Skip to main content
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2017 Mar 22.
Published in final edited form as: Circulation. 2016 Feb 25;133(12):1155–1159. doi: 10.1161/CIRCULATIONAHA.116.021765

Reducing Cardiovascular Risk Using Genomic Information in the Era of Precision Medicine

Pradeep Natarajan 1,2,3, Christopher J O’Donnell 3,4,5
PMCID: PMC4803573  NIHMSID: NIHMS763369  PMID: 26915631

Overview

With the pace of current advances in genomics technologies, we are fast approaching an era when patients will have complete genome sequence information on which clinicians will need to act when making routine clinical decisions. Precision medicine is “an approach to disease treatment and prevention that seeks to maximize effectiveness by taking into account individual variability in genes, environment and lifestyle” (PMI Working Group report to the Advisory Committee to the Director, NIH, 2015). One central aim of the recently launched US Precision Medicine Initiative is the return of genetic results for clinical utility. Atherosclerotic cardiovascular disease (CVD), the leading cause of death in men and women, is a chronic disease influenced by lifelong exposure to inherited and environmental risk factors. The major clinical and biochemical atherosclerosis risk factors for coronary heart disease (CHD) and other forms of CVD have been well defined over the past fifty years by prospective population cohorts like the Framingham Heart Study and resulting randomized controlled treatment trials (RCTs). Genetics for CVD risk prediction provides the opportunity to more precisely identify individuals at high risk for developing disease for whom preventive therapy can be directed.

Our initial understanding regarding genetic risk for myocardial infarction (MI) and other forms of CHD has focused on rare (<1:100 carrier rate) monogenic etiologies conferring exceptional risk, such as mutations in genes for the low-density lipoprotein (LDL) receptor (LDLR), PCSK9 or APOB underlying the predisposition for familial hypercholesterolemia.1-4 However, due to the efforts of international consortia of investigators over the past decade, genome wide association studies (GWAS) of hundreds of thousands of research participants have led to the discovery of well over fifty common (>1:20 carrier rate) gene variants with strong evidence for modest increases in CHD risk5 as well as over one hundred and fifty common genetic variants with strong evidence for modest alterations of levels of key lipid fractions.6

Genetic risk scores for CHD

Despite many novel discoveries, translation of this rapidly growing catalogue of associated genetic variants into clinical application has lagged behind the pace of discovery. While some variants associated with CHD risk proximally influence traditional risk factors, the molecular mechanisms conferring CHD risk for a large proportion of these variants currently remains limited. Nevertheless, even without an understanding of the causal etiology for the many novel genetic variants, current investigation includes a focus on how the information can be used to predict and prevent disease. An individual’s CHD genetic risk score (GRS) is an additive score of the burden of discovered CHD risk alleles that is often weighted by the estimated disease effect of each allele. From available studies in older adults, an aggregate of CHD risk alleles can predict future risk of MI and other forms of CHD independently of conventional risk factors, although the incremental predictive benefit of current models appears modest.7-11 Nevertheless, in a recent post-hoc analysis of RCTs of statin therapy for primary and secondary prevention of CHD, persons with the highest burden of CHD risk alleles were not only at increased risk for CHD events but also, surprisingly, experienced enhanced absolute and relative clinical benefit despite similar LDL-cholesterol lowering.12 These data suggest that a CHD GRS may identify persons at increased risk who may be more likely to benefit from preventive interventions.

GRS for CHD risk reduction using lipid lowering in the MIGENES Trial

In a study published in in this issue of Circulation, Kullo et al have added to our understanding of the potential utility of a CHD GRS for lipid lowering in the MIGENES Clinical Trial13. In MIGENES, 203 participants without clinical CHD were randomized to receive a 10-year CHD risk estimation based upon a conventional risk factor score (CRS) alone or also with a CHD GRS. Participants randomized to CRS+GRS received risk information by a genetic counselor in addition to “shared decision-making regarding statin therapy” with a physician. Participants randomized to GRS + CRS were more likely to receive statins (39% vs 22%) and had 9.4 mg/dL lower LDL-cholesterol level than the CRS group. There was no evidence of adverse levels of anxiety in those randomized to GRS but there were also no beneficial changes in lifestyle behaviors, such as lower dietary fat intake or improved levels of physical activity. Within the CRS + GRS participants, participants with a high GRS did not have a significantly lower LDL-cholesterol than those with an average/low GRS.

MIGENES Trial strengths

Kullo et al are to be commended for an ambitious design and meticulous training program for use of genetic information by implementation of GRS based algorithm within the real world context of a health system with an electronic health record (EHR). The conceptualization of genetic risk by both providers and patients can be highly varied, and Kullo et al provide initial insights about applying common, complex genetics in the clinic. Kullo et al have provided an important initial step demonstrating that trials integrating genomics-based decision-making for the prevention and treatment of CHD and other forms of CVD can be successfully conducted. A careful review of this important trial warrants careful interpretation of its results and of the implications for future precision medicine trials.

Challenges for clinical trials using GRS information

Evolving Clinical Risk Score Algorithm

Even during the short course of this RCT, CHD clinical risk calculation and statin therapy guidelines have evolved. Framingham 10 year CHD risk factor scores14 have been updated more recently to assess “global” CVD risk.15 Recently updated risk scores in multiethnic populations have employed these risk factors in the prediction of global future CVD risk in multiple US populations, and the recent ACC/AHA guideline incorporates information from multiple cohorts to refine baseline risk estimates.16, 17 Such newer approaches appear to better determine statin eligibility.18

Evolving Genetic Risk Score Algorithm

The authors use a CHD GRS for their algorithm that became outdated over the course of the RCT. While the authors cite the 2013 CHD meta-GWAS19, they were unable to update the RCT for the updated 2015 meta-GWAS with additional novel loci and refined effect estimates at previously discovered loci.5

Interpretation of GRS prediction

The authors’ clinical-teaching tools provide evidence for patients indicating that GRS can increase the number of patients who will develop CHD events over and above risk factor score. However, only limited data are available for use to derive the teaching tools used to communicate risk/benefits to patients for the modest incremental prediction of risk from a CRS+GRS. Furthermore, it is unclear to what extent providers and patients are influenced by subtler changes in estimated risk using GRS, related to notions of “genetic determinism.”

Generalizability of GRS and risk factor algorithms for race/ethnicity

The authors conducted their study in only non-Hispanic whites and they acknowledge the need for further independent study of the generalizability of the risk factor algorithm, GRS and the teaching algorithms used to communicate risk / benefit information. That most research participants in CHD GWAS studies are largely of European ancestry is a major gap in population cardiovascular genomics research.

Interpretation of GRS actionability

The authors provide evidence that randomization to CRS+GRS versus CRS leads to increased statin prescriptions and decreased LDL cholesterol. In the “shared decision making” study design, it is difficult to know whether increased statin prescriptions were due to a lower threshold for statin prescriptions by the physician versus a lower threshold for patient refusal of a statin prescription. Interestingly, despite GRS value, LDL-cholesterol was similarly lowered suggesting that more understanding regarding appropriate interpretation and implementation is required. Furthermore, LDL-cholesterol lowering solely via statin prescription without accompanying alterations in lifestyle factors may not be the optimal approach. While recent analyses suggest that those at high genetic risk may enjoy greater clinical benefit for similar LDL-cholesterol lowering from statins,12 it is possible that personalized non-pharmacologic LDL-cholesterol lowering may also provide meaningful benefits.

Laying the groundwork for precision medicine

While there are a number of challenges to the conduct of this type of RCT and proper interpretation of its results, the authors have boldly laid the groundwork for implementation of future precision medicine trials using genetic information. Key considerations going forward will include:

Infrastructural challenges

The incorporation of dynamic risk assessment from evolving literature on clinical and genetic risk prediction acknowledges the exponential pace of the field attempting to catch up with the rapid data generation and urgent interest in clinical translation. Furthermore, with routine addition of genome sequence information, EHR systems will need to manage a huge bolus of diagnostic test data conferred by adding over 3 billion base pairs per individual to the EHR and continually updating the clinical annotation of genome sequence.

Educating patients and providers

The field of human genetics has evolved rapidly over the last two decades. Patients and providers typically have limited understanding of human genetics. Accurate representations and education regarding incremental risk and modifiable risk from polygenic risk scores are required to for appropriate interpretation.

Incorporating biology

Recent successful examples of effective novel pharmacotherapy tailored to genetics include new drugs for cystic fibrosis20-22 as well as the development of PCSK9 inhibitors. Rare genetic mutations in LDLR, PCSK9, and APOB result in high LDL cholesterol and increased risk for premature CHD; approaches to lower LDL-cholesterol reduce CHD risk in such patients. While current GRS approaches use an aggregate of CHD risk alleles, such alleles represent a diverse range of mechanisms influencing atherosclerosis many of which have yet to be characterized. Whether the driving biology from molecularly uncharacterized risk alleles is modifiable through alternative therapeutics is unknown.

Proper design of RCT in the genomic era

An RCT is optimal when included participants are at high risk for disease, the intervention carries a large relative benefit, and adverse events are minimal. A key question is the incremental value of genetics beyond clinical factors. Current approaches have focused on identifying those at highest absolute risk as preventive approaches with statins in clinical subgroups have had similar relative effects.23 With recent data suggesting that relative risk is increased in those at highest genetic risk,12 a 2×2 trial of statin vs placebo and high vs low genetic test may be needed to test whether there is an absolute risk difference between the two genetic groups and whether there is a relative risk difference with similar LDL-cholesterol lowering. In a “genotype-first” approach, eligible patients may be selected for lower risk for developing statin-related complications.24

Opportunities for the large precision medicine cohorts

We are now entering an exciting time in cardiovascular medicine with the advent of new health care associated cohorts orders of magnitude larger than traditional epidemiological cohorts, such as the planned Precision Medicine Initiative Cohort and related mega-cohorts including the Veterans Administration Million Veteran Program,. Such efforts will allow us to study the role of genetics on common diseases, such as atherosclerotic cardiovascular disease, in the real world at an unprecedented scale. Building on the lessons of the Framingham Heart Study and related cohorts, these 21st century precision medicine cohorts will provide key insights into cardiovascular biology CVD prevention and treatment.

Conclusion

Kullo et al show that when patients and providers are presented with an interpretation of polygenic CHD risk in an RCT of 203 participants, LDL-cholesterol is lowered largely via statin medications. There are several uncertainties and challenges about the role of genetics in the management and prevention of chronic disease. The national focus on precision medicine aims to catalyze an effort to better understand human disease biology, and optimize disease treatment and prevention using a combination of clinical, biochemical, and genetic factors.

Footnotes

Disclosures: None.

References

  • 1.Abifadel M, Varret M, Rabes JP, Allard D, Ouguerram K, Devillers M, Cruaud C, Benjannet S, Wickham L, Erlich D, Derre A, Villeger L, Farnier M, Beucler I, Bruckert E, Chambaz J, Chanu B, Lecerf JM, Luc G, Moulin P, Weissenbach J, Prat A, Krempf M, Junien C, Seidah NG, Boileau C. Mutations in PCSK9 cause autosomal dominant hypercholesterolemia. Nat Genet. 2003;34:154–6. doi: 10.1038/ng1161. [DOI] [PubMed] [Google Scholar]
  • 2.Innerarity TL, Weisgraber KH, Arnold KS, Mahley RW, Krauss RM, Vega GL, Grundy SM. Familial defective apolipoprotein B-100: low density lipoproteins with abnormal receptor binding. Proc Natl Acad Sci U S A. 1987;84:6919–23. doi: 10.1073/pnas.84.19.6919. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Goldstein JL, Basu SK, Brunschede GY, Brown MS. Release of low density lipoprotein from its cell surface receptor by sulfated glycosaminoglycans. Cell. 1976;7:85–95. doi: 10.1016/0092-8674(76)90258-0. [DOI] [PubMed] [Google Scholar]
  • 4.Do R, Stitziel NO, Won HH, Jorgensen AB, Duga S, Angelica Merlini P, Kiezun A, Farrall M, Goel A, Zuk O, Guella I, Asselta R, Lange LA, Peloso GM, Auer PL, Project NES, Girelli D, Martinelli N, Farlow DN, DePristo MA, Roberts R, Stewart AF, Saleheen D, Danesh J, Epstein SE, Sivapalaratnam S, Kees Hovingh G, Kastelein JJ, Samani NJ, Schunkert H, Erdmann J, Shah SH, Kraus WE, Davies R, Nikpay M, Johansen CT, Wang J, Hegele RA, Hechter E, Marz W, Kleber ME, Huang J, Johnson AD, Li M, Burke GL, Gross M, Liu Y, Assimes TL, Heiss G, Lange EM, Folsom AR, Taylor HA, Olivieri O, Hamsten A, Clarke R, Reilly DF, Yin W, Rivas MA, Donnelly P, Rossouw JE, Psaty BM, Herrington DM, Wilson JG, Rich SS, Bamshad MJ, Tracy RP, Adrienne Cupples L, Rader DJ, Reilly MP, Spertus JA, Cresci S, Hartiala J, Wilson Tang WH, Hazen SL, Allayee H, Reiner AP, Carlson CS, Kooperberg C, Jackson RD, Boerwinkle E, Lander ES, Schwartz SM, Siscovick DS, McPherson R, Tybjaerg-Hansen A, Abecasis GR, Watkins H, Nickerson DA, Ardissino D, Sunyaev SR, O’Donnell CJ, Altshuler D, Gabriel S, Kathiresan S. Exome sequencing identifies rare LDLR and APOA5 alleles conferring risk for myocardial infarction. Nature. 2015;518:102–6. doi: 10.1038/nature13917. doi: 10.1038/nature13917. Epub 2014 Dec 10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.CARDIoGRAMplusC4D A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat Genet. 2015;47:1121–30. doi: 10.1038/ng.3396. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, Ganna A, Chen J, Buchkovich ML, Mora S, Beckmann JS, Bragg-Gresham JL, Chang HY, Demirkan A, Den Hertog HM, Do R, Donnelly LA, Ehret GB, Esko T, Feitosa MF, Ferreira T, Fischer K, Fontanillas P, Fraser RM, Freitag DF, Gurdasani D, Heikkila K, Hypponen E, Isaacs A, Jackson AU, Johansson A, Johnson T, Kaakinen M, Kettunen J, Kleber ME, Li X, Luan J, Lyytikainen LP, Magnusson PK, Mangino M, Mihailov E, Montasser ME, Muller-Nurasyid M, Nolte IM, O’Connell JR, Palmer CD, Perola M, Petersen AK, Sanna S, Saxena R, Service SK, Shah S, Shungin D, Sidore C, Song C, Strawbridge RJ, Surakka I, Tanaka T, Teslovich TM, Thorleifsson G, Van den Herik EG, Voight BF, Volcik KA, Waite LL, Wong A, Wu Y, Zhang W, Absher D, Asiki G, Barroso I, Been LF, Bolton JL, Bonnycastle LL, Brambilla P, Burnett MS, Cesana G, Dimitriou M, Doney AS, Doring A, Elliott P, Epstein SE, Eyjolfsson GI, Gigante B, Goodarzi MO, Grallert H, Gravito ML, Groves CJ, Hallmans G, Hartikainen AL, Hayward C, Hernandez D, Hicks AA, Holm H, Hung YJ, Illig T, Jones MR, Kaleebu P, Kastelein JJ, Khaw KT, Kim E, Klopp N, Komulainen P, Kumari M, Langenberg C, Lehtimaki T, Lin SY, Lindstrom J, Loos RJ, Mach F, McArdle WL, Meisinger C, Mitchell BD, Muller G, Nagaraja R, Narisu N, Nieminen TV, Nsubuga RN, Olafsson I, Ong KK, Palotie A, Papamarkou T, Pomilla C, Pouta A, Rader DJ, Reilly MP, Ridker PM, Rivadeneira F, Rudan I, Ruokonen A, Samani N, Scharnagl H, Seeley J, Silander K, Stancakova A, Stirrups K, Swift AJ, Tiret L, Uitterlinden AG, van Pelt LJ, Vedantam S, Wainwright N, Wijmenga C, Wild SH, Willemsen G, Wilsgaard T, Wilson JF, Young EH, Zhao JH, Adair LS, Arveiler D, Assimes TL, Bandinelli S, Bennett F, Bochud M, Boehm BO, Boomsma DI, Borecki IB, Bornstein SR, Bovet P, Burnier M, Campbell H, Chakravarti A, Chambers JC, Chen YD, Collins FS, Cooper RS, Danesh J, Dedoussis G, de Faire U, Feranil AB, Ferrieres J, Ferrucci L, Freimer NB, Gieger C, Groop LC, Gudnason V, Gyllensten U, Hamsten A, Harris TB, Hingorani A, Hirschhorn JN, Hofman A, Hovingh GK, Hsiung CA, Humphries SE, Hunt SC, Hveem K, Iribarren C, Jarvelin MR, Jula A, Kahonen M, Kaprio J, Kesaniemi A, Kivimaki M, Kooner JS, Koudstaal PJ, Krauss RM, Kuh D, Kuusisto J, Kyvik KO, Laakso M, Lakka TA, Lind L, Lindgren CM, Martin NG, Marz W, McCarthy MI, McKenzie CA, Meneton P, Metspalu A, Moilanen L, Morris AD, Munroe PB, Njolstad I, Pedersen NL, Power C, Pramstaller PP, Price JF, Psaty BM, Quertermous T, Rauramaa R, Saleheen D, Salomaa V, Sanghera DK, Saramies J, Schwarz PE, Sheu WH, Shuldiner AR, Siegbahn A, Spector TD, Stefansson K, Strachan DP, Tayo BO, Tremoli E, Tuomilehto J, Uusitupa M, van Duijn CM, Vollenweider P, Wallentin L, Wareham NJ, Whitfield JB, Wolffenbuttel BH, Ordovas JM, Boerwinkle E, Palmer CN, Thorsteinsdottir U, Chasman DI, Rotter JI, Franks PW, Ripatti S, Cupples LA, Sandhu MS, Rich SS, Boehnke M, Deloukas P, Kathiresan S, Mohlke KL, Ingelsson E, Abecasis GR. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45:1274–83. doi: 10.1038/ng.2797. doi: 10.1038/ng.2797. Epub 2013 Oct 6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Paynter NP, Chasman DI, Pare G, Buring JE, Cook NR, Miletich JP, Ridker PM. Association between a literature-based genetic risk score and cardiovascular events in women. JAMA. 2010;303:631–7. doi: 10.1001/jama.2010.119. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Ripatti S, Tikkanen E, Orho-Melander M, Havulinna AS, Silander K, Sharma A, Guiducci C, Perola M, Jula A, Sinisalo J, Lokki ML, Nieminen MS, Melander O, Salomaa V, Peltonen L, Kathiresan S. A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses. Lancet. 2010;376:1393–400. doi: 10.1016/S0140-6736(10)61267-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Thanassoulis G, Peloso GM, Pencina MJ, Hoffmann U, Fox CS, Cupples LA, Levy D, D’Agostino RB, Hwang SJ, O’Donnell CJ. A genetic risk score is associated with incident cardiovascular disease and coronary artery calcium: the Framingham Heart Study. Circ Cardiovasc Genet. 2012;5:113–21. doi: 10.1161/CIRCGENETICS.111.961342. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Ganna A, Magnusson PK, Pedersen NL, de Faire U, Reilly M, Arnlov J, Sundstrom J, Hamsten A, Ingelsson E. Multilocus genetic risk scores for coronary heart disease prediction. Arterioscler Thromb Vasc Biol. 2013;33:2267–72. doi: 10.1161/ATVBAHA.113.301218. [DOI] [PubMed] [Google Scholar]
  • 11.Tikkanen E, Havulinna AS, Palotie A, Salomaa V, Ripatti S. Genetic risk prediction and a 2-stage risk screening strategy for coronary heart disease. Arterioscler Thromb Vasc Biol. 2013;33:2261–6. doi: 10.1161/ATVBAHA.112.301120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Mega JL, Stitziel NO, Smith JG, Chasman DI, Caulfield MJ, Devlin JJ, Nordio F, Hyde CL, Cannon CP, Sacks FM, Poulter NR, Sever PS, Ridker PM, Braunwald E, Melander O, Kathiresan S, Sabatine MS. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet. 2015;385:2264–71. doi: 10.1016/S0140-6736(14)61730-X. doi: 10.1016/S0140-6736(14)61730-X. Epub 2015 Mar 4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Kullo IJ, Jouni H, Austin EE, Brown SA, Kruisselbrink TM, Isseh IN, Haddad RA, Marroush TS, Shameer K, Olson JE, Broeckel U, Green RC, Schaid DJ, Montori VM, Bailey KR. Incorporating a Genetic Risk Score into Coronary Heart Disease Risk Estimates: Effect on LDL Cholesterol Levels (the MIGENES Clinical Trial) Circulation. 2016;133:XX–XXX. doi: 10.1161/CIRCULATIONAHA.115.020109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Wilson PW, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97:1837–47. doi: 10.1161/01.cir.97.18.1837. [DOI] [PubMed] [Google Scholar]
  • 15.D’Agostino RB, Sr., Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, Kannel WB. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117:743–53. doi: 10.1161/CIRCULATIONAHA.107.699579. [DOI] [PubMed] [Google Scholar]
  • 16.Goff DC, Jr., Lloyd-Jones DM, Bennett G, Coady S, D’Agostino RB, Gibbons R, Greenland P, Lackland DT, Levy D, O’Donnell CJ, Robinson JG, Schwartz JS, Shero ST, Smith SC, Jr., Sorlie P, Stone NJ, Wilson PW, Jordan HS, Nevo L, Wnek J, Anderson JL, Halperin JL, Albert NM, Bozkurt B, Brindis RG, Curtis LH, DeMets D, Hochman JS, Kovacs RJ, Ohman EM, Pressler SJ, Sellke FW, Shen WK, Smith SC, Jr., Tomaselli GF, American College of Cardiology. American Heart Association Task Force on Practice G ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014;129:S49–73. doi: 10.1161/01.cir.0000437741.48606.98. 2013. [DOI] [PubMed] [Google Scholar]
  • 17.Stone NJ, Robinson JG, Lichtenstein AH, Bairey Merz CN, Blum CB, Eckel RH, Goldberg AC, Gordon D, Levy D, Lloyd-Jones DM, McBride P, Schwartz JS, Shero ST, Smith SC, Jr., Watson K, Wilson PW. ACC/AHA Guideline on the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014;129:S1–S45. doi: 10.1161/01.cir.0000437738.63853.7a. 2013. [DOI] [PubMed] [Google Scholar]
  • 18.Pursnani A, Massaro JM, D’Agostino RB, Sr., O’Donnell CJ, Hoffmann U. Guideline-Based Statin Eligibility, Coronary Artery Calcification, and Cardiovascular Events. JAMA. 2015;314:134–41. doi: 10.1001/jama.2015.7515. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.CARDIoGRAMplusC4D. Deloukas P, Kanoni S, Willenborg C, Farrall M, Assimes TL, Thompson JR, Ingelsson E, Saleheen D, Erdmann J, Goldstein BA, Stirrups K, Konig IR, Cazier JB, Johansson A, Hall AS, Lee JY, Willer CJ, Chambers JC, Esko T, Folkersen L, Goel A, Grundberg E, Havulinna AS, Ho WK, Hopewell JC, Eriksson N, Kleber ME, Kristiansson K, Lundmark P, Lyytikainen LP, Rafelt S, Shungin D, Strawbridge RJ, Thorleifsson G, Tikkanen E, Van Zuydam N, Voight BF, Waite LL, Zhang W, Ziegler A, Absher D, Altshuler D, Balmforth AJ, Barroso I, Braund PS, Burgdorf C, Claudi-Boehm S, Cox D, Dimitriou M, Do R, Consortium D, Consortium C, Doney AS, El Mokhtari N, Eriksson P, Fischer K, Fontanillas P, Franco-Cereceda A, Gigante B, Groop L, Gustafsson S, Hager J, Hallmans G, Han BG, Hunt SE, Kang HM, Illig T, Kessler T, Knowles JW, Kolovou G, Kuusisto J, Langenberg C, Langford C, Leander K, Lokki ML, Lundmark A, McCarthy MI, Meisinger C, Melander O, Mihailov E, Maouche S, Morris AD, Muller-Nurasyid M, Mu TC, Nikus K, Peden JF, Rayner NW, Rasheed A, Rosinger S, Rubin D, Rumpf MP, Schafer A, Sivananthan M, Song C, Stewart AF, Tan ST, Thorgeirsson G, van der Schoot CE, Wagner PJ, Wellcome Trust Case Control C. Wells GA, Wild PS, Yang TP, Amouyel P, Arveiler D, Basart H, Boehnke M, Boerwinkle E, Brambilla P, Cambien F, Cupples AL, de Faire U, Dehghan A, Diemert P, Epstein SE, Evans A, Ferrario MM, Ferrieres J, Gauguier D, Go AS, Goodall AH, Gudnason V, Hazen SL, Holm H, Iribarren C, Jang Y, Kahonen M, Kee F, Kim HS, Klopp N, Koenig W, Kratzer W, Kuulasmaa K, Laakso M, Laaksonen R, Lee JY, Lind L, Ouwehand WH, Parish S, Park JE, Pedersen NL, Peters A, Quertermous T, Rader DJ, Salomaa V, Schadt E, Shah SH, Sinisalo J, Stark K, Stefansson K, Tregouet DA, Virtamo J, Wallentin L, Wareham N, Zimmermann ME, Nieminen MS, Hengstenberg C, Sandhu MS, Pastinen T, Syvanen AC, Hovingh GK, Dedoussis G, Franks PW, Lehtimaki T, Metspalu A, Zalloua PA, Siegbahn A, Schreiber S, Ripatti S, Blankenberg SS, Perola M, Clarke R, Boehm BO, O’Donnell C, Reilly MP, Marz W, Collins R, Kathiresan S, Hamsten A, Kooner JS, Thorsteinsdottir U, Danesh J, Palmer CN, Roberts R, Watkins H, Schunkert H, Samani NJ. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet. 2013;45:25–33. doi: 10.1038/ng.2480. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Accurso FJ, Rowe SM, Clancy JP, Boyle MP, Dunitz JM, Durie PR, Sagel SD, Hornick DB, Konstan MW, Donaldson SH, Moss RB, Pilewski JM, Rubenstein RC, Uluer AZ, Aitken ML, Freedman SD, Rose LM, Mayer-Hamblett N, Dong Q, Zha J, Stone AJ, Olson ER, Ordonez CL, Campbell PW, Ashlock MA, Ramsey BW. Effect of VX-770 in persons with cystic fibrosis and the G551D-CFTR mutation. N Engl J Med. 2010;363:1991–2003. doi: 10.1056/NEJMoa0909825. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Ramsey BW, Davies J, McElvaney NG, Tullis E, Bell SC, Drevinek P, Griese M, McKone EF, Wainwright CE, Konstan MW, Moss R, Ratjen F, Sermet-Gaudelus I, Rowe SM, Dong Q, Rodriguez S, Yen K, Ordonez C, Elborn JS, Group VXS A CFTR potentiator in patients with cystic fibrosis and the G551D mutation. N Engl J Med. 2011;365:1663–72. doi: 10.1056/NEJMoa1105185. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Wainwright CE, Elborn JS, Ramsey BW, Marigowda G, Huang X, Cipolli M, Colombo C, Davies JC, De Boeck K, Flume PA, Konstan MW, McColley SA, McCoy K, McKone EF, Munck A, Ratjen F, Rowe SM, Waltz D, Boyle MP, Group TS. Group TS Lumacaftor-Ivacaftor in Patients with Cystic Fibrosis Homozygous for Phe508del CFTR. N Engl J Med. 2015;373:220–31. doi: 10.1056/NEJMoa1409547. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Cholesterol Treatment Trialists C. Baigent C, Blackwell L, Emberson J, Holland LE, Reith C, Bhala N, Peto R, Barnes EH, Keech A, Simes J, Collins R. Efficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000 participants in 26 randomised trials. Lancet. 2010;376:1670–81. doi: 10.1016/S0140-6736(10)61350-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Link E, Parish S, Armitage J, Bowman L, Heath S, Matsuda F, Gut I, Lathrop M, Collins R. SLCO1B1 variants and statin-induced myopathy--a genomewide study. N Engl J Med. 2008;359:789–99. doi: 10.1056/NEJMoa0801936. [DOI] [PubMed] [Google Scholar]

RESOURCES