Emma K. Larkin

Genome-Wide Association Study of Coronary Heart Disease and Its Risk Factors in 8,090 African Americans: The NHLBI CARe Project

Coronary heart disease (CHD) is the leading cause of mortality in African Americans. To identify common genetic polymorphisms associated with CHD and its risk factors (LDL- and HDL-cholesterol (LDL-C and HDL-C), hypertension, smoking, and type-2 diabetes) in individuals of African ancestry, we performed a genome-wide association study (GWAS) in 8,090 African Americans from five population-based cohorts. We replicated 17 loci previously associated with CHD or its risk factors in Caucasians. For five of these regions (CHD: CDKN2A/CDKN2B; HDL-C: FADS1-3, PLTP, LPL, and ABCA1), we could leverage the distinct linkage disequilibrium (LD) patterns in African Americans to identify DNA polymorphisms more strongly associated with the phenotypes than the previously reported index SNPs found in Caucasian populations. We also developed a new approach for association testing in admixed populations that uses allelic and local ancestry variation. Using this method, we discovered several loci that would have been missed using the basic allelic and global ancestry information only. Our conclusions suggest that no major loci uniquely explain the high prevalence of CHD in African Americans. Our project has developed resources and methods that address both admixture- and SNP-association to maximize power for genetic discovery in even larger African-American consortia.

Variation of C-Reactive Protein Levels in Adolescents Association With Sleep-Disordered Breathing and Sleep Duration

Background—There is increasing evidence that sleep-disordered breathing (SDB) is an independent risk factor for cardiovascular disease (CVD) in adults. C-reactive protein (CRP), a marker of systemic inflammation, is an important predictor of future cardiovascular events. The goal of this study was to quantify the associations of SDB, sleep duration, and CRP in adolescents to better understand the role of SDB in CVD risk. Methods and Results—Adolescents (n=143; age, 13 to 18 years; 36% black; 50% female) with a wide range of SDB severity underwent polysomnography and measurement of high-sensitivity CRP. SDB was quantified with the apnea hypopnea index (AHI) and oxygen desaturation measures. Sleep duration was estimated from 7-day actigraphy. The independent and dose-response associations of SDB with CRP were addressed through linear mixed-effects models. Forty-eight percent were overweight or obese, and 12% had SDB (AHI ≥5). CRP levels varied with increasing body mass index and SDB. After adjustment for body mass index, age, sex, and race, mean CRP levels were 0.50, 0.43, 0.97, and 1.66 mg/L for SDB severity levels of AHI <1, 1 to 4.9, 5 to 14.9, and ≥15, respectively (P=0.0049, AHI ≥15 versus <1). Adjusted mean CRP levels demonstrated a dose response with SDB above a threshold AHI of 5. This association was partially explained by overnight hypoxemia and less so by sleep duration. Conclusions—In adolescents free of known CVD, an AHI ≥5 is associated with increasing levels of CRP, suggesting that pediatric SDB may confer additional CVD risk beyond that of obesity.

A Whole-Genome Scan for Obstructive Sleep Apnea and Obesity

Obstructive sleep apnea (OSA) is a common, chronic, complex disease associated with serious cardiovascular and neuropsychological sequelae and with substantial social and economic costs. Along with male gender, obesity is the most characteristic feature of OSA in adults. To identify susceptibility loci for OSA, we undertook a 9-cM genome scan in 66 white pedigrees (n=349 subjects) ascertained on the basis of either an affected individual with laboratory-confirmed OSA or a proband who was a neighborhood control individual. Multipoint variance-component linkage analysis was performed for the OSA-associated quantitative phenotypes apnea-hypopnea index (AHI) and body mass index (BMI). Candidate regions on chromosomes 1p (LOD score 1.39), 2p (LOD score 1.64), 12p (LOD score 1.43), and 19p (LOD score 1.40) gave the most evidence for linkage to AHI. BMI was also linked to multiple regions, most significantly to markers on chromosomes 2p (LOD score 3.08), 7p (LOD score 2.53), and 12p (LOD score 3.41). Extended modeling indicated that the evidence for linkage to AHI was effectively removed after adjustment for BMI, with the exception of the candidate regions on chromosomes 2p (adjusted LOD score 1.33) and 19p (adjusted LOD score 1.45). After adjustment for AHI, the primary linkages to BMI remained suggestive but were roughly halved. Our results suggest that there are both shared and unshared genetic factors underlying susceptibility to OSA and obesity and that the interrelationship of OSA and obesity in white individuals may be partially explained by a common causal pathway involving one or more genes regulating both AHI and BMI levels.

Short duration of sleep increases risk of colorectal adenoma.

Short duration and poor quality of sleep have been associated with increased risks of obesity, cardiovascular disease, diabetes mellitus, and total mortality. However, few studies have investigated their associations with risk of colorectal neoplasia.

Development and validation of school-based asthma and allergy screening instruments for parents and students

BACKGROUNDnThe increasing morbidity attributable to asthma among school-aged children suggests the potential utility of school-based asthma screening programs.nnnOBJECTIVEnWe report our efforts to develop and validate culturally sensitive and clinically useful screening questionnaires (parent and child versions) for asthma and allergies among urban US school children.nnnMETHODSnInstrument development was accomplished through literature review, expert medical and child developmental input, focus group feedback, and a rigorous trial of the instruments in a public school setting. Questionnaires were distributed to 2,800 children and their families in an urban public school system (grades kindergarten through 6). Validity was evaluated by blinded comparison of results against a standardized clinical evaluation in 107 children, with final designations determined by an expert panel.nnnRESULTSnQuestionnaires pertaining to 2,083 children were returned (participation rate of 74%). A moderate level of agreement was observed between parent and student questionnaire responses (r values = 0.36 to 0.50; P values < 0.001). The highest frequency of asthma-like symptoms was reported for African-American boys and the lowest for Caucasian girls. The items from the parent questionnaire that best predicted asthma were breathing problems (occurring rarely or more; odds ratio 12.8; 95% confidence interval, 4.5 to 36.1) and problems coughing (sometimes or more; odds ratio 9.7; 95% confidence interval, 3.6 to 26.5). Considering the presence of cough (sometimes or more) and/or breathing problem (rarely or more) yielded a sensitivity of 80%; a specificity of 75%, a positive predictive value of 50%, and a negative predictive value of 92%. Similar levels of prediction were observed for the items trouble breathing and noisy breathing as directly reported by the students. Allergic rhinitis was best predicted by report of a runny/stuffy no se (sometimes or more; sensitivity of 83%, specificity of 61%). Allergic conjunctivitis was best predicted by itchy eyes.nnnCONCLUSIONSnAdministration of a school-based questionnaire is feasible, with a high response rate and excellent internal consistency. A high sensitivity and acceptable specificity was achieved by using one to two questions for asthma, allergic rhinitis, and allergic conjunctivitis. Among the children in grades 2 or above, comparable levels of prediction could be achieved with the student or parent version.

Using linkage analysis to identify quantitative trait loci for sleep apnea in relationship to body mass index.

To understand the genetics of sleep apnea, we evaluated the relationship between the apnea hypopnea index (AHI) and body mass index (BMI) through linkage analysis to identify genetic loci that may influence AHI and BMI jointly and AHI independent of BMI.

Genome-wide linkage screen for stature and body mass index in 3.032 families: evidence for sex- and population-specific genetic effects.

Stature (adult body height) and body mass index (BMI) have a strong genetic component explaining observed variation in human populations; however, identifying those genetic components has been extremely challenging. It seems obvious that sample size is a critical determinant for successful identification of quantitative trait loci (QTL) that underlie the genetic architecture of these polygenic traits. The inherent shared environment and known genetic relationships in family studies provide clear advantages for gene mapping over studies utilizing unrelated individuals. To these ends, we combined the genotype and phenotype data from four previously performed family-based genome-wide screens resulting in a sample of 9.371 individuals from 3.032 African-American and European-American families and performed variance-components linkage analyses for stature and BMI. To our knowledge, this study represents the single largest family-based genome-wide linkage scan published for stature and BMI to date. This large study sample allowed us to pursue population- and sex-specific analyses as well. For stature, we found evidence for linkage in previously reported loci on 11q23, 12q12, 15q25 and 18q23, as well as 15q26 and 19q13, which have not been linked to stature previously. For BMI, we found evidence for two loci: one on 7q35 and another on 11q22, both of which have been previously linked to BMI in multiple populations. Our results show both the benefit of (1) combining data to maximize the sample size and (2) minimizing heterogeneity by analyzing subgroups where within-group variation can be reduced and suggest that the latter may be a more successful approach in genetic mapping.

Population stratification and patterns of linkage disequilibrium

Although the importance of selecting cases and controls from the same population has been recognized for decades, the recent advent of genome‐wide association studies has heightened awareness of this issue. Because these studies typically deal with large samples, small differences in allele frequencies between cases and controls can easily reach statistical significance. When, unbeknownst to a researcher, cases and controls have different substructures, the number of false‐positive findings is inflated. There have been three recent developments of purely statistical approaches to assessing the ancestral comparability of case and control samples: genomic control, structured association, and multivariate reduction analyses. The widespread use of high‐throughput technology has allowed the quick and accurate genotyping of the large number of markers required by these methods. Group 13 dealt with four population stratification issues: single‐nucleotide polymorphism marker selection, association testing, nonstandard methods, and linkage disequilibrium calculations in stratified or mixed ethnicity samples. We demonstrated that there are continuous axes of ethnic variation in both data sets of Genetic Analysis Workshop 16. Furthermore, ignoring this structure created P‐value inflation for a variety of phenotypes. Principal‐components analysis (or multidimensional scaling) can control inflation as covariates in a logistic regression. One can weigh for local ancestry estimation and allow the use of related individuals. Problems arise in the presence of extremely high association or unusually strong linkage disequilibrium (e.g., in chromosomal inversions). Our group also reported a method for performing an association test controlling for substructure, when genome‐wide markers are not available, to explicitly compute stratification Genet. Epidemiol. 33 (Suppl. 1):S88–S92, 2009.

Assessing the impact of global versus local ancestry in association studies

BackgroundTo account for population stratification in association studies, principal-components analysis is often performed on single-nucleotide polymorphisms (SNPs) across the genome. Here, we use Framingham Heart Study (FHS) Genetic Analysis Workshop 16 data to compare the performance of local ancestry adjustment for population stratification based on principal components (PCs) estimated from SNPs in a local chromosomal region with global ancestry adjustment based on PCs estimated from genome-wide SNPs.MethodsStandardized height residuals from unrelated adults from the FHS Offspring Cohort were averaged from longitudinal data. PCs of SNP genotype data were calculated to represent individuals ancestry either 1) globally using all SNPs across the genome or 2) locally using SNPs in adjacent 20-Mbp regions within each chromosome. We assessed the extent to which there were differences in association studies of height depending on whether PCs for global, local, or both global and local ancestry were included as covariates.ResultsThe correlations between local and global PCs were low (r < 0.12), suggesting variability between local and global ancestry estimates. Genome-wide association tests without any ancestry adjustment demonstrated an inflated type I error rate that decreased with adjustment for local ancestry, global ancestry, or both. A known spurious association was replicated for SNPs within the lactase gene, and this false-positive association was abolished by adjustment with local or global ancestry PCs.ConclusionPopulation stratification is a potential source of bias in this seemingly homogenous FHS population. However, local and global PCs derived from SNPs appear to provide adequate information about ancestry.

Modeling the complex gene × environment interplay in the simulated rheumatoid arthritis GAW15 data using latent variable structural equation modeling

Rheumatoid arthritis is a complex disease that appears to involve multiple genetic and environmental factors. Using the Genetic Analysis Workshop 15 simulated rheumatoid arthritis data and the structural equation modeling framework, we tested hypothesized causal rheumatoid arthritis model(s) by employing a novel latent gene construct approach that models individual genes as latent variables defined by multiple dense and non-dense single-nucleotide polymorphisms (SNPs). Our approach produced valid latent gene constructs, particularly with dense SNPs, which when coupled with other factors involved in rheumatoid arthritis, were able to generate good fitting models by certain goodness of fit indices. We observed that Gene F, C, DR, sex and smoking were significant predictors of rheumatoid arthritis but Genes A and E were not, which was generally, but not entirely, consistent with how the data were simulated. Our approach holds promise in unravelling complex diseases and improves upon current one SNP (haplotype)-at-a-time regression approaches by decreasing the number of statistical tests while minimizing problems with multicolinearity and haplotype estimation algorithm error. Furthermore, when genes are modeled as latent constructs simultaneously with other key cofactors, the approach provides enhanced control of confounding that should lead to less biased effect estimates among genes as well as between gene(s) and the complex disease. However, further study is needed to quantify bias, evaluate fit index disparity, and resolve multiplicative latent gene interactions. Moreover, because some a priori biological information is needed to form an initial substantive model, our approach may be most appropriate for candidate gene SNP panel applications.