Interpretation of association signals and identification of causal variants from genome-wide association studies

Excess of rare noncoding variants in several type 2 diabetes candidate genes among Asian Indian families

BACKGROUND

Type 2 diabetes (T2D) etiology is highly complex due to its multiple roots of origin. Polygenic risk scores (PRS) based on genome-wide association studies (GWAS) can partially explain T2D risk. Asian Indian people have up to six times higher risk of developing T2D than European people, and underlying causes of this disparity are unknown.

METHODS

We have performed targeted sequencing of ten T2D GWAS/candidate regions using endogamous Punjabi Sikh families and replication studies using unrelated Sikh people and families from three other Indian endogamous ethnic groups (EEGs).

RESULTS

We detect rare and ultra-rare variants (RVs) in KCNJ11-ABCC8 and HNF4A (MODY genes) cosegregated with late-onset T2D. We also identify RV enrichment in two new genes, SLC38A11 and ANPEP, associated with T2D. Gene-burden analysis reveals the highest RV burden contributed by HNF4A (p = 0.0003), followed by KCNJ11/ABCC8 (p = 0.0061) and SLC38A11 (p = 0.03). Some RVs detected in Sikh people are also found in Agarwals from Jaipur, both from Northern India, but were monomorphic in other two EEGs from South Indian people. Despite carrying a high burden of T2D and RVs, most families have a significantly lower burden of PRS. Functional studies show that an intronic regulatory variant (RV) in ABCC8 affects the binding of Pax4 and NF-kB transcription factors, influencing downstream gene regulation.

CONCLUSIONS

The high burden of T2D in these families may stem from the enrichment of noncoding RVs in a small number of major known genes (including MODY genes) with oligogenic inheritance alongside RVs from genes associated with polygenic susceptibility. These findings highlight the need to conduct deeper evaluations of families from non-European ancestries to identify potential novel therapeutics and implement preventative strategies.

Multi-omics approaches for understanding gene-environment interactions in noncommunicable diseases: techniques, translation, and equity issues

Non-communicable diseases (NCDs) such as cardiovascular diseases, chronic respiratory diseases, cancers, diabetes, and mental health disorders pose a significant global health challenge, accounting for the majority of fatalities and disability-adjusted life years worldwide. These diseases arise from the complex interactions between genetic, behavioral, and environmental factors, necessitating a thorough understanding of these dynamics to identify effective diagnostic strategies and interventions. Although recent advances in multi-omics technologies have greatly enhanced our ability to explore these interactions, several challenges remain. These challenges include the inherent complexity and heterogeneity of multi-omic datasets, limitations in analytical approaches, and severe underrepresentation of non-European genetic ancestries in most omics datasets, which restricts the generalizability of findings and exacerbates health disparities. This scoping review evaluates the global landscape of multi-omics data related to NCDs from 2000 to 2024, focusing on recent advancements in multi-omics data integration, translational applications, and equity considerations. We highlight the need for standardized protocols, harmonized data-sharing policies, and advanced approaches such as artificial intelligence/machine learning to integrate multi-omics data and study gene-environment interactions. We also explore challenges and opportunities in translating insights from gene-environment (GxE) research into precision medicine strategies. We underscore the potential of global multi-omics research in advancing our understanding of NCDs and enhancing patient outcomes across diverse and underserved populations, emphasizing the need for equity and fairness-centered research and strategic investments to build local capacities in underrepresented populations and regions.

The HDAC2-MTA3 interaction induces nonsmall cell lung cancer cell migration and invasion by targeting c-Myc and cyclin D1

Genome-wide association studies have identified numerous single-nucleotide polymorphisms (SNPs) associated with lung cancer; however, the functions of histone deacetylase 2 (HDAC2) rs13213007 and HDAC2 in nonsmall cell lung cancer (NSCLC) remain unclear. Here we identified HDAC2 rs13213007 as a risk SNP and showed that HDAC2 was upregulated in both peripheral blood mononuclear cells (PBMCs) and NSCLC tissues with the rs13213007 A/A genotype compared with those with the rs13213007 G/G or G/A genotype. Patient clinical data indicated strong associations between rs13213007 genotype and N classification. Immunohistochemical staining confirmed that higher expression of HDAC2 was associated with NSCLC progression. Furthermore, we generated 293T cells with the rs13213007 A/A genotype using CRISPR (clustered regularly interspaced short palindromic repeats)/Cas9 gene editing. Chromatin immunoprecipitation sequencing followed by motif analysis showed that HDAC2 can bind to c-Myc in rs13213007 A/A 293T cells. Cell Counting Kit-8, colony formation, wound-healing, and Transwell assays revealed that HDAC2 upregulates c-Myc and cyclin D1 expression and promotes NSCLC cell proliferation, migration, and invasion. Co-immunoprecipitation, quantitative reverse transcription-polymerase chain reaction, and western blot analysis assays showed that MTA3 interacts with HDAC2, decreases HDAC2 expression, and rescues the migration and invasion abilities of NSCLC cells. Taken together, these findings identify HDAC2 as a potential therapeutic biomarker in NSCLC.

Genome-wide Association Study Identifies Novel Risk Loci for Apical Periodontitis

INTRODUCTION

Apical periodontitis (AP) is a common consequence of root canal infection leading to periapical bone resorption. Microbial and host genetic factors and their interactions have been shown to play a role in AP development and progression. Variations in a few genes have been reported in association with AP; however, the lack of genome-wide studies has hindered progress in understanding the molecular mechanisms involved. Here, we report the first genome-wide association study of AP in a large and well-characterized population.

METHODS

Male and female adults (n = 932) presenting with deep caries and AP (cases), or deep caries without AP (controls) were included. Genotyping was performed using the Illumina Expanded Multi-Ethnic Genotyping Array (MEGA). Single-variant association testing was performed adjusting for sex and 5 principal components. Subphenotype association testing, analyses of genetically regulated gene expression, polygenic risk score, and phenome-wide association (PheWAS) analyses were also conducted.

RESULTS

Eight loci reached near genome-wide significant association with AP (P < 5 × 10-6); gene-focused analyses replicated 3 previously reported associations (P < 8.9 × 10-5). Sex-specific and subphenotype-specific analyses revealed additional significant associations with variants genome-wide. Functionally oriented gene-based analyses revealed 8 genes significantly associated with AP (P < 5 × 10-5), and PheWAS analysis revealed 33 phecodes associated with AP risk score (P < 3.08 × 10-5).

CONCLUSIONS

This study identified novel genes/loci contributing to AP and specific contributions to AP risk in men and women. Importantly, we identified additional systemic conditions significantly associated with AP risk. Our findings provide strong evidence for host-mediated effects on AP susceptibility.

Fine mapping spatiotemporal mechanisms of genetic variants underlying cardiac traits and disease

The causal variants and genes underlying thousands of cardiac GWAS signals have yet to be identified. Here, we leverage spatiotemporal information on 966 RNA-seq cardiac samples and perform an expression quantitative trait locus (eQTL) analysis detecting eQTLs considering both eGenes and eIsoforms. We identify 2,578 eQTLs associated with a specific developmental stage-, tissue- and/or cell type. Colocalization between eQTL and GWAS signals of five cardiac traits identified variants with high posterior probabilities for being causal in 210 GWAS loci. Pulse pressure GWAS loci are enriched for colocalization with fetal- and smooth muscle- eQTLs; pulse rate with adult- and cardiac muscle- eQTLs; and atrial fibrillation with cardiac muscle- eQTLs. Fine mapping identifies 79 credible sets with five or fewer SNPs, of which 15 were associated with spatiotemporal eQTLs. Our study shows that many cardiac GWAS variants impact traits and disease in a developmental stage-, tissue- and/or cell type-specific fashion.

Genome-wide association study identifies novel risk loci for apical periodontitis

Apical periodontitis (AP) is a common consequence of root canal infection leading to periapical bone resorption. Microbial and host genetic factors, and their interactions, have been shown to play a role in AP development and progression. Variations in a few genes have been reported in association with AP, however, the lack of genome-wide studies has hindered progress in understanding the mechanisms involved in AP. Here, we report the first genome-wide association study of AP in a well-characterized population. Male and female adults (n=932) presenting with deep caries with AP (cases) or without AP (controls) were included. Genotyping was performed using the Illumina Expanded Multi-Ethnic Genotyping Array. Single-variant association testing was performed adjusting for sex and five principal components. Subphenotype association testing, analyses of genetically regulated gene expression, polygenic risk score and phenome-wide association (PheWAS) analyses were also performed. Eight loci reached near-genome-wide significant association with AP (p < 5 x 10-6); gene-focused analyses replicated three previously reported associations (p < 8.9 x 10-5). Sex-specific and subphenotype analyses revealed additional significant associations with variants genome-wide. Functionally oriented gene-based analyses revealed eight genes significantly associated with AP (p < 5 x 10-5), and PheWAS analysis revealed 33 phecodes associated with AP risk score (p < 3.08 x 10-5). This study identified novel genes/loci contributing to AP and revealed specific contributions to AP risk in males and females. Importantly, we identified additional systemic conditions significantly associated with AP risk. Our findings provide strong evidence for host-mediated effects on AP susceptibility.

Transhumanist Genetic Enhancement: Creation of a 'New Man' Through Technological Innovation

The transhumanist project of reshaping human beings by promoting their improvement through technological innovations has a broad agenda. This study focuses on the enhancement of the human organism through genetic modification techniques. Transhumanism values and a discussion of their philosophical background provide a framework to understand its ideals. Genetics and ethics are employed to assess the claims of the transhumanist program of human enhancement. A succinct description of central concepts in genetics and an explanation of current techniques to edit the human genome serve to assess the capabilities and limitations of editing techniques. Potential benefits and liabilities of human enhancement through genome editing are discussed to appraise its feasibility. Ethical considerations of genome editing inform a reflection on the implications of introducing heritable changes in the genome of individuals. It is concluded that the transhumanist program is underpinned by a large number of hypotheses rather than by sufficient evidence.

Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction

Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction.

Genetic and Non-genetic Determinants of Cardiovascular Disease in South Asians

South Asians (SAs), people from the Indian subcontinent (e.g., India, Pakistan, Bangladesh, Sri Lanka, and Nepal) have a higher prevalence of cardiovascular disease (CVD) and suffer from a greater risk of CVD-associated mortality compared to other global populations. These problems are compounded by the alterations in lifestyles due to urbanization and changing cultural, social, economic, and political environments. Current methods of CV risk prediction are based on white populations that under-estimate the CVD risk in SAs. Prospective studies are required to obtain actual CVD morbidity/mortality rates so that comparisons between predicted CVD risk can be made with actual events. Overwhelming data support a strong influence of genetic factors. Genome-Wide Association Studies (GWAS) serve as a starting point for future genetic and functional studies since the mechanisms of action by which these associated loci influence CVD is still unclear. It is difficult to predict the potential implication of these findings in clinical settings. This review provides a systematic assessment of the risk factors, genetics, and environmental causes of CV health disparity in SAs, and highlights progress made in clinical and genomics discoveries in the rapidly evolving field, which has the potential to show clinical relevance in the near future.

FKBP-CaN-NFAT pathway polymorphisms selected by in silico biological function prediction are associated with tacrolimus efficacy in renal transplant patients

AIM

The aim of the present study was to investigate the potential effects of genetic variations in the FKBP-CaN-NFAT pathway on clinical events associated with tacrolimus efficacy in Chinese renal transplant patients.

METHODS

One hundred and forty Chinese renal transplant patients of Han ethnicity with over five years of follow-up were enrolled in our study. A pool of single nucleotide polymorphisms (SNPs) (1284 SNPs) was extracted from the Ensembl database according to chromosomal regions of the candidate genes. Next, 109 SNPs were screened out from this pool using multiple bioinformatics tools for subsequent genotyping using the MALDI-TOF-MS method. The associations of these candidate SNPs with acute rejection, nephrotoxicity, pneumonia and post-transplant estimated glomerular filtration rate (eGFR) were explored.

RESULTS

Fourty-four SNPs were found to be associated with tacrolimus-related clinical drug response. Specifically, eight SNPs were associated with the incidence of biopsy-proven acute rejection, four SNPs were associated with the rate of nephrotoxicity, 16 SNPs were correlated with the onset of pneumonia, and 26 SNPs were found to significantly influence post-transplant eGFR trend. An elaborate scoring system was implemented to prioritize the validation of these potentially causal SNPs. In particular, NFATC2 rs150348438 (G>T) performed well during integrative scoring (P_total=23.8) and was significantly associated with the occurrence of pneumonia (P = 0.0035, HR=0.91, 95% CI=0.85-0.97) and post-transplant eGFR levels (P = 0.000003).

CONCLUSIONS

NFATC2 rs150348438, rs6013219, rs1052653, and NFATC1 rs754093, ranking high in scoring, significantly affected the post-transplant eGFR and the incidence of pneumonia, acute rejection, and nephrotoxicity in renal transplant patients taking tacrolimus. Those SNPs may alter the expression and regulation of FKBP-CaN-NFAT pathway by influencing transcription regulation, mature mRNA degradation and RNA splicing, or protein coding. Critical SNPs of high ranking may serve as PD-associated pharmacogenetic biomarkers indicating individual response variability of TAC, and thus aid the clinical management of renal transplant patients.

Exome Array Analysis of Early-Onset Ischemic Stroke

BACKGROUND AND PURPOSE

The genetic contribution to ischemic stroke may include rare- or low-frequency variants of high-penetrance and large-effect sizes. Analyses focusing on early-onset disease, an extreme-phenotype, and on the exome, the protein-coding portion of genes, may increase the likelihood of identifying such rare functional variants. To evaluate this hypothesis, we implemented a 2-stage discovery and replication design, and then addressed whether the identified variants also associated with older-onset disease.

METHODS

Discovery was performed in UMD-GEOS Study (University of Maryland-Genetics of Early-Onset Stroke), a biracial population-based study of first-ever ischemic stroke cases 15 to 49 years of age (n=723) and nonstroke controls (n=726). All participants had prior GWAS (Genome Wide Association Study) and underwent Illumina exome-chip genotyping. Logistic-regression was performed to test single-variant associations with all-ischemic stroke and TOAST (Trial of ORG 10172 in Acute Stroke Treatment) subtypes in Whites and Blacks. Population level results were combined using meta-analysis. Gene-based aggregation testing and meta-analysis were performed using seqMeta. Covariates included age and gender, and principal-components for population structure. Pathway analyses were performed across all nominally associated genes for each stroke outcome. Replication was attempted through lookups in a previously reported meta-analysis of early-onset stroke and a large-scale stroke genetics study consisting of primarily older-onset cases.

RESULTS

Gene burden tests identified a significant association with NAT10 in small-vessel stroke (P=3.79×10-6). Pathway analysis of the top 517 genes (P<0.05) from the gene-based analysis of small-vessel stroke identified several signaling and metabolism-related pathways related to neurotransmitter, neurodevelopmental notch-signaling, and lipid/glucose metabolism. While no individual SNPs reached chip-wide significance (P<2.05×10-7), several were near, including an intronic variant in LEXM (rs7549251; P=4.08×10-7) and an exonic variant in TRAPPC11 (rs67383011; P=5.19×10-6).

CONCLUSIONS

Exome-based analysis in the setting of early-onset stroke is a promising strategy for identifying novel genetic risk variants, loci, and pathways.

Systems Genetics for Mechanistic Discovery in Heart Diseases

Cardiovascular diseases are the leading cause of death worldwide. Complex diseases with highly heterogenous disease progression among patient populations, cardiovascular diseases feature multifactorial contributions from both genetic and environmental stressors. Despite significant effort utilizing multiple approaches from molecular biology to genome-wide association studies, the genetic landscape of cardiovascular diseases, particularly for the nonfamilial forms of heart failure, is still poorly understood. In the past decade, systems-level approaches based on omics technologies have become an important approach for the study of complex traits in large populations. These advances create opportunities to integrate genetic variation with other biological layers to identify and prioritize candidate genes, understand pathogenic pathways, and elucidate gene-gene and gene-environment interactions. In this review, we will highlight some of the recent progress made using systems genetics approaches to uncover novel mechanisms and molecular bases of cardiovascular pathophysiological manifestations. The key technology and data analysis platforms necessary to implement systems genetics will be described, and the current major challenges and future directions will also be discussed. For complex cardiovascular diseases, such as heart failure, systems genetics represents a powerful strategy to obtain mechanistic insights and to develop individualized diagnostic and therapeutic regiments, paving the way for precision cardiovascular medicine.

Convolutional neural network model to predict causal risk factors that share complex regulatory features

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.

An evaluation of noncoding genome annotation tools through enrichment analysis of 15 genome-wide association studies

Functionally annotating genetic variations is an essential yet challenging topic in human genetics research. As large consortia including ENCODE and Roadmap Epigenomics Project continue to generate high-throughput transcriptomic and epigenomic data, many computational frameworks have been developed to integrate these experimental data to predict functionality of genetic variations in both protein-coding and noncoding regions. Here, we compare a number of recently developed annotation frameworks for noncoding regions through enrichment analysis on genome-wide association studies (GWASs). We also compare several different strategies to quantify enrichment using GWAS summary statistics. Our analyses highlight the importance of jointly modeling context-specific annotations with genome-wide data in providing statistically powerful and biologically interpretable enrichment for complex disease associations. Our findings provide insights into when and how computational genome annotations may benefit future complex disease studies on the genome-wide scale.

SNP rs17079281 decreases lung cancer risk through creating an YY1-binding site to suppress DCBLD1 expression

Genome-wide association studies (GWAS) have identified numerous genetic variants that are associated with lung cancer risk, but the biological mechanisms underlying these associations remain largely unknown. Here we investigated the functional relevance of a genetic region in 6q22.2 which was identified to be associated with lung cancer risk in our previous GWAS. We performed linkage disequilibrium (LD) analysis and bioinformatic prediction to screen functional SNPs linked to a tagSNP in 6q22.2 loci, followed by two case-control studies and a meta-analysis with 4403 cases and 5336 controls to identify if these functional SNPs were associated with lung cancer risk. A novel SNP rs17079281 in the DCBLD1 promoter was identified to be associated with lung cancer risk in Chinese populations. Compared with those with C allele, patients with T allele had lower risk of adenocarcinoma (adjusted OR = 0.86; 95% CI: 0.80–0.92), but not squamous cell carcinoma (adjusted OR = 0.99; 95% CI: 0.91–1.10), and patients with the C/T or T/T genotype had lower levels of DCBLD1 expression than those with C/C genotype in lung adenocarcinoma tissues. We performed functional assays to characterize its biological relevance. The results showed that the T allele of rs17079281 had higher binding affinity to transcription factor YY1 than the C allele, which suppressed DCBLD1 expression. DCBLD1 behaved like an oncogene, promoting tumor growth by influencing cell cycle progression. These findings suggest that the functional variant rs17079281C>T decreased lung adenocarcinoma risk by creating an YY1-binding site to suppress DCBLD1 expression, which may serve as a biomarker for assessing lung cancer susceptibility.

Benefits and limitations of genome-wide association studies

Genome-wide association studies (GWAS) involve testing genetic variants across the genomes of many individuals to identify genotype-phenotype associations. GWAS have revolutionized the field of complex disease genetics over the past decade, providing numerous compelling associations for human complex traits and diseases. Despite clear successes in identifying novel disease susceptibility genes and biological pathways and in translating these findings into clinical care, GWAS have not been without controversy. Prominent criticisms include concerns that GWAS will eventually implicate the entire genome in disease predisposition and that most association signals reflect variants and genes with no direct biological relevance to disease. In this Review, we comprehensively assess the benefits and limitations of GWAS in human populations and discuss the relevance of performing more GWAS.

A clinal polymorphism in the insulin signaling transcription factor foxo contributes to life-history adaptation in Drosophila

A fundamental aim of adaptation genomics is to identify polymorphisms that underpin variation in fitness traits. In Drosophila melanogaster, latitudinal life-history clines exist on multiple continents and make an excellent system for dissecting the genetics of adaptation. We have previously identified numerous clinal single-nucleotide polymorphism in insulin/insulin-like growth factor signaling (IIS), a pathway known from mutant studies to affect life history. However, the effects of natural variants in this pathway remain poorly understood. Here we investigate how two clinal alternative alleles at foxo, a transcriptional effector of IIS, affect fitness components (viability, size, starvation resistance, fat content). We assessed this polymorphism from the North American cline by reconstituting outbred populations, fixed for either the low- or high-latitude allele, from inbred DGRP lines. Because diet and temperature modulate IIS, we phenotyped alleles across two temperatures (18°C, 25°C) and two diets differing in sugar source and content. Consistent with clinal expectations, the high-latitude allele conferred larger body size and reduced wing loading. Alleles also differed in starvation resistance and expression of insulin-like receptor, a transcriptional target of FOXO. Allelic reaction norms were mostly parallel, with few GxE interactions. Together, our results suggest that variation in IIS makes a major contribution to clinal life-history adaptation.

A local score approach improves GWAS resolution and detects minor QTL: application to Medicago truncatula quantitative disease resistance to multiple Aphanomyces euteiches isolates

Quantitative trait loci (QTL) with small effects, which are pervasive in quantitative phenotypic variation, are difficult to detect in genome-wide association studies (GWAS). To improve their detection, we propose to use a local score approach that accounts for the surrounding signal due to linkage disequilibrium, by accumulating association signals from contiguous single markers. Simulations revealed that, in a GWAS context with high marker density, the local score approach outperforms single SNP p-value-based tests for detecting minor QTL (heritability of 5-10%) and is competitive with regard to alternative methods, which also aggregate p-values. Using more than five million SNPs, this approach was applied to identify loci involved in Quantitative Disease Resistance (QDR) to different isolates of the plant root rot pathogen Aphanomyces euteiches, from a GWAS performed on a collection of 174 accessions of the model legume Medicago truncatula. We refined the position of a previously reported major locus, underlying MYB/NB-ARC/tyrosine kinase candidate genes conferring resistance to two closely related A. euteiches isolates belonging to pea pathotype I. We also discovered a diversity of minor resistance QTL, not detected using p-value-based tests, some of which being putatively shared in response to pea (pathotype I and III) and/or alfalfa (race 1 and 2) isolates. Candidate genes underlying these QTL suggest pathogen effector recognition and plant proteasome as key functions associated with M. truncatula resistance to A. euteiches. GWAS on any organism can benefit from the local score approach to uncover many weak-effect QTL.

Patients with SATB2-associated syndrome exhibiting multiple odontomas

Special AT-rich sequence-binding protein 2 (SATB2)-associated syndrome (SAS) is characterized by alterations of SATB2. Its clinical features include intellectual disability and craniofacial abnormalities, such as cleft palate, dysmorphic features, and dental abnormalities. Here, we describe three previously undiagnosed, unrelated patients with SAS who exhibited dental abnormalities, including multiple odontomas. Although isolated odontomas are common, multiple odontomas are rare. Individuals in families 1 and 3 underwent whole-exome sequencing. Patient 2 and parents underwent targeted amplicon sequencing. On the basis of the hg19/GRCh37 reference and the RefSeq mRNA NM_001172517, respective heterozygous mutations were found and validated in Patients 1, 2, and 3: a splice-site mutation (chr2:g.200137396C > T, c.1741-1G > A), a nonsense mutation (chr2:g.200213750G > A, c.847C > T, p.R283*), and a frame-shift mutations (chr2:g.200188589_200188590del, c.1478_1479del, p.Q493Rfs*19). All mutations occurred de novo. The mutations in Patients 1 and 3 were novel; the mutation in Patient 2 has been described previously. Tooth mesenchymal cells derived from Patient 2 showed diminished SATB2 expression. Multiple odontomas were evident in the patients in this report; however, this has not been recognized previously as a SAS-associated phenotype. We propose that multiple odontomas be considered as an occasional manifestation of SAS.

The Emerging Immunogenetic Architecture of Schizophrenia

Schizophrenia is a severe psychiatric disorder of complex etiology. Immune processes have long been proposed to contribute to the development of schizophrenia, and accumulating evidence supports immune involvement in at least a subset of cases. In recent years, large-scale genetic studies have provided new insights into the role of the immune system in this disease. Here, we provide an overview of the immunogenetic architecture of schizophrenia based on findings from genome-wide association studies (GWAS). First, we review individual immune loci identified in secondary analyses of GWAS, which implicate over 30 genes expressed in both immune and brain cells. The function of the proteins encoded by these immune candidates highlight the role of the complement system, along with regulation of apoptosis in both immune and neuronal cells. Next, we review hypothesis-free pathway analyses which have so far been inconclusive with respect to identifying immune pathways involved in schizophrenia. Finally, we explore the genetic overlap between schizophrenia and immune-mediated diseases. Although there have been some inconsistencies across studies, genome-wide pleiotropy has been reported between schizophrenia and Crohn's disease, multiple sclerosis, rheumatoid arthritis, systemic lupus erythematosus, type 1 diabetes, and ulcerative colitis. Overall, there are multiple lines of evidence supporting the role of immune genes in schizophrenia. Current evidence suggests that specific immune pathways are involved-likely those with dual functions in the central nervous system. Future studies focused on further elucidating the relevant pathways hold the potential to identify novel biomarkers and therapeutic targets for schizophrenia.

Novel functional variants at the GWAS-implicated loci might confer risk to major depressive disorder, bipolar affective disorder and schizophrenia

BACKGROUND

A challenge of understanding the mechanisms underlying cognition including neurodevelopmental and neuropsychiatric disorders is mainly given by the potential severity of cognitive disorders for the quality of life and their prevalence. However, the field has been focused predominantly on protein coding variation until recently. Given the importance of tightly controlled gene expression for normal brain function, the goal of the study was to assess the functional variation including non-coding variation in human genome that is likely to play an important role in cognitive functions. To this end, we organized and utilized available genome-wide datasets from genomic, transcriptomic and association studies into a comprehensive data corpus. We focused on genomic regions that are enriched in regulatory activity-overlapping transcriptional factor binding regions and repurpose our data collection especially for identification of the regulatory SNPs (rSNPs) that showed associations both with allele-specific binding and allele-specific expression. We matched these rSNPs to the nearby and distant targeted genes and then selected the variants that could implicate the etiology of cognitive disorders according to Genome-Wide Association Studies (GWAS). Next, we use DeSeq 2.0 package to test the differences in the expression of the certain targeted genes between the controls and the patients that were diagnosed bipolar affective disorder and schizophrenia. Finally, we assess the potential biological role for identified drivers of cognition using DAVID and GeneMANIA.

RESULTS

As a result, we selected fourteen regulatory SNPs locating within the loci, implicated from GWAS for cognitive disorders with six of the variants unreported previously. Grouping of the targeted genes according to biological functions revealed the involvement of processes such as 'posttranscriptional regulation of gene expression', 'neuron differentiation', 'neuron projection development', 'regulation of cell cycle process' and 'protein catabolic processes'. We identified four rSNP-targeted genes that showed differential expression between patient and control groups depending on brain region: NRAS-in schizophrenia cohort, CDC25B, DDX21 and NUCKS1-in bipolar disorder cohort.

CONCLUSIONS

Overall, our findings are likely to provide the keys for unraveling the mechanisms that underlie cognitive functions including major depressive disorder, bipolar disorder and schizophrenia etiopathogenesis.

Fine-mapping of 98 obesity loci in Mexican children

BACKGROUND/OBJECTIVES

Mexico has one of the highest prevalence of childhood obesity in the world. Genome-wide association studies (GWAS) for obesity have identified multiple single-nucleotide polymorphisms (SNPs) in populations of European, East Asian, and African descent. The contribution of these loci to obesity in Mexican children is unclear. We assessed the transferability of 98 obesity loci in Mexican children and fine-mapped the association signals.

SUBJECTS/METHODS

The study included 405 and 390 Mexican children with normal weight and obesity. Participants were genotyped with a genome-wide dense SNP array designed for Latino populations, allowing for the analysis of GWAS index SNPs as well as fine-mapping SNPs, totaling 750 SNPs covering 98 loci. Two genetic risk scores (GRS) were constructed: a "discovery GRS" and a "best-associated GRS", representing the number of effect alleles at the GWAS index SNPs and at the best-associated SNPs after fine-mapping for each subject.

RESULTS

Seventeen obesity loci were significantly associated with obesity, and five had fine-mapping SNPs significantly better associated with obesity than their corresponding GWAS index SNPs in Mexican children. Six obesity-associated SNPs significantly departed from additive to dominant (N = 5) or recessive (N = 1) models, and a significant interaction was found between rs274609 (TNNI3K) and rs1010553 (ITIH4) on childhood obesity risk. The best-associated GRS was significantly more associated with childhood obesity (OR = 1.21 per additional risk allele [95%CI:1.17-1.25], P = 4.8 × 10^-25) than the discovery GRS (OR = 1.05 per additional risk allele [95%CI:1.02-1.08], P = 8.0 × 10^-4), and was also associated with waist-to-hip ratio, fasting glucose, fasting insulin and triglyceride levels, the association being mediated by obesity. An overall depletion of obesity risk alleles was observed in Mexican children with normal weight when compared to GWAS discovery populations.

CONCLUSIONS

Our study indicates a partial transferability of GWAS obesity loci in Mexican children, and supports the pertinence of post-GWAS fine-mapping experiments in the admixed Mexican population.

Regional evaluation of childhood acute lymphoblastic leukemia genetic susceptibility loci among Japanese

Genome-wide association studies (GWAS) performed mostly in populations of European and Hispanic ancestry have confirmed an inherited genetic basis for childhood acute lymphoblastic leukemia (ALL), but these associations are less clear in other races/ethnicities. DNA samples from ALL patients (aged 0–19 years) previously enrolled onto a Tokyo Children’s Cancer Study Group trial were collected during 2013–2015, and underwent single nucleotide polymorphism (SNP) microarray genotyping resulting in 527 B-cell ALL for analysis. Cases and control data for 3,882 samples from the Nagahama Study Group and Aichi Cancer Center Study were combined, and association analyses across 10 previous GWAS-identified regions were performed after targeted SNP imputation. Linkage disequilibrium (LD) patterns in Japanese and other populations were evaluated using the varLD score based on 1000 Genomes data. Risk associations for ARID5B (rs10821936, OR = 1.84, P = 6 × 10⁻¹⁷) and PIP4K2A (rs7088318, OR = 0.76, P = 2 × 10⁻⁴) directly transferred to Japanese, and the IKZF1 association was detected by an alternate SNP (rs1451367, OR = 1.52, P = 2 × 10⁻⁶). Marked regional LD differences between Japanese and Europeans was observed for most of the remaining loci for which associations did not transfer, including CEBPE, CDKN2A, CDKN2B, and ELK3. This study represents a first step towards characterizing the role of genetic susceptibility in childhood ALL risk in Japanese.

Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans

Background

Genome-wide association studies are useful for discovering genotype–phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into “gene level” effects.

Methods

Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions.

Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression—on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals.

Results

We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations.

Conclusions

Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions.

MATLAB code is available at https://github.com/assafgo/warfarin-cohort

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-017-0495-0) contains supplementary material, which is available to authorized users.

Fine mapping by composite genome-wide association analysis

Genome-wide association (GWA) studies play a key role in current genetics research, unravelling genomic regions linked to phenotypic traits of interest in multiple species. Nevertheless, the extent of linkage disequilibrium (LD) may provide confounding results when significant genetic markers span along several contiguous cM. In this study, we have adapted the composite interval mapping approach to the GWA framework (composite GWA), in order to evaluate the impact of including competing (possibly linked) genetic markers when testing for the additive allelic effect inherent to a given genetic marker. We tested model performance on simulated data sets under different scenarios (i.e., qualitative trait loci effects, LD between genetic markers and width of the genomic region involved in the analysis). Our results showed that the genomic region had a small impact on the number of competing single nucleotide polymorphisms (SNPs) as well as on the precision of the composite GWA analysis. A similar conclusion was derived from the preferable range of LD between the tested SNP and competing SNPs, although moderate-to-high LD seemed to attenuate the loss of statistical power. The composite GWA improved specificity and reduced the number of significant genetic markers. The composite GWA model contributes a novel point of view for GWA analyses where testing circumscribed to the genomic region flanking each SNP (delimited by the nearest competing SNPs) and conditioning on linked markers increases the precision to locate causal mutations, but possibly at the expense of power.

Gene Set Enrichment Analyses: lessons learned from the heart failure phenotype

BACKGROUND

Genetic studies for complex diseases have predominantly discovered main effects at individual loci, but have not focused on genomic and environmental contexts important for a phenotype. Gene Set Enrichment Analysis (GSEA) aims to address this by identifying sets of genes or biological pathways contributing to a phenotype, through gene-gene interactions or other mechanisms, which are not the focus of conventional association methods.

RESULTS

Approaches that utilize GSEA can now take input from array chips, either gene-centric or genome-wide, but are highly sensitive to study design, SNP selection and pruning strategies, SNP-to-gene mapping, and pathway definitions. Here, we present lessons learned from our experience with GSEA of heart failure, a particularly challenging phenotype due to its underlying heterogeneous etiology.

CONCLUSIONS

This case study shows that proper data handling is essential to avoid false-positive results. Well-defined pipelines for quality control are needed to avoid reporting spurious results using GSEA.

Dynamic Role of trans Regulation of Gene Expression in Relation to Complex Traits

Identifying causal genetic variants and understanding their mechanisms of effect on traits remains a challenge in genome-wide association studies (GWASs). In particular, how genetic variants (i.e., trans-eQTLs) affect expression of remote genes (i.e., trans-eGenes) remains unknown. We hypothesized that some trans-eQTLs regulate expression of distant genes by altering the expression of nearby genes (cis-eGenes). Using published GWAS datasets with 39,165 single-nucleotide polymorphisms (SNPs) associated with 1,960 traits, we explored whole blood gene expression associations of trait-associated SNPs in 5,257 individuals from the Framingham Heart Study. We identified 2,350 trans-eQTLs (at p < 10^-7); more than 80% of them were found to have cis-associated eGenes. Mediation testing suggested that for 35% of trans-eQTL-trans-eGene pairs in different chromosomes and 90% pairs in the same chromosome, the disease-associated SNP may alter expression of the trans-eGene via cis-eGene expression. In addition, we identified 13 trans-eQTL hotspots, affecting from ten to hundreds of genes, suggesting the existence of master genetic regulators. Using causal inference testing, we searched causal variants across eight cardiometabolic traits (BMI, systolic and diastolic blood pressure, LDL cholesterol, HDL cholesterol, total cholesterol, triglycerides, and fasting blood glucose) and identified several cis-eGenes (ALDH2 for systolic and diastolic blood pressure, MCM6 and DARS for total cholesterol, and TRIB1 for triglycerides) that were causal mediators for the corresponding traits, as well as examples of trans-mediators (TAGAP for LDL cholesterol). The finding of extensive evidence of genome-wide mediation effects suggests a critical role of cryptic gene regulation underlying many disease traits.

Identification of Candidate Genes for Reactivity in Guzerat (Bos indicus) Cattle: A Genome-Wide Association Study

Temperament is fundamental to animal production due to its direct influence on the animal-herdsman relationship. When compared to calm animals, the aggressive, anxious or fearful ones exhibit less weight gain, lower reproductive efficiency, decreased milk production and higher herd maintenance costs, all of which contribute to reduced profits. However, temperament is a trait that is complex and difficult to assess. Recently, a new quantitative system, REATEST^®, for assessing reactivity, a phenotype of temperament, was developed. Herein, we describe the results of a Genome-wide association study for reactivity, assessed using REATEST^® with a sample of 754 females from five dual-purpose (milk and meat production) Guzerat (Bos indicus) herds. Genotyping was performed using a 50k SNP chip and a two-step mixed model approach (Grammar-Gamma) with a one-by-one marker regression was used to identify QTLs. QTLs for reactivity were identified on chromosomes BTA1, BTA5, BTA14, and BTA25. Five intronic and two intergenic markers were significantly associated with reactivity. POU1F1, DRD3, VWA3A, ZBTB20, EPHA6, SNRPF and NTN4 were identified as candidate genes. Previous QTL reports for temperament traits, covering areas surrounding the SNPs/genes identified here, further corroborate these associations. The seven genes identified in the present study explain 20.5% of reactivity variance and give a better understanding of temperament biology.

Trans-ethnic follow-up of breast cancer GWAS hits using the preferential linkage disequilibrium approach

Leveraging population-distinct linkage equilibrium (LD) patterns, trans-ethnic follow-up of variants discovered from genome-wide association studies (GWAS) has proved to be useful in facilitating the identification of bona fide causal variants. We previously developed the preferential LD approach, a novel method that successfully identified causal variants driving the GWAS signals within European-descent populations even when the causal variants were only weakly linked with the GWAS-discovered variants. To evaluate the performance of our approach in a trans-ethnic setting, we applied it to follow up breast cancer GWAS hits identified mostly from populations of European ancestry in African Americans (AA). We evaluated 74 breast cancer GWAS variants in 8,315 AA women from the African American Breast Cancer Epidemiology and Risk (AMBER) consortium. Only 27% of them were associated with breast cancer risk at significance level α=0.05, suggesting race-specificity of the identified breast cancer risk loci. We followed up on those replicated GWAS hits in the AMBER consortium utilizing the preferential LD approach, to search for causal variants or better breast cancer markers from the 1000 Genomes variant catalog. Our approach identified stronger breast cancer markers for 80% of the GWAS hits with at least nominal breast cancer association, and in 81% of these cases, the marker identified was among the top 10 of all 1000 Genomes variants in the corresponding locus. The results support trans-ethnic application of the preferential LD approach in search for candidate causal variants, and may have implications for future genetic research of breast cancer in AA women.

Identification of a haplotype associated with cholesterol deficiency and increased juvenile mortality in Holstein cattle

Over the last decades, several genetic disorders have been discovered in cattle. However, the genetic background of disorders in calves is less reported. Recently, German cattle farmers reported on calves from specific matings with chronic diarrhea and retarded growth of unknown etiology. Affected calves did not respond to any medical treatment and died within the first months of life. These calves were underdeveloped in weight and showed progressive and severe emaciation despite of normal feed intake. Hallmark findings of the blood biochemical analysis were pronounced hypocholesterolemia and deficiency of fat-soluble vitamins. Results of the clinical and blood biochemical examination had striking similarities with findings reported in human hypobetalipoproteinemia. Postmortem examination revealed near-complete atrophy of the body fat reserves including the spinal canal and bone marrow. To identify the causal region, we performed a genome-wide association study with 9 affected and 21,077 control animals genotyped with the Illumina BovineSNP50 BeadChip (Illumina Inc., San Diego, CA), revealing a strong association signal on BTA 11. Subsequent autozygosity mapping identified a disease-associated haplotype encompassing 1.01 Mb. The segment of extended homozygosity contains 6 transcripts, among them the gene APOB, which is causal for cholesterol disorders in humans. However, results from multi-sample variant calling of 1 affected and 47 unaffected animals did not detect any putative causal mutation. The disease-associated haplotype has an important adverse effect on calf mortality in the homozygous state when comparing survival rates of risk matings vs. non-risk matings. Blood cholesterol values of animals are significantly associated with the carrier status indicating a codominant inheritance. The frequency of the haplotype in the current Holstein population was estimated to be 4.2%. This study describes the identification and phenotypic manifestation of a new Holstein haplotype characterized by pronounced hypocholesterolemia, chronic emaciation, growth retardation, and increased mortality in young cattle, denominated as cholesterol deficiency haplotype. Our genomic investigations and phenotypic examinations provide additional evidence for a mutation within the APOB gene causing cholesterol deficiency in Holstein cattle.

MAP3K1 May be a Promising Susceptibility Gene for Type 2 Diabetes Mellitus in an Iranian Population

Considering that MAPK (mitogen- activated protein kinase) signaling pathway has an important role in the progression of inflammatory cytokine secretion in type 2 diabetes mellitus (T2DM), we have recently investigated the reported genetic polymorphism from genome wide association study in MAP3K1 (mitogen-activated protein kinase kinase kinase 1) in diabetes as an important member of MAPK signaling. This study aimed to investigate the possible association of rs10461617 at the upstream of MAP3K1 gene in an Iranian case-control study with the risk of T2DM. The study population was comprised of 342 unrelated Iranian individuals including 177 patients with T2DM and 165 unrelated healthy control subjects. Genotyping was performed using PCR-RFLP and confirmed with sequencing. In a logistic regression analysis, the rs10461617A allele was associated with a significantly higher risk of T2DM assuming the log- additive model (OR: 1.44, 95% CI: 1.01-2.05, P = 0.039). In conclusion, we provided the first evidence for the association of rs10461617 at the upstream of MAP3K1 with the risk of T2DM in an Iranian population.

Insights into kidney diseases from genome-wide association studies

Over the past decade, genome-wide association studies (GWAS) have considerably improved our understanding of the genetic basis of kidney function and disease. Population-based studies, used to investigate traits that define chronic kidney disease (CKD), have identified >50 genomic regions in which common genetic variants associate with estimated glomerular filtration rate or urinary albumin-to-creatinine ratio. Case-control studies, used to study specific CKD aetiologies, have yielded risk loci for specific kidney diseases such as IgA nephropathy and membranous nephropathy. In this Review, we summarize important findings from GWAS and clinical and experimental follow-up studies. We also compare risk allele frequency, effect sizes, and specificity in GWAS of CKD-defining traits and GWAS of specific CKD aetiologies and the implications for study design. Genomic regions identified in GWAS of CKD-defining traits can contain causal genes for monogenic kidney diseases. Population-based research on kidney function traits can therefore generate insights into more severe forms of kidney diseases. Experimental follow-up studies have begun to identify causal genes and variants, which are potential therapeutic targets, and suggest mechanisms underlying the high allele frequency of causal variants. GWAS are thus a useful approach to advance knowledge in nephrology.

SUGP1 is a novel regulator of cholesterol metabolism

A large haplotype on chromosome 19p13.11 tagged by rs10401969 in intron 8 of SURP and G patch domain containing 1 (SUGP1) is associated with coronary artery disease (CAD), plasma LDL cholesterol levels, and other energy metabolism phenotypes. Recent studies have suggested that TM6SF2 is the causal gene within the locus, but we postulated that this locus could harbor additional CAD risk genes, including the putative splicing factor SUGP1. Indeed, we found that rs10401969 regulates SUGP1 exon 8 skipping, causing non-sense-mediated mRNA decay. Hepatic Sugp1 overexpression in CD1 male mice increased plasma cholesterol levels 20–50%. In human hepatoma cell lines, SUGP1 knockdown stimulated 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) alternative splicing and decreased HMGCR transcript stability, thus reducing cholesterol synthesis and increasing LDL uptake. Our results strongly support a role for SUGP1 as a novel regulator of cholesterol metabolism and suggest that it contributes to the relationship between rs10401969 and plasma cholesterol.

Haplotype synthesis analysis reveals functional variants underlying known genome-wide associated susceptibility loci

MOTIVATION

The functional mechanisms underlying disease association remain unknown for Genome-wide Association Studies (GWAS) susceptibility variants located outside coding regions. Synthesis of effects from multiple surrounding functional variants has been suggested as an explanation of hard-to-interpret findings. We define filter criteria based on linkage disequilibrium measures and allele frequencies which reflect expected properties of synthesizing variant sets. For eligible candidate sets, we search for haplotype markers that are highly correlated with associated variants.

RESULTS

Via simulations we assess the performance of our approach and suggest parameter settings which guarantee 95% sensitivity at 20-fold reduced computational cost. We apply our method to 1000 Genomes data and confirmed Crohn's Disease (CD) and Type 2 Diabetes (T2D) variants. A proportion of 36.9% allowed explanation by three-variant-haplotypes carrying at least two functional variants, as compared to 16.4% for random variants ([Formula: see text]). Association could be explained by missense variants for MUC19, PER3 (CD) and HMG20A (T2D). In a CD GWAS-imputed using haplotype reference consortium data (64 976 haplotypes)-we could confirm the syntheses of MUC19 and PER3 and identified synthesis by missense variants for 6 further genes (ZGPAZ, GPR65, CLN3/NPIPB8, LOC102723878, rs2872507, GCKR). In all instances, the odds ratios of the synthesizing haplotypes were virtually identical to that of the index SNP. In summary, we demonstrate the potential of synthesis analysis to guide functional follow-up of GWAS findings.

AVAILABILITY AND IMPLEMENTATION

All methods are implemented in the C/C ++ toolkit GetSynth, available at http://sourceforge.net/projects/getsynth/

CONTACT

tim.becker@uni-greifswald.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Common and Low Frequency Variants in MERTK Are Independently Associated with Multiple Sclerosis Susceptibility with Discordant Association Dependent upon HLA-DRB1*15:01 Status

Multiple Sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system. The risk of developing MS is strongly influenced by genetic predisposition, and over 100 loci have been established as associated with susceptibility. However, the biologically relevant variants underlying disease risk have not been defined for the vast majority of these loci, limiting the power of these genetic studies to define new avenues of research for the development of MS therapeutics. It is therefore crucial that candidate MS susceptibility loci are carefully investigated to identify the biological mechanism linking genetic polymorphism at a given gene to the increased chance of developing MS. MERTK has been established as an MS susceptibility gene and is part of a family of receptor tyrosine kinases known to be involved in the pathogenesis of demyelinating disease. In this study we have refined the association of MERTK with MS risk to independent signals from both common and low frequency variants. One of the associated variants was also found to be linked with increased expression of MERTK in monocytes and higher expression of MERTK was associated with either increased or decreased risk of developing MS, dependent upon HLA-DRB1*15:01 status. This discordant association potentially extended beyond MS susceptibility to alterations in disease course in established MS. This study provides clear evidence that distinct polymorphisms within MERTK are associated with MS susceptibility, one of which has the potential to alter MERTK transcription, which in turn can alter both susceptibility and disease course in MS patients.

Multiple sclerosis (MS) is the most common neurological disease of young Caucasian adults. Oligodendrocytes are the key cell type damaged in MS, a process that is accompanied by loss of the myelin sheath that these cells produce, resulting in demyelination and ultimately in secondary damage to nerve cells. Susceptibility to MS is strongly influenced by genes, and over 100 genes have now been linked with the risk of developing MS. However, surprisingly little is known about the biological mechanism by which any one of these genes increases the probability of developing MS. In this study we have explored in detail the links between one known MS risk gene, MERTK, and MS susceptibility. We found that a number of different alterations in the MERTK gene are independently associated with the risk of developing MS. One these changes was also linked with changes in the level of expression of MERTK in monocytes, an immune cell type known to be involved in the etiology of MS. In an unexpected result, we found this expression-linked alteration in MERTK was either protective or risk-associated, depending on the genotype of the individual at another well known MS risk gene known as HLA-DRB1. In addition, we found that not only were alterations in MERTK associated with MS susceptibility, but potentially with ongoing disease course, indicating that MERTK may be a good target for the development of novel MS therapeutics.

Regulatory polymorphisms modulate the expression of HLA class II molecules and promote autoimmunity

Targeted sequencing of sixteen SLE risk loci among 1349 Caucasian cases and controls produced a comprehensive dataset of the variations causing susceptibility to systemic lupus erythematosus (SLE). Two independent disease association signals in the HLA-D region identified two regulatory regions containing 3562 polymorphisms that modified thirty-seven transcription factor binding sites. These extensive functional variations are a new and potent facet of HLA polymorphism. Variations modifying the consensus binding motifs of IRF4 and CTCF in the XL9 regulatory complex modified the transcription of HLA-DRB1, HLA-DQA1 and HLA-DQB1 in a chromosome-specific manner, resulting in a 2.5-fold increase in the surface expression of HLA-DR and DQ molecules on dendritic cells with SLE risk genotypes, which increases to over 4-fold after stimulation. Similar analyses of fifteen other SLE risk loci identified 1206 functional variants tightly linked with disease-associated SNPs and demonstrated that common disease alleles contain multiple causal variants modulating multiple immune system genes.

The Nature of Genetic Variation for Complex Traits Revealed by GWAS and Regional Heritability Mapping Analyses

We use computer simulations to investigate the amount of genetic variation for complex traits that can be revealed by single-SNP genome-wide association studies (GWAS) or regional heritability mapping (RHM) analyses based on full genome sequence data or SNP chips. We model a large population subject to mutation, recombination, selection, and drift, assuming a pleiotropic model of mutations sampled from a bivariate distribution of effects of mutations on a quantitative trait and fitness. The pleiotropic model investigated, in contrast to previous models, implies that common mutations of large effect are responsible for most of the genetic variation for quantitative traits, except when the trait is fitness itself. We show that GWAS applied to the full sequence increases the number of QTL detected by as much as 50% compared to the number found with SNP chips but only modestly increases the amount of additive genetic variance explained. Even with full sequence data, the total amount of additive variance explained is generally below 50%. Using RHM on the full sequence data, a slightly larger number of QTL are detected than by GWAS if the same probability threshold is assumed, but these QTL explain a slightly smaller amount of genetic variance. Our results also suggest that most of the missing heritability is due to the inability to detect variants of moderate effect (∼0.03-0.3 phenotypic SDs) segregating at substantial frequencies. Very rare variants, which are more difficult to detect by GWAS, are expected to contribute little genetic variation, so their eventual detection is less relevant for resolving the missing heritability problem.

A robust GWSS method to simultaneously detect rare and common variants for complex disease

The rapid advances in sequencing technologies and the resulting next-generation sequencing data provide the opportunity to detect disease-associated variants with a better solution, in particular for low-frequency variants. Although both common and rare variants might exert their independent effects on the risk for the trait of interest, previous methods to detect the association effects rarely consider them simultaneously. We proposed a class of test statistics, the generalized weighted-sum statistic (GWSS), to detect disease associations in the presence of common and rare variants with a case-control study design. Information of rare variants was aggregated using a weighted sum method, while signal directions and strength of the variants were considered at the same time. Permutations were performed to obtain the empirical p-values of the test statistics. Our simulation showed that, compared to the existing methods, the GWSS method had better performance in most of the scenarios. The GWSS (in particular VDWSS-t) method is particularly robust for opposite association directions, association strength, and varying distributions of minor-allele frequencies. It is therefore promising for detecting disease-associated loci. For empirical data application, we also applied our GWSS method to the Genetic Analysis Workshop 17 data, and the results were consistent with the simulation, suggesting good performance of our method. As re-sequencing studies become more popular to identify putative disease loci, we recommend the use of this newly developed GWSS to detect associations with both common and rare variants.

The contribution of genomic research to explaining racial disparities in cardiovascular disease: a systematic review

After nearly a decade of genome-wide association studies, no assessment has yet been made of their contribution toward an explanation of the most prominent racial health disparities observed at the population level. We examined populations of African and European ancestry and focused on cardiovascular diseases, which are collectively the largest contributor to the racial mortality gap. We conducted a systematic search for review articles and meta-analyses published in 2007-2013 in which genetic data from both populations were available. We identified 68 articles relevant to this question; however, few reported significant associations in both racial groups, with just 3 variants meeting study-specific significance criteria. For most outcomes, there were too few estimates for quantitative summarization, but when summarization was possible, racial group did not contribute to heterogeneity. Most associations reported from genome-wide searches were small, difficult to replicate, and in no consistent direction that favored one racial group or another. Although the substantial investment in this technology might have produced clinical advances, it has thus far made little or no contribution to our understanding of population-level racial health disparities in cardiovascular disease.

The genetic architecture of alopecia areata

A major impetus to initiating the Human Genome Project was the belief that information encoded in the human genome would "accelerate progress in understanding disease pathogenesis and in developing new approaches to diagnosis, treatment, and prevention in many areas of medicine". Alopecia areata (AA) is a notable example of how understanding the genetic basis of a disease can have an impact on the care of patients in a relatively short time. Our first genome-wide association study in AA identified an initial set of common variants that increase risk of AA, some of which are shared with other autoimmune diseases. Thus, there has already been rapid progress in the translation of this information into new therapeutic strategies for patients, as drugs are already on the market for some of these disorders that can now be tested in AA. Informed by the progress achieved with genetic studies for mechanistically aligned autoimmune diseases, we are poised to carry this work forward and interrogate the underlying disease mechanisms in AA. Importantly, future genetic studies aimed at identifying additional susceptibility genes will further establish the foundation for the application of precision medicine in the care of AA patients.

A multiple sclerosis-associated variant of CBLB links genetic risk with type I IFN function

Multiple sclerosis (MS) is an autoimmune disease of the CNS, and autoreactive CD4(+) T cells are considered important for its pathogenesis. The etiology of MS involves a complex genetic trait and environmental triggers that include viral infections, particularly the EBV. Among the risk alleles that have repeatedly been identified by genome-wide association studies, three are located near the Casitas B-lineage lymphoma proto-oncogene b gene (CBLB). The CBLB protein (CBL-B) is a key regulator of peripheral immune tolerance by limiting T cell activation and expansion and hence T cell-mediated autoimmunity through its ubiquitin E3-ligase activity. In this study, we show that CBL-B expression is reduced in CD4(+) T cells from relapsing-remitting MS (RR-MS) patients during relapse. The MS risk-related single nucleotide polymorphism of CBLB rs12487066 is associated with diminished CBL-B expression levels and alters the effects of type I IFNs on human CD4(+) T cell proliferation. Mechanistically, the CBLB rs12487066 risk allele mediates increased binding of the transcription factor C/EBPβ and reduced CBL-B expression in human CD4(+) T cells. Our data suggest a role of the CBLB rs12487066 variant in the interactions of a genetic risk factor and IFN function during viral infections in MS.

The contribution of genetic variants to disease depends on the ruler

Our understanding of the genetic basis of disease has evolved from descriptions of overall heritability or familiality to the identification of large numbers of risk loci. One can quantify the impact of such loci on disease using a plethora of measures, which can guide future research decisions. However, different measures can attribute varying degrees of importance to a variant. In this Analysis, we consider and contrast the most commonly used measures - specifically, the heritability of disease liability, approximate heritability, sibling recurrence risk, overall genetic variance using a logarithmic relative risk scale, the area under the receiver-operating curve for risk prediction and the population attributable fraction - and give guidelines for their use that should be explicitly considered when assessing the contribution of genetic variants to disease.

Principal component analysis characterizes shared pathogenetics from genome-wide association studies

Genome-wide association studies (GWASs) have recently revealed many genetic associations that are shared between different diseases. We propose a method, disPCA, for genome-wide characterization of shared and distinct risk factors between and within disease classes. It flips the conventional GWAS paradigm by analyzing the diseases themselves, across GWAS datasets, to explore their "shared pathogenetics". The method applies principal component analysis (PCA) to gene-level significance scores across all genes and across GWASs, thereby revealing shared pathogenetics between diseases in an unsupervised fashion. Importantly, it adjusts for potential sources of heterogeneity present between GWAS which can confound investigation of shared disease etiology. We applied disPCA to 31 GWASs, including autoimmune diseases, cancers, psychiatric disorders, and neurological disorders. The leading principal components separate these disease classes, as well as inflammatory bowel diseases from other autoimmune diseases. Generally, distinct diseases from the same class tend to be less separated, which is in line with their increased shared etiology. Enrichment analysis of genes contributing to leading principal components revealed pathways that are implicated in the immune system, while also pointing to pathways that have yet to be explored before in this context. Our results point to the potential of disPCA in going beyond epidemiological findings of the co-occurrence of distinct diseases, to highlighting novel genes and pathways that unsupervised learning suggest to be key players in the variability across diseases.

Transcriptome profiling of human ulcerative colitis mucosa reveals altered expression of pathways enriched in genetic susceptibility loci

Human colonic mucosa altered by inflammation due to ulcerative colitis (UC) displays a drastically altered pattern of gene expression compared with healthy tissue. We aimed to understand the underlying molecular pathways influencing these differences by analyzing three publically-available, independently-generated microarray datasets of gene expression from endoscopic biopsies of the colon. Gene set enrichment analysis (GSEA) revealed that all three datasets share 87 gene sets upregulated in UC lesions and 8 gene sets downregulated (false discovery rate <0.05). The upregulated pathways were dominated by gene sets involved in immune function and signaling, as well as the control of mitosis. We applied pathway analysis to genotype data derived from genome-wide association studies (GWAS) of UC, consisting of 5,584 cases and 11,587 controls assembled from eight European-ancestry cohorts. The upregulated pathways derived from the gene expression data showed a highly significant overlap with pathways derived from the genotype data (33 of 56 gene sets, hypergeometric P = 1.49×10^–19). This study supports the hypothesis that heritable variation in gene expression as measured by GWAS signals can influence key pathways in the development of disease, and that comparison of genetic susceptibility loci with gene expression signatures can differentiate key drivers of inflammation from secondary effects on gene expression of the inflammatory process.

Explaining additional genetic variation in complex traits

Genome-wide association studies (GWAS) have provided valuable insights into the genetic basis of complex traits, discovering >6000 variants associated with >500 quantitative traits and common complex diseases in humans. The associations identified so far represent only a fraction of those that influence phenotype, because there are likely to be many variants across the entire frequency spectrum, each of which influences multiple traits, with only a small average contribution to the phenotypic variance. This presents a considerable challenge to further dissection of the remaining unexplained genetic variance within populations, which limits our ability to predict disease risk, identify new drug targets, improve and maintain food sources, and understand natural diversity. This challenge will be met within the current framework through larger sample size, better phenotyping, including recording of nongenetic risk factors, focused study designs, and an integration of multiple sources of phenotypic and genetic information. The current evidence supports the application of quantitative genetic approaches, and we argue that one should retain simpler theories until simplicity can be traded for greater explanatory power.

Risk loci for chronic obstructive pulmonary disease: a genome-wide association study and meta-analysis

BACKGROUND

The genetic risk factors for susceptibility to chronic obstructive pulmonary disease (COPD) are still largely unknown. Additional genetic variants are likely to be identified by genome-wide association studies in larger cohorts or specific subgroups. We sought to identify risk loci for moderate to severe and severe COPD with data from several cohort studies.

METHODS

We combined genome-wide association analysis data from participants in the COPDGene study (non-Hispanic white and African-American ethnic origin) and the ECLIPSE, NETT/NAS, and Norway GenKOLS studies (self-described white ethnic origin). We did analyses comparing control individuals with individuals with moderate to severe COPD and with a subset of individuals with severe COPD. Single nucleotide polymorphisms yielding a p value of less than 5 × 10(-7) in the meta-analysis at loci not previously described were genotyped in individuals from the family-based ICGN study. We combined results in a joint meta-analysis (threshold for significance p<5 × 10(-8)).

FINDINGS

Analysis of 6633 individuals with moderate to severe COPD and 5704 control individuals confirmed association at three known loci: CHRNA3 (p=6·38 × 10(-14)), FAM13A (p=1·12 × 10(-14)), and HHIP (p=1·57 × 10(-12)). We also showed significant evidence of association at a novel locus near RIN3 (p=5·25 × 10(-9)). In the overall meta-analysis (ie, including data from 2859 ICGN participants), the association with RIN3 remained significant (p=5·4 × 10(-9)). 3497 individuals were included in our analysis of severe COPD. The effect estimates for the loci near HHIP and CHRNA3 were significantly stronger in severe disease than in moderate to severe disease (p<0·01). We also identified associations at two additional loci: MMP12 (overall joint meta-analysis p=2·6 × 10(-9)) and TGFB2 (overall joint meta-analysis p=8·3 × 10(-9)).

INTERPRETATION

We have confirmed associations with COPD at three known loci and identified three new genome-wide significant associations. Genetic variants other than in α-1 antitrypsin increase the risk of COPD.

FUNDING

US National Heart, Lung, and Blood Institute; the Alpha-1 Foundation; the COPD Foundation through contributions from AstraZeneca, Boehringer Ingelheim, Novartis, and Sepracor; GlaxoSmithKline; Centers for Medicare and Medicaid Services; Agency for Healthcare Research and Quality; and US Department of Veterans Affairs.

Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism

Genetic studies have identified dozens of autism spectrum disorder (ASD) susceptibility genes, raising two critical questions: (1) do these genetic loci converge on specific biological processes, and (2) where does the phenotypic specificity of ASD arise, given its genetic overlap with intellectual disability (ID)? To address this, we mapped ASD and ID risk genes onto coexpression networks representing developmental trajectories and transcriptional profiles representing fetal and adult cortical laminae. ASD genes tightly coalesce in modules that implicate distinct biological functions during human cortical development, including early transcriptional regulation and synaptic development. Bioinformatic analyses suggest that translational regulation by FMRP and transcriptional coregulation by common transcription factors connect these processes. At a circuit level, ASD genes are enriched in superficial cortical layers and glutamatergic projection neurons. Furthermore, we show that the patterns of ASD and ID risk genes are distinct, providing a biological framework for further investigating the pathophysiology of ASD.