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Genetic diagnosis in first or second trimester pregnancy loss using exome sequencing: a systematic review of human essential genes

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Abstract

Purpose

Non-aneuploid recurrent pregnancy loss (RPL) affects approximately 100,000 pregnancies worldwide annually. Exome sequencing (ES) may help uncover the genetic etiology of RPL and, more generally, pregnancy loss as a whole. Previous studies have attempted to predict the genes that, when disrupted, may cause human embryonic lethality. However, predictions by these early studies rarely point to the same genes. Case reports of pathogenic variants identified in RPL cases offer another clue. We evaluated known genetic etiologies of RPL identified by ES.

Methods

We gathered primary research articles from PubMed and Embase involving case reports of RPL reporting variants identified by ES. Two authors independently reviewed all articles for eligibility and extracted data based on predetermined criteria. Preliminary and amended analysis isolated 380 articles; 15 met all inclusion criteria.

Results

These 15 articles described 74 families with 279 reported RPLs with 34 candidate pathogenic variants in 19 genes (NOP14, FOXP3, APAF1, CASP9, CHRNA1, NLRP5, MMP10, FGA, FLT1, EPAS1, IDO2, STIL, DYNC2H1, IFT122, PADI6, CAPS, MUSK, NLRP2, NLRP7) and 26 variants of unknown significance in 25 genes. These genes cluster in four essential pathways: (1) gene expression, (2) embryonic development, (3) mitosis and cell cycle progression, and (4) inflammation and immunity.

Conclusions

For future studies of RPL, we recommend trio-based ES in cases with normal parental karyotypes. In vitro fertilization with preimplantation genetic diagnosis can be pursued if causative variants are found. Utilization of other sequencing technologies in concert with ES should improve understanding of the causes of early embryonic lethality in humans.

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References

  1. World Health Organization. Not every pregnancy is welcome. Accessed at: http://www.who.int/whr/2005/chapter3/en/index3.html. Accessed 8 Aug 2018.

  2. Practice Committee Opinion of the ASRM. Evaluation and treatment of recurrent pregnancy loss: a committee opinion. Fertil Steril. 2012;98(5):1103–11.

    Article  Google Scholar 

  3. Page JM, Silver RM. Genetic causes of recurrent pregnancy loss. Clin Obstet Gynecol. 2016;59(3):498–508.

    Article  Google Scholar 

  4. Shahine L, Lathi R. Recurrent pregnancy loss: evaluation and treatment. Obstet Gynecol Clin N Am. 2015;42(1):117–34.

    Article  Google Scholar 

  5. Popescu F, Jaslow CR, Kutteh WH. Recurrent pregnancy loss evaluation combined with 24-chromosome microarray of miscarriage tissue provides a probable or definite cause of pregnancy loss in over 90% of patients. Hum Reprod. 2018;33(4):579–87.

    Article  CAS  Google Scholar 

  6. Steward CG, Newbury-Ecob RA, Hastings R, Smithson SF, Tsai-Goodman B, Quarrell OW, et al. Barth syndrome: an X-linked cause of fetal cardiomyopathy and stillbirth. Prenat Diagn. 2010;30(1):970–6.

    Article  CAS  Google Scholar 

  7. Kareminijad A, Ghaderi-Sohi S, Hossein-Nejad NH, et al. Lethal multiple pterygium, the extreme end of the RYR1 spectrum. BMC Musculoskelet Disord. 2016;17:109.

    Article  Google Scholar 

  8. McKusick VA, Kelly TE, Dorst JP. Observations suggesting allelism of the achondroplasia and hypochondroplasia genes. J Med Genet. 1973;10(1):11–6.

    Article  CAS  Google Scholar 

  9. Hammarsjö A, Wang Z, Vaz R, Taylan F, Sedghi M, Girisha KM, et al. Novel KIAA0753 mutations extend the phenotype of skeletal ciliopathies. Sci Rep. 2017;7(1):15585.

    Article  Google Scholar 

  10. Rai R, Backos M, Elgaddal S, Shlebak A, Regan L. Factor V Leiden and recurrent miscarriage – prospective outcome of untreated pregnancies. Hum Reprod. 2002;17(2):442–5.

    Article  CAS  Google Scholar 

  11. Lathi RB, Gustin SL, Keller J, Maisenbacher MK, Sigurjonsson S, Tao R, et al. Reliability of 46,XX results on miscarriage specimens: a review of 1,222 first-trimester miscarriage specimens. Fertil Steril. 2014 Jan;101(1):178–82.

    Article  Google Scholar 

  12. Dickinson ME, Flenniken AM, Ji X, et al. High-throughput discovery of novel developmental phenotypes. Nature. 2016;537(7621):508–14.

    Article  CAS  Google Scholar 

  13. Chong JX, Buckingham KJ, Jhangiani SN, Boehm C, Sobreira N, Smith JD, et al. The genetic basis of Mendelian phenotypes: discoveries, challenges, and opportunities. Am J Hum Genet. 2015;97(2):199–215.

    Article  CAS  Google Scholar 

  14. Ng SB, Buckingham KJ, Lee C, Bigham AW, Tabor HK, Dent KM, et al. Exome sequencing identifies the cause of a mendelian disorder. Nat Genet. 2010;42:30–5.

    Article  CAS  Google Scholar 

  15. Spellicy CJ, Norris J, Bend R, Bupp C, Mester P, Reynolds T, et al. Key apoptotic genes APAF1 and CASP9 implicated in recurrent folate-resistant neural tube defects. Eur J Hum Genet. 2018;26(3):420–7.

    Article  CAS  Google Scholar 

  16. Reichert SL, McKay EM, Moldenhauer JS. Identification of a novel nonsense mutation in the FOXP3 gene in a fetus with hydrops--expanding the phenotype of IPEX syndrome. Am J Med Genet. 2016;170A(1):226–32.

    Article  Google Scholar 

  17. Cristofoli F, De Keersmaecker B, De Catte L, et al. Novel STIL compound heterozygous mutations cause severe fetal microcephaly and centriolar lengthening. Mol Syndromol. 2017;8(6):282–93.

    Article  CAS  Google Scholar 

  18. Filges I, Manokhina I, Peñaherrera MS, McFadden DE, Louie K, Nosova E, et al. Recurrent triploidy due to a failure to complete maternal meiosis II: whole-exome sequencing reveals candidate variants. Mol Hum Reprod. 2015;21(4):339–46.

    Article  CAS  Google Scholar 

  19. Quintero-Ronderos P, Mercier E, Fukuda M, González R, Suárez CF, Patarroyo MA, et al. Novel genes and mutations in patients affected by recurrent pregnancy loss. PLoS One. 2017;12(1):e0186149.

    Article  Google Scholar 

  20. Pan H, Xiang H, Wang J, Wei Z, Zhou Y, Liu B, et al. CAPS mutations are potentially associated with unexplained recurrent pregnancy loss. Am J Pathol. 2019;189(1):124–31.

    Article  CAS  Google Scholar 

  21. Suzuki T, Behnam M, Ronasian F, Salehi M, Shiina M, Koshimizu E, et al. A homozygous NOP14 variant is likely to cause recurrent pregnancy loss. J Hum Genet. 2018;63(4):425–30.

    Article  CAS  Google Scholar 

  22. Shamseldin HE, Swaid A, Alkuraya FS. Lifting the lid on unborn lethal Mendelian phenotypes through exome sequencing. Genet Med. 2013;15(4):307–9.

    Article  CAS  Google Scholar 

  23. Shaheen R, Shamseldin HE, Loucks CM, Seidahmed MZ, Ansari S, Ibrahim Khalil M, et al. Mutations in CSPP1, encoding a core centrosomal protein, cause a range of ciliopathy phenotypes in humans. Am J Hum Genet. 2014;94(1):73–9.

    Article  CAS  Google Scholar 

  24. Qiao Y, Wen J, Tang F, Martell S, Shomer N, Leung PCK, et al. Whole exome sequencing in recurrent early pregnancy loss. Mol Hum Reprod. 2016;22(5):364–72.

    Article  CAS  Google Scholar 

  25. Tsurusaki Y, Yonezawa R, Furuya M, Nishimura G, Pooh RK, Nakashima M, et al. Whole exome sequencing revealed biallelic IFT122 mutations in a family with CED1 and recurrent pregnancy loss. Clin Genet. 2014;85:592–4.

    Article  CAS  Google Scholar 

  26. Qian J, Nguyen NMP, Rezael M, et al. Biallelic PADI6 variants linking infertility, miscarriages, and hydatidiform moles. Eur J Hum Genet. 2018;26:1007–13.

    Article  CAS  Google Scholar 

  27. Wilbe M, Ekvall S, Eurenius K, Ericson K, Casar-Borota O, Klar J, et al. MuSK: a new target for lethal fetal akinesia deformation sequence (FADS). J Med Genet. 2015;52:195–202.

    Article  CAS  Google Scholar 

  28. Shehab O, Tester DJ, Ackerman NC, Cowchock FS, Ackerman MJ. Whole genome sequencing identifies etiology of recurrent male intrauterine fetal death. Prenat Diagn. 2017;37:1040–5.

    Article  CAS  Google Scholar 

  29. Begemann M, Rezwan FI, Beygo J, Docherty LE, Kolarova J, Schroeder C, et al. Maternal variants in NLRP and other maternal effect proteins are associated with multilocus imprinting disturbance in offspring. J Med Genet. 2018;55:497–504.

    Article  CAS  Google Scholar 

  30. Chavan AR, Griffith OW, Wagner GP. The inflammation paradox in the evolution of mammalian pregnancy: turning foe into friend. Curr Opin Genet Dev. 2017;47:24–32.

    Article  CAS  Google Scholar 

  31. Exome Aggregation Consortium, Lek M, Karczewski KJ, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536(7616):285–91.

    Article  Google Scholar 

  32. BRAVO Top-Med Database. Accessed at: https://bravo.sph.umich.edu/freeze5/hg38/about. Accessed 25 Oct 2018.

  33. 1000 Genomes Project Consortium, Auton A, Brooks LD, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68–74.

    Article  Google Scholar 

  34. Exome Variant Server, NHLBI GO Exome Sequencing Project (ESP), Seattle WA. http://evs.gs.washington.edu/EVS/. Accessed 25 Oct 2018.

  35. Davydov EV, Goode DL, Sirota M, Cooper GM, Sidow A, Batzoglou S. Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLoS Comput Biol. 2010;6(1):e1001025.

    Article  Google Scholar 

  36. Adzuhubei IA, Schmidt S, Peshkin L, et al. A method and server for predicting damaging missense mutations. Nat Methods. 2010;7(4):248–9.

    Article  Google Scholar 

  37. Kumar P, Henikoff S, Ng PC. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc. 2009;4(7):1073–81.

    Article  CAS  Google Scholar 

  38. Schwarz JM, Rodelsperger C, Scheulke M, et al. MutationTaster evaluates disease-causing potential of sequence alterations. Nat Methods. 2010;7:575–6.

    Article  CAS  Google Scholar 

  39. Bartha I, di Iulio J, Venter JC, Telenti A. Human gene essentiality. Nat Rev Genet. 2018;19(1):51–62.

    Article  CAS  Google Scholar 

  40. Sobreira N, Schiettecatte F, Valle D, Hamosh A. GeneMatcher: a matching tool for connecting investigators with an interest in the same gene. Hum Mutat. 2015;36(1):928–30.

  41. Tschopp J, Martinon F, Burns K. NALPs: a novel protein family involved in inflammation. Nat Rev Mol Cell Biol. 2003;4:94–105.

    Article  Google Scholar 

  42. Bult CJ, Blake JA, Smith CL, Kadin JA, Richardson JE, the Mouse Genome Database Group. Mouse Genome Database (MGD) 2019. Nucleic Acids Res. 2019;47(D1):D801–6.

    Article  CAS  Google Scholar 

  43. Farwell KD, Shahmirzadi L, El-Khechen D, et al. Enhanced utility of family-centered diagnostic exome sequencing with inheritance model-based analysis: results from 500 unselected families with undiagnosed genetic conditions. Genet Med. 2017;17:578–86.

    Article  Google Scholar 

  44. Veltman J, Brunner HG. De novo mutations in human genetic disease. Nat Rev Genet. 2012;13(8):565–75.

    Article  CAS  Google Scholar 

  45. Choi Y, Sims GE, Murphy S, Miller JR, Chain AP. Predicting the functional effect of amino acid substitutions and indels. PLoS One. 2012;7(1):e46688.

    Article  CAS  Google Scholar 

  46. Kircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. 2014;46:310–5.

    Article  CAS  Google Scholar 

  47. Carter H, Douville C, Stenson PD, Cooper DN, Karchin R. Identifying Mendelian disease genes with the variant effect scoring tool. BMC Genomics. 2013;14(Suppl 3):S3.

    Article  Google Scholar 

  48. Ioannidis NM, Rothstein JH, Pejaver V, Middha S, McDonnell SK, Baheti S, et al. REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am J Hum Genet. 2016;99(4):877–85.

    Article  CAS  Google Scholar 

  49. Sundaram L, Gao H, Padigepati SR, McRae JF, Li Y, Kosmicki JA, et al. Predicting the clinical impact of human mutation with deep neural networks. Nat Genet. 2018;50(8):1161–70.

    Article  CAS  Google Scholar 

  50. Philippakis AA, Azzariti DR, Beltran S, Brookes AJ, Brownstein CA, Brudno M, et al. The Matchmaker Exchange: a platform for rare disease gene discovery. Hum Mutat. 2015;36(10):915–21.

    Article  Google Scholar 

  51. Vora NL, Powell B, Brandt A, Strande N, Hardisty E, Gilmore K, et al. Prenatal exome sequencing in anomalous fetuses: new opportunities and challenges. Genet Med. 2017;19(11):1207–16.

    Article  CAS  Google Scholar 

  52. Jelin AC, Vora N. Whole exome sequencing: applications in prenatal genetics. Obstet Gynecol Clin N Am. 2018;45(1):69–81.

    Article  Google Scholar 

  53. Quinlan-Jones E, Kilby MD, Green S, et al. Prenatal whole exome sequencing: the views of clinicians, scientists, genetic counsellors and patient representatives. Prenat Diagn. 2016;36(1):10–9.

    Article  Google Scholar 

  54. Capalbo A, Romanelli V, Cimadomo D, Girardi L, Stoppa M, Dovere L, et al. Implementing PGD/PGD-A in IVF clinics: considerations for the best laboratory approach and management. J Assist Reprod Genet. 2016;33(1):1279–86.

    Article  Google Scholar 

  55. Hanschmidt F, Linde K, Hilbert A, Riedel- Heller SG, Kersting A. Abortion stigma: a systematic review. Perspect Sex Reprod Health. 2016;48(4):169–77.

    Article  Google Scholar 

  56. Gray SW, Park ER, Najita J, Martins Y, Traeger L, Bair E, et al. Oncologists’ and cancer patients’ views on whole- exome sequencing and incidental findings: results from the CanSeq study. Genet Med. 2016;18:1011–9.

    Article  CAS  Google Scholar 

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Funding

This review was funded by the Baylor-Hopkins Center for Mendelian Genomics (NHGRI/NHLBI UM1HG006542) and the Johns Hopkins Women's Health Scholarship.

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Authors and Affiliations

  1. McKusick-Nathans Institute in the Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Sarah M. Robbins & David Valle

  2. Predoctoral Training Program in Human Genetics, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Sarah M. Robbins

  3. Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Matthew A. Thimm

  4. Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Angie C. Jelin

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  1. Sarah M. Robbins
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  2. Matthew A. Thimm
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Correspondence to Sarah M. Robbins.

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Robbins, S.M., Thimm, M.A., Valle, D. et al. Genetic diagnosis in first or second trimester pregnancy loss using exome sequencing: a systematic review of human essential genes. J Assist Reprod Genet 36, 1539–1548 (2019). https://doi.org/10.1007/s10815-019-01499-6

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  • DOI: https://doi.org/10.1007/s10815-019-01499-6

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