PIIS0092867412002887

Leading Edge e 5 4 y s s a ti o . ders, inherited DNA sequence variants play a role in conferring techniques, including sequencing and modeling human genetic a patient with homozygous familial hypercholesterolemia and of an interplay between multiple genes and nongenetic factors. es e Approaches to Discover Genes for CVD To discover genes for CVD and its risk factors in humans, two major approaches—linkage analysis and genetic association— 2007, approaches to accomplish this goal have matured, and genetic mapping for complex traits in humans has become a reality. The intellectual foundations that enabled a systematic have been utilized. The choice of approach has depended on the pattern of segregation, whether consistent with the ratios genome-wide screen of common variants (termed genome- wide association study [GWAS]) and results from this approach disease in cells. Mapping gene loci associated with complex traits requir substantial levels of information and analysis, but sinc risk for disease. For example, in the general population, a history of premature atherosclerotic CVD in a parent confers �3.0-fold increase in CVD risk to offspring (Lloyd-Jones et al., 2004). The precise magnitude of the role of inheritance, however, varies by disease and by other factors such as age of disease onset and subtype of disease. Over the past century, a key goal of biomedical research has been to correlate genotype with phenotype, i.e., to identify the specific genes and DNA sequence variants responsible for trait variation in humans. What is the principal reason to pursue this goal? Naturally occurring genetic variation has the unique potential to reveal causal biologic mechanisms in humans. This is particularly important, as some diseases like myocardial infarction (MI) are poorly modeled in nonhuman species. In this Review, we consider the approaches used to discover genes for human CVD and the lessons learned from the study of Mendelian and of common, complex forms of CVD, and we take a look forward at research driven by next generation uncovered a 5 kilobase deletion that eliminated several exons, representing the first demonstration of a mutation for Mendelian CVD (Lehrman et al., 1985). In 1989, linkage analysis was used to localize the chromosomal position of a causal gene for hypertrophic cardiomyopathy, and in the subsequent year, mutations in the beta cardiac myosin heavy chain were discov- ered as causal for the phenotype (Geisterfer-Lowrance et al., 1990; Jarcho et al., 1989). Other prominent examples in the CVD field include long QT syndrome, severe hypercholesterol- emia, a Mendelian family with early coronary artery disease, Mendelian forms of hypertension, Marfan’s syndrome, and several forms of congenital heart disease, including septal defects and valve defects (Abifadel et al., 2003; Basson et al., 1997; Berge et al., 2000; Curran et al., 1995; Dietz et al., 1991; Garcia et al., 2001; Garg et al., 2003, 2005; Lifton et al., 2001; Mani et al., 2007; Schott et al., 1998; Soria et al., 1989; Tartaglia et al., 2001). However, most CVD traits, such as MI or concentrations of plasma LDL cholesterol, show complex inheritance, suggestive Genetics of Human Card Sekar Kathiresan1,2,3,* and Deepak Srivastava4,5,* 1Center for Human Genetic Research and Cardiovascular Research C 2Department of Medicine, Harvard Medical School, Boston, MA 0211 3Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA 4Gladstone Institute of Cardiovascular Disease, San Francisco, CA, 9 5Departments of Pediatrics and Biochemistry & Biophysics, Universit *Correspondence: skathiresan@partners.org (S.K.), dsrivastava@glad DOI 10.1016/j.cell.2012.03.001 Cardiovascular disease encompasses a range of to congenital heart disease, most of which are in understanding the genes and specific DNA heritability. Here, we review the lessons learned cardiovascular disease. We also discuss key ch moving from genomic localization to mechanis next-generation sequencing and the use of plurip by which genetic variation contributes to disease Introduction Cardiovascular disease (CVD) is a leading health problem, affecting more than 80,000,000 individuals in the United States alone. CVD encompasses a broad range of disorders, including diseases of the vasculature, diseases of the myocardium, diseases of the heart’s electrical circuit, and congenital heart disease (Roger et al., 2012). For nearly all of these disor- 1242 Cell 148, March 16, 2012 ª2012 Elsevier Inc. Review iovascular Disease nter, Massachusetts General Hospital, Boston, MA 02114, USA , USA 158, USA of California, San Francisco, San Francisco, CA 94158, USA tone.ucsf.edu (D.S.) conditions extending from myocardial infarction heritable. Enormous effort has been invested equence variants that are responsible for this for monogenic and common, complex forms of llenges that remain for gene discovery and for c insights, with an emphasis on the impact of tent human cells to understand the mechanism described by Mendel or more complex. Some forms of CVD exhibit a simple pattern of inheritance suggestive of a single causal gene that confers a large effect on phenotype. For many of these Mendelian forms of CVD, direct DNA sequencing and/or linkage analysis have successfully yielded the causal gene and mutation. In 1985, Lehrman and colleagues directly sequenced the low-density lipoprotein receptor (LDLR) gene in were recently reviewed (Altshuler et al., 2008; O’Donnell and Nabel, 2011). The tools and methods included catalogs of polymorphisms, techniques to genotype these DNA sequence variants, and the analytical framework to distinguish true association signal from false positives. The initial focus has been on utilizing common DNA sequence variants (variants with allele frequency > 1:20) as a discovery tool, largely because it was practical to do so; recent advances in DNA sequencing and genotyping technology will allow interrogation of less frequent variants and will be considered below. The National Human Genome Research Institute hosts a catalog of published GWAS results, and as of January 14, 2012, the catalog includes 1,143 publications and 5,585 single-nucleotide polymorphisms (SNPs) with association evidence at p < 10�5 (Hindorff et al., 2009). What have we learned from these gene discovery efforts? Below, we discuss lessons that have emerged from geno- type-to-phenotype correlation studies for Mendelian diseases, followed by lessons from studies of common, complex diseases. Lessons Learned from the Study of Mendelian Forms of CVD Rare Variants Lead to Broadly Relevant Insights Because Mendelian diseases are rare in the population, there was initial skepticism about whether the genes and mechanisms that cause these diseases would inform our understanding of common forms of CVD. Linkage studies involve identifying and recruiting families with unique, often severe phenotypes, isolating a chromosomal segment that tracks with disease status in the family, and then pinpointing the causal gene and mutation in the linked segment. For a range of conditions, the genes identified by these linkage studies have transformed our understanding of CVD. Selected examples of Mendelian diseases, the responsible genes, and the gleaned biological and clinical insights are detailed in Table 1. Of particular significance is monogenic severe hypercholesterolemia, for which the six responsible genes have led to fundamental new biological concepts and have supported the development of new therapies. Genotype-Phenotype Correlation Can Be Complex Even in Monogenic Disorders Although there are cases in which single gene mutations lead to straightforward genotype-phenotype associations, other more complex relationships exist as well. This complexity can arise from three distinct genetic phenomena: pleiotropy, penetrance, and expressivity. Sometimes, mutations in a single gene can influence multiple phenotypic traits (i.e., pleiotropy). In 1995, Wang, Keating, and colleagues identified the a subunit of the type V voltage-gated sodium channel (SCN5A) as the cause of inherited long QT syndrome type 3 (Wang et al., 1995). Since then, mutations in the same gene have been demonstrated to cause Brugada syndrome (right precordial ST-segment eleva- tion and increased risk for ventricular arrhythmias), cardiac conduction system disease, and dilated cardiomyopathy (Chen et al., 1998; McNair et al., 2004; Schott et al., 1999). This range of disease phenotypes may reflect the underlying functionality of the channel. Among carriers of a Mendelian mutation in a given family, some may exhibit the condition and others may not. Penetrance refers to the proportion of individuals with a given genotype who exhibit the phenotype associated with the genotype. In many Mendelian cardiovascular conditions inherited in an autosomal-dominant manner, there is evidence for incomplete penetrance. For example, Hobbs and colleagues reported that, in a pedigree with familial hypercholesterolemia due to a point mutation in LDLR, only 12 out of 18 heterozygotes had high LDL cholesterol (>95th percentile), whereas some of the remain- ing 6 had LDL cholesterol as low as 28th percentile for the population (Hobbs et al., 1989). The lack of a high-cholesterol phenotype given the same genotype may be due to modifier genes or environmental influences. Individuals with the same Mendelian genotype can also show different degrees of the same phenotype. Expressivity is the degree to which trait expression differs among individuals. Marfan’s syndrome is a multisystem Mendelian disorder that can include a range of signs and symptoms involving the skeletal system (pectus excavatum, increased arm span to height ratio, craniofacial alterations), ocular system (eye lens dislocation, flat cornea), and cardiovascular system (aortic aneurysm, dissection of the ascending aorta, mitral valve prolapse), among others (Can˜adas et al., 2010). Dietz and colleagues identified mutations in the FBN1 gene encoding the extracellularmatrix protein fibrillin 1 as responsible forMarfan’s syndrome (Dietz et al., 1991).When a specific mutation in the fibrillin 1 (FBN1) gene causes Marfan’s syndrome in a family, carriers of the same mutation can display variable clinical manifestations (Faivre et al., 2007). Pleiotropy, penetrance, expressivity, and nongenetic factors conspire to ensure that, even in a single gene disorder, genotype does not ‘‘equal’’ a specific phenotype. This complexity has several consequences. First, gene discovery is more difficult, as genotype may not segregate perfectly with phenotype, thereby reducing the power of linkage. Second, there is intense interest in identifying modifiers—genetic or environmental—that may modulate the relationship between genotype and pheno- type. Finally, because of this complexity, in many Mendelian diseases, it has been difficult to develop genotype-specific prognostic or treatment recommendations. The Long Road from Genotype to Mechanism to Treatment For Marfan’s syndrome, the path from the discovery of FBN1 as the causal gene to a breakthrough in the molecular under- standing of the disease has spanned more than two decades. Historically, Marfan’s syndrome had been viewed as a structural disease due to a defect in elastic fibers (Lindsay andDietz, 2011). The identification of mutations in an extracellular matrix protein seemed to confirm this view. However, more recent studies suggest that microfibrils normally bind the large latent complex of the cytokine transforming growth factor b (TGF-b) and that failure of this event to occur results in increased TGF-b activation and signaling. Now, investigators are exploring the hypothesis that blocking TGF-b signaling will ameliorate the growth of aortic aneurysms in Marfan’s syndrome. For further examples of therapeutic approaches derived from the study of Mendelian disorders, we refer the reader to a recent review on this topic (Dietz, 2010). Cell 148, March 16, 2012 ª2012 Elsevier Inc. 1243 e ica r- r f o an 5) c Table 1. Selected Examples of Mendelian Diseases and Insights D Mendelian Condition Causal Genes Key Biolog Severe hypercholesterolemia LDLR, APOB, ABCG5, ABCG8, ARH, PCSK9 (1) Recepto (2) receptor regulation o mechanism absorption excretion; ( sufficient to Lessons Learned from the Study of Common, Complex Forms of CVD Gene Variants across the Spectrum of Allele Frequency Contribute to Most Complex, Common Diseases and Quantitative Traits Variants associated with common, complex traits range in frequency from common (>1:20 frequency) to low-frequency (1:1,000 to 1:20) to very rare (<1:1,000). In other words, genetic heterogeneity from variants across the frequency spectrum may be the rule. Consider the example of plasma triglycerides, a phenotype that marks triglyceride-rich lipoproteins, including very low-density lipoprotein particles, chylomicrons, and remnant products of their metabolism. Roughly 50% of the inter- individual variability in plasma triglycerides is estimated to be Familial hypobetalipoproteinemia APOB, PCSK9, ANGPTL3 (1) Lifelong lo (from loss-of sufficient to p other corona Mendelian forms of low and high blood pressure SLC12A3, SLC12A1, KCNJ1, CLCNKB, NR3C2, SCNN1A, SCNN1B, SCNN1G; CYP11B2, CYP11B1, HSD11B2, NR3C2, SCNN1B, SCNN1G, WNK1, WNK4, KLHL3, CUL3 (1) Genes co pathway of a handling and of new target of blood pres Hypertrophic cardiomyopathy MYH7, TNNT2, TPM1, TNNI3, MYL2, MYBPC3, ACTC, MYL3 (1) Mutations of the molecu muscle contr cause increa myocyte with neighboring fi fibrosis and s Marfan’s syndrome FBN1 (1) Aneurysm perturbations cascades an contractile ap in the extrace role for TGF- Atrial or ventricular septal defects NKX2-5, GATA-4, TBX5 (1) These tran discovered in for proper he and function Bicuspid aortic valve, Calcific aortic valve disease NOTCH1 (1) NOTCH1 osteoblast fa cells; (2) NOT in a derepres subsequent d into an osteo 1244 Cell 148, March 16, 2012 ª2012 Elsevier Inc. rived from the Study of Causal Genes l and Clinical Insights References mediated endocytosis; ecycling; (3) feedback receptors; (4) molecular f intestinal cholesterol d biliary cholesterol high LDL cholesterol is ause MI. (Abifadel et al., 2003; Brown and Goldstein, 1986; Garcia et al., 2001; Lehrman et al., 1985) on the basis of DNA sequence variants. Johansen, Hegele, and colleagues studied individuals from the extremes of the plasma triglyceride distribution (438 individuals with high triglycerides [mean triglycerides = 14.2 mmol/l] and 327 individuals with low triglycerides [mean triglycerides = 1.2 mmol/l]) (Johansen et al., 2010) using both GWAS and resequencing of selected genes. In the GWAS, common variants at seven loci were associated with plasma triglycerides, and in the resequencing study, there was an excess of rare, nonsynonymous variants across four genes in individuals with high triglycerides when compared with those with low triglycerides. A comprehensive logistic regression model including clinical variables and both common and rare genetic variants explained 42% of total variation in hypertriglyceridemia diagnosis: clinical variables explained w-LDL cholesterol -PCSK9 function) is rotect from MI despite ry risk factors. (Cohen et al., 2006; Musunuru et al., 2010a; Soria et al., 1989) nverge on a final common ltering net renal sodium balance; (2) identification s for the treatment sure. (Boyden et al., 2012; Chang et al., 1996; Geller et al., 2000; Geller et al., 1998; Hansson et al., 1995; Lifton et al., 1992a, 1992b, 2001; Mune et al., 1995; Shimkets et al., 1994; Simon et al., 1996a, 1996b, 1996c, 1997; Wilson et al., 2001) have expanded knowledge lar mechanisms of heart action; (2) mutations may sed TGF-b signaling in the subsequent effects on broblasts, leading to carring. (Bonne et al., 1995; Carrier et al., 1993; Geisterfer-Lowrance et al., 1990; Kimura et al., 1997; Olson et al., 2000; Poetter et al., 1996; Seidman and Seidman, 2001; Thierfelder et al., 1994; Watkins et al., 1995) formation is likely due to in cytokine signaling d the smooth muscle paratus rather than defects llular matrix; (2) unexpected b pathway in disease. (Dietz et al., 1991; Lindsay and Dietz, 2011) scription factors, originally flies and mice, are critical art development in humans in a common complex. (Basson et al., 1997; Garg et al., 2003; Schott et al., 1998) functions to repress a default te of the valve mesenchymal CH1 mutations likely result sion of this fate choice and ifferentiation of valve cells blast-like phenotype. (Garg et al., 2005) 20%, common genetic variants in seven loci explained 21%, and rare genetic variants in four loci explained 1%. The genetic architecture for triglycerides in the population appears to be that of a mosaic comprised of large-effect variants rare in frequency, small-effect variants common in frequency, and environmental influences. More generally, the concept of a mosaic model is supported by the fact that, for many cardiovascular traits and diseases, there is strong overlap between the genes mapped using GWAS and those identified earlier through Mendelian families. Nineteen genes have been identified as monogenic causes of extremely low or high levels of LDL cholesterol, high-density lipo- protein (HDL) cholesterol, and triglycerides; loci harboring 16 of these geneswere alsomapped usingGWAS (Figure 1) (Teslovich et al., 2010). Rare mutations in FBN1 cause the thoracic aortic aneurysms and dissections seen inMarfan’s syndrome, whereas common SNPs in the introns of FBN1 are the top association result in a GWAS for spontaneous, nonsyndromic thoracic aortic aneurysm and dissection (Lemaire et al., 2011). Rare mutations in SCN5A, KCNQ1, KCNH2, KCNE1, and KCNJ2 cause mono- genic long QT syndrome, whereas common SNPs in these five genes are associated with QT interval measured on electrocar- diograms in the population (Newton-Cheh et al., 2009). New Biological Insights from GWAS Genes Plasma lipids, platelets, and sickle cell disease represent three fields in which there has been progress toward new biology based on GWAS. GWAS for plasma LDL cholesterol, HDL cholesterol, and triglycerides have evaluated > 100,000 partic- ipants and have mapped 95 distinct loci associated with at least one of these traits at a stringent statistical threshold (p < 5 3 10�8) (Kathiresan et al., 2008, 2009b; Pollin et al., 2008; Teslovich et al., 2010; Willer et al., 2008). Approximately one-third of the loci harbored genes previously appreciated to play a role in lipoprotein metabolism, including five targets of lipid-modifying therapies: HMGCR (statins), NPC1L1 (ezeti- mibe), APOB (mipomersen), CETP (anacetrapib, dalcetrapib, and evacetrapib), and PCSK9 (therapies in development by several pharmaceutical companies) (Figure 1). Of note, the proportion of overall phenotypic variance ex- plained by a genetic variant may have little correlation with the ultimate therapeutic or biological value of the gene mapped by the variant. Phenotypic variance explained by a variant is a function of two key parameters: allele frequency and effect size. For Mendelian diseases, the causal variants typically confer large effects but explain a small proportion of trait vari- ance due to their rare frequencies. Variants from GWAS are common but explain a small proportion of trait variance due to modest effects. Nevertheless, variants that explain a small proportion of phenotypic variance may provide substantial bio- logical or therapeutic insights. This has been highlighted f