Biomedical subjects

S C Heath

Publications and source records attributed to S C Heath.

17 recordsLinked to original sources

PubMed2000

A cholesterol-lowering gene maps to chromosome 13q.

A cholesterol-lowering gene has been postulated from familial hypercholesterolemia (FH) families having heterozygous persons with normal LDL levels and homozygous individuals with LDL levels similar to those in persons with heterozygous FH. We studied such a family with FH that also had members without FH and with lower-than-normal LDL levels. We performed linkage analyses and identified a locus at 13q, defined by markers D13S156 and D13S158. FASTLINK and GENEHUNTER yielded LOD scores >5 and >4, respectively, whereas an affected-sib-pair analysis gave a peak multipoint LOD score of 4.8, corresponding to a P value of 1.26x10-6. A multipoint quantitative-trait-locus (QTL) linkage analysis with maximum-likelihood binomial QTL verified this locus as a QTL for LDL levels. To test the relevance of this QTL in an independent normal population, we studied MZ and DZ twin subjects. An MZ-DZ comparison confirmed genetic variance with regard to lipid concentrations. We then performed an identity-by-descent linkage analysis on the DZ twins, with markers at the 13q locus. We found strong evidence for linkage at this locus with LDL (P<.0002), HDL (P<.004), total cholesterol (P<.0002), and body-mass index (P<.0001). These data provide support for the existence of a new gene influencing lipid concentrations in humans.

Adult↗

PubMed2000

An analytic solution to single nucleotide polymorphism error-detection rates in nuclear families: implications for study design.

Recently, there has been increased interest in using Single Nucleotide Polymorphisms (SNPs) as a method for detecting genes for complex traits. SNPs are diallelic markers that have the potential to be inexpensively produced using chip technology. It has been suggested that SNPs will be beneficial in study designs that utilize trio data (father, mother, child). In our previous work, we calculated the probability of detecting Mendelian errors at a SNP locus for a trio randomly selected from a population in Hardy-Weinberg equilibrium. The highest error-detection rate was 30%. Here we investigate the error-detection rate when additional sibs are genotyped. We define an error to be a change from a 1 allele to a 2 allele, or vice versa. Typing one additional sib increases the detection rate on average by 10-13%. Typing two additional sibs increases the detection rate on average by 14-19%. The increase in the detection rate is dependent on the allele frequencies. Equal allele frequencies produce the lowest detection rates, independent of true error rates and number of offspring genotyped. Typing additional siblings not only improves error-detection rates, but can also provide additional linkage information. In order to increase linkage information and error-detection rates, at least two additional siblings should be ascertained when available.

Alleles↗

PubMed1999

Distribution of alleles of the methylenetetrahydrofolate reductase (MTHFR) C677T gene polymorphism in familial spina bifida.

Spina bifida cystica (SB) is one of the most common and disabling of birth defects. Folic acid supplementation in mothers during the periconceptional period has been shown to prevent more than 70% of neural tube defects (NTD) including SB. However, the mechanism is unknown. We tested a series of multicase SB families in which 224 individuals were genotyped and a group of 215 unrelated unaffected (external) control individuals for association of SB with the T allele of methylenetetrahydrofolate reductase (MTHFR) C677T polymorphism that produces a heat-labile enzyme protein. The data were analyzed using first the transmission/disequilibrium test (TDT) and second a modified case-control study design with Monte Carlo sampling methods. No association of SB with the MTHFR T allele was found by either method. Presently, association between SB and the T allele has been found in four studies, a Dutch study, an Irish study, a North American study, and an Italian study. But no association was found in four other studies, a British study, a French study, a Turkish study, and a German study. A California population-based study found only modestly increased risk of SB with this allele that was not significant at the P < 0.05 level. The present study finds no evidence of the association. Only one other study, the German study, has used TDT analysis. The present study is the first to use a modified case-control study design with Monte Carlo sampling methods to test this association. Thus, it appears that the MTHFR T allele is a risk factor for SB in some populations but not others. Major genetic risk factors for folate-related SB remain to be found.

Alleles↗

PubMed1999

Searching for alcoholism susceptibility genes using Markov chain Monte Carlo methods.

Markov chain Monte Carlo (MCMC) methods offer a rapid parametric approach that can test for linkage throughout the entire genome. It has an advantage similar to nonparametric methods in that the model does not have to be completely specified a priori. However, unlike nonparametric methods, there are no limitations on pedigree size and MCMC methods can also handle relatively complex pedigree structures. In addition MCMC methods can be used to carry segregation analysis in order to answer questions on the genetic components of a disease phenotype. Segregation analysis gave evidence for between two and eight alcoholism susceptibility loci, each having a modest effect on the phenotype. MCMC methods were used to map alcoholism loci using the phenotypes ALDX1 (DSM-III-R and Feighner criteria) and ALDX2 (World Health Organization diagnosis ICD-10 criteria). There was mild evidence for quantitative trait loci on chromosomes 2, 10, and 11.

Adolescent↗

PubMed1999

Power loss for multiallelic transmission/disequilibrium test when errors introduced: GAW11 simulated data.

Many researchers are considering the use of transmission/disequilibrium tests (TDT) for trios of genotypes (father, mother, child) as a method for localizing genes associated with complex diseases. We evaluate the effect of random errors (allele changes) in trios on the power to detect linkage. For a marker in the simulated data set, one allele is associated with the fictitious disease in a certain subpopulation. For the data as given (no errors), our power to detect linkage using the multiallelic TDT (TDTmhet) is 68% (critical p-value set at 0.0001). We introduce errors into trios at various rates (1%, 5%, or 10%), remove only trios displaying mendelian inconsistencies, and recalculate power to detect linkage. Our principal finding is that there is power loss to detect linkage with the TDTmhet when errors are introduced. We observe power losses of 8%, 16%, and 48% for error rates of 1%, 5%, and 10%, respectively. To determine the source of the power loss, we perform Monte Carlo simulations. At the 1% and 5% rates, we conclude that power loss is due primarily to loss in sample size. At the 10% rate, we observe substantial power loss due to error introduction in addition to sample size reduction. We also determine, given a particular error rate, the probability that we detect errors if we use only mendelian consistency as a check. We find that the mean detection rates for the data sets with 1%, 5%, or 10% error rates are 58%, 60%, and 62%, respectively. As a result, the apparent error rate appears to be almost half the true error rate. Based on these results, we recommend that researchers maintain error rates below 5% when using the TDTmhet for linkage, use additional methods beyond mendelian consistency checks when searching for errors in their data, and modify sample size calculations when accounting for errors in their genotype data.

Alleles↗

PubMed1999

Multipoint oligogenic analysis of age-at-onset data with applications to Alzheimer disease pedigrees.

It is usually difficult to localize genes that cause diseases with late ages at onset. These diseases frequently exhibit complex modes of inheritance, and only recent generations are available to be genotyped and phenotyped. In this situation, multipoint analysis using traditional exact linkage analysis methods, with many markers and full pedigree information, is a computationally intractable problem. Fortunately, Monte Carlo Markov chain sampling provides a tool to address this issue. By treating age at onset as a right-censored quantitative trait, we expand the methods used by Heath (1997) and illustrate them using an Alzheimer disease (AD) data set. This approach estimates the number, sizes, allele frequencies, and positions of quantitative trait loci (QTLs). In this simultaneous multipoint linkage and segregation analysis method, the QTLs are assumed to be diallelic and to interact additively. In the AD data set, we were able to localize correctly, quickly, and accurately two known genes, despite the existence of substantial genetic heterogeneity, thus demonstrating the great promise of these methods for the dissection of late-onset oligogenic diseases.

Age Factors↗

PubMed1999

Detecting QTLs for uni- and bipolar disorder using a variance component method.

The objective of this study was to use a robust variance component method to analyse unipolar and bipolar disorder in a large Scottish extended family (n = 168) in which linkage between markers and disease has been previously reported on the short arm of chromosome 4. Data consisted of diagnosed clinical uni- or bipolar disorder on 143 individuals, with microsatellite marker information on 109 of these individuals. The incidence of unipolar and bipolar disorder in the family was 17/143, and 11/143, respectively. Eleven linked markers on chromosome 4, spanning a region of approximately 26 cM, were used in the analysis. The statistical analysis was performed in two steps. First, pairwise identify-by-descent (IBD) coefficients for all individuals in the pedigree were calculated at 1 cM intervals, using all marker data simultaneously, with a Monte Carlo Markov Chain algorithm. Second, the variance in the trait of interest was partitioned using residual maximum likelihood (REML). Three components of variance were estimated: (i) a genetic component associated with the average relationship between individuals using the numerator relationship matrix, (ii) a genetic component associated with a chromosome location using the estimated IBD coefficients, and (iii) a residual component. The test statistic (LOD score) was calculated from the maximum likelihood of the full model, fitting all three variance components, and the maximum likelihood value from the reduced model, fitting a polygenic and residual component. The largest LOD scores (maximum LOD = 5.9), were found in a region spanning about 10 cM, when the trait was defined as the occurrence of either uni- or bipolar disorder. The putative QTL explained about 25% of the total variation in the trait.

Bipolar Disorder↗

PubMed1999

True pedigree errors more frequent than apparent errors for single nucleotide polymorphisms.

Single nucleotide polymorphisms (SNPs) are currently being developed for use in disequilibrium analyses. These SNPs consist of two alleles with varying degrees of polymorphism. A natural design for use with SNPs is the 'haplotype relative risk' sampling design in which a father, mother, and child are typed at an SNP locus. Given such a trio of genotypes, we ask: what is the probability that a pedigree error (a change from one allele to the other) at an SNP locus will be detected using only Mendel's laws as a check? We calculate the probability of detecting such errors for a hypothetical SNP locus with varying degrees of polymorphism and for various true error rates. For the sets of allele frequencies considered, we find that the detection rates range between 25 and 30%, the detection rate being lowest when the two alleles have equal frequencies and the highest when one allele has a frequency of 10%. Based on this detection rate, we determine that the true error rate is roughly 3.3-4 times that of the apparent error rate at an SNP locus. The greatest discrepancy between true and apparent error rates occurs when allele frequencies are equal.

Alleles↗

PubMed1999

Heterogeneity for multiple disease loci in linkage analysis.

Many mendelian traits show heterogeneity; that is, the disease phenotype in different families may be caused by genes at different locations. In linkage analysis, this admixture type of heterogeneity (locus heterogeneity) has often been accommodated with one of the HOMOG programs, which thus far have been restricted to at most two disease gene locations. Here, an extension to an arbitrary number of disease locations is described. It has been implemented in a computer program, HOMOGM. This approach is also suitable as an approximation to the situation of complex traits, in which multiple disease genes may occur in the same family.

Algorithms↗

PubMed1999

Genetic background determines the extent of atherosclerosis in ApoE-deficient mice.

Two strains of ApoE-deficient mice were found to have markedly different plasma lipoprotein profiles and susceptibility to atherosclerosis when fed either a low-fat chow or a high-fat Western-type diet. FVB/NJ ApoE-deficient (FVB E0) mice had higher total cholesterol, HDL cholesterol, ApoA1, and ApoA2 levels when compared with C57BL/6J ApoE-deficient (C57 E0) mice. At 16 weeks of age, mean aortic root atherosclerotic lesion area was 7- to 9-fold higher in chow diet-fed C57 E0 mice and 3.5-fold higher in Western diet-fed C57 E0 mice compared with FVB E0 mice fed similar diets. Lesion area in chow diet-fed first-generation mice from a strain intercross was intermediate in size compared with parental values. The distribution of the lesion area in 150 chow diet-fed second-generation progeny spanned the range of the lesion area in both parental strains. There were no correlations between total cholesterol, non-HDL cholesterol, HDL cholesterol, ApoA1, ApoA2, ApoJ, or anti-cardiolipin antibodies and lesion area in the second-generation progeny. Thus, a genomic approach may succeed in identifying the genes responsible for the variation in atherosclerosis susceptibility in these 2 strains of ApoE-deficient mice, which could not be explained by measured plasma parameters.

Animals↗

PubMed1997

MCMC segregation and linkage analysis.

Our objective was to infer the genetic model for the quantitative traits using a variety of methods developed in our group. Only a single data set was analyzed in any one analysis, although some comparison between data sets was made. In addition, the simulated model was not known during the course of the analysis. Basic modeling and segregation analyses for the five quantitative traits was followed by several simple genome scans to indicate areas of interest. A Markov chain Monte Carlo (MCMC) multipoint quantitative trait locus (QTL) mapping approach was then used to estimate the posterior probabilities of linkage of QTL to each chromosome simultaneously with trait model parameters, and to further localize the genes. Comparisons between the nuclear family and pedigree data sets indicated a greater power for QTL detection and mapping with the pedigree data sets. Even with the pedigree data, however, precise localization of the QTL did not appear to be possible using single replicate data sets. Two of the three genes with effects on trait Q1 were detected by the MCMC method.

Chromosome Mapping↗

PubMed1997

Mapping quantitative trait loci for body weight on the X chromosome in mice. II. Analysis of congenic backcrosses.

In a QTL mapping study with an F2 population of mice, we have shown that one or more sex-linked factors account for a large part of the divergence between mouse lines selected for high and low body weight. Here, we describe a study undertaken to map the putative X-linked quantitative trait loci (QTLs) by backcrossing segments of chromosome from the high line onto an inbred line derived from the low line, thereby removing possible contributions from the autosomes and linked segments of the X chromosome. Sublines containing a regional at the proximal end of the X chromosome were found to be associated with large differences in body weight, and to account for almost all the difference between the lines. A Markov chain Monte Carlo based multipoint linkage analysis incorporating the available marker and phenotypic information from the backcross pedigree was used to map the QTL to a region of about 6 cM. There was no evidence for QTLs elsewhere on the chromosome. The estimated QTL effect is approximately 20% of mean body weight in males and females at 10 weeks. From results obtained from this study and the accompanying F2 analysis, we conclude the presence of a single factor for body weight localizing to about position (+/- SE) 26.4 +/- 1.2 cM on the X chromosome, which increases body weight by approximately 18% at 10 weeks. A strategy to positionally clone the QTL is discussed.

Animals↗

PubMed1997

Markov chain Monte Carlo segregation and linkage analysis for oligogenic models.

A new method for segregation and linkage analysis, with pedigree data, is described. Reversible jump Markov chain Monte Carlo methods are used to implement a sampling scheme in which the Markov chain can jump between parameter subspaces corresponding to models with different numbers of quantitative-trait loci (QTL's). Joint estimation of QTL number, position, and effects is possible, avoiding the problems that can arise from misspecification of the number of QTL's in a linkage analysis. The method is illustrated by use of a data set simulated for the 9th Genetic Analysis Workshop; this data set had several oligogenic traits, generated by use of a 1,497-member pedigree. The mixing characteristics of the method appear to be good, and the method correctly recovers the simulated model from the test data set. The approach appears to have great potential both for robust linkage analysis and for the answering of more general questions regarding the genetic control of complex traits.

Computer Simulation↗

PubMed1997

Markov chain Monte Carlo methods for radiation hybrid mapping.

The ordering of genetic loci is central to genetic mapping at all levels. Markov chain Monte Carlo (MCMC) techniques can provide estimates of the posterior density of orders while accounting naturally for missing data, data errors, and unknown parameters. MCMC sampling schemes have been proposed for mapping problems such as linkage mapping and radiation hybrid mapping. The sampling schemes tend, however, to suffer from poor mixing caused by strong correlations between the model parameters. The method described here investigates the effect of using a modified sampling scheme, simulated tempering, on the mixing characteristics of the Markov chain. The method is illustrated by the analysis of haploid radiation hybrid mapping data; the principles are, however, applicable to a range of mapping problems. The results demonstrate that simulated tempering greatly improves the performance of the MCMC sampling scheme. For the radiation hybrid problem, the approach is probably not suitable for simultaneously ordering very large number of loci (> 100); it could, however, be useful for fine scale mapping of subsections of chromosomes.

Algorithms↗

PubMed1997

Imperfect genes, Fisherian mutation and the evolution of sex.

In this paper we present a mathematical model of mutation and selection that allows for the coexistence of multiple alleles at a locus with very small selective differences between alleles. The model also allows for the determination of fitness by multiple loci. Models of this sort are biologically plausible. However, some previous attempts to construct similar models have assumed that all mutations produce a decrease in fitness, and this has led to a tendency for the average fitness of population members to decline when population numbers are finite. In our model we incorporate some of the ideas of R. A. FISHER, so that both deleterious and beneficial mutations are possible. As a result, average fitness tends to approach a stationary distribution. We have used computer simulation methods to apply the Fisherian mutation model to the problem of the evolution of sex and recombination. The results suggest that sex and recombination can provide very large benefits in terms of average fitness. The results also suggest that obligately sexual species will win ecological competitions with species that produce a substantial fraction of their offspring asexually, so long as the number of sites under selection within the genomes of the competing species is not too small and the population sizes are not too large. Our model focuses on fertility selection in an hermaphroditic plant. However, the results are likely to generalize to a wide variety of other situations as well.

Biological Evolution↗

PubMed1995

Rates of change of genetic parameters of body weight in selected mouse lines.

A method based on the animal model is described which allows the estimation of continuous changes in variance components over time using restricted maximum likelihood (REML). The method was applied to the analysis of a selection experiment in which a foundation population formed from a cross between two inbred strains of mice (C57BL/6J and DBA/2J) was divergently selected for 6 week body weight over 20 generations. The analysis suggested that there was an increase in phenotypic variance of about 50% in the low selected lines over the course of the experiment which was attributed to increases in the environmental and additive variance components. Variance changes in the High selected lines were generally smaller than in the Low lines, although there was an estimated 20% increase in the environmental variance. Simple models to explain these effects involving dominance, linkage and epistasis were explored. Testing which of these was responsible for the variance changes noted in this experiment (if any) is difficult, although the epistasis and dominance models require less stringent conditions than the linkage model, and the dominance model is supported by evidence of heterosis in the F1.

Analysis of Variance↗

PubMedDate not supplied

Generating consistent genotypic configurations for multi-allelic loci and large complex pedigrees.

Markov chain Monte Carlo techniques have been applied to many different genetic analysis problems. Gibbs sampling in particular has been widely used because of its simplicity and because it can be extended to arbitrarily complex pedigrees and genetic models (albeit with modifications for multi-allelic loci). Gibbs sampling requires an initial genotypic configuration, consistent with observed data, the generation of which is not trivial with large complex pedigrees and multi-allelic loci. A method to generate feasible genotype configurations in these circumstances using a combination of peeling and genotype elimination is described. The method is illustrated using two complex multi-generation pedigrees, one real and one simulated, each partially typed for one highly polymorphic marker locus.

Alleles↗