Biomedical subjects

Mary Sara McPeek

Publications and source records attributed to Mary Sara McPeek.

12 recordsLinked to original sources

PubMed2006

Multipoint linkage-disequilibrium mapping with haplotype-block structure.

The HapMap Project is providing a great deal of new information on high-resolution haplotype structure in various human populations. This information has the potential to greatly increase the power of association mapping for a fixed amount of genotyping. A number of methods have been proposed for the identification of haplotype blocks, common haplotypes, and tagging single-nucleotide polymorphisms. Here, we build on this work by developing novel methods for case-control multipoint linkage-disequilibrium (LD) mapping that gain power and speed by making explicit use of the inferred block structure. Specifically, we developed a virtual-variant approach that uses the haplotype-block information to greatly increase power for detection of untyped common variants associated with a trait. Because full multipoint LD mapping can be slow, we exploited the haplotype-block information to develop a fast single-block multipoint mapping method. Our methods are appropriate for genotype data and take into account the uncertainty in phase. We describe the methods in the context of case-parents trios, although they are also applicable to unrelated cases and controls. Our simulations indicate that the most important gains from taking into account the haplotype-block structure at the analysis stage of multipoint LD mapping come from (1) greatly increased power to detect association with untyped variants and (2) greatly improved localization of untyped variants associated with the trait. More-modest gains are obtained in improving power to detect association with a variant that is typed with a moderate amount of missing data. The methods are applied to a Crohn disease data set.

Algorithms↗

PubMed2005

Incorporating interference into linkage analysis for experimental crosses.

The phenomenon of interference in genetic recombination is well-known and studied in a wide variety of organisms. Multilocus linkage analysis, which makes use of recombination patterns among all genetic markers simultaneously, is routinely used with data on humans and experimental organisms to build genetic maps. It is also used to try to determine the genes involved in traits of interest, such as common diseases. Most linkage analyses performed today ignore the occurrence of genetical interference. We present an extension to the Lander-Green algorithm for experimental crosses (backcross and intercross) to incorporate crossover interference according to the chi2 model. Simulation results show the impact of using this model on the accuracy of estimated genetic maps.

Algorithms↗

PubMed2005

Multilocus linkage disequilibrium mapping by the decay of haplotype sharing with samples of related individuals.

We consider the problem of multilocus linkage disequilibrium (LD) mapping of a trait-associated variant from case-control samples in which some individuals may be related. Our method, which we call DHS-R, is an extension of the decay of haplotype sharing (DHS) method of McPeek and Strahs and Strahs and McPeek. The DHS-R method shares the main features of the DHS method: (1) it allows construction of a confidence interval for the location of a trait-associated variant; (2) it allows for missing observations and unphased genotype data, with the uncertainty in the haplotypes taken into account in the analysis; and (3) it allows for heterogeneity, mutation, recombination, and background LD. The main advances of the DHS-R are (1) the ability to include individuals of arbitrary known relationship (including inbreeding) in the case and control samples; (2) an extension to allow partially-phased haplotypes derived from case-parent trio genotype data; and (3) an extension to allow for genotyping error in the model. Our method, which uses a hidden Markov model for likelihood calculation and maximization, has the advantage of being computationally feasible even in a large, complex pedigree. Simulations based on a 13-generation, 1,623-member Hutterite pedigree demonstrate accurate coverage of the confidence intervals for location of the variant. We apply the method to fine-mapping of a susceptibility locus for bronchial hyperresponsiveness (BHR) in the Hutterites. The results confirm the importance of taking into account the relatedness of individuals in LD mapping.

Algorithms↗

PubMed2004

Testing for Hardy-Weinberg equilibrium in samples with related individuals.

When the classical chi(2) goodness-of-fit test for Hardy-Weinberg (HW) equilibrium is used on samples with related individuals, the type I error can be greatly inflated. In particular the test is inappropriate in population isolates where the individuals are related through multiple lines of descent. In this article, we propose a new test for HW (the QL-HW test) suitable for any sample with related individuals, including large inbred pedigrees, provided that their genealogy is known. Performed conditional on the pedigree structure, the QL-HW test detects departures from HW that are not due to the genealogy. Because the computation of the QL-HW test becomes intractable for very polymorphic loci in large inbred pedigrees, a simpler alternative, the GCC-HW test, is also proposed. The statistical properties of the QL-HW and GCC-HW tests are studied through simulations considering a sample of independent nuclear families, a sample of extended outbred genealogies, and samples from the Hutterite population, a North American highly inbred isolate. Finally, the method is used to test a set of 143 biallelic markers spanning 82 genes in this latter population.

Chi-Square Distribution↗

PubMed2004

Are common disease susceptibility alleles the same in outbred and founder populations?

Founder populations have been the subjects of complex disease studies because of their decreased genetic heterogeneity, increased linkage disequilibrium and more homogeneous environmental exposures. However, it is possible that disease alleles identified in founder populations may not contribute significantly to susceptibility in outbred populations. In this study we examine the Hutterites, a founder population of European descent, for 103 polymorphisms in 66 genes that are candidates for cardiovascular or inflammatory diseases. We compare the frequencies of alleles at these loci in the Hutterites to their frequencies in outbred European-American populations and test for associations with cardiovascular disease-associated phenotypes in the Hutterites. We show that alleles at these loci are found at similar frequencies in the Hutterites and in outbred populations. In addition, we report associations between 39 alleles or haplotypes and cardiovascular disease phenotypes (P<0.05), with five loci remaining significant after adjusting for multiple comparisons. These data indicate that this founder population is informative and offers considerable advantages for genetic studies of common complex diseases.

Cardiovascular Diseases↗

PubMed2004

Genome-wide association study identifies ITGB3 as a QTL for whole blood serotonin.

Serotonin has been implicated in common disorders involving the central nervous, gastrointestinal, cardiovascular, and pulmonary systems. We describe the first genome-wide screen to identify quantitative trait loci (QTLs) influencing whole blood serotonin in 567 members of a single large pedigree, using a novel association-based mapping approach. We identified an association between the beta3 integrin (ITGB3) Leu33Pro polymorphism on 17q21 and whole blood serotonin levels (P-value = 9.8 x 10(-5)). This variant explained the evidence for linkage in this region when included as a covariate in the linkage analysis (change in LOD from 1.87 to 0.16), indicating that ITGB3 may be an important serotonin QTL.

Female↗

PubMed2004

Best linear unbiased allele-frequency estimation in complex pedigrees.

Many types of genetic analyses depend on estimates of allele frequencies. We consider the problem of allele-frequency estimation based on data from related individuals. The motivation for this work is data collected on the Hutterites, an isolated founder population, so we focus particularly on the case in which the relationships among the sampled individuals are specified by a large, complex pedigree for which maximum likelihood estimation is impractical. For this case, we propose to use the best linear unbiased estimator (BLUE) of allele frequency. We derive this estimator, which is equivalent to the quasi-likelihood estimator for this problem, and we describe an efficient algorithm for computing the estimate and its variance. We show that our estimator has certain desirable small-sample properties in common with the maximum likelihood estimator (MLE) for this problem. We treat both the case when parental origin of each allele is known and when it is unknown. The results are extended to prediction of allele frequency in some set of individuals S based on genotype data collected on a set of individuals R. We compare the mean-squared error of the BLUE, the commonly used naive estimator (sample frequency) and the MLE when the latter is feasible to calculate. The results indicate that although the MLE performs the best of the three, the BLUE is close in performance to the MLE and is substantially easier to calculate, making it particularly useful for large complex pedigrees in which MLE calculation is impractical or infeasible. We apply our method to allele-frequency estimation in a Hutterite data set.

Algorithms↗

PubMed2003

Novel case-control test in a founder population identifies P-selectin as an atopy-susceptibility locus.

To avoid problems related to unknown population substructure, association studies may be conducted in founder populations. In such populations, however, the relatedness among individuals may be considerable. Neglecting such correlations among individuals can lead to seriously spurious associations. Here, we propose a method for case-control association studies of binary traits that is suitable for any set of related individuals, provided that their genealogy is known. Although we focus here on large inbred pedigrees, this method may also be used in outbred populations for case-control studies in which some individuals are relatives. We base inference on a quasi-likelihood score (QLS) function and construct a QLS test for allelic association. This approach can be used even when the pedigree structure is far too complex to use an exact-likelihood calculation. We also present an alternative approach to this test, in which we use the known genealogy to derive a correction factor for the case-control association chi2 test. We perform analytical power calculations for each of the two tests by deriving their respective noncentrality parameters. The QLS test is more powerful than the corrected chi2 test in every situation considered. Indeed, under certain regularity conditions, the QLS test is asymptotically the locally most powerful test in a general class of linear tests that includes the corrected chi2 test. The two methods are used to test for associations between three asthma-associated phenotypes and 48 SNPs in 35 candidate genes in the Hutterites. We report a highly significant novel association (P=2.10-6) between atopy and an amino acid polymorphism in the P-selectin gene, detected with the QLS test and also, but less significantly (P=.0014), with the transmission/disequilibrium test.

Alleles↗

PubMed2003

Major loci influencing serum triglyceride levels on 2q14 and 9p21 localized by homozygosity-by-descent mapping in a large Hutterite pedigree.

Serum triglyceride (TG) level is a well-known risk factor for cardiovascular disease, a leading cause of morbidity and mortality in Western countries. Although genome-wide scans for TG have been conducted in several populations, few loci have shown strong evidence for linkage. The Hutterites are a founder population, which practices a communal lifestyle that includes a uniformly high-fat, high-cholesterol diet. We measured serum TG in 485 Hutterites >or=14 years old and performed a genome-wide scan to find genetic determinants of the observed variation in TG levels, using mapping methods that take advantage of the extensive inbreeding and linkage disequilibrium (LD) in this single, 1623-member pedigree. We report two highly significant associations with TG levels, alleles at D2S410 on 2q14 (locus P=5.8 x 10(-6), genome-wide P=0.005) and at IFNA on chromosome 9p21 (locus P=4.3 x 10(-5), genome-wide P=0.024). In each case, homozygosity at the locus is associated with low TG levels, suggesting that alleles at nearby loci may protect against high TG levels.

Adult↗

PubMed2002

Quantitative-trait homozygosity and association mapping and empirical genomewide significance in large, complex pedigrees: fasting serum-insulin level in the Hutterites.

We present methods for linkage and association mapping of quantitative traits for a founder population with a large, known genealogy. We detect linkage to quantitative-trait loci (QTLs) through a multipoint homozygosity-mapping method. We propose two association methods, one of which is single point and uses a general two-allele model and the other of which is multipoint and uses homozygosity by descent for a particular allele. In all three methods, we make extensive use of the pedigree and genotype information, while keeping the computations simple and efficient. To assess significance, we have developed a permutation-based test that takes into account the covariance structure due to relatedness of individuals and can be used to determine empirical genomewide and locus-specific P values. In the case of multivariate-normally distributed trait data, the permutation-based test is asymptotically exact. The test is broadly applicable to a variety of mapping methods that fall within the class of linear statistical models (e.g., variance-component methods), under the assumption of random ascertainment with respect to the phenotype. For obtaining genomewide P values, our proposed method is appropriate when positions of markers are independent of the observed linkage signal, under the null hypothesis. We apply our methods to a genome screen for fasting insulin level in the Hutterites. We detect significant genomewide linkage on chromosome 19 and suggestive evidence of QTLs on chromosomes 1 and 16.

Alleles↗

PubMed2002

A statistical method for identification of polymorphisms that explain a linkage result.

Suppose that many polymorphic sites have been identified and genotyped in a region showing strong linkage with a trait. A key question of interest is which site (or combination of sites) in the region influences susceptibility to the trait. We have developed a novel statistical approach to this problem, in the context of qualitative-trait mapping, in which we use linkage data to identify the polymorphic sites whose genotypes could fully explain the observed linkage to the region. The information provided by this analysis is different from that provided by tests of either linkage or association. Our approach is based on the observation that if a particular site is the only site in the region that influences the trait, then-conditional on the genotypes at that site for the affected relatives-there should be no unexplained oversharing in the region among affected individuals. We focus on the affected sib-pair study design and develop test statistics that are variations on the usual allele-sharing methods used in linkage studies. We perform hypothesis tests and derive a confidence set for the true causal polymorphic site, under the assumption that there is only one site in the region influencing the trait. Our method is appropriate under a very general model for how the site influences the trait, including epistasis with unlinked loci, correlated environmental effects within families, and gene-environment interaction. We extend our method to larger sibships and apply it to an NIDDM1 data set.

Alleles↗

PubMed2002

Enhanced pedigree error detection.

Accurate information on the relationships among individuals in a study is critical for valid linkage analysis. We extend the MLLR, EIBD, AIBS and IBS tests for detection of misspecified relationships to a broader range of relative pairs, and we improve the two-stage screening procedure for analyzing large data sets. We have developed software, PREST, which calculates the test statistics and performs the corresponding hypothesis tests for relationship misclassification in general outbred pedigrees. When a potential pedigree error is detected, our companion program, ALTERTEST, can be used to determine which relationships are compatible with the genotype data. Both programs are now freely available on the web.

Computer Simulation↗