Skip main navigation

Familial Hypercholesterolemia in the Electronic Medical Records and Genomics Network: Prevalence, Penetrance, Cardiovascular Risk, and Outcomes After Return of Results

Originally published Genomic and Precision Medicine. 2023;16


    The implications of secondary findings detected in large-scale sequencing projects remain uncertain. We assessed prevalence and penetrance of pathogenic familial hypercholesterolemia (FH) variants, their association with coronary heart disease (CHD), and 1-year outcomes following return of results in phase III of the electronic medical records and genomics network.


    Adult participants (n=18 544) at 7 sites were enrolled in a prospective cohort study to assess the clinical impact of returning results from targeted sequencing of 68 actionable genes, including LDLR, APOB, and PCSK9. FH variant prevalence and penetrance (defined as low-density lipoprotein cholesterol >155 mg/dL) were estimated after excluding participants enrolled on the basis of hypercholesterolemia. Multivariable logistic regression was used to estimate the odds of CHD compared to age- and sex-matched controls without FH-associated variants. Process (eg, referral to a specialist or ordering new tests), intermediate (eg, new diagnosis of FH), and clinical (eg, treatment modification) outcomes within 1 year after return of results were ascertained by electronic health record review.


    The prevalence of FH-associated pathogenic variants was 1 in 188 (69 of 13,019 unselected participants). Penetrance was 87.5%. The presence of an FH variant was associated with CHD (odds ratio, 3.02 [2.00–4.53]) and premature CHD (odds ratio, 3.68 [2.34–5.78]). At least 1 outcome occurred in 92% of participants; 44% received a new diagnosis of FH and 26% had treatment modified following return of results.


    In a multisite cohort of electronic health record–linked biobanks, monogenic FH was prevalent, penetrant, and associated with presence of CHD. Nearly half of participants with an FH-associated variant received a new diagnosis of FH and a quarter had treatment modified after return of results. These results highlight the potential utility of sequencing electronic health record–linked biobanks to detect FH.


    *O. Dikilitas and A. Sherafati contributed equally.

    For Sources of Funding and Disclosures, see page 139.

    Supplemental Material is available at

    Correspondence to: Iftikhar J. Kullo, MD, Department of Cardiovascular Medicine, Gonda Vascular Center, Mayo Clinic, 200 First Street SW, Rochester, MN 55905. Email


    • 1. Safarova MS, Kullo IJ. My approach to the patient with familial hypercholesterolemia.Mayo Clin Proc. 2016; 91:770–786. doi: 10.1016/j.mayocp.2016.04.013CrossrefMedlineGoogle Scholar
    • 2. Hajighasemi S, Gorabi AM, Bianconi V, Pirro M, Banach M, Tafti HA, Reiner Z, Sahebkar A. A review of gene-and cell-based therapies for familial hypercholesterolemia.Pharmacol Res. 2019; 143:119–132. doi: 10.1016/j.phrs.2019.03.016CrossrefMedlineGoogle Scholar
    • 3. Abul-Husn NS, Manickam K, Jones LK, Wright EA, Hartzel DN, Gonzaga-Jauregui C, O’Dushlaine C, Leader JB, Kirchner HL, D’Andra ML. Genetic identification of familial hypercholesterolemia within a single US health care system.Science. 2016; 354:aaf7000. doi: 10.1126/science.aaf7000CrossrefMedlineGoogle Scholar
    • 4. Austin MA, Hutter CM, Zimmern RL, Humphries SE. Familial hypercholesterolemia and coronary heart disease: a HuGE association review.Am J Epidemiol. 2004; 160:421–429. doi: 10.1093/aje/kwh237CrossrefMedlineGoogle Scholar
    • 5. Hopkins PN, Toth PP, Ballantyne CM, Rader DJ; National Lipid Association Expert Panel on Familial Hypercholesterolemia. Familial hypercholesterolemias: prevalence, genetics, diagnosis and screening recommendations from the National Lipid Association Expert Panel on Familial Hypercholesterolemia.J Clin Lipidol. 2011; 5:S9–17. doi: 10.1016/j.jacl.2011.03.452CrossrefMedlineGoogle Scholar
    • 6. Schmidt HH-J, Hill S, Makariou EV, Feuerstein IM, Dugi KA, Hoeg JM. Relation of cholesterol-year score to severity of calcific atherosclerosis and tissue deposition in homozygous familial hypercholesterolemia.Am J Cardiol. 1996; 77:575–580. doi: 10.1016/s0002-9149(97)89309-5CrossrefMedlineGoogle Scholar
    • 7. Neil A, Cooper J, Betteridge J, Capps N, McDowell I, Durrington P, Seed M, Humphries SE. Reductions in all-cause, cancer, and coronary mortality in statin-treated patients with heterozygous familial hypercholesterolaemia: a prospective registry study.Eur Heart J. 2008; 29:2625–2633. doi: 10.1093/eurheartj/ehn422CrossrefMedlineGoogle Scholar
    • 8. Versmissen J, Oosterveer DM, Yazdanpanah M, Defesche JC, Basart DC, Liem AH, Heeringa J, Witteman JC, Lansberg PJ, Kastelein JJ, et al. Efficacy of statins in familial hypercholesterolaemia: a long term cohort study.BMJ. 2008; 337:a2423. doi: 10.1136/bmj.a2423CrossrefMedlineGoogle Scholar
    • 9. Barkas F, Elisaf M, Milionis H. Statins decrease the risk of stroke in individuals with heterozygous familial hypercholesterolemia: a systematic review and meta-analysis.Atherosclerosis. 2015; 243:60–64. doi: 10.1016/j.atherosclerosis.2015.08.038CrossrefMedlineGoogle Scholar
    • 10. Miller DT, Lee K, Gordon AS, Amendola LM, Adelman K, Bale SJ, Chung WK, Gollob MH, Harrison SM, Herman GE, et al; ACMG Secondary Findings Working Group. Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2021 update: a policy statement of the American College of Medical Genetics and Genomics (ACMG).Genet Med. 2021; 23:1391–1398. doi: 10.1038/s41436-021-01171-4CrossrefMedlineGoogle Scholar
    • 11. Miller DT, Lee K, Chung WK, Gordon AS, Herman GE, Klein TE, Stewart DR, Amendola LM, Adelman K, Bale SJ, et al; ACMG Secondary Findings Working Group. ACMG SF v3. 0 list for reporting of secondary findings in clinical exome and genome sequencing: a policy statement of the American College of Medical Genetics and Genomics (ACMG).Genet Med. 2021; 23:1381–1390. doi: 10.1038/s41436-021-01172-3CrossrefMedlineGoogle Scholar
    • 12. Nordestgaard BG, Chapman MJ, Humphries SE, Ginsberg HN, Masana L, Descamps OS, Wiklund O, Hegele RA, Raal FJ, Defesche JC, et al; European Atherosclerosis Society Consensus Panel. Familial hypercholesterolaemia is underdiagnosed and undertreated in the general population: guidance for clinicians to prevent coronary heart disease: consensus statement of the European Atherosclerosis Society.Eur Heart J. 2013; 34:3478–390a. doi: 10.1093/eurheartj/eht273CrossrefMedlineGoogle Scholar
    • 13. Gordon AS, Zouk H, Venner E, Eng CM, Funke BH, Amendola LM, Carrell DS, Chisholm RL, Chung WK, Denny JC, et al. Frequency of genomic incidental findings among 21,915 eMERGE network participants.Genet Med. 2020; 22:1470–1477. doi: 10.1038/s41436-020-0810-9CrossrefMedlineGoogle Scholar
    • 14. Vassy JL, Christensen KD, Schonman EF, Blout CL, Robinson JO, Krier JB, Diamond PM, Lebo M, Machini K, Azzariti DR, et al; MedSeq Project. The impact of whole-genome sequencing on the primary care and outcomes of healthy adult patients: a pilot randomized trial.Ann Intern Med. 2017; 167:159–169. doi: 10.7326/M17-0188CrossrefMedlineGoogle Scholar
    • 15. Alver M, Palover M, Saar A, Läll K, Zekavat SM, Tõnisson N, Leitsalu L, Reigo A, Nikopensius T, Ainla T, et al. Recall by genotype and cascade screening for familial hypercholesterolemia in a population-based biobank from Estonia.Genet Med. 2019; 21:1173–1180. doi: 10.1038/s41436-018-0311-2CrossrefMedlineGoogle Scholar
    • 16. Buchanan AH, Lester Kirchner H, Schwartz MLB, Kelly MA, Schmidlen T, Jones LK, Hallquist MLG, Rocha H, Betts M, Schwiter R, et al. Clinical outcomes of a genomic screening program for actionable genetic conditions.Genet Med. 2020; 22:1874–1882. doi: 10.1038/s41436-020-0876-4CrossrefMedlineGoogle Scholar
    • 17. Wiesner GL, Kulchak Rahm A, Appelbaum P, Aufox S, Bland ST, Blout CL, Christensen KD, Chung WK, Clayton EW, Green RC, et al. Returning results in the genomic era: initial experiences of the eMERGE Network.J Pers Med. 2020; 10:30. doi: 10.3390/jpm10020030CrossrefMedlineGoogle Scholar
    • 18. Zouk H, Venner E, Lennon NJ, Muzny DM, Abrams D, Adunyah S, Albertson-Junkans L, Ames DC, Appelbaum P, Aronson S. Harmonizing clinical sequencing and interpretation for the eMERGE III network.Am J Hum Genet. 2019; 105:588–605. doi: 10.1016/j.ajhg.2019.07.018CrossrefMedlineGoogle Scholar
    • 19. eMERGE Consortium. Lessons learned from the eMERGE Network: balancing genomics in discovery and practice.HGG Adv. 2021; 2:100018. doi: 10.1016/j.xhgg.2020.100018CrossrefMedlineGoogle Scholar
    • 20. Leppig KA, Kulchak Rahm A, Appelbaum P, Aufox S, Bland ST, Buchanan A, Christensen KD, Chung WK, Clayton EW, Crosslin D, et al. The reckoning: the return of genomic results to 1444 participants across the eMERGE3 Network.Genet Med. 2022; 24:1130–1138. doi: 10.1016/j.gim.2022.01.015CrossrefMedlineGoogle Scholar
    • 21. Wenger BM, Patel N, Lui M, Moscati A, Do R, Stewart DR, Tartaglia M, Muiño-Mosquera L, De Backer J, Kontorovich AR, et al. A genotype-first approach to exploring Mendelian cardiovascular traits with clear external manifestations.Genet Med. 2021; 23:94–102. doi: 10.1038/s41436-020-00973-2CrossrefMedlineGoogle Scholar
    • 22. Haer-Wigman L, van der Schoot V, Feenstra I, Vulto-van Silfhout AT, Gilissen C, Brunner HG, Vissers LE, Yntema HG. 1 in 38 individuals at risk of a dominant medically actionable disease.Eur J Hum Genet. 2019; 27:325–330. doi: 10.1038/s41431-018-0284-2CrossrefMedlineGoogle Scholar
    • 23. Natarajan P, Gold NB, Bick AG, McLaughlin H, Kraft P, Rehm HL, Peloso GM, Wilson JG, Correa A, Seidman JG, et al. Aggregate penetrance of genomic variants for actionable disorders in European and African Americans.Sci Transl Med. 2016; 8:364ra151–364ra151. doi: 10.1126/scitranslmed.aag2367CrossrefMedlineGoogle Scholar
    • 24. Khera AV, Won H-H, Peloso GM, Lawson KS, Bartz TM, Deng X, van Leeuwen EM, Natarajan P, Emdin CA, Bick AG, et al. Diagnostic yield and clinical utility of sequencing familial hypercholesterolemia genes in patients with severe hypercholesterolemia.J Am Coll Cardiol. 2016; 67:2578–2589. doi: 10.1016/j.jacc.2016.03.520CrossrefMedlineGoogle Scholar
    • 25. Sampson M, Ling C, Sun Q, Harb R, Ashmaig M, Warnick R, Sethi A, Fleming JK, Otvos JD, Meeusen JW, et al. A new equation for calculation of low-density lipoprotein cholesterol in patients with normolipidemia and/or hypertriglyceridemia.JAMA Cardiol. 2020; 5:540–548. doi: 10.1001/jamacardio.2020.0013CrossrefMedlineGoogle Scholar
    • 26. Trinder M, Francis GA, Brunham LR. Association of monogenic vs polygenic hypercholesterolemia with risk of atherosclerotic cardiovascular disease.JAMA Cardiol. 2020; 5:390–399. doi: 10.1001/jamacardio.2019.5954CrossrefMedlineGoogle Scholar
    • 27. Clarke SL, Tcheandjieu C, Hilliard AT, Lee M, Lynch J, Chang K-M, Miller D, Knowles JW, O’Donnell C, Tsao P. Coronary artery disease risk of familial hypercholesterolemia genetic variants independent of clinically observed longitudinal cholesterol exposure.Circ Genom Precis Med. 2022; 15:e003501. doi: 10.1161/CIRCGEN.121.003501LinkGoogle Scholar
    • 28. Shaibi GQ, Kullo IJ, Singh DP, Hernandez V, Sharp RR, Cuellar I, De Filippis E, Levey S, Breitkopf CR, Mandarino LJ, et al. Returning genomic results in a Federally Qualified Health Center: the intersection of precision medicine and social determinants of health.Genet Med. 2020; 22:1552–1559. doi: 10.1038/s41436-020-0806-5CrossrefMedlineGoogle Scholar
    • 29. Garcia-Garcia A-B, Ivorra C, Martinez-Hervas S, Blesa S, Fuentes MJ, Puig O, Martín-de-Llano JJ, Carmena R, Real JT, Chaves FJ. Reduced penetrance of autosomal dominant hypercholesterolemia in a high percentage of families: importance of genetic testing in the entire family.Atherosclerosis. 2011; 218:423–430. doi: 10.1016/j.atherosclerosis.2011.07.106CrossrefMedlineGoogle Scholar
    • 30. Jones LK, Chen N, Hassen D, McMinn M, Klinger T, Hartzel DN, Veenstra D, Spencer S, Snyder SR, Peterson JF, et al. Impact of a population genomic screening program on health behaviors related to familial hypercholesterolemia risk reduction.Circ Genom Precis Med. 2022; 15:e003549. doi: 10.1161/circgen.121.003549LinkGoogle Scholar
    • 31. Zhang L, Bao Y, Riaz M, Tiller J, Liew D, Zhuang X, Amor DJ, Huq A, Petelin L, Nelson M, et al. Population genomic screening of all young adults in a health-care system: a cost-effectiveness analysis.Genet Med. 2019; 21:1958–1968. doi: 10.1038/s41436-019-0457-6CrossrefMedlineGoogle Scholar
    • 32. Chen CX, Hay JW. Cost-effectiveness analysis of alternative screening and treatment strategies for heterozygous familial hypercholesterolemia in the United States.Int J Cardiol. 2015; 181:417–424. doi: 10.1016/j.ijcard.2014.12.070CrossrefMedlineGoogle Scholar
    • 33. Marquina C, Lacaze P, Tiller J, Riaz M, Sturm AC, Nelson MR, Ference BA, Pang J, Watts GF, Nicholls SJ. Population genomic screening of young adults for familial hypercholesterolaemia: a cost-effectiveness analysis.Eur Heart J. 2022; 43:ehab770. doi: 10.1093/eurheartj/ehab770CrossrefGoogle Scholar
    • 34. Von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.Bull World Health Organ. 2007; 85:867–872. doi: 10.2471/blt.07.045120CrossrefMedlineGoogle Scholar
    • 35. Gottesman O, Kuivaniemi H, Tromp G, Faucett WA, Li R, Manolio TA, Sanderson SC, Kannry J, Zinberg R, Basford MA, et al; eMERGE Network. The electronic medical records and genomics (eMERGE) network: past, present, and future.Genet Med. 2013; 15:761–771. doi: 10.1038/gim.2013.72CrossrefMedlineGoogle Scholar
    • 36. Fossey R, Kochan D, Winkler E, Pacyna JE, Olson J, Thibodeau S, Connolly JJ, Harr M, Behr MA, Prows CA, et al. Ethical considerations related to return of results from genomic medicine projects: the eMERGE network (phase III) experience.J Pers Med. 2018; 8:2. doi: 10.3390/jpm8010002CrossrefMedlineGoogle Scholar
    • 37. Green RC, Berg JS, Grody WW, Kalia SS, Korf BR, Martin CL, McGuire AL, Nussbaum RL, O’Daniel JM, Ormond KE, et al; American College of Medical Genetics and Genomics. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing.Genet Med. 2013; 15:565–574. doi: 10.1038/gim.2013.73CrossrefMedlineGoogle Scholar
    • 38. Kullo IJ, Olson J, Fan X, Jose M, Safarova M, Breitkopf CR, Winkler E, Kochan DC, Snipes S, Pacyna JE. The Return of Actionable Variants Empirical (RAVE) Study, a Mayo Clinic genomic medicine implementation study: design and initial results.Mayo Clin Proc. 2018; 93:1600–1610. doi: 10.1016/j.mayocp.2018.06.026CrossrefMedlineGoogle Scholar
    • 39. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, Grody WW, Hegde M, Lyon E, Spector E, et al; ACMG Laboratory Quality Assurance Committee. 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; 17:405–424. doi: 10.1038/gim.2015.30CrossrefMedlineGoogle Scholar
    • 40. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support.J Biomed Inform. 2009; 42:377–381. doi: 10.1016/j.jbi.2008.08.010CrossrefMedlineGoogle Scholar
    • 41. Balder JW, de Vries JK, Nolte IM, Lansberg PJ, Kuivenhoven JA, Kamphuisen PW. Lipid and lipoprotein reference values from 133,450 Dutch Lifelines participants: age- and gender-specific baseline lipid values and percentiles.J Clin Lipidol. 2017; 11:1055–1064.e6. doi: 10.1016/j.jacl.2017.05.007CrossrefMedlineGoogle Scholar
    • 42. Newton KM, Peissig PL, Kho AN, Bielinski SJ, Berg RL, Choudhary V, Basford M, Chute CG, Kullo IJ, Li R, et al. Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network.J Am Med Inform Assoc. 2013; 20:e147–e154. doi: 10.1136/amiajnl-2012-000896CrossrefMedlineGoogle Scholar
    • 43. Mach F, Baigent C, Catapano AL, Koskinas KC, Casula M, Badimon L, Chapman MJ, De Backer GG, Delgado V, Ference BA, et al; ESC Scientific Document Group. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk: the Task Force for the management of dyslipidaemias of the European Society of Cardiology (ESC) and European Atherosclerosis Society (EAS).Eur Heart J. 2020; 41:111–188. doi: 10.1093/eurheartj/ehz455CrossrefMedlineGoogle Scholar
    • 44. Safarova MS, Liu H, Kullo IJ. Rapid identification of familial hypercholesterolemia from electronic health records: the SEARCH study.J Clin Lipidol. 2016; 10:1230–1239. doi: 10.1016/j.jacl.2016.08.001CrossrefMedlineGoogle Scholar
    • 45. Williams MS. Early lessons from the implementation of genomic medicine programs.Annu Rev Genomics Hum Genet. 2019; 20:389–411. doi: 10.1146/annurev-genom-083118-014924CrossrefMedlineGoogle Scholar
    • 46. Peterson JF, Roden DM, Orlando LA, Ramirez AH, Mensah GA, Williams MS. Building evidence and measuring clinical outcomes for genomic medicine.Lancet. 2019; 394:604–610. doi: 10.1016/S0140-6736(19)31278-4CrossrefMedlineGoogle Scholar


    eLetters should relate to an article recently published in the journal and are not a forum for providing unpublished data. Comments are reviewed for appropriate use of tone and language. Comments are not peer-reviewed. Acceptable comments are posted to the journal website only. Comments are not published in an issue and are not indexed in PubMed. Comments should be no longer than 500 words and will only be posted online. References are limited to 10. Authors of the article cited in the comment will be invited to reply, as appropriate.

    Comments and feedback on AHA/ASA Scientific Statements and Guidelines should be directed to the AHA/ASA Manuscript Oversight Committee via its Correspondence page.