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Low-pass genome sequencing–based detection of absence of heterozygosity: validation in clinical cytogenetics



Absence of heterozygosity (AOH) is a genetic characteristic known to cause human genetic disorders through autosomal recessive or imprinting mechanisms. However, the analysis of AOH via low-pass genome sequencing (GS) is not yet clinically available.


Low-pass GS (fourfold) with different types of libraries was performed on 17 clinical samples with previously ascertained AOH by chromosomal microarray analysis (CMA). In addition, AOH detection was performed with low-pass GS data in 1,639 cases that had both GS and high-probe density CMA data available from the 1000 Genomes Project. Cases with multiple AOHs (coefficient of inbreeding F ≥ 1/32) or terminal AOHs ≥5 Mb (suspected uniparental disomy [UPD]) were reported based on the guidelines of the American College of Medical Genetics and Genomics.


Low-pass GS revealed suspected segmental UPD and multiple AOHs (F ≥ 1/32) in nine and eight clinical cases, respectively, consistent with CMA. Among the 1,639 samples, low-pass GS not only consistently detected multiple AOHs (F ≥ 1/32) in 18 cases, but also reported 60 terminal AOHs in 44 cases including four mosaic AOHs at a level ranging from 50% to 75%.


Overall, our study demonstrates the feasibility of AOH analysis (≥5 Mb) with low-pass GS data and shows high concordance compared with CMA.

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Fig. 1: Absence of heterozygosity (AOH) detected in an amniotic fluid (AF) sample with uniparental disomy (UPD)(6).
Fig. 2: Mosaic terminal absence of heterozygosity (AOH).
Fig. 3: The spectrum of absence of heterozygosity (AOH) identified by chromosomal microarray analysis (CMA) and by low-pass genome sequencing (GS).

Data availability

Genome sequencing data used in this study have been made available in the CNGB Nucleotide Sequence Archive (CNSA: under the accession number CNP0000558.

Code availability

All programs relevant to this pipeline are available at


  1. 1.

    Liu, S. et al. Uniparental disomy of chromosome 15 in two cases by chromosome microarray: a lesson worth thinking. Cytogenet. Genome Res. 152, 1–8 (2017).

    CAS  Article  Google Scholar 

  2. 2.

    Margraf, R. L. et al. Utilization of whole-exome next-generation sequencing variant read frequency for detection of lesion-specific, somatic loss of heterozygosity in a neurofibromatosis type 1 cohort with tibial pseudarthrosis. J. Mol. Diagn. 19, 468–474 (2017).

    CAS  Article  Google Scholar 

  3. 3.

    Liu, X., Li, A., Xi, J., Feng, H. & Wang, M. Detection of copy number variants and loss of heterozygosity from impure tumor samples using whole exome sequencing data. Oncol. Lett. 16, 4713–4720 (2018).

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    D’Amours, G. et al. SNP arrays: comparing diagnostic yields for four platforms in children with developmental delay. BMC Med. Genomics 7, 70 (2014).

    Article  Google Scholar 

  5. 5.

    Rehder, C. W. et al. American College of Medical Genetics and Genomics: standards and guidelines for documenting suspected consanguinity as an incidental finding of genomic testing. Genet. Med. 15, 150–152 (2013).

    Article  Google Scholar 

  6. 6.

    Cooper, W. N., Curley, R., Macdonald, F. & Maher, E. R. Mitotic recombination and uniparental disomy in Beckwith-Wiedemann syndrome. Genomics. 89, 613–617 (2007).

    CAS  Article  Google Scholar 

  7. 7.

    Potapova, T. & Gorbsky, G. J. The consequences of chromosome segregation errors in mitosis and meiosis. Biology (Basel). 6, 12 (2017).

  8. 8.

    Carvalho, C. M. et al. Absence of heterozygosity due to template switching during replicative rearrangements. Am. J. Hum. Genet. 96, 555–564 (2015).

    CAS  Article  Google Scholar 

  9. 9.

    Robinson, W. P. Mechanisms leading to uniparental disomy and their clinical consequences. Bioessays. 22, 452–459 (2000).

    CAS  Article  Google Scholar 

  10. 10.

    Conlin, L. K. et al. Mechanisms of mosaicism, chimerism and uniparental disomy identified by single nucleotide polymorphism array analysis. Hum. Mol. Genet. 19, 1263–1275 (2010).

    CAS  Article  Google Scholar 

  11. 11.

    Yauy, K., de Leeuw, N., Yntema, H. G., Pfundt, R. & Gilissen, C. Accurate detection of clinically relevant uniparental disomy from exome sequencing data. Genet. Med. 22, 803–808 (2020).

    CAS  Article  Google Scholar 

  12. 12.

    King, D. A. et al. A novel method for detecting uniparental disomy from trio genotypes identifies a significant excess in children with developmental disorders. Genome Res 24, 673–687 (2014).

    CAS  Article  Google Scholar 

  13. 13.

    Sensi, A. et al. Nonhomologous Robertsonian translocations (NHRTs) and uniparental disomy (UPD) risk: an Italian multicentric prenatal survey. Prenat. Diagn. 24, 647–652 (2004).

    CAS  Article  Google Scholar 

  14. 14.

    Wang, B. T. et al. Abnormalities in spontaneous abortions detected by G-banding and chromosomal microarray analysis (CMA) at a national reference laboratory. Mol. Cytogenet. 7, 33 (2014).

    Article  Google Scholar 

  15. 15.

    Nakka, P. et al. Characterization of prevalence and health consequences of uniparental disomy in four million individuals from the general population. Am. J. Hum. Genet. 105, 921–932 (2019).

    CAS  Article  Google Scholar 

  16. 16.

    Del Gaudio, D. et al. Diagnostic testing for uniparental disomy: a points to consider statement from the American College of Medical Genetics and Genomics (ACMG). Genet. Med. 22, 1133–1141 (2020).

  17. 17.

    Wiszniewska, J. et al. Combined array CGH plus SNP genome analyses in a single assay for optimized clinical testing. Eur. J. Hum. Genet. 22, 79–87 (2014).

    CAS  Article  Google Scholar 

  18. 18.

    Liang, D. et al. Copy number variation sequencing for comprehensive diagnosis of chromosome disease syndromes. J. Mol. Diagn. 16, 519–526 (2014).

    CAS  Article  Google Scholar 

  19. 19.

    Dong, Z. et al. Low-pass whole-genome sequencing in clinical cytogenetics: a validated approach. Genet. Med. 18, 940–948 (2016).

    CAS  Article  Google Scholar 

  20. 20.

    Wang, H. et al. Low-pass genome sequencing versus chromosomal microarray analysis: implementation in prenatal diagnosis. Genet. Med. 22, 500–510 (2020).

    CAS  Article  Google Scholar 

  21. 21.

    Dong, Z. et al. Identification of balanced chromosomal rearrangements previously unknown among participants in the 1000 Genomes Project: implications for interpretation of structural variation in genomes and the future of clinical cytogenetics. Genet. Med. 20, 697–707 (2018).

    CAS  Article  Google Scholar 

  22. 22.

    Dong, Z. et al. A robust approach for blind detection of balanced chromosomal rearrangements with whole-genome low-coverage sequencing. Hum. Mutat. 35, 625–636 (2014).

    CAS  Article  Google Scholar 

  23. 23.

    Redin, C. et al. The genomic landscape of balanced cytogenetic abnormalities associated with human congenital anomalies. Nat. Genet. 49, 36–45 (2017).

    CAS  Article  Google Scholar 

  24. 24.

    Dong, Z. et al. Genome sequencing explores complexity of chromosomal abnormalities in recurrent miscarriage. Am. J. Hum. Genet. 105, 1102–1111 (2019).

    CAS  Article  Google Scholar 

  25. 25.

    Gross, A. M. et al. Copy-number variants in clinical genome sequencing: deployment and interpretation for rare and undiagnosed disease. Genet. Med. 21, 1121–1130 (2019).

    CAS  Article  Google Scholar 

  26. 26.

    Chaubey, A. et al. Low-pass genome sequencing: validation and diagnostic utility from 409 clinical cases of low-pass genome sequencing for the detection of copy number variants to replace constitutional microarray. J. Mol. Diagn. 22, 823–840 (2020).

    CAS  Article  Google Scholar 

  27. 27.

    Cheng, Y. K. et al. The detection of mosaicism by prenatal BoBs. Prenat. Diagn. 33, 42–49 (2013).

    CAS  Article  Google Scholar 

  28. 28.

    Dong, Z., et al. Development of coupling controlled polymerizations by adapter-ligation in mate-pair sequencing for detection of various genomic variants in one single assay. DNA Res. 26, 313–325 (2019).

  29. 29.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics. 25, 1754–1760 (2009).

    CAS  Article  Google Scholar 

  30. 30.

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 25, 2078–2079 (2009).

    Article  Google Scholar 

  31. 31.

    Chau, M. H. K. et al. Low-pass genome sequencing: a validated method in clinical cytogenetics. Hum. Genet. 139, 1403–1415 (2020).

    CAS  Article  Google Scholar 

  32. 32.

    Genomes Project, C. et al. An integrated map of genetic variation from 1,092 human genomes. Nature. 491, 56–65 (2012).

    Article  Google Scholar 

  33. 33.

    Sudmant, P. H. et al. An integrated map of structural variation in 2,504 human genomes. Nature. 526, 75–81 (2015).

    CAS  Article  Google Scholar 

  34. 34.

    Shirley, M. D. et al. Chromosomal variation in lymphoblastoid cell lines. Hum. Mutat. 33, 1075–1086 (2012).

    CAS  Article  Google Scholar 

  35. 35.

    Oniya, O., Neves, K., Ahmed, B. & Konje, J. C. A review of the reproductive consequences of consanguinity. Eur. J. Obstet. Gynecol. Reprod. Biol. 232, 87–96 (2019).

    Article  Google Scholar 

  36. 36.

    Hoppman, N., Rumilla, K., Lauer, E., Kearney, H. & Thorland, E. Patterns of homozygosity in patients with uniparental disomy: detection rate and suggested reporting thresholds for SNP microarrays. Genet. Med. 20, 1522–1527 (2018).

    CAS  Article  Google Scholar 

  37. 37.

    Collins, R. L. et al. Defining the diverse spectrum of inversions, complex structural variation, and chromothripsis in the morbid human genome. Genome Biol. 18, 36 (2017).

    Article  Google Scholar 

  38. 38.

    Dong, Z., et al. Deciphering the complexity of simple chromosomal insertions by genome sequencing. Hum. Genet. 140, 361–380 (2021).

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This project is supported by the National Natural Science Foundation of China (31801042), the Health and Medical Research Fund (04152666, 07180576), 2018 Shenzhen Virtue University Park Laboratory Support Special Fund (YFJGJS1.0) for Key Laboratory for Regenerative Medicine, Ministry of Education (Shenzhen Base), General Research Fund (14115418), and Direct Grant (2019.051). C.C.M. is supported by NIH/NIGMS P01 GM0613S4 and the NIHR Manchester Biomedical Research Centre, UK. We thank the 1000 Genomes Project for releasing the GS and CMA data. The following cell lines/DNA samples were obtained from the NIGMS Human Genetic Cell Repository at the Coriell Institute for Medical Research: [NA06984, NA06985, NA06986, NA06989, NA06994, NA07000, NA07037, NA07048, NA07051, NA07056, NA07347, NA07357, NA10847, NA10851, NA11829, NA11830, NA11831, NA11832, NA11840, NA11843, NA11881, NA11892, NA11893, NA11894, NA11918, NA11919, NA11920, NA11930. NA11931, NA11932, NA11933, NA11992, NA11994, NA11995, NA12003, NA12004, NA12005, NA12006, NA12043, NA12044, NA12045, NA12046, NA12058, NA12144, NA12154, NA12155, NA12156, NA12234, NA12249, NA12272, NA12273, NA12275, NA12282, NA12283, NA12286, NA12287, NA12340, NA12341, NA12342, NA12347, NA12348, NA12383, NA12399, NA12400, NA12413,, NA12414, NA12489, NA12546, NA12716, NA12717, NA12718, NA12748, NA12749, NA12750, NA12751, NA12760, NA12761, NA12762, NA12763, NA12775, NA12776, NA12777, NA12778, NA12812, NA12813, NA12814, NA12815, NA12827, NA12828, NA12829, NA12830, NA12842, NA12843, NA12872, NA12873, NA12874, NA12878, NA12889, NA12890]. These genome sequencing data were generated at the New York Genome Center with funds provided by NHGRI Grant 3UM1HG008901-03S1.

Author information




Z.D., Y.K.K., and K.W.C. designed the study. Y.M.W., T.Y.L., and Y.K.K. collected the samples. Y.Z., M.S., and M.H.K.C. performed genome sequencing and conducted validation. Z.Y. and Z.D. performed the analysis. Z.D., M.H.K.C., Z.Y., Y.K.K., C.C.M., and K.W.C. carried out the data interpretation and wrote the manuscript.

Corresponding authors

Correspondence to Cynthia C. Morton or Kwong Wai Choy.

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Ethics Declaration

The study protocol was approved by the Ethics Committee of the Joint Chinese University of Hong Kong–New Territories East Cluster Clinical Research Ethics Committee (CREC reference number 2016.713). Written consent for sample storage and genetic analyses was obtained from each participant.

Competing interests

The authors declare no competing interests.

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Dong, Z., Chau, M.H.K., Zhang, Y. et al. Low-pass genome sequencing–based detection of absence of heterozygosity: validation in clinical cytogenetics. Genet Med (2021).

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