Robert C. Elston

Bio: Robert C. Elston is an academic researcher from Case Western Reserve University. The author has contributed to research in topics: Population & Linkage (software). The author has an hindex of 70, co-authored 380 publications receiving 15348 citations. Previous affiliations of Robert C. Elston include University Hospitals of Cleveland & MetroHealth.

Natural selection on EPAS1 (HIF2α) associated with low hemoglobin concentration in Tibetan highlanders

Abstract: By impairing both function and survival, the severe reduction in oxygen availability associated with high-altitude environments is likely to act as an agent of natural selection. We used genomic and candidate gene approaches to search for evidence of such genetic selection. First, a genome-wide allelic differentiation scan (GWADS) comparing indigenous highlanders of the Tibetan Plateau (3,200-3,500 m) with closely related lowland Han revealed a genome-wide significant divergence across eight SNPs located near EPAS1. This gene encodes the transcription factor HIF2alpha, which stimulates production of red blood cells and thus increases the concentration of hemoglobin in blood. Second, in a separate cohort of Tibetans residing at 4,200 m, we identified 31 EPAS1 SNPs in high linkage disequilibrium that correlated significantly with hemoglobin concentration. The sex-adjusted hemoglobin concentration was, on average, 0.8 g/dL lower in the major allele homozygotes compared with the heterozygotes. These findings were replicated in a third cohort of Tibetans residing at 4,300 m. The alleles associating with lower hemoglobin concentrations were correlated with the signal from the GWADS study and were observed at greatly elevated frequencies in the Tibetan cohorts compared with the Han. High hemoglobin concentrations are a cardinal feature of chronic mountain sickness offering one plausible mechanism for selection. Alternatively, as EPAS1 is pleiotropic in its effects, selection may have operated on some other aspect of the phenotype. Whichever of these explanations is correct, the evidence for genetic selection at the EPAS1 locus from the GWADS study is supported by the replicated studies associating function with the allelic variants.

A Generalized Combinatorial Approach for Detecting Gene-by-Gene and Gene-by-Environment Interactions with Application to Nicotine Dependence

Abstract: The determination of gene-by-gene and gene-by-environment interactions has long been one of the greatest challenges in genetics. The traditional methods are typically inadequate because of the problem referred to as the "curse of dimensionality." Recent combinatorial approaches, such as the multifactor dimensionality reduction (MDR) method, the combinatorial partitioning method, and the restricted partition method, have a straightforward correspondence to the concept of the phenotypic landscape that unifies biological, statistical genetics, and evolutionary theories. However, the existing approaches have several limitations, such as not allowing for covariates, that restrict their practical use. In this study, we report a generalized MDR (GMDR) method that permits adjustment for discrete and quantitative covariates and is applicable to both dichotomous and continuous phenotypes in various population-based study designs. Computer simulations indicated that the GMDR method has superior performance in its ability to identify epistatic loci, compared with current methods in the literature. We applied our proposed method to a genetics study of four genes that were reported to be associated with nicotine dependence and found significant joint action between CHRNB4 and NTRK2. Moreover, our example illustrates that the newly proposed GMDR approach can increase prediction ability, suggesting that its use is justified in practice. In summary, GMDR serves the purpose of identifying contributors to population variation better than do the other existing methods.

A quantitative-trait analysis of human plasma-dopamine β-hydroxylase activity: Evidence for a major functional polymorphism at the DBH locus

Abstract: Dopamine-β-hydroxylase (DβH) catalyzes the conversion of dopamine to norepinephrine and is released from sympathetic neurons into the circulation. Plasma-DβH activity varies widely between individuals, and a subgroup of the population has very low activity levels. Mounting evidence suggests that the DBH structural gene is itself the major quantitative-trait locus (QTL) for plasma-DβH activity, and a single unidentified polymorphism may account for a majority of the variation in activity levels. Through use of both sequencing-based mutational analysis of extreme phenotypes and genotype/phenotype correlations in samples from African American, European American (EA), and Japanese populations, we have identified a novel polymorphism (−1021C→T), in the 5′ flanking region of the DBH gene, that accounts for 35%–52% of the variation in plasma-DβH activity in these populations. In EAs, homozygosity at the T allele predicted the very low DβH–activity trait, and activity values in heterozygotes formed an intermediate distribution, indicating codominant inheritance. Our findings demonstrate that −1021C→T is a major genetic marker for plasma-DβH activity and provide new tools for investigation of the role of both DβH and the DBH gene in human disease.

Sibling correlations and segregation analysis of age-related maculopathy: the Beaver Dam Eye Study.

Abstract: Sibling correlations were evaluated and segregation analysis was performed on age-dependent maculopathy scores of the right and left eyes of individuals from 564 families in the Beaver Dam Eye study. There is evidence of significant sibling correlations. The data fit a mixture of two normal distributions, especially after undergoing the Box and Cox power transformation. In each eye, the hypothesis of mendelian transmission of a major effect cannot be rejected under the tau AB free model, but is rejected under the tau's free model. The hypothesis of a random environmental major effect is rejected. Similar major gene parameter estimates are found for both eyes. The results are consistent with a major effect accounting for 62% and 59%, in the right and left eyes, respectively, of the determination of age-related maculopathy scores. A single major gene can account for about 89% and 97% of this variability due to a major effect, or for about 55% and 57% of the total variability, in the right and left eyes, respectively.

Linkage and Association Analysis of Angiotensin I–Converting Enzyme (ACE)–Gene Polymorphisms with ACE Concentration and Blood Pressure

Abstract: Considerable effort has been expended to determine whether the gene for angiotensin I–converting enzyme (ACE) confers susceptibility to cardiovascular disease. In this study, we genotyped 13 polymorphisms in the ACE gene in 1,343 Nigerians from 332 families. To localize the genetic effect, we first performed linkage and association analysis of all the markers with ACE concentration. In multipoint variance-component analysis, this region was strongly linked to ACE concentration (maximum LOD score 7.5). Likewise, most of the polymorphisms in the ACE gene were significantly associated with ACE ( P

Human biochemical genetics

Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Heart Disease and Stroke Statistics-2018 Update: A Report From the American Heart Association.

Abstract: Each chapter listed in the Table of Contents (see next page) is a hyperlink to that chapter. The reader clicks the chapter name to access that chapter. Each chapter listed here is a hyperlink. Click on the chapter name to be taken to that chapter. Each year, the American Heart Association (AHA), in conjunction with the Centers for Disease Control and Prevention, the National Institutes of Health, and other government agencies, brings together in a single document the most up-to-date statistics related to heart disease, stroke, and the cardiovascular risk factors listed in the AHA’s My Life Check - Life’s Simple 7 (Figure1), which include core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure [BP], and glucose control) that contribute to cardiovascular health. The Statistical Update represents …

Mapping mendelian factors underlying quantitative traits using rflp linkage maps

Abstract: The advent of complete genetic linkage maps consisting of codominant DNA markers [typically restriction fragment length polymorphisms (RFLPs)] has stimulated interest in the systematic genetic dissection of discrete Mendelian factors underlying quantitative traits in experimental organisms. We describe here a set of analytical methods that modify and extend the classical theory for mapping such quantitative trait loci (QTLs). These include: (i) a method of identifying promising crosses for QTL mapping by exploiting a classical formula of SEWALL WRIGHT; (ii) a method (interval mapping) for exploiting the full power of RFLP linkage maps by adapting the approach of LOD score analysis used in human genetics, to obtain accurate estimates of the genetic location and phenotypic effect of QTLs; and (iii) a method (selective genotyping) that allows a substantial reduction in the number of progeny that need to be scored with the DNA markers. In addition to the exposition of the methods, explicit graphs are provided that allow experimental geneticists to estimate, in any particular case, the number of progeny required to map QTLs underlying a quantitative trait.

Complement Factor H Polymorphism in Age-Related Macular Degeneration

Abstract: Age-related macular degeneration (AMD) is a major cause of blindness in the elderly. We report a genome-wide screen of 96 cases and 50 controls for polymorphisms associated with AMD. Among 116,204 single-nucleotide polymorphisms genotyped, an intronic and common variant in the complement factor H gene ( CFH ) is strongly associated with AMD (nominal P value -7 ). In individuals homozygous for the risk allele, the likelihood of AMD is increased by a factor of 7.4 (95% confidence interval 2.9 to 19). Resequencing revealed a polymorphism in linkage disequilibrium with the risk allele representing a tyrosine-histidine change at amino acid 402. This polymorphism is in a region of CFH that binds heparin and C-reactive protein. The CFH gene is located on chromosome 1 in a region repeatedly linked to AMD in family-based studies.

Structural Equation Modeling

Abstract: Structural equation modeling (SEM) is a multivariate statistical framework that is used to model complex relationships between directly observed and indirectly observed (latent) variables. SEM is a general framework that involves simultaneously solving systems of linear equations and encompasses other techniques such as regression, factor analysis, path analysis, and latent growth curve modeling. Recently, SEM has gained popularity in the analysis of complex genetic traits because it can be used to better analyze the relationships between correlated variables (traits), to model genes as latent variables as a function of multiple observed genetic variants, and to assess the association between multiple genetic variants and multiple correlated phenotypes of interest. Though the general SEM framework only allows for the analysis of independent observations, recent work has extended SEM for the analysis of data on general pedigrees. Here, we review the theory of SEM for both unrelated and family data, describe the available software for SEM, and provide examples of SEM analysis.