships. We compiled a set of GWAS trait gene SNPs that were predicted to affect microRNA regulation of mRNA. Trait associations were tested in a sample of 6725 European-American (EA) and African-American (AA) subjects that were interviewed using the polydiagnostic SSADDA to diagnose major psychiatric disorders. A predicted miR-330-3p target site SNP (rs41305272) in mitogen-activated protein kinase kinase 5 (MAP2K5) mRNA was in LD (d' = 1.0, r(2) = 0.02) with a reported GWAS-identified variant for restless legs syndrome (RLS), a disorder frequently comorbid with anxiety and depression, possibly because of a shared pathophysiology. We examined the SNP's association with mood and anxiety-related disorders. Rs41305272 was associated with agoraphobia (Ag) in EAs (odds ratio [OR] = 1.95, P = 0.007; 195 cases) and AAs (OR = 3.2, P = 0.03; 148 cases) and major depressive disorder (MDD) in AAs (OR = 2.64, P = 0.01; 427 cases), but not EAs (465 cases). Rs41305272*T carrier frequency was correlated with the number of anxiety and depressive disorders diagnosed per subject. RLS was not evaluated in our subjects. Predicted miR-330-3p target genes were enriched in pathways relevant to psychiatric disorders. These findings suggest that microRNA target site information may be useful in the analysis of GWAS signals for complex traits. MiR-330-3p and MAP2K5 are potentially important contributors to mood and anxiety-related traits. With support from additional studies, these findings could add to the large number of risk genes identified through association to medical disorders that have primary psychiatric effects.

The architecture of risk for type 2 diabetes: understanding Asia in the context of global findings

The prevalence of Type 2 diabetes is rising rapidly in both developed and developing countries. Asia is developing as the epicentre of the escalating pandemic, reflecting rapid transitions in demography, migration, diet, and lifestyle patterns. The effective management of Type 2 diabetes in Asia may be complicated by differences in prevalence, risk factor profiles, genetic risk allele frequencies, and gene-environment interactions between different Asian countries, and between Asian and other continental populations. To reduce the worldwide burden of T2D, it will be important to understand the architecture of T2D susceptibility both within and between populations. This review will provide an overview of known genetic and nongenetic risk factors for T2D, placing the results from Asian studies in the context of broader global research. Given recent evidence from large-scale genetic studies of T2D, we place special emphasis on emerging knowledge about the genetic architecture of T2D and the potential contribution of genetic effects to population differences in risk.

Quantifying missing heritability at known GWAS loci

Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain more heritability than GWAS-associated SNPs on average (). For some diseases, this increase was individually significant: for Multiple Sclerosis (MS) () and for Crohn's Disease (CD) (); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained more MS heritability than known MS SNPs () and more CD heritability than known CD SNPs (), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with more heritability from all SNPs at GWAS loci () and more heritability from all autoimmune disease loci () compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.

Heritable diseases have an unknown underlying “genetic architecture” that defines the distribution of effect-sizes for disease-causing mutations. Understanding this genetic architecture is an important first step in designing disease-mapping studies, and many theories have been developed on the nature of this distribution. Here, we evaluate the hypothesis that additional heritable variation lies at previously known associated loci but is not fully explained by the single most associated marker. We develop methods based on variance-components analysis to quantify this type of “local” heritability, demonstrating that standard strategies can be falsely inflated or deflated due to correlation between neighboring markers and propose a robust adjustment. In analysis of nine common diseases we find a significant average increase of local heritability, consistent with multiple common causal variants at an average locus. Intriguingly, for autoimmune diseases we also observe significant local heritability in loci not associated with the specific disease but with other autoimmune diseases, implying a highly correlated underlying disease architecture. These findings have important implications to the design of future studies and our general understanding of common disease.

A fast multilocus test with adaptive SNP selection for large-scale genetic-association studies

As increasing evidence suggests that multiple correlated genetic variants could jointly influence the outcome, a multilocus test that aggregates association evidence across multiple genetic markers in a considered gene or a genomic region may be more powerful than a single-marker test for detecting susceptibility loci. We propose a multilocus test, AdaJoint, which adopts a variable selection procedure to identify a subset of genetic markers that jointly show the strongest association signal, and defines the test statistic based on the selected genetic markers. The P-value from the AdaJoint test is evaluated by a computationally efficient algorithm that effectively adjusts for multiple-comparison, and is hundreds of times faster than the standard permutation method. Simulation studies demonstrate that AdaJoint has the most robust performance among several commonly used multilocus tests. We perform multilocus analysis of over 26,000 genes/regions on two genome-wide association studies of pancreatic cancer. Compared with its competitors, AdaJoint identifies a much stronger association between the gene CLPTM1L and pancreatic cancer risk (6.0 × 10(-8)), with the signal optimally captured by two correlated single-nucleotide polymorphisms (SNPs). Finally, we show AdaJoint as a powerful tool for mapping cis-regulating methylation quantitative trait loci on normal breast tissues, and find many CpG sites whose methylation levels are jointly regulated by multiple SNPs nearby.

Three SNPs in chromosome 11q23.3 are independently associated with systemic lupus erythematosus in Asians

Systemic lupus erythematosus (SLE) has a complex etiology and is affected by both genetic and environmental factors. Although more than 40 loci have shown robust association with SLE, the details of these loci, such as the independent contributors and the genes involved, are still unclear. In this study, we performed meta-analysis of two existing genome-wide association studies (GWASs) on Chinese Han populations from Hong Kong and Anhui, China, and followed the findings by further replication on three additional Chinese and Thailand cohorts with a total of 4254 cases and 6262 controls matched geographically and ethnically. We discovered multiple susceptibility variants for SLE in the 11q23.3 region, including variants in/near PHLDB1 (rs11603023, P(_combined) = 1.25E-08, OR = 1.20), DDX6 (rs638893, P(_combined) = 5.19E-07, OR = 1.22) and CXCR5 (rs10892301, P(_combined) = 2.51E-08, OR = 0.85). Genetic contributions from the newly identified variants were all independent of SNP rs4639966, whose association was reported from the previous GWAS. In addition, the three newly identified variants all showed independent association with the disease through modeling by both stepwise and conditional logistic regression. The presence of multiple independent variants in this region emphasizes its role in SLE susceptibility, and also hints the possibility that distinct biological mechanisms might be involved in the disease involving this genomic region.

Postgwas: advanced GWAS interpretation in R

We present a comprehensive toolkit for post-processing, visualization and advanced analysis of GWAS results. In the spirit of comparable tools for gene-expression analysis, we attempt to unify and simplify several procedures that are essential for the interpretation of GWAS results. This includes the generation of advanced Manhattan and regional association plots including rare variant display as well as novel interaction network analysis tools for the investigation of systems-biology aspects. Our package supports virtually all model organisms and represents the first cohesive implementation of such tools for the popular language R. Previous software of that range is dispersed over a wide range of platforms and mostly not adaptable for custom work pipelines. We demonstrate the utility of this package by providing an example workflow on a publicly available dataset.

Mapping of immune-mediated disease genes

Genetic studies in immune-mediated diseases have yielded a large number of disease-associated loci. Here we review the progress being made in 12 such diseases, for which 199 independently associated non-HLA loci have been identified by genome-wide association studies since 2007. It is striking that many of the loci are not unique to a single disease but shared between different immune-mediated diseases. The challenge now is to understand how the unique and shared genetic factors can provide insight into the underlying disease biology. We annotated disease-associated variants using the Encyclopedia of DNA Elements (ENCODE) database and demonstrate that, of the predisposing disease variants, the majority have the potential to be regulatory. We also demonstrate that many of these variants affect the expression of nearby genes. Furthermore, we summarize results from the Immunochip, a custom array, which allows a detailed comparison between five of the diseases that have so far been analyzed using this platform.