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R C Elston

Publications and source records attributed to R C Elston.

At least 55 records · Page 3Linked to original sources
PubMed2000

Haseman and Elston revisited.

Haseman and Elston (H-E) [1972] proposed a method to detect quantitative trait loci by linkage to a marker. The squared sib-pair trait difference is regressed on the proportion of marker alleles the pair is estimated to share identical by descent: a significantly negative regression coefficient suggests linkage. It has been shown that a maximum likelihood method that directly models the sib-pair covariance has more power. This increase in power can also be obtained using the H-E regression procedure by changing the dependent variable from the squared difference to the mean-corrected product of the sibs' trait values. Multiple sibs in a sibship can be accommodated by allowing for the correlations between pairs of products in a generalized least squares procedure. Multiple trait loci, including epistatic interactions, involve only multiple linear regression. Multivariate traits can use the method of Amos et al. [1990] to find the linear function of the traits that maximizes the evidence for linkage, which now leads more simply to a test of significance. Multiple markers can be the basis of a multipoint analysis. Results of simulation studies for a continuous trait are presented that investigate Type I error and power. A similar general scheme can be used to study affected sib pairs, testing whether their identity by descent sharing probabilities are greater than would be expected in the absence of linkage, and to study other types of relative pairs.

Alleles↗
PubMed2000

Model-free sib-pair linkage analysis: combining full-sib and half-sib pairs.

When sampling full-sibs for linkage studies, half-sibs are often available. Not only are half-sibs convenient to sample, but they can sometimes offer greater power than full-sibs. We propose a method to combine the information from full-sibs and half-sibs into a single test for linkage. This method is based on the Haseman and Elston [1972] method of regressing the squared trait-difference for a pair of sibs (either full- or half-sibs) on the estimated proportion of alleles shared identical by descent. To approximate the distribution of the test statistic, we propose a correction factor that considers the correlation among sibs, and demonstrate by simulations that this approximation works well in many situations, although there are some conditions for which the statistic can have an inflated Type-I error rate. The main appeal of our proposed method is the speed at which it can be computed, offering a rapid way to perform genome-wide linkage screens.

Alleles↗
PubMed2000

On estimating the heterozygosity and polymorphism information content value.

The polymorphism information content (PIC) value is commonly used in genetics as a measure of polymorphism for a marker locus used in linkage analysis. In this communication we have derived the uniformly minimum variance unbiased estimator of PIC along with its exact variance. We have also calculated the exact variance of the maximum likelihood estimator of PIC which is asymptotically an unbiased estimator. In order to find this variance we have derived a recursive formula to calculate the moments of any polynomial in a set of variables that are multinomially distributed.

Genetic Linkage↗
PubMed2000

The relationship between the sibling recurrence-risk ratio and genotype relative risk.

The recurrence-risk ratio of disease in siblings, lambdaS, is a standard parameter used in genetic analysis to estimate the statistical power for detection of a disease locus. However, the relationship between the underlying risk conferred by a disease-susceptibility allele and lambdaS has not been well described. The former is generally quantified as a genotype relative risk, gamma, and measures the ratio of disease risks between those with and those without the susceptibility genotype(s). We demonstrate that lambdaS varies significantly more with respect to gamma and the disease-allele frequency for two-locus multiplicative models than for other two-locus and for single-locus models. For the single- and two-locus dominant-inheritance models that we studied, when a disease-susceptibility allele had a frequency >/=.2, lambdaS had an upper limit of <10. In general, lambdaS values >10 are possible only under recessive inheritance, dominant inheritance with relatively rare (<5%) disease-susceptibility alleles, or when two or more disease loci have alleles acting either epistatically or multiplicatively. We introduce the idea of a restricted sib recurrence-risk ratio (lambda*S) estimated by restriction of sibships to those ascertained through a proband who already has a putative high-risk allele. A lambda*S larger than the lambdaS value estimated from randomly selected probands can serve as an indirect way of testing whether the posited susceptibility allele increases disease risk. Our results demonstrate that a lambdaS of 2-3 may portend successful mapping for a variety of genetic models but that, for some two-locus models, a lambdaS as high as 10 does not guarantee underlying genes easily mapped by linkage.

Alleles↗
PubMed2000

A genome screen of multiplex sibships with prostate cancer.

Analysis of a genome screen of 504 brothers with prostate cancer (CaP) who were from 230 multiplex sibships identified five regions with nominally positive linkage signals, on chromosomes 2q, 12p, 15q, 16p, and 16q. The strongest signal in these data is found on chromosome 16q, between markers D16S515 and D16S3040, a region suspected to contain a tumor-suppressor gene. On the basis of findings from previous genome screens of families with CaP, three preplanned subanalyses were carried out, in the hope of increasing the subgroup homogeneity. Subgroups were formed by dividing the sibships into a group with a positive family history (FH+) that met criteria for "hereditary" CaP (n=111) versus those which did not meet the criteria (n=119) and by dividing the families into those with a mean onset age below the median (n=115) versus those with a mean onset age above the median (n=115). A separate subanalysis was carried out for families with a history of breast cancer (CaB+ [n=53]). Analyses of these subgroups revealed a number of potentially important differences in regions that were nonsignificant when all the families were analyzed together. In particular, the subgroup without a positive family history (FH-) had a signal in a region that is proximal to the putative site of the HPC1 locus on chromosome 1, whereas the late-age-at-onset group had a signal on 4q. The CaB+ subgroup revealed a strong linkage signal at 1p35.1.

Age of Onset↗
PubMed2000

Testing drug response in the presence of genetic information: sampling issues for clinical trials.

Progress towards construction of a dense map of di-allelic markers across the human genome has generated considerable enthusiasm for pharmacogenomic applications. To date, however, nearly all of the effort on single nucleotide polymorphism (SNP) projects has been focused on marker identification and screening, not on how the SNP genotype data actually can be used in clinical trials to advance medical practice. Here, we explore how different properties of SNPs impact the size, scope and design of clinical trials using a simple trial design. We evaluate the clinical trial sampling requirements under different allele frequencies, gene action, gene effect size and number of markers in a genome screen. Power and sample size calculations suggest that allele frequency and type of gene action can have a dramatic impact on trial sample sizes, in that under some conditions the required sample sizes are too large to be applicable in a costly clinical trial setting. In other situations, however, pharmacogenomic clinical trials can yield significant sampling/cost savings over traditional trials. These properties are discussed with regard to the general usage of genetic information in clinical trial settings.

Clinical Trials as Topic↗
PubMed2000

A century of biometrical genetics.

We briefly review the major contribution of biometrics to genetics over the last century (population genetic models, familial correlations, segregation analysis, and gene mapping) and current areas of active research and then speculate about what problems will be tackled in the next century.

Biometry↗
PubMed2000

The E1E4 protein of human papillomavirus type 16 associates with a putative RNA helicase through sequences in its C terminus.

Human papillomavirus type 16 (HPV16) infects cervical epithelium and is associated with the majority of cervical cancers. The E1E4 protein of HPV16 but not those of HPV1 or HPV6 was found to associate with a novel member of the DEAD box protein family of RNA helicases through sequences in its C terminus. This protein, termed E4-DBP (E4-DEAD box protein), has a molecular weight of 66,000 (66K) and can shuttle between the nucleus and the cytoplasm. It binds to RNA in vitro, including the major HPV16 late transcript (E1E4. L1), and has an RNA-independent ATPase activity which can be partially inhibited by E1E4. E4-DBP was detectable in the cytoplasm of cells expressing HPV16 E1E4 (in vivo and in vitro) and could be immunoprecipitated as an E1E4 complex from cervical epithelial cell lines. In cell lines lacking cytoplasmic intermediate filaments, loss of the leucine cluster-cytoplasmic anchor region of HPV16 E1wedgeE4 resulted in both proteins colocalizing exclusively to the nucleoli. Two additional HPV16 E1E4-binding proteins, of 80K and 50K, were identified in pull-down experiments but were not recognized by antibodies to E4-DBP or the conserved DEAD box motif. Sequence analysis of E4-DBP revealed homology in its E4-binding region with three Escherichia coli DEAD box proteins involved in the regulation of mRNA stability and degradation (RhlB, SrmB, and DeaD) and with the Rrp3 protein of Saccharomyces cerevisiae, which is involved in ribosome biogenesis. The synthesis of HPV16 coat proteins occurs after E1E4 expression and genome amplification and is regulated at the level of mRNA stability and translation. Identification of E4-DBP as an HPV16 E1E4-associated protein indicates a possible role for E1E4 in virus synthesis.

Cell Nucleus↗
PubMed2000

Genetic determinants of acute hypoxic ventilation: patterns of inheritance in mice.

Acutely lowering ambient O(2) tension increases ventilation in many mammalian species, including humans and mice. Inheritance patterns among kinships and between mouse strains suggest that a robust genetic influence determines individual hypoxic ventilatory responses (HVR). Here, we tested specific genetic hypotheses to describe the inheritance patterns of HVR phenotypes among two inbred mouse strains and their segregant and nonsegregant progeny. Using whole body plethysmography, we assessed the magnitude and pattern of ventilation in C3H/HeJ (C3) and C57BL/6J (B6) progenitor strains at baseline and during acute (3-5 min) hypoxic [mild hypercapnic hypoxia, inspired O(2) fraction (FI(O(2))) = 0.10] and normoxic (mild hypercapnic normoxia, FI(O(2)) = 0.21) inspirate challenges in mild hypercapnia (inspired CO(2) fraction = 0.03). First- and second-filial generations and two backcross progeny were also studied to assess response distributions of HVR phenotypes relative to the parental strains. Although the minute ventilation (VE) during hypoxia was comparable between the parental strains, breathing frequency (f) and tidal volume were significantly different; C3 mice demonstrated a slow, deep HVR relative to a rapid, shallow phenotype of B6 mice. The HVR profile in B6C3F(1)/J mice suggested that this offspring class represented a third phenotype, distinguishable from the parental strains. The distribution of HVR among backcross and intercross offspring suggested that the inheritance patterns for f and VE during mild hypercapnic hypoxia are consistent with models that incorporate two genetic determinants. These results further suggest that the quantitative genetic expression of alleles derived from C3 and B6 parental strains interact to significantly attenuate individual HVR in the first- and second-filial generations. In conclusion, the genetic control of HVR in this model was shown to exhibit a relatively simple genetic basis in terms of respiratory timing characteristics.

Acute Disease↗
PubMed2000

Introduction and overview. Statistical methods in genetic epidemiology.

Common terms used in genetics with multiple meanings are explained and a brief overview given of the four major areas of genetic epidemiology--the study of familial aggregation, segregation, cosegregation and association. Familial aggregation measures the potential for a trait to have a genetic aetiology. Segregation analysis uncovers single gene segregation. Cosegregation with genetic markers gives rise to linkage, which is used to locate trait genes on the genome. Association analysis is used for fine mapping, but rests on the assumption that linkage disequilibrium exists.

Chromosome Mapping↗
PubMed2000

Response of the hypothalamic-pituitary-adrenal axis to lumbar puncture induced stress.

To assess lumbar puncture (LP) stress as measured by Hypothalamic-Pituitary-Adrenal axis response, serum cortisols were measured before and after LP in Alzheimer's disease (AD) and healthy elderly individuals. There were no differences in baseline serum cortisol. AD group had significantly higher cortisols at 165 minutes post-LP. Growth curve analysis confirmed these findings and showed significant differences in cortisol levels overall. The AD males had higher cortisols at 105 minutes post-LP, but no other consistent gender differences emerged. The findings are consistent with the relative reduction in HPA negative feedback seen in AD. Overall, LP induces little change in group mean cortisol levels relative to inter-individual variation, indicating that while LP is an interesting model of neuroendocrine challenge, it needs to be tested in larger populations.

Journal Article↗
PubMed1999

Genetic mapping of complex traits.

Statistical genetic mapping methods are powerful tools for finding genes that contribute to complex human traits. Mapping methods combine knowledge of the biological mechanisms of inheritance and the randomness inherent in those mechanisms to locate, with increasing precision, trait genes on the human genome. We provide an overview of the two major classes of mapping methods, genetic linkage analysis and linkage disequilibrium analysis, and related concepts of genetic inheritance.

Chromosome Mapping↗
PubMed1999

The study of candidate genes in drug trials: sample size considerations.

With discovery of an increasing number of candidate genes that may affect inter-individual variability in response to drugs, the design of drug trials that incorporate their study has become relevant. We discuss the determination of sample size for such studies when the number of tests to perform is given, or, alternatively, the number of tests to perform when the sample size is given. In many cases, a uniformly most powerful test does not exist and normal approximations are not sufficiently accurate to determine sample size. We discuss briefly various tests of interest and we give simple examples to illustrate some of the problems that arise.

Clinical Trials, Phase I as Topic↗
PubMed1999

Multipoint linkage disequilibrium mapping with particular reference to the African-American population.

A new approach to scanning the genome is presented to detect linkage disequilibrium caused specifically by population admixture. In contrast to current linkage genome scanning methods to find causal genes for complex diseases, this new method should be powerful to find genes for multilocus traits, particularly those genes that lead to the highest population attributable risk. Such a scan using the African-American population is generally feasible for mapping common diseases. A conservative threshold is also provided for such association mapping.

Alleles↗
PubMed1999

Fieller's theorem and linkage disequilibrium mapping.

Linkage disequilibrium mapping exploits the fact that at genetic markers close enough to a disease locus on a particular chromosome, we expect to find an association between the disease and marker alleles. Furthermore, the magnitude of the association is expected to follow a unimodal curve when plotted against location, with the peak at the disease location. In practice, for real data, we usually see deviations from such a curve due to other influences such as evolutionary variability, mutation, and selection. Here we propose fitting a quadratic curve to data of this nature, estimating the location of the disease locus by the point at which the curve is maximum. A key feature of our method is the use of transformations of both location and disequilibrium, so that departures from a unimodal curve are incorporated by fitting the curve not to the original location and disequilibrium values but to the transformed values. In addition, we estimate the covariances between the disequilibrium values at linked loci using either a multinomial approximation or a bootstrap procedure. The location estimate from our method is the ratio of two quantities that, in large samples, are normally distributed, and so we use Fieller's theorem to obtain a confidence interval for the disease gene location. We successfully apply our method to data from several published studies in which the true disease gene location is known.

Alleles↗
PubMed1999

Linkage and association analyses of alcoholism using a regression-based transmission/disequilibrium test.

Recently, George et al. proposed a regression-based transmission/disequilibrium test for linkage using information on the parent-to-offspring transmission status of an allele at a marker locus. We extended this test by simultaneously testing for any population association by incorporating the presence/absence status of the associated allele as a covariate in the model. We used this method to analyze markers on chromosomes 1 through 21 of the Collaborative Study on the Genetics of Alcoholism data on alcoholism for possible association and linkage. We found nominal significance (at the 0.02 level) at eight different regions for linkage, though statistical significance may not be concluded due to multiple testing. The strongest evidence of linkage was observed for markers D4S2639 and D12S397 with p-values less than 0.005. We also found strong association between the trait and alleles 149 of D7S691 and 131 of D21S1437.

Age Factors↗
PubMed1999

Model-based and model-free multipoint genome-wide linkage analysis of alcoholism.

We analyzed a subset of the Collaborative Study on the Genetics of Alcoholism (COGA) data set as provided by the 11th Genetic Analysis Workshop (GAW11). Linkage analyses were performed using each of the diagnostic criteria for alcoholism included in the data: the COGA criteria (DSM-III-R plus the Feighner criteria) and the narrower World Health Organization diagnosis ICD-10 criteria. Formal segregation analysis using these data was not attempted because only a subset of all the originally ascertained families was made available. Nevertheless, an attempt was made to estimate the best one-locus two-allele genetic model for these data. Model-based multipoint linkage analysis was performed using the results of our trait model fitting, and model-free multipoint linkage analysis was performed with an improved version of the Haseman and Elston linkage method for sib pairs.

Adolescent↗