Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
Materials and Methods
Parameter Estimation and Hypothesis Testing
Model Hypothesis
Simulated Data
Data for TBC1D4 in a Greenlandic Cohort
Results
Simulation-Based Results
Discussion
References
All Topics
Medicine and Health Sciences
Public Health

Ancestry-specific association mapping in admixed populations

Profile image of Thorfinn KorneliussenThorfinn Korneliussen

2015

https://doi.org/10.1101/014001
visibility

…

description

71 pages

link

1 file

Abstract

During the last decade genome–wide association studies have proven to be a powerful approach to identifying disease-causing variants. However, for admixed populations, most current methods for association testing are based on the assumption that the effect of a genetic variant is the same regardless of its ancestry. This is a reasonable assumption for a causal variant, but may not hold for the genetic variants that are tested in genome–wide association studies, which are usually not causal. The effects of non-causal genetic variants depend on how strongly their presence correlate with the presence of the causal variant, which may vary between ancestral populations because of different linkage disequilibrium patterns and allele frequencies.Motivated by this, we here introduce a new statistical method for association testing in recently admixed populations, where the effect size is allowed to depend on the ancestry of a given allele. Our method does not rely on accurate inference of l...

Related papers

Identifying Rare Variant Associations in Admixed Populations
Jinying Zhao

Scientific Reports, 2019

An admixed population and its ancestral populations bear different burdens of a complex disease. The ancestral populations may have different haplotypes of deleterious alleles and thus ancestry-gene interaction can influence disease risk in the admixed population. Among admixed individuals, deleterious haplotypes and their ancestries are dependent and can provide non-redundant association information. Herein we propose a local ancestry boosted sum test (LABST) for identifying chromosomal blocks that harbor rare variants but have no ancestry switches. For such a stable ancestral block, our LABST exploits ancestry-gene interaction and the number of rare alleles therein. Under the null of no genetic association, the test statistic asymptotically follows a chi-square distribution with one degree of freedom (1-df). Our LABST properly controlled type I error rates under extensive simulations, suggesting that the asymptotic approximation was accurate for the null distribution of the test s...

View PDFchevron_right
Genome-wide association studies in diverse populations
Ivana Jankovic

Nature Reviews …, 2010

View PDFchevron_right
genome-Wide Association Studies and Beyond
carolina cruz rodriguez

Genome-wide association studies (GWAS) provide an important avenue for undertaking an agnostic evaluation of the association between common genetic variants and risk of disease. Recent advances in our understanding of human genetic variation and the technology to measure such variation have made GWAS feasible. Over the past few years a multitude of GWAS have identified and replicated many associated variants. These findings are enriching our knowledge about the genetic basis of disease and leading some to advocate using GWA study results for genetic testing. For many of the GWA study results, however, the underlying mechanisms remain unclear and the findings explain only a limited amount of heritability. These issues may be clarified by more detailed investigations, including analyses of less common variants, sequence-level data, and environmental exposures. Such studies should help clarify the potential value of genetic testing to the public's health.

View PDFchevron_right
Recovering unused information in genome-wide association studies: the benefit of analyzing SNPs out of Hardy–Weinberg equilibrium
David Fardo

European Journal of Human Genetics, 2009

Although the rapid advancements in high throughput genotyping technology have made genome-wide association studies possible, these studies remain an expensive undertaking, especially when considering the large sample sizes necessary to find the small to moderate effect sizes that define complex diseases. It is therefore prudent to utilize all possible information contained in a genome-wide scan. We propose a straightforward analytical approach that tests often unused SNP data without sacrificing statistical validity. We simulate genotype miscalls under a variety of models consistent with observed miscall rates and test for departures from HWE using the standard Pearson's v 2-test. We find that true disease susceptibility loci subjected to various patterns of genotype miscalls can be largely out of HWE and, thus, be candidates for removal before association testing. These loci, we demonstrate, can maintain sufficient statistical power even under extreme error models. We additionally show that random miscalls of null SNPs, independent of the phenotype, do not induce bias in case-control or cohort studies, and we suggest that a significant HWE test should not prevent a SNP from being tested when conducting genome-wide association studies in these scenarios. However, association findings for SNPs that are out of HWE must be treated more carefully than 'regular' findings, for example, by re-genotyping the SNP in the same study using a different genotyping technology.

View PDFchevron_right
Assessment of Population Structure and Its Effects on Genome-Wide Association Studies
Hongyan Xu

Communications in Statistics - Theory and Methods, 2009

Large-scale genome-wide association studies are promising for unraveling the genetic basis of complex diseases. However, population structure is a potential problem, the effects of which on genetic association studies are controversial. Quantification of the effects of population structure on large scale genetic association studies is needed for valid analysis of data and correct interpretation of results. In this study, we performed extensive coalescent-based simulation study with varying levels of population structure to investigate the effects of population structure on large-scale genetic association studies. The effects of population structure are measured by the multiplicative changes of the probability of type I error, which is then correlated with the levels of population structure. It is found that at each nominal level of association tests, there is a positive relationship between the level of population structure and its effects, which could be summarized well with a regression function. It is also found that at a specific level of population structure, its effect on association study increases drastically as the significance level of the test decreases. The type I error is inflated by an amount approximately equal to Wright's F ST , a measure that is used to quantify the magnitude of population structure. Therefore, in genome-wide association studies, the effects of population structure cannot be safely ignored, and must be accounted for with proper methods. This study provides quantitative guidelines to account for the effects of population structure on genome-wide association studies in admixed populations.

View PDFchevron_right
Comparison of Six Statistics of Genetic Association Regarding Their Ability to Discriminate between Causal Variants and Genetically Linked Markers
A GP

Human Heredity, 2011

Objectives: Genome-wide association (GWA) studies still rely on the common-disease common-variant hypothesis since the assumption is associated with increased power. In GWA studies, polymorphisms are genotyped and their association with disease is investigated. Most of the identified associations are indirect and reflect a shared inheritance of the genotyped markers and genetically linked causal variants. We have compared six statistics of genetic association regarding their ability to discriminate between markers and causal susceptibility variants, including a probability value (Pval) and a Bayes Factor (BF) based on logistic regression, and the attributable familial relative risk (FRR). Methods: We carried out a simulation-based sensitivity analysis to explore several conceivable scenarios. Theoretical results were illustrated by established causal associations with age-related macular degeneration and by using imputed data based on HapMap for a case-control study of breast cancer...

View PDFchevron_right
Evaluating variations of genotype calling: a potential source of spurious associations in genome-wide association studies
Roger Perkins

Journal of Genetics, 2010

Genome-wide association studies (GWAS) examine the entire human genome with the goal of identifying genetic variants (usually single nucleotide polymorphisms (SNPs)) that are associated with phenotypic traits such as disease status and drug response. The discordance of significantly associated SNPs for the same disease identified from different GWAS indicates that false associations exist in such results. In addition to the possible sources of spurious associations that have been investigated and discussed intensively, such as sample size and population stratification, an accurate and reproducible genotype calling algorithm is required for concordant GWAS results from different studies. However, variations of genotype calling of an algorithm and their effects on significantly associated SNPs identified in downstream association analyses have not been systematically investigated. In this paper, the variations of genotype calling using the Bayesian Robust Linear Model with Mahalanobis distance classifier (BRLMM) algorithm and the resulting influence on the lists of significantly associated SNPs were evaluated using the raw data of 270 HapMap samples analysed with the Affymetrix Human Mapping 500K Array Set (Affy500K) by changing algorithmic parameters. Modified were the Dynamic Model (DM) call confidence threshold (threshold) and the number of randomly selected SNPs (size). Comparative analysis of the calling results and the corresponding lists of significantly associated SNPs identified through association analysis revealed that algorithmic parameters used in BRLMM affected the genotype calls and the significantly associated SNPs. Both the threshold and the size affected the called genotypes and the lists of significantly associated SNPs in association analysis. The effect of the threshold was much larger than the effect of the size. Moreover, the heterozygous calls had lower consistency compared to the homozygous calls. : a potential source of spurious associations in genome-wide association studies J. Genet. 89,[55][56][57][58][59][60][61][62][63][64]

View PDFchevron_right
An analysis pipeline for genome-wide association studies
Bert Gold

Cancer informatics, 2008

We developed an efficient pipeline to analyze genome-wide association study single nucleotide polymorphism scan results. Purl scripts were used to convert genotypes called using the BRLMM algorithm into a modified PB format. We computed summary statistics characteristic of our case and control populations including allele counts, missing values, heterozygosity, measures of compliance with Hardy-Weinberg equilibrium, and several population difference statistics. In addition, we computed association tests, including exact tests of association for genotypes, alleles, the Cochran-Armitage linear trend test, and dominant, recessive, and over dominant models at every single nucleotide polymorphism (SNP). In addition, pairwise linkage disequilibrium statistics were elaborated, using the command line version of HaploView, which was possible by writing a reformatting script. Additional Perl scripts permit loading the results into a MySQL database conjoined with a Generic Genome Browser (gbro...

View PDFchevron_right
Consistency of genome-wide associations across major ancestral groups
Evangelia Ntzani

Human genetics, 2012

It is not well known whether genetic markers identified through genome-wide association studies (GWAS) confer similar or different risks across people of different ancestry. We screened a regularly updated catalog of all published GWAS curated at the NHGRI website for GWAS-identified associations that had reached genome-wide significance (p ≤ 5 × 10−8) in at least one major ancestry group (European, Asian, African) and for which replication data were available for comparison in at least two different major ancestry groups. These groups were compared for the correlation between and differences in risk allele frequencies and genetic effects’ estimates. Data on 108 eligible GWAS-identified associations with a total of 900 datasets (European, n = 624; Asian, n = 217; African, n = 60) were analyzed. Risk-allele frequencies were modestly correlated between ancestry groups, with >10% absolute differences in 75–89% of the three pairwise comparisons of ancestry groups. Genetic effect (odds ratio) point estimates between ancestry groups correlated modestly (pairwise comparisons’ correlation coefficients: 0.20–0.33) and point estimates of risks were opposite in direction or differed more than twofold in 57%, 79%, and 89% of the European versus Asian, European versus African, and Asian versus African comparisons, respectively. The modest correlations, differing risk estimates, and considerable between-association heterogeneity suggest that differential ancestral effects can be anticipated and genomic risk markers may need separate further evaluation in different ancestry groups.

View PDFchevron_right
Efficiency of genome-wide association studies in random cross populations
José Marcelo S Viana

Molecular Breeding, 2017

Genome-wide association studies (GWAS) with plant species have employed inbred lines panels. We evaluated the efficiency of GWAS in non-inbred and inbred populations and assessed factors affecting GWAS. Fifty samples of 800 individuals from populations with linkage disequilibrium were simulated. Individuals were genotyped for 10,000 single nucleotide polymorphisms (SNPs) and phenotyped for traits controlled by ten quantitative trait loci (QTLs) and 90 minor genes, assuming different degrees of dominance and broad sense heritabilities of 40 and 80%. The average SNP density was 0.1 centiMorgan (cM) and the QTL heritabilities ranged from 3.2 to 11.8%. The results for random cross populations evidenced that to increase the QTL detection power, the additive-dominance model must be fitted for traits controlled by dominance effects but must not be fitted for traits showing no dominance. The power of detection was maximized by increasing the sample size to 400 and the false discovery rate (FDR) to 5%. The average power of detection for the low, intermediate, and high heritability QTLs achieved 52.4, 87.0, and 100.0%, respectively. Assuming sample sizes of 400 and 800, the observed FDR was equal to or lower than the specified level of significance. The association mapping was highly precise, since at least 97% of the declared QTLs were detected by the SNP inside it (average bias of 0.4 cM). Besides controlling the FDR, relatedness (and identity by state) efficiently controls the number of significant associations outside the QTL interval (not all false positive associations). The analysis of the inbred random cross population provided essentially the same results as the non-inbred populations.

View PDFchevron_right

References (30)

  1. Alexander, D. H., Novembre, J. & Lange, K. (2009), 'Fast model-based es- timation of ancestry in unrelated individuals', Genome Res. 19(9), 1655- 1664.
  2. Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D. P., McCarthy, M. I., Ouwehand, W. H., Samani, N. J., Todd, J. A., Donnelly, P., Barrett, J. C., Burton, P. R., Davison, D., Donnelly, P., Easton, D., Evans, D., Leung, H. T., Mar- chini, J. L., Morris, A. P., Spencer, C. C., Tobin, M. D., Cardon, L. R., Clayton, D. G., Attwood, A. P., Boorman, J. P., Cant, B., Everson, U., Hussey, J. M., Jolley, J. D., Knight, A. S., Koch, K., Meech, E., Nut- land, S., Prowse, C. V., Stevens, H. E., Taylor, N. C., Walters, G. R., Walker, N. M., Watkins, N. A., Winzer, T., Todd, J. A., Ouwehand, W. H., Jones, R. W., McArdle, W. L., Ring, S. M., Strachan, D. P., Pembrey, M., Breen, G., St Clair, D., Caesar, S., Gordon-Smith, K., Jones, L., Fraser, C., Green, E. K., Grozeva, D., Hamshere, M. L., Holmans, P. A., Jones, I. R., Kirov, G., Moskvina, V., Nikolov, I., O'Donovan, M. C., Owen, M. J., Craddock, N., Collier, D. A., Elkin, A., Farmer, A., Williamson, R., McGuffin, P., Young, A. H., Ferrier, I. N., Ball, S. G., Balmforth, A. J., Barrett, J. H., Bishop, D. T., Iles, M. M., Maqbool, A., Yuldasheva, N., Hall, A. S., Braund, P. S., Burton, P. R., Dixon, R. J., Mangino, M., Suzanne, S., Tobin, M. D., Thompson, J. R., Samani, N. J., Bredin, 43
  3. F., Tremelling, M., Parkes, M., Drummond, H., Lees, C. W., Nimmo, E. R., Satsangi, J., Fisher, S. A., Forbes, A., Lewis, C. M., Onnie, C. M., Prescott, N. J., Sanderson, J., Mathew, C. G., Barbour, J., Mohiuddin, M. K., Todhunter, C. E., Mansfield, J. C., Ahmad, T., Cummings, F. R., Jewell, D. P., Webster, J., Brown, M. J., Clayton, D. G., Lathrop, G. M., Connell, J., Dominczak, A., Samani, N. J., Marcano, C. A., Burke, B., Dobson, R., Gungadoo, J., Lee, K. L., Munroe, P. B., Newhouse, S. J., Onipinla, A., Wallace, C., Xue, M., Caulfield, M., Farrall, M., Barton, A., Bruce, I. N., Donovan, H., Eyre, S., Gilbert, P. D., Hider, S. L., Hinks, A. M., John, S. L., Potter, C., Silman, A. J., Symmmons, D. P., Thomson, W., Worthington, J., Clayton, D. G., Dunger, D. B., Nutland, S., Stevens, H. E., Walker, N. M., Widmer, B., Todd, J. A., Frayling, T. A., Freathy, R. M., Lango, H., Perry, J. R., Shields, B. M., Weedon, M. N., Hattersley, A. T., Hitman, G. A., Walker, M., Elliott, K. S., Groves, C. J., Lindgren, C. M., Rayner, N. W., Timpson, N. J., Zeggini, E., McCarthy, M. I., New- port, M., Sirugo, G., Lyons, E., Vannberg, F., Hill, A. V., Bradbury, L. A., Farrar, C., Pointon, J. J., Wordsworth, P., Brown, M. A., Franklyn, J. A., Heward, J. M., Simmonds, M. J., Gough, S. C., Seal, S., Stratton, M. R., Rahman, N., Ban, M., Goris, A., Sawcer, S. J., Compston, A., Conway, D., Jallow, M., Newport, M., Sirugo, G., Rockett, K. A., Kwiatowski, D. P., Bumpstead, S. J., Chaney, A., Downes, K., Ghori, M. J., Gwilliam, R., Hunt, S. E., Inouye, M., Keniry, A., King, E., McGinnis, R., Potter, S., Ravindrarajah, R., Whittaker, P., Widden, C., Withers, D., Deloukas, P., Leung, H. T., Nutland, S., Stevens, H. E., Walker, N. M., Todd, J. A., Eas- ton, D., Clayton, D. G., Burton, P. R., Tobin, M. D., Barrett, J. C., Evans, D., Morris, A. P., Cardon, L. R., Cardin, N. J., Davison, D., Ferreira, T., Pereira-Gale, J., Hallgrimsdottir, I. B., Howie, B. N., Marchini, J. L., Spencer, C. C., Su, Z., Teo, Y. Y., Vukcevic, D., Donnelly, P., Bentley, D., Brown, M. A., Gordon, L. R., Caulfield, M., Clayton, D. G., Compston, A., Craddock, N., Deloukas, P., Donnelly, P., Farrall, M., Gough, S. C., Hall, A. S., Hattersley, A. T., Hill, A. V., Kwiatkowski, D. P., Mathew, C., McCarthy, M. I., Ouwehand, W. H., Parkes, M., Pembrey, M., Rahman, N., Samani, N. J., Stratton, M. R., Todd, J. A. & Worthington, J. (2007), 'Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls', Nature 447(7145), 661-678.
  4. Devlin, B. & Roeder, K. (1999), 'Genomic control for association studies', Biometrics 55(4), 997-1004.
  5. Duan, Q., Xu, Z., Raffield, L. M., Chang, S., Wu, D., Lange, E. M., Reiner, A. P. & Li, Y. (2017), 'A robust and powerful two-step testing procedure for local ancestry adjusted allelic association analysis in admixed popula- tions.', Genetic Epidemiology 42(3), 288-302.
  6. Duerr, R. H., Taylor, K. D., Brant, S. R., Rioux, J. D., Silverberg, M. S., Daly, M. J., Steinhart, A. H., Abraham, C., Regueiro, M., Griffiths, A., Dassopoulos, T., Bitton, A., Yang, H., Targan, S., Datta, L. W., Kistner, E. O., Schumm, L. P., Lee, A. T., Gregersen, P. K., Barmada, M. M., Rot- ter, J. I., Nicolae, D. L. & Cho, J. H. (2006), 'A genome-wide association study identifies IL23R as an inflammatory bowel disease gene', Science 314(5804), 1461-1463.
  7. Guan, Y. (2014), 'Detecting structure of haplotypes and local ancestry', Ge- netics 196(3), 625-642.
  8. Holm, H., Gudbjartsson, D. F., Sulem, P., Masson, G., Helgadottir, H. T., Zanon, C., Magnusson, O. T., Helgason, A., Saemundsdottir, J., Gylfason, A., Stefansdottir, H., Gretarsdottir, S., Matthiasson, S. E., Thorgeirsson, G. M., Jonasdottir, A., Sigurdsson, A., Stefansson, H., Werge, T., Rafnar, T., Kiemeney, L. A., Parvez, B., Muhammad, R., Roden, D. M., Darbar, D., Thorleifsson, G., Walters, G. B., Kong, A., Thorsteinsdottir, U., Arnar, D. O. & Stefansson, K. (2011), 'A rare variant in MYH6 is associated with high risk of sick sinus syndrome', Nat. Genet. 43(4), 316-320.
  9. Jorgensen, M. E., Borch-Johnsen, K., Stolk, R. & Bjerregaard, P. (2013), 'Fat distribution and glucose intolerance among Greenland Inuit', Diabetes Care 36(10), 2988-2994.
  10. Kang, H. M., Zaitlen, N. A., Wade, C. M., Kirby, A., Heckerman, D., Daly, M. J. & Eskin, E. (2008), 'Efficient control of population structure in model organism association mapping', Genetics 178(3), 1709-1723.
  11. Klein, R. J., Zeiss, C., Chew, E. Y., Tsai, J. Y., Sackler, R. S., Haynes, C., Henning, A. K., SanGiovanni, J. P., Mane, S. M., Mayne, S. T., Bracken, M. B., Ferris, F. L., Ott, J., Barnstable, C. & Hoh, J. (2005), 'Comple- ment factor H polymorphism in age-related macular degeneration', Science 308(5720), 385-389.
  12. Lake, S. L., Lyon, H., Tantisira, K., Silverman, E. K., Weiss, S. T., Laird, N. M. & Schaid, D. J. (2003), 'Estimation and tests of haplotype- environment interaction when linkage phase is ambiguous', Hum. Hered. 55(1), 56-65.
  13. Maples, B. K., Gravel, S., Kenny, E. E. & Bustamante, C. D. (2013), 'RFMix: a discriminative modeling approach for rapid and robust local-ancestry inference', Am. J. Hum. Genet. 93(2), 278-288.
  14. Moltke, I., Fumagalli, M., Korneliussen, T. S., Crawford, J. E., Bjerregaard, Jorgensen, M. E., Grarup, N., Gullov, H. C., Linneberg, A., Pedersen, O., Hansen, T., Nielsen, R. & Albrechtsen, A. (2015), 'Uncovering the genetic history of the present-day greenlandic population', Am. J. Hum. Genet. 96(1), 54-69.
  15. Moltke, I., Grarup, N., Jorgensen, M. E., Bjerregaard, P., Treebak, J. T., Fumagalli, M., Korneliussen, T. S., Andersen, M. A., Nielsen, T. S., Krarup, N. T., Gjesing, A. P., Zierath, J. R., Linneberg, A., Wu, X., Sun, G., Jin, X., Al-Aama, J., Wang, J., Borch-Johnsen, K., Pedersen, O., Nielsen, R., Albrechtsen, A. & Hansen, T. (2014), 'A common Greenlandic TBC1D4 variant confers muscle insulin resistance and type 2 diabetes', Nature 512(7513), 190-193.
  16. Morris, A., Voight, B., Teslovich, T., Ferreira, T., Segre, A., Steinthorsdottir, V., Strawbridge, R., Khan, H., Grallert, H., Mahajan, A. & et al. (2012), 'Large-scale association analysis provides insights into the genetic archi- tecture and pathophysiology of type 2 diabetes', Nat. Genet. 44, 981-990.
  17. B., Zaitlen, N., Lettre, G., Chen, G. K., Tandon, A., Kao, W. H., Ruczinski, I., Fornage, M., Siscovick, D. S., Zhu, X., Larkin, E., Lange, L. A., Cupples, L. A., Yang, Q., Akylbekova, E. L., Musani, S. K., Divers, J., Mychaleckyj, J., Li, M., Papanicolaou, G. J., Millikan, R. C., Am- brosone, C. B., John, E. M., Bernstein, L., Zheng, W., Hu, J. J., Ziegler, R. G., Nyante, S. J., Bandera, E. V., Ingles, S. A., Press, M. F., Chanock, S. J., Deming, S. L., Rodriguez-Gil, J. L., Palmer, C. D., Buxbaum, S., Ekunwe, L., Hirschhorn, J. N., Henderson, B. E., Myers, S., Haiman, C. A., Reich, D., Patterson, N., Wilson, J. G. & Price, A. L. (2011), 'En- hanced statistical tests for GWAS in admixed populations: assessment using African Americans from CARe and a Breast Cancer Consortium', PLoS Genet. 7(4), e1001371.
  18. Patterson, N., Hattangadi, N., Lane, B., Lohmueller, K. E., Hafler, D. A., Oksenberg, J. R., Hauser, S. L., Smith, M. W., O'Brien, S. J., Altshuler, D., Daly, M. J. & Reich, D. (2004), 'Methods for high-density admixture mapping of disease genes', Am. J. Hum. Genet. 74(5), 979-1000.
  19. Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A. & Reich, D. (2006), 'Principal components analysis corrects for stratifica- tion in genome-wide association studies', Nat. Genet. 38(8), 904-909.
  20. Price, A. L., Tandon, A., Patterson, N., Barnes, K. C., Rafaels, N., Ruczin- ski, I., Beaty, T. H., Mathias, R., Reich, D. & Myers, S. (2009), 'Sensitive detection of chromosomal segments of distinct ancestry in admixed popu- lations', PLoS Genet. 5(6), e1000519.
  21. Sankararaman, S., Sridhar, S., Kimmel, G. & Halperin, E. (2008), 'Es- timating local ancestry in admixed populations', Am. J. Hum. Genet. 82(2), 290-303.
  22. Scott, L. J., Mohlke, K. L., Bonnycastle, L. L., Willer, C. J., Li, Y., Duren, W. L., Erdos, M. R., Stringham, H. M., Chines, P. S., Jackson, A. U., Prokunina-Olsson, L., Ding, C.-J., Swift, A. J., Narisu, N., Hu, T., Pruim, R., Xiao, R., Li, X.-Y., Conneely, K. N., Riebow, N. L., Sprau, A. G., Tong, M., White, P. P., Hetrick, K. N., Barnhart, M. W., Bark, C. W., Goldstein, J. L., Watkins, L., Xiang, F., Saramies, J., Buchanan, T. A., Watanabe, R. M., Valle, T. T., Kinnunen, L., Abecasis, G. R., Pugh, E. W., Doheny, K. F., Bergman, R. N., Tuomilehto, J., Collins, F. S. & Boehnke, M. (2007), 'A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants', Science 316(5829), 1341- 1345.
  23. Sparso, T., Grarup, N., Andreasen, C., Albrechtsen, A., Holmkvist, J., An- dersen, G., Jorgensen, T., Borch-Johnsen, K., Sandbaek, A., Lauritzen, T., Madsbad, S., Hansen, T. & Pedersen, O. (2009), 'Combined analysis of 19 common validated type 2 diabetes susceptibility gene variants shows moderate discriminative value and no evidence of gene-gene interaction', Diabetologia 52(7), 1308-1314.
  24. The SIGMA Type 2 Diabetes Consortium (2013), 'Sequence variants in SLC16A11 are a common risk factor for type 2 diabetes in Mexico', Nature 506, 97-101.
  25. Thorleifsson, G., Walters, G. B., Gudbjartsson, D. F., Steinthorsdottir, V., Sulem, P., Helgadottir, A., Styrkarsdottir, U., Gretarsdottir, S., Thor- lacius, S., Jonsdottir, I., Jonsdottir, T., Olafsdottir, E. J., Olafsdottir, G. H., Jonsson, T., Jonsson, F., Borch-Johnsen, K., Hansen, T., Andersen, G., Jorgensen, T., Lauritzen, T., Aben, K. K., Verbeek, A. L., Roeleveld, N., Kampman, E., Yanek, L. R., Becker, L. C., Tryggvadottir, L., Rafnar, T., Becker, D. M., Gulcher, J., Kiemeney, L. A., Pedersen, O., Kong, A., Thorsteinsdottir, U. & Stefansson, K. (2009), 'Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity', Nat. Genet. 41(1), 18-24.
  26. Unoki, H., Takahashi, A., Kawaguchi, T., Hara, K., Horikoshi, M., Ander- sen, G., Ng, D. P., Holmkvist, J., Borch-Johnsen, K., Jorgensen, T., Sand- baek, A., Lauritzen, T., Hansen, T., Nurbaya, S., Tsunoda, T., Kubo, M., Babazono, T., Hirose, H., Hayashi, M., Iwamoto, Y., Kashiwagi, A., Kaku, K., Kawamori, R., Tai, E. S., Pedersen, O., Kamatani, N., Kadowaki, T., 50
  27. Kikkawa, R., Nakamura, Y. & Maeda, S. (2008), 'SNPs in KCNQ1 are as- sociated with susceptibility to type 2 diabetes in East Asian and European populations', Nat. Genet. 40(9), 1098-1102.
  28. Voight B.F., et al. (2010), 'Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis', Nat. Genet. 42, 579-589.
  29. Yorgov, D., Edwards, K. L. & Santorico, S. A. (2014), 'Use of admixture and association for detection of quantitative trait loci in the Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Ethnic Samples (T2D-GENES) study', BMC Proceedings 8(Suppl 1), S6.
  30. Zhou, X. & Stephens, M. (2012), 'Genome-wide efficient mixed-model anal- ysis for association studies', Nat. Genet. 44(7), 821-824.

Related papers

Adjustment for local ancestry in genetic association analysis of admixed populations
Xiaomin Wang

Bioinformatics, 2011

Admixed populations offer a unique opportunity for mapping diseases that have large disease allele frequency differences between ancestral populations. However, association analysis in such populations is challenging because population stratification may lead to association with loci unlinked to the disease locus. Methods and results: We show that local ancestry at a test single nucleotide polymorphism (SNP) may confound with the association signal and ignoring it can lead to spurious association. We demonstrate theoretically that adjustment for local ancestry at the test SNP is sufficient to remove the spurious association regardless of the mechanism of population stratification, whether due to local or global ancestry differences among study subjects; however, global ancestry adjustment procedures may not be effective. We further develop two novel association tests that adjust for local ancestry. Our first test is based on a conditional likelihood framework which models the distribution of the test SNP given disease status and flanking marker genotypes. A key advantage of this test lies in its ability to incorporate different directions of association in the ancestral populations. Our second test, which is computationally simpler, is based on logistic regression, with adjustment for local ancestry proportion. We conducted extensive simulations and found that the Type I error rates of our tests are under control; however, the global adjustment procedures yielded inflated Type I error rates when stratification is due to local ancestry difference.

View PDFchevron_right
Joint Genotype- and Ancestry-based Genome-wide Association Studies in Admixed Populations
Malgorzata Bogdan

2016

In Genome-Wide Association Studies (GWAS) genetic loci that influence complex traits are localized by inspecting associations between genotypes of genetic markers and the values of the trait of interest. On the other hand Admixture Mapping, which is performed in case of populations consisting of a recent mix of two ancestral groups, relies on the ancestry information at each locus (locus-specific ancestry).Recently it has been proposed to jointly model genotype and locus-specific ancestry within the framework of single marker tests. Here we extend this approach for population-based GWAS in the direction of multi marker models. A modified version of the Bayesian Information Criterion is developed for building a multi-locus model, which accounts for the differential correlation structure due to linkage disequilibrium and admixture linkage disequilibrium. Simulation studies and a real data example illustrate the advantages of this new approach compared to single-marker analysis and mod...

View PDFchevron_right
A Novel Test for Gene-Ancestry Interactions in Genome-Wide Association Data
Jean-Baptiste Cazier

PLoS ONE, 2012

Genome-wide association study (GWAS) data on a disease are increasingly available from multiple related populations. In this scenario, meta-analyses can improve power to detect homogeneous genetic associations, but if there exist ancestryspecific effects, via interactions on genetic background or with a causal effect that co-varies with genetic background, then these will typically be obscured. To address this issue, we have developed a robust statistical method for detecting susceptibility gene-ancestry interactions in multi-cohort GWAS based on closely-related populations. We use the leading principal components of the empirical genotype matrix to cluster individuals into ''ancestry groups'' and then look for evidence of heterogeneous genetic associations with disease or other trait across these clusters. Robustness is improved when there are multiple cohorts, as the signal from true gene-ancestry interactions can then be distinguished from genecollection artefacts by comparing the observed interaction effect sizes in collection groups relative to ancestry groups. When applied to colorectal cancer, we identified a missense polymorphism in iron-absorption gene CYBRD1 that associated with disease in individuals of English, but not Scottish, ancestry. The association replicated in two additional, independentlycollected data sets. Our method can be used to detect associations between genetic variants and disease that have been obscured by population genetic heterogeneity. It can be readily extended to the identification of genetic interactions on other covariates such as measured environmental exposures. We envisage our methodology being of particular interest to researchers with existing GWAS data, as ancestry groups can be easily defined and thus tested for interactions.

View PDFchevron_right
Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations
Allan Kalungi

Cell, 2019

Genome-wide association studies (GWASs) have focused primarily on populations of European descent, but it is essential that diverse populations become better represented. Increasing diversity among study participants will advance our understanding of genetic architecture in all populations and ensure that genetic research is broadly applicable. To facilitate and promote research in multiancestry and admixed cohorts, we outline key methodological considerations and highlight opportunities, challenges, solutions, and areas in need of development. Despite the perception that analyzing genetic data from diverse populations is difficult, it is scientifically and ethically imperative, and there is an expanding analytical toolbox to do it well.

View PDFchevron_right
Accounting for ancestry: population substructure and genome-wide association studies
Peter Gregersen

Human Molecular Genetics, 2008

Accounting for the genetic substructure of human populations has become a major practical issue for studying complex genetic disorders. Allele frequency differences among ethnic groups and subgroups and admixture between different ethnic groups can result in frequent false-positive results or reduced power in genetic studies. Here, we review the problems and progress in defining population differences and the application of statistical methods to improve association studies. It is now possible to take into account the confounding effects of population stratification using thousands of unselected genome-wide single-nucleotide polymorphisms or, alternatively, selected panels of ancestry informative markers. These methods do not require any demographic information and therefore can be widely applied to genotypes available from multiple sources. We further suggest that it will be important to explore results in homogeneous population subsets as we seek to define the extent to which genomic variation influences complex phenotypes.

View PDFchevron_right

Related topics

  • Genetics
  • Biology
  • Public health systems and servic...
    • 580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved