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Translating genetic association of lipid levels for biological and clinical application

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Cardiovascular Drugs and Therapy Aims and scope Submit manuscript

Abstract

Purpose of review

This review focuses on the foundational evidence from the last two decades of lipid genetics research and describes the current status of data-driven approaches for transethnic GWAS, fine-mapping, transcriptome informed fine-mapping, and disease prediction.

Recent findings

Current lipid genetics research aims to understand the association mechanisms and clinical relevance of lipid loci as well as to capture population specific associations found in global ancestries. Recent genome-wide trans-ethnic association meta-analyses have identified 118 novel lipid loci reaching genome-wide significance. Gene-based burden tests of whole exome sequencing data have identified three genes—PCSK9, LDLR, and APOB—with significant rare variant burden associated with familial dyslipidemia. Transcriptome-wide association studies discovered five previously unreported lipid-associated loci. Additionally, the predictive power of genome-wide genetic risk scores amalgamating the polygenic determinants of lipid levels can potentially be used to increase the accuracy of coronary artery disease prediction.

Conclusions

Lipids are one of the most successful group of traits in the era of genome-wide genetic discovery for identification of novel loci and plausible drug targets. However, a substantial fraction of lipid trait heritability remains unexplained. Further analysis of diverse ancestries and state of the art methods for association locus refinement could potentially reveal some of this missing heritability and increase the clinical application of the genomic association results.

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References

  1. Psaty BM, O'Donnell CJ, Gudnason V, et al. Cohorts for heart and aging research in genomic epidemiology (CHARGE) consortium: design of prospective meta-analyses of genome-wide association studies from 5 cohorts. Circ Cardiovasc Genet. 2009;2:73–80.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Teslovich TM, Musunuru K, Smith AV, et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature. 2010;466:707–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Lango Allen H, Estrada K, Lettre G, et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature. 2010;467:832–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. O'Donnell CJ, Kavousi M, Smith AV, et al. Genome-wide association study for coronary artery calcification with follow-up in myocardial infarction. Circulation. 2011;124:2855–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Schunkert H, König IR, Kathiresan S, et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat Genet. 2011;43:333–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Klein RJ, Zeiss C, Chew EY, et al. Complement factor H polymorphism in age-related macular degeneration. Science. 2005;308:385–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Nielsen JB, Thorolfsdottir RB, Fritsche LG, et al. Biobank-driven genomic discovery yields new insight into atrial fibrillation biology. Nat Genet. 2018;50:1234–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Klarin D, Damrauer SM, Cho K, et al. Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program. Nat Genet. 2018;50:1514–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Richardson TG, Sanderson E, Palmer TM, et al. Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: a multivariable Mendelian randomisation analysis. PLoS Med. 2020;17:e1003062.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Dron JS, Hegele RA. Polygenic influences on dyslipidemias. Curr Opin Lipidol. 2018;29:133–43.

    Article  CAS  PubMed  Google Scholar 

  11. Hachem SB, Mooradian AD. Familial dyslipidaemias: an overview of genetics, pathophysiology and management. Drugs. 2006;66:1949–69.

    Article  CAS  PubMed  Google Scholar 

  12. Bamshad MJ, Ng SB, Bigham AW, et al. Exome sequencing as a tool for Mendelian disease gene discovery. Nat Rev Genet. 2011;12:745–55.

    Article  CAS  PubMed  Google Scholar 

  13. Wolford BN, Hornsby WE, Guo D, et al. Clinical implications of identifying pathogenic variants in individuals with thoracic aortic dissection. Circ Genom Precis Med. 2019;12:e002476.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Lange LA, Willer CJ, Rich SS. Recent developments in genome and exome-wide analyses of plasma lipids. Curr Opin Lipidol. 2015;26:96–102.

    Article  CAS  PubMed  Google Scholar 

  15. van der Laan SW, Harshfield EL, Hemerich D, Stacey D, Wood AM, Asselbergs FW. From lipid locus to drug target through human genomics. Cardiovasc Res. 2018;114:1258–70.

    PubMed  Google Scholar 

  16. Zeggini E, Ioannidis JP. Meta-analysis in genome-wide association studies. Pharmacogenomics. 2009;10:191–201.

    Article  PubMed  Google Scholar 

  17. Frazer KA, Murray SS, Schork NJ, Topol EJ. Human genetic variation and its contribution to complex traits. Nat Rev Genet. 2009;10:241–51.

    Article  CAS  PubMed  Google Scholar 

  18. Gaziano JM, Concato J, Brophy M, et al. Million Veteran Program: a mega-biobank to study genetic influences on health and disease. J Clin Epidemiol. 2016;70:214–23.

    Article  PubMed  Google Scholar 

  19. Lee CH, Cook S, Lee JS, Han B. Comparison of two meta-analysis methods: inverse-variance-weighted average and weighted sum of Z-scores. Genomics Inform. 2016;14:173–80.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Lacey S, Chung JY, Lin H. A comparison of whole genome sequencing with exome sequencing for family-based association studies. BMC Proc. 2014;8:S38.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Lee S, Abecasis GR, Boehnke M, Lin X. Rare-variant association analysis: study designs and statistical tests. Am J Hum Genet. 2014;95:5–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Guo MH, Plummer L, Chan YM, Hirschhorn JN, Lippincott MF. Burden testing of rare variants identified through exome sequencing via publicly available control data. Am J Hum Genet. 2018;103:522–34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Lange LA, Hu Y, Zhang H, et al. Whole-exome sequencing identifies rare and low-frequency coding variants associated with LDL cholesterol. Am J Hum Genet. 2014;94:233–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Hegele RA, Tsimikas S. Lipid-lowering agents. Circ Res. 2019;124:386–404.

    Article  CAS  PubMed  Google Scholar 

  25. Raal FJ, Kallend D, Ray KK, et al. Inclisiran for the treatment of heterozygous familial hypercholesterolemia. N Engl J Med. 2020;382:1520–30.

    Article  CAS  PubMed  Google Scholar 

  26. Greig JA, Limberis MP, Bell P, et al. Non-clinical study examining AAV8.TBG.hLDLR vector-associated toxicity in chow-fed wild-Type and LDLR(+/-) Rhesus Macaques. Hum Gene Ther Clin Dev. 2017;28:39–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Greig JA, Limberis MP, Bell P, et al. Nonclinical pharmacology/toxicology study of AAV8.TBG.mLDLR and AAV8.TBG.hLDLR in a mouse model of homozygous familial hypercholesterolemia. Hum Gene Ther Clin Dev. 2017;28:28–38.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Wong E, Goldberg T. Mipomersen (kynamro): a novel antisense oligonucleotide inhibitor for the management of homozygous familial hypercholesterolemia. P t. 2014;39:119–22.

    PubMed  PubMed Central  Google Scholar 

  29. Abifadel M, Varret M, Rabès JP, et al. Mutations in PCSK9 cause autosomal dominant hypercholesterolemia. Nat Genet. 2003;34:154–6.

    Article  CAS  PubMed  Google Scholar 

  30. Cohen J, Pertsemlidis A, Kotowski IK, Graham R, Garcia CK, Hobbs HH. Low LDL cholesterol in individuals of African descent resulting from frequent nonsense mutations in PCSK9. Nat Genet. 2005;37:161–5.

    Article  CAS  PubMed  Google Scholar 

  31. Crosby J, Peloso GM, Auer PL, et al. Loss-of-function mutations in APOC3, triglycerides, and coronary disease. N Engl J Med. 2014;371:22–31.

    Article  PubMed  Google Scholar 

  32. Cohen JC, Kiss RS, Pertsemlidis A, Marcel YL, McPherson R, Hobbs HH. Multiple rare alleles contribute to low plasma levels of HDL cholesterol. Science. 2004;305:869–72.

    Article  CAS  PubMed  Google Scholar 

  33. Willer CJ, Schmidt EM, Sengupta S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45:1274–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Surakka I, Horikoshi M, Mägi R, et al. The impact of low-frequency and rare variants on lipid levels. Nat Genet. 2015;47:589–97.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Sanna S, Li B, Mulas A, et al. Fine mapping of five loci associated with low-density lipoprotein cholesterol detects variants that double the explained heritability. PLoS Genet. 2011;7:e1002198.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Génin E. Missing heritability of complex diseases: case solved? Hum Genet. 2020;139:103–13.

    Article  PubMed  Google Scholar 

  37. Schaid DJ, Chen W, Larson NB. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat Rev Genet. 2018;19:491–504.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Lu X, Peloso GM, Liu DJ, et al. Exome chip meta-analysis identifies novel loci and East Asian-specific coding variants that contribute to lipid levels and coronary artery disease. Nat Genet. 2017;49:1722–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Zubair N, Graff M, Luis Ambite J, et al. Fine-mapping of lipid regions in global populations discovers ethnic-specific signals and refines previously identified lipid loci. Hum Mol Genet. 2016;25:5500–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Pers TH, Karjalainen JM, Chan Y, et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat Commun. 2015;6:5890.

    Article  CAS  PubMed  Google Scholar 

  41. Weeks EM, Ulirsch JC, Cheng NY et al. Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases. medRxiv 2020:2020.09.08.20190561.

  42. Morris AP. Transethnic meta-analysis of genomewide association studies. Genet Epidemiol. 2011;35:809–22.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Ruan X, Li P, Chen Y, et al. In vivo functional analysis of non-conserved human lncRNAs associated with cardiometabolic traits. Nat Commun. 2020;11:45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Muret K, Désert C, Lagoutte L, et al. Long noncoding RNAs in lipid metabolism: literature review and conservation analysis across species. BMC Genomics. 2019;20:882.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Lee KH, Hwang HJ, Cho JY. Long non-coding RNA associated with cholesterol homeostasis and its involvement in metabolic diseases. Int J Mol Sci. 2020;21.

  46. Ruan X, Li P, Ma Y, et al. Identification of human long noncoding RNAs associated with nonalcoholic fatty liver disease and metabolic homeostasis. J Clin Invest. 2021;131.

  47. Mittelstraß K, Waldenberger M. DNA methylation in human lipid metabolism and related diseases. Curr Opin Lipidol. 2018;29:116–24.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Braun KV, Voortman T, Dhana K, et al. The role of DNA methylation in dyslipidaemia: a systematic review. Prog Lipid Res. 2016;64:178–91.

    Article  CAS  PubMed  Google Scholar 

  49. Sayols-Baixeras S, Irvin MR, Arnett DK, Elosua R, Aslibekyan SW. Epigenetics of lipid phenotypes. Curr Cardiovasc Risk Rep. 2016;10.

  50. Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 2019;47:D886–d894.

    Article  CAS  PubMed  Google Scholar 

  51. Adzhubei IA, Schmidt S, Peshkin L, et al. A method and server for predicting damaging missense mutations. Nat Methods. 2010;7:248–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Ng PC, Henikoff S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003;31:3812–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Boyle AP, Hong EL, Hariharan M, et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 2012;22:1790–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Zhou J, Troyanskaya OG. Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods. 2015;12:931–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Wainberg M, Sinnott-Armstrong N, Mancuso N, et al. Opportunities and challenges for transcriptome-wide association studies. Nat Genet. 2019;51:592–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Carithers LJ, Ardlie K, Barcus M, et al. A novel approach to high-quality postmortem tissue procurement: the GTEx project. Biopreserv Biobank. 2015;13:311–9.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Meng XH, Chen XD, Greenbaum J, et al. Integration of summary data from GWAS and eQTL studies identified novel causal BMD genes with functional predictions. Bone. 2018;113:41–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Vilhjálmsson BJ, Yang J, Finucane HK, et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am J Hum Genet. 2015;97:576–92.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Ge T, Chen CY, Ni Y, Feng YA, Smoller JW. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat Commun. 2019;10:1776.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Mak TSH, Porsch RM, Choi SW, Zhou X, Sham PC. Polygenic scores via penalized regression on summary statistics. Genet Epidemiol. 2017;41:469–80.

    Article  PubMed  Google Scholar 

  61. Inouye M, Abraham G, Nelson CP, et al. Genomic risk prediction of coronary artery disease in 480,000 adults: implications for primary prevention. J Am Coll Cardiol. 2018;72:1883–93.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Khera AV, Chaffin M, Zekavat SM, et al. Whole-genome sequencing to characterize monogenic and polygenic contributions in patients hospitalized with early-onset myocardial infarction. Circulation. 2019;139:1593–602.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Sinnott-Armstrong N, Tanigawa Y, Amar D et al. Genetics of 38 blood and urine biomarkers in the UK Biobank. Nat Genet. 2021;53:185–94.

  64. Dias R, Torkamani A. Artificial intelligence in clinical and genomic diagnostics. Genome Med. 2019;11:70.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Mars N, Koskela JT, Ripatti P, et al. Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers. Nat Med. 2020;26:549–57.

    Article  CAS  PubMed  Google Scholar 

  66. Widen E, Junna N, Ruotsalainen S et al. Communicating polygenic and non-genetic risk for atherosclerotic cardiovascular disease—an observational follow-up study. medRxiv 2020:2020.09.18.20197137.

  67. Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019;51:584–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. The International HapMap Project. Nature. 2003;426:789–96.

    Article  Google Scholar 

  69. A haplotype map of the human genome. Nature. 2005;437:1299–320.

  70. Abecasis GR, Auton A, Brooks LD, et al. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491:56–65.

    Article  PubMed  Google Scholar 

  71. McCarthy S, Das S, Kretzschmar W, et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet. 2016;48:1279–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Diogo D, Tian C, Franklin CS, et al. Phenome-wide association studies across large population cohorts support drug target validation. Nat Commun. 2018;9:4285.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Nielsen JRO, Surakka I, Graham S, Zhou W, Roychowdhury T, Fritsche L, et al. Loss-of-function genetic variants with impact on liver-related blood traits highlight potential therapeutic targets for cardiovascular disease. Nat Commun. 2020;11:6417.

  74. Van Hout CV, Tachmazidou I, Backman JD, et al. Exome sequencing and characterization of 49,960 individuals in the UK Biobank. Nature. 2020;586:749–56.

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

Cristen J. Willer is supported by the National Institutes of Health (R01-HL127564, R35-HL135824, R01-HL142023, and R01-DK075787). Ida Surakka is partially funded by the Michigan Medicine Precision Health Scholarship award.

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Authors and Affiliations

  1. Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA

    Bradley Crone & Cristen J. Willer

  2. Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Michigan Medicine, Ann Arbor, MI, USA

    Amelia M. Krause, Whitney E. Hornsby, Cristen J. Willer & Ida Surakka

  3. Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA

    Cristen J. Willer

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  1. Bradley Crone
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  2. Amelia M. Krause
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  4. Cristen J. Willer
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  5. Ida Surakka
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Contributions

B. C.: conception and design; drafting the article and revising for important intellectual content; final approval.

A. M. K.: acquisition of data; drafting the article or revising for important intellectual content; final approval.

W. E. H.: conception and design; drafting the article or revising for important intellectual content; final approval.

C. J. W.: conception and design; drafting the article or revising it critically for important intellectual content; final approval.

I. S.: conception and design; drafting the article or revising it critically for important intellectual content; final approval.

Corresponding author

Correspondence to Ida Surakka.

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Dr. Willer's spouse works for Regeneron.

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Crone, B., Krause, A.M., Hornsby, W.E. et al. Translating genetic association of lipid levels for biological and clinical application. Cardiovasc Drugs Ther 35, 617–626 (2021). https://doi.org/10.1007/s10557-021-07156-4

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