ABSTRACT Modern improvement of complex traits in agricultural species relies on successful associations of heritable molecular variation with observable phenotypes. Historically, this pursuit has primarily been based on easily measurable genetic markers. The recent advent of new technologies allows assaying and quantifying biological intermediates (hereafter endophenotypes) which are now readily measur- able at a large scale across diverse individuals. The usefulness of endophenotypes for delineating the regulatory landscape of the genome and genetic dissection of complex trait variation remains underexplored in plants. The work presented here illustrated the utility of a large-scale (299-genotype and seven-tissue) gene expression resource to dissect traits across multiple levels of biological organization. Using single-tissue- and multi-tissue-based transcriptome-wideassociation studies (TWAS), we revealed that about half of the functional variation acts through altered transcript abundance for maize kernel traits, including 30 grain carotenoid abundance traits, 20 grain tocochromanol abundance traits, and 22 ﬁ eld-measured agronomic traits. Comparing the ef ﬁ cacy of TWAS with genome-wideassociation studies (GWAS) and an ensemble approach that combines both GWAS and TWAS, we demonstrated that results of TWAS in combination with GWAS increase the power to detect known genes and aid in prioritizing likely causal genes. Using a variance partitioning approach in the largely independent maize Nested Association Mapping (NAM) population, we also showed that the most strongly associated genes identiﬁed by combining GWAS and TWAS explain more heritable variance for a majority of traits than the heritability captured by the random genes and the genes identiﬁed by GWAS or TWAS alone. This not only improves the ability to link genes to phenotypes, but also highlights the phenotypic consequences of regulatory variation in plants.
Genome-wideassociation studies (GWAS) analyze the genetic component of a phenotype or the etiology of a disease. Despite the success of many GWAS, little progress has been made in uncovering the underlying mechanisms for many diseases. The use of metabolomics as a readout of molecular phenotypes has enabled the discovery of previously undetected associations between diseases and signaling and metabolic pathways. In addition, combining GWAS and metabolomic information allows the simultaneous analysis of the genetic and environmental impacts on homeostasis. Most success has been seen in metabolic diseases such as diabetes, obesity and dyslipidemia.
investigated genome-wideassociation with FEV 1 and FEV 1 !FVC in the SpiroMeta consortium using 20,288 individuals of European ancestry. They further per- formed a meta-analysis of top signals with data from direct genotyping in up to 32,184 individuals and in silico summary association data relating to a further 22,092 individuals. 29 The previously reported locus at 4q31 was confirmed, and associations with FEV 1 or FEV 1 !FVC and common variants at five additional loci were identified: 2q35 in TNS1 (P = 1.11 × 10 -12 ), 4q24 in GSTCD (2.18 × 10 -23 ), 5q33 in HTR4 (P = 4.29 × 10 -9 ), 6p21 in AGER (P = 3.07 × 10 -15 ) and 15q 23 in THSD4 (P = 7.24 × 10 -15 ). Reduction of FEV 1 ! FVC is a characteristic of obstructive lung diseases such as asthma, and natural variations in lung func- tion may have implications for asthma. Further stud- ies are necessary to clarify the relationship between genetic determinants of lung function and susceptibil- ity to asthma as well as its severity.
Correspondence to: Lan Tan. Department of Neurology, Qingdao Municipal Hospital, School of Medicine, Qingdao University, No. 5 Donghai Middle Road, Qingdao 266071, China. Email: email@example.com; Jin-Tai Yu. Department of Neurology, Qingdao Municipal Hospital, School of Medicine, Qingdao University, No. 5 Donghai Middle Road, Qingdao 266071, China. Email: firstname.lastname@example.org.
Abstract: Genome-wideassociation studies (GWAS) are a powerful tool for understanding the genetic underpinnings of human disease. In this article, we briefly review the role and findings of GWAS in common neurological diseases, including Stroke, Alzheimer’s disease, Parkinson’s disease, epilepsy, multiple sclerosis, migraine, amyotrophic lateral sclerosis, frontotemporal lobar degeneration, restless legs syndrome, intracranial aneurysm, human prion diseases and moyamoya disease. We then discuss the present and future implications of these findings with regards to disease prediction, uncovering basic biology, and the development of potential therapeutic agents.
Addis Ababa University, College of Health Sciences, Department of Pharmacology, P.O.Box 9086, Addis Ababa, Ethiopia
The aim of this study is to review the applications of genome-wideassociation studies (GWAS) in pharmacogenomics. GWAS have matured into a powerful tool to identify single nucleotide polymorphisms (SNPs) that are associated with various phenotypes. GWAS in pharmacogenomics are increasingly being performed to identify variants that affect therapeutic response and susceptibility to adverse drug reactions (ADRs). Such studies not only confirm previous findings but also identify novel variants. GWAS-identified and replication-confirmed variants for therapeutic response could be exemplified with SNPs in VKORC1 gene for coumarin anticoagulants, CYP2C19 gene for clopidogrel, and IL28B gene for interferon-alpha. For serious ADRs, significantly associated SNPs have been reported in human leukocyte antigen (HLA)-A*31:01 for carbamazepine-induced skin rash, SLCO1B1 gene for simvastatin-induced myopathy, and HLA-B*57:01 for flucloxacillin and HLA-DRB1*15:01 for lumiracoxib-induced liver injuries among others. Subsequent GWAS using larger sample sizes, and genotyping platforms with better marker SNP density could enhance the discovery of genetic variants on pharmacogenomic traits to advance clinical care.
Since the first reports in 2009, genome-wideassociation studies (GWAS) have been successful in identifying germline variants associated with glioma susceptibility. In this review we describe a chronological history of glioma GWAS, culminating in the most recent study comprising 12,496 cases and 18,190 controls. We additionally summarise associations at the 27 glioma risk SNPs that have been reported so far. Future efforts are likely to be principally focused on assessing association of germline risk SNPs with particular molecular subgroups of glioma, as well as investigating the functional basis of the risk loci in tumour formation. These ongoing studies will be important to maximise the impact of research into glioma susceptibility, both in terms of insight into tumour aetiology as well as opportunities for clinical translation.
310 Journal of Neuro-Oncology (2020) 147:309–315
Prognosis for medulloblastoma patients is poor, with a ten-year survival rate of 63% [ 6 ]. As a consequence of the disease and intensive treatment, the children who survive have an increased risk of long-term neurocognitive dysfunc- tion and secondary malignancies [ 7 ]. To improve treatment and prevention strategies for this devastating disease, a better understanding of medulloblastoma etiology is needed. We have conducted a genome-wideassociation study (GWAS) with the aim to identify genetic variants that are associated with medulloblastoma development in children and young adults. Identifying genetic variants that predispose to medul- loblastoma development may provide new insights into the genetic pathways that contribute to the development of the disease and potential new targets for therapy.
importance of checking for population specific risks as some variants may only confer risks in certain populations, as was the case in this study.
A pilot genome-wideassociation study of Extrapulmonary Tuberculosis
In the fifth chapter, I perform a GWAS analysis on the same population sample used in the candidate gene analysis of the previous chapter. In addition to associations that were found, the results also demonstrate the necessity of correcting for populations stratification in any GWAS as some associations that were specific to one of the subpopulations would have been missed had the population sample been used without accounting for differences in genetic ancestry. It is also important to note that the amount of noise in a GWAS makes population stratification more problematic than for the candidate gene study where very few noise variants, if any are expected since many of the variants in the study were known to have prior association with the disease. The results here demonstrate the advantage GWAS has in discovering novel biology, and while I was able to find new associations to
asthma, atopic dermatitis, genetics, Genome-WideAssociation Study, polymorphisms
The genome is the set of all genes, regulatory se- quences and other information included in the non- coding regions. 1,2 The Human Genome Project is a coordinated international project with the primary goal of determining the consensus sequence of the human genome. 1,2 After the draft sequence of the hu- man genome was reported in 2001, researchers fo- cused on the genomic variation among individuals. 3-6 A base variation is referred to as a single-nucleotide polymorphism (SNP), and humans have a heterozy- gous site roughly every 300 bases in their genome. 5 It has been suggested that there are about 10 million SNPs across the genome and one million SNPs in gene regions. 5 Some of them, especially around genes, are considered to influence variations in the timing, amount, or function of a protein produced from a gene and to contribute to disease risk. Most common diseases are caused by the interaction of ge- netic and environmental factors, and genetic vari-
PLoS ONE 2008;3:e3962
38. Gieger C, Geistlinger L, Altmaier E, Hrabe´ de Angelis M, Kronenberg F, Meitinger T, Mewes HW, Wichmann HE, Weinberger KM, Adamski J, Illig T, Suhre K. Genetics meets metabolomics: a genome-wideassociation study of metabolite profiles in human serum. PLoS Genet 2008;4:e1000282 39. Schaeffer L, Gohlke H, Mu¨ller M, Heid IM, Palmer LJ, Kompauer I, Demmelmair H, Illig T, Koletzko B, Heinrich J. Common genetic variants of the FADS1 FADS2 gene cluster and their reconstructed haplotypes are associated with the fatty acid composition in phospholipids. Hum Mol Genet 2006;15:1745–1756
Joint association analysis of multiple traits in a genome-wideassociation study (GWAS), i.e. a multivariate GWAS, offers several advantages over analyzing each trait in a separate GWAS. In this study we directly compared a number of multivariate GWAS methods using simulated data. We focused on six methods that are implemented in the software packages PLINK, SNPTEST, MultiPhen, BIMBAM, PCHAT and TATES, and also compared them to standard univariate GWAS, analysis of the first principal component of the traits, and meta-analysis of univariate results. We simulated data (N = 1000) for three quantitative traits and one bi-allelic quantitative trait locus (QTL), and varied the number of traits associated with the QTL (explained variance 0.1%), minor allele frequency of the QTL, residual correlation between the traits, and the sign of the correlation induced by the QTL relative to the residual correlation. We compared the power of the methods using empirically fixed significance thresholds (a = 0.05). Our results showed that the multivariate methods implemented in PLINK, SNPTEST, MultiPhen and BIMBAM performed best for the majority of the tested scenarios, with a notable increase in power for scenarios with an opposite sign of genetic and residual correlation. All multivariate analyses resulted in a higher power than univariate analyses, even when only one of the traits was associated with the QTL. Hence, use of multivariate GWAS methods can be recommended, even when genetic correlations between traits are weak.
Better understanding of biological system requires considering all markers simultaneously in the model. This makes the model capable of explaining phenotipic variance and consequently predicting quantitative traits or disease susceptibility of future individuals. The main challenge for this kind of stud- ies is the large number of markers in the model. Typically, in genome-wideassociation studies the number of markers, p, vastly exceeds the number of observations, n, that breaks down the main assumption in classical methods. To deal with p n problem, penalization or thresholding methods have been introduced in the frequentist context. On the other hand, Bayesian ap- proaches attempt to overcome this difficulty by specifying new form of priors. These priors can be divided into two main categories, shrinkage priors and mixture priors. Shrinkage priors are continuous priors concentrated at zero in order to shrink marker effects toward the origin. The rate of shrinkage that is controlled by hyperparameters of the priors should be adjusted auto- matically with the effect sizes, i.e, the magnitude of small effects toward zero should be stranger than the one for large effects. Another prior specification in high dimensional settings is based on discrete mixture of distributions. The main assumption of mixture priors is that set of markers is a collection of some set of markers with different patterns for size of effect. A widely applied mixture prior is mixture of set of zero and nonzero effect sizes.
Genome-WideAssociation Study (GWAS) — 27/37 — GWAS: independent single-variant tests across all genome-wide variants
• Quality control (QC) of the study dataset
• Choose a model/test for the phenotype of interest (e.g., linear regression model for quantitative traits, logistic regression model for dichotomous traits, other
Given the centrality of weight dysregulation to AN, genes implicated in the regulation of body weight might also be involved in the etiology of AN. 39, 40 Therefore genetic variants with a profound effect on BMI are worthy of consideration. 38
Two genome-wideassociation studies (GWAS) of AN have been conducted. One study that used DNA pooling and genotyping with a modest number of microsatellite markers with follow-up genotyping detected evidence for association with rs2048332 on chromosome 1, but this finding did not reach genome-wide significance. 41 A GWAS of 1033 AN cases from the USA, Canada, and Europe compared with 3733 pediatric controls yielded no genome- wide significant findings. 42 Recently, a sequencing and genotyping study of 152 candidate genes in 1205 AN cases and 1948 controls suggested a novel association of a cholesterol metabolism influencing EPHX2 gene with susceptibility to AN. 43
Recently, genome-wideassociation studies revealed that a genetic variant in the loci corresponding to histone deacetylase 9 (HDAC9) is associated with large vessel stroke. Moreover, HDAC9 expression was identified in human atherosclerotic plaques in different arteries. How- ever, the molecular mechanism of HDAC9 on atherogenesis is unknown. In this study, we demonstrate that HDAC9 in macrophage is athero- genic. Systemic and bone marrow cell deletion of HDAC9 decreases atherosclerosis in LDLr −/− mice. Macrophages lacking HDAC9 suppress foam cell formation by increasing cholesterol efflux via increased expression of macrophage ABCA1 and ABCG1 gene expression. Moreover, macrophages lacking HDAC9 produce less inflammatory mediators and polarize toward M2-like macrophages. These important finding points toward development of epigenetic therapy for atherosclerosis by using small molecule inhibitors that targets HDAC9 isoform in macrophages.
Genome-wideassociation studies are widely used today to discover genetic factors that modify the risk of complex diseases. Usually, these methods work in a SNP-by-SNP fashion. We present a gene-based test that can be applied in the context of genome-wideassociation studies. We compare both strategies, SNP-based and gene-based, in a sample of cases and controls for rheumatoid arthritis.
Summary: In the past decade, many genomewideassociation studies (GWASs) have been conducted to explore association of genetic variants with complex diseases using a case-control design. These GWASs not only collect information on the complex-disease status (primary phenotype, D) and the genetic variants (genotypes, X), but also collect extensive data on several risk factors and other quantitative traits. These additional traits (secondary phenotypes, Y ) may be associated with the primary disease outcome. One may analyze these secondary traits with the hope that the primary and the secondary traits share common genetic factors. An association study using these multiple traits can have improved power to identify the genetic variants associated with the complex disease than a study using the binary disease status alone. Secondary trait analysis is also conducted with the view that such traits measure one common underlying trait in which one may be interested in studying. This underlying trait of interest is different from the primary outcome and might be associated with it. Multivariate genetic association analysis with the secondary traits from a case-control sample is not straightforward since one may need to take care of the ascertainment bias arising from over-representation of cases in the sample. In this chapter, we explore the behavior of several methods for multivariate association test between a genetic variant and multiple secondary phenotypes under various scenarios of dependency among Y , X, and D. One popular strategy is to adjust for the case-control status in the analysis model. We have shown that the bias of estimated genetic effect from an approach with adjustment for disease status can be very different from that without adjustment. This bias, and hence type I error, is substantial when X as well as Y are associated with D, even though there is no association
ABSTRACT Genome-wideassociation studies (GWAS) are designed to identify the portion of single-nucleotide polymorphisms (SNPs) in genome sequences associated with a complex trait. Strategies based on the gene list enrichment concept are currently applied for the functional analysis of GWAS, according to which a signiﬁcant overrepresentation of candidate genes associated with a biological pathway is used as a proxy to infer overrepresentation of candidate SNPs in the pathway. Here we show that such inference is not always valid and introduce the program SNP2GO, which implements a new method to properly test for the overrepresentation of candidate SNPs in biological pathways.
San Diego, CA, USA. 4 - 8 March 2017
Genome-wideassociation studies often collect multiple phenotypes for complex diseases. Multivariate joint analyses have higher power to detect genetic variants compared with the marginal analysis of each phenotype and are also able to identify loci with pleiotropic effects. We extend the unified score-based association test to incorporate family structure, apply different approaches to analyze multiple traits in GAW20 real samples, and compare the results. Through simulation studies, we confirm that the Type I error rate of the pedigree-based unified score association test is appropriately controlled. In marginalanalysis of triglyceride levels, we found 1 subgenome-wide significant variant on chromosome 6. Joint analyses identified several suggestive genome-wide significant signals, with the pedigree-based unified score association test yielding the greatest number of significant results.