Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “statistical genetics”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 577 records · Page 32Linked to original sources
PubMed2004

Breed relationships and definition in British cattle: a genetic analysis.

The genetic diversity of eight British cattle breeds was quantified in this study. In all, 30 microsatellites from the FAO panel of markers were used to characterise the DNA samples from nearly 400 individuals. A variety of methods were applied to analyse the data in order to look at diversity within and between breeds. The relationships between breeds were not highly resolved and breed clusters were not associated with geographical distribution. Analyses also defined the cohesiveness or definition of the various breeds, with Highland, Guernsey and Jersey as the best defined and most distinctive of the breeds.

Animals↗
PubMed2001

Quantitative trait linkage analysis of the liability underlying a common oligogenic disease.

We utilized pedigree discriminant and factor analytic approaches to combine multivariate phenotypic information into a single liability phenotype in the isolate and general populations. We applied two-stage relative-pair quantitative trait linkage analysis to detect genetic contributions to variation in the resulting liability phenotypes. Linkage analysis revealed several regions of suggestive linkage in both the general and isolate populations, the majority of which appear in retrospect to be false positives. A likely explanation is an overall lack of power given that we tested hypotheses in data from only one replicate. However, it may be possible that a construct that ignores affection status when using liability-associated characteristics as indicators of this construct is not the most effective approach in modeling the liability underlying a complex phenotype.

Chromosome Mapping↗
PubMed2004

Power of variance component linkage analysis-II. Discrete traits.

We determine the power of variance component linkage analysis in the case of discrete, dichotomous traits analyzed under a classical liability threshold model. For simplicity we consider randomly ascertained samples and an additive model of variation incorporating a qtl, residual additive genetic factors, and individual-specific random environmental effects. We derive an expression for the power of variance component linkage analysis in arbitrary relative pairs, and compare the power of discrete and quantitative trait linkage analysis in the specific case of sibpairs. The predicted sample sizes required in linkage analysis of sibpairs are confirmed by analysis of simulated data. Unlike the affected-sibpair method, the power of discrete trait variance component analysis increases with trait prevalence. The relative efficiency of a discrete trait for linkage analysis increases with population trait prevalence, but does not exceed about 40% and is typically much less.

Analysis of Variance↗
PubMed2001

Haplotype diversity and linkage disequilibrium at human G6PD: recent origin of alleles that confer malarial resistance.

The frequencies of low-activity alleles of glucose-6-phosphate dehydrogenase in humans are highly correlated with the prevalence of malaria. These "deficiency" alleles are thought to provide reduced risk from infection by the Plasmodium parasite and are maintained at high frequency despite the hemopathologies that they cause. Haplotype analysis of "A-" and "Med" mutations at this locus indicates that they have evolved independently and have increased in frequency at a rate that is too rapid to be explained by random genetic drift. Statistical modeling indicates that the A- allele arose within the past 3840 to 11,760 years and the Med allele arose within the past 1600 to 6640 years. These results support the hypothesis that malaria has had a major impact on humans only since the introduction of agriculture within the past 10,000 years and provide a striking example of the signature of selection on the human genome.

Africa↗
PubMed2004

Allelic frequency of the PAI-1 4G/5G promoter polymorphism in patients with type 2 diabetes mellitus and lack of association with PAI-1 plasma levels.

Plasminogen activator inhibitor-1 (PAI-1) levels were found to be associated with obesity, indicating that adipocytes might influence PAI-1 plasma levels. In addition, the 4G/5G promoter polymorphism of the PAI-1 gene possibly modulates PAI-1 gene transcription and, as a consequence, PAI-1 plasma levels. Metabolic parameters, diabetes complications, PAI-1 plasma levels, and PAI-1 promoter genotypes were determined and were tested for correlation in 547 Caucasian patients with type 2 diabetes. Genotyping was performed by using allele-specific PCR, and PAI-1 plasma levels were measured in 547 well-characterized subjects with type 2 diabetes. The allelic frequencies of the polymorphism (0.56 for the 4G-genotype, 0.44 for the 5G-genotype) were not different from those observed in nondiabetic controls. The PAI-1 concentration was positively associated with MI, but not with the 4G/5G polymorphism. Statistical analysis of metabolic parameters, diabetic complications, and the 4G/5G polymorphism revealed that serum fibrinogen levels were significantly higher in the 4G/4G subgroup compared with the 4G/5G and 5G/5G subgroups. The correlation between serum fibrinogen and 4G allele remained significant, even when additional variables, such as gender, age, BMI, duration of diabetes, and HbA1c, were controlled. In patients with type 2 diabetes mellitus, the PAI-1 4G/5G promoter polymorphism does not predict PAI-1 plasma levels and is not associated with common metabolic parameters besides fibrinogen levels.

Aged↗
PubMedDate not supplied

The covariance between relatives conditional on genetic markers.

The development of molecular genotyping techniques makes it possible to analyze quantitative traits on the basis of individual loci. With marker information, the classical theory of estimating the genetic covariance between relatives can be reformulated to improve the accuracy of estimation. In this study, an algorithm was derived for computing the conditional covariance between relatives given genetic markers. Procedures for calculating the conditional relationship coefficients for additive, dominance, additive by additive, additive by dominance, dominance by additive and dominance by dominance effects were developed. The relationship coefficients were computed based on conditional QTL allelic transmission probabilities, which were inferred from the marker allelic transmission probabilities. An example data set with pedigree and linked markers was used to demonstrate the methods developed. Although this study dealt with two QTLs coupled with linked markers, the same principle can be readily extended to the situation of multiple QTL. The treatment of missing marker information and unknown linkage phase between markers for calculating the covariance between relatives was discussed.

Analysis of Variance↗
PubMed2004

Direct estimation of genetic principal components: simplified analysis of complex phenotypes.

Estimating the genetic and environmental variances for multivariate and function-valued phenotypes poses problems for estimation and interpretation. Even when the phenotype of interest has a large number of dimensions, most variation is typically associated with a small number of principal components (eigen-vectors or eigenfunctions). We propose an approach that directly estimates these leading principal components; these then give estimates for the covariance matrices (or functions). Direct estimation of the principal components reduces the number of parameters to be estimated, uses the data efficiently, and provides the basis for new estimation algorithms. We develop these concepts for both multivariate and function-valued phenotypes and illustrate their application in the restricted maximum-likelihood framework.

Algorithms↗
PubMed2006

Utility of correlation measures in analysis of gene expression.

The role of the correlation structure of gene expression data are two-fold: It is a source of complications and useful information at the same time. Ignoring the strong stochastic dependence between gene expression levels in statistical methodologies for microarray data analysis may deteriorate their performance. However, there is a host of valuable information in the correlation structure that deserves a closer look. A proper use of correlation measures can remedy deficiencies of currently practiced methods that are focused too heavily on strong effects in terms of differential expression of genes. The present paper discusses the utility of correlation measures in microarray data analysis and gene regulatory network reconstruction, along with various pitfalls in both research areas that have been uncovered in methodological studies. These issues have broad applicability to all genomic studies examining the biology, diagnosis, and treatment of neurological disorders.

Animals↗
PubMed1985

A comparison of sib-pair linkage tests for disease susceptibility loci.

An analytical study is conducted of the properties of statistical tests to detect linkage between a disease locus and a very polymorphic marker locus when data on sib pairs are available. In most instances the most powerful test is the test based on the mean number of marker alleles shared identical by descent by the two members of a sib pair, and the most efficient sampling strategy is almost always to sample only pairs with both sibs affected. We show it is valid to use the information from all possible sib pairs as though they came from separate families when data on sibships of size three or larger are available, though more power may be obtained if different weights are given to the different sibship sizes.

Alleles↗
PubMed1996

Testing for homogeneity of Hardy-Weinberg disequilibrium using data sampled from several populations.

Olson (1993, Annals of Human Genetics 57, 291-295) proposed a large-sample test of Hardy-Weinberg equilibrium when genotype data are sampled from several populations with different allele frequencies. The test assumes that a ratio measure of disequilibrium is constant across the populations. In this paper, we consider the problem of testing the assumption of homogeneity of that ratio and propose both a large-sample test and an exact test. The large-sample test is appropriate if sample sizes in all strata are sufficiently large, but is strongly anticonservative if some strata are small. In the latter case, the exact test is preferred and we approximate the P-value of this test using a Markov chain Monte Carlo approach.

Alleles↗
PubMed2002

ALFRED: An allele frequency database for anthropology.

The deluge of data from the human genome project (HGP) presents new opportunities for molecular anthropologists to study human variation through the promise of vast numbers of new polymorphisms (e.g., single nucleotide polymorphisms or SNPs). Collecting the resulting data into a single, easily accessible resource will be important to facilitate this research. We created a prototype Web-accessible database named ALFRED (ALelle FREquency Database, http://alfred.med.yale.edu/alfred/) to store and make publicly available allele frequency data on diverse polymorphic sites for many populations. In constructing this database, we considered many different concerns relating to the types of information needed for anthropology, population genetics, molecular genetics, and statistics, as well as issues of data integrity and ease of access to data. We also developed links to other Web-based databases as well as procedures for others to make links to the data in ALFRED. Here we present an overview of the issues considered and provisional solutions, as well as an example of data already available. It is our hope that this database will be useful for research and teaching in a wide range of fields, and that colleagues from various fields will contribute to making ALFRED an important resource for many studies as yet unforeseen.

Anthropology, Physical↗
PubMed2026

TL-HDMR: a transfer learning framework for advancing equitable causal inference reveals metabolic signatures of stroke across multiple ancestries.

The limited genetic diversity in genome-wide association studies (GWAS) poses a significant challenge to the generalizability and equity of biomedical discoveries. Most causal inferences, particularly from high-dimensional phenomes (e.g. metabolomics), are primarily based on European populations, and their applicability to other ancestries remains uncertain. Traditional multivariable Mendelian randomization (MVMR) methods further struggle in high-dimensional and correlated settings due to collinearity and model instability. To bridge this gap, we present a two-step transfer learning framework for high-dimensional MR (TL-HDMR), designed to enhance causal exposure detection in understudied populations. Our approach leverages the Minimax Concave Penalty for asymptotically unbiased estimation amidst exposure correlations. Crucially, we introduce two novel pre-transfer procedures-HDMR.TSD for sourcing beneficial data and HDMR.PRESSO for filtering pleiotropic instruments-to ensure robust knowledge transfer. Extensive simulations demonstrated TL-HDMR's superior performance in ROC curves and mean absolute error over alternative methods. When applied to identify causal metabolites for stroke across multi-ancestry cohorts (European, East Asian, South Asian, and African), TL-HDMR successfully pinpointed both shared and ethnic-specific causal biomarkers, showcasing its unique capability for equitable causal inference. This work provides a powerful statistical tool that not only addresses critical methodological challenges but also promotes inclusivity and fairness in human health research.

Humans↗
PubMed2004

Linkage analysis using single nucleotide polymorphisms.

We performed multipoint linkage analysis using 83 markers from the SNP Consortium (TSC) SNP linkage map in 3 regions covering 190 cM previously scanned with microsatellite markers and found to be linked to type 2 diabetes. Since the average linkage disequilibrium present in the TSC SNP marker clusters is relatively low, we assumed the intracluster genetic distances were a reasonable small nonzero distance (0.03 cM) and performed linkage analysis using GENEHUNTER PLUS and ASM linkage analysis software. We found that for the pedigree structures and missing data patterns in our samples the average information content in all three regions and the LOD score curves in two regions obtained from the TSC SNP markers were similar to results obtained from microsatellite marker maps with 10 cM average spacing. We also give an algorithm which extends the Lander-Green algorithm to permit multipoint linkage analysis of clusters of tightly linked markers with arbitrarily high levels of intracluster linkage disequilibrium.

Chromosome Mapping↗
PubMed2002

Simulation study of the reliability and robustness of the statistical methods for detecting positive selection at single amino acid sites.

Inferring positive selection at single amino acid sites is of biological and medical importance. Parsimony-based and likelihood-based methods have been developed for this purpose, but the reliabilities of these methods are not well understood. Because the evolutionary models assumed in these methods are only rough approximations to reality, it is desirable that the methods are not very sensitive to violation of the assumptions made. In this study we show by computer simulation that the likelihood-based method is sensitive to violation of the assumptions and produces many false-positive results under certain conditions, whereas the parsimony-based method tends to be conservative. These observations, together with those from previous studies, suggest that the positively selected sites inferred by the parsimony-based method are more reliable than those inferred by the likelihood-based method.

Amino Acid Sequence↗
PubMed1997

Alternative test for linkage between two loci.

When using the maximum likelihood method to test for linkage it is traditionally assumed that the female (theta f) and male (theta m) recombination fractions are equal. However, this assumption is not always realistic. In this paper we present a testing for linkage that does not require this assumption. Specifically, we propose testing for linkage by testing the null hypothesis H0: theta f + theta m = 1 vs. the alternative HA: theta f + theta m < 1, treating theta f - theta m as a nuisance parameter. This leads to a likelihood ratio test statistic that is asymptotically distributed as a chi-square with one degree of freedom. By examining the expected values of the maximum lod scores, we show that for data from phase-known meioses this test can provide a more powerful test for linkage-especially so when theta f not equal to theta m-unless the recombination fraction is zero. For data from phase-unknown meioses, the proposed test is a more powerful test for linkage only for large values of theta f - theta m; otherwise, the traditional test has higher power. Thus, the proposed test can lead to gain in power for detecting linkage when either phase is known for most meioses or when there is a large absolute difference between the male and female recombination fractions.

Chromosome Mapping↗
PubMed2003

The distribution of fitness effects among beneficial mutations.

We know little about the distribution of fitness effects among new beneficial mutations, a problem that partly reflects the rarity of these changes. Surprisingly, though, population genetic theory allows us to predict what this distribution should look like under fairly general assumptions. Using extreme value theory, I derive this distribution and show that it has two unexpected properties. First, the distribution of beneficial fitness effects at a gene is exponential. Second, the distribution of beneficial effects at a gene has the same mean regardless of the fitness of the present wild-type allele. Adaptation from new mutations is thus characterized by a kind of invariance: natural selection chooses from the same spectrum of beneficial effects at a locus independent of the fitness rank of the present wild type. I show that these findings are reasonably robust to deviations from several assumptions. I further show that one can back calculate the mean size of new beneficial mutations from the observed mean size of fixed beneficial mutations.

Data Interpretation, Statistical↗
PubMed2006

Inference of population genetic parameters in metagenomics: a clean look at messy data.

Metagenomic projects generate short, overlapping fragments of DNA sequence, each deriving from a different individual. We report a new method for inferring the scaled mutation rate, theta = 2Neu, and the scaled exponential growth rate, R = Ner, from the site-frequency spectrum of these data while accounting for sequencing error via Phred quality scores. After obtaining maximum likelihood parameter estimates for theta and R, we calculate empirical Bayes quality scores reflecting the posterior probability that each apparently polymorphic site is truly polymorphic; these scores can then be used for other applications such as SNP discovery. For realistic parameter ranges, analytic and simulation results show our estimates to be essentially unbiased with tight confidence intervals. In contrast, choosing an arbitrary quality score cutoff (e.g., trimming reads) and ignoring further quality information during inference yields biased estimates with greater variance. We illustrate the use of our technique on a new project analyzing activated sludge from a lab-scale bioreactor seeded by a wastewater treatment plant.

Bacteria↗
PubMed2005

A multidimensional model of obsessive-compulsive disorder.

OBJECTIVE: Obsessive-compulsive disorder (OCD) is a clinically heterogeneous condition. This heterogeneity can reduce the power and obscure the findings from natural history studies to genome scans, neuroimaging, and clinical trials. The authors review the evidence supporting a multidimensional model of OCD. METHOD: Computerized and manual literature searches were performed to identify factor-analytic studies of obsessive-compulsive symptoms before data from disciplines that bear on the potential usefulness of these dimensions were considered. Selection criteria included the novelty and importance of studies and their relevance to outcomes of interest to well-informed mental health professionals. RESULTS: Twelve factor-analytic studies involving more than 2,000 patients were identified that consistently extracted at least four symptom dimensions: symmetry/ordering, hoarding, contamination/cleaning, and obsessions/checking. These dimensions were associated with distinct patterns of comorbidity, genetic transmission, neural substrates, and treatment response. The evidence supporting the hoarding dimension is particularly robust. CONCLUSIONS: The complex clinical presentation of OCD can be summarized with a few consistent, temporally stable symptom dimensions. These can be understood as a spectrum of potentially overlapping syndromes that may 1) coexist in any patient, 2) be continuous with normal obsessive-compulsive phenomena, and 3) extend beyond the traditional nosological boundaries of OCD. Although the dimensional structure of obsessive-compulsive symptoms is imperfect, this quantitative approach to phenotypic traits has the potential to advance our understanding of OCD and may aid in the identification of more robust endophenotypes. The need for a dimensional rating scale and suggestions for future research aimed at reducing the burden of this disorder are discussed.

Brain↗