Haplotype affinities resolve a major component of goat (<i>Capra hircus</i>) MtDNA D-loop diversity and reveal specific features of the Sardinian stock

Goat mtDNA haplogroup A is a poorly resolved lineage absorbing most of the overall diversity and is found in locations as distant as Eastern Asia and Southern Africa. Its phylogenetic dissection would cast light on an important portion of the spread of goat breeding. The aims of this work were 1) to provide an operational definition of meaningful mtDNA units within haplogroup A, 2) to investigate the mechanisms underlying the maintenance of diversity by considering the modes of selection operated by breeders and 3) to identify the peculiarities of Sardinian mtDNA types. We sequenced the mtDNA D-loop in a large sample of animals (1,591) which represents a non-trivial quota of the entire goat population of Sardinia. We found that Sardinia mirrors a large quota of mtDNA diversity of Western Eurasia in the number of variable sites, their mutational pattern and allele frequency. By using Bayesian analysis, a distance-based tree and a network analysis, we recognized demographically coherent groups of sequences identified by particular subsets of the variable positions. The results showed that this assignment system could be reproduced in other studies, capturing the greatest part of haplotype diversity. We identified haplotype groups overrepresented in Sardinian goats as a result of founder effects. We found that breeders maintain diversity of matrilines most likely through equalization of the reproductive potential. Moreover, the relevant amount of inter-farm mtDNA diversity found does not increase proportionally with distance. Our results illustrate the effects of breeding practices on the composition of maternal gene pool and identify mtDNA types that may be considered in projects aimed at retrieving the maternal component of the oldest breeds of Sardinia.</br

Estimation of N-acetyltransferase 2 haplotypes

N-Acetyltransferase 2 (NAT2) genotyping may result in a considerable percentage in several ambiguous allele combinations. PHASE 2.1 is a statistical program which is designed to estimate the probability of different allele combinations. We have investigated haplotypes of 2088 subjects genotyped for NAT2 according to standard PCR/RFLP methods. In 856 out of 2088 cases the genotype was clearly defined by PCR/RFLP only. In many of the remaining cases the program clearly defined the most probable allele combination: In the case of *5A/*6C, *5B/*6A the probability for *5B/*6A is 99% whereas the alternative allele combination *5A/*6C can be neglected. Other combinations cannot be allocated with a comparable high probability. For example the allele combination *5A/*5C, *5B/*5D provides for *5A/*5C a probability of 69% whereas the estimation for *5B/*5D allele is only 31%. In the two most often observed constellations in our data [(*12A/*5B, *12C/*5C); (*12A/*6A, *12B/*6B, *4/*6C)] the probability of allele combination was ascertained as follows: *12A/*5B, 98%; *12C/*5C, 1.4% and *12A/*6A, 82%; *4/*6C, 17%; *12B/*6B, 0%. The estimation of the NAT2 haplotype is important because the assignment of the NAT2 alleles *12A, *12B or *13 as a rapid or slow genotype has been discussed controversially. Otherwise the classification of alleles in subjects which are not showing a clearly allocation can result in a rapid or slow acetylation state. This assignment has an important role in survey of bladder cancer cases in the scope of occupational exposure with aromatic amines. --PHASE 2.1,NAT2 genotyping,single nucleotide polymorphism

Direct maximum parsimony phylogeny reconstruction from genotype data

<p>Abstract</p> <p>Background</p> <p>Maximum parsimony phylogenetic tree reconstruction from genetic variation data is a fundamental problem in computational genetics with many practical applications in population genetics, whole genome analysis, and the search for genetic predictors of disease. Efficient methods are available for reconstruction of maximum parsimony trees from haplotype data, but such data are difficult to determine directly for autosomal DNA. Data more commonly is available in the form of genotypes, which consist of conflated combinations of pairs of haplotypes from homologous chromosomes. Currently, there are no general algorithms for the direct reconstruction of maximum parsimony phylogenies from genotype data. Hence phylogenetic applications for autosomal data must therefore rely on other methods for first computationally inferring haplotypes from genotypes.</p> <p>Results</p> <p>In this work, we develop the first practical method for computing maximum parsimony phylogenies directly from genotype data. We show that the standard practice of first inferring haplotypes from genotypes and then reconstructing a phylogeny on the haplotypes often substantially overestimates phylogeny size. As an immediate application, our method can be used to determine the minimum number of mutations required to explain a given set of observed genotypes.</p> <p>Conclusion</p> <p>Phylogeny reconstruction directly from unphased data is computationally feasible for moderate-sized problem instances and can lead to substantially more accurate tree size inferences than the standard practice of treating phasing and phylogeny construction as two separate analysis stages. The difference between the approaches is particularly important for downstream applications that require a lower-bound on the number of mutations that the genetic region has undergone.</p

Parsimony-based genetic algorithm for haplotype resolution and block partitioning

This dissertation proposes a new algorithm for performing simultaneous haplotype resolution and block partitioning. The algorithm is based on genetic algorithm approach and the parsimonious principle. The multiloculs LD measure (Normalized Entropy Difference) is used as a block identification criterion. The proposed algorithm incorporates missing data is a part of the model and allows blocks of arbitrary length. In addition, the algorithm provides scores for the block boundaries which represent measures of strength of the boundaries at specific positions. The performance of the proposed algorithm was validated by running it on several publicly available data sets including the HapMap data and comparing results to those of the existing state-of-the-art algorithms. The results show that the proposed genetic algorithm provides the accuracy of haplotype decomposition within the range of the same indicators shown by the other algorithms. The block structure output by our algorithm in general agrees with the block structure for the same data provided by the other algorithms. Thus, the proposed algorithm can be successfully used for block partitioning and haplotype phasing while providing some new valuable features like scores for block boundaries and fully incorporated treatment of missing data. In addition, the proposed algorithm for haplotyping and block partitioning is used in development of the new clustering algorithm for two-population mixed genotype samples. The proposed clustering algorithm extracts from the given genotype sample two clusters with substantially different block structures and finds haplotype resolution and block partitioning for each cluster

Haplotype Affinities Resolve a Major Component of Goat (Capra hircus) MtDNA D-Loop Diversity and Reveal Specific Features of the Sardinian Stock

Goat mtDNA haplogroup A is a poorly resolved lineage absorbing most of the overall diversity and is found in locations as distant as Eastern Asia and Southern Africa. Its phylogenetic dissection would cast light on an important portion of the spread of goat breeding. The aims of this work were 1) to provide an operational definition of meaningful mtDNA units within haplogroup A, 2) to investigate the mechanisms underlying the maintenance of diversity by considering the modes of selection operated by breeders and 3) to identify the peculiarities of Sardinian mtDNA types. We sequenced the mtDNA D-loop in a large sample of animals (1,591) which represents a non-trivial quota of the entire goat population of Sardinia. We found that Sardinia mirrors a large quota of mtDNA diversity of Western Eurasia in the number of variable sites, their mutational pattern and allele frequency. By using Bayesian analysis, a distance-based tree and a network analysis, we recognized demographically coherent groups of sequences identified by particular subsets of the variable positions. The results showed that this assignment system could be reproduced in other studies, capturing the greatest part of haplotype diversity

Bayesian Statistical Methods for Genetic Association Studies with Case-Control and Cohort Design

Large-scale genetic association studies are carried out with the hope of discovering single nucleotide polymorphisms involved in the etiology of complex diseases. We propose a coalescent-based model for association mapping which potentially increases the power to detect disease-susceptibility variants in genetic association studies with case-control and cohort design. The approach uses Bayesian partition modelling to cluster haplotypes with similar disease risks by exploiting evolutionary information. We focus on candidate gene regions and we split the chromosomal region of interest into sub-regions or windows of high linkage disequilibrium (LD) therein assuming a perfect phylogeny. The haplotype space is then partitioned into disjoint clusters within which the phenotype-haplotype association is assumed to be the same. The novelty of our approach consists in the fact that the distance used for clustering haplotypes has an evolutionary interpretation, as haplotypes are clustered according to the time to their most recent common mutation. Our approach is fully Bayesian and we develop Markov Chain Monte Carlo algorithms to sample efficiently over the space of possible partitions. We have also developed a Bayesian survival regression model for high-dimension and small sample size settings. We provide a Bayesian variable selection procedure and shrinkage tool by imposing shrinkage priors on the regression coefficients. We have developed a computationally efficient optimization algorithm to explore the posterior surface and find the maximum a posteriori estimates of the regression coefficients. We compare the performance of the proposed methods in simulation studies and using real datasets to both single-marker analyses and recently proposed multi-marker methods and show that our methods perform similarly in localizing the causal allele while yielding lower false positive rates. Moreover, our methods offer computational advantages over other multi-marker approaches

Discrete Algorithms for Analysis of Genotype Data

Accessibility of high-throughput genotyping technology makes possible genome-wide association studies for common complex diseases. When dealing with common diseases, it is necessary to search and analyze multiple independent causes resulted from interactions of multiple genes scattered over the entire genome. The optimization formulations for searching disease-associated risk/resistant factors and predicting disease susceptibility for given case-control study have been introduced. Several discrete methods for disease association search exploiting greedy strategy and topological properties of case-control studies have been developed. New disease susceptibility prediction methods based on the developed search methods have been validated on datasets from case-control studies for several common diseases. Our experiments compare favorably the proposed algorithms with the existing association search and susceptibility prediction methods

Graph algorithms for the haplotyping problem

Evidence from investigations of genetic differences among human beings shows that genetic diseases are often the results of genetic mutations. The most common form of these mutations is single nucleotide polymorphism (SNP). A complete map of all SNPs in the human genome will be extremely valuable for studying the relationships between specific haplotypes and specific genetic diseases. Some recent discoveries show that the DNA sequence of human beings can be partitioned into long blocks where genetic recombination has been rare. Then, inferring both haplotypes from chromosome sequences is a biologically meaningful research topic, which has compounded mathematical and computational problems.;We are interested in the algorithmic implications to infer haplotypes from long blocks of DNA that have not undergone recombination in populations. The assumption justifies a model of haplotype evolution---haplotypes in a population evolves along a coalescent, based on the standard population-genetic assumption of infinite sites, which as a rooted tree is a perfect phylogeny. The Perfect Phylogeny Haplotyping (PPH) Problem was introduced by Daniel Gusfield in 2002. A nearly linear-time solution to the PPH problem (O( nmalpha(nm)), where alpha is the extremely slowly growing inverse Ackerman function) is provided. However, it is very complex and difficult to implement. So far, even the best practical solution to the PPH problem has the worst-case running time of O( nm2). D. Gusfield conjectured that a linear-time ( O(nm)) solution to the PPH problem should be possible.;We solve the conjecture of Gusfield by introducing a linear-time algorithm for the PPH problem. Different kinds of posets for haplotype matrices and genotype matrices are designed and the relationships between them are studied. Since redundant calculations can be avoided by the transitivity of partial ordering in posets, we design a linear-time (O(nm )) algorithm for the PPH problem that provides all the possible solutions from an input. The algorithm is fully implemented and the simulation shows that it is much faster than previous methods