Article

Correcting for measurement error in individual ancestry estimates in structured association tests.

Center for Public Health Genomics, Department of Biostatistical Sciences, Division of Public Health Services, Wake Forest University Health Sciences, Winston-Salem, North Carolina 27101, USA.
Genetics (Impact Factor: 4.87). 08/2007; 176(3):1823-33. DOI: 10.1534/genetics.107.075408
Source: PubMed

ABSTRACT We present theoretical explanations and show through simulation that the individual admixture proportion estimates obtained by using ancestry informative markers should be seen as an error-contaminated measurement of the underlying individual ancestry proportion. These estimates can be used in structured association tests as a control variable to limit type I error inflation or reduce loss of power due to population stratification observed in studies of admixed populations. However, the inclusion of such error-containing variables as covariates in regression models can bias parameter estimates and reduce ability to control for the confounding effect of admixture in genetic association tests. Measurement error correction methods offer a way to overcome this problem but require an a priori estimate of the measurement error variance. We show how an upper bound of this variance can be obtained, present four measurement error correction methods that are applicable to this problem, and conduct a simulation study to compare their utility in the case where the admixed population results from the intermating between two ancestral populations. Our results show that the quadratic measurement error correction (QMEC) method performs better than the other methods and maintains the type I error to its nominal level.

Download full-text

Full-text

Available from: Jose Fernandez, Feb 26, 2014
0 Followers
 · 
85 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Cardiovascular disease (CVD) affects many people in the United States. Compared with other population groups in the United States, epidemiologic data suggest that Hispanic Americans are at a disproportionate risk for CVD. The etiology of this disparity is complex, with genetic, behavioral, cultural, and other environmental factors acting in an independent, interactive, and/or synergistic fashion. Because many complex conditions mediate risk of CVD, including diabetes, obesity, and hyperlipidemia, genes associated with these conditions have been considered as possible contributors to CVD in Hispanics. In addition, the diversity of background and heritage within this population creates a plethora of environmental determinants that interact with behaviors, cultural practices, and genetic makeup to influence disease risk. In this review, we explore the recent literature on genetic determinants of CVD and explain that effective efforts to reduce CVD disparities in Hispanics in the United States will require an understanding of the interactions of genes, the environment, and health-related practices.
    Current Cardiovascular Risk Reports 05/2009; 3(3):175-180. DOI:10.1007/s12170-009-0028-5
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The objective of the present study was to map candidate loci influencing naturally occurring variation in triacylglycerol (TAG) storage using quantitative complementation procedures in Drosophila melanogaster. Based on our results from Drosophila, we performed a human population-based association study to investigate the effect of natural variation in LAMA5 gene on body composition in humans. We identified four candidate genes that contributed to differences in TAG storage between two strains of D. melanogaster, including Laminin A (LanA), which is a member of the alpha subfamily of laminin chains. We confirmed the effects of this gene using a viable LanA mutant and showed that female flies homozygous for the mutation had significantly lower TAG storage, body weight, and total protein content than control flies. Drosophila LanA is closely related to human LAMA5 gene, which maps to the well-replicated obesity-linkage region on chromosome 20q13.2-q13.3. We tested for association between three common single nucleotide polymorphisms (SNPs) in the human LAMA5 gene and variation in body composition and lipid profile traits in a cohort of unrelated women of European American (EA) and African American (AA) descent. In both ethnic groups, we found that SNP rs659822 was associated with weight (EA: P = 0.008; AA: P = 0.05) and lean mass (EA: P= 0.003; AA: P = 0.03). We also found this SNP to be associated with height (P = 0.01), total fat mass (P = 0.01), and HDL-cholesterol (P = 0.003) but only in EA women. Finally, significant associations of SNP rs944895 with serum TAG levels (P = 0.02) and HDL-cholesterol (P = 0.03) were observed in AA women. Our results suggest an evolutionarily conserved role of a member of the laminin gene family in contributing to variation in weight and body composition.
    BMC Genetics 02/2008; 9:52. DOI:10.1186/1471-2156-9-52 · 2.36 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Diseases with an inherited component that demonstrate different prevalence in various ancestral populations can now be studied using admixture mapping in an appropriate admixed population. This strategy called mapping by admixture linkage disequilibrium or MALD utilizes polymorphic genetic markers that are spaced throughout the genome to identify genomic regions where the estimated admixture proportion is significantly different than its expected value. These genetic markers are selected based on their ancestry informativeness content. The MALD approach assumes that genomic regions showing excess ancestry from the ancestral population with higher disease prevalence, in the sample of admixed individuals, are more likely to harbor polymorphisms that confer higher risk to disease than others. Certain conditions including essential hypertension, type 2 diabetes mellitus and common complex forms of nephropathy demonstrate clear differences in disease frequency in individuals of African and European descent and appear particularly suited to this type of analysis. Genetic admixture can also cause confounding in association studies conducted on an admixed sample leading to inflated type I error rates and possible loss of power. This manuscript describes the background, methodologies and uses for admixture mapping in the search for genes that underlie type 2 diabetes mellitus and its associated nephropathy in the African American population, and statistical methods to address the confounding issues in genetic association tests.
    Ethnicity & disease 02/2008; 18(3):384-8. · 0.92 Impact Factor