Off the street phasing (OTSP): no hassle haplotype phasing for molecular PGD applications

Abstract

Purpose

Pre-implantation genetic diagnosis (PGD) for molecular disorders requires the construction of parental haplotypes. Classically, haplotype resolution (“phasing”) is obtained by genotyping multiple polymorphic markers in both parents and at least one additional relative. However, this process is time-consuming, and immediate family members are not always available. The recent availability of massive genomic data for many populations promises to eliminate the needs for developing family-specific assays and for recruiting additional family members. In this study, we aimed to validate population-assisted haplotype phasing for PGD.

Methods

Targeted sequencing of CFTR gene variants and ~ 1700 flanking polymorphic SNPs (± 2 Mb) was performed on 54 individuals from 12 PGD families of (a) Full Ashkenazi (FA; n = 16), (b) mixed Ashkenazi (MA; n = 23 individuals with at least one Ashkenazi and one non-Ashkenazi grandparents), or (c) non-Ashkenazi (NA; n = 15) descent. Heterozygous genotype calls in each individual were phased using various whole genome reference panels and appropriate computational models. All computationally derived haplotype predictions were benchmarked against trio-based phasing.

Results

Using the Ashkenazi reference panel, phasing of FA was highly accurate (99.4% ± 0.2% accuracy); phasing of MA was less accurate (95.4% ± 4.5% accuracy); and phasing of NA was predictably low (83.4% ± 6.6% accuracy). Strikingly, for founder mutation carriers, our haplotyping approach facilitated near perfect phasing accuracy (99.9% ± 0.1% and 98.2% ± 2.8% accuracy for W1282X and delF508 carriers, respectively).

Conclusions

Our results demonstrate the feasibility of replacing classical haplotype phasing with population-based phasing with uncompromised accuracy.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. 1.

    Handyside AH, Kontogianni EH, Hardy K, Winston RM. Pregnancies from biopsied human preimplantation embryos sexed by Y-specific DNA amplification. Nature. 1990;344(6268):768–70. https://doi.org/10.1038/344768a0.

    Article  CAS  PubMed  Google Scholar 

  2. 2.

    Yan L, Huang L, Xu L, Huang J, Ma F, Zhu X, et al. Live births after simultaneous avoidance of monogenic diseases and chromosome abnormality by next-generation sequencing with linkage analyses. Proc Natl Acad Sci U S A. 2015;112(52):15964–9. https://doi.org/10.1073/pnas.1523297113.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Thornhill AR, Handyside AH, Ottolini C, Natesan SA, Taylor J, Sage K, et al. Karyomapping-a comprehensive means of simultaneous monogenic and cytogenetic PGD: comparison with standard approaches in real time for Marfan syndrome. J Assist Reprod Genet. 2015;32(3):347–56. https://doi.org/10.1007/s10815-014-0405-y.

    Article  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Ottolini CS, Rogers S, Sage K, Summers MC, Capalbo A, Griffin DK, et al. Karyomapping identifies second polar body DNA persisting to the blastocyst stage: implications for embryo biopsy. Reprod BioMed Online. 2015;31(6):776–82. https://doi.org/10.1016/j.rbmo.2015.07.005.

    Article  CAS  PubMed  Google Scholar 

  5. 5.

    Natesan SA, Handyside AH, Thornhill AR, Ottolini CS, Sage K, Summers MC, et al. Live birth after PGD with confirmation by a comprehensive approach (karyomapping) for simultaneous detection of monogenic and chromosomal disorders. Reprod BioMed Online. 2014;29(5):600–5. https://doi.org/10.1016/j.rbmo.2014.07.007.

    Article  PubMed  Google Scholar 

  6. 6.

    Natesan SA, Bladon AJ, Coskun S, Qubbaj W, Prates R, Munne S, et al. Genome-wide karyomapping accurately identifies the inheritance of single-gene defects in human preimplantation embryos in vitro. Genet Med. 2014;16(11):838–45. https://doi.org/10.1038/gim.2014.45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Handyside AH, Harton GL, Mariani B, Thornhill AR, Affara N, Shaw MA, et al. Karyomapping: a universal method for genome wide analysis of genetic disease based on mapping crossovers between parental haplotypes. J Med Genet. 2010;47(10):651–8. https://doi.org/10.1136/jmg.2009.069971.

    Article  PubMed  Google Scholar 

  8. 8.

    Handyside AH. Live births following karyomapping—a “key” milestone in the development of preimplantation genetic diagnosis. Reprod BioMed Online. 2015;31(3):307–8. https://doi.org/10.1016/j.rbmo.2015.07.003.

    Article  PubMed  Google Scholar 

  9. 9.

    Gould RL, Griffin DK. Karyomapping and how is it improving preimplantation genetics? Expert Rev Mol Diagn. 2017;17(6):611–21. https://doi.org/10.1080/14737159.2017.1325736.

    Article  CAS  PubMed  Google Scholar 

  10. 10.

    Dimitriadou E, Melotte C, Debrock S, Esteki MZ, Dierickx K, Voet T, et al. Principles guiding embryo selection following genome-wide haplotyping of preimplantation embryos. Hum Reprod. 2017;32(3):687–97. https://doi.org/10.1093/humrep/dex011.

    Article  CAS  PubMed  Google Scholar 

  11. 11.

    Ben-Nagi J, Wells D, Doye K, Loutradi K, Exeter H, Drew E, et al. Karyomapping: a single centre’s experience from application of methodology to ongoing pregnancy and live-birth rates. Reprod BioMed Online. 2017;35:264–71. https://doi.org/10.1016/j.rbmo.2017.06.004.

    Article  PubMed  Google Scholar 

  12. 12.

    Zamani Esteki M, Dimitriadou E, Mateiu L, Melotte C, Van der Aa N, Kumar P, et al. Concurrent whole-genome haplotyping and copy-number profiling of single cells. Am J Hum Genet. 2015;96(6):894–912. https://doi.org/10.1016/j.ajhg.2015.04.011.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Genomes Project C, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68–74. https://doi.org/10.1038/nature15393.

    Article  CAS  Google Scholar 

  14. 14.

    McCarthy S, Das S, Kretzschmar W, Delaneau O, Wood AR, Teumer A, et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet. 2016;48(10):1279–83. https://doi.org/10.1038/ng.3643.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Consortium UK, Walter K, Min JL, Huang J, Crooks L, Memari Y, et al. The UK10K project identifies rare variants in health and disease. Nature. 2015;526(7571):82–90. https://doi.org/10.1038/nature14962.

    Article  CAS  Google Scholar 

  16. 16.

    Loh PR, Palamara PF, Price AL. Fast and accurate long-range phasing in a UK Biobank cohort. Nat Genet. 2016;48(7):811–6. https://doi.org/10.1038/ng.3571.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    O’Connell J, Sharp K, Shrine N, Wain L, Hall I, Tobin M, et al. Haplotype estimation for biobank-scale data sets. Nat Genet. 2016;48(7):817–20. https://doi.org/10.1038/ng.3583.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Loh PR, Danecek P, Palamara PF, Fuchsberger C, Y AR, H KF, et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat Genet. 2016;48(11):1443–8. https://doi.org/10.1038/ng.3679.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Choi Y, Chan AP, Kirkness E, Telenti A, Schork NJ. Comparison of phasing strategies for whole human genomes. PLoS Genet. 2018;14(4):e1007308. https://doi.org/10.1371/journal.pgen.1007308.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Williams AL, Patterson N, Glessner J, Hakonarson H, Reich D. Phasing of many thousands of genotyped samples. Am J Hum Genet. 2012;91(2):238–51. https://doi.org/10.1016/j.ajhg.2012.06.013.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Kong A, Masson G, Frigge ML, Gylfason A, Zusmanovich P, Thorleifsson G, et al. Detection of sharing by descent, long-range phasing and haplotype imputation. Nat Genet. 2008;40(9):1068–75. https://doi.org/10.1038/ng.216.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Browning SR, Browning BL. Haplotype phasing: existing methods and new developments. Nat Rev Genet. 2011;12(10):703–14. https://doi.org/10.1038/nrg3054.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Browning SR, Browning BL. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am J Hum Genet. 2007;81(5):1084–97. https://doi.org/10.1086/521987.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol. 2010;34(8):816–34. https://doi.org/10.1002/gepi.20533.

    Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009;5(6):e1000529. https://doi.org/10.1371/journal.pgen.1000529.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Delaneau O, Marchini J, Zagury JF. A linear complexity phasing method for thousands of genomes. Nat Methods. 2011;9(2):179–81. https://doi.org/10.1038/nmeth.1785.

    Article  CAS  PubMed  Google Scholar 

  27. 27.

    Delaneau O, Zagury JF, Marchini J. Improved whole-chromosome phasing for disease and population genetic studies. Nat Methods. 2013;10(1):5–6. https://doi.org/10.1038/nmeth.2307.

    Article  CAS  PubMed  Google Scholar 

  28. 28.

    Delaneau O, Howie B, Cox AJ, Zagury JF, Marchini J. Haplotype estimation using sequencing reads. Am J Hum Genet. 2013;93(4):687–96. https://doi.org/10.1016/j.ajhg.2013.09.002.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Daetwyler HD, Wiggans GR, Hayes BJ, Woolliams JA, Goddard ME. Imputation of missing genotypes from sparse to high density using long-range phasing. Genetics. 2011;189(1):317–27. https://doi.org/10.1534/genetics.111.128082.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Hickey JM, Kinghorn BP, Tier B, Wilson JF, Dunstan N, van der Werf JH. A combined long-range phasing and long haplotype imputation method to impute phase for SNP genotypes. Genetics, selection, evolution : GSE. 2011;43(12):12. https://doi.org/10.1186/1297-9686-43-12.

    Article  PubMed  Google Scholar 

  31. 31.

    Palin K, Campbell H, Wright AF, Wilson JF, Durbin R. Identity-by-descent-based phasing and imputation in founder populations using graphical models. Genet Epidemiol. 2011;35(8):853–60. https://doi.org/10.1002/gepi.20635.

    Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    SHAPEIT. https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html#home. Accessed July 19, 2018 2018.

  33. 33.

    Genomes Project C, Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, et al. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491(7422):56–65. https://doi.org/10.1038/nature11632.

    Article  Google Scholar 

  34. 34.

    Carmi S, Hui KY, Kochav E, Liu X, Xue J, Grady F, et al. Sequencing an Ashkenazi reference panel supports population-targeted personal genomics and illuminates Jewish and European origins. Nat Commun. 2014;5:4835. https://doi.org/10.1038/ncomms5835.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Lencz T, Yu J, Palmer C, Carmi S, Ben-Avraham D, Barzilai N, et al. High-depth whole genome sequencing of an Ashkenazi Jewish reference panel: enhancing sensitivity, accuracy, and imputation. Hum Genet. 2018;137(4):343–55. https://doi.org/10.1007/s00439-018-1886-z.

    Article  CAS  PubMed  Google Scholar 

  36. 36.

    Gudbjartsson DF, Helgason H, Gudjonsson SA, Zink F, Oddson A, Gylfason A, et al. Large-scale whole-genome sequencing of the Icelandic population. Nat Genet. 2015;47(5):435–44. https://doi.org/10.1038/ng.3247.

    Article  CAS  PubMed  Google Scholar 

  37. 37.

    Genome England. http://genomicsengland.co.uk. Accessed 2018 2018.

  38. 38.

    All of Us. https://allofus.nih.gov/. 2018.

  39. 39.

    Cyranoski D. China embraces precision medicine on a massive scale. Nature. 2016;529(7584):9–10. https://doi.org/10.1038/529009a.

    Article  CAS  PubMed  Google Scholar 

  40. 40.

    Korean Reference Genome Project. http://152.99.75.168/KRGDB/menuPages/intro.jsp. 2018.

  41. 41.

    Abu-Elmagd M, Assidi M, Schulten HJ, Dallol A, Pushparaj P, Ahmed F, et al. Individualized medicine enabled by genomics in Saudi Arabia. BMC Med Genet. 2015;8(Suppl 1):S3. https://doi.org/10.1186/1755-8794-8-S1-S3.

    CAS  Article  Google Scholar 

  42. 42.

    Kerem B, Rommens JM, Buchanan JA, Markiewicz D, Cox TK, Chakravarti A, et al. Identification of the cystic fibrosis gene: genetic analysis. Science. 1989;245(4922):1073–80.

    Article  CAS  PubMed  Google Scholar 

  43. 43.

    Worldwide survey of the delta F508 mutation—report from the cystic fibrosis genetic analysis consortium. Am J Hum Genet. 1990;47(2):354–9.

    Google Scholar 

  44. 44.

    Wu D, Dou J, Chai X, Bellis C, Wilm A, Shih CC, et al. Large-scale whole-genome sequencing of three diverse Asian populations in Singapore. bioRxiv. 2018. https://doi.org/10.1101/390070.

  45. 45.

    Bai H, Guo X, Narisu N, Lan T, Wu Q, Xing Y, et al. Whole-genome sequencing of 175 Mongolians uncovers population-specific genetic architecture and gene flow throughout North and East Asia. Nat Genet. 2018;50:1696–704. https://doi.org/10.1038/s41588-018-0250-5.

    Article  CAS  PubMed  Google Scholar 

  46. 46.

    Mooney JA, Huber CD, Service S, Sul JH, Marsden CD, Zhang Z, et al. Understanding the hidden complexity of Latin American population isolates. Am J Hum Genet. 2018;103(5):707–26. https://doi.org/10.1016/j.ajhg.2018.09.013.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Fakhro KA, Staudt MR, Ramstetter MD, Robay A, Malek JA, Badii R, et al. The Qatar genome: a population-specific tool for precision medicine in the Middle East. Human Genome Variation. 2016;3:16016. https://doi.org/10.1038/hgv.2016.16.

    Article  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Low-Kam C, Rhainds D, Lo KS, Provost S, Mongrain I, Dubois A, et al. Whole-genome sequencing in French Canadians from Quebec. Hum Genet. 2016;135(11):1213–21. https://doi.org/10.1007/s00439-016-1702-6.

    Article  CAS  PubMed  Google Scholar 

  49. 49.

    Gurdasani D, Carstensen T, Tekola-Ayele F, Pagani L, Tachmazidou I, Hatzikotoulas K, et al. The African Genome Variation Project shapes medical genetics in Africa. Nature. 2015;517(7534):327–32. https://doi.org/10.1038/nature13997.

    Article  CAS  PubMed  Google Scholar 

  50. 50.

    Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562(7726):203–9. https://doi.org/10.1038/s41586-018-0579-z.

    Article  CAS  PubMed  Google Scholar 

Download references

Funding

The authors thank the Shaare Zedek Mirsky intramural grant and Rabbi David Fuld for funding this research. S. C. thanks the Israel Science Foundation grant 407/17.

Author information

Affiliations

Authors

Corresponding author

Correspondence to David A. Zeevi.

Ethics declarations

All individuals agreed to use their DNA samples for this study, and ethical approval was obtained according to Shaare Zedek Medical Center institutional review board guidelines.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zeevi, D.A., Zahdeh, F., Kling, Y. et al. Off the street phasing (OTSP): no hassle haplotype phasing for molecular PGD applications. J Assist Reprod Genet 36, 727–739 (2019). https://doi.org/10.1007/s10815-018-1392-1

Download citation

Keywords

  • PGD
  • Haplotype phasing
  • Population-based phasing
  • Identity by descent
  • CFTR