Progress in understanding the biological basis of polygenic disorders.

The completion of the Human Genome Project has unleashed a wealth of human genomics information, but it remains unclear how best to implement this information for the benefit of patients. The standard approach of biomedical research, with researchers pursuing advances in knowledge in the laboratory and, separately, clinicians translating research findings into the clinic as much as decades later, will need to give way to new interdisciplinary models for research in genomic medicine. These models should include scientists and clinicians actively working as teams to study patients and populations recruited in clinical settings and communities to make genomics discoveries-through the combined efforts of data scientists, clinical researchers, epidemiologists, and basic scientists-and to rapidly apply these discoveries in the clinic for the prediction, prevention, diagnosis, prognosis, and treatment of cardiovascular diseases and stroke. The highly publicized US Precision Medicine Initiative, also known as All of Us, is a large-scale program funded by the US National Institutes of Health that will energize these efforts, but several ongoing studies such as the UK Biobank Initiative; the Million Veteran Program; the Electronic Medical Records and Genomics Network; the Kaiser Permanente Research Program on Genes, Environment and Health; and the DiscovEHR collaboration are already providing exemplary models of this kind of interdisciplinary work. In this statement, we outline the opportunities and challenges in broadly implementing new interdisciplinary models in academic medical centers and community settings and bringing the promise of genomics to fruition.

Advances in the Genetics of Attention-Deficit/Hyperactivity Disorder

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.

Cardiovascular diseases (CVDs) are a major source of morbidity and mortality worldwide. Despite a decline of ≈30% over the past decade, heart disease remains the leading killer of Americans.1 For rare and familial forms of CVD, we are increasingly recognizing single-gene mutations that impart relatively large effects on individual phenotype. Examples include inherited forms of cardiomyopathy, arrhythmias, and aortic diseases. However, the prevalence of monogenic disorders typically accounts for a small proportion of the total CVD observed in the population. CVDs in the general population are complex diseases, with several contributing genetic and environmental factors. Although recent progress in monogenic disorders has occurred, we have seen a period of intense investigation to identify the genetic architecture of more common forms of CVD and related traits. Genomics serves several roles in cardiovascular health and disease, including disease prediction, discovery of genetic loci influencing CVD, functional evaluation of these genetic loci to understand mechanisms, and identification of therapeutic targets. For single-gene CVDs, progress has led to several clinically useful diagnostic tests, extending our ability to inform the management of afflicted patients and their family members. However, there has been little progress in developing genetic testing for complex CVD because individual common variants have only a modest impact on risk. The study of the genomics of complex CVDs is further challenged by the influence of environmental variables, phenotypic heterogeneity, and pathogenic complexity. Characterization of the clinical phenotype requires consideration of the clinical details of the diseases and traits under study. This update expands the prior scientific statement on the relevance of genetics and genomics for the prevention and treatment of CVDs.2 In the earlier report, we focused on the current status of the field, which consisted of predominantly family-based linkage studies and single-gene or mendelian mutations of relatively large phenotypic effect …

Common Disease: Are Causative Alleles Common or Rare?

A Genome-Wide Association Meta-Analysis of Attention-Deficit/Hyperactivity Disorder Symptoms in Population-Based Pediatric Cohorts

Sequence Kernel Association Tests for the Combined Effect of Rare and Common Variants

Diamonds in the Rough: Rare Variants Scratch the Surface

Author response: Genetic architecture of natural variation of cardiac performance from flies to humans

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Deciphering the genetic architecture of human cardiac disorders is of fundamental importance but their underlying complexity is a major hurdle. We investigated the natural variation of cardiac performance in the sequenced inbred lines of the Drosophila Genetic Reference Panel (DGRP). Genome-wide associations studies (GWAS) identified genetic networks associated with natural variation of cardiac traits which were used to gain insights as to the molecular and cellular processes affected. Non-coding variants that we identified were used to map potential regulatory non-coding regions, which in turn were employed to predict transcription factors (TFs) binding sites. Cognate TFs, many of which themselves bear polymorphisms associated with variations of cardiac performance, were also validated by heart-specific knockdown. Additionally, we showed that the natural variations associated with variability in cardiac performance affect a set of genes overlapping those associated with average traits but through different variants in the same genes. Furthermore, we showed that phenotypic variability was also associated with natural variation of gene regulatory networks. More importantly, we documented correlations between genes associated with cardiac phenotypes in both flies and humans, which supports a conserved genetic architecture regulating adult cardiac function from arthropods to mammals. Specifically, roles for PAX9 and EGR2 in the regulation of the cardiac rhythm were established in both models, illustrating that the characteristics of natural variations in cardiac function identified in Drosophila can accelerate discovery in humans. Editor's evaluation The authors investigated natural variation and new genetic mechanisms underlying cardiac performance using sequenced inbred lines of the Drosophila Genetic Reference Panel. The study provides insights into the genetic architecture of complex cardiac performance traits and represents an important resource for researchers studying cardiac performance. https://doi.org/10.7554/eLife.82459.sa0 Decision letter eLife's review process Introduction Heart diseases is a major cause of mortality (Bezzina et al., 2015). Although a large number of genome-wide association studies (GWAS) have identified hundreds of genetic variants related to cardiovascular traits (Roselli et al., 2018; van Setten et al., 2018; Shah et al., 2020; Verweij et al., 2020), we are very far from a comprehensive understanding of the genetic architecture of these complex traits. Deciphering the impact of genetic variations on quantitative traits is however critical for the prediction of disease risk. But disentangling the relative genetic and environmental contributions to pathologies is challenging due to the difficulty in accounting for environmental influences and disease comorbidities. Underlying epistatic interactions may also contribute to problems with replication in human GWAS performed in distinct populations which rarely take epistatic effects into account. In addition, linking a trait associated locus to a candidate gene or a set of genes for prioritization is not straightforward (Mackay, 2014, Boyle et al., 2017). Furthermore, the analysis of genetic factors related to cardiac traits is complicated by their interactions with several risk factors, such as increasing age, hypertension, diabetes mellitus, ischemic, and structural heart disease (Paludan-Müller et al., 2016). These pitfalls can be overcome using animal models. Model organisms allow precise controlling of the genetic background and environmental rearing conditions. They can provide generally applicable insights into the genetic underpinnings of complex traits and human diseases, due to the evolutionary conservation of biological pathways. Numerous studies have highlighted the conservation of cardiac development and function from flies to mammals. Indeed, orthologous genes control the early development as well as the essential functional elements of the heart. The fly is the simplest genetic model with a heart muscle and is increasingly used to identify the genes involved in heart disease and aging (Ocorr et al., 2007b; Diop and Bodmer, 2015; Rosenthal et al., 2010). Although a large number of genes are implicated in establishing and maintaining cardiac function in Drosophila (Neely et al., 2010), the extent to which genes identified from mutant analysis reflect naturally occurring variants is neither known, nor do we know how allelic variants at several segregating loci combine to affect cardiac performance. We previously showed that wild populations of flies harbor rare polymorphisms of major effects that predispose them to cardiac dysfunction (Ocorr et al., 2007a). Here, we analyzed the genetic architecture of the natural variation of cardiac performance in Drosophila. Our aims were to (i) identify the variants associated with cardiac traits found in a natural population, (ii) decipher how these variants interact with each other and with the environment to impact cardiac performance, and (iii) gain insights into the molecular and cellular processes affected. For this, we used the Drosophila Genetic Reference Panel (DGRP) (Mackay et al., 2012; Huang et al., 2014), a community resource of sequenced inbred lines. Previous GWAS performed in the DGRP indicate that inheritance of most quantitative traits in Drosophila is complex, involving many genes with small additive effects as well as epistatic interactions (Mackay and Huang, 2018). The use of inbred lines allows us to assess the effects of genetic variations in distinct but constant genetic backgrounds and discriminate genetic and environmental effects. We demonstrated substantial among-lines variations of cardiac performance and identified genetic variants associated with the cardiac traits together with epistatic interactions among polymorphisms. Candidate loci were enriched for genes encoding transcription factors (TFs) and signaling pathways, which we validated in vivo. We used non-coding variants - which represented the vast majority of identified polymorphisms – for predicting transcriptional regulators of associated genes. Corresponding TFs were further validated in vivo by heart-specific RNAi-mediated knockdown (KD). This illustrates that natural variations of gene regulatory networks have widespread impact on cardiac function. In addition, we analyzed the phenotypic variability of cardiac traits between individuals within each of the DGRP lines (i.e., with the same genotype), and we documented significant diversity in phenotypic variability among the DGRP lines, suggesting genetic variations influenced phenotypic variability of cardiac performance. Genetic variants associated with this phenotypic variability were identified and shown to affect a set of genes that overlapped with those associated with trait means, although through different genetic variants in the same genes. Comparison of human GWAS of cardiac disorders with results in flies identified a set of orthologous genes associated with cardiac traits both in Drosophila and in humans, supporting the conservation of the genetic architecture of cardiac performance traits, from arthropods to mammals. siRNA-mediated gene KD were performed in human induced pluripotent stem cells derived cardiomyocytes (hiPSC-CMs) to indeed show that dmPox-meso/hPAX9 and dmStripe/hEGR2 have conserved functions in cardiomyocytes from both flies and humans. These new insights into the fly’s genetic architecture and the connections between natural variations and cardiac performance permit the accelerated identification of essential cardiac genes and pathways in humans. Results Quantitative genetics of heart performances in the DGRP In this study, we aimed to evaluate how naturally occurring genetic variations affect cardiac performance in young Drosophila adults and identify variants and genes involved in the genetic architecture of cardiac traits. To assess the magnitude of naturally occurring variations of the traits, we measured heart parameters in 1-week-old females for 167 lines from the DGRP, a publicly available population of sequenced inbred lines derived from wild caught females (Figure 1A). Briefly, semi-intact preparations of individual flies (Ocorr et al., 2007c) were used for high-speed video recording combined with Semi-automated Heartbeat Analysis (SOHA) software (http://www.sohasoftware.com/) which allows precise quantification of a number of cardiac function parameters (Fink et al., 2009; Cammarato et al., 2015). Fly cardiac function parameters are highly influenced by sex (Wessells et al., 2004). Due to the experimental burden of analyzing individual cardiac phenotypes, we focused on female flies only and designed our experiment in the following way: we randomly selected 14 lines out of 167 that were replicated twice. The remaining 153 lines were replicated once. Each replicate was composed of 12 individuals. No block effect was observed due to the replicates in the 14 selected lines (see Supplementary file 1a). This allowed us to perform our final analysis on one replicate of each of the 167 lines. A total sample of 1956 individuals was analyzed. Seven cardiac traits were analyzed across the whole population (Figure 1—source data 1 and Table 1). As illustrated in Figure 1A, we analyzed phenotypes related to the rhythmicity of cardiac function: the systolic interval (SI) is the time elapsed between the beginning and the end of one contraction, the diastolic interval (DI) is the time elapsed between two contractions and the heart period (HP) is the duration of a total cycle (contraction+relaxation (DI+SI)). The arrhythmia index (AI, std-dev(HP)/mean (HP)) is used to evaluate the variability of the cardiac rhythm. In addition, three traits related to contractility were measured. The diameters of the heart in diastole (end diastolic diameter [EDD]), in systole (end systolic diameter [ESD]), and the fractional shortening (FS), which measures the contraction efficacy (EDD −ESD/EDD). We found significant genetic variation for all traits (Figure 1B and Figure 1—figure supplement 1) with broad sense heritability ranging from 0.30 (AI) to 0.56 (EDD) (Table 1). Except for EDD/ESD and HP/DI, quantitative traits were poorly correlated with each other (Figure 1—figure supplement 1). Figure 1 with 2 supplements see all Download asset Open asset Quantitative genetics and genome-wide associations studies (GWAS) for cardiac traits in the Drosophila Genetic Reference Panel (DGRP). (A) Left: Cardiac performance traits were analyzed in 167 sequenced inbred lines from the DGRP population. Approximately 12 females per line were analyzed. Right panels: Schematic of the Drosophila adult heart assay and example of M-mode generated from video recording of a beating fly heart. Semi-intact preparations of 1-week-old adult females were used for high-speed video recording followed by automated and quantitative assessment of heart size and function. The representative M-mode trace illustrate the cardiac traits analyzed. DI: diastolic interval; SI: systolic interval; HP: heart period (duration of one heartbeat); EDD: end diastolic diameter (fully relaxed cardiac tube diameter); ESD: end systolic diameter (fully contracted cardiac tube diameter). Fractional shortening (FS=EDD − ESD/EDD) and arrythmia index (AI=Std Dev (HP)/HP) were additionally calculated and analyzed. (B) Distribution of line means and within lines variations (box plots) from 167 measured DGRP lines for HP and EDD. DGRP lines are ranked by their increasing mean phenotypic values. For both phenotypes, representative M-modes from extreme lines are shown below (other traits are displayed in Figure 1—figure supplement 1). (C) Pearson residuals of chi-square test from the comparison of indicated single nucleotide polymorphism (SNP) categories in the DGRP and among variants associated with cardiac traits. According to DGRP annotations, SNPs are attributed to genes if they are within the gene transcription unit (5’ and 3’ UTR, synonymous and non-synonymous coding, introns) or within 1 kb from transcription start and end sites (1 kb upstream, 1 kb downstream). NA: SNPs not attributed to genes (>1 kb from transcription start site [TSS] and transcription end sites [TES]). (D) Comparison of gene sets identified by single marker using Fast-LMM (LMM) and in interaction using FastEpistasis (Epistasis). The Venn diagram illustrates the size of the two populations and their overlap. (E) Overlap coefficient of gene sets associated with the different cardiac traits analyzed. Figure 1—source data 1 Individual values for cardiac traits analyzed across the 167 Drosophila Genetic Reference Panel (DGRP) lines. Individual and DGRP line number are indicated. Phenotypic values were determined from high-speed video recording on dissected flies and movie analysis using Semi-automated Heartbeat Analysis (SOHA) (Mackay et al., 2012). https://cdn.elifesciences.org/articles/82459/elife-82459-fig1-data1-v1.xlsx Download elife-82459-fig1-data1-v1.xlsx Figure 1—source data 2 Variants identified by FastLMM as associated to indicated phenotypes. Among the 100 best ranked associations, only variants with MAF >4% were retained. Tables for variants mapped to genes and for variants that are not within gene mapping criteria (>1 kb from transcription start site [TSS] and transcription end sites [TES]) are indicated. https://cdn.elifesciences.org/articles/82459/elife-82459-fig1-data2-v1.xlsx Download elife-82459-fig1-data2-v1.xlsx Figure 1—source data 3 All FastEpistasis data on mean phenotypes, per quantitative trait. Single nucleotide polymorphism (SNP) ID, position, associated genes, and statistics are indicated for both focal SNPs (left) and their interacting SNPs (right). Each sheet displays the results for indicated quantitative traits, except for the first one which is a merge of all quantitative traits association analyses. https://cdn.elifesciences.org/articles/82459/elife-82459-fig1-data3-v1.xlsx Download elife-82459-fig1-data3-v1.xlsx Table 1 Quantitative genetics of cardiac traits in the Drosophila Genetic Reference Panel (DGRP). Summary statistics over all DGRP genotypes assayed. Number of lines and individuals (after outlier removal, see Materials and methods) analyzed for each cardiac trait is indicated. Mean, standard deviation (Std dev.), and coefficient of variation (Coef. Var) among the whole population are indicated. Genetic, environment, and phenotypic variance (respectively Genet. var, Env. var, and Phen. var) were calculated for each trait. Broad sense heritability of traits means (H2) suggested heritability of corresponding traits. Levene test indicated significant heterogeneity of the variance among the lines. DiastolicintervalsSystolicintervalsHeartperiodDiastolic DiameterSystolic diameterFractional shorteningArrhythmia Indextotal.nb.lines167167167167167167167mean0.46380.21660.688379.420051.05000.35380.2475Std dev.0.263300.032160.2769014.090009.493000.068370.29230Coef. var0.56770.14850.40220.17740.18600.19331.1810lines (mean)165166165159157158166Indiv. (mean)1914191119201779175317671832lines (Cve)165166165159157158166Indiv. (Cve)1914191119201779175317671832Genet. var2.59e-025.03e-042.87e-021.13e+024.39e+011.57e-032.21e-02Env. var4.36e-025.35e-044.82e-028.64e+014.65e+013.11e-036.35e-02Phen. var6.95e-021.04e-037.68e-021.99e+029.04e+014.68e-038.56e-02H20.3730.4850.3730.5660.4850.3350.258F value76,86411,68674,71546,95015,04111,16465,308Pr(F)8.8e-1202.3e-1875.8e-1167.1e-621.9e-2318.8e-1751.8e-96Levene test1.9e-101.9e-101.7e-081.6e-052.1e-131.6e-052.1e-13 GWAS analyses of heart performance To identify candidate variants associated with cardiac performance variation, we performed GWAS analyses and evaluated single marker associations of line means with common variants using a linear mixed model (Lippert et al., 2011) and after accounting for effects of Wolbachia infection and common polymorphism inversions (see Materials and methods). Genotype-phenotype associations were performed separately for all seven quantitative traits and variants were ranked based on their p-values. For most of the phenotypes analyzed, quantile-quantile (QQ) plots were uniform (Figure 1—figure supplement 2) and none of the variants reached the strict Bonferroni correction threshold for multiple tests (2 · 10–8), which is usual in the DGRP given the size of the population. However, the decisive advantage of the Drosophila system is that we can use GWA analyses as primary screens for candidate genes and mechanisms that can be subsequently validated by different means. We therefore chose to analyze the 100 top ranked variants for each quantitative trait. This choice is based on our strategy to test the selected single nucleotide polymorphisms (SNPs) and associated genes by a variety of approaches – data mining and experimental validation (see below) – in order to provide a global validation of association results and to gain insights into the characteristics of the genetic architecture of the cardiac traits. This cut-off was chosen in order to be able to test a significant number of variants while being globally similar to the nominal cut-off (10–5) generally used in DGRP analyses. A large proportion of the variants retained have indeed a p-value below 10–5. Selected variants were further filtered on the basis of minor allele frequency (MAF >4%) (Figure 1—source data 2, Supplementary file 1b). Among the seven quantitative traits analyzed, we identified 530 unique variants. These variants were associated to genes if they were within 1 kb of transcription start site (TSS) or transcription end sites (TES). Using these criteria, 417 variants were mapped to 332 genes (Supplementary file 1c). We performed a chi-squared test to determine if the genomic location of variants associated with cardiac traits is biased toward any particular genomic region when compared with the whole set of variants with MAF >4% in the DGRP population and obtained a p-value of 2.778e-13. Genomic locations of the variants were biased toward regions within 1 kb upstream of genes TSS, and, to a lesser extent, to genes 5’ UTR (Figure 1C). Variants not mapped to genes (located at >1 kb from TSS or TES) were slightly depleted in our set. In GWAS analyses, loci associated with a complex trait collectively account for only a small proportion of the observed genetic variation (Manolio et al., 2009) and part of this ‘missing heritability’ is thought to come from interactions between variants (Flint and Mackay, 2009; Manolio et al., 2009; Huang et al., 2012; Shorter et al., 2015). As a first step toward identifying such interactions, we used FastEpistasis (Schüpbach et al., 2010). SNP identified by GWAS were used as focal SNPs and were tested for interactions with all other SNPs in the DGRP. FastEpistasis reports best ranked interacting SNP for each starting focal SNP, thus extending the network of variants and genes associated to natural variation of cardiac performance, which were used for hypothesis generation and functional validations; 288 unique SNPs were identified, which were mapped to 261 genes (Figure 1—source data 3, Supplementary file 1e). While none of the focal SNPs interacted with each other, there is a significant overlap between the 332 genes associated with single marker GWAS and the 261 genes identified by epistasis (n=31, Figure 1D and Supplementary file 1e, fold change (FC)=6; hypergeometric pval=6.8 × 10–16). This illustrates that the genes that contribute to quantitative variations in cardiac performance have a tendency to interact with each other, although through distinct alleles. Taken together, single marker GWAS and epistatic interactions performed on the seven cardiac phenotypes identified a compendium of 562 genes associated with natural variations of heart performance (Supplementary file 1f). In line with the correlation noted between their phenotypes (Figure 1—figure supplement 1B), the GWAS for HP and DI identified partially overlapping gene sets (overlap index 0.23, Figure 1E). The same was true, to a lesser extent, for ESD and EDD (0.15). Otherwise, the sets of genes associated with each of the cardiac traits are poorly correlated with each other. Functional annotations and network analyses of association results Our next objective was to identify the biological processes potentially affected by natural variation in cardiac performance. Gene Ontology (GO) enrichment analysis of the combined single marker GWAS and epistatic interactions analyses indicated that genes encoding signaling receptors, TFs, and cell adhesion molecules were over-represented among these gene sets (pval=1.4 × 10–9 [FC=2.9], 5×10–4 [FC=2], and 3×10–3 [FC=4.6], respectively). There was also a bias for genes encoding proteins located at the plasma membrane, at ion channel complexes as well as components of contractile fibers (pval=3.4 × 10–10 [FC=3], 7×10–5 [FC=4.2], and 4×10–2 [FC=3.6]; Figure 2A; Supplementary file 2a). Of note, although a number of genes have previously been identified as being required during heart development or for the establishment and maintenance of cardiac function by single gene approaches, we found no enrichment for these gene categories in our analysis. In addition, genes identified in a global screen for stress-induced lethality following heart-specific RNAi KD (Neely et al., 2010) were also not enriched in GWAS detected genes (FC=1; Supplementary file 2b). This indicates that genes associated to natural variations of cardiac traits are typically missed by traditional forward or reverse genetics approaches, which highlights the value of our approach. Figure 2 with 2 supplements see all Download asset Open asset Functional annotations and validations of genes associated with genome-wide associations studies (GWAS) for cardiac performance. (A) Gene Ontology (GO) enrichment analyses. Selected molecular functions (MF, left) and cellular components (CC, right) associated with cardiac performances at FDR < 0.05 are shown. Enrichment analysis was performed using G:profiler with a correction for multitesting (see Materials and methods). (B) Interaction network of genes associated with natural variations of cardiac performance. Direct genetic and interactions between cardiac fly GWAS genes are genes interactions to single marker epistasis to the cardiac performances for which associations were and proteins highlighted in to transcription factors, in and to signaling pathways and and in to ion (C) the effects on indicated cardiac traits of heart-specific RNAi-mediated knockdown of genes identified in GWAS for cardiac performance. Results of test of the effects of indicated heart-specific RNAi-mediated gene KD for cardiac performance traits analyzed on semi-intact females data are in Figure data of genes tested to significant effects on cardiac performance traits indicate the for which the corresponding gene was associated in not were for multiple using Bonferroni Comparison with heart-specific effect of selected genes is displayed in Figure supplement (D) Schematic of and pathways in Drosophila. (E) Genetic interactions between and genes. Genetic interactions tested between and for and between and for (other phenotypes are shown in Figure supplement Cardiac traits were measured on each single and on that the interaction between and for and between and for are data for interaction effect corresponding to all phenotypes are displayed in Figure supplement Figure data 1 of and genetic interactions identified in Drosophila. Download Figure data 2 Data from validation RNAi validations and tests for genetic interactions among Download In order to gain the cellular and molecular pathways affected by natural variations of cardiac traits, we have mapped the associated genes and gene interaction networks. Of the 562 identified genes, were mapped to the fly that both and genetic interactions from both et al., 2011) and (see Materials and methods and Figure data 1). a proportion of the GWAS identified genes were within the fly and a large network of interacting (Supplementary file and Figure suggesting that they in common biological This network several TFs and ion channel complex genes, with their potential in the genetic architecture of natural variation of heart performance. components of signaling pathways are also in the of the and pathways (see Functional validations of candidate genes To assess in an in genes SNPs associated with variation in cardiac traits to these phenotypes, we selected GWAS associated genes for RNAi KD and tested the effects on cardiac performance. We selected genes that were identified in at two GWAS for two traits or that were to be in the adult heart et al., and for which RNAi lines were were tested in 1-week-old adult female using the heart-specific et al., and the same semi-intact heart preparations and analyses as for DGRP lines of the selected genes to of cardiac performance following heart-specific KD (Figure In we tested the effect on cardiac performance of genes randomly selected in the – the GWAS associated genes being from the (see Materials and methods and Figure supplement 1). Although a number of these genes to cardiac phenotypes when – which is with that quantitative traits can be influenced by a large number of genes et al., – when in the the genes selected from GWAS to phenotypes compared to the randomly selected genes (Figure supplement 1). These results therefore our association is important to that our is to the effects of gene of the variants may to gene function this can to a that is to In addition, of the associated variants may heart function by which not be replicated by RNAi We further focused on the of both and pathways were identified in our analyses. We tested different of the for cardiac phenotypes using RNAi KD (Figure and the of the and the and pathways are (Figure identification in our GWAS that they in a to heart function. may reflect their in different of cardiac development functional In order to discriminate between these two we tested if different components of these pathways interacted Single for of function show effects of and on several phenotypes, an of their in several cardiac traits (Figure supplement compared to each single flies showed phenotypes · suggesting a genetic interaction (Figure and Figure supplement is however that is also a transcriptional of the et al., The effect observed in can therefore as a of an signaling the We thus tested other allelic for of function of and pathways.

Editor's evaluation: Genetic architecture of natural variation of cardiac performance from flies to humans

Decision letter: Multiple alleles at a single locus control seed dormancy in Swedish Arabidopsis

T64 - Genome-Wide Association Study Of Posttraumatic Stress Disorder Symptoms In Two Cohorts Of United States Army Soldiers

Over the past 2 years, the pace of scientific discovery in human genetics related to atherothrombotic disease and vascular biology has been rapid, with no shortage of innovative articles published in ATVB. Several studies have identified novel loci by genetic association approaches, whereas others focused on validating genome-wide association study (GWAS) data functionally. Strides were also made with micro-RNAs (miRNAs) and their use as biomarkers and therapeutic targets in disease. Furthermore, molecular and mechanistic bases of certain genetic conditions, including dyslipidemias, were characterized. Here, we review genetic-themed articles published in ATVB since 2015, which highlight rapid advances in the field. A popular type of study in human genetics is the association study, which classically is performed in 1 of 2 forms.1 In the first, a quantitative phenotypic feature is measured in a population sample, genotypic strata are created from alleles of a common DNA variant, and differences between genotypic classes are tested statistically. In the second, cases with a discrete trait or disorder are matched with controls who are free of the trait. Both groups are genotyped for a common DNA variant, and differences in allele or genotype frequencies between cases and controls are evaluated statistically. Both forms of association studies can be performed millions of times with microarrays that genotype single nucleotide polymorphisms (SNPs) from across the human genome, with adjustments for multiple testing; the extreme case is GWAS. Often, when results are reported, there is no direct experimental testing of biological impact of the associated alleles. For instance, in 812 participants of the 15-year Bruneck study, a noncoding DNA microsatellite polymorphism in the promoter region of the HMOX1 gene, encoding heme oxidase-1 was associated with increased carotid atherosclerosis and a trend to higher levels of oxidized phospholipids; however, …

Cancer genetics has moved into a new era of discovering germ-line genetic variants that influence risk for specific cancers as a consequence of large-scale annotation of common genetic variation across the human genome. With the advent of fixed genotyping platforms and the investment in well-designed molecular epidemiology studies, the focus has shifted away from candidate gene studies towards large scale GWAS. In the process, we have seen clear evidence that substantial replication efforts are needed to conclusively identify new signals associated with cancer etiology. These studies seek to discover markers for disease, but rarely directly identify the variants biologically underlying the observed effect. Consequently, GWAS have primarily identified surrogate markers that necessitate follow-up fine mapping and laboratory analyses. So far, the published literature in cancer has reported approximately 100 novel regions of the genome associated with cancer etiology. GWAS have also demonstrated that the spectrum of cancers could have distinct genomic architectures, which reflects differential contributions of alleles with a range of frequencies and effect sizes. For nearly all regions, the estimated per allele effect sizes are less than 1.3 and the susceptibility alleles are common, with minor allele frequencies greater than 5%. The notable exception is for testicular cancer, which has a very strong tendency towards familial aggregation. In the GWAS of testicular cancer, common genetic markers on chromosome 12q21.32 were found to have per allele estimated risks of greater than 2.5; there is a plausible candidate gene in this region, KITLG. Still, because each common allele appears to confer a small effect, we are beginning to better appreciate the complex nature of common genetic variants that contribute to primary carcinogenesis. Furthermore, it is unlikely that there are many other common alleles with large effects for the common diseases, such as breast, colorectal, lung or prostate cancer because a sufficiently large enough number of cases and controls have been scanned. It is notable that the distribution of the number of regions identified by GWAS varies greatly by cancers, and does not appear to closely mimic hereditary estimates based on twins or family aggregation. For instance, GWAS have identified nearly 30 regions in the genome associated with prostate cancer, all of which are associated with early and aggressive cancers. On the other hand, for lung cancer, only three have been identified and one of them on chromosome 15q24/25 is also strongly associated with smoking behavior phenotypes. These observations raise the likelihood that for some cancers, a substantial environmental effect, such as smoking and lung cancer inhibits the ability to discover low penetrance, common alleles for these diseases. Because GWAS have been conducted in more than a dozen cancers, it is interesting to note that multiple cancers have mapped to two distinct regions, 8q24 and 5p15.33. The former, a ~600MB region centromeric to the MYC gene harbors a series of independent markers associated with breast, colorectal, prostate and urinary bladder cancer as well as chronic lymphocytic leukemia. Similarly, an unexpected spectrum of GWAS have pointed to common variants in a region of chromosome 5p15.33, which harbors the TERT-CLPTM1L locus; these include lung cancer (specifically adenocarcinoma), brain tumors, skin cancer (melanoma and basal cell carcinoma) and pancreatic cancer. This region also harbors less common mutations linked to acute myelogenous leukemia, dyskeratosis congenital (an inherited bone marrow failure syndrome) and pulmonary fibrosis. For each of the regions conclusively identified by GWAS, a series of follow-up studies are needed to fine map the most promising variants, preferably in different populations that can point investigators towards variants for studies designed to investigate plausibility for the association. Subsequently, laboratory investigation will be required to investigate each region that can lead to new insights into pathways and mechanisms underlying the association. To date, the reported GWAS have concentrated on regions associated with cancer etiology and not survival or other clinical outcomes. These suggests that common genetic variation may influence distinct processes important for carcinogenesis as opposed to those associated with disease progression. Though there has been considerable discussion of the promise of common variants as markers for disease risk, it is too early to apply the common variants detected in cancer GWAS to clinical and public health venues. Further studies are needed to map the markers and assess the relationship of multiple markers in sufficiently large studies. In addition, the analytical approach should account for the exposures to environmental and lifestyle choices that interact with germ-line genetic variants. The success of GWAS has opened new horizons for exploration and highlighted the complex genomic architecture of disease susceptibility. Citation Format: Stephen J. Chanock. Genome-wide association studies in cancer: What have we found and what next [abstract]. In: Proceedings of the AACR 101st Annual Meeting 2010; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr PL02-05