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Polygenic risk scores for low-density lipoprotein cholesterol and familial hypercholesterolemia

Journal of Human Genetics volume 66, pages 1079–1087 (2021)Cite this article

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Abstract

Familial hypercholesterolemia (FH) is an autosomal dominant monogenic disorder characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C) and an increased risk of premature coronary artery disease (CAD). Recently, it has been shown that a high polygenic risk score (PRS) could be an independent risk factor for CAD in FH patients of European ancestry. However, it is uncertain whether PRS is also useful for risk stratification of FH patients in East Asia. We recruited and genotyped clinically diagnosed FH (CDFH) patients from the Kanazawa University Mendelian Disease FH registry and controls from the Shikamachi Health Improvement Practice genome cohort in Japan. We calculated PRS from 3.6 million variants of each participant (imputed from the 1000 Genome phase 3 Asian dataset) for LDL-C (PRSLDLC) using a genome-wide association study summary statistic from the BioBank Japan Project. We assessed the association of PRSLDLC with LDL-C and CAD among and within monogenic FH, mutation negative CDFH, and controls. We tested a total of 1223 participants (376 FH patients, including 173 with monogenic FH and 203 with mutation negative CDFH, and 847 controls) for the analyses. PRSLDLC was significantly higher in mutation negative CDFH patients than in controls (p = 3.1 × 10−13). PRSLDLC was also significantly linked to LDL-C in controls (p trend = 3.6 × 10−4) but not in FH patients. Moreover, we could not detect any association between PRSLDLC and CAD in any of the groups. In conclusion, mutation negative CDFH patients demonstrated significantly higher PRSLDLC than controls. However, PRSLDLC may have little additional effect on LDL-C and CAD among FH patients.

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Fig. 1: Histograms of PRSLDLC across all FH, monogenic FH, mutation negative CDFH, and control groups
Fig. 2: LDL-C levels by PRSLDLC tertile in each group
Fig. 3: Prevalence of coronary artery disease by PRSLDLC tertile in each group

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References

  1. Harada-Shiba M, Arai H, Oikawa S, Ohta T, Okada T, Okamura T, et al. Guidelines for the management of familial hypercholesterolemia. J Atheroscler Thromb. 2012;19:1043–60.

    Article  CAS  PubMed  Google Scholar 

  2. Kawashiri MA, Hayashi K, Konno T, Fujino N, Ino H, Yamagishi M. Current perspectives in genetic cardiovascular disorders: from basic to clinical aspects. Heart Vessels. 2014;29:129–41.

    Article  PubMed  Google Scholar 

  3. Tada H, Kawashiri MA, Nohara A, Inazu A, Mabuchi H, Yamagishi M. Impact of clinical signs and genetic diagnosis of familial hypercholesterolaemia on the prevalence of coronary artery disease in patients with severe hypercholesterolaemia. Eur Heart J. 2017;38:1573–9.

    Article  CAS  PubMed  Google Scholar 

  4. Khera AV, Won HH, Peloso GM, Lawson KS, Bartz TM, Deng X, et al. Diagnostic yield and clinical utility of sequencing familial hypercholesterolemia genes in patients with severe hypercholesterolemia. J Am Coll Cardiol. 2016;67:2578–89.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Mabuchi H. Half a century tales of familial hypercholesterolemia (FH) in Japan. J Atheroscler Thromb. 2017;24:189–207.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Niemi MEK, Martin HC, Rice DL, Gallone G, Gordon S, Kelemen M, et al. Common genetic variants contribute to risk of rare severe neurodevelopmental disorders. Nature. 2018;562:268–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Choi SW, Mak T, O’Reilly PF. Tutorial: a guide to performing polygenic risk score analyses. Nat Protoc. 2020;15:2759–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Paquette M, Chong M, Theriault S, Dufour R, Pare G, Baass A. Polygenic risk score predicts prevalence of cardiovascular disease in patients with familial hypercholesterolemia. J Clin Lipido. 2017;11:725–32 e5.

    Article  Google Scholar 

  9. Trinder M, Li X, DeCastro ML, Cermakova L, Sadananda S, Jackson LM, et al. Risk of premature atherosclerotic disease in patients with monogenic versus polygenic familial hypercholesterolemia. J Am Coll Cardiol. 2019;74:512–22.

    Article  PubMed  Google Scholar 

  10. Sarraju A, Knowles JW. Genetic testing and risk scores: impact on familial hypercholesterolemia. Front Cardiovasc Med. 2019;6:5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Peloso GM, Nomura A, Khera AV, Chaffin M, Won HH, Ardissino D, et al. Rare protein-truncating variants in APOB, lower low-density lipoprotein cholesterol, and protection against coronary heart disease. Circ Genom Precis Med. 2019;12:e002376.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Nomura A, Emdin CA, Won HH, Peloso GM, Natarajan P, Ardissino D, et al. Heterozygous ABCG5 gene deficiency and risk of coronary artery disease. Circ Genom Precis Med. 2020;13:417–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Kanai M, Akiyama M, Takahashi A, Matoba N, Momozawa Y, Ikeda M, et al. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nat Genet. 2018;50:390–400.

    Article  CAS  PubMed  Google Scholar 

  14. Harada-Shiba M, Arai H, Ishigaki Y, Ishibashi S, Okamura T, Ogura M, et al. Guidelines for diagnosis and treatment of familial hypercholesterolemia 2017. J Atheroscler Thromb. 2018;25:751–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Nomura A, Won HH, Khera AV, Takeuchi F, Ito K, McCarthy S, et al. Protein-truncating variants at the cholesteryl ester transfer protein gene and risk for coronary heart disease. Circ Res. 2017;121:81–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Tada H, Kawashiri MA, Nomura A, Teramoto R, Hosomichi K, Nohara A, et al. Oligogenic familial hypercholesterolemia, LDL cholesterol, and coronary artery disease. J Clin Lipido. 2018;12:1436–44.

    Article  Google Scholar 

  17. DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. 2011;43:491–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. McLaren W, Pritchard B, Rios D, Chen Y, Flicek P, Cunningham F. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics .2010;26:2069–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Liu X, Jian X, Boerwinkle E. dbNSFP v2.0: a database of human non-synonymous SNVs and their functional predictions and annotations. Hum Mutat. 2013;34:E2393–402.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature .2016;536:285–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Fromer M, Moran JL, Chambert K, Banks E, Bergen SE, Ruderfer DM, et al. Discovery and statistical genotyping of copy-number variation from whole-exome sequencing depth. Am J Hum Genet. 2012;91:597–607.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Fromer M, Purcell SM. Using XHMM software to detect copy number variation in whole-exome sequencing data. Curr Protoc Hum Genet. 2014;81:7.23.1–21.

    Google Scholar 

  23. Landrum MJ, Lee JM, Riley GR, Jang W, Rubinstein WS, Church DM, et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 2014;42:D980–5.

    Article  CAS  PubMed  Google Scholar 

  24. Mabuchi H, Nohara A, Noguchi T, Kobayashi J, Kawashiri MA, Inoue T, et al. Genotypic and phenotypic features in homozygous familial hypercholesterolemia caused by proprotein convertase subtilisin/kexin type 9 (PCSK9) gain-of-function mutation. Atherosclerosis .2014;236:54–61.

    Article  CAS  PubMed  Google Scholar 

  25. Ohta N, Hori M, Takahashi A, Ogura M, Makino H, Tamanaha T, et al. Proprotein convertase subtilisin/kexin 9 V4I variant with LDLR mutations modifies the phenotype of familial hypercholesterolemia. J Clin Lipido. 2016;10:547–55 e5.

    Article  Google Scholar 

  26. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17:405–24.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Kawai Y, Mimori T, Kojima K, Nariai N, Danjoh I, Saito R, et al. Japonica array: improved genotype imputation by designing a population-specific SNP array with 1070 Japanese individuals. J Hum Genet. 2015;60:581–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience .2015;4:7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38:904–9.

    Article  CAS  PubMed  Google Scholar 

  31. Browning SR, Browning BL. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am J Hum Genet. 2007;81:1084–97.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Browning BL, Browning SR. Genotype imputation with millions of reference samples. Am J Hum Genet. 2016;98:116–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. The 1000 Genomes Project Consortium, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, et al. A global reference for human genetic variation. Nature .2015;526:68–74.

    Article  CAS  Google Scholar 

  34. Vilhjalmsson BJ, Yang J, Finucane HK, Gusev A, Lindstrom S, Ripke S, et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am J Hum Genet. 2015;97:576–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Wu H, Forgetta V, Zhou S, Bhatnagar SR, Pare G, Richards JB. A polygenic risk score for low-density lipoprotein cholesterol is associated with risk of ischemic heart disease and enriches for individuals with familial hypercholesterolemia. Circ Genom Precis Med. 2021;14:e003106.

    Article  CAS  PubMed  Google Scholar 

  36. Rieck L, Bardey F, Grenkowitz T, Bertram L, Helmuth J, Mischung C, et al. Mutation spectrum and polygenic score in German patients with familial hypercholesterolemia. Clin Genet. 2020;98:457–67.

    Article  CAS  PubMed  Google Scholar 

  37. Trinder M, Paquette M, Cermakova L, Ban MR, Hegele RA, Baass A, et al. Polygenic contribution to low-density lipoprotein cholesterol levels and cardiovascular risk in monogenic familial hypercholesterolemia. Circ Genom Precis Med. 2020;13:515–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Trinder M, Francis GA, Brunham LR. Association of monogenic vs polygenic hypercholesterolemia with risk of atherosclerotic cardiovascular disease. JAMA Cardiol. 2020;5:390–99.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Ripatti P, Ramo JT, Mars NJ, Fu Y, Lin J, Soderlund S, et al. Polygenic hyperlipidemias and coronary artery disease risk. Circ Genom Precis Med. 2020;13:e002725.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Elliott J, Bodinier B, Bond TA, Chadeau-Hyam M, Evangelou E, Moons KGM, et al. Predictive accuracy of a polygenic risk score-enhanced prediction model vs a clinical risk score for coronary artery disease. JAMA .2020;323:636–45.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Mosley JD, Gupta DK, Tan J, Yao J, Wells QS, Shaffer CM, et al. Predictive accuracy of a polygenic risk score compared with a clinical risk score for incident coronary heart disease. JAMA. 2020;323:627–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank all the participants and medical staff that were involved in this study. Also, this study was supported in part by the Japan Heart Foundation Research Grant, Astellas Foundation for Research on Metabolic Disorders, Japan Research Foundation for Clinical Pharmacology, Japan Cardiovascular Research Foundation (The Bayer Scholarship for Cardiovascular Research), The Mochida Memorial Foundation for Medical and Pharmaceutical Research, and the KAKEN Grant-in-Aid for Scientific Research (C) (19K08553), (B) (21H03179), and for Challenging Research (Exploratory) (19K22753).

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Author notes

  1. These authors contributed equally: Akihiro Nomura, Takehiro Sato.

Authors and Affiliations

  1. Department of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan

    Akihiro Nomura, Hayato Tada, Masayuki Takamura & Masa-aki Kawashiri

  2. Innovative Clinical Research Center, Kanazawa University, Kanazawa, Japan

    Akihiro Nomura

  3. Department of Bioinformatics and Genomics, Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan

    Takehiro Sato, Takayuki Kannon, Kazuyoshi Hosomichi & Atsushi Tajima

  4. Department of Environmental and Preventive Medicine, Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan

    Hiromasa Tsujiguchi & Hiroyuki Nakamura

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Correspondence to Hayato Tada.

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AN received consulting fees from CureApp, Inc. and was a co-founder of the CureApp Institute. The other authors declare no conflicts of interest.

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Nomura, A., Sato, T., Tada, H. et al. Polygenic risk scores for low-density lipoprotein cholesterol and familial hypercholesterolemia. J Hum Genet 66, 1079–1087 (2021). https://doi.org/10.1038/s10038-021-00929-7

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