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At least 1,171 records · Page 65Linked to original sources
PubMed2004

Data mining techniques for cancer detection using serum proteomic profiling.

OBJECTIVE: Pathological changes in an organ or tissue may be reflected in proteomic patterns in serum. It is possible that unique serum proteomic patterns could be used to discriminate cancer samples from non-cancer ones. Due to the complexity of proteomic profiling, a higher order analysis such as data mining is needed to uncover the differences in complex proteomic patterns. The objectives of this paper are (1) to briefly review the application of data mining techniques in proteomics for cancer detection/diagnosis; (2) to explore a novel analytic method with different feature selection methods; (3) to compare the results obtained on different datasets and that reported by Petricoin et al. in terms of detection performance and selected proteomic patterns. METHODS AND MATERIAL: Three serum SELDI MS data sets were used in this research to identify serum proteomic patterns that distinguish the serum of ovarian cancer cases from non-cancer controls. A support vector machine-based method is applied in this study, in which statistical testing and genetic algorithm-based methods are used for feature selection respectively. Leave-one-out cross validation with receiver operating characteristic (ROC) curve is used for evaluation and comparison of cancer detection performance. RESULTS AND CONCLUSIONS: The results showed that (1) data mining techniques can be successfully applied to ovarian cancer detection with a reasonably high performance; (2) the classification using features selected by the genetic algorithm consistently outperformed those selected by statistical testing in terms of accuracy and robustness; (3) the discriminatory features (proteomic patterns) can be very different from one selection method to another. In other words, the pattern selection and its classification efficiency are highly classifier dependent. Therefore, when using data mining techniques, the discrimination of cancer from normal does not depend solely upon the identity and origination of cancer-related proteins.

Biomarkers, Tumor↗
PubMed2001

Genetic consequences of polygyny and social structure in an Indian fruit bat, Cynopterus sphinx. I. Inbreeding, outbreeding, and population subdivision.

Population subdivision into behaviorally cohesive kin groups influences rates of inbreeding and genetic drift and has important implications for the evolution of social behavior. Here we report the results of a study designed to test the hypothesis that harem social structure promotes inbreeding and genetic subdivision in a population with overlapping generations. Genetic consequences of harem social structure were investigated in a natural population of a highly polygynous fruit bat, Cynopterus sphinx (Chiroptera: Pteropodidae), in western India. The partitioning of genetic variance within and among breeding groups was assessed using 10-locus microsatellite genotypes for 431 individually marked bats. Genetic analysis of the C. sphinx study population was integrated with field data on demography and social structure to determine the specific ways in which mating, dispersal, and new social group formation influenced population genetic structure. Microsatellite data revealed striking contrasts in genetic structure between consecutive offspring cohorts and between generations. Relative to the 1998 (dry-season) offspring cohort, the 1997 (wet-season) cohort was characterized by a more extensive degree of within-group heterozygote excess (F(IS) = -0.164 vs. -0.050), a greater degree of among-group subdivision (F(ST) = 0.123 vs. 0.008), and higher average within-group relatedness (r = 0.251 vs. 0.017). Differences in genetic structure between the two offspring cohorts were attributable to seasonal differences in the number and proportional representation of male parents. Relative to adult age-classes, offspring cohorts were characterized by more extensive departures from allelic and genotypic equilibria and a greater degree of genetic subdivision. Generational differences in F-statistics indicated that genetic structuring of offspring cohorts was randomized by natal dispersal prior to recruitment into the breeding population. Low relatedness among harem females (r = 0.002-0.005) was primarily attributable to high rates of natal dispersal and low rates of juvenile survivorship. Kin selection is therefore an unlikely explanation for the formation and maintenance of behaviorally cohesive breeding groups in this highly social mammal.

Animals↗
PubMed2002

[Research and application of animal genetic marker-assisted selection].

With the development of molecular and quantitative genetics and its related subjects, it made a great progress on the research about animal genetic marker-assisted selection (MAS). There were also some successful examples on the application of MAS to animal genetic improvement. The statistical method which using phenotypic, pedigree and genetic marker information to predict individual breeding values has already been developed. Many achievements were obtained from the researches, which carried on MAS relative efficiency and its affecting factors and selection schemes. The present paper reviewed some progresses of MAS research and discussed some problems about MAS application to animal breeding.

English Abstract↗
PubMed1990

Sib-pair linkage tests for disease susceptibility loci: common tests vs. the asymptotically most powerful test.

Several statistical tests for linkage between a disease susceptibility locus and a marker locus for sib-pair data are examined analytically. Two common statistics, a test based on the mean number of marker alleles shared identical by descent by sib-pairs, and a test based on the proportion of sib-pairs sharing exactly two marker alleles, are shown to be special cases of a more general statistic. We use this more general statistic to derive the asymptotically most powerful statistic for a given genetic alternative hypothesis, and then compare this statistic with the "mean" statistic and the "proportion" statistic. Results indicate that the "mean" statistic generally compares well with the most powerful statistic. However, in some instances the "mean" statistic may lose power, relative to the most powerful. To guard against this, a new statistic (the maximum of the "mean" and "proportions" statistics) is considered and its asymptotic distribution is derived. Results indicate that this new statistic performs well.

Alleles↗
PubMed1999

Genetic anticipation and breast cancer: a prospective follow-up study.

Genetic anticipation is characterized by an earlier age of disease onset, increased severity, and a greater proportion of affected individuals in succeeding generations. The discovery of trinucleotide repeat expansion (TRE) mutations as the molecular correlate of anticipation in a number of rare Mendelian neurodegenerative disorders has led to a resurgence of interest in this phenomenon. Because of the difficulties presented to traditional genetics by complex diseases, the testing for genetic anticipation coupled with TRE detection has been proposed as a strategy for expediting the identification of susceptibility genes for complex disorders. In the case of breast cancer, a number of previous studies found evidence consistent with genetic anticipation. It is known that a proportion of such families are linked to either BRCA1 or BRCA2, but no TRE mutations have been identified. It has been shown that the typical ascertainment employed in studies purporting to demonstrate genetic anticipation combined with unadjusted statistical analysis can dramatically elevate the type I error. We re-examine the evidence for anticipation in breast cancer by applying a new statistical approach that appears to have validity in the analysis of anticipation to data ascertained from a recent follow-up of a large prospective cohort family study of breast cancer. Using this approach, we find no statistically significant evidence for genetic anticipation in familial breast cancer. We discuss the limitations of our analysis, including the problem of adequate sample size for this new statistical test.

Adult↗
PubMed1983

[Genetic analysis of the structure of the predisposition to diabetes mellitus. I. Genetic epidemiological approaches to an analysis of age-dependent multifactorial diseases].

The statistical methods for genetic analysis of multifactorial diseases (MFD) with variable age-at-onset are developed. It is emphasized that the index numbers used in the genetical-epidemiological studies, such as frequency of MFD in the population (qg--incidence) and that among family members (PR), should be independent on the demographic structure of both the population and the families. The mathematical grounds for the estimating methods as well as biological meaning for such index numbers are given. The approach suggested will be used later in analysis of the structure of predisposition to diabetes mellitus.

Age Factors↗
PubMed2003

Theoretical basis of the Beavis effect.

The core of statistical inference is based on both hypothesis testing and estimation. The use of inferential statistics for QTL identification thus includes estimation of genetic effects and statistical tests. Typically, QTL are reported only when the test statistics reach a predetermined critical value. Therefore, the estimated effects of detected QTL are actually sampled from a truncated distribution. As a result, the expectations of detected QTL effects are biased upward. In a simulation study, William D. Beavis showed that the average estimates of phenotypic variances associated with correctly identified QTL were greatly overestimated if only 100 progeny were evaluated, slightly overestimated if 500 progeny were evaluated, and fairly close to the actual magnitude when 1000 progeny were evaluated. This phenomenon has subsequently been called the Beavis effect. Understanding the theoretical basis of the Beavis effect will help interpret QTL mapping results and improve success of marker-assisted selection. This study provides a statistical explanation for the Beavis effect. The theoretical prediction agrees well with the observations reported in Beavis's original simulation study. Application of the theory to meta-analysis of QTL mapping is discussed.

Chromosome Mapping↗
PubMed2004

A behavior genetic investigation of the relationship between leadership and personality.

Phenotypic research on leadership style has long considered the importance of individual differences in personality when identifying the behaviors associated with good leaders. Although leadership and many personality traits have been separately shown to be heritable, these constructs have not been examined with genetically informative data to identify common sources of heritability in the two domains. A logical extension to current research, therefore, is to examine the extent to which factors of personality are predictive of leadership dimensions and the extent to which unique genetic contributions to the relationship between personality and leadership style may be identified. Adult twin pairs (183 MZ and 64 same-sex DZ) completed the Multifactor Leadership Questionnaire (MLQ) and the Personality Research Form (PRF). Univariate analyses indicated that both leadership factors (transformational and transactional leadership) and all five of the "Big Five" factors (openness, conscientiousness, extraversion, disagreeableness, and neuroticism) were best fit by genetic models. Multivariate genetic analyses suggest that transformational leadership shows a statistically significant positive genetic correlation with conscientiousness, extraversion, and openness to experience. Transactional leadership shows a significant negative genetic correlation with conscientiousness and extraversion, and a significant positive genetic correlation with disagreeableness. These results underscore the importance of conscientiousness and extraversion in predicting leadership style, and illustrate important differences between transformational and transactional leaders.

Adult↗
PubMed2004

Early- versus late-onset obsessive-compulsive disorder: investigating genetic and clinical correlates.

There is increasing evidence that obsessive-compulsive disorder (OCD) is mediated by genetic factors. Although the precise mechanism of inheritance is unclear, recent evidence has pointed towards the involvement of the serotonergic and dopaminergic systems in the disorder's development. Furthermore, early-onset OCD appears to be a subtype that exhibits distinct clinical features and that is associated with greater familial loading. In the present investigation, South African OCD patients (n=252) were stratified according to age of onset and were clinically assessed. Additionally, selected variants in genes encoding serotonergic and dopaminergic components were investigated in a Caucasian OCD subset (n=180). This subgroup was further stratified to evaluate the role that these candidate genes may play in the genetically homogeneous Afrikaner subset (n=80). Analysis of the clinical data revealed an association between early age of onset and an increased frequency of tics, Tourette's disorder, and trichotillomania (TTM). The genetic studies yielded statistically significant results when the allelic distributions of genetic variants in the dopamine receptor type 4 gene (DRD4) were analysed in the Caucasian OCD cohort. These data support a role for the dopaminergic system, which may be relevant to the development of early-onset OCD.

Adult↗
PubMed2005

A fine-scale map of recombination rates and hotspots across the human genome.

Genetic maps, which document the way in which recombination rates vary over a genome, are an essential tool for many genetic analyses. We present a high-resolution genetic map of the human genome, based on statistical analyses of genetic variation data, and identify more than 25,000 recombination hotspots, together with motifs and sequence contexts that play a role in hotspot activity. Differences between the behavior of recombination rates over large (megabase) and small (kilobase) scales lead us to suggest a two-stage model for recombination in which hotspots are stochastic features, within a framework in which large-scale rates are constrained.

Animals↗
PubMed1979

A comparison of two methods for making statistical inferences on Nei's measure of genetic distance.

The delta and jackknife methods can be used to estimate Nei's measure of genetic distance and calculate confidence intervals for this estimate. Computer stimulations were used to study the bias and variance of each estimator and the accuracy of the corresponding approximate 95% confidence intervals. The simulations were conducted using 3 sets of data and several sample sizes. The results showed: (1) the jackknife reduced bias; (2) in 8 out of 9 cases the variance and mean square error of the jackknife estimator were less; (3) a second order jackknife reduced the bias the most but suffered a corresponding increase in variance; (4) both the first order jackknife and delta methods yielded intervals whose confidence levels were approximately equal but less than 95%.

Alleles↗
PubMed1999

On a randomization procedure in linkage analysis.

Although much theoretical work has been undertaken to derive thresholds for statistical significance in genetic linkage studies, real data are often complicated by many factors, such as missing individuals or uninformative markers, which make the validity of these theoretical results questionable. Many simulation-based methods have been proposed in the literature to determine empirically the statistical significance of the observed test statistics. However, these methods either are not generally applicable to complex pedigree structures or are too time-consuming. In this article, we propose a computationally efficient simulation procedure that is applicable to arbitrary pedigree structures. This procedure can be combined with statistical tests, to assess the statistical significance for genetic linkage between a locus and a qualitative or quantitative trait. Furthermore, the genomewide significance level can be appropriately controlled when many linked markers are studied in a genomewide scan. Simulated data and a diabetes data set are analyzed to demonstrate the usefulness of this novel simulation method.

Computer Simulation↗
PubMed2006

A statistical model for dissecting genomic imprinting through genetic mapping.

As a result of nonequivalent genetic contribution of maternal and paternal genomes to offsprings, genomic imprinting or called parent-of-origin effect, has been broadly identified in plants, animals and humans. Its role in shaping organism's development has been unanimously recognized. However, statistical methods for identifying imprinted quantitative trait loci (iQTL) and estimating the imprinted effect have not been well developed. In this article, we propose an efficient statistical procedure for genomewide estimating and testing the effects of significant iQTL underlying the quantitative variation of interested traits. The developed model can be applied to two different genetic cross designs, backcross and F(2) families derived from inbred lines. The proposed procedure is built within the maximum likelihood framework and implemented with the EM algorithm. Extensive simulation studies show that the proposed model is well performed in a variety of situations. To demonstrate the usefulness of the proposed approach, we apply the model to a published data in an F(2) family derived from LG/S and SM/S mouse stains. Two partially maternal imprinting iQTL are identified which regulate the growth of body weight. Our approach provides a testable framework for identifying and estimating iQTL involved in the genetic control of complex traits.

Algorithms↗
PubMed2000

Large scale population structure and gene flow in the planktonic developing periwinkle, Littorina striata, in Macaronesia (Mollusca: Gastropoda).

Allozymes were used to investigate the genetic structure of 42 populations of the planktonic developing, Macaronesian periwinkle Littorina striata, throughout its entire geographic range (Azores, Madeira, Canary Islands and Cape Verde Islands). This periwinkle is presumed to have a high dispersal and gene flow potential, because it has a planktonic development. It is therefore expected to show little population genetic differentiation. Indeed, based on Wright's hierarchical F-statistics, no significant genetic differentiation could be detected among populations, at any of the specified hierarchical levels (i.e. population, island, and archipelago). Nevertheless, the Cape Verde Islands seemed genetically more diverse (highest mean number of alleles per locus). The number of loci revealing a significant genetic heterogeneity increased with increasing distance between populations, while private alleles based gene flow (Nm) estimates also revealed a tendency towards a geographical pattern. The distribution of rare and private alleles, might account for these observations which suggested some geographical effect. Because of the low frequency at which these alleles occur, their influence on the genetic population structure is negligible, and not picked up by F-statistics.

Journal Article↗