Hum Mol Genet. 2014 Feb 1;23(3):796-809.

Genome-wide association study of ancestry-specific TB risk in the South African Coloured population.

Chimusa ER, Zaitlen N, Daya M, Möller M, van Helden PD, Mulder NJ, Price AL, Hoal EG.

Department of Clinical Laboratory Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa.

 

Abstract

The worldwide burden of tuberculosis (TB) remains an enormous problem, and is particularly severe in the admixed South African Coloured (SAC) population residing in the Western Cape. Despite evidence from twin studies suggesting a strong genetic component to TB resistance, only a few loci have been identified to date. In this work, we conduct a genome-wide association study (GWAS), meta-analysis and trans-ethnic fine mapping to attempt the replication of previously identified TB susceptibility loci. Our GWAS results confirm the WT1 chr11 susceptibility locus (rs2057178: odds ratio = 0.62, P = 2.71e(-06)) previously identified by Thye et al., but fail to replicate previously identified polymorphisms in the TLR8 gene and locus 18q11.2. Our study demonstrates that the genetic contribution to TB risk varies between continental populations, and illustrates the value of including admixed populations in studies of TB risk and other complex phenotypes. Our evaluation of local ancestry based on the real and simulated data demonstrates that case-only admixture mapping is currently impractical in multi-way admixed populations, such as the SAC, due to spurious deviations in average local ancestry generated by current local ancestry inference methods. This study provides insights into identifying disease genes and ancestry-specific disease risk in multi-way admixed populations.

PMID: 24057671

 

Supplement:

The worldwide burden of tuberculosis (TB) remains an enormous problem, and is particularly severe in the admixed South African Coloured (SAC) population residing in the Western Cape. SAC has a high level of intercontinental admixture as result of unions between African Bantu (33%±0.226) and Khoesan(31%±0.20), Europeans (16%±0.12), population groups of Indian (13%±0.09) and East Asian (7%±0.04). We used a combination of two complementary methods to examine whether the genetic contribution can increase tuberculosis risk, and evaluated the contribution of socio-economic status to the ancestry-tuberculosis relationship in SAC. We have demonstrated significant evidence of an association between Khoisan ancestry and tuberculosis status that is not confounded by socio-economic status. This an important epidemiological result.

We conducted a genome-wide association study (GWAS) from 733 unrelated SAC individuals (642 cases and 91 controls), resulting in the identification of a moderate-frequency variant at SNP rs17175227 (p=8.99e-09, OR=0.141, MAF=0.54). After imputation we also identified a rare variant at SNP rs12294076 (p=9.5e-08) at the borderline of genome-wide significance and we moderately replicate a recently reported susceptibility locus, rs2057178 (p=2.71e-06,OR = 0.62,). Because of the imperfect asymptotic distribution of mixed model association or logistic regression in the specific case of low-frequency variants, we computed Fishers exact test values for variants that achieved the most significant mixed model association p-values. This resulted in rs17175227 (p=2.77e-06, OR=0.141) not reaching the genome-wide cut-off. Power to detect association is a function of allele frequency and rare variants are underpowered when sample sizes are limited. However, because current mixed models association do not account for rare variants, we have addressed this challenge by computing Fishers exact test p-values for variants that achieve the most significant mixed model association p-values.

To achieve sufficient power to detect associations at a level of genome-wide significance and identify shared risk loci with a previously reported African tuberculosis case-control study [1,2] and four polymorphisms in the TLR8 gene on chromosome X previously identified in Davila et al. 2008 [3], a imputation meta-analysis was performed under random-effect and binary-effect models to attempt the replication of previously identified TB susceptibility loci. We confirmed the WT1 chr11 susceptibility locus (rs2057178: OR = 0.62, p=2.71e-06) previously identified in [1,2], but fail to replicate previously identified polymorphisms in the TLR8 gene and locus 18q11.2. WT1 is a tumor suppressor gene located on chromosome 11p13. WT1 is known as Wilms’s Tumor Protein, which provides instructions for making a protein that is involved in the development of the kidneys and gonads (ovaries in females and testes in males) before birth [4].

 

Fig1

Figure 1: Sub-network of WT1 from TB imputation GWAS in the SAC. The size of a node denotes its significance from small to big size.

 

It is also known as a transcription factor, since it regulates the activity of other genes by binding to specific regions of DNA. Querying a comprehensive human Protein-Protein Interaction (PPI) network (http://cbg.garvan.unsw.edu.au/pina/), we have obtained sub-network of WT1 has known direct interactions (Figure 1). In particular, this gene is unusually expressed in certain types of lung and prostate cancer, and is seen in some cancers of blood-forming cells (leukemias), such as acute lymphoblastic leukemia, chronic myeloid leukemia, and childhood acute myeloid leukemia [4].

Because the SAC is an admixed population, the ancestry association suggested that admixture mapping might work to find a locus. Since admixture mapping can be done as a case-only analysis, it should be more powerful than a case-control analysis which was limited by the small sample size of controls. However, a major limitation of admixture mapping and admixture association is that the inference of locus-specific ancestry in complex multi-way admixed populations such as the SAC may suffer from spurious deviations in average local ancestry at particular chromosomal regions from cases and controls, resulting in spurious case-only admixture associations [5]. Existing methods may attain high accuracy on average but may suffer from spurious deviations in average local ancestry at particular regions (e.g. regions in which the modelled ancestral population is unusually different from the true ancestral population due to the historical action of natural selection). These spurious deviations would be present in both affected and unaffected individuals, and would lead to spurious mapping of genes underlying ethnic difference in disease risk. Our evaluation of local ancestry based on the real and simulated data demonstrates that case-only admixture mapping is currently impractical in multi-way admixed populations, such as the SAC, due to systematic inaccuracies and spurious deviations in average local ancestry generated by current local ancestry inference methods.

Overall, this study tackles an important question about TB susceptibility, genetic ancestry and genetic susceptibility to infection and has major implications on public health and on the biology of infectious diseases. It demonstrates that the genetic contribution to TB risk varies between continental populations, and illustrates the value of including admixed populations in studies of TB risk and other complex phenotypes. Some limitations should be noted in association analyses. Firstly, the present study is underpowered to detect risk variants of more modest effect size, because of the modest sample size. Secondly, imputing missing genotype data of a complex admixed population is an important challenge based on the choice and size of haplotype of existing reference panels.

Finally, despite applying Fisher’s Exact test to correct the imperfection of the mixed model for association, the implementation of newer sequencing technologies is still required to search for rare risk variants.

This study open opportunity to examine a tractable, accurate and unbiased method that models historical gene flow and pinpoint ancestry along the genome of a multi-way admixed population. This will contribute to different methods that account for combined genome-wide SNP case-control and admixture analysis and postGWAS models that leverage GWAS or admixture mapping summary statistics to combine effects of genes to fully characterize the susceptible genes and the genetic structure of complex diseases.

Acknowledgements:

We are grateful to all the participants in the study. This project was supported by a Carnegie Corporation Grant by

the University of Cape Town and by NIH grant R01 HG006399 (N.Z. and A.L.P.).

 

References

  1. Thye, Fredrik OV, Sunny HW, Ellis O, Ivy O, Gyapong J, et al. (2010) Genome-wide association analyses identifies a susceptibility locus for tuberculosis on chromosome 18q11.2.Nature Genetics,Vol 42 Num 9.
  2. Thye, Owusu-Dabo E, Vannberg FO, Crevel RV, Curtis J, Sahiratmadja E, et al. (2012) Common variants at 11p13 are associated with susceptibility to tuberculosis. Proc. Nature Genetics,Vol 44 Num 3.
  3. Sonia D, Martin LH, Ranjeeta HD, Hazel EE. Wong, Edhyana S, Bonnard C, et al. (2008) Genetic Association and Expression Studies Indicate a Role of Toll-Like Receptor 8 in Pulmonary Tuberculosis. PLoS Gen. Vol 4 Issue 10 e1000218.
  4. Sum S, Eleanor Y, Peng B, Yu X, Chen J, Byrne J, et al (2002) The lim domain protein lmo4 interacts with the cofactor ctip and the tumor suppressor brca1 and inhibits brca1 activity. J.Biol.Chem. 277 (10), 7849-56, PMID 11751867.
  5. Pasaniuc B, Sankararaman S, Dara GT, Gignoux C, Zaitlen N, Eng C, et al. (2013) Analysis of Latino populations from GALA and MEC studies reveals genomic loci with biased local ancestry estimation. Bioinformatics: Vol.29,11:p1407-1415.

 

Fig2Correspondence:

Dr Emile R CHIMUSA is a full-time lecturer/Researcher at the University of Cape Town, Department of Integrative Biomedical Sciences at the Institute of Infectious Disease and Molecular Medicine. He is a mathematical population geneticist whose research focuses on analysing genome wide patterns of variation within and between species to address fundamental questions in biology, anthropology, and medicine.

emile.chimusa@uct.ac.za or emile@cbio.uct.ac.za

 

 

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