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The results of investigation of calving features in 371 cows of Ukrainian beef cattle were summarized. Variance and correlation analyses of factors affecting calving in cows were used. Breeds, pedigrees of parents and progeny, assessment of complicated and lethal calvings, duration of pregnancies and mass of newborns were registered. The influence of selection type on the normal calving was shown.
Statistical genetics software packages for linkage analysis have their own unique constraints on the size and shape of the pedigrees they can process. As a result, researchers are often forced to exclude from analysis some individuals in a given family. Existing procedures for reducing pedigree size to fit computational constraints use arbitrary rules and are not interactive. However, judicious evaluation of which subject(s) to remove to minimize loss of information involves consideration of many factors, including informativeness owing to position in pedigree, availability of genotypic information, and quality of phenotypic information. Thus, automation of this task would be of significant benefit. We designed an interactive algorithm (PowerTrim) that provides the user access to detailed information with which to make informed decisions. In addition, PowerTrim checks for transcriptional and data-entry errors, which can be very time-consuming to localize manually.
Statistical procedures and molecular genetic techniques have attained a fine degree of resolution. Their ability to find disease genes has revolutionized medicine and raised hopes for breakthroughs in psychiatry. However, such breakthroughs may require an equally discriminating nosology. A psychiatric genetic nosology seeks to classify patients into categories that correspond to distinct genetic entities by addressing the problem of diagnostic accuracy: the degree to which a diagnosis correctly classifies people with and without a putative genetic illness. We review methods that deal with misclassification in genetic studies. These are clinical and epidemiological approaches that deal directly with how to define the observable manifestation of a putative genotype. We discuss two groups of methods: those that use known phenotypes and those that design new phenotypes.
Statistical analyses are used in many fields of genetic research. Most geneticists are taught classical statistics, which includes hypothesis testing, estimation and the construction of confidence intervals; this framework has proved more than satisfactory in many ways. What does a Bayesian framework have to offer geneticists? Its utility lies in offering a more direct approach to some questions and the incorporation of prior information. It can also provide a more straightforward interpretation of results. The utility of a Bayesian perspective, especially for complex problems, is becoming increasingly clear to the statistics community; geneticists are also finding this framework useful and are increasingly utilizing the power of this approach.
Recent years have seen rapid progress in several areas of both biomedical and anthropological genetics. While genetic analyses have come to play a significant role in biological anthropology, there has been little use of modern methods for linkage mapping of quantitative trait loci (QTLs). It is now feasible to design research studies to investigate the quantitative genetics of complex phenotypes that are of primary importance to traditional questions in biological anthropology. Complex traits such as functionally significant morphological features, physiological characteristics or aspects of behavior can be examined to estimate the influence of genetic variation on within-species phenotypic variation. In addition, new methods for mapping quantitative trait loci provide opportunities to identify the regions within chromosomes that contain the functional genes of interest. This review summarizes molecular genetic and statistical genetic approaches to QTL mapping, and presents examples of how this approach can expand the scope of anthropological genetics to include mapping and identifying individual genes that influence complex phenotypic traits relevant to fundamental questions in biological anthropology.
Quality control for likelihood ratio (LR) tests on restriction fragment length polymorphisms (RFLPs) and polymerase chain reaction (PCR) markers is provided by the 4N6 program. Examples are given for test of exclusion, coincidence, and kinship that should be used routinely in validation of DNA profiling. They confirm that "in a knowledgeable court DNA profiling is no longer exposed to risk of illogical presentation, blind acceptance or arbitrary rejection" (N. E. Morton, Eur. J. Hum. Genet. 1993, 1, 172-179). However, they remain to be implemented in national systems of quality control. There can be no effective quality control until attempts to fudge gene frequencies in order to retain the Hardy-Weinberg assumption (recommended by the National Research Council) ar abandoned in favour of the population genetic approach that takes gene frequencies from a real population and uses kinship to express genotype probabilities. Since an expert witness cannot know the identity of the culprit, he is obliged to consider multiple hypothesis without usurping the prerogative of the court to favour one of them on the basis of other evidence. It remains to be seen whether the new NRC Committee (and more importantly the international community of forensic scientists) will adopt this fundamental principle, which is especially important in populations like India with preferential endogamy and consanguineous marriage. Alternative procedures and the causes, effects, and remedies of four types of error are also considered.
Ten genetic markers were studied in seven Spanish native cattle breeds, using a total of 725 animals. Of the ten, two were found to be monomorphic in all seven breeds. The genetic relationships of the seven breeds are estimated by three different genetic-statistical methods (genetic distances, main coordinate analysis and cluster analysis), which indicate three clearly distinct groups of populations: one where the Cárdena Andaluza and Alistana Sanabresa are very closely related, one comprising Sayaguesa, Morucha, Asturiana de los Valles and Asturiana de la Montaña cattle, and a third, genetically distant from the other two, comprising only Blanca Cacereña. The dendrogram drawn from the genetic distances matrix would seem to imply that the seven breeds are descended from different ancestors.
The choice of allele-sharing statistics can have a great impact on the power of robust affected relative methods. Similarly, when allele-sharing statistics from several pedigrees are combined, the weight applied to each pedigree's statistic can affect power. Here we describe the direct connection between the affected relative methods and traditional parametric linkage analysis, and we use this connection to give explicit formulae for the optimal sharing statistics and weights, applicable to all pedigree types. One surprising consequence is that under any single gene model, the value of the optimal allele-sharing statistic does not depend on whether observed sharing is between more closely or more distantly related affected relatives. This result also holds for any multigene model with loci unlinked, additivity between loci, and all loci having small effect. For specific classes of two-allele models, we give the most powerful statistics and optimal weights for arbitrary pedigrees. When the effect size is small, these also extend to multigene models with additivity between loci. We propose a useful new statistic, S(rob dom), which performs well for dominant and additive models with varying phenocopy rates and varying predisposing allele frequency. We find that the statistic S(_#alleles), performs well for recessive models with varying phenocopy rates and varying redisposing allele frequency. We also find that for models with large deviation from null sharing, the correspondence between allele-sharing statistics and the models for which they are optimal may also depend on which method is used to test for linkage.
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The Swedish State Institute for Blood Group Serology is a central government laboratory handling all blood typing in paternity cases in Sweden, each year testing 1,500-2,000 cases using about 13 polymorphisms. Of the accused men, 35%-40% are nonfathers, but in one-man cases (about 78% of all cases), approximately 75% are the true fathers. Exclusions appear to be distributed as expected from allele frequencies, and the paternity probability of nonexcluded men is assessed with a Bayesian approach. Some cases are retested in extended investigations which raise theoretical exclusion capability from about 87% to about 99%. Both the results of extended investigations and the theoretical consideration of the distribution of paternity probabilities support the use of such positive statistical evidence for the attribution of paternity.
The past 25 years has seen an explosion in the number of genetic markers that can be measured on DNA samples at an ever decreasing cost. Although basic statistical methods for analysing such data gathered on samples of either independent individuals or family members, one or two markers at a time, were already well developed before this explosion occurred, there has been a corresponding burst in activity to develop multiple marker models to find disease-causing gene variants, capitalizing on the data that have become available, to increase the power of such methods. This has required the concomitant development of faster algorithms to speed up the computation of various likelihoods. For linkage analysis, to obtain the approximate locations for genes of interest, Mendelian segregation models have been extended to be more realistic and statistical models that do not assume specific modes of inheritance have been extended to allow for the analysis of larger pedigree structures. For association analysis, to obtain more precise locations for genes of interest, the recent completion of the first stage of the HapMap project has spurred the development, still underway, of novel experimental designs and analytical methods to combat the curse of dimensionality and the resulting multiple testing problem. Perhaps the greatest current challenge concerns how best to gather and synthesize the many lines of evidence possible in order to discover the genetic determinants underlying complex diseases.
BACKGROUND: Concurrent tumors can be synchronous, independently derived, non-metastatic tumors or metastatic tumors. The prognosis and clinical management of patients with these different concurrent tumor types are different. METHODS: DNA from normal and tumor tissues of 62 patients with synchronous endometrial and ovarian, bilateral ovarian, or endometrial and bilateral ovarian tumors was analyzed for loss of heterozygosity and microsatellite instability using eight polymorphic microsatellite markers at loci frequently deleted in ovarian and/or endometrial cancers. A statistical algorithm was designed to assess the clonal relationship between the tumors. RESULTS: The original histopathology reports classified 26 (42%) case patients with single primary tumors and related metastatic lesions and 21 (34%) with independent primary tumors; 15 (24%) were unclassified. Genetic data identified 35 (56%) case patients with single primary tumors and related metastatic lesions, 18 (29%) with independent primary tumors, and nine (15%) that could not be typed. Excluding case patients with histopathology reports for which a clonal relationship was uncertain or was not reported, there was 53% concordance between genetic and histopathology diagnoses. Increasing the stringency of the statistical analysis increased the number of uncertain diagnoses but did not affect the proportion of discordant genetic and histologic diagnoses. CONCLUSIONS: We have developed a rapid and robust combined genetic and statistical method to establish whether multiple tumors from the same patient represent distinct primary tumors or whether they are clonally related and therefore metastatic. For the majority of case patients, histopathology reports and genetic analyses were in agreement and diagnostic confidence was improved. Importantly, in approximately one-fourth of all case patients, genetic and histopathologic analyses suggested alternative diagnoses. The results suggest that genetic analysis has implications for clinical management and can be performed rapidly as a diagnostic test with paraffin-embedded tissues.
A large microsatellite data set from three species of bear (Ursidae) was used to empirically test the performance of six genetic distance measures in resolving relationships at a variety of scales ranging from adjacent areas in a continuous distribution to species that diverged several million years ago. At the finest scale, while some distance measures performed extremely well, statistics developed specifically to accommodate the mutational processes of microsatellites performed relatively poorly, presumably because of the relatively higher variance of these statistics. At the other extreme, no statistic was able to resolve the close sister relationship of polar bears and brown bears from more distantly related pairs of species. This failure is most likely due to constraints on allele distributions at microsatellite loci. At intermediate scales, both within continuous distributions and in comparisons to insular populations of late Pleistocene origin, it was not possible to define the point where linearity was lost for each of the statistics, except that it is clearly lost after relatively short periods of independent evolution. All of the statistics were affected by the amount of genetic diversity within the populations being compared, significantly complicating the interpretation of genetic distance data.
A new statistic for detecting genetic differentiation of subpopulations is described. The statistic can be calculated when genetic data are collected on individuals sampled from two or more localities. It is assumed that haplotypic data are obtained, either in the form of DNA sequences or data on many tightly linked markers. Using a symmetric island model, and assuming an infinite-sites model of mutation, it is found that the new statistic is as powerful or more powerful than previously proposed statistics for a wide range of parameter values.
We develop a general population genetic framework for analyzing selection on many loci, and apply it to strong truncation and disruptive selection on an additive polygenic trait. We first present statistical methods for analyzing the infinitesimal model, in which offspring breeding values are normally distributed around the mean of the parents, with fixed variance. These show that the usual assumption of a Gaussian distribution of breeding values in the population gives remarkably accurate predictions for the mean and the variance, even when disruptive selection generates substantial deviations from normality. We then set out a general genetic analysis of selection and recombination. The population is represented by multilocus cumulants describing the distribution of haploid genotypes, and selection is described by the relation between mean fitness and these cumulants. We provide exact recursions in terms of generating functions for the effects of selection on non-central moments. The effects of recombination are simply calculated as a weighted sum over all the permutations produced by meiosis. Finally, the new cumulants that describe the next generation are computed from the non-central moments. Although this scheme is applied here in detail only to selection on an additive trait, it is quite general. For arbitrary epistasis and linkage, we describe a consistent infinitesimal limit in which the short-term selection response is dominated by infinitesimal allele frequency changes and linkage disequilibria. Numerical multilocus results show that the standard Gaussian approximation gives accurate predictions for the dynamics of the mean and genetic variance in this limit. Even with intense truncation selection, linkage disequilibria of order three and higher never cause much deviation from normality. Thus, the empirical deviations frequently found between predicted and observed responses to artificial selection are not caused by linkage-disequilibrium-induced departures from normality. Disruptive selection can generate substantial four-way disequilibria, and hence kurtosis; but even then, the Gaussian assumption predicts the variance accurately. In contrast to the apparent simplicity of the infinitesimal limit, data suggest that changes in genetic variance after 10 or more generations of selection are likely to be dominated by allele frequency dynamics that depend on genetic details.
The age of the molecular organization of life as expressed in the genetic code can be estimated from experimental data. Comparative sequence analysis of transfer RNA by the method of statistical geometry in sequence space suggests that about one-third of the present transfer RNA sequence divergence was present at the urkingdom level about the time when archaebacteria separated from eubacteria. It is concluded that the genetic code is not older than, but almost as old as our planet. While this result may not be unexpected, it was not clear until now that interpretable data exist that permit inferences about such early stages of life as the establishment of the genetic code.
We report the results of statistical genetic analyses of data from the Collaborative Study on the Genetics of Alcoholism prepared for the Genetic Analysis Workshop 14 to detect and characterize maternally inherited mitochondrial genetic effects on variation in latent class psychiatric/behavioral variables employed in the diagnosis of alcoholism. Using published extensions to variance decomposition methods for statistical genetic analysis of continuous and discrete traits we: 1) estimated the proportion of the variance in each trait due to the effects of mitochondrial DNA (mtDNA), 2) tested for pleiotropy, both mitochondrial genetic and residual additive genetic, between trait pairs, and 3) evaluated whether the simultaneous estimation of mitochondrial genetic effects on these traits improves our ability to detect and localize quantitative trait loci (QTL) in the nuclear genome. After correction for multiple testing, we find significant (p < 0.009) mitochondrial genetic contributions to the variance for two latent class variables. Although we do detect significant residual additive genetic correlations between the two traits, there is no evidence of a residual mitochondrial genetic correlation between them. Evidence for autosomal QTL for these traits is improved when linkage screens are conditioned on significant mitochondrial genetic effects. We conclude that mitochondrial genes may contribute to variation in some latent class psychiatric/behavioral variables associated with alcoholism.
Statistical genetic mapping methods are powerful tools for finding genes that contribute to complex human traits. Mapping methods combine knowledge of the biological mechanisms of inheritance and the randomness inherent in those mechanisms to locate, with increasing precision, trait genes on the human genome. We provide an overview of the two major classes of mapping methods, genetic linkage analysis and linkage disequilibrium analysis, and related concepts of genetic inheritance.