Genome-Wide Association Studies of Plasma Lipids
2010; Lippincott Williams & Wilkins; Volume: 30; Issue: 11 Linguagem: Inglês
10.1161/atvbaha.110.214643
ISSN1524-4636
Autores Tópico(s)Genetic Associations and Epidemiology
ResumoHomeArteriosclerosis, Thrombosis, and Vascular BiologyVol. 30, No. 11Genome-Wide Association Studies of Plasma Lipids Free AccessEditorialPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessEditorialPDF/EPUBGenome-Wide Association Studies of Plasma LipidsHave We Reached the Limit? Robert A. Hegele Robert A. HegeleRobert A. Hegele From the Schulich School of Medicine and Dentistry and Robarts Research Institute, University of Western Ontario, London, Ontario, Canada. Originally published1 Nov 2010https://doi.org/10.1161/ATVBAHA.110.214643Arteriosclerosis, Thrombosis, and Vascular Biology. 2010;30:2084–2086For those who have witnessed the evolution of genetic association studies of plasma lipids since 1983, the field has had its roller-coaster moments. The early high points followed from the application of "restriction fragment length polymorphism" analysis to yield candidate genotypes that were statistically evaluated for association with plasma lipoproteins. After 2 decades, though, the candidate gene approach had produced only negligible replicable associations, notably with genotypes for the canonical APOE isoforms and some others.1 Because of this inconsistency, enthusiasm for candidate gene association studies had essentially been exhausted by 2005. However, in late 2007, association studies experienced a renaissance and have generated important new findings for lipoprotein metabolism.See accompanying article on page 2264The reasons underlying this resurrection have been widely discussed2 and include both new technology—namely, microarrays to genotype hundreds of thousands of single-nucleotide polymorphisms (SNPs)—plus very large sample sizes, permitting metaanalysis of individual-level or summary data from tens of thousands of individuals. Over the last 3 years, genome-wide association studies (GWASs) have identified robust, replicable statistical associations for numerous complex diseases and traits, including plasma lipids. The basic GWAS design and workflow are shown in the Figure. The study by Waterworth et al in the current issue of Arteriosclerosis, Thrombosis, and Vascular Biology illustrates both strengths and limitations of the GWAS approach.3Download figureDownload PowerPointFigure. A path for discovery and translation phases of GWASs is shown. Step 1 indicates the study design: population-based or case-control samples for continuous and discrete trait analysis, respectively. After microarray-based genotyping, step 2 indicates the statistical testing approach: generally, linear regression analysis for a quantitative trait and χ2 analysis for a discrete trait. Step 3 shows a graphical display of the voluminous statistical testing from step 2: a "Manhattan plot" in which the genome is laid out in linear fashion along the x axis, with each point representing the negative logarithm of the probability value for association for the SNP genotype at that genomic location with the specific trait. A higher "peak" represents a more significant probability value. Step 4 indicates the selection step in which results are ranked by probability value and the most significantly associated loci are identified (threshold for significance is usually set at ≈5×10−8 to adjust for the large number of individual comparisons). Step 5 indicates the essential step of replicating the significant associations in independent data sets. This is the point at which Waterworth et al reported their findings.3 Additional experiments after this point include replications of the associations in a broader range of study samples (step 6), fine mapping to narrow the associated region to a particular gene or locus (step 7), resequencing of the narrowed locus to detect additional variants that are not on the microarray (step 8), and follow-up experiments involving the implicated genes and gene products, including studies of function in vitro and in vivo, studies of gene-gene and gene-environment interactions (step 9), and eventually clinical translation and application (step 10).The authors report metaanalyses of genotype associations with plasma lipids from >17,000 white European subjects, by combining GWAS data from 8 studies, followed by replications in >37,000 individuals from 8 additional studies and in a smaller sample of South Asian subjects. Significant SNP genotypes were then examined for their association with coronary artery disease risk in >9,600 cases and >38,000 controls.The findings confirm past associations of low-density lipoprotein–cholesterol (LDL-C), high-density lipoprotein–cholesterol, and triglyceride concentrations with 9, 10, and 9 loci, respectively. Several loci showed pleiotropic associations with ≥2 traits. Similar, albeit fewer significant associations were found in the smaller South Asian sample. The authors emphasized 4 new replicated genetic loci: MYLIP/GMPR and PPP1R3B for LDL-C, SLC39A8 for high-density lipoprotein–cholesterol, and AFF for triglyceride. They showed that ≈1/4 of the lipid-associated loci were also associated with coronary artery disease risk.However, despite its strong design and its interesting findings, this study is probably among the last of a dying breed. The landscape for reporting GWASs of plasma lipids changed forever in August 2010. Going forward, all future reports of genetic determinants of plasma lipids should be considered in light of the major achievement of organization and collaboration that characterized the Global Lipids Genetics Consortium (GLGC) metaanalysis.4In GLGC, >200 collaborators pooled results in which they screened the genomes of >100,000 white Europeans with ≈2.6 million common SNP genotypes and tested them for associations with plasma lipids. They reported 95 significantly associated loci, of which 59 were completely novel. The new loci contained genes that were both known to have roles in lipoprotein metabolism from nongenetic research but also contained previously unknown genes.The same 95 loci contributed to variation in lipid traits from both population-based epidemiological samples and to severe dyslipidemia phenotypes. Studies in 3 non-European ethnic groups showed similar associated loci. In a case-control analysis, almost all LDL-C-associated SNPs and a smaller proportion of triglyceride and high-density lipoprotein–cholesterol–associated SNPs were associated with coronary artery disease risk. Finally, functional studies validated 3 novel genes—GALNT2, PPP1R3B, and TTC39B—as playing mechanistic roles in determining plasma lipid concentrations.The GLGC report had all the desirable attributes of a comprehensive genetic association study, including huge numbers of study subjects drawn from multiple diverse samples, sustainable replications, and demonstration of a mechanistic impact of some genes and polymorphisms.5 The main findings from Waterworth et al were also seen in GLGC, including all 4 new replicated loci, although the triglyceride locus marked by AFF by Waterworth et al was designated KLHL8 in GLGC.3 The overlap of findings between the 2 studies is unsurprising: most samples (and authors) from Waterworth et al were subsumed into GLGC.3Another interesting wrinkle found by Waterworth et al was attribution of an association signal with LDL-C to CELSR2.3 In contrast, GLGC and other studies have implicated the neighboring gene SORT1 (encoding sortilin 1) as the source of the association signal at this locus; functional studies in a companion article to the GLGC report6 provide mechanistic data further supporting a causal role for SORT1 as the basis for the association signal. Interestingly, the locus also harbors another gene, PSRC1; perhaps all 3 genes somehow contribute to the LDL-C association. This will be an interesting story to monitor.With the GLGC, GWASs of lipids in whites may have reached the limit of what can be practically expected. The enormous statistical power in GLGC permitted sensitive detection of some loci with exquisitely small genetic effects. Although these loci may prove to be biologically relevant, the clinical or diagnostic relevance of any specific variant may be harder to demonstrate. Cumulatively, ≈30 SNP genotypes explain ≈10% of the total variation (and ≈25% of variation attributable to genetic factors) for each lipid trait.4An important lingering question regards the source or sources of the remaining unattributed or "missing" variability. One possibility seems to be low frequency genetic variants with large biochemical effects that cannot be detected with DNA microarrays. Such variants would require different types of arrays7 or DNA sequencing to be detected. In hypertriglyceridemia, rare heterozygous variants have already been shown to explain some of this missing genetic component.8Thus, as quickly as they illuminated the landscape, GWASs of plasma lipids may soon begin to fade from the scene. Future studies will include the following features: (1) assessing additional geographical ancestries; (2) evaluating rare genetic variants; (3) screening larger cohorts with clinical dyslipidemia; (4) using high-throughput functional characterization of gene products; (5) evaluating nonlinear and nonadditive genetic models; and (6) assessing gene-gene and gene-environment interactions. However, much of the groundwork has now been laid by reports like those from Waterworth et al and the GLGC.3 It will be exciting to follow the progress of GWAS data as they begin to have an impact on biological and medical research.Christopher Johansen provided helpful comments and assisted with the figure preparation. The author is supported by operating grants from the Heart and Stroke Foundation of Ontario (NA 6018 and NA 6059), the Canadian Institutes for Health Research (MOP 13430 and 79533), and Genome Canada through the Ontario Genomics Institute.FootnotesCorrespondence to Robert A. Hegele, MD, FRCPC, FACP, Blackburn Cardiovascular Genetics Laboratory, 4288A-100 Perth Drive, London, Ontario N6A 5K8, Canada. E-mail [email protected] References 1 Hegele RA. Plasma lipoproteins: genetic influences and clinical implications. Nat Rev Genet. 2009; 10: 109–121.CrossrefMedlineGoogle Scholar2 Hunter DJ, Altshuler D, Rader DJ. From Darwin's finches to canaries in the coal mine: mining the genome for new biology. N Engl J Med. 2008; 358: 2760–2763.CrossrefMedlineGoogle Scholar3 Waterworth D, et al. Genetic variants influencing circulating lipid levels and risk of coronary artery disease. Arterioscler Thromb Vasc Biol. 2010; 30: 2264–2276.LinkGoogle Scholar4 Teslovich TM, Musunuru K, Smith AV, et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature. 2010; 466: 707–713.CrossrefMedlineGoogle Scholar5 Hegele RA. SNP judgments and freedom of association. Arterioscler Thromb Vasc Biol. 2002; 22: 1058–1061.LinkGoogle Scholar6 Musunuru K, Strong A, Frank-Kamenetsky M, Lee NE, Ahfeldt T, Sachs KV, Li X, Li H, Kuperwasser N, Ruda VM, Pirruccello JP, Muchmore B, Prokunina-Olsson L, Hall JL, Schadt EE, Morales CR, Lund-Katz S, Phillips MC, Wong J, Cantley W, Racie T, Ejebe KG, Orho-Melander M, Melander O, Koteliansky V, Fitzgerald K, Krauss RM, Cowan CA, Kathiresan S, Rader DJ. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature. 466: 714–719.CrossrefMedlineGoogle Scholar7 Keating BJ, Tischfield S, Murray SS, Bhangale T, Price TS, Glessner JT, Galver L, Barrett JC, Grant SF, Farlow DN, Chandrupatla HR, Hansen M, Ajmal S, Papanicolaou GJ, Guo Y, Li M, Derohannessian S, de Bakker PI, Bailey SD, Montpetit A, Edmondson AC, Taylor K, Gai X, Wang SS, Fornage M, Shaikh T, Groop L, Boehnke M, Hall AS, Hattersley AT, Frackelton E, Patterson N, Chiang CW, Kim CE, Fabsitz RR, Ouwehand W, Price AL, Munroe P, Caulfield M, Drake T, Boerwinkle E, Reich D, Whitehead AS, Cappola TP, Samani NJ, Lusis AJ, Schadt E, Wilson JG, Koenig W, McCarthy MI, Kathiresan S, Gabriel SB, Hakonarson H, Anand SS, Reilly M, Engert JC, Nickerson DA, Rader DJ, Hirschhorn JN, Fitzgerald GA. Concept, design and implementation of a cardiovascular gene-centric 50 k SNP array for large-scale genomic association studies. PLoS One. 2008; 3: e3583.CrossrefMedlineGoogle Scholar8 Johansen CT, Wang J, Lanktree MB, Cao H, McIntyre AD, Ban MR, Martins RA, Kennedy BA, Hassell RG, Visser ME, Schwartz SM, Voight BF, Elosua R, Salomaa V, O'Donnell CJ, Dallinga-Thie GM, Anand SS, Yusuf S, Huff MW, Kathiresan S, Hegele RA. Excess of rare variants in genes identified by genome-wide association study of hypertriglyceridemia. Nat Genet. 42: 684–687.CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsCited By Cheung K, Li Y, Sham P and Chan D (2016) Genetics of Lumbar Disk Degeneration Advanced Concepts in Lumbar Degenerative Disk Disease, 10.1007/978-3-662-47756-4_6, (67-88), . O'Hare E, Wang X, Montasser M, Chang Y, Mitchell B and Zaghloul N (2014) Disruption of ldlr causes increased LDL-c and vascular lipid accumulation in a zebrafish model of hypercholesterolemia, Journal of Lipid Research, 10.1194/jlr.M046540, 55:11, (2242-2253), Online publication date: 1-Nov-2014. Ganesh S, Arnett D, Assimes T, Basson C, Chakravarti A, Ellinor P, Engler M, Goldmuntz E, Herrington D, Hershberger R, Hong Y, Johnson J, Kittner S, McDermott D, Meschia J, Mestroni L, O'Donnell C, Psaty B, Vasan R, Ruel M, Shen W, Terzic A and Waldman S (2013) Genetics and Genomics for the Prevention and Treatment of Cardiovascular Disease: Update, Circulation, 128:25, (2813-2851), Online publication date: 1-Dec-2013. Sharma A, Gulbahce N, Pevzner S, Menche J, Ladenvall C, Folkersen L, Eriksson P, Orho-Melander M and Barabási A (2013) Network-based Analysis of Genome Wide Association Data Provides Novel Candidate Genes for Lipid and Lipoprotein Traits, Molecular & Cellular Proteomics, 10.1074/mcp.M112.024851, 12:11, (3398-3408), Online publication date: 1-Nov-2013. Timpson N, Wade K and Smith G (2011) Mendelian Randomization: Application to Cardiovascular Disease, Current Hypertension Reports, 10.1007/s11906-011-0242-7, 14:1, (29-37), Online publication date: 1-Feb-2012. Nordestgaard B and Tybjærg-Hansen A (2011) Genetic determinants of LDL, lipoprotein(a), triglyceride-rich lipoproteins and HDL: concordance and discordance with cardiovascular disease risk, Current Opinion in Lipidology, 10.1097/MOL.0b013e32834477d2, 22:2, (113-122), Online publication date: 1-Apr-2011. November 2010Vol 30, Issue 11 Advertisement Article InformationMetrics https://doi.org/10.1161/ATVBAHA.110.214643PMID: 20962295 Originally publishedNovember 1, 2010 PDF download Advertisement
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