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Circ Res. Author manuscript; available in PMC 2017 June 24. Published in final edited form as: Circ Res. 2016 June 24; 119(1): 21–24. doi:10.1161/CIRCRESAHA.116.308830.
The Case of “Missing Causal Genes” and the Practice of Medicine: A Sherlock Holmes Approach of Deductive Reasoning AJ Marian, M.D. Center for Cardiovascular Genetics, Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center and Texas Heart Institute, Houston, TX, 77030
Author Manuscript
The practice of medicine is an exceedingly gratifying privilege, a notion that is commonly shared among the physicians. Also commonly understood but often not expressed is the humbling nature of this practice. Seldom, if ever, we know the true answers to our patients’ questions. Yet, we adeptly offer explanations, commonly based on the group data, and yet the essence of the practice of medicine is recognizing the uniqueness of the individual and his/her disease. To quote Sir William Osler, the father of western medicine, “the good physician treats the disease; the great physician treats the patient who has the disease”. Still, our answers typically gratify the patients and perhaps give the physicians an incontrovertibly false sense of being omniscient.
Author Manuscript
The landscape of the practice of medicine has been recently enriched with the entry of the medical genetics. The impetus provided by the recent elucidation of the genetic causes of single gene cardiovascular diseases has ushered in clinical applications of the molecular genetic discoveries and along with it our exuberance about genetic-based diagnosis, risk stratification, prevention, and treatment. Molecular Genetic testing is now routinely performed as part of the medical evaluation of patients with medical conditions commonly recognized as single gene diseases, such as inherited cardiomyopathies and arrhythmic syndromes1–3. It is also being advocated for risk stratification of patients with complex phenotypes, albeit its validity remains unsettled. Along with these advances, the need to educate and train the practicing physicians in understanding the fundamentals of genetics and their applications to the practice of medicine are increasingly recognized4.
Author Manuscript
Despite the remarkable progress in elucidation of genetic etiology of cardiovascular diseases over the last 25 years, the causal genes in approximately 30 to 50% of various single gene cardiovascular diseases, including hereditary cardiomyopathies and arrhythmic syndromes, have yet-to-be identified (Figure 1). The common causal genes, typically mapped through robust genetic linkage studies in large families, are responsible for about half and at most 2/3rd of single gene cardiovascular diseases. The remaining genes are uncommon and typically occur in the sporadic cases and small families, which are not suitable for genetic linkage studies. Consequently, the approach to identify the remaining elusive causal genes
Address for Correspondence: AJ Marian, M.D., Center for Cardiovascular Genetics, The Brown Foundation Institute of Molecular Medicine, The University of Texas Health Sciences Center, 6770 Bertner Street, Suite C900A, Houston, TX 77030, Phone: 713 500 2350, Fax: 713 383 0313, [email protected]. CONFLICT OF INTEREST None
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for single gene cardiovascular disorders has shifted toward DNA sequencing, primarily through whole exome sequencing (WES) and less commonly, whole genome sequencing (WGS).
Author Manuscript
The findings from WGS and WES projects have illustrated the complexity of the human genome. Fundamental to this complexity is the rare error rate of the DNA replication machinery, which is empirically calculated at about 1.0 × 10−8 per nucleotide5, 6. Consequently, each newborn (or meiosis) introduces approximately 50 to 60 de novo genetic variations to the existing pool. The explosive growth of the human population during the recent epochs has introduced a huge number of genetic variants in the population, rendering the humans genetically extremely diverse. Recognition of such genetic complexity has infused a dose of sobriety as well as a sense of intimidation, because of the challenges it has posed in applying the discoveries to the practice of medicine. Genetic testing laboratories have taken the conservative approach in reporting only the well-characterized genes and variants, an appropriate measure to avoid over-interpretation, particularly by those lacking an adequate training in clinical genetics. And yet this approach often leads to a null test result and is anticlimactic. It is often a source of a disappointment for the patients and the physicians alike. The disappointment is even greater for the physician-scientists who are expected not only to make the medical discoveries and pave the way for tomorrow’s practice of medicine but also apply the new discoveries to the care of their patients.
Author Manuscript
Staring into the read out of the exome sequencing of a proband with hypertrophic cardiomyopathy (HCM), whose genetic etiology is among the best characterized cardiovascular diseases7, the daunting task is to identify the causal variants among > 20,000 heterozygous variants that reside in the exons and exon-intron boundaries. WES approach also captures a huge number of variants in regions adjacent to the exons, which do not code for proteins. A Sherlock Holmes approach of deductive (or inductive) reasoning is needed to deduce the putative causal variant(s). Considering that the vast majority of the known causal mutations for single gene disorders, including HCM, are located in the protein coding regions (http://www.hgmd.org), it is logical to follow the bank robber Willie Sutton’s principle (“because that is where the money is”) and focus on the exons. The exon-centric approach has the inherent risk of the “winner’s curse”, and eliminating the chance of discovering new genetic mechanisms. An alternative approach of WGS that also encompasses introns and intergenic regions, however, adds further complexity, because of the presence of about 4 million genetic variants in each genome, the vast majority of which have yet to annotated8, 9. Whether the current exome-centric approach is responsible, in part, for failure to identify the “missing causal genes” for cardiovascular single gene disorders remains an open question.
Author Manuscript
Having settled on following the Willie Sutton’s principle, at the risk of the “winner’s curse”, the focus is on the variants that affect structure and potentially function of their respective encoded proteins. The approach eliminates the synonymous variants, even though not all synonymous variants are innocuous, as best illustrated for the synonymous p.G608G variant in the LMNA gene, which activate a cryptic splice site, and leads to Hutchinson-Gilford progeria syndrome10. Nevertheless, despite this generally accepted but not flawless filtering approach, the remaining list includes about 10,000 non-synonymous, 150 frameshift, 350
Circ Res. Author manuscript; available in PMC 2017 June 24.
Marian
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non-frameshift insertion/deletion, and 100 stop codon gain or loss variants, all having the potential to be functional and pathogenic. Computational algorithms that incorporate population variant frequency, evolutionary conservation of the involved amino acids, and the predicted effects of the variant on biophysical and biochemical properties of the involved protein are useful in discerning the pathogenic and deleterious variants. Regardless of the prediction by in silico algorithms, however, it is hard to argue that over 600 variants that are expected to affect their respective protein length and structure are biologically inert and innocuous. Eliminating variants that are more common in the population than the prevalence of HCM, estimated at ~ 1 in 500 in the general population of young adults11, and considering genetically heterogeneity of HCM7, one could further narrow down the search to rare putatively functional variants. The filtering approach and integration of the biological data, further restricts the candidate causal variants to a handful. In best situations, one of a rare pathogenic candidate variant resides in a gene known to cause HCM, and hence, is isolated, with a reasonable accuracy, but not with an unambiguous certainty, as the causal variant/gene. Otherwise, the approach has met the “Peter Principle”, rendering identification of the causal variant quite challenging, if not impossible, even if the candidate variant resides in a biologically plausible candidate protein. Moreover, the failure to identify might also reflect, despite all technical advances, technical imperfectness of WES variant detection, annotation, and filtering algorithms.
Author Manuscript Author Manuscript
To narrow down the list of the candidate causal variants in a proband or members of a small family, a commonly used approach, typically for molecular diagnostic purposes, is to perform WES followed by a focused analysis restricted to genes known to cause the phenotype of interest. This targeted approach typically entails analysis of about 50 to 100 known causal genes, as opposed to ~ 20,000 genes in the human genome, and therefore, irrefutably, reduces the number of the putative candidate pathogenic variants. However, this focused approach explicably is not applicable to discovering novel genes and inherently has a high rate of a negative test result, as the causal gene(s) in approximately 30 to 50% of each of the single gene cardiovascular disease have yet-to-be-identified. Moreover, it merits to be noted that “no gene or protein is a perfect molecule”. Accordingly, even the best-established causal genes for monogenic cardiovascular diseases carry variants that not only are considered functional but also pathogenic, as predicted by various in silico algorithms, and yet they occur in individuals free of the relevant clinical phenotype. A notable example is the case of TTN, encoding the giant sarcomere protein titin, which is the most common causal gene for dilated cardiomyopathy (DCM)12. Yet, the gene carries a large number of missense, stop codon, and frame shift (truncating) variants that occur in normal individuals12–14. In the best-case scenario in the candidate gene approach, the list of the pathogenic variants might include a previously established causal variant. Such finding typically satisfies the inquisitive physician for discovering a genetic etiology for his/her patient’s condition. Yet, the extrapolation dismisses the possibility of the potential effects of the genetic background and/or the environmental factors in precipitating the causality in one specific individual but not in the other and hence, restricting generalizability of the previous findings. Given that establishing causality of a genetic variant in a single individual unambiguously, even with a well-defined single gene disorder, is almost impossible, it is best to focus on families, as inclusion of each family member could enable reducing the number of the Circ Res. Author manuscript; available in PMC 2017 June 24.
Marian
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candidate genes/variants by up to 50%. The causal genes in most large families with single gene disorders have already been identified (Figure 1). Therefore, one is often limited to analyzing the data in nuclear families composed of often 2, sometimes 3 individuals, and occasionally more. At such situation, one, at best, might infer causality, but seldom is able establish it with a high level of confidence.
Author Manuscript Author Manuscript
The discoveries ushered in by the large-scale DNA sequencing technologies are also blurring the classic categorical distinction between single gene and complex disorders, rendering the disease spectrum a continuum, wherein the classical single gene and complex disorders represent the opposite ends of the spectrum15, 16. Accordingly, a subset of the “single gene disorders” might be digenic and even oligogenic, wherein two or more distinct pathogenic variants are responsible for the phenotype while none perfectly co-segregating with inheritance of the phenotype. An oligogenic etiology might explain the elusive genetic etiology of a subset of the conventionally considered single gene disorders. However, it is practically impossible to ascertain causality of a single, let alone multiple variants, in a single individual and even in members of a small family, in an unambiguous manner. Thus, to test an oligogenic etiology of a subset of presumably single gene disorders, therefore, one has to depend on the group data and apply various statistical methods to assess enrichment of the pathogenic variants in the genes of interest in those with the phenotype. Statistical evidence typically mandate complementation with in vitro and in vitro functional studies, in order to fulfill the analogous elements of the Koch postulates of causality17, 18. Until then, whether such digenic or oligogenic etiology could in part explain the “missing genes” in approximately 30 to 50% of the conventionally considered single gene cardiovascular disorders is an open question in need of empiric testing. Such etiological complexity along with possible involvement of copy number variants and mitochondrial DNA have posed further challenges in the clinical implications of genetic screening. The view presented probably reflects the conviction that the most profound impact of molecular genetic discoveries is in elucidating the fundamental mechanisms that govern the pathogenesis of the disease. This notion is best illustrated in the case of PCSK9 gene, which was mapped in a family with a rare form of autosomal dominant hypercholesterolemia19. Within a decade or so, the discovery led to development of powerful new therapies for patients with hypercholesterolemia20–22. Thus, despite recent technological advances and our exuberance in application of the modern genetic discoveries to the practice of medicine, the complexity of the human molecular genetics has eluded us in identifying the remaining “missing causal genes” in patients/families with single gene cardiovascular disorders. The complexity of the human genome has made it amply evident that “the more we know, the more we know that we do not know”.
Author Manuscript
Acknowledgments None FUNDING SOURCES Supported in part by grants from NIH, National Heart, Lung and Blood Institute (R01 HL088498, 1R01HL132401, and R34 HL105563), Leducq Foundation (14 CVD 03), Roderick MacDonald Foundation (13RDM005), TexGen
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Fund from Greater Houston Community Foundation, Texas Heart Institute Foundation, and George and Mary Josephine Hamman Foundation.
References
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1. Authors/Task Force m. Elliott PM, Anastasakis A, Borger MA, Borggrefe M, Cecchi F, Charron P, Hagege AA, Lafont A, Limongelli G, Mahrholdt H, McKenna WJ, Mogensen J, Nihoyannopoulos P, Nistri S, Pieper PG, Pieske B, Rapezzi C, Rutten FH, Tillmanns C, Watkins H. 2014 esc guidelines on diagnosis and management of hypertrophic cardiomyopathy: The task force for the diagnosis and management of hypertrophic cardiomyopathy of the european society of cardiology (esc). European heart journal. 2014; 35:2733–2779. [PubMed: 25173338] 2. Ackerman MJ, Priori SG, Willems S, Berul C, Brugada R, Calkins H, Camm AJ, Ellinor PT, Gollob M, Hamilton R, Hershberger RE, Judge DP, Le Marec H, McKenna WJ, Schulze-Bahr E, Semsarian C, Towbin JA, Watkins H, Wilde A, Wolpert C, Zipes DP. Hrs/ehra expert consensus statement on the state of genetic testing for the channelopathies and cardiomyopathies this document was developed as a partnership between the heart rhythm society (hrs) and the european heart rhythm association (ehra). Heart rhythm: the official journal of the Heart Rhythm Society. 2011; 8:1308– 1339. [PubMed: 21787999] 3. Bezzina CR, Lahrouchi N, Priori SG. Genetics of sudden cardiac death. Circulation research. 2015; 116:1919–1936. [PubMed: 26044248] 4. Korf BR, Berry AB, Limson M, Marian AJ, Murray MF, O’Rourke PP, Passamani ER, Relling MV, Tooker J, Tsongalis GJ, Rodriguez LL. Framework for development of physician competencies in genomic medicine: Report of the competencies working group of the inter-society coordinating committee for physician education in genomics. Genetics in medicine: official journal of the American College of Medical Genetics. 2014 5. Campbell CD, Chong JX, Malig M, Ko A, Dumont BL, Han L, Vives L, O’Roak BJ, Sudmant PH, Shendure J, Abney M, Ober C, Eichler EE. Estimating the human mutation rate using autozygosity in a founder population. Nature genetics. 2012; 44:1277–1281. [PubMed: 23001126] 6. Kong A, Frigge ML, Masson G, Besenbacher S, Sulem P, Magnusson G, Gudjonsson SA, Sigurdsson A, Jonasdottir A, Jonasdottir A, Wong WS, Sigurdsson G, Walters GB, Steinberg S, Helgason H, Thorleifsson G, Gudbjartsson DF, Helgason A, Magnusson OT, Thorsteinsdottir U, Stefansson K. Rate of de novo mutations and the importance of father’s age to disease risk. Nature. 2012; 488:471–475. [PubMed: 22914163] 7. Ho CY, Charron P, Richard P, Girolami F, Van Spaendonck-Zwarts KY, Pinto Y. Genetic advances in sarcomeric cardiomyopathies: State of the art. Cardiovascular research. 2015; 105:397–408. [PubMed: 25634555] 8. Levy S, Sutton G, Ng PC, Feuk L, Halpern AL, Walenz BP, Axelrod N, Huang J, Kirkness EF, Denisov G, Lin Y, MacDonald JR, Pang AW, Shago M, Stockwell TB, Tsiamouri A, Bafna V, Bansal V, Kravitz SA, Busam DA, Beeson KY, McIntosh TC, Remington KA, Abril JF, Gill J, Borman J, Rogers YH, Frazier ME, Scherer SW, Strausberg RL, Venter JC. The diploid genome sequence of an individual human. PLoS Biol. 2007; 5:e254. [PubMed: 17803354] 9. Wheeler DA, Srinivasan M, Egholm M, Shen Y, Chen L, McGuire A, He W, Chen YJ, Makhijani V, Roth GT, Gomes X, Tartaro K, Niazi F, Turcotte CL, Irzyk GP, Lupski JR, Chinault C, Song XZ, Liu Y, Yuan Y, Nazareth L, Qin X, Muzny DM, Margulies M, Weinstock GM, Gibbs RA, Rothberg JM. The complete genome of an individual by massively parallel DNA sequencing. Nature. 2008; 452:872–876. [PubMed: 18421352] 10. De Sandre-Giovannoli A, Bernard R, Cau P, Navarro C, Amiel J, Boccaccio I, Lyonnet S, Stewart CL, Munnich A, Le Merrer M, Levy N. Lamin a truncation in hutchinson-gilford progeria. Science. 2003; 300:2055. [PubMed: 12702809] 11. Maron BJ, Gardin JM, Flack JM, Gidding SS, Kurosaki TT, Bild DE. Prevalence of hypertrophic cardiomyopathy in a general population of young adults. Echocardiographic analysis of 4111 subjects in the cardia study. Coronary artery risk development in (young) adults. Circulation. 1995; 92:785–789. [PubMed: 7641357] 12. Herman DS, Lam L, Taylor MR, Wang L, Teekakirikul P, Christodoulou D, Conner L, DePalma SR, McDonough B, Sparks E, Teodorescu DL, Cirino AL, Banner NR, Pennell DJ, Graw S, Merlo
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M, Di Lenarda A, Sinagra G, Bos JM, Ackerman MJ, Mitchell RN, Murry CE, Lakdawala NK, Ho CY, Barton PJ, Cook SA, Mestroni L, Seidman JG, Seidman CE. Truncations of titin causing dilated cardiomyopathy. The New England journal of medicine. 2012; 366:619–628. [PubMed: 22335739] 13. Golbus JR, Puckelwartz MJ, Fahrenbach JP, Dellefave-Castillo LM, Wolfgeher D, McNally EM. Population-based variation in cardiomyopathy genes. Circulation Cardiovascular genetics. 2012; 5:391–399. [PubMed: 22763267] 14. Norton N, Li D, Rampersaud E, Morales A, Martin ER, Zuchner S, Guo S, Gonzalez M, Hedges DJ, Robertson PD, Krumm N, Nickerson DA, Hershberger RE, National Heart L, Blood Institute GOESP, the Exome Sequencing Project Family Studies Project T. Exome sequencing and genomewide linkage analysis in 17 families illustrate the complex contribution of ttn truncating variants to dilated cardiomyopathy. Circulation Cardiovascular genetics. 2013; 6:144–153. [PubMed: 23418287] 15. Marian AJ. Nature’s genetic gradients and the clinical phenotype. Circulation Cardiovascular genetics. 2009; 2:537–539. [PubMed: 20031631] 16. Marian AJ, Belmont J. Strategic approaches to unraveling genetic causes of cardiovascular diseases. Circulation research. 2011; 108:1252–1269. [PubMed: 21566222] 17. MacArthur DG, Manolio TA, Dimmock DP, Rehm HL, Shendure J, Abecasis GR, Adams DR, Altman RB, Antonarakis SE, Ashley EA, Barrett JC, Biesecker LG, Conrad DF, Cooper GM, Cox NJ, Daly MJ, Gerstein MB, Goldstein DB, Hirschhorn JN, Leal SM, Pennacchio LA, Stamatoyannopoulos JA, Sunyaev SR, Valle D, Voight BF, Winckler W, Gunter C. Guidelines for investigating causality of sequence variants in human disease. Nature. 2014; 508:469–476. [PubMed: 24759409] 18. Marian AJ. Causality in genetics: The gradient of genetic effects and back to koch’s postulates of causality. Circulation research. 2014; 114:e18–21. [PubMed: 24436434] 19. 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. Nature genetics. 2003; 34:154–156. [PubMed: 12730697] 20. Seidah NG, Awan Z, Chretien M, Mbikay M. Pcsk9: A key modulator of cardiovascular health. Circulation research. 2014; 114:1022–1036. [PubMed: 24625727] 21. Sabatine MS, Giugliano RP, Wiviott SD, Raal FJ, Blom DJ, Robinson J, Ballantyne CM, Somaratne R, Legg J, Wasserman SM, Scott R, Koren MJ, Stein EA. Open-Label Study of LongTerm Evaluation against LDLCI. Efficacy and safety of evolocumab in reducing lipids and cardiovascular events. The New England journal of medicine. 2015; 372:1500–1509. [PubMed: 25773607] 22. Robinson JG, Farnier M, Krempf M, Bergeron J, Luc G, Averna M, Stroes ES, Langslet G, Raal FJ, El Shahawy M, Koren MJ, Lepor NE, Lorenzato C, Pordy R, Chaudhari U, Kastelein JJ, Investigators OLT. Efficacy and safety of alirocumab in reducing lipids and cardiovascular events. The New England journal of medicine. 2015; 372:1489–1499. [PubMed: 25773378]
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Figure 1. Known and yet-to-be identified causal genes for hypertrophic cardiomyopathy (HCM)
The common causal genes, identified through linkage analysis in multiple large families, are responsible for about 60% of HCM. Evidence for causality for such genes is quite strong. In contrast, the causal genes in approximately 40% of HCM, occurring in small families and sporadic cases, remain unknown. Causality is difficult to establish in such cases.
Author Manuscript Circ Res. Author manuscript; available in PMC 2017 June 24.
Circ Res. Author manuscript; available in PMC 2017 June 24. Published in final edited form as: Circ Res. 2016 June 24; 119(1): 21–24. doi:10.1161/CIRCRESAHA.116.308830.
The Case of “Missing Causal Genes” and the Practice of Medicine: A Sherlock Holmes Approach of Deductive Reasoning AJ Marian, M.D. Center for Cardiovascular Genetics, Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center and Texas Heart Institute, Houston, TX, 77030
Author Manuscript
The practice of medicine is an exceedingly gratifying privilege, a notion that is commonly shared among the physicians. Also commonly understood but often not expressed is the humbling nature of this practice. Seldom, if ever, we know the true answers to our patients’ questions. Yet, we adeptly offer explanations, commonly based on the group data, and yet the essence of the practice of medicine is recognizing the uniqueness of the individual and his/her disease. To quote Sir William Osler, the father of western medicine, “the good physician treats the disease; the great physician treats the patient who has the disease”. Still, our answers typically gratify the patients and perhaps give the physicians an incontrovertibly false sense of being omniscient.
Author Manuscript
The landscape of the practice of medicine has been recently enriched with the entry of the medical genetics. The impetus provided by the recent elucidation of the genetic causes of single gene cardiovascular diseases has ushered in clinical applications of the molecular genetic discoveries and along with it our exuberance about genetic-based diagnosis, risk stratification, prevention, and treatment. Molecular Genetic testing is now routinely performed as part of the medical evaluation of patients with medical conditions commonly recognized as single gene diseases, such as inherited cardiomyopathies and arrhythmic syndromes1–3. It is also being advocated for risk stratification of patients with complex phenotypes, albeit its validity remains unsettled. Along with these advances, the need to educate and train the practicing physicians in understanding the fundamentals of genetics and their applications to the practice of medicine are increasingly recognized4.
Author Manuscript
Despite the remarkable progress in elucidation of genetic etiology of cardiovascular diseases over the last 25 years, the causal genes in approximately 30 to 50% of various single gene cardiovascular diseases, including hereditary cardiomyopathies and arrhythmic syndromes, have yet-to-be identified (Figure 1). The common causal genes, typically mapped through robust genetic linkage studies in large families, are responsible for about half and at most 2/3rd of single gene cardiovascular diseases. The remaining genes are uncommon and typically occur in the sporadic cases and small families, which are not suitable for genetic linkage studies. Consequently, the approach to identify the remaining elusive causal genes
Address for Correspondence: AJ Marian, M.D., Center for Cardiovascular Genetics, The Brown Foundation Institute of Molecular Medicine, The University of Texas Health Sciences Center, 6770 Bertner Street, Suite C900A, Houston, TX 77030, Phone: 713 500 2350, Fax: 713 383 0313, [email protected]. CONFLICT OF INTEREST None
Marian
Page 2
Author Manuscript
for single gene cardiovascular disorders has shifted toward DNA sequencing, primarily through whole exome sequencing (WES) and less commonly, whole genome sequencing (WGS).
Author Manuscript
The findings from WGS and WES projects have illustrated the complexity of the human genome. Fundamental to this complexity is the rare error rate of the DNA replication machinery, which is empirically calculated at about 1.0 × 10−8 per nucleotide5, 6. Consequently, each newborn (or meiosis) introduces approximately 50 to 60 de novo genetic variations to the existing pool. The explosive growth of the human population during the recent epochs has introduced a huge number of genetic variants in the population, rendering the humans genetically extremely diverse. Recognition of such genetic complexity has infused a dose of sobriety as well as a sense of intimidation, because of the challenges it has posed in applying the discoveries to the practice of medicine. Genetic testing laboratories have taken the conservative approach in reporting only the well-characterized genes and variants, an appropriate measure to avoid over-interpretation, particularly by those lacking an adequate training in clinical genetics. And yet this approach often leads to a null test result and is anticlimactic. It is often a source of a disappointment for the patients and the physicians alike. The disappointment is even greater for the physician-scientists who are expected not only to make the medical discoveries and pave the way for tomorrow’s practice of medicine but also apply the new discoveries to the care of their patients.
Author Manuscript
Staring into the read out of the exome sequencing of a proband with hypertrophic cardiomyopathy (HCM), whose genetic etiology is among the best characterized cardiovascular diseases7, the daunting task is to identify the causal variants among > 20,000 heterozygous variants that reside in the exons and exon-intron boundaries. WES approach also captures a huge number of variants in regions adjacent to the exons, which do not code for proteins. A Sherlock Holmes approach of deductive (or inductive) reasoning is needed to deduce the putative causal variant(s). Considering that the vast majority of the known causal mutations for single gene disorders, including HCM, are located in the protein coding regions (http://www.hgmd.org), it is logical to follow the bank robber Willie Sutton’s principle (“because that is where the money is”) and focus on the exons. The exon-centric approach has the inherent risk of the “winner’s curse”, and eliminating the chance of discovering new genetic mechanisms. An alternative approach of WGS that also encompasses introns and intergenic regions, however, adds further complexity, because of the presence of about 4 million genetic variants in each genome, the vast majority of which have yet to annotated8, 9. Whether the current exome-centric approach is responsible, in part, for failure to identify the “missing causal genes” for cardiovascular single gene disorders remains an open question.
Author Manuscript
Having settled on following the Willie Sutton’s principle, at the risk of the “winner’s curse”, the focus is on the variants that affect structure and potentially function of their respective encoded proteins. The approach eliminates the synonymous variants, even though not all synonymous variants are innocuous, as best illustrated for the synonymous p.G608G variant in the LMNA gene, which activate a cryptic splice site, and leads to Hutchinson-Gilford progeria syndrome10. Nevertheless, despite this generally accepted but not flawless filtering approach, the remaining list includes about 10,000 non-synonymous, 150 frameshift, 350
Circ Res. Author manuscript; available in PMC 2017 June 24.
Marian
Page 3
Author Manuscript Author Manuscript
non-frameshift insertion/deletion, and 100 stop codon gain or loss variants, all having the potential to be functional and pathogenic. Computational algorithms that incorporate population variant frequency, evolutionary conservation of the involved amino acids, and the predicted effects of the variant on biophysical and biochemical properties of the involved protein are useful in discerning the pathogenic and deleterious variants. Regardless of the prediction by in silico algorithms, however, it is hard to argue that over 600 variants that are expected to affect their respective protein length and structure are biologically inert and innocuous. Eliminating variants that are more common in the population than the prevalence of HCM, estimated at ~ 1 in 500 in the general population of young adults11, and considering genetically heterogeneity of HCM7, one could further narrow down the search to rare putatively functional variants. The filtering approach and integration of the biological data, further restricts the candidate causal variants to a handful. In best situations, one of a rare pathogenic candidate variant resides in a gene known to cause HCM, and hence, is isolated, with a reasonable accuracy, but not with an unambiguous certainty, as the causal variant/gene. Otherwise, the approach has met the “Peter Principle”, rendering identification of the causal variant quite challenging, if not impossible, even if the candidate variant resides in a biologically plausible candidate protein. Moreover, the failure to identify might also reflect, despite all technical advances, technical imperfectness of WES variant detection, annotation, and filtering algorithms.
Author Manuscript Author Manuscript
To narrow down the list of the candidate causal variants in a proband or members of a small family, a commonly used approach, typically for molecular diagnostic purposes, is to perform WES followed by a focused analysis restricted to genes known to cause the phenotype of interest. This targeted approach typically entails analysis of about 50 to 100 known causal genes, as opposed to ~ 20,000 genes in the human genome, and therefore, irrefutably, reduces the number of the putative candidate pathogenic variants. However, this focused approach explicably is not applicable to discovering novel genes and inherently has a high rate of a negative test result, as the causal gene(s) in approximately 30 to 50% of each of the single gene cardiovascular disease have yet-to-be-identified. Moreover, it merits to be noted that “no gene or protein is a perfect molecule”. Accordingly, even the best-established causal genes for monogenic cardiovascular diseases carry variants that not only are considered functional but also pathogenic, as predicted by various in silico algorithms, and yet they occur in individuals free of the relevant clinical phenotype. A notable example is the case of TTN, encoding the giant sarcomere protein titin, which is the most common causal gene for dilated cardiomyopathy (DCM)12. Yet, the gene carries a large number of missense, stop codon, and frame shift (truncating) variants that occur in normal individuals12–14. In the best-case scenario in the candidate gene approach, the list of the pathogenic variants might include a previously established causal variant. Such finding typically satisfies the inquisitive physician for discovering a genetic etiology for his/her patient’s condition. Yet, the extrapolation dismisses the possibility of the potential effects of the genetic background and/or the environmental factors in precipitating the causality in one specific individual but not in the other and hence, restricting generalizability of the previous findings. Given that establishing causality of a genetic variant in a single individual unambiguously, even with a well-defined single gene disorder, is almost impossible, it is best to focus on families, as inclusion of each family member could enable reducing the number of the Circ Res. Author manuscript; available in PMC 2017 June 24.
Marian
Page 4
Author Manuscript
candidate genes/variants by up to 50%. The causal genes in most large families with single gene disorders have already been identified (Figure 1). Therefore, one is often limited to analyzing the data in nuclear families composed of often 2, sometimes 3 individuals, and occasionally more. At such situation, one, at best, might infer causality, but seldom is able establish it with a high level of confidence.
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The discoveries ushered in by the large-scale DNA sequencing technologies are also blurring the classic categorical distinction between single gene and complex disorders, rendering the disease spectrum a continuum, wherein the classical single gene and complex disorders represent the opposite ends of the spectrum15, 16. Accordingly, a subset of the “single gene disorders” might be digenic and even oligogenic, wherein two or more distinct pathogenic variants are responsible for the phenotype while none perfectly co-segregating with inheritance of the phenotype. An oligogenic etiology might explain the elusive genetic etiology of a subset of the conventionally considered single gene disorders. However, it is practically impossible to ascertain causality of a single, let alone multiple variants, in a single individual and even in members of a small family, in an unambiguous manner. Thus, to test an oligogenic etiology of a subset of presumably single gene disorders, therefore, one has to depend on the group data and apply various statistical methods to assess enrichment of the pathogenic variants in the genes of interest in those with the phenotype. Statistical evidence typically mandate complementation with in vitro and in vitro functional studies, in order to fulfill the analogous elements of the Koch postulates of causality17, 18. Until then, whether such digenic or oligogenic etiology could in part explain the “missing genes” in approximately 30 to 50% of the conventionally considered single gene cardiovascular disorders is an open question in need of empiric testing. Such etiological complexity along with possible involvement of copy number variants and mitochondrial DNA have posed further challenges in the clinical implications of genetic screening. The view presented probably reflects the conviction that the most profound impact of molecular genetic discoveries is in elucidating the fundamental mechanisms that govern the pathogenesis of the disease. This notion is best illustrated in the case of PCSK9 gene, which was mapped in a family with a rare form of autosomal dominant hypercholesterolemia19. Within a decade or so, the discovery led to development of powerful new therapies for patients with hypercholesterolemia20–22. Thus, despite recent technological advances and our exuberance in application of the modern genetic discoveries to the practice of medicine, the complexity of the human molecular genetics has eluded us in identifying the remaining “missing causal genes” in patients/families with single gene cardiovascular disorders. The complexity of the human genome has made it amply evident that “the more we know, the more we know that we do not know”.
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Acknowledgments None FUNDING SOURCES Supported in part by grants from NIH, National Heart, Lung and Blood Institute (R01 HL088498, 1R01HL132401, and R34 HL105563), Leducq Foundation (14 CVD 03), Roderick MacDonald Foundation (13RDM005), TexGen
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Fund from Greater Houston Community Foundation, Texas Heart Institute Foundation, and George and Mary Josephine Hamman Foundation.
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Figure 1. Known and yet-to-be identified causal genes for hypertrophic cardiomyopathy (HCM)
The common causal genes, identified through linkage analysis in multiple large families, are responsible for about 60% of HCM. Evidence for causality for such genes is quite strong. In contrast, the causal genes in approximately 40% of HCM, occurring in small families and sporadic cases, remain unknown. Causality is difficult to establish in such cases.
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