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R C Elston

Publications and source records attributed to R C Elston.

At least 37 records · Page 2Linked to original sources
PubMed2001

Comparison of marker intervals and number of sib pairs used for linkage analysis on simulated nuclear family data.

Using a two-stage global scan design, we analyzed general population replicates 1 and 42 of the Genetic Analysis Workshop (GAW) 12 simulated data set using three methods: revisited Haseman-Elston (HER), maximum likelihood variance estimation (ML), and variance components (VC). Three marker densities, 5-, 10-, and 15-cM intervals, were examined in the first-stage scan. We found that the 10-cM interval appears to be the most cost-effective approach in genotyping without sacrificing power when using a first stage significance level of 0.01. Subsequently, we performed the second-stage scan at 1-cM intervals for those putative positive regions identified in the first-stage scan at a significance level of 0.01. We also compared the power to detect linkage using different numbers of sib pairs for a genome-wide scan at a 10-cM interval and found that power decreases nonlinearly as the number of sib pairs decreases.

Analysis of Variance↗
PubMed2001

Impact of preadjusting a quantitative phenotype prior to sib-pair linkage analysis when gene x environment interaction exists.

The investigation of potential gene x environment (G x E) interactions is an important facet in the study of complex diseases. When G x E interaction exists, linkage analyses of the interacting gene must treat the environmental factor appropriately. Specifically, the common approach of regressing out an environmental factor prior to linkage analysis may be inappropriate if that factor has an interaction with the gene. This is explored here in the Genetic Analysis Workshop 12 simulated data set using the G x E interaction between major gene four (MG4) and environmental factor two (E2). The analysis shows that preadjusting the quantitative trait three (Q3) phenotype for the main effects of several environmental variables, including one (E2) that interacts with MG4, affects the results of a Haseman-Elston linkage analysis. In particular, the agreement in detecting linkage between preadjusting versus not preadjusting was only 78% and 66% using alpha levels of 0.05 and 0.10, respectively. For both approaches, incorporating an interaction term in the regression models enabled linkage to be detected where the evidence was either minimal or not present in an identical-by-descent main effects-only model. Furthermore, preadjustment for E2 did not appear to account for the major discrepancies between the approaches.

Chromosome Mapping↗
PubMed2001

Overview of model-free methods for linkage analysis.

Methods of model-free linkage analysis do not require a detailed specification for the mode of inheritance of the trait locus being linked. Beginning with methods proposed by Penrose in the 1930s, which allowed detection of linkage only, these methods now allow one to use multipoint analysis both to locate trait genes and to estimate variance components that give information on the genetic mechanism underlying the trait. The newer methods can utilize data on multiple types of pairs of relatives other than just sibpairs, and they can detect multiple trait loci. In combination with special sampling schemes, these methods give hope that they may play a crucial role in unraveling the genetic etiology of multifactorial traits, regardless of whether epistatic interactions are present. The results of such analyses can guide the use of more powerful model-based linkage analyses.

Chromosome Mapping↗
PubMed2001

One-stage versus two-stage strategies for genome scans.

One way to determine the genetic etiology of a complex disease is to find the chromosomal regions that tend to be shared among affected relatives and yet tend to differ between affected and unaffected relatives. This can be done by using equally spaced markers to perform a global linkage search of the whole genome. With the rapid development of molecular technology to readily type individuals, it is becoming relatively easy to carry out such genome searches for disease genes. The simplest approach is to type every individual in the sample at every marker locus. However, such an approach wastes a great deal of effort in genotyping large areas of the genome that are eventually found not to show any evidence for linkage. Efficient design of genome scans for linkage analysis is therefore an important issue. We first describe strategies in which a single sample is typed for markers in one or two stages, with neither the use of a second sample nor any attempt at replication of a linkage result. Then in the discussion we briefly mention other multistage strategies, including one that involves a degree of replication.

Chromosome Mapping↗
PubMed2001

Electrocardiographic prediction of abnormal genotype in congenital long QT syndrome: experience in 101 related family members.

INTRODUCTION: Previous studies showed that diagnosing congenital long QT syndrome (LQTS) is difficult due to variable penetrance and genetic heterogeneity, especially when subjects from multiple families with diverse mutations are combined. We hypothesized that a combination of clinical and ECG techniques could identify gene carriers within a single family with congenital LQTS. METHODS AND RESULTS: One hundred one genotyped members of a family with LQTS, including 26 carriers of a HERG mutation, underwent history and ECG analysis. Forty-eight family members also underwent exercise testing with QT and T wave alternans (TWA) analysis and 24-hour Holter monitoring with QT and heart rate variability analysis. A logistic regression model, which included age, gender, QTc, and QTc by age, provided the best prediction of gene carrier status, although there was substantial overlap (78%) of QTc among subjects with and without the mutation. QTc was not helpful as a discriminator in children < or = 13 years. TWA (observed infrequently) did not add significantly to the model's ability to predict abnormal genotype. CONCLUSION: Even in this homogeneous LQTS population, the phenotype was so variable that clinical and detailed ECG analyses did not permit an accurate diagnosis of gene carrier status, especially in children. Sustained microvolt TWA was a specific (100%) but insensitive (18%) marker for LQTS. Its ability to predict risk of arrhythmia in this population remains to be determined. Genetic testing serves an essential role in screening for carriers of LQTS.

Adolescent↗
PubMed2001

Pooling analysis of genetic data: the association of leptin receptor (LEPR) polymorphisms with variables related to human adiposity.

Analysis of raw pooled data from distinct studies of a single question generates a single statistical conclusion with greater power and precision than conventional metaanalysis based on within-study estimates. However, conducting analyses with pooled genetic data, in particular, is a daunting task that raises important statistical issues. In the process of analyzing data pooled from nine studies on the human leptin receptor (LEPR) gene for the association of three alleles (K109R, Q223R, and K656N) of LEPR with body mass index (BMI; kilograms divided by the square of the height in meters) and waist circumference (WC), we encountered the following methodological challenges: data on relatives, missing data, multivariate analysis, multiallele analysis at multiple loci, heterogeneity, and epistasis. We propose herein statistical methods and procedures to deal with such issues. With a total of 3263 related and unrelated subjects from diverse ethnic backgrounds such as African-American, Caucasian, Danish, Finnish, French-Canadian, and Nigerian, we tested effects of individual alleles; joint effects of alleles at multiple loci; epistatic effects among alleles at different loci; effect modification by age, sex, diabetes, and ethnicity; and pleiotropic genotype effects on BMI and WC. The statistical methodologies were applied, before and after multiple imputation of missing observations, to pooled data as well as to individual data sets for estimates from each study, the latter leading to a metaanalysis. The results from the metaanalysis and the pooling analysis showed that none of the effects were significant at the 0.05 level of significance. Heterogeneity tests showed that the variations of the nonsignificant effects are within the range of sampling variation. Although certain genotypic effects could be population specific, there was no statistically compelling evidence that any of the three LEPR alleles is associated with BMI or waist circumference in the general population.

Adipose Tissue↗
PubMed2001

Testing quantitative traits for association and linkage in the presence or absence of parental data.

Zhu and Elston developed a transmission disequilibrium test for quantitative traits by defining a linear transformation to condition out founder information. The method tests the null hypothesis of no linkage or association and can be applied to general pedigree structures. However, this method requires both genotype and phenotype parental information, which may be difficult to obtain. In this paper, we describe parametric and non-parametric methods to relax this requirement when only nuclear families are sampled. We show that neither method is affected by population stratification in the absence of linkage. The statistical power and validity of the tests are investigated by simulation. A simple simulation method to calculate the power of the nonparametric method is also discussed. In practice, the data may have some families with parental phenotype and genotype information available and some without. We briefly discuss how all the data may be analyzed jointly.

Computer Simulation↗
PubMed2001

A tournament of linkage tests in complex inheritance.

The performance of some weakly parametric linkage tests in common use was compared on 200 replicates of oligogenic inheritance from Genetic Analysis Workshop 10. Each random sample for the quantitative trait was dichotomized at different thresholds and also selected through 2 affected sibs, generating 8 combinations of sample and variable. The variance component program SOLAR performed best with a continuous trait, even in selected samples, when the population mean was used. The sib-pair program SIBPAL2 was best in most other cases when the phenotype product, population mean, and empirical estimates of pair correlations were used. The BETA program that introduced phenotype products was slightly more powerful than maximum likelihood scores under the null hypothesis and approached but did not exceed SIBPAL2 under its optimal conditions. Type I errors generally exceeded expectations from a chi(2) test, but were conservative with respect to bounds on lods. All methods can be improved by use of the population mean, empirical correlations, logistic representation for affection status, and correct lods for samples that favour the null hypothesis. It remains uncertain whether all information can be extracted by weakly parametric methods and whether correction for ascertainment bias demands a strongly parametric model. Performance on a standard set of simulated data is indispensable for recognising optimal methods.

Algorithms↗
PubMed2001

Linkage analysis of alpha 1-antitrypsin deficiency: lessons for complex diseases.

OBJECTIVES: Severe alpha 1-antitrypsin (A1AT) deficiency is the one proven genetic risk factor for chronic obstructive pulmonary disease (COPD). Familial aggregation has been demonstrated for COPD among individuals who do not have A1AT deficiency, but linkage analysis of COPD has not been reported. To investigate the optimal phenotype definitions and analytical methods for the linkage analysis of COPD, we examined a set of 28 A1AT- deficient families containing 155 individuals. We have used the protease inhibitor (PI) type as a genetic marker rather than a disease gene, and we have performed linkage analysis between PI type and serum A1AT level and spirometry-related phenotypes. METHODS: Linkage analysis was performed on the quantitative phenotypes forced expiratory volume at 1 s (FEV(1) as % predicted), the ratio of FEV(1) to forced vital capacity (FEV(1)/FVC as % predicted), and serum A1AT level using the variance component approach in SOLAR, the generalized estimating equation approach in RELPAL, and the model-based classical lod score method in LINKAGE. Linkage analysis with qualitative A1AT and spirometry phenotypes was performed using a model-based method (LINKAGE) and a model-free method (GENEHUNTER). Adjustments for smoking effects were investigated under each method. RESULTS: All of the methods demonstrated linkage of PI type to serum A1AT level. Interestingly, however, the other quantitative phenotypes provided only weak evidence for linkage of PI type to lung disease. Better evidence for linkage of lung disease to PI type was found using a moderate or a mild threshold for the definition of airflow obstruction. CONCLUSIONS: For linkage analysis of spirometry phenotypes in A1AT deficiency, qualitative phenotypes provided stronger evidence for linkage than quantitative phenotypes. Possible contributors to the stronger evidence for linkage to qualitative spirometry phenotypes include the ascertainment scheme and the nonnormality of the pulmonary function data in PI Z subjects. This study provides guidelines for studies of the genetics of COPD unrelated to A1AT deficiency.

Female↗
PubMed2000

Ascertainment adjustment: where does it take us?

It is commonly assumed that the parameter estimates of a statistical genetics model that has been adjusted for ascertainment will estimate parameters in the general population from which the ascertained subpopulation was originally drawn. We show that this is true only in certain restricted circumstances. More generally, ascertainment-adjusted parameter estimates reflect parameters in the ascertained subpopulation. In many situations, this shift in perspective is immaterial: the parameters of interest are the same in the ascertained sample and in the population from which it was drawn, and it is therefore irrelevant to which population inferences are presumed to apply. In other circumstances, however, this is not so. This has important implications, particularly for studies investigating the etiology of complex diseases.

Computer Simulation↗
PubMed2000

Major gene segregation of actinic prurigo among North American Indians in Saskatchewan.

Actinic prurigo is an idiopathic, familial photodermatosis seen especially in American Indians. Segregation analysis was performed on 12 Saskatchewan pedigrees with American Indian ancestry, comprising a total of 1,148 individuals, ascertained via probands diagnosed with actinic prurigo. Although a high degree of familial aggregation has been noted in the past and dominant inheritance has been suggested, no formal segregation analysis has been attempted. Actinic prurigo has a variable age of onset and, therefore, age at the time of censoring must be taken into account in the analysis. However, as these ages of 57% of the unaffected individuals were missing, an algorithm was devised to impute the missing ages from known birth years in the family based on the age differences among relatives and spouses. Using these imputed ages, simple dominant inheritance with incomplete penetrance and a single age of onset distribution was found. The method for imputing the ages at examination was evaluated, as was the correction for ascertainment, by using alternative methods and comparing the results. Regardless of the method used, a dominant mode of inheritance without any multifactorial component remained the best hypothesis.

Adolescent↗
PubMed2000

Genomewide scan for prostate cancer-aggressiveness loci.

The aggressiveness of prostate cancer (PCa) varies widely: some tumors progress to invasive, potentially life-threatening disease, whereas others stay latent for the remainder of an individual's lifetime. The mechanisms resulting in this variability are not yet understood, but they are likely to involve both genetic and environmental influences. To investigate genetic factors, we conducted a genomewide linkage analysis of 513 brothers with PCa, using the Gleason score, which reflects tumor histology, as a quantitative measure of PCa aggressiveness. To our knowledge, this is the first time that a measure of PCa aggressiveness has been directly investigated as a quantitative trait in a genomewide scan. We employed a generalized multipoint Haseman-Elston linkage-analysis approach that regresses the mean-corrected cross product between the brothers' Gleason scores on the estimated proportion of alleles shared by brothers identical by descent at each marker location. Our results suggest that candidate regions on chromosomes 5q, 7q, and 19q give evidence for linkage to PCa-aggressiveness genes. In particular, the strongest signals detected in these regions were at the following markers (with corresponding P values): for chromosome 5q31-33, between markers D5S1480 and D5S820 (P=.0002); for chromosome 7q32, between markers D7S3061 and D7S1804 (P=.0007); and, for chromosome 19q12, at D19S433 (P=.0004). This indicates that one or more of these candidate regions may contain genes that influence the progression of PCa from latent to invasive disease. Identification of such genes would be extremely valuable for elucidation of the mechanism underlying PCa progression and for determination of treatment in men in whom this disease has been diagnosed.

Adult↗
PubMed2000

Multilocus linkage tests based on affected relative pairs.

For complex diseases, recent interest has focused on methods that take into account joint effects at interacting loci. Conditioning on effects of disease loci at known locations can lead to increased power to detect effects at other loci. Moreover, use of joint models allows investigation of the etiologic mechanisms that may be involved in the disease. Here we present a method for simultaneous analysis of the joint genetic effects at several loci that uses affected relative pairs. The method is a generalization of the two-locus LOD-score analysis for affected sib pairs proposed by Cordell et al. We derive expressions for the relative risk, lambdaR, to a relative of an affected individual, in terms of the additive and epistatic components of variance at an arbitrary number of disease loci, and we show how these can be used to fit a likelihood model to the identity-by-descent sharing among pairs of affected relatives in extended pedigrees. We implement the method by use of a stepwise strategy in which, given evidence of linkage to disease at m-1 locations on the genome, we calculate the conditional likelihood curve across the genome for an mth disease locus, using multipoint methods similar to those proposed by Kruglyak et al. We evaluate the properties of our method by use of simulated data and present an application to real data from families with insulin-dependent diabetes mellitus.

Alleles↗
PubMed2000

On the relative sample size required for multiple comparisons.

Multiple comparisons are commonly made in epidemiologic and genetic research. How to appropriately adjust for multiple comparisons remains a controversial issue. This note demonstrates, however, that large increases in the number of comparisons has a limited effect on the sample size required to maintain an experimentwise alpha-level. In particular, the relative sample size required increases only linearly with the logarithm of the number of comparisons made.

Models, Statistical↗
PubMed2000

Two-stage global search designs for linkage analysis II: including discordant relative pairs in the study.

The optimal two-stage designs proposed by Elston [1992, 1994] for a global search of the genome to locate disease genes by linkage analysis typically halve the cost of a study compared to one-stage designs [Elston et al., 1996]. These designs were based on sampling affected pairs of relatives. Usually, using affected relative pairs is more powerful than using discordant relative pairs [Blackwelder and Elston, 1985; Risch 1990b]. However, in certain situations discordant relative pairs can be as powerful as, or even more powerful than, affected relative pairs. In addition, combining discordant pairs with affected pairs provides a control study. In this paper, we investigate optimal two-stage designs when: 1) using only discordant relative pairs, and 2) combining discordant with affected relative pairs. We show that including discordant relatives pairs into the study not only provides a control, but is also cost effective.

Genetic Diseases, Inborn↗
PubMed2000

Two-stage global search designs for linkage analysis I: use of the mean statistic for affected sib pairs.

Two-stage global search designs for linkage analysis using pairs of affected relatives were shown by Elston et al. [1996] to typically halve the cost of a study compared to a one-stage design. The statistic used for testing linkage in that study was based on the proportion of pairs sharing no marker alleles identical by descent (IBD). However, it has been established that the mean statistic often has the greatest power for full sib pairs [Blackwelder and Elston, 1985; Schaid and Nick, 1990; Knapp et al. 1994]. In this paper, we study optimal two-stage global search designs, in the case of affected full sib pairs, when using the mean test statistic to test for linkage. When dominant genetic variance is present, using the mean statistic is usually more cost efficient than using the proportion of pairs sharing no maker alleles IBD; in the case when there is no dominant genetic variance, the mean statistic leads to a better design, in the sense of being more cost-saving, provided that the relative risk ratio for first-degree relatives is small. The effect of heterogeneity and markers' informativeness is also investigated, the latter using the Linkage Information Content value for sibs.

Alleles↗
PubMed2000

Power comparison of regression methods to test quantitative traits for association and linkage.

George et al. [1999 Am J Hum Genet 65:236-245] proposed a regression-based TDT method for quantitative traits consisting of regressing the trait on the parental transmission of a marker allele. Zhu and Elston [2000] also developed a TDT method for quantitative traits by defining a linear transformation to condition out founder information. Both methods test the null hypothesis of no linkage or association and can be applied to general pedigree structures. In this paper, we compare the power of these two methods through simulation, sampling those nuclear families with at least one heterozygous parent. Overall, we find that a variant of Zhu and Elston's method with 2 d.f. is more powerful. However, if the mode of inheritance is known, then a most powerful test with 1 d.f. can be found. All these regression TDT tests require linkage to detect association, but a test that does not require linkage will be more powerful.

Alleles↗