A key unresolved question in population ecology concerns the relationship between a population's size and its growth rate. We estimated this relationship for 1780 time series of mammals, birds, fish, and insects. We found that rates of population growth are high at low population densities but, contrary to previous predictions, decline rapidly with increasing population size and then flatten out, for all four taxa. This produces a strongly concave relationship between a population's growth rate and its size. These findings have fundamental implications for our understanding of animals' lives, suggesting in particular that many animals in these taxa will be found living at densities above the carrying capacity of their environments.

In cross between inbred lines, linear regression can be used to estimate the correlation of markers with a trait of interest; these marker effects then allow marker assisted selection (MAS) for quantitative traits. Usually a subset of markers to include in the model must be selected: no completely satisfactory method of doing this exists. We show that replacing this selection of markers by ridge regression can improve the mean response to selection and reduce the variability of selection response.

We investigated risk models for the inherited susceptibility of breast and ovarian cancer, using data from both high-risk families and a population based series of ovarian cancer. The first data set consisted of 112 families containing 2 or more relatives with epithelial ovarian cancer. BRCA1 and BRCA2 germline mutations were detected in 50 % of these families. The second study involved 374 ovarian cancer cases, collected at the Royal Marsden Hospital, London, who had DNA samples analysed for BRCA1 mutations. 12 women were found to be carriers. We constructed genetic models for ovarian and breast cancer using the computer program MENDEL. In the first study we modelled the effects of BRCA1 and BRCA2 simultaneously and allowed for a third gene predisposing to ovarian cancer. None of the models fitted gave significant evidence for a third gene. Population frequencies of BRCA1 and BRCA2 mutations were estimated to be 0n13 % and 0n17 % respectively. Our results suggest that BRCA1 and BRCA2 may be sufficient to explain the majority of familial ovarian cancer and that families without mutations can be explained by sensitivity of the mutation testing and chance clusters of sporadic cases. Using data on the families of the 12 mutation carriers in the second study, we estimated age specific ovarian and breast cancer risks for BRCA1 mutation carriers. Under the best fitting model the cumulative ovarian cancer risk was 66 % by age 70, and the corresponding breast cancer risk was 45 %. The differences in penetrance estimates among studies suggest that modifying genetic or environmental factors may be important determinants of risk.An example of complex segregation analysis of plant pedigree : reversion of cytoplasm type in Sugar Beet (Beta vulgaris L.) YU. S. AULCHENKO, S. G. VEPREV and T. I. AXENOVICH. Institute of Cytology and Genetics, Novosibirsk, Russia.As a rule, the genetic analysis of traits in animals and plants is performed using ' intercross experiment '. This approach requires obtaining two genetically homogeneous strains contrasting by the trait under analysis ; it is usually time and money-consuming procedure. At the same time there is a possibility to obtain useful information from data, which were not designed for genetic analysis, for example, from stock-maintenance pedigrees.To extract information from pedigrees of arbitrary structure the likelihood-based technique of complex segregation analysis (CSA) might be applied (Elston & Stewart, 1971). This method is widely used in human genetics and it is applied for analysis of animal data sometimes. However, up 352 Abstracts to date it was not used for analysis of traits in plants : although the technique of CSA allows analysis of plant pedigrees theoretically, practically the modifications are to be made because of specific features of plants' breeding structure and traits. We developed such a modification, which allows analysis of plant pedigree data.The object under the study was Sugar Beet (Beta vulgaris L. ). It is normally hermaphroditic, cross-pollinating...

No abstract

Most evolutionary tree estimation methods for DNA sequences ignore or inefficiently use the phylogenetic information contained within shared patterns of gaps. This is largely due to the computational difficulties in implementing models for insertions and deletions. A simple way to incorporate this information is to treat a gap as a fifth character (with the four nucleotides being the other four) and to incorporate it within a Markov model of nucleotide substitution. This idea has been dismissed in the past, since it treats a multiple-site insertion or deletion as a sequence of independent events rather than a single event. While this is true, we have found that under many circumstances it is better to incorporate gap information inadequately than to ignore it, at least for topology estimation. We propose an extension to a class of nucleotide substitution models to incorporate the gap character and show that, for data sets (both real and simulated) with short and medium gaps, these models do lead to effective use of the information contained within insertions and deletions. We also implement an ad hoc method in which the likelihood at columns containing multiple-site gaps is downweighted in order to avoid giving them undue influence. The precision of the estimated tree, assessed using Markov chain Monte Carlo techniques to find the posterior distribution over tree space, improves under these five-state models compared with standard methods which effectively ignore gaps.

Partial least squares regression has been widely adopted within some areas as a useful alternative to ordinary least squares regression in the manner of other shrinkage methods such as principal components regression and ridge regression. In this paper we examine the nature of this shrinkage and demonstrate that partial least squares regression exhibits some undesirable properties.

We investigated risk models for the inherited susceptibility of breast and ovarian cancer, using data from both high-risk families and a population based series of ovarian cancer. The first data set consisted of 112 families containing 2 or more relatives with epithelial ovarian cancer. BRCA1 and BRCA2 germline mutations were detected in 50 % of these families. The second study involved 374 ovarian cancer cases, collected at the Royal Marsden Hospital, London, who had DNA samples analysed for BRCA1 mutations. 12 women were found to be carriers. We constructed genetic models for ovarian and breast cancer using the computer program MENDEL. In the first study we modelled the effects of BRCA1 and BRCA2 simultaneously and allowed for a third gene predisposing to ovarian cancer. None of the models fitted gave significant evidence for a third gene. Population frequencies of BRCA1 and BRCA2 mutations were estimated to be 0n13 % and 0n17 % respectively. Our results suggest that BRCA1 and BRCA2 may be sufficient to explain the majority of familial ovarian cancer and that families without mutations can be explained by sensitivity of the mutation testing and chance clusters of sporadic cases. Using data on the families of the 12 mutation carriers in the second study, we estimated age specific ovarian and breast cancer risks for BRCA1 mutation carriers. Under the best fitting model the cumulative ovarian cancer risk was 66 % by age 70, and the corresponding breast cancer risk was 45 %. The differences in penetrance estimates among studies suggest that modifying genetic or environmental factors may be important determinants of risk.An example of complex segregation analysis of plant pedigree : reversion of cytoplasm type in Sugar Beet (Beta vulgaris L.) YU. S. AULCHENKO, S. G. VEPREV and T. I. AXENOVICH. Institute of Cytology and Genetics, Novosibirsk, Russia.As a rule, the genetic analysis of traits in animals and plants is performed using ' intercross experiment '. This approach requires obtaining two genetically homogeneous strains contrasting by the trait under analysis ; it is usually time and money-consuming procedure. At the same time there is a possibility to obtain useful information from data, which were not designed for genetic analysis, for example, from stock-maintenance pedigrees.To extract information from pedigrees of arbitrary structure the likelihood-based technique of complex segregation analysis (CSA) might be applied (Elston & Stewart, 1971). This method is widely used in human genetics and it is applied for analysis of animal data sometimes. However, up Abstracts to date it was not used for analysis of traits in plants : although the technique of CSA allows analysis of plant pedigrees theoretically, practically the modifications are to be made because of specific features of plants' breeding structure and traits. We developed such a modification, which allows analysis of plant pedigree data.The object under the study was Sugar Beet (Beta vulgaris L. ). It is normally hermaphroditic, cross-pollinating plan...

No abstract