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 415 records · Page 23Linked to original sources
PubMed1997

[A new wave of behavior genetic modeling using covariance structure analysis].

A number of useful methods for analyzing covariance structure have been proposed in the studies of human behavior genetics, reflecting the fact that the behavior genetic studies are one of the main origins of covariance structure model. In this paper, I review recent progress on methodology for behavior genetic studies of twins and families from the standpoint of the structural equation modeling. Especially, genetic ACE (additive genetic, common environment and random environment) model, multivariate ACE model, genetic factor analysis model and twin-parent model are focused upon. This review also discusses how to construct applied structural equation models which are useful for psychological research.

Factor Analysis, Statistical↗
PubMed2002

Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network.

We propose a new statistical method for constructing genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network construction is in the estimation of the conditional distribution of each random variable. We consider fitting nonparametric regression models with heterogeneous error variances to the microarray gene expression data to capture the nonlinear structures between genes. A problem still remains to be solved in selecting an optimal graph, which gives the best representation of the system among genes. We theoretically derive a new graph selection criterion from Bayes approach in general situations. The proposed method includes previous methods based on Bayesian networks. We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae gene expression data newly obtained by disrupting 100 genes.

Artificial Intelligence↗
PubMed2002

[Roaming through methodology. XXXVII. Unintentional population stratification in genetic case-control studies].

In the field of genetic epidemiology, the appropriate selection of patients and controls within a case-control study is essential to obtain accurate results. Within the genetic case-control study design, unintentional population stratification can cause confounding of results. The probability of this type of confounding arising is determined by the selection of cases and controls from different populations and by the differences in their genetic background. In follow-up studies, confounding by population stratification may also occur. Recent developments in the fields of statistics and genetics now make it reasonably simple to test for population stratification in case-control studies, and to prevent confounding by this stratification.

Case-Control Studies↗
PubMedDate not supplied

What about R.A. Fisher's statement of the "too good" data of J.G. Mendel's Pisum paper?

Mendel was accused by Fisher that his observed data, which corresponded to expectations, were too good to be true, and, further, that Mendel, growing only 10 plants per offspring, disregarded in his genotypical analysis the loss of recessives by assuming a ratio of 1:2 instead of 1.1126:1.8874. In contrast, it is proposed here that all chi-square statistics of genetic segregations fall short because the variance of genetic segregations is smaller and not of a binomial type as assumed. Furthermore, this variance and the corresponding chi-square statistics are not homogeneous in different segregation types. Consequently, it is not possible to summarize the different chi-square statistics as Fisher did. It is only in this way that he was able to obtain his unrealistic result (a probability of "seven times in 100,000 cases"). Regarding Fisher's second accusation, it should be taken into account that Mendel selected his 10 plants from offspring with a finite and not an infinite number of entities. Although this number is different from offspring to offspring, the average number is about 30. This means that the loss of recessives must be calculated by using a hypergeometric and not a binomial model as Fisher did. Consequently, the real deviation from the 1:2 ratio can be disregarded.

Genetic Variation↗
PubMed2004

Genotype-assisted optimum contribution selection to maximize selection response over a specified time period.

Genotype-assisted selection (GAS), i.e. selection for an identified quantitative trait locus (QTL) and polygenic background genes, has been shown to increase short-term genetic gain but may reduce long-term genetic gains. In order to avoid this reduction of long-term gain, multi-generation optimization of truncation selection schemes is needed. This paper presents a multi-generation optimization of optimum contribution (OC) selection with selection on an identified QTL. This genotype-assisted optimum contribution (GAOC) selection method assumes that the optimum selection differential at the QTL is constant over the time horizon, and achieves this by controlling the increase of the frequency of the positive QTL allele. Implementation was straightforward by an additional linear restriction in the OC algorithm. GAOC achieved 35.2%, 2.3% and 1.1%, respectively, more cumulative genetic gain than OC selection (ignoring the QTL) using time horizons of 5, 10 and 15 generations. When one-generation optimization of GAS was used instead of multi-generation optimization, these figures were 2.8%, 3.1% and 3.2%, respectively. Simulated annealing was used to optimize the increases of the frequency of the positive QTL allele in order to test the optimality of GAOC. This latter resulted in genetic gains that were always within 0.4% of those of GAOC. In practice, short-term genetic gains are also important, which makes one-generation optimization of genetic gain closer to optimal.

Data Interpretation, Statistical↗
PubMed2003

Population genetic structure of mahogany (Swietenia macrophylla King, Meliaceae) across the Brazilian Amazon, based on variation at microsatellite loci: implications for conservation.

Mahogany (Swietenia macrophylla, Meliaceae) is the most valuable and intensively exploited Neotropical tree. No information is available regarding the genetic structure of mahogany in South America, yet the region harbours most of the unlogged populations of this prized hardwood. Here we report on the genetic diversity within and the differentiation among seven natural populations separated by up to 2100 km along the southern arc of the Brazilian Amazon basin. We analysed the variation at eight microsatellite loci for 194 adult individuals. All loci were highly variable, with the number of alleles per locus ranging from 13 to 27 (mean = 18.4). High levels of genetic diversity were found for all populations at the eight loci (mean HE = 0.781, range 0.754-0.812). We found moderate but statistically significant genetic differentiation among populations considering both estimators of FST and RST, theta = 0.097 and rho = 0.147, respectively. Estimates of theta and rho were significantly greater than zero for all pairwise population comparisons. Pairwise rho-values were positively and significantly correlated with geographical distance under the isolation-by-distance model. Furthermore, four of the populations exhibited a significant inbreeding coefficient. The finding of local differentiation among Amazonian mahogany populations underscores the need for in situ conservation of multiple populations of S. macrophylla across its distribution in the Brazilian Amazon. In addition, the occurrence of microgeographical genetic differentiation at a local scale indicates the importance of maintaining populations in their diverse habitats, especially in areas with mosaics of topography and soil.

Brazil↗
PubMedDate not supplied

Linkage analysis in nuclear families. 2: Relationship between affected sib-pair tests and lod score analysis.

It is believed that the main advantage of affected sib-pair tests is that their application requires no information about the underlying genetic mechanism of the disease. However, here it is proved that the mean test, which can be considered the most prominent of the affected sib-pair tests, is equivalent to lod score analysis for an assumed recessive mode of inheritance, irrespective of the true mode of the disease. Further relationships of certain sib-pair tests and lod score analysis under specific assumed genetic modes are investigated.

Data Interpretation, Statistical↗
PubMed2003

Geographic patterns of functional categories of HLA-DRB1 alleles: a new approach to analyse associations between HLA-DRB1 and disease.

Because specific amino acids found within the peptide-binding cleft of human leukocyte antigen (HLA) molecules have been implicated in HLA/disease associations, an approach which consists in grouping the alleles according to their functional properties at the protein level may enable us to better understand HLA associations than the conventional allelic classification. In this study, we applied this methodology to investigate the associations between HLA-DRB1 and rheumatoid arthritis. The alleles were first classified into seven functional categories [restrictive supertype patterns (RSPs)], among which three were known to be significantly associated with susceptibility (one category) or resistance (two categories) to rheumatoid arthritis. The frequencies of these categories were then estimated in 104 population samples previously tested for HLA-DRB1, and their variability was analysed spatially on a worldwide scale by applying an original methodology for detecting discontinuities in geographically patterned data. RSP frequencies were also compared to known values of rheumatoid arthritis prevalence in some populations. The results indicated that the three RSP frequency distributions were geographically structured, and that these patterns could generally be explained by the history of human migrations. However, the peculiar pattern observed for RSP 'A' (conferring susceptibility to rheumatoid arthritis) indicated a possible association with some latitude-dependent disease. Furthermore, the very high correlation coefficient found between RSP 'A' frequencies and rheumatoid arthritis prevalence confirmed the significant disease association of this functional category. In contrast, the putative protective effect of the other RSPs ('De' and 'Q') was not detectable at the worldwide level, but may be significant in specific geographic areas. This study shows that population genetic diversity analyses based on a functional grouping of HLA alleles provide an efficient way to explore the mutual influence of HLA genetic variation and disease.

Alleles↗
PubMed2004

First microsatellite loci of red mullet (Mullus barbatus) and their application to genetic structure analysis of Adriatic shared stock.

In order to study the genetic structure of the Adriatic shared stock of red mullet (Mullus barbatus), we developed a set of dinucleotide microsatellite markers. A dinucleotide-enriched genomic library was obtained, and 6 polymorphic dinucleotide loci were successfully optimized. The markers showed high expected heterozygosity (from 0.68 to 0.92) and allele number (from 12 to 33); thus they appear to be suitable for detecting genetic differences in the population of red mullet. Four Adriatic samples were subsequently analyzed for microsatellite variation, and the results showed subtle but statistically significant genetic differentiation, indicating that the Adriatic red mullet may group into local, genetically isolated populations. No correlation between geographic distance and genetic differentiation was observed. In addition, the evidence of recent bottlenecks in the Adriatic samples indicates that the observed population subdivision might reflect random local allelic variations, generated by reproductive success, survival rates, or fishing pressure.

Animals↗
PubMed1997

A permutation test for the robust sib-pair linkage method.

The robust sib-pair method introduced by Haseman & Elston (1972) is one of the most widely circulated allele-sharing methods for linkage analysis. The procedure evaluates linkage by significance testing of a regression coefficient and, hence, a standard t-test has traditionally been applied despite known violations of the statistical assumptions underlying the test. We present a permutation based reference distribution for the estimate of the regression coefficient that is motivated by genetic principles rather than by standard regression testing procedures. The permutation test approximates Mendelian co-segregation under the null hypothesis of no linkage, making it a very natural approach. Theory and simulations show that the conventional t-test approximates the permutation test quite well, even when dependent sib pairs are used for analysis. These results thus indirectly address concerns over the t-test. To illustrate the permutation test using real data we applied the procedure to two lipoprotein systems that have been well characterized.

Animals↗
PubMed2003

Comparison of selection methods for optimizing genetic gain and gene diversity in a red pine ( Pinus resinosa Ait.) seedling seed orchard.

Four selection methods, individual selection (IS), family selection (FS), family plus within-family selection (FWFS) and combined selection (CS), were used to estimate genetic gain [ E( g)] for stem volume and gene diversity ( GD) for ten theoretical selection intensities in a 108-family, 12-year-old red pine seedling seed orchard. Estimated genetic gain for stem volume ranged from 4.6% to 11.8% across all selection methods and intensities with CS consistently having the highest gains and FS the lowest for any given selection intensity. Genetic diversity ranged from 0.9797 to 0.9954 across all selection methods and intensities. Individual selection was the best selection method for retaining GD, especially at the higher selection intensities, while FWFS was more efficient at the lowest selection intensity. An optimization point, which maximized E( g) and GD relative to each other, was calculated for each selection method. In all cases the optimization point indicated that both E( g) and GD would be favorably high when optimized relative to each other. The implications for volume gain, genetic diversity and potential inbreeding in red pine, a species with inherently low levels of genetic variation, are discussed.

Biological Evolution↗