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“Mendelian Code” in the Genetic Structure of Common Multifactorial Diseases

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Abstract

The phenomenon of comorbidity between monogenic and multifactorial diseases suggests the involvement of a certain common number of genes and biological pathways in the formation of the predisposition to diseases, known as the Mendelian code, which links each multifactorial disease with a unique set of Mendelian loci. Within the omnigenic model of multifactorial diseases, genes of Mendelian diseases can be represented by core genes that function in the cells of the target organs of the pathology and participate in their pathogenesis. Mendelian diseases can be used as a starting point for prioritizing loci/genes related to complex traits and diseases. This approach was applied in this review by the example of prioritizing genes in loci associated with hypertrophic and dilated cardiomyopathies as a result of genome-wide association studies. The functional characteristics of the Mendelian disease genes in the genetic structure of the susceptibility to multifactorial diseases will provide new knowledge about core and peripheral genes and their areas of competence. It is important to analyze the Mendelian code of multifactorial diseases using the multiomic approach, which will allow one to identify driver genes and biological pathways associated with the development of diseases.

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REFERENCES

  1. Timpson, N.J., Greenwood, C.M.T., Soranzo, N., et al., Genetic architecture: the shape of the genetic contribution to human traits and disease, Nat. Rev. Genet., 2018, vol. 19, pp. 110—124. https://doi.org/10.1038/nrg.2017.101

    Article  CAS  PubMed  Google Scholar 

  2. Dzau, V. and Braunwald, E., Resolved and unresolved issues in the prevention and treatment of coronary artery disease: a workshop consensus statement, Am. Heart J., 1991, vol. 121, pp. 1244—1263. https://doi.org/10.1016/0002-8703(91)90694-d

    Article  CAS  PubMed  Google Scholar 

  3. Puzyrev, V.P., Makeeva, O.A., and Golubenko, M.V., Syntropic genes and the cardiovascular continuum, Inf. Vestn. Vavilovskogo O-va. Genet. Sel., 2006, vol. 10, no. 3, pp. 479—491.

    Google Scholar 

  4. Puzyrev, V.P. and Freidin, M.B., Genetic view on the phenomenon of combined diseases in man, Acta Nat., 2009, vol. 1, no. 3, pp. 52—57.

    Article  CAS  Google Scholar 

  5. Gottesman, O., Drill, E., Lotay, V., et al., Can genetic pleiotropy replicate common clinical constellations of cardiovascular disease and risk?, PLoS One, 2012, vol. 7. e46419. https://doi.org/10.1371/journal.pone.0046419

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Puzyrev, V.P., Genetic bases of human comorbidity, Russ. J. Genet., 2015, vol. 51, no. 4, pp. 408—417. https://doi.org/10.1134/S1022795415040092

    Article  CAS  Google Scholar 

  7. Rankinen, T., Sarzynski, M.A., Ghosh, S., and Bouchard, C., Are there genetic paths common to obesity, cardiovascular disease outcomes, and cardiovascular risk factors?, Circ. Res., 2015, vol. 116, pp. 909—922. https://doi.org/10.1161/CIRCRESAHA.116.302888

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Jia, X., Yang, Y., Chen, Y., et al., Multivariate analysis of genome-wide data to identify potential pleiotropic genes for type 2 diabetes, obesity and coronary artery disease using MetaCCA, Int. J. Cardiol., 2019, vol. 283, pp. 144—150. https://doi.org/10.1016/j.ijcard.2018.10.102

    Article  PubMed  Google Scholar 

  9. Tabarés-Seisdedos, R., Dumont, N., Baudot, A., et al., No paradox, no progress: inverse cancer comorbidity in people with other complex diseases, Lancet Oncol., 2011, vol. 12, pp. 604—608. https://doi.org/10.1016/S1470-2045(11)70041-9

    Article  PubMed  Google Scholar 

  10. Catalá-López, F., Suárez-Pinilla, M., Suárez-Pinilla, P., et al., Inverse and direct cancer comorbidity in people with central nervous system disorders: a meta-analysis of cancer incidence in 577 013 participants of 50 observational studies, Psychother. Psychosom., 2014, vol. 83, pp. 89—105. https://doi.org/10.1159/000356498

    Article  PubMed  Google Scholar 

  11. Seo, J. and Park, M., Molecular crosstalk between cancer and neurodegenerative diseases, Cell Mol. Life Sci., 2020, vol. 77, pp. 2659—2680. https://doi.org/10.1007/s00018-019-03428-3

    Article  CAS  PubMed  Google Scholar 

  12. Houck, A.L., Seddighi, S., and Driver, J.A., At the crossroads between neurodegeneration and cancer: a review of overlapping biology and its implications, Curr. Aging Sci., 2018, vol. 11, pp. 77—89. https://doi.org/10.2174/1874609811666180223154436

    Article  PubMed  PubMed Central  Google Scholar 

  13. Manolio, T.A., Collins, F.S., Cox, N.J., et al., Finding the missing heritability of complex diseases, Nature, 2009, vol. 461, pp. 747—753. https://doi.org/10.1038/nature08494

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Claussnitzer, M., Cho, J.H., Collins, R., et al., A brief history of human disease genetics, Nature, 2020, vol. 577, pp. 179—189. https://doi.org/10.1038/s41586-019-1879-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Dipple, K.M. and McCabe, E.R., Phenotypes of patients with simple Mendelian disorders are complex traits: thresholds, modifiers, and systems dynamics, Am. J. Hum. Genet., 2000, vol. 66, pp. 1729—1735. https://doi.org/10.1086/302938

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Badano, J.L. and Katsanis, N., Beyond Mendel: an evolving view of human genetic disease transmission, Nat. Rev. Genet., 2002, vol. 3, pp. 779—789. https://doi.org/10.1038/nrg910

    Article  CAS  PubMed  Google Scholar 

  17. Sidransky, E., Heterozygosity for a Mendelian disorder as a risk factor for complex disease, Clin. Genet., 2006, vol. 70, pp. 275—282. https://doi.org/10.1111/j.1399-0004.2006.00688.x

    Article  CAS  PubMed  Google Scholar 

  18. Katsanis, N., The continuum of causality in human genetic disorders, Genome Biol., 2016, vol. 17, p. 233. https://doi.org/10.1186/s13059-016-1107-9

    Article  PubMed  PubMed Central  Google Scholar 

  19. Blair, D.R., Lyttle, C.S., Mortensen, J.M., et al., A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk, Cell, 2013, vol. 155, pp. 70—80. https://doi.org/10.1016/j.cell.2013.08.030

    Article  CAS  PubMed  Google Scholar 

  20. Cohen, J., Pertsemlidis, A., Kotowski, I.K., et al., Low LDL cholesterol in individuals of African descent resulting from frequent nonsense mutations in PCSK9, Nat. Genet., 2005, vol. 37, pp. 161—165. https://doi.org/10.1038/ng1509

    Article  CAS  PubMed  Google Scholar 

  21. Han, K., Holder, J.L.J., Schaaf, C.P., et al., SHANK3 overexpression causes manic-like behaviour with unique pharmacogenetic properties, Nature, 2013, vol. 503, pp. 72—77. https://doi.org/10.1038/nature12630

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Auer, P.L., Teumer, A., Schick, U., et al., Rare and low-frequency coding variants in CXCR2 and other genes are associated with hematological traits, Nat. Genet., 2014, vol. 46, pp. 629—634. https://doi.org/10.1038/ng.2962

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Lin, A., Ching, C.R.K., Vajdi, A., et al., Mapping 22q11.2 gene dosage effects on brain morphometry, J. Neurosci., 2017, vol. 37, pp. 6183—6199. https://doi.org/10.1523/JNEUROSCI.3759-16.2017

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Wainschtein, P., Jain, D.P., Yengo, L., et al., Recovery of trait heritability from whole genome sequence data, BioRxiv, 2019. https://www.biorxiv.org/content/10.1101/588020v1.

  25. Bezzina, C.R., Lahrouchi, N., and Priori, S.G., Genetics of sudden cardiac death, Circ. Res., 2015, vol. 116, pp. 1919—1936. https://doi.org/10.1161/CIRCRESAHA.116.304030

    Article  CAS  PubMed  Google Scholar 

  26. Bečanović, K., Nørremølle, A., Neal, S.J., et al., A SNP in the HTT promoter alters NF-κB binding and is a bidirectional genetic modifier of Huntington disease, Nat. Neurosci., 2015, vol. 18, pp. 807—816. https://doi.org/10.1038/nn.4014

    Article  CAS  PubMed  Google Scholar 

  27. Corvol, H., Blackman, S.M., Boëlle, P.Y., et al., Genome-wide association meta-analysis identifies five modifier loci of lung disease severity in cystic fibrosis, Nat. Commun., 2015, vol. 6, p. 8382. https://doi.org/10.1038/ncomms9382

    Article  CAS  PubMed  Google Scholar 

  28. Jordan, D.M., Frangakis, S.G., Golzio, C., et al., Identification of cis-suppression of human disease mutations by comparative genomics, Nature, 2015, vol. 524, pp. 225—229. https://doi.org/10.1038/nature14497

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Castel, S.E., Cervera, A., Mohammadi, P., et al., Modified penetrance of coding variants by cis-regulatory variation contributes to disease risk, Nat. Genet., 2018, vol. 50, pp. 1327—1334. https://doi.org/10.1038/s41588-018-0192-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Holmans, P. and Stone, T., Using genomic data to find disease-modifying loci in Huntington’s disease (HD), Methods Mol. Biol., 2018, vol. 1780, pp. 443—461. https://doi.org/10.1007/978-1-4939-7825-0_20

    Article  CAS  PubMed  Google Scholar 

  31. Kim, K.H., Hong, E.P., Shin, J.W., et al., Genetic and functional analyses point to FAN1 as the source of multiple Huntington disease modifier effects, Am. J. Hum. Genet., 2020, vol. 107, pp. 96—110. https://doi.org/10.1016/j.ajhg.2020.05.012

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Posey, J.E., Harel, T., Liu, P., et al., Resolution of disease phenotypes resulting from multilocus genomic variation, N. Engl. J. Med., 2017, vol. 376, pp. 21—31. https://doi.org/10.1056/NEJMoa1516767

    Article  CAS  PubMed  Google Scholar 

  33. Lupski, J.R., Belmont, J.W., Boerwinkle, E., et al., Clan genomics and the complex architecture of human disease, Cell, 2011, vol. 147, pp. 32—43. https://doi.org/10.1016/j.cell.2011.09.008

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Gibson, G., Rare and common variants: twenty arguments, Nat. Rev. Genet., 2012, vol. 13, pp. 135—145. https://doi.org/10.1038/nrg3118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Gaugler, T., Klei, L., Sanders, S.J., et al., Most genetic risk for autism resides with common variation, Nat. Genet., 2014, vol. 46, pp. 881—885. https://doi.org/10.1038/ng.3039

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Dean, M., Approaches to identify genes for complex human diseases: lessons from Mendelian disorders, Hum. Mutat., 2003, vol. 22, pp. 261—274. https://doi.org/10.1002/humu.10259

    Article  CAS  PubMed  Google Scholar 

  37. Peltonen, L., Perola, M., Naukkarinen, J., et al., Lessons from studying monogenic disease for common disease, Hum. Mol. Genet., 2006, vol. 15, no. 1, pp. R67—R74. https://doi.org/10.1093/hmg/ddl060

    Article  CAS  PubMed  Google Scholar 

  38. Scheuner, M.T., Yoon, P.W., and Khoury, M.J., Contribution of Mendelian disorders to common chronic disease: opportunities for recognition, intervention, and prevention, Am. J. Med. Genet., Part C, 2004, vol. 125, pp. 50—65. https://doi.org/10.1002/ajmg.c.30008

    Article  Google Scholar 

  39. Kathiresan, S., Willer, C.J., Peloso, G.M., et al., Common variants at 30 loci contribute to polygenic dyslipidemia, Nat. Genet., 2009, vol. 41, pp. 56—65. https://doi.org/10.1038/ng.291

    Article  CAS  PubMed  Google Scholar 

  40. van der Harst, P., van Setten, J., Verweij, N., et al., 52 Genetic loci influencing myocardial mass, J. Am. Coll. Cardiol., 2016, vol. 68, pp. 1435—1448. https://doi.org/10.1016/j.jacc.2016.07.729

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Freund, M.K., Burch, K.S., Shi, H., et al., Phenotype-specific enrichment of Mendelian disorder genes near GWAS regions across 62 complex traits, Am. J. Hum. Genet., 2018, vol. 103, pp. 535—552. https://doi.org/10.1016/j.ajhg.2018.08.017

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Spataro, N., Rodríguez, J.A., Navarro, A., et al., Properties of human disease genes and the role of genes linked to Mendelian disorders in complex disease aetiology, Hum. Mol. Genet., 2017, vol. 26, pp. 489—500. https://doi.org/10.1093/hmg/ddw405

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Jin, W., Qin, P., Lou, H., et al., A systematic characterization of genes underlying both complex and Mendelian diseases, Hum. Mol. Genet., 2012, vol. 21, pp. 1611—1624. https://doi.org/10.1093/hmg/ddr599

    Article  CAS  PubMed  Google Scholar 

  44. Van, D.P., Veldink, J.H., Van, B.M., et al., Expanded ATXN2 CAG repeat size in ALS identifies genetic overlap between ALS and SCA2, Neurology, 2011, vol. 76, pp. 2066—2072. https://doi.org/10.1212/WNL.0b013e31821f445b

    Article  CAS  Google Scholar 

  45. Lattante, S., Pomponi, M.G., Conte, A., et al., ATXN1 intermediate-length polyglutamine expansions are associated with amyotrophic lateral sclerosis, Neurobiol. Aging, 2018, vol. 64, pp. 157.e1—157.e5. https://doi.org/10.1016/j.neurobiolaging.2017.11.011

  46. Choubtum, L., Witoonpanich, P., Kulkantrakorn, K., et al., Trinucleotide repeat expansion of TATA-binding protein gene associated with Parkinson’s disease: a Thai multicenter study, Parkinsonism Relat. Disord., 2016, vol. 28, pp. 146—149. https://doi.org/10.1016/j.parkreldis.2016.05.008

    Article  PubMed  Google Scholar 

  47. Vasil’ev, V.B., Geneticheskie osnovy mitokhondrial’nykh boleznei (Genetic Basis of Mitochondrial Diseases), Nestor-Istoriya, 2006.

  48. Sulaiman, S.A., Rani, Z.M., Radin, F.Z.M., et al., Advancement in the diagnosis of mitochondrial diseases, J. Transl. Genet. Genom., 2020, vol. 4, pp. 159—187.

    Google Scholar 

  49. Kang, D. and Hamasaki, N., Alterations of mitochondrial DNA in common diseases and disease states: aging, neurodegeneration, heart failure, diabetes, and cancer, Curr. Med. Chem., 2005, vol. 12, pp. 429—441. https://doi.org/10.2174/0929867053363081

    Article  CAS  PubMed  Google Scholar 

  50. Veronese, N., Stubbs, B., Koyanagi, A., et al., Mitochondrial genetic haplogroups and cardiovascular diseases: data from the Osteoarthritis Initiative, PLoS One, 2019, vol. 14, no. 3. e0213656. https://doi.org/10.1371/journal.pone.0213656

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Wallace, D.C. and Chalkia, D., Mitochondrial DNA genetics and the heteroplasmy conundrum in evolution and disease, Cold Spring Harb. Perspect. Biol., 2013, vol. 5, no. 11, p. a021220. https://doi.org/10.1101/cshperspect.a021220

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Boyle, E.A., Li, Y.I., and Pritchard, J.K., An expanded view of complex traits: from polygenic to omnigenic, Cell, 2017, vol. 169, pp. 1177—1186. https://doi.org/10.1016/j.cell.2017.05.038

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Melamed, R.D., Emmett, K.J., Madubata, C., et al., Genetic similarity between cancers and comorbid Mendelian diseases identifies candidate driver genes, Nat. Commun., 2015, vol. 6, p. 7033. https://doi.org/10.1038/ncomms8033

    Article  CAS  PubMed  Google Scholar 

  54. ClinGen. https://clinicalgenome.org.

  55. Ingles, J., Goldstein, J., Thaxton, C., et al., Evaluating the clinical validity of hypertrophic cardiomyopathy genes, Circ. Genom. Precis. Med., 2019, vol. 12, no. 2. e002460. https://doi.org/10.1161/CIRCGEN.119.002460

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Alimohamed, M.Z., Johansson, L.F., Posafalvi, A., et al., Diagnostic yield of targeted next generation sequencing in 2002 Dutch cardiomyopathy patients, Int. J. Cardiol., 2021, vol. 332, pp. 99—104. https://doi.org/10.1016/j.ijcard.2021.02.069

    Article  PubMed  Google Scholar 

  57. Tadros, R., Francis, C., Xu, X., et al., Shared genetic pathways contribute to risk of hypertrophic and dilated cardiomyopathies with opposite directions of effect, Nat. Genet., 2021, vol. 53, pp. 128—134. https://doi.org/10.1038/s41588-020-00762-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Wooten, E.C., Hebl, V.B., Wolf, M.J., et al., Formin homology 2 domain containing 3 variants associated with hypertrophic cardiomyopathy, Circ. Cardiovasc. Genet., 2013, vol. 6, pp. 10—18. https://doi.org/10.1161/CIRCGENETICS.112.965277

    Article  CAS  PubMed  Google Scholar 

  59. Harper, A.R., Goel, A., Grace, C., et al., Common genetic variants and modifiable risk factors underpin hypertrophic cardiomyopathy susceptibility and expressivity, Nat. Genet., 2021, vol. 53, pp. 135—142. https://doi.org/10.1038/s41588-020-00764-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Villard, E., Perret, C., Gary, F., et al., A genome-wide association study identifies two loci associated with heart failure due to dilated cardiomyopathy, Eur. Heart J., 2011, vol. 32, pp. 1065—1076. https://doi.org/10.1093/eurheartj/ehr105

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Meder, B., Rühle, F., Weis, T., et al., A genome-wide association study identifies 6p21 as novel risk locus for dilated cardiomyopathy, Eur. Heart J., 2014, vol. 35, pp. 1069—1077. https://doi.org/10.1093/eurheartj/eht251

    Article  CAS  PubMed  Google Scholar 

  62. Esslinger, U., Garnier, S., Korniat, A., et al., Exome-wide association study reveals novel susceptibility genes to sporadic dilated cardiomyopathy, PLoS One, 2017, vol. 12, no. 3. e0172995. https://doi.org/10.1371/journal.pone.0172995

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. MendelVar. https://mendelvar.mrcieu.ac.uk/.

  64. GTEx Portal. https://gtexportal.org/home/.

  65. Verweij, N., Benjamins, J.W., Morley, M.P., et al., The genetic makeup of the electrocardiogram, Cell Syst., 2020, vol. 11, pp. 229—238. https://doi.org/10.1016/j.cels.2020.08.005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. UCSC genome browser. http://genome.ucsc.edu/.

  67. Pirruccello, J.P., Bick, A., Wang, M., et al., Analysis of cardiac magnetic resonance imaging in 36 000 individuals yields genetic insights into dilated cardiomyopathy, Nat. Commun., 2020, vol. 11, p. 2254. https://doi.org/10.1038/s41467-020-15823-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Garnier, S., Harakalova, M., Weiss, S., et al., Genome-wide association analysis in dilated cardiomyopathy reveals two new players in systolic heart failure on chromosomes 3p25.1 and 22q11.23, Eur. Heart J., 2021, vol. 42, pp. 2000—2011. https://doi.org/10.1093/eurheartj/ehab030

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Diets, I.J., Prescott, T., Champaigne, N.L., et al., A recurrent de novo missense pathogenic variant in SMARCB1 causes severe intellectual disability and choroid plexus hyperplasia with resultant hydrocephalus, Genet. Med., 2019, vol. 21, pp. 572—579. https://doi.org/10.1038/s41436-018-0079-4

    Article  CAS  PubMed  Google Scholar 

  70. Wieczorek, D., Bögershausen, N., Beleggia, F., et al., A comprehensive molecular study on Coffin Siris and Nicolaides Baraitser syndromes identifies a broad molecular and clinical spectrum converging on altered chromatin remodeling, Hum. Mol. Genet., 2013, vol. 22, no. 25, pp. 5121—5135. https://doi.org/10.1093/hmg/ddt366

    Article  CAS  PubMed  Google Scholar 

  71. Radio, F.C., Pang, K., Ciolfi, A., et al., SPEN haploinsufficiency causes a neurodevelopmental disorder overlapping proximal 1p36 deletion syndrome with an episignature of X chromosomes in females, Am. J. Hum. Genet., 2021, vol. 108, pp. 502—516. https://doi.org/10.1016/j.ajhg.2021.01.015

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Zollino, M., Marangi, G., Ponzi, E., et al., Intragenic KANSL1 mutations and chromosome 17q21.31 deletions: broadening the clinical spectrum and genotype—phenotype correlations in a large cohort of patients, J. Med. Genet., 2015, vol. 52, pp. 804—814. https://doi.org/10.1136/jmedgenet-2015-103184

    Article  CAS  PubMed  Google Scholar 

  73. Rattka, M., Westphal, S., Gahr, B.M., et al., Spen deficiency interferes with Connexin 43 expression and leads to heart failure in zebrafish, J. Mol. Cell Cardiol., 2021, vol. 155, pp. 25—35. https://doi.org/10.1016/j.yjmcc.2021.01.006

    Article  CAS  PubMed  Google Scholar 

  74. León, L.E., Benavides, F., Espinoza, K., et al., Partial microduplication in the histone acetyltransferase complex member KANSL1 is associated with congenital heart defects in 22q11.2 microdeletion syndrome patients, Sci. Rep., 2017, vol. 7, p. 1795. https://doi.org/10.1038/s41598-017-01896-w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Yang, M., Zhang, Y., and Ren, J., Acetylation in cardiovascular diseases: molecular mechanisms and clinical implications, Biochim. Biophys. Acta, Mol. Basis Dis., 2020, vol. 1866, p. 165836. https://doi.org/10.1016/j.bbadis.2020.165836

    Article  CAS  Google Scholar 

  76. Backs, J. and Olson, E.N., Control of cardiac growth by histone acetylation/deacetylation, Circ. Res., 2006, vol. 98, pp. 15—24. https://doi.org/10.1161/01.RES.0000197782.21444.8f

    Article  CAS  PubMed  Google Scholar 

  77. Bick, A.G., Flannick, J., Ito, K., et al., Burden of rare sarcomere gene variants in the Framingham and Jackson Heart Study cohorts, Am. J. Hum. Genet., 2012, vol. 91, pp. 513—519. https://doi.org/10.1016/j.ajhg.2012.07.017

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Watanabe, K., Stringer, S., Frei, O., et al., A global overview of pleiotropy and genetic architecture in complex traits, Nat. Genet., 2019, vol. 51, pp. 1339—1348. https://doi.org/10.1038/s41588-019-0481-0

    Article  CAS  PubMed  Google Scholar 

  79. Broekema, R.V., Bakker, O.B., and Jonkers, I.H., A practical view of fine-mapping and gene prioritization in the post-genome-wide association era, Open Biol., 2020, vol. 10, p. 190221. https://doi.org/10.1098/rsob.190221

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Cano-Gamez, E. and Trynka, G., From GWAS to function: using functional genomics to identify the mechanisms underlying complex diseases, Front. Genet., 2020, vol. 11, p. 424. https://doi.org/10.3389/fgene.2020.00424

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Bekker, O.B., Claringbould, A., Westra, H.-J., et al., Linking common and rare disease genetics through gene regulatory networks, medRxive, 2021. https://doi.org/10.1101/2021.10.21.21265342

  82. King, E.A., Davis, J.W., and Degner, J.F., Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval, PLoS Genet., 2019, vol. 15, no. 12. e1008489. https://doi.org/10.1371/journal.pgen.1008489

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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This work was carried out within the State Task of the Ministry of Science and Higher Education.

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  1. Research Institute of Medical Genetics, Tomsk National Research Medical Center, Russian Academy of Sciences, 634050, Tomsk, Russia

    M. S. Nazarenko, A. A. Sleptcov & V. P. Puzyrev

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  1. M. S. Nazarenko
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Nazarenko, M.S., Sleptcov, A.A. & Puzyrev, V.P. “Mendelian Code” in the Genetic Structure of Common Multifactorial Diseases. Russ J Genet 58, 1159–1168 (2022). https://doi.org/10.1134/S1022795422100052

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