Is percentage body fat differentially related to body mass index in Hispanic Americans,African Americans,and European Americans?

BACKGROUND: Limited research has been done to explore differences between ethnic groups, including Hispanic Americans (HAs), in the association between percentage body fat (PBF) and body mass index (BMI; in kg/m(2)); the numbers of HAs are increasing in the US population. OBJECTIVE: We investigated whether the relation between PBF and BMI in adult HAs differed from that of African Americans (AAs) and European Americans (EAs). DESIGN: We used a multiple regression model in which PBF measured with dual energy X-ray absorptiometry was predicted by the reciprocal of BMI (1/BMI; in m(2)/kg) in a sample of 487 men (n(EA) = 192, n(AA) = 148, and n(HA) = 147) and 933 women (n(EA) = 448, n(AA) = 304, and n(HA) = 181). RESULTS: For men, our results showed no significant differences between HAs and EAs, AAs and EAs, or HAs and AAs in the slope of the line relating 1/BMI to PBF. In women, there were significant differences in PBF as predicted by BMI between HAs and EAs (P < 0.002) and AAs and HAs (P = 0.020), but not between AAs and EAs. When PBF was estimated on the basis of predicting equations, the trend of the predicted PBF value in women differed according to ethnic group and BMI category. At a BMI < 30, HAs tended to have more body fat than did EAs and AAs, and at a BMI > 35, EAs tended to have more body fat than did the other groups. CONCLUSIONS: Our results show that the relation between PBF and BMI in HA women differs from that of EA and AA women.     

Association mapping,using a mixture model for complex traits

Association mapping for complex diseases using unrelated individuals can be more powerful than family-based analysis in many settings. In addition, this approach has major practical advantages, including greater efficiency in sample recruitment. Association mapping may lead to false-positive findings, however, if population stratification is not properly considered. In this paper, we propose a method that makes it possible to infer the number of subpopulations by a mixture model, using a set of independent genetic markers and then testing the association between a genetic marker and a trait. The proposed method can be effectively applied in the analysis of both qualitative and quantitative traits. Extensive simulations demonstrate that the method is valid in the presence of a population structure.     

African and non‐African admixture components in African Americans and an African Caribbean population

Admixture is a potential source of confounding in genetic association studies, so it becomes important to detect and estimate admixture in a sample of unrelated individuals. Populations of African descent in the US and the Caribbean share similar historical backgrounds but the distributions of African admixture may differ. We selected 416 ancestry informative markers (AIMs) to estimate and compare admixture proportions using STRUCTURE in 906 unrelated African Americans (AAs) and 294 Barbadians (ACs) from a study of asthma. This analysis showed AAs on average were 72.5% African, 19.6% European and 8% Asian, while ACs were 77.4% African, 15.9% European, and 6.7% Asian which were significantly different. A principal components analysis based on these AIMs yielded one primary eigenvector that explained 54.04% of the variation and captured a gradient from West African to European admixture. This principal component was highly correlated with African vs. European ancestry as estimated by STRUCTURE (r²=0.992, r²=0.912, respectively). To investigate other African contributions to African American and Barbadian admixture, we performed PCA on ∼14,000 (14k) genome‐wide SNPs in AAs, ACs, Yorubans, Luhya and Maasai African groups, and estimated genetic distances (F_ST). We found AAs and ACs were closest genetically (F_ST=0.008), and both were closer to the Yorubans than the other East African populations. In our sample of individuals of African descent, ∼400 well‐defined AIMs were just as good for detecting substructure as ∼14,000 random SNPs drawn from a genome‐wide panel of markers. Genet. Epidemiol. 34:561–568, 2010.© 2010 Wiley‐Liss, Inc.     

湖北钉螺扩增片段长度多态性分子标记遗传多样性研究的合理样本量与分子位点数

目的探讨用扩增片段长度多态性（AFLP）分子标记研究湖北钉螺遗传多样性的合理样本量与分子位点数。方法选取湖南省岳阳市遗传变异较大的肋壳钉螺为研究材料，用AFLP方法对钉螺基因组DNA进行扩增，然后分析湖北钉螺样本量和分子位点数与遗传变异信息可靠性的关系。结果湖北钉螺样本量和分子位点数与遗传多样性信息的可靠性之间存在明显的关系。当样本量低于7只时，AFLP总位点数、多态位点数、多态位点频率、Nei’s基因多样性指数和Shannon’s信息指数变化很大，而当样本量超过30只时，这些指标值的变化趋于平稳。当AFLP分子位点数低于128时，多态位点频率、Nei’s基因多样性指数、Shannon’s信息指数以及这两个指数的标准差变化相当剧烈，当分子位点数超过338时，这些指标值的变化趋于稳定。结论在用AFLP分子标记技术研究湖北钉螺的遗传多样性时，每个钉螺种群内的样本量最好不应低于30只，用于研究分析的分子位点数最好不低于338个。     

Detecting interacting genetic loci with effects on quantitative traits where the nature and order of the interaction are unknown

Standard techniques for single marker quantitative trait mapping perform poorly in detecting complex interacting genetic influences. When a genetic marker interacts with other genetic markers and/or environmental factors to influence a quantitative trait, a sample of individuals will show different effects according to their exposure to other interacting factors. This paper presents a Bayesian mixture model, which effectively models heterogeneous genetic effects apparent at a single marker. We compute approximate Bayes factors which provide an efficient strategy for screening genetic markers (genome‐wide) for evidence of a heterogeneous effect on a quantitative trait. We present a simulation study which demonstrates that the approximation is good and provide a real data example which identifies a population‐specific genetic effect on gene expression in the HapMap CEU and YRI populations. We advocate the use of the model as a strategy for identifying candidate interacting markers without any knowledge of the nature or order of the interaction. The source of heterogeneity can be modeled as an extension. Genet. Epidemiol. 34: 299–308, 2010. © 2009 Wiley‐Liss, Inc.     

On a semiparametric test to detect associations between quantitative traits and candidate genes using unrelated individuals

Although genetic association studies using unrelated individuals may be subject to bias caused by population stratification, alternative methods that are robust to population stratification such as family-based association designs may be less powerful. Recently, various statistical methods robust to population stratification were proposed for association studies, using unrelated individuals to identify associations between candidate markers and traits of interest (both qualitative and quantitative). Here, we propose a semiparametric test for association (SPTA). SPTA controls for population stratification through a set of genomic markers by first deriving a genetic background variable for each sampled individual through his/her genotypes at a series of independent markers, and then modeling the relationship between trait values, genotypic scores at the candidate marker, and genetic background variables through a semiparametric model. We assume that the exact form of relationship between the trait value and the genetic background variable is unknown and estimated through smoothing techniques. We evaluate the performance of SPTA through simulations both with discrete subpopulation models and with continuous admixture population models. The simulation results suggest that our procedure has a correct type I error rate in the presence of population stratification and is more powerful than statistical association tests for family-based association designs in all the cases considered. Moreover, SPTA is more powerful than the Quantitative Similarity-Based Association Test (QSAT) developed by us under continuous admixture populations, and the number of independent markers needed by SPTA to control for population stratification is substantially fewer than that required by QSAT.     

Health policy and genetic endowments: Understanding sources of response to Minimum Legal Drinking Age laws

This paper uses policy‐induced variation in legal access to alcohol in the United States to explore interactions between genetic predispositions and health behaviors. It is well known that Minimum Legal Drinking Age (MLDA) laws have discrete impacts on binge drinking behaviors, but less is known about heterogeneity of the effects and the characteristics of individuals most and least affected. Using the Add Health data, this paper explores differential policy effects based on polygenic scores (PGS), which are genome‐wide summary measures predicting health outcomes. Specifically, we leverage PGS for alcoholism and for a broader set of risk‐taking behaviors to explore heterogeneities in response to the policy and consider mechanisms for the responses. Like previous literature using the Add Health and other datasets, we find main effects of MLDA in increasing recent binge drinking episodes by approximately 5 percentage points. We find MLDA effects are concentrated entirely in individuals with high PGS for alcohol use. We are also able to compare these results with measures of parental alcoholism as a global proxy for family history.     

Possible role of serum 25-hydroxyvitamin D in black-white health disparities in the United States

Significant health disparities exist between African Americans (AAs) and White Americans (WAs). The all-cause mortality rate for AAs in 2006 was 26% higher than for non-Hispanic WAs. Explanations for the disparities usually include socioeconomic status, lifestyle behaviors, social environment, and access to preventive health care services. However, several studies indicate that these factors do not account for the observed disparities. Many studies report that vitamin D has important health benefits through paracrine and autocrine mechanisms and that higher serum 25-hydroxyvitamin D (25[OH]D) levels are associated with better health outcomes. AAs have a population mean serum 25(OH)D level of 16 ng/mL, whereas WAs have a level of 26 ng/mL. From preliminary meta-analyses of serum 25(OH)D level-disease outcome from observational studies, differences in serum 25(OH)D level for AAs and WAs can explain many of the health disparities. The ratios of mortality rates for AAs to WAs for female breast cancer, colorectal cancer, cardiovascular disease, and all-cause mortality rate in 2006 were 1.34, 1.43, 1.29, and 1.26, respectively. The 25(OH)D level-disease outcome ratios for 16 ng/mL versus 26 ng/mL for the same diseases were 1.26, 1.44, 1.27, and 1.26, respectively. The close agreement between these 2 sets of numbers suggests that low serum 25(OH)D level is an important health risk for AAs. Given the widespread vitamin D deficiency in the AA population and the potential widespread health benefits that accompany adequate replacement, we believe that addressing this issue may be the single most important public health measure that can be undertaken.     

Diagnostic test accuracy and prevalence inferences based on joint and sequential testing with finite population sampling

The two-test two-population model, originally formulated by Hui and Walter, for estimation of test accuracy and prevalence estimation assumes conditionally independent tests, constant accuracy across populations and binomial sampling. The binomial assumption is incorrect if all individuals in a population e.g. child-care centre, village in Africa, or a cattle herd are sampled or if the sample size is large relative to population size. In this paper, we develop statistical methods for evaluating diagnostic test accuracy and prevalence estimation based on finite sample data in the absence of a gold standard. Moreover, two tests are often applied simultaneously for the purpose of obtaining a 'joint' testing strategy that has either higher overall sensitivity or specificity than either of the two tests considered singly. Sequential versions of such strategies are often applied in order to reduce the cost of testing. We thus discuss joint (simultaneous and sequential) testing strategies and inference for them. Using the developed methods, we analyse two real and one simulated data sets, and we compare 'hypergeometric' and 'binomial-based' inferences. Our findings indicate that the posterior standard deviations for prevalence (but not sensitivity and specificity) based on finite population sampling tend to be smaller than their counterparts for infinite population sampling. Finally, we make recommendations about how small the sample size should be relative to the population size to warrant use of the binomial model for prevalence estimation.     

A computer simulation analysis of the accuracy of partial genome sequencing and restriction fragment analysis in the reconstruction of phylogenetic relationships.

Partial genome sequencing (PGS) and restriction fragment analysis (RFA) are used frequently in molecular epidemiologic investigations. The relative accuracy of PGS and RFA in phylogenetic reconstruction has not been assessed. In this study, 32 model phylogenetic trees with 16 extant lineages were generated, for which DNA sequences were simulated under varying conditions of genome length, nucleotide substitution rate, and between-site substitution rate variation. Genotyping using PGS and RFA was simulated. The effect of tree structure (stemminess, imbalance, lineage variation) on the accuracy of phylogenetic reconstruction (topological and branch length similarity) was evaluated. Overall, PGS was more accurate than RFA. The accuracy of PGS increased with increasing sequence length. The accuracy of RFA increased with the number of restriction enzymes used. In fragment size comparison, the Dice and Nei-Li algorithms differed little, with both more accurate than the Fragment Size Distribution algorithm. For RFA, higher tree stemminess and longer genome length were associated with higher topological accuracy, whereas lower tree stemminess and lower substitution rates were associated with higher branch length accuracy. For PGS, lower tree imbalance was associated with higher topological accuracy, whereas lower tree stemminess, higher substitution rate, and lower between-site substitution rate variation were associated with higher branch length accuracy. RFA had higher topological accuracy than PGS only for the shortest sequence length (200 bps) at a low substitution rate, high tree stemminess, and long genome length. PGS had equal or higher accuracy in branch length reconstruction than RFA under all conditions investigated. Thus, partial genome sequencing is recommended over restriction fragment analysis for conditions within the parameter space examined.     

Sequencing and imputation in GWAS: Cost-effective strategies to increase power and genomic coverage across diverse populations

A key aim for current genome-wide association studies (GWAS) is to interrogate the full spectrum of genetic variation underlying human traits, including rare variants, across populations. Deep whole-genome sequencing is the gold standard to fully capture genetic variation, but remains prohibitively expensive for large sample sizes. Array genotyping interrogates a sparser set of variants, which can be used as a scaffold for genotype imputation to capture a wider set of variants. However, imputation quality depends crucially on reference panel size and genetic distance from the target population. Here, we consider sequencing a subset of GWAS participants and imputing the rest using a reference panel that includes both sequenced GWAS participants and an external reference panel. We investigate how imputation quality and GWAS power are affected by the number of participants sequenced for admixed populations (African and Latino Americans) and European population isolates (Sardinians and Finns), and identify powerful, cost-effective GWAS designs given current sequencing and array costs. For populations that are well-represented in existing reference panels, we find that array genotyping alone is cost-effective and well-powered to detect common- and rare-variant associations. For poorly represented populations, sequencing a subset of participants is often most cost-effective, and can substantially increase imputation quality and GWAS power.     

GEE‐Based SNP Set Association Test for Continuous and Discrete Traits in Family‐Based Association Studies

Family‐based genetic association studies of related individuals provide opportunities to detect genetic variants that complement studies of unrelated individuals. Most statistical methods for family association studies for common variants are single marker based, which test one SNP a time. In this paper, we consider testing the effect of an SNP set, e.g., SNPs in a gene, in family studies, for both continuous and discrete traits. Specifically, we propose a generalized estimating equations (GEEs) based kernel association test, a variance component based testing method, to test for the association between a phenotype and multiple variants in an SNP set jointly using family samples. The proposed approach allows for both continuous and discrete traits, where the correlation among family members is taken into account through the use of an empirical covariance estimator. We derive the theoretical distribution of the proposed statistic under the null and develop analytical methods to calculate the P‐values. We also propose an efficient resampling method for correcting for small sample size bias in family studies. The proposed method allows for easily incorporating covariates and SNP‐SNP interactions. Simulation studies show that the proposed method properly controls for type I error rates under both random and ascertained sampling schemes in family studies. We demonstrate through simulation studies that our approach has superior performance for association mapping compared to the single marker based minimum P‐value GEE test for an SNP‐set effect over a range of scenarios. We illustrate the application of the proposed method using data from the Cleveland Family GWAS Study.     

Issues concerning association studies for fine mapping a susceptibility gene for a complex disease

The usefulness of association studies for fine mapping loci with common susceptibility alleles for complex genetic diseases in outbred populations is unclear. We investigate this issue for a battery of tightly linked anonymous genetic markers spanning a candidate region centered around a disease locus, and study the joint behavior of chi-square statistics used to discover and to localize the disease locus. We used simulation methods based on a coalescent process with mutation, recombination, and genetic drift to examine the spatial distribution of markers with large noncentrality parameters in a case-control study design. Simulations with a disease allele at intermediate frequency, presumably representing an old mutation, tend to exhibit the largest noncentrality parameter values at markers near the disease locus. In contrast, simulations with a disease allele at low frequency, presumably representing a young mutation, often exhibit the largest noncentrality parameter values at markers scattered over the candidate region. In the former cases, sample sizes or marker densities sufficient to detect association are likely to lead to useful localization, whereas, in the latter case, localization of the disease locus within the candidate region is much less likely, regardless of the sample size or density of the map. The effects of increasing sample size or marker density are also investigated. Based upon a single marker analysis, we find that a simple strategy of choosing the marker with the smallest associated P value to begin a laboratory search for the disease locus performs adequately for a common disease allele. We also investigated a strategy of pooling nearby sites to form multiple allele markers. Using multiple degree of freedom chi-square tests for two or three nearby sites, we found no clear advantage of this form of pooling over a single marker analysis. Genet. Epidemiol. 20:432-457, 2001. Published by Wiley-Liss, 2001.     

Using case-control designs for genome-wide screening for associations between genetic markers and disease susceptibility loci

We used a case-control design to scan the genome for any associations between genetic markers and disease susceptibility loci using the first two replicates of the Mycenaean population from the GAW11 (Problem 2) data. Using a case-control approach, we constructed a series of 2-by-3 tables for each allele of every marker on all six chromosomes. Odds ratios (ORs) and 95% confidence intervals (95% CI) were estimated for all alleles of every marker. We selected the one allele for which the estimated OR had the minimum p-value to plot in the graph. Among these selected ORs, we calculated 95% CI for those that had a p-value < or = adjusted alpha level. Significantly high ORs were taken to indicate an association between a marker locus and a suspected disease-susceptibility gene. For the Mycenaean population, the case-control design identified allele number 1 of marker 24 on chromosome 1 to be associated with a disease susceptibility gene, OR = 2.10 (95% CI 1.66-2.62). Our approach failed to show any other significant association between case-control status and genetic markers. Stratified analysis on the environmental risk factor (E1) provided no further evidence of significant association other than allele 1 of marker 24 on chromosome 1. These data indicate the absence of linkage disequilibrium for markers flanking loci A, B, and C. Finally, we examined the effect of gene x environment (G x E) interaction for the identified allele. Our results provided no evidence of G x E interaction, but suggested that the environmental exposure alone was a risk factor for the disease.     

Understanding local population genetics of tsetse: the case of an isolated population of Glossina palpalis gambiensis in Burkina Faso

Tsetse flies are the vectors of human and animal trypanosomiases. For tsetse eradication programs, it is crucial to be able to identify and target isolated populations, because they can be targeted for eradication without risk of reinvasion. However, most data that are available on non-isolated populations fail to find how these populations are locally structured, because Wahlund effect (admixture of individuals from genetically different units) always interfere with interpretations. In this paper, we investigated the genetic population structure of a possibly isolated population of Glossina palpalis gambiensis in a sacred wood in South Burkina Faso, using microsatellite DNA markers. We found that genotypic proportions in this population were in agreement with random mating model and that these tsetse were genetically highly differentiated from other populations of the same Mouhoun river basin only a few kilometers away, confirming its genetic isolation. The population also displayed substantial temporal differentiation in a two years period that lead to an estimate of effective population size of ～100 individuals. The fact that no Wahlund effect was identified allowed us to accurately measure the basic genetic parameters of this isolated population. Identifying such isolated and small populations is crucial for eradication programs and should be implemented more often.     

Practical considerations for imputation of untyped markers in admixed populations

Imputation of genotypes for markers untyped in a study sample has become a standard approach to increase genome coverage in genome‐wide association studies at practically zero cost. Most methods for imputing missing genotypes extend previously described algorithms for inferring haplotype phase. These algorithms generally fall into three classes based on the underlying model for estimating the conditional distribution of haplotype frequencies: a cluster‐based model, a multinomial model, or a population genetics‐based model. We compared BEAGLE, PLINK, and MACH, representing the three classes of models, respectively, with specific attention to measures of imputation success and selection of the reference panel for an admixed study sample of African Americans. Based on analysis of chromosome 22 and after calibration to a fixed level of 90% concordance between experimentally determined and imputed genotypes, MACH yielded the largest absolute number of successfully imputed markers and the largest gain in coverage of the variation captured by HapMap reference panels. Following the common practice of performing imputation once, the Yoruba in Ibadan, Nigeria (YRI) reference panel outperformed other HapMap reference panels, including (1) African ancestry from Southwest USA (ASW) data, (2) an unweighted combination of the Northern and Western Europe (CEU) and YRI data into a single reference panel, and (3) a combination of the CEU and YRI data into a single reference panel with weights matching estimates of admixture proportions. For our admixed study sample, the optimal strategy involved imputing twice with the HapMap CEU and YRI reference panels separately and then merging the data sets. Genet. Epidemiol. 34: 258–265, 2010. © 2009 Wiley‐Liss, Inc.     

Biogeographic ancestry, self-identified race, and admixture-phenotype associations in the Heart SCORE Study

Large epidemiologic studies examining differences in cardiovascular disease (CVD) risk factor profiles between European Americans and African Americans have exclusively used self-identified race (SIR) to classify individuals. Recent genetic epidemiology studies of some CVD risk factors have suggested that biogeographic ancestry (BGA) may be a better predictor of CVD risk than SIR. This hypothesis was investigated in 464 African Americans and 771 European Americans enrolled in the Heart Strategies Concentrating on Risk Evaluation (Heart SCORE) Study in March and April 2010. Individual West African and European BGA were ascertained by means of a panel of 1,595 genetic ancestry informative markers. Individual BGA varied significantly among African Americans and to a lesser extent among European Americans. In the total cohort, BGA was not found to be a better predictor of CVD risk factors than SIR. Both measures predicted differences in the presence of the metabolic syndrome, waist circumference, triglycerides, body mass index, very low density lipoprotein cholesterol, lipoprotein A, and systolic and diastolic blood pressure between European Americans and African Americans. These results suggest that for most nongenetic cardiovascular epidemiology studies, SIR is sufficient for predicting CVD risk factor differences between European Americans and African Americans. However, higher body mass index and diastolic blood pressure were significantly associated with West African BGA among African Americans, suggesting that BGA should be considered in genetic cardiovascular epidemiology studies carried out among African Americans.     

A review of childhood obesity prevention interventions targeting African American children

Obesity is a significant public health concern. Unfortunately, obesity affects minority youth populations disproportionately, with African Americans (AAs) 1.4 times as likely to be obese as non-Hispanic whites. There are a variety of reasons for the disparity of the obesogenic risk in AA children. In addition to genetic factors, cultural differences related to the nutritional habits, level of physical activity and acceptance of surplus weight among AAs play a major role in the development of obesity in this population. Considering these high-risk behaviors, and the associated barriers, the Institute of Medicine has expressed an urgent need to initiate childhood obesity interventions among diverse ethnic groups. Therefore, the purpose of this article was to review existing childhood obesity prevention interventions targeting AA children that were published between 2005 and 2010. This review was limited to interventions in which the population included more than 35% of AA children and adolescents. There was an abundance of interventions related to childhood obesity prevention, but fewer targeting specifically AA children and adolescents. A total of 18 interventions have been summarized, including behavioral, social and environmental approaches. Recommendations are presented to enhance childhood obesity interventions among diverse ethnic groups.     

Heterogeneity of marker allele frequencies hinders interpretation of linkage analysis: Illustration on chromosome 18 markers

In the first part of our study we tested linkage with chromosome 18 markers in a sample of bipolar I sib pairs. We did not obtain evidence for linkage but showed that we could not exclude the presence of a disease locus (having even a non-negligible effect). The limitation of the sib-pair sample size, and consequently of the conclusions, was a result of our care in assuring that the linkage analysis was free of possible errors in the marker allele frequencies. In the second part, we illustrated the possible impact of such heterogeneity in a single data set when applying the multipoint (APM) method. An Amish pedigree included in the study of Berrettini et al. Was analyzed under two sets of marker allele frequencies. One set corresponds to estimates from the entire data set and the second to estimates from the Amish pedigree only. Very different values for the APM statistics were obtained. Although the real frequencies are unknown for this family belonging to an isolated population, this example illustrates that heterogeneity in the populations from which familial data are collected may artificially increase evidence for linkage and hinder interpretation of the analysis. © 1997 Wiley-Liss, Inc.     

Power calculation for overall hypothesis testing with high‐dimensional commensurate outcomes

The complexity of system biology means that any metabolic, genetic, or proteomic pathway typically includes so many components (e.g., molecules) that statistical methods specialized for overall testing of high‐dimensional and commensurate outcomes are required. While many overall tests have been proposed, very few have power and sample size methods. We develop accurate power and sample size methods and software to facilitate study planning for high‐dimensional pathway analysis. With an account of any complex correlation structure between high‐dimensional outcomes, the new methods allow power calculation even when the sample size is less than the number of variables. We derive the exact (finite‐sample) and approximate non‐null distributions of the ‘univariate’ approach to repeated measures test statistic, as well as power‐equivalent scenarios useful to generalize our numerical evaluations. Extensive simulations of group comparisons support the accuracy of the approximations even when the ratio of number of variables to sample size is large. We derive a minimum set of constants and parameters sufficient and practical for power calculation. Using the new methods and specifying the minimum set to determine power for a study of metabolic consequences of vitamin B6 deficiency helps illustrate the practical value of the new results. Free software implementing the power and sample size methods applies to a wide range of designs, including one group pre‐intervention and post‐intervention comparisons, multiple parallel group comparisons with one‐way or factorial designs, and the adjustment and evaluation of covariate effects. Copyright © 2013 John Wiley & Sons, Ltd.