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PubMed2006

Integrated analysis of genetic data with R.

Genetic data are now widely available. There is, however, an apparent lack of concerted effort to produce software systems for statistical analysis of genetic data compared with other fields of statistics. It is often a tremendous task for end-users to tailor them for particular data, especially when genetic data are analysed in conjunction with a large number of covariates. Here, R (http://www.r-project.org), a free, flexible and platform-independent environment for statistical modelling and graphics is explored as an integrated system for genetic data analysis. An overview of some packages currently available for analysis of genetic data is given. This is followed by examples of package development and practical applications. With clear advantages in data management, graphics, statistical analysis, programming, internet capability and use of available codes, it is a feasible, although challenging, task to develop it into an integrated platform for genetic analysis; this will require the joint efforts of many researchers.

Algorithms↗
PubMed2006

Statistical methods for the analysis of genetic association studies.

This paper applies a retrospective logistic regression model (Prentice, 1976) using a sandwich variance estimator (White, 1982; Zeger et al. 1985) to genetic association studies in which alleles are treated as dependent variables. The validity of switching the positions of allele and trait variables in the regression model is ensured by the invariance property of the odds ratio. The approach is shown to be able to accommodate many commonly seen designs, matched or unmatched alike, having either binary or quantitative traits. The resultant score statistic has potentially higher power than those that have previously appeared in the genetics literature. As a regression model in general, this approach may also be applied to incorporate covariates. Numerical examples implemented with standard software are presented.

Alleles↗
PubMedDate not supplied

Group-structured genetic models in analyses of the population and behavioral ecology of poikilothermic vertebrates.

Estimates of gene correlations among individuals within and among populations are frequently derived from statistical analyses of genetic data (e.g., F statistics). These measures can be important tools in molecular ecology and conservation, and offer important insights into population breeding structure. Using recently derived theory developed for group-structured populations, we show that fixation indices, when combined with basic population ecological and demographic data can be used to investigate population mating systems and to predict dispersal rates, trajectories and asymptotic levels of fixation indices, and effective population size. Four case studies of poikilothermic vertebrates are used to demonstrate the broad utility of evolutionary and ecological inferences afforded by group-structured models.

Animals↗
PubMed1989

Genetic differentiation among some endogamous populations of Andhra Pradesh, India.

Endogamous populations belonging to Brahmin, Vysya, Mala, and Madiga castes of Andhra Pradesh, South India, were investigated for certain red cell enzyme and serum protein genetic markers. Frequency values were statistically analyzed to assess genetic variation among the populations. Average heterozygosity of ten loci and genetic diversity within and between the populations were calculated by using the methods of Nei. Nei's index was used to calculate genetic distances between the pairs of populations. A dendrogram was drawn adapting the modified unweighted pair group method suggested by Li, which agreed with the history of the populations.

Blood Group Antigens↗
PubMed1995

Genetic heterogeneity among the Hindus and their relationships with the other "Caucasoid" populations: new data on Punjab-Haryana and Rajasthan Indian states.

The genetic structure of Rajasthan Hindus and Punjab-Haryana Hindus and Sikhs has been studied for ABO, RH, APOC2, C6, C7, F13A, F13B, HP, ORM1, ACP1, ADA, AK1, ESD, GLO1, PGD, PGM1 subtyping, and PGP. This is the first genetic survey on Hindus of Rajasthan. Furthermore, many of these markers have never been studied on Hindus before (APOC2, C6, C7, F13A, F13B, ORM1, PGP). These data, together with those previously available for Hindus, have been utilized to analyze the within-Hindus genetic heterogeneity by RST statistic and correspondence analysis. The genetic relationships of Hindus to other Causcasoid populations were also investigated. In the first analysis, two eastern states (Orissa and Andhra Pradesh) were found to be quite separate from each other and clearly distinct from the northwestern and western states. Out of the markers which could not be utilized in this analysis, PGM1 subtyping turned out to discriminate between the Dravidian-speaking and the Indo-Aryan-speaking Hindus. The second analysis shows a clear-cut separation of Hindus from Europeans, with Near Eastern and Middle Eastern populations genetically in an intermediate position.

Blood Group Antigens↗
PubMed2006

No contribution of angiotensin-converting enzyme (ACE) gene variants to severe obesity: a model for comprehensive case/control and quantitative cladistic analysis of ACE in human diseases.

Candidate gene analyses are often inconclusive owing to genetic or phenotypic heterogeneity, low statistical power, selection of nonfunctional SNPs, and inadequate statistical analysis of the genetic architecture. Angiotensin-converting enzyme (ACE) is involved in adipocyte growth and function and the ACE-processed angiotensin II inhibits adipocyte differentiation. Associations between body mass index (BMI) and ACE polymorphisms have been reported in general populations, but the contribution to severe obesity of this gene, which is located under an obesity genome-scan linkage peak on 17q23, is unknown. ACE is one of the most studied genes and markers responsible for variation in circulating ACE enzyme levels have been extensively characterised. Eight of these variants were genotyped in 1054 severely obese cases and 918 nonobese controls, as well as 116 nuclear families from the genome scan (n=447), enabling the known clades to be inferred. Qualitative analysis of individual single-nucleotide polymorphisms (SNPs), haplotypes, clades, and diploclades demonstrated no significant associations (P<0.05) after minimal correction for multiple testing. Quantitative analysis of clades and diploclades for BMI, waist-to-hip ratio, or ZBMI in children were also not significant. This rigorous, large-scale study of common, well-defined, severe polygenic obesity provides strong evidence that functionally relevant sequence variation in ACE, whether it is defined at the level of SNPs, haplotypes, or clades, is not associated with severe obesity in French Caucasians. Such a study design exemplifies the strategy needed to clearly define the contribution of the ACE gene to the plethora of complex genetic diseases where weak associations have been previously reported.

Case-Control Studies↗
PubMed2005

Mechanistic mapping of ontogenetic growth based on biological principles.

Ontogenetic growth can be described by mathematical equations constructed on the goodness of fit. Recently, the biological mechanism underlying mathematical growth equations has been explored using basic cellular properties. Here, we derive a general statistical model for understanding the genetic regulation of ontogenetic growth by integrating those biologically-proven meaningful growth equations into a quantitative trait locus (QTL) mapping framework. We can characterize the dynamic patterns of effects of QTL governing growth curves and estimate the global effect of the underlying QTL throughout the entire course of growth. The model provides the basis for deciphering genetic relationships for growth rates and the timing of life history events for any kind of organisms.

Animals↗
PubMed2001

Towards the genetic basis of periodic catatonia: pedigree sample for genome scan I and II.

In a genome-wide linkage study, we mapped two major susceptibility loci for periodic catatonia, a phenotype with qualitative disturbances of the psychomotor sphere and a morbidity risk of 26.9% in first-degree relatives of index cases, to chromosome 15q15, and to chromosome 22q13 using nonparametric as well as parametric (autosomal dominant model) analyses. The study included 12 multiplex pedigrees with 135 individuals, among them 57 affected persons. A second genome scan is in progress investigating four families with 21 affected individuals, aiming to confirm linkage results. Age at onset patterns as well as the clinical outcome were similar among affected individuals in both sets of families. Within the pedigrees we observed no physical diseases segregating with periodic catatonia. Under the assumption of genetic homogeneity, the statistical power to detect LOD scores > or = 2.0 was 98.5% in the first set of families, and 57.9% in the second set. Thus, the panel of multiplex pedigrees segregating periodic catatonia seems to represent a homogenous clinical sample, and possesses sufficient statistical power to delineate and confirm linkage to major genetic loci for periodic catatonia.

Adult↗
PubMed2007

Systematic pathway analysis using high-resolution fitness profiling of combinatorial gene deletions.

Systematic genetic interaction studies have illuminated many cellular processes. Here we quantitatively examine genetic interactions among 26 Saccharomyces cerevisiae genes conferring resistance to the DNA-damaging agent methyl methanesulfonate (MMS), as determined by chemogenomic fitness profiling of pooled deletion strains. We constructed 650 double-deletion strains, corresponding to all pairings of these 26 deletions. The fitness of single- and double-deletion strains were measured in the presence and absence of MMS. Genetic interactions were defined by combining principles from both statistical and classical genetics. The resulting network predicts that the Mph1 helicase has a role in resolving homologous recombination-derived DNA intermediates that is similar to (but distinct from) that of the Sgs1 helicase. Our results emphasize the utility of small molecules and multifactorial deletion mutants in uncovering functional relationships and pathway order.

DEAD-box RNA Helicases↗
PubMedDate not supplied

Microsatellite variation and differentiation in North Atlantic eels.

We screened 11 populations of American, European, and Icelandic eels (Anguillidae) for allelic variation and genetic divergence at six polymorphic microsatellite loci. Within either of the two recognized Anguilla species in the North Atlantic (rostrata in the Americas, anguilla in Europe), population genetic structure was statistically significant but weak; fully 95% of the total genetic variation was present within geographic locales rather than distributed among them. The two Anguilla species also overlap greatly in allelic frequencies, so the available data proved ineffective for addressing hypotheses about the possible hybrid origins of some Icelandic eels. The overlapping microsatellite profiles contrast with nearly diagnostic species differences documented previously in allozymes and mtDNA. This and similar empirical findings in several other species support theoretical concerns that homoplasy (convergent evolution) in allelic states can compromise the utility of rapidly mutating microsatellite loci for certain types of microevolutionary questions regarding gene flow and species differences.

Anguilla↗
PubMed1998

Measuring departures from Hardy-Weinberg: a Markov chain Monte Carlo method for estimating the inbreeding coefficient.

Many well-established statistical methods in genetics were developed in a climate of severe constraints on computational power. Recent advances in simulation methodology now bring modern, flexible statistical methods within the reach of scientists having access to a desktop workstation. We illustrate the potential advantages now available by considering the problem of assessing departures from Hardy-Weinberg (HW) equilibrium. Several hypothesis tests of HW have been established, as well as a variety of point estimation methods for the parameter which measures departures from HW under the inbreeding model. We propose a computational, Bayesian method for assessing departures from HW, which has a number of important advantages over existing approaches. The method incorporates the effects-of uncertainty about the nuisance parameters--the allele frequencies--as well as the boundary constraints on f (which are functions of the nuisance parameters). Results are naturally presented visually, exploiting the graphics capabilities of modern computer environments to allow straightforward interpretation. Perhaps most importantly, the method is founded on a flexible, likelihood-based modelling framework, which can incorporate the inbreeding model if appropriate, but also allows the assumptions of the model to he investigated and, if necessary, relaxed. Under appropriate conditions, information can be shared across loci and, possibly, across populations, leading to more precise estimation. The advantages of the method are illustrated by application both to simulated data and to data analysed by alternative methods in the recent literature.

Algorithms↗
PubMed2000

Hitchhiking under positive Darwinian selection.

Positive selection can be inferred from its effect on linked neutral variation. In the restrictive case when there is no recombination, all linked variation is removed. If recombination is present but rare, both deterministic and stochastic models of positive selection show that linked variation hitchhikes to either low or high frequencies. While the frequency distribution of variation can be influenced by a number of evolutionary processes, an excess of derived variants at high frequency is a unique pattern produced by hitchhiking (derived refers to the nonancestral state as determined from an outgroup). We adopt a statistic, H, to measure an excess of high compared to intermediate frequency variants. Only a few high-frequency variants are needed to detect hitchhiking since not many are expected under neutrality. This is of particular utility in regions of low recombination where there is not much variation and in regions of normal or high recombination, where the hitchhiking effect can be limited to a small (<1 kb) region. Application of the H test to published surveys of Drosophila variation reveals an excess of high frequency variants that are likely to have been influenced by positive selection.

Animals↗
PubMed1995

Biological effect monitoring in industrial workers following incidental exposure to high concentrations of ethylene oxide.

Peripheral blood from four groups of seven workers from a chemical manufacturing plant in The Netherlands was analyzed for hemoglobin adducts in erythrocytes and for hprt mutants, micronuclei and SCEs in lymphocytes. Group I workers were incidentally exposed to acute high doses of ethylene oxide ranging from 52 to 785 mg/m3. Group II and III workers were chronically exposed to low doses of ethylene oxide for < 5 years or > 15 years respectively. Group IV workers served as unexposed controls and came from the Occupational Health Department. Hemoglobin adduct levels in group I workers were very high and ranged from 1461 to 19913 pmol HOEtVal/g Hb approximately 1 month after the accident. HOEtVal values for group II and III workers fluctuated between 0 and 190 pmol/g Hb corresponding with average EtO exposure levels in the range of < 0.01 and 0.06 mg/m3 EtO. The statistical analysis of the genetic data did not reveal any statistically significant differences between any combination of worker groups. The genetic tests for group I workers were performed on blood samples collected 89-180 days after the incidental exposure. The absence of enhanced frequencies of mutations, micronuclei and SCEs suggests that significant induction of hprt mutations in vivo did not occur and that persistent preclastogenic lesions were not present in significant amounts when the exposed lymphocytes were put in culture to visualize any induced cytogenetic damage. This finding implies that the incidental exposure to high concentrations of EtO did not cause any measurable permanent mutational/cytogenetic damage in exposed lymphocytes.

Accidents, Occupational↗
PubMed1992

Statistical methods for short-term tests in genetic toxicology: the first fifteen years.

Short-term tests (STTs) for detecting and assessing genotoxic or mutagenic effects have catalyzed the development of biostatistical methods for more than one decade. Most notably, the Ames Salmonella/microsome assay created statistical methodology with a range of applications going beyond genotoxicity. Early approaches with parametric statistical methods appeared to be insufficient and have been replaced by non-parametric ones requiring less restrictive distributional assumptions. There have also been successful attempts to use biomathematical models for establishing dose-response relationships. Overdispersion has been recognized as a major problem for the evaluation of mutagenic count data and methods to cope with it have became available. A theory of generalized linear modelling is emerging to combine dose-response modeling with much less restrictive distributional assumptions, while allowing the inclusion of concomitant factors arising from the experimental conditions. The methodological survey below reviews the present state of this development and is intended to promote further research into biostatistical issues and methods of analysis. Appropriate methods for the design and analysis of STTs are discussed. The progress for the Ames assay was only partially transmitted to the analysis of the large number of other short-term assays. Several such assays are reviewed with respect to their present state of statistical evaluation.

Animals↗
PubMed2003

Statistical significance for hierarchical clustering in genetic association and microarray expression studies.

BACKGROUND: With the increasing amount of data generated in molecular genetics laboratories, it is often difficult to make sense of results because of the vast number of different outcomes or variables studied. Examples include expression levels for large numbers of genes and haplotypes at large numbers of loci. It is then natural to group observations into smaller numbers of classes that allow for an easier overview and interpretation of the data. This grouping is often carried out in multiple steps with the aid of hierarchical cluster analysis, each step leading to a smaller number of classes by combining similar observations or classes. At each step, either implicitly or explicitly, researchers tend to interpret results and eventually focus on that set of classes providing the "best" (most significant) result. While this approach makes sense, the overall statistical significance of the experiment must include the clustering process, which modifies the grouping structure of the data and often removes variation. RESULTS: For hierarchically clustered data, we propose considering the strongest result or, equivalently, the smallest p-value as the experiment-wise statistic of interest and evaluating its significance level for a global assessment of statistical significance. We apply our approach to datasets from haplotype association and microarray expression studies where hierarchical clustering has been used. CONCLUSION: In all of the cases we examine, we find that relying on one set of classes in the course of clustering leads to significance levels that are too small when compared with the significance level associated with an overall statistic that incorporates the process of clustering. In other words, relying on one step of clustering may furnish a formally significant result while the overall experiment is not significant.

Cluster Analysis↗
PubMed1989

Comments on estimations of risks to translocation carriers.

Two projects for estimation of risks to carriers of reciprocal translocations based on data from more than 1,100 families are compared. The 2 projects differ considerably with regard to the amount of information utilized in the data, the statistical methods used--either standard computer programs or methods specially developed for the data--and the relevance and variety of the obtained results. The comparison shows the strength of utilizing tailor-made statistical methods for extracting relevant information from available data in human genetics instead of statistical standard methods from general computer programs.

Congenital Abnormalities↗
PubMed1998

Genetic and environmental influences on prolactin secretion during wake and during sleep.

To delineate the contributions of genetic and environmental factors in the regulation of human prolactin (PRL) secretion, the 24-h profile of plasma PRL was obtained at 15-min intervals in 10 pairs of monozygotic and 10 pairs of dizygotic twins. Sleep was monitored polygraphically. PRL secretory rates were derived from plasma concentrations by deconvolution. Diurnal (24-h) variations were quantified by a regression curve to define nadir, acrophase, and amplitude. Pulses of PRL secretion were identified using a computerized algorithm. A procedure specifically developed for twin studies was used to partition the variance into genetic and environmental contributions. Significant genetic effects were identified for daytime PRL concentrations, rhythm amplitude, and overall wave-shape of the daily PRL profile. In contrast, environmental effects were dominant for mean concentrations during sleep, total secretory output during sleep, overall 24-h concentrations, and total 24-h secretion. However, when interindividual variations in sleep fragmentation were taken into account, the estimates of genetic variance for PRL concentrations and secretion during sleep approached statistical significance. Significant genetic influences were identified for slow-wave sleep (SWS). Because SWS is associated with increased nocturnal PRL secretion, it is possible that genetic effects on PRL secretion during sleep reflect genetic influences on SWS. In conclusion, genetic factors determine partially both the basal daytime concentrations of PRL and the temporal organization of PRL secretion over the 24-h cycle in normal young men.

Adult↗
PubMed2003

Approaches to identifying genetic predictors of clinical outcome in rheumatoid arthritis.

Predicting which patients with rheumatoid arthritis (RA), at presentation, are likely to suffer a severe disease course based on genotype data would be a major clinical advance. It would ensure that patients at highest risk of a severe outcome could be targeted with early aggressive therapies. With a better understanding of interactions between genotype and drug response it would be possible to prescribe treatments most likely to be efficacious and safe for specific patient subgroups. While a clear genetic component has been demonstrated in RA severity, the identification of genetic factors poses a challenge to researchers in the field. Initiatives such as the SNP Consortium and advances in genotyping technology have facilitated the investigation of genetic factors in both disease susceptibility and severity. However, several other factors, such as the availability of suitable longitudinal cohorts, definition of outcome measures, study design, selection of genetic markers, and statistical power, will all contribute to the likely success of genetic studies. Several strategies that have been applied in the pursuit of genetic predictors of clinical outcome in RA. While some encouraging results have been generated, it has so far been difficult to quantify the predictive value of genetic markers and extrapolate the results from genetic studies to clinic patients. Establishing high quality prospective inception cohorts, a more systemic approach to defining suitable outcome measures, and understanding the effects of treatment, will be critical to the eventual identification of good predictive genetic markers.

Animals↗