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Characterization of Greater Middle Eastern genetic variation for enhanced disease gene discovery

Nature Genetics 48, 1071–1076 (2016) | Download Citation

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Abstract

The Greater Middle East (GME) has been a central hub of human migration and population admixture. The tradition of consanguinity, variably practiced in the Persian Gulf region, North Africa, and Central Asia1,2,3, has resulted in an elevated burden of recessive disease4. Here we generated a whole-exome GME variome from 1,111 unrelated subjects. We detected substantial diversity and admixture in continental and subregional populations, corresponding to several ancient founder populations with little evidence of bottlenecks. Measured consanguinity rates were an order of magnitude above those in other sampled populations, and the GME population exhibited an increased burden of runs of homozygosity (ROHs) but showed no evidence for reduced burden of deleterious variation due to classically theorized 'genetic purging'. Applying this database to unsolved recessive conditions in the GME population reduced the number of potential disease-causing variants by four- to sevenfold. These results show variegated genetic architecture in GME populations and support future human genetic discoveries in Mendelian and population genetics.

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Acknowledgements

The authors thank S. Sunyaev and D. Reich for help with PolyPhen-2 and DAF corrections, M. Turchin for help with purging analysis, J. Pickrell for help with TreeMix, and V. Bafna, N. Schork, and S. Bonissone for suggestions. Work was supported by grants from the US National Institutes of Health (P01HD070494 and R01NS048453), the Qatari National Research Foundation (NPRP6-1463), the Simons Foundation Autism Research Initiative (175303 and 275275) to J.G.G., the Yale Center for Mendelian Disorders (U54HG006504), the Broad Institute (U54HG003067), the Rockefeller University CTSA (5UL1RR024143-04), the Howard Hughes Medical Institute (to J.G.G. and J.-L.C.), INSERM, the St. Giles Foundation, and the Candidoser Association and by grants R01AI088364, R37AI095983, P01AI061093, U01AI109697 (to J.-L.C.), U01AI088685 (to J.-L.C. and L.A.), R21AI107508 (to E. Jouanguy), the DHFMR Collaborative Research Grant, and KACST 13-BIO1113-20 (to F.S.A.).

Author information

Affiliations

  1. Howard Hughes Medical Institute, Rockefeller University, New York, New York, USA.

    • Eric M Scott
    • , Emily G Spencer
    • , Yupeng He
    • , Mostafa Abdellateef Azab
    • , Jean-Laurent Casanova
    •  & Joseph G Gleeson

  2. Rady Children's Institute for Genomic Medicine, Department of Neurosciences, University of California, San Diego, La Jolla, California, USA.

    • Eric M Scott
    • , Emily G Spencer
    • , Yupeng He
    • , Mostafa Abdellateef Azab
    •  & Joseph G Gleeson

  3. Laboratory for Pediatric Brain Disease, Rockefeller University, New York, New York, USA.

    • Eric M Scott
    • , Emily G Spencer
    • , Yupeng He
    • , Mostafa Abdellateef Azab
    •  & Joseph G Gleeson

  4. Department of Biostatistics, Epidemiology and Scientific Computing, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia.

    • Anason Halees

  5. St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller University, New York, New York, USA.

    • Yuval Itan
    • , Bertrand Boisson
    • , Laurent Abel
    •  & Jean-Laurent Casanova

  6. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

    • Stacey B Gabriel

  7. Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U1163, Necker Hospital for Sick Children, INSERM, Paris, France.

    • Aziz Belkadi
    • , Bertrand Boisson
    • , Laurent Abel
    •  & Jean-Laurent Casanova

  8. Paris Descartes University, Imagine Institute, Paris, France.

    • Aziz Belkadi
    • , Bertrand Boisson
    • , Laurent Abel
    •  & Jean-Laurent Casanova

  9. Department of Molecular Biology and Genetics, Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.

    • Andrew G Clark

  10. Department of Genetics, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia.

    • Fowzan S Alkuraya

  11. Department of Anatomy and Cell Biology, College of Medicine, Alfaisal University, Riyadh, Saudi Arabia.

    • Fowzan S Alkuraya

  12. Pediatric Hematology–Immunology Unit, Necker Hospital for Sick Children, Paris, France.

    • Jean-Laurent Casanova

Consortia

  1. Greater Middle East Variome Consortium

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Contributions

E.M.S. performed analysis and generated all figures. A.H., Y.I., Y.H., and M.A.A. consulted on analysis. E.G.S., A.B., B.B., L.A., F.S.A., J.-L.C., and J.G.G. contributed subjects and jointly wrote and edited the manuscript. S.B.G. oversaw sequencing. A.G.C. consulted on population studies. The GME Variome Consortium identified subjects for study.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Joseph G Gleeson.

Integrated supplementary information

Supplementary figures

  1. 1.

    Country distribution of GME samples and designation of geographical subregions.

  2. 2.

    Unbiased genetic clustering demonstrates shorter genetic distance between samples from proximal geographical subregions.

  3. 3.

    ADMIXTURE cross-validation.

  4. 4.

    Unsupervised ADMIXTURE analysis of GME populations shows genetic history.

  5. 5.

    Introgression analysis of GME and 1000 Genomes Project exome samples shows consistent Neanderthal introgression on all GME, European, and East Asian samples except for NWA.

  6. 6.

    Heat map of pairwise FST values among all 1000 Genomes Project and GME populations identifies three clusters with a low degree of differentiation.

  7. 7.

    Principal-component analysis on GME and 1000 Genomes Project populations showed that PC3 and PC4 explained inter-GME variance.

  8. 8.

    Reported consanguineous marriage rates many fold higher in GME than in other continental populations.

  9. 9.

    GME samples carried longer and rarer runs of homozygosity than 1000 Genomes Project populations.

  10. 10.

    Identity-by-state distance comparing human and chimpanzee reference genomes showed burden bias associated with hg19 corrected using estimated ancestral alleles.

  11. 11.

    Correction of PolyPhen-2 predictions for derived variants resolved missense burden bias.

  12. 12.

    Mean derived allele frequencies for GME and 1000 Genomes Project populations across seven functional and deleteriousness variant classes suggested equivalent selective pressure.

  13. 13.

    Comparison of allele frequency estimates from Exome Variant Server European-American and African-American populations showed poor correlation.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–13 and Supplementary Tables 1, 2 and 4.

Excel files

  1. 1.

    Supplementary Table 3

    List of variants predicted to be potentially homozygous loss of function in verified healthy GME samples.

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DOI

https://doi.org/10.1038/ng.3592

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