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Abstract

The case–control study design is one of the main tools for detecting associations between genetic markers and diseases. It is well known that population substructure can lead to spurious association between disease status and a genetic marker if the prevalence of disease and the marker allele frequency vary across subpopulations. In this paper, we propose a novel statistical method to estimate the association in case–control studies with unmeasured population substructure. The proposed method takes two steps. First, the information on genomic markers and disease status is used to infer the population substructure; second, the association between the disease and the test marker adjusting for the population substructure is modeled and estimated parametrically through polytomous logistic regression. The performance of the proposed method, relative to the existing methods, on bias, coverage probability and computational time, is assessed through simulations. The method is applied to an end-stage renal disease study in African Americans population.

Keywords

Case–control study latent class model population substructure pseudolikelihood two stage estimation

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References

Cardon

Palmer

LJ.

Population stratification and spurious allelic association. Lancet 2003; 361: 598–604.

Reich

Goldstein

DB.

Detecting association in a case-control study while correcting for population stratification. Genetic Epidemiol 2001; 20: 4–16.

Devlin

Roeder

Genomic control for association studies.

Biometrics 1999; 55: 997–1004.

Devlin

Roeder

Wasserman

Genomic control, a new approach to genetic-based association studies.

Theor Popul Biol 2001; 60: 155–166.

Bacanu

Devlin

Roeder

The power of genomic control.

Am J Human Genet 2000; 66: 1933–1944.

Gorroochurn

Hodge

Heiman

, et al. A unified approach for quantifying, testing and correcting population stratification in case-control association studies. Hum Hered 2007; 64: 149–159.

Pritchard

Stephens

Donnelly

Inference of population structure using multilocus genotype data. Genetics 2000; 155: 945–959.

Gorroochurn

Heiman

Hodge

, et al. Centralizing the non-central chi-square: a new method to correct for population stratification in genetic case-control association studies. Genet Epidemiol 2006; 30: 277–289.

Satten

Flanders

Yang

Accounting for unmeasured population substructure in case-control studies of genetic association using a novel latent-class model.

Am J Hum Genet 2001; 68: 466–477.

10.

Pritchard

Stephens

Rosenberg

, et al. Association mapping in structured populations. Am J Hum Genet 2000; 67: 170–181.

11.

Zhu

Zhang

Zhao

, et al. Association mapping, using a mixture model for complex traits. Genet Epidemiol 2002; 23: 181–196.

12.

Epstein

Allen

Satten

GA.

A simple and improved correction for population stratification in case-control studies.

Am J Hum Genet 2007; 80: 921–930.

13.

Pritchard

Rosenberg

NA.

Use of unlinked genetic markers to detect population stratification in association studies.

Am J Hum Genet 1999; 65: 220–228.

14.

Pritchard

Donnelly

Case-control studies of association in structured or admixed populations.

Theoret Popul Biol 2001; 60: 227–238.

15.

Price

Patterson

Plenge

, et al. Principal components analysis corrects for stratification in genome-wide association studies. Nature 2006; 38: 904–909.

16.

Liu

Zhao

PSMIX: an R package for population structure inference via maximum likelihood method.

BMC Bioinform 2006; 7: 317.

17.

Gong

G and

Samaniego

FJ.

Pseudo maximum likelihood estimation: theory and applications. Ann Stat 1981; 9: 861–869.

18.

Bandeen-Roche

Miglioretti

Zeger

, et al. Latent variable regression for multiple discrete outcomes. J Am Stat Assoc 1997; 92: 1375–1386.

19.

Liang

KY and

Self

SG.

On the asymptotic behaviour of the pseudolikelihood ratio test statistic. J R Stat Soc Ser B (Methodological) 1996; 58: 785–796.

20.

Chen

Y and

Liang

KY.

On the asymptotic behavior of the pseudolikelihood ratio test statistic with boundary problems.

Biometrika 2010; 97: 603–620.

21.

Arminger

Clogg

Sobel

ME.

Handbook of statistical modeling for the social and behavioral sciences. USA: Springer, 1995.

22.

Dempster

Laird

Rubin

, et al. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B (Methodological) 1977; 39: 1–38.

23.

Devlin

Roeder

Bacanu

SA.

Unbiased methods for population-based association studies.

Genet Epidemiol 2001; 21: 273–284.

24.

Parke

WR.

Pseudo maximum likelihood estimation: the asymptotic distribution. Ann Stat 1986; 14: 355–357.

25.

Hauck

Donner

Wald’s test as applied to hypotheses in logit analysis. J Am Stat Assoc 1977; 72: 851–853.

26.

Aitkin

M and

Rubin

DB.

Estimation and hypothesis testing in finite mixture models. J R Stat Soc Ser B (Methodological) 1985; 47: 67–75.

27.

Akaike

Likelihood of a model and information criteria. J Econom 1981; 16: 3–14.

28.

Woodroofe

On model selection and the arc sine laws. Ann Stat 1982; 10: 1182–1194.

29.

Yang

CC.

Evaluating latent class analysis models in qualitative phenotype identification. Comput Stat Data Anal 2006; 50: 1090–1104.

30.

Haughton

DMA.

On the choice of a model to fit data from an exponential family. Ann Stat 1988; 16: 342–355.

31.

Armitage

Tests for linear trends in proportions and frequencies. Biometrics 1955; 11: 375–386.

32.

Balding

Nichols

RA.

A method for quantifying differentiation between populations at multi-allelic loci and its implications for investigating identity and paternity.

Genetica 1995; 96: 3–12.

33.

Eknoyan

G and

Levin

NW.

K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis 2002; 39: S1–S266.

34.

Collins

Kasiske

Herzog

, et al. Excerpts from the United States Renal Data System 2004 annual data report: atlas of end-stage renal disease in the United States. Am J Kidney Dis 2005; 45: A5.

35.

Coresh

Selvin

Stevens

, et al. Prevalence of chronic kidney disease in the United States. JAMA 2007; 298: 2038–2047.

36.

Klag

Whelton

Randall

, et al. End-stage renal disease in African-American and white men. JAMA 1997; 277: 1293–1298.

37.

Fox

Yang

Cupples

, et al. Genomewide linkage analysis to serum creatinine, GFR, and creatinine clearance in a community-based population: the Framingham Heart Study. J Am Soc Nephrol 2004; 15: 2457–61.

38.

Iyengar

Fox

Schachere

, et al. Linkage analysis of candidate loci for end-stage renal disease due to diabetic nephropathy. J Am Soc Nephrol 2003; 14: S195–S201.

39.

Remuzzi

Benigni

Remuzzi

, et al. Mechanisms of progression and regression of renal lesions of chronic nephropathies and diabetes. J Clin Investig 2006; 116: 288.

40.

Knowler

Coresh

Elston

, et al. The Family Investigation of Nephropathy and Diabetes (FIND) – design and methods. J Diabetes Complications 2005; 19: 1–9.

41.

Kao

WHL

Klag

Meoni

, et al. MYH9 is associated with nondiabetic end-stage renal disease in African Americans. Nature Genet 2008; 40: 1185–1192.

42.

Falush

Stephens

Pritchard

JK.

Inference of population structure using multilocus genotype data linked loci and correlated allele frequencies.

Genetics 2003; 164: 1567–1587.

43.

Zou

Lee

Knowles

, et al. Quantification of population structure using correlated SNPs by shrinkage principal components. Hum Hered 2010; 70: 9–22.

44.

Cochran

WG.

The effectiveness of adjustment by subclassification in removing bias in observational studies.

Biometrics 1968; 24: 295–313.

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A pseudolikelihood approach for assessing genetic association in case–control studies with unmeasured population structure

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