Faculty Bibliography

OBJECTIVES/OBJECTIVE:Structured association tests (SAT), like any statistical model, assumes that all variables are measured without error. Measurement error can bias parameter estimates and confound residual variance in linear models. It has been shown that admixture estimates can be contaminated with measurement error causing SAT models to suffer from the same afflictions. Multiple imputation (MI) is presented as a viable tool for correcting measurement error problems in SAT linear models with emphasis on correcting measurement error contaminated admixture estimates. METHODS:Several MI methods are presented and compared, via simulation, in terms of controlling Type I error rates for both non-additive and additive genotype coding. RESULTS:Results indicate that MI using the Rubin or Cole method can be used to correct for measurement error in admixture estimates in SAT linear models. CONCLUSION/CONCLUSIONS:Although MI can be used to correct for admixture measurement error in SAT linear models, the data should be of reasonable quality, in terms of marker informativeness, because the method uses the existing data to borrow information in which to make the measurement error corrections. If the data are of poor quality there is little information to borrow to make measurement error corrections.

BACKGROUND:MYH9 is a podocyte-expressed gene encoding nonmuscle myosin IIA that is associated with idiopathic and human immunodeficiency virus-associated focal segmental glomerulosclerosis (FSGS) and hypertensive end-stage renal disease in African Americans. METHODS:Four single nucleotide polymorphisms comprising the major MYH9 E1 risk haplotype were tested for association with estimated glomerular filtration rate (eGFR) and urine albumin:creatinine ratio (ACR) in 2,903 HyperGEN participants (1,458 African Americans (AA) in 895 families and 1,445 European Americans (EA) in 859 families) to determine the role of MYH9 in subclinical nephropathy. Association analyses employed general linear models in unrelated probands and generalized estimating equations in families. Adjustment was performed for age, sex, diabetes, BMI, medications, and mean arterial pressure separately in each race. RESULTS:Mean (SD) eGFR and ACR were 74.3 (16.0) ml/min/1.73 m(2) and 20.3 (119.9) mg/g in EA, and 88.6 (20.9) ml/min/1.73 m(2) and 76.8 (394.5) mg/g in AA (both p < 0.0001 across ethnicities). Urine ACR was associated with rs3752462 (p = 0.01) and rs4821481 (p = 0.05) in unrelated AA and with rs4821481 (p = 0.03), rs2032487 (p = 0.04) and the E1 3224 haplotype (p = 0.013) in AA families. Single nucleotide polymorphisms and the haplotype were not associated with ACR in EA or with eGFR in either ethnic group. CONCLUSIONS:MYH9 variants are associated with albuminuria in hypertensive AA. The strength of the association was weaker than that in FSGS and hypertensive end-stage renal disease. MYH9 risk variants appear to be associated with primary FSGS with secondary hypertension, although nephrosclerosis may develop in response to hypertension in subjects homozygous for the MYH9 E1 risk haplotype.

As end-stage renal disease (ESRD) has a four times higher incidence in African Americans compared to European Americans, we hypothesized that susceptibility alleles for ESRD have a higher frequency in the West African than the European gene pool. We carried out a genome-wide admixture scan in 1,372 ESRD cases and 806 controls and found a highly significant association between excess African ancestry and nondiabetic ESRD (lod score = 5.70) but not diabetic ESRD (lod = 0.47) on chromosome 22q12. Each copy of the European ancestral allele conferred a relative risk of 0.50 (95% CI = 0.39-0.63) compared to African ancestry. Multiple common SNPs (allele frequencies ranging from 0.2 to 0.6) in the gene encoding nonmuscle myosin heavy chain type II isoform A (MYH9) were associated with two to four times greater risk of nondiabetic ESRD and accounted for a large proportion of the excess risk of ESRD observed in African compared to European Americans.

Admixture and population stratification are major concerns in genetic association studies. We wished to evaluate the impact of admixture using empirically derived data from genetic association studies of African Americans (AA) with type 2 diabetes (T2DM) and end-stage renal disease (ESRD). Seventy ancestry informative markers (AIMs) were genotyped in 577 AA with T2DM-ESRD, 596 AA controls, 44 Yoruba Nigerian (YRI) and 39 European American (EA) controls. Genotypic data and association results for eight T2DM candidate gene studies in our AA population were included. Ancestral estimates were calculated using FRAPPE, ADMIXMAP and STRUCTURE for all AA samples, using varying numbers of AIMs (25, 50, and 70). Ancestry estimates varied significantly across all three programs with the highest estimates obtained using STRUCTURE, followed by ADMIXMAP; while FRAPPE estimates were the lowest. FRAPPE estimates were similar using varying numbers of AIMs, while STRUCTURE estimates using 25 AIMs differed from estimates using 50 and 70 AIMs. Female T2DM-ESRD cases showed higher mean African proportions as compared to female controls, male cases, and male controls. Age showed a weak but significant correlation with individual ancestral estimates in AA cases (r2 = 0.101; P = 0.019) and in the combined set (r2 = 0.131; P = 3.57 x 10(-5)). The absolute difference between frequencies in parental populations, absolute delta, was correlated with admixture impact for dominant, additive, and recessive genotypic models of association. This study presents exploratory analyses of the impact of admixture on studies of AA with T2DM-ESRD and supports the use of ancestral proportions as a means of reducing confounding effects due to admixture.

OBJECTIVE:Several whole-genome association studies have reported identification of type 2 diabetes susceptibility genes in various European-derived study populations. Little investigation of these loci has been reported in other ethnic groups, specifically African Americans. Striking differences exist between these populations, suggesting they may not share identical genetic risk factors. Our objective was to examine the influence of type 2 diabetes genes identified in whole-genome association studies in a large African American case-control population. RESEARCH DESIGN AND METHODS/METHODS:Single nucleotide polymorphisms (SNPs) in 12 loci (e.g., TCF7L2, IDE/KIF11/HHEX, SLC30A8, CDKAL1, PKN2, IGF2BP2, FLJ39370, and EXT2/ALX4) associated with type 2 diabetes in European-derived populations were genotyped in 993 African American type 2 diabetic and 1,054 African American control subjects. Additionally, 68 ancestry-informative markers were genotyped to account for the impact of admixture on association results. RESULTS:Little evidence of association was observed between SNPs, with the exception of those in TCF7L2, and type 2 diabetes in African Americans. One TCF7L2 SNP (rs7903146) showed compelling evidence of association with type 2 diabetes (admixture-adjusted additive P [P(a)] = 1.59 x 10(-6)). Only the intragenic SNP on 11p12 (rs9300039, dominant P [P(d)] = 0.029) was also associated with type 2 diabetes after admixture adjustments. Interestingly, four of the SNPs are monomorphic in the Yoruba population of the HAPMAP project, with only the risk allele from the populations of European descent present. CONCLUSIONS:Results suggest that these variants do not significantly contribute to interindividual susceptibility to type 2 diabetes in African Americans. Consequently, genes contributing to type 2 diabetes in African Americans may, in part, be different from those in European-derived study populations. High frequency of risk alleles in several of these genes may, however, contribute to the increased prevalence of type 2 diabetes in African Americans.

Fat accumulation, in general, is the result of a breakdown in the homeostatic regulation of energy balance. Although, the specific factors influencing the disruption of energy balance and why these factors affect individuals differently are not completely understood, numerous studies have identified multiple contributors. Environmental components influence food acquisition, eating, and lifestyle habits. However, the variability in obesity-related outcomes observed among individuals placed in similar controlled environments supports the notion that genetic components also wield some control. Multiple genetic regions have been associated with measures related to energy balance; however, the replication of these genetic contributors to energy intake and energy expenditure in humans is relatively small perhaps because of the heterogeneity of human populations. Genetic tools such as genetic admixture account for individual's genetic background in gene association studies, reducing the confounding effect of population stratification, and promise to be a relevant tool on the identification of genetic contributions to energy balance, particularly among individuals of diverse racial/ethnic backgrounds. Although it has been recognized that genes are expressed according to environmental influences, the search toward the understanding of nature and nurture in obesity will require the detailed study of the effect of genes under diverse physiologic and behavioral environments. It is evident that more research is needed to elucidate the methodological and statistical issues that underlie the interactions between genes and environments in obesity and its related comorbidities.

Systemic lupus erythematosus (SLE) is a common systemic autoimmune disease with complex etiology but strong clustering in families (lambda(S) = approximately 30). We performed a genome-wide association scan using 317,501 SNPs in 720 women of European ancestry with SLE and in 2,337 controls, and we genotyped consistently associated SNPs in two additional independent sample sets totaling 1,846 affected women and 1,825 controls. Aside from the expected strong association between SLE and the HLA region on chromosome 6p21 and the previously confirmed non-HLA locus IRF5 on chromosome 7q32, we found evidence of association with replication (1.1 x 10(-7) < P(overall) < 1.6 x 10(-23); odds ratio = 0.82-1.62) in four regions: 16p11.2 (ITGAM), 11p15.5 (KIAA1542), 3p14.3 (PXK) and 1q25.1 (rs10798269). We also found evidence for association (P < 1 x 10(-5)) at FCGR2A, PTPN22 and STAT4, regions previously associated with SLE and other autoimmune diseases, as well as at > or =9 other loci (P < 2 x 10(-7)). Our results show that numerous genes, some with known immune-related functions, predispose to SLE

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.

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.

Individual genetic admixture estimates, determined both across the genome and at specific genomic regions, have been proposed for use in identifying specific genomic regions harboring loci influencing phenotypes in regional admixture mapping (RAM). Estimates of individual ancestry can be used in structured association tests (SAT) to reduce confounding induced by various forms of population substructure. Although presented as two distinct approaches, we provide a conceptual framework in which both RAM and SAT are special cases of a more general linear model. We clarify which variables are sufficient to condition upon in order to prevent spurious associations and also provide a simple closed form "semiparametric" method of evaluating the reliability of individual admixture estimates. An estimate of the reliability of individual admixture estimates is required to make an inherent errors-in-variables problem tractable. Casting RAM and SAT methods as a general linear model offers enormous flexibility enabling application to a rich set of phenotypes, populations, covariates, and situations, including interaction terms and multilocus models. This approach should allow far wider use of RAM and SAT, often using standard software, in addressing admixture as either a confounder of association studies or a tool for finding loci influencing complex phenotypes in species as diverse as plants, humans, and nonhuman animals.