Era Of Genome-wide Association Studies Research Articles

Genetic and functional research strategies of non-syndromic cleft lip with or without cleft palate in the post genome-wide association study era

The emergence of genome-wide association studies (GWAS) has greatly promoted the genetic research of non-syndromic cleft lip with or without cleft palate (NSCL/P). There have been more than 40 regions concerning NSCL/P identified by GWAS, whereas specific susceptible loci and their potential function remains unclear. In the post-GWAS era, precise localization of susceptible loci in candidate regions and exploration of underlying biological mechanism will contribute to further understanding of genetic etiology of NSCL/P. The present article reviewed the genetic and functional research strategies of NSCL/P in post-GWAS era.

Searching for gene-gene interactions through variance quantitative trait loci of 29 continuous Taiwan Biobank phenotypes.

Introduction: After the era of genome-wide association studies (GWAS), thousands of genetic variants have been identified to exhibit main effects on human phenotypes. The next critical issue would be to explore the interplay between genes, the so-called "gene-gene interactions" (GxG) or epistasis. An exhaustive search for all single-nucleotide polymorphism (SNP) pairs is not recommended because this will induce a harsh penalty of multiple testing. Limiting the search of epistasis on SNPs reported by previous GWAS may miss essential interactions between SNPs without significant marginal effects. Moreover, most methods are computationally intensive and can be challenging to implement genome-wide. Methods: I here searched for GxG through variance quantitative trait loci (vQTLs) of 29 continuous Taiwan Biobank (TWB) phenotypes. A discovery cohort of 86,536 and a replication cohort of 25,460 TWB individuals were analyzed, respectively. Results: A total of 18 nearly independent vQTLs with linkage disequilibrium measure r 2 < 0.01 were identified and replicated from nine phenotypes. 15 significant GxG were found with p-values <1.1E-5 (in the discovery cohort) and false discovery rates <2% (in the replication cohort). Among these 15 GxG, 11 were detected for blood traits including red blood cells, hemoglobin, and hematocrit; 2 for total bilirubin; 1 for fasting glucose; and 1 for total cholesterol (TCHO). All GxG were observed for gene pairs on the same chromosome, except for the APOA5 (chromosome 11)-TOMM40 (chromosome 19) interaction for TCHO. Discussion: This study provided a computationally feasible way to search for GxG genome-wide and applied this approach to 29 phenotypes.

Perspectives on genetic studies of type 2 diabetes from the genome-wide association studies era to precision medicine.

Genome-wide association studies (GWAS) have facilitated a substantial and rapid increase in the number of confirmed genetic susceptibility variants for complex diseases. Approximately 700 variants predisposing individuals to the risk for type 2 diabetes have been identified through GWAS until 2023. From 2018 to 2022, hundreds of type 2 diabetes susceptibility loci with smaller effect sizes were identified through large-scale GWAS with sample sizes of 200,000 to >1 million. The clinical translation of genetic information for type 2 diabetes includes the development of novel therapeutics and risk predictions. Although drug discovery based on loci identified in GWAS remains challenging owing to the difficulty of functional annotation, global efforts have been made to identify novel biological mechanisms and therapeutic targets by applying multi-omics approaches or searching for disease-associated coding variants in isolated founder populations. Polygenic risk scores (PRSs), comprising up to millions of associated variants, can identify individuals with higher disease risk than those in the general population. In populations of European descent, PRSs constructed from base GWAS data with a sample size of approximately 450,000 have predicted the onset of diseases well. However, European GWAS-derived PRSs have limited predictive performance in non-European populations. The predictive accuracy of a PRS largely depends on the sample size of the base GWAS data. The results of GWAS meta-analyses for multi-ethnic groups as base GWAS data and cross-population polygenic prediction methodology have been applied to establish a universal PRS applicable to small isolated ethnic populations.

Germline mechanisms of immunotherapy toxicities in the era of genome-wide association studies.

Cancer immunotherapy has revolutionized the treatment of advanced cancers and is quickly becoming an option for early-stage disease. By reactivating the host immune system, immunotherapy harnesses patients' innate defenses to eradicate the tumor. By putatively similar mechanisms, immunotherapy can also substantially increase the risk of toxicities or immune-related adverse events (irAEs). Severe irAEs can lead to hospitalization, treatment discontinuation, lifelong immune complications, or even death. Many irAEs present with similar symptoms to heritable autoimmune diseases, suggesting that germline genetics may contribute to their onset. Recently, genome-wide association studies (GWAS) of irAEs have identified common germline associations and putative mechanisms, lending support to this hypothesis. A wide range of well-established GWAS methods can potentially be harnessed to understand the etiology of irAEs specifically and immunotherapy outcomes broadly. This review summarizes current findings regarding germline effects on immunotherapy outcomes and discusses opportunities and challenges for leveraging germline genetics to understand, predict, and treat irAEs.

New "drugs and targets" in the GWAS era of bipolar disorder.

Due to the phenotypic heterogeneity and etiological complexity of bipolar disorder (BD), many patients do not respond well to the current medications, and developing novel effective treatment is necessary. Whether any BD genome-wide association study (GWAS) risk genes were targets of existing drugs or novel drugs that can be repurposed in the clinical treatment of BD is a hot topic in the GWAS era of BD. A list of 425 protein-coding BD risk genes was distilled through the BD GWAS, and 4479 protein-coding druggable targets were retrieved from the druggable genome. The overlapped genes/targets were subjected to further analyses in DrugBank, Pharos, and DGIdb datasets in terms of their FDA status, mechanism of action and primary indication, to identify their potential for repurposing. We identified 58 BD GWAS risk genes grouped as the druggable targets, and several genes were given higher priority. These BD risk genes were targets of antipsychotics, antidepressants, antiepileptics, calcium channel antagonists, as well as anxiolytics and analgesics, either existing clinically-approved drugs for BD or the drugs than can be repurposed for treatment of BD in the future. Those genes were also likely relevant to BD pathophysiology, as many of them encode ion channel, ion transporter or neurotransmitter receptor, or the mice manipulating those genes are likely to mimic the phenotypes manifest in BD patients. This study identifies several targets that may facilitate the discovery of novel treatments in BD, and implies the value of conducting GWAS into clinical translation.

A Varying Coefficient Model to Jointly Test Genetic and Gene-Environment Interaction Effects.

Most human traits are influenced by the interplay between genetic and environmental factors. Many statistical methods have been proposed to screen for gene-environment interaction (GxE) in the post genome-wide association study era. However, most of the existing methods assume a linear interaction between genetic and environmental factors toward phenotypic variations, which diminishes statistical power in the case of nonlinear GxE. In this paper, we present a flexible statistical procedure to detect GxE regardless of whether the underlying relationship is linear or not. By modeling the joint genetic and GxE effects as a varying-coefficient function of the environmental factor, the proposed model is able to capture dynamic trajectories of GxE. We employ a likelihood ratio test with a fast Monte Carlo algorithm for hypothesis testing. Simulations were conducted to evaluate validity and power of the proposed model in various settings. Real data analysis was performed to illustrate its power, in particular, in the case of nonlinear GxE.

Dissecting Meta-Analysis in GWAS Era: Bayesian Framework for Gene/Subnetwork-Specific Meta-Analysis.

Over the past decades, advanced high-throughput technologies have continuously contributed to genome-wide association studies (GWASs). GWAS meta-analysis has been increasingly adopted, has cross-ancestry replicability, and has power to illuminate the genetic architecture of complex traits, informing about the reliability of estimation effects and their variability across human ancestries. However, detecting genetic variants that have low disease risk still poses a challenge. Designing a meta-analysis approach that combines the effect of various SNPs within genes or genes within pathways from multiple independent population GWASs may be helpful in identifying associations with small effect sizes and increasing the association power. Here, we proposed ancMETA, a Bayesian graph-based framework, to perform the gene/pathway-specific meta-analysis by combining the effect size of multiple SNPs within genes, and genes within subnetwork/pathways across multiple independent population GWASs to deconvolute the interactions between genes underlying the pathogenesis of complex diseases across human populations. We assessed the proposed framework on simulated datasets, and the results show that the proposed model holds promise for increasing statistical power for meta-analysis of genetic variants underlying the pathogenesis of complex diseases. To illustrate the proposed meta-analysis framework, we leverage seven different European bipolar disorder (BD) cohorts, and we identify variants in the angiotensinogen (AGT) gene to be significantly associated with BD across all 7 studies. We detect a commonly significant BD-specific subnetwork with the ESR1 gene as the main hub of a subnetwork, associated with neurotrophin signaling (p = 4e −14) and myometrial relaxation and contraction (p = 3e −08) pathways. ancMETA provides a new contribution to post-GWAS methodologies and holds promise for comprehensively examining interactions between genes underlying the pathogenesis of genetic diseases and also underlying ethnic differences.

The UK Biobank: A Shining Example of Genome-Wide Association Study Science with the Power to Detect the Murky Complications of Real-World Epidemiology.

Genome-wide association studies (GWASs) have successfully identified thousands of genetic variants that are reliably associated with human traits. Although GWASs are restricted to certain variant frequencies, they have improved our understanding of the genetic architecture of complex traits and diseases. The UK Biobank (UKBB) has brought substantial analytical opportunity and performance to association studies. The dramatic expansion of many GWAS sample sizes afforded by the inclusion of UKBB data has improved the power of estimation of effect sizes but, critically, has done so in a context where phenotypic depth and precision enable outcome dissection and the application of epidemiological approaches. However, at the same time, the availability of such a large, well-curated, and deeply measured population-based collection has the capacity to increase our exposure to the many complications and inferential complexities associated with GWASs and other analyses. In this review, we discuss the impact that UKBB has had in the GWAS era, some of the opportunities that it brings, and exemplar challenges that illustrate the reality of using data from this world-leading resource.

The “Golden Age” of Behavior Genetics?

The search for genetic risk factors underlying the presumed heritability of all human behavior has unfolded in two phases. The first phase, characterized by candidate-gene-association (CGA) studies, has fallen out of favor in the behavior-genetics community, so much so that it has been referred to as a “cautionary tale.” The second and current iteration is characterized by genome-wide association studies (GWASs), single-nucleotide polymorphism (SNP) heritability estimates, and polygenic risk scores. This research is guided by the resurrection of, or reemphasis on, Fisher’s “infinite infinitesimal allele” model of the heritability of complex phenotypes, first proposed over 100 years ago. Despite seemingly significant differences between the two iterations, they are united in viewing the discovery of risk alleles underlying heritability as a matter of finding differences in allele frequencies. Many of the infirmities that beset CGA studies persist in the era of GWASs, accompanied by a host of new difficulties due to the human genome’s underlying complexities and the limitations of Fisher’s model in the postgenomics era.

Current Developments in Detection of Identity-by-Descent Methods and Applications

Identity-by-descent (IBD), the detection of shared segments inherited from a common ancestor, is a fundamental concept in genomics with broad applications in the characterization and analysis of genomes. While historically the concept of IBD was extensively utilized through linkage analyses and in studies of founder populations, applications of IBD-based methods subsided during the genome-wide association study era. This was primarily due to the computational expense of IBD detection, which becomes increasingly relevant as the field moves toward the analysis of biobank-scale datasets that encompass individuals from highly diverse backgrounds. To address these computational barriers, the past several years have seen new methodological advances enabling IBD detection for datasets in the hundreds of thousands to millions of individuals, enabling novel analyses at an unprecedented scale. Here, we describe the latest innovations in IBD detection and describe opportunities for the application of IBD-based methods across a broad range of questions in the field of genomics.

Gene action, genetic variation, and GWAS: A user-friendly web tool.

Fisher’s partitioning of genotypic values and genetic variance is highly relevant in the current era of genome-wide association studies (GWASs). However, despite being more than a century old, a number of persistent misconceptions related to nonadditive genetic effects remain. We developed a user-friendly web tool, the Falconer ShinyApp, to show how the combination of gene action and allele frequencies at causal loci translate to genetic variance and genetic variance components for a complex trait. The app can be used to demonstrate the relationship between a SNP effect size estimated from GWAS and the variation the SNP generates in the population, i.e., how locus-specific effects lead to individual differences in traits. In addition, it can also be used to demonstrate how within and between locus interactions (dominance and epistasis, respectively) usually do not lead to a large amount of nonadditive variance relative to additive variance, and therefore, that these interactions usually do not explain individual differences in a population.

The Genetics of Reading and Language.

Recounts how our collaboration with Nick Martin was shaped over two decades, leading to the first studies of predictions from the 'Dual Route Cascaded' computational model of reading in twins, and extending into the molecular work, first linkage, fine mapping of genes identified in pedigree studies, into now the genomewide association study era and the first polygenic risk scores for reading and their potential in early clarifying causality and validating interventions, as well as for future global collaborations in improving these predictors and identifying causal variants. We highlight Nick's warm, future-focused optimism, support and inclusive approach without which none of this would have been possible. The circle of Nick asking, over half a century ago, 'What genes do you think make some kids get better grades?' has built a diverse scientific legacy involving thousands of papers and collaborations. The (heritable) traits of curiosity, boldness, warmth, interest in societally important questions, openness to new methods, ambition and collaborative skill to bring into being the infrastructure and samples needed for this research are rare, and we are grateful.

Familial association of attention-deficit hyperactivity disorder with autoimmune diseases in the population of Sweden.

In the era of genome-wide association studies, familial risks are used to estimate disease heritability and success in gene identification. We wanted to estimate associations of 42 autoimmune diseases with attention-deficit hyperactivity disorder (ADHD) between individuals and family members. The availability of a Multigeneration Register in Sweden provides reliable access to family data that covers the last century. An open cohort design of the diseases in individual and family members was obtained through linkage to the Hospital Discharge Register. Standardized incidence ratios were calculated as relative risks for ADHD in family members of affected patients compared with those without affected family members. Among a total of 86 493 patients, 18 153 had a family history of autoimmune diseases. ADHD was associated with 14 autoimmune diseases in the first-degree relatives, including ankylosing spondylitis (standardized incidence ratio:1.13), celiac disease (1.16), Crohn's disease (1.07), diabetes mellitus type 1 (1.19), discoid lupus erythematosus (1.26), glomerular nephritis chronic (1.13), Hashimoto/hypothyroidism (1.11), lupoid hepatitis (1.44), multiple sclerosis (1.11), psoriasis (1.18), Reiter's disease (1.38), rheumatoid arthritis (1.07), Sjögren's syndrome (1.21), and ulcerative colitis (1.05). Familial associations with several autoimmune diseases suggest genetic sharing and challenge to gene identification.

Genome-Wide Identification of N6-Methyladenosine (m6A) SNPs Associated With Rheumatoid Arthritis.

Previous large scale genome-wide association studies (GWAS) have identified more than one hundred susceptibility loci for rheumatoid arthritis (RA) (Stahl et al., 2010; Eyre et al., 2012; Okada et al., 2012, 2014b). One major issue in the post GWAS era is to identify functional (causal) variants in the disease-associated loci. Recently, whole-exome sequencing studies have identified several missense mutations for RA (Mitsunaga et al., 2013; Okada et al., 2014a). Nevertheless, few studies have focused on potentially functional variants which could affect N6-methyladenosine (m6A) methylation. N6-methyladenosine (m6A) has been discovered as the first example of reversible RNA methylation and is a pervasive modification in the mRNA of all eukaryotes. It plays critical roles in regulating many fundamental biological processes such as gene expression control (Meyer and Jaffrey, 2014) and regulation of mRNA stability (Wang et al., 2014) and homeostasis (Edupuganti et al., 2017). Evidence has increasingly shown that m6A modification modulates almost all aspects of RNA processing such as nuclear export, translatability, splicing, and miRNA processing (Visvanathan and Somasundaram, 2018). Thus, m6A modification adds a new layer of gene expression regulation, and it has been implicated in T cell differentiation, homeostasis, and response to HIV infection (Li et al., 2018). Moreover, it was found to be involved in the etiology of various diseases (Visvanathan and Somasundaram, 2018). Given the vital function of m6A modification in gene expression regulation and immune response, it is possible that m6A is involved in the etiology of RA. Recent studies have suggested that SNPs would affect m6A by altering the RNA sequences of the target sites or key flanking nucleotides (Zheng et al., 2018). It means that m6A-associated SNPs (m6A-SNPs) may have regulatory potential to affect gene expression and mRNA stability and homeostasis, which may consequently affect disease such as RA. However, what is the relationship between m6A-SNPs and RA is still unclear. Thus, we examined the effect of the m6A-SNPs on RA in a large scale GWAS. Then, to test whether the RA-associated m6A-SNPs have potential regulatory effects on gene expression and whether these disruptions may contribute to RA, we performed eQTL and differential expression analyses using whole genome data of about 10.5 million SNPs and 21,323 mRNAs from 26 RA cases and 17 controls, as well as public data.

Rigor and reproducibility in genetic research on eating disorders

We explored both within-method and between-method rigor and reproducibility in the field of eating disorders genetics. We present critical evaluation and commentary on component methods of genetic research (family studies, twin studies, molecular genetic studies) and discuss both successful and unsuccessful efforts in the field. Eating disorders genetics has had a number of robust results that converge across component methodologies. Familial aggregation of eating disorders, twin-based heritability estimates of eating disorders, and genome-wide association studies (GWAS) all point toward a substantial role for genetics in eating disorders etiology and support the premise that genes do not act alone. Candidate gene and linkage studies have been less informative historically. The eating disorders field has entered the GWAS era with studies of anorexia nervosa. Continued growth of sample sizes is essential for rigorous discovery of actionable variation. Molecular genetic studies of bulimia nervosa, binge-eating disorder, and other eating disorders are virtually nonexistent and lag seriously behind other major psychiatric disorders. Expanded efforts are necessary to reveal the fundamental biology of eating disorders, inform clinical practice, and deliver new therapeutic targets.

Scientific Contributions of Population-Based Studies to Cardiovascular Epidemiology in the GWAS Era.

Longitudinal, well phenotyped, population-based cohort studies offer unique research opportunities in the context of genome-wide association studies (GWAS), including GWAS for new-onset (incident) cardiovascular disease (CVD) events, the assessment of gene x lifestyle interactions, and evaluating the incremental predictive utility of genetic information in apparently healthy individuals. Furthermore, comprehensively phenotyped community-dwelling samples have contributed to GWAS of numerous traits that reflect normal organ function (e.g., cardiac structure and systolic and diastolic function) and for many traits along the CVD continuum (e.g., risk factors, circulating biomarkers, and subclinical disease traits). These GWAS have heretofore identified many genetic loci implicated in normal organ function and different stages of the CVD continuum. Finally, population-based cohort studies have made important contributions to Mendelian Randomization analyses, a statistical approach that uses genetic information to assess observed associations between cardiovascular traits and clinical CVD outcomes for potential causality.

Human Genetics of Obesity and Type 2 Diabetes Mellitus: Past, Present, and Future.

Type 2 diabetes mellitus (T2D) and obesity already represent 2 of the most prominent risk factors for cardiovascular disease, and are destined to increase in importance given the global changes in lifestyle. Ten years have passed since the first round of genome-wide association studies for T2D and obesity. During this decade, we have witnessed remarkable developments in human genetics. We have graduated from the despair of candidate gene-based studies that generated few consistently replicated genotype-phenotype associations, to the excitement of an exponential harvest of loci robustly associated with medical outcomes through ever larger genome-wide association study meta-analyses. As well as discovering hundreds of loci, genome-wide association studies have provided transformative insights into the genetic architecture of T2D and other complex traits, highlighting the extent of polygenicity and the tiny effect sizes of many common risk alleles. Genome-wide association studies have also provided a critical starting point for discovering new biology relevant to these traits. Expectations are high that these discoveries will foster development of more effective strategies for intervention, through optimization of precision medicine approaches. In this article, we review current knowledge and provide suggestions for the next steps in genetic research for T2D and obesity. We focus on four areas relevant to precision medicine: genetic architecture, pharmacogenetics and other gene-environment interactions, mechanistic inference, and drug development. As we describe, the genetic architecture of complex traits has major implications for the prospects of precision medicine, rendering some anticipated approaches decidedly unrealistic. We highlight obstacles to the translation of human genetic findings into mechanism inference but are optimistic that, as these are overcome, there is untapped potential for novel drugs and more effective strategies for treating and preventing T2D and obesity.

Statistical methods to detect novel genetic variants using publicly available GWAS summary data

We propose statistical methods to detect novel genetic variants using only genome-wide association studies (GWAS) summary data without access to raw genotype and phenotype data. With more and more summary data being posted for public access in the post GWAS era, the proposed methods are practically very useful to identify additional interesting genetic variants and shed lights on the underlying disease mechanism. We illustrate the utility of our proposed methods with application to GWAS meta-analysis results of fasting glucose from the international MAGIC consortium. We found several novel genome-wide significant loci that are worth further study.

Genome-Wide Association Studies of Autoimmune Thyroid Diseases, Thyroid Function, and Thyroid Cancer.

Thyroid diseases, including autoimmune thyroid diseases and thyroid cancer, are known to have high heritability. Family and twin studies have indicated that genetics plays a major role in the development of thyroid diseases. Thyroid function, represented by thyroid stimulating hormone (TSH) and free thyroxine (T4), is also known to be partly genetically determined. Before the era of genome-wide association studies (GWAS), the ability to identify genes responsible for susceptibility to thyroid disease was limited. Over the past decade, GWAS have been used to identify genes involved in many complex diseases, including various phenotypes of the thyroid gland. In GWAS of autoimmune thyroid diseases, many susceptibility loci associated with autoimmunity (human leukocyte antigen [HLA], protein tyrosine phosphatase, non-receptor type 22 [PTPN22], cytotoxic T-lymphocyte associated protein 4 [CTLA4], and interleukin 2 receptor subunit alpha [IL2RA]) or thyroid-specific genes (thyroid stimulating hormone receptor [TSHR] and forkhead box E1 [FOXE1]) have been identified. Regarding thyroid function, many susceptibility loci for levels of TSH and free T4 have been identified through genome-wide analyses. In GWAS of differentiated thyroid cancer, associations at FOXE1, MAP3K12 binding inhibitory protein 1 (MBIP)-NK2 homeobox 1 (NKX2-1), disrupted in renal carcinoma 3 (DIRC3), neuregulin 1 (NRG1), and pecanex-like 2 (PCNXL2) have been commonly identified in people of European and Korean ancestry, and many other susceptibility loci have been found in specific populations. Through GWAS of various thyroid-related phenotypes, many susceptibility loci have been found, providing insights into the pathogenesis of thyroid diseases and disease co-clustering within families and individuals.

Sasquatch: predicting the impact of regulatory SNPs on transcription factor binding from cell- and tissue-specific DNase footprints.

In the era of genome-wide association studies (GWAS) and personalized medicine, predicting the impact of single nucleotide polymorphisms (SNPs) in regulatory elements is an important goal. Current approaches to determine the potential of regulatory SNPs depend on inadequate knowledge of cell-specific DNA binding motifs. Here, we present Sasquatch, a new computational approach that uses DNase footprint data to estimate and visualize the effects of noncoding variants on transcription factor binding. Sasquatch performs a comprehensive k-mer-based analysis of DNase footprints to determine any k-mer's potential for protein binding in a specific cell type and how this may be changed by sequence variants. Therefore, Sasquatch uses an unbiased approach, independent of known transcription factor binding sites and motifs. Sasquatch only requires a single DNase-seq data set per cell type, from any genotype, and produces consistent predictions from data generated by different experimental procedures and at different sequence depths. Here we demonstrate the effectiveness of Sasquatch using previously validated functional SNPs and benchmark its performance against existing approaches. Sasquatch is available as a versatile webtool incorporating publicly available data, including the human ENCODE collection. Thus, Sasquatch provides a powerful tool and repository for prioritizing likely regulatory SNPs in the noncoding genome.