Ancestry-related Distribution of Runs of Homozygosity and Functional Variants in Qatari Population

Overview

Journal BMC Genom Data

Publisher Biomed Central

Specialty Genetics

Date 2022 Sep 21

PMID 36131251

Authors

Massimo Mezzavilla

Massimiliano Cocca

Pierpaolo Maisano Delser

Ramin Badii

Fatemeh Abbaszadeh

Khalid Abdul Hadi

Girotto Giorgia

Paolo Gasparini

Affiliations

Soon will be listed here.

Abstract

Background: Describing how genetic history shapes the pattern of medically relevant variants could improve the understanding of how specific loci interact with each other and affect diseases and traits prevalence. The Qatari population is characterized by a complex history of admixture and substructure, and the study of its population genomic features would provide valuable insights into the genetic landscape of functional variants. Here, we analyzed the genomic variation of 186 newly-genotyped healthy individuals from the Qatari peninsula.

Results: We discovered an intricate genetic structure using ancestry related analyses. In particular, the presence of three different clusters, Cluster 1, Cluster 2 and Cluster 3 (with Near Eastern, South Asian and African ancestry, respectively), was detected with an additional fourth one (Cluster 4) with East Asian ancestry. These subpopulations show differences in the distribution of runs of homozygosity (ROH) and admixture events in the past, ranging from 40 to 5 generations ago. This complex genetic history led to a peculiar pattern of functional markers under positive selection, differentiated in shared signals and private signals. Interestingly we found several signatures of shared selection on SNPs in the FADS2 gene, hinting at a possible common evolutionary link to dietary intake. Among the private signals, we found enrichment for markers associated with HDL and LDL for Cluster 1(Near Eastern ancestry) and Cluster 3 (South Asian ancestry) and height and blood traits for Cluster 2 (African ancestry). The differences in genetic history among these populations also resulted in the different frequency distribution of putative loss of function variants. For example, homozygous carriers for rs2884737, a variant linked to an anticoagulant drug (warfarin) response, are mainly represented by individuals with predominant Bedouin ancestry (risk allele frequency G at 0.48).

Conclusions: We provided a detailed catalogue of the different ancestral pattern in the Qatari population highlighting differences and similarities in the distribution of selected variants and putative loss of functions. Finally, these results would provide useful guidance for assessing genetic risk factors linked to consanguinity and genetic ancestry.

References

Fakhro K, Robay A, Rodrigues-Flores J, Mezey J, Al-Shakaki A, Chidiac O . Point of Care Exome Sequencing Reveals Allelic and Phenotypic Heterogeneity Underlying Mendelian disease in Qatar. Hum Mol Genet. 2019; 28(23):3970-3981. DOI: 10.1093/hmg/ddz134. View

Browning S, Browning B . Accurate Non-parametric Estimation of Recent Effective Population Size from Segments of Identity by Descent. Am J Hum Genet. 2015; 97(3):404-18. PMC: 4564943. DOI: 10.1016/j.ajhg.2015.07.012. View

Mezzavilla M, Vozzi D, Badii R, Alkowari M, Abdulhadi K, Girotto G . Increased rate of deleterious variants in long runs of homozygosity of an inbred population from Qatar. Hum Hered. 2015; 79(1):14-9. DOI: 10.1159/000371387. View

OConnell J, Gurdasani D, Delaneau O, Pirastu N, Ulivi S, Cocca M . A general approach for haplotype phasing across the full spectrum of relatedness. PLoS Genet. 2014; 10(4):e1004234. PMC: 3990520. DOI: 10.1371/journal.pgen.1004234. View

Loh P, Palamara P, Price A . Fast and accurate long-range phasing in a UK Biobank cohort. Nat Genet. 2016; 48(7):811-6. PMC: 4925291. DOI: 10.1038/ng.3571. View

Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira M, Bender D . PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007; 81(3):559-75. PMC: 1950838. DOI: 10.1086/519795. View

Elewa H, Alhaddad A, Al-Rawi S, Nounou A, Mahmoud H, Singh R . Trends in oral anticoagulant use in Qatar: a 5-year experience. J Thromb Thrombolysis. 2017; 43(3):411-416. DOI: 10.1007/s11239-017-1474-4. View

Rodriguez-Flores J, Fakhro K, Hackett N, Salit J, Fuller J, Agosto-Perez F . Exome sequencing identifies potential risk variants for Mendelian disorders at high prevalence in Qatar. Hum Mutat. 2013; 35(1):105-16. PMC: 3908915. DOI: 10.1002/humu.22460. View

Clark S, Adkins D, Aberg K, Hettema J, McClay J, Souza R . Pharmacogenomic study of side-effects for antidepressant treatment options in STAR*D. Psychol Med. 2011; 42(6):1151-62. PMC: 3627503. DOI: 10.1017/S003329171100239X. View

10.

Prohaska A, Racimo F, Schork A, Sikora M, Stern A, Ilardo M . Human Disease Variation in the Light of Population Genomics. Cell. 2019; 177(1):115-131. DOI: 10.1016/j.cell.2019.01.052. View

11.

Turk-Adawi K, Sarrafzadegan N, Fadhil I, Taubert K, Sadeghi M, Wenger N . Cardiovascular disease in the Eastern Mediterranean region: epidemiology and risk factor burden. Nat Rev Cardiol. 2017; 15(2):106-119. DOI: 10.1038/nrcardio.2017.138. View

12.

OBeirne S, Salit J, Rodriguez-Flores J, Staudt M, Abi Khalil C, Fakhro K . Exome sequencing-based identification of novel type 2 diabetes risk allele loci in the Qatari population. PLoS One. 2018; 13(9):e0199837. PMC: 6136697. DOI: 10.1371/journal.pone.0199837. View

13.

Goldstein J, Crenshaw A, Carey J, Grant G, Maguire J, Fromer M . zCall: a rare variant caller for array-based genotyping: genetics and population analysis. Bioinformatics. 2012; 28(19):2543-5. PMC: 3463112. DOI: 10.1093/bioinformatics/bts479. View

14.

Ferrer-Admetlla A, Liang M, Korneliussen T, Nielsen R . On detecting incomplete soft or hard selective sweeps using haplotype structure. Mol Biol Evol. 2014; 31(5):1275-91. PMC: 3995338. DOI: 10.1093/molbev/msu077. View

15.

McLaren W, Gil L, Hunt S, Riat H, Ritchie G, Thormann A . The Ensembl Variant Effect Predictor. Genome Biol. 2016; 17(1):122. PMC: 4893825. DOI: 10.1186/s13059-016-0974-4. View

16.

Mcquillan R, Leutenegger A, Abdel-Rahman R, Franklin C, Pericic M, Barac-Lauc L . Runs of homozygosity in European populations. Am J Hum Genet. 2008; 83(3):359-72. PMC: 2556426. DOI: 10.1016/j.ajhg.2008.08.007. View

17.

Li T, Lange L, Li X, Susswein L, Bryant B, Malone R . Polymorphisms in the VKORC1 gene are strongly associated with warfarin dosage requirements in patients receiving anticoagulation. J Med Genet. 2006; 43(9):740-4. PMC: 2564574. DOI: 10.1136/jmg.2005.040410. View

18.

Mozaffarian D, Kabagambe E, Johnson C, Lemaitre R, Manichaikul A, Sun Q . Genetic loci associated with circulating phospholipid trans fatty acids: a meta-analysis of genome-wide association studies from the CHARGE Consortium. Am J Clin Nutr. 2015; 101(2):398-406. PMC: 4307209. DOI: 10.3945/ajcn.114.094557. View

19.

Buckley M, Racimo F, Allentoft M, Jensen M, Jonsson A, Huang H . Selection in Europeans on Fatty Acid Desaturases Associated with Dietary Changes. Mol Biol Evol. 2017; 34(6):1307-1318. PMC: 5435082. DOI: 10.1093/molbev/msx103. View

20.

Jorsboe E, Albrechtsen A . Efficient approaches for large-scale GWAS with genotype uncertainty. G3 (Bethesda). 2021; 12(1). PMC: 8727990. DOI: 10.1093/g3journal/jkab385. View