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      A comprehensive literature review of haplotyping software and methods for use with unrelated individuals

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          Abstract

          Interest in the assignment and frequency analysis of haplotypes in samples of unrelated individuals has increased immeasurably as a result of the emphasis placed on haplotype analyses by, for example, the International HapMap Project and related initiatives. Although there are many available computer programs for haplotype analysis applicable to samples of unrelated individuals, many of these programs have limitations and/or very specific uses. In this paper, the key features of available haplotype analysis software for use with unrelated individuals, as well as pooled DNA samples from unrelated individuals, are summarised. Programs for haplotype analysis were identified through keyword searches on PUBMED and various internet search engines, a review of citations from retrieved papers and personal communications, up to June 2004. Priority was given to functioning computer programs, rather than theoretical models and methods. The available software was considered in light of a number of factors: the algorithm(s) used, algorithm accuracy, assumptions, the accommodation of genotyping error, implementation of hypothesis testing, handling of missing data, software characteristics and web-based implementations. Review papers comparing specific methods and programs are also summarised. Forty-six haplotyping programs were identified and reviewed. The programs were divided into two groups: those designed for individual genotype data (a total of 43 programs) and those designed for use with pooled DNA samples (a total of three programs). The accuracy of programs using various criteria are assessed and the programs are categorised and discussed in light of: algorithm and method, accuracy, assumptions, genotyping error, hypothesis testing, missing data, software characteristics and web implementation. Many available programs have limitations (eg some cannot accommodate missing data) and/or are designed with specific tasks in mind (eg estimating haplotype frequencies rather than assigning most likely haplotypes to individuals). It is concluded that the selection of an appropriate haplotyping program for analysis purposes should be guided by what is known about the accuracy of estimation, as well as by the limitations and assumptions built into a program.

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          Score tests for association between traits and haplotypes when linkage phase is ambiguous.

          A key step toward the discovery of a gene related to a trait is the finding of an association between the trait and one or more haplotypes. Haplotype analyses can also provide critical information regarding the function of a gene; however, when unrelated subjects are sampled, haplotypes are often ambiguous because of unknown linkage phase of the measured sites along a chromosome. A popular method of accounting for this ambiguity in case-control studies uses a likelihood that depends on haplotype frequencies, so that the haplotype frequencies can be compared between the cases and controls; however, this traditional method is limited to a binary trait (case vs. control), and it does not provide a method of testing the statistical significance of specific haplotypes. To address these limitations, we developed new methods of testing the statistical association between haplotypes and a wide variety of traits, including binary, ordinal, and quantitative traits. Our methods allow adjustment for nongenetic covariates, which may be critical when analyzing genetically complex traits. Furthermore, our methods provide several different global tests for association, as well as haplotype-specific tests, which give a meaningful advantage in attempts to understand the roles of many different haplotypes. The statistics can be computed rapidly, making it feasible to evaluate the associations between many haplotypes and a trait. To illustrate the use of our new methods, they are applied to a study of the association of haplotypes (composed of genes from the human-leukocyte-antigen complex) with humoral immune response to measles vaccination. Limited simulations are also presented to demonstrate the validity of our methods, as well as to provide guidelines on how our methods could be used.
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            Linkage disequilibrium in the human genome.

            With the availability of a dense genome-wide map of single nucleotide polymorphisms (SNPs), a central issue in human genetics is whether it is now possible to use linkage disequilibrium (LD) to map genes that cause disease. LD refers to correlations among neighbouring alleles, reflecting 'haplotypes' descended from single, ancestral chromosomes. The size of LD blocks has been the subject of considerable debate. Computer simulations and empirical data have suggested that LD extends only a few kilobases (kb) around common SNPs, whereas other data have suggested that it can extend much further, in some cases greater than 100 kb. It has been difficult to obtain a systematic picture of LD because past studies have been based on only a few (1-3) loci and different populations. Here, we report a large-scale experiment using a uniform protocol to examine 19 randomly selected genomic regions. LD in a United States population of north-European descent typically extends 60 kb from common alleles, implying that LD mapping is likely to be practical in this population. By contrast, LD in a Nigerian population extends markedly less far. The results illuminate human history, suggesting that LD in northern Europeans is shaped by a marked demographic event about 27,000-53,000 years ago.
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              A vision for the future of genomics research.

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                Author and article information

                Journal
                Hum Genomics
                Hum. Genomics
                Human Genomics
                BioMed Central
                1473-9542
                1479-7364
                2005
                1 March 2005
                : 2
                : 1
                : 39-66
                Affiliations
                [1 ]Polymorphism Research Laboratory, Department of Psychiatry, University of California, San Diego, CA, USA
                [2 ]Department of Family and Preventive Medicine, University of California, San Diego, CA, USA
                [3 ]Graduate School of Public Health, San Diego State University, San Diego, CA, USA
                Article
                1479-7364-2-1-39
                10.1186/1479-7364-2-1-39
                3525117
                15814067
                ff06fc0b-ce5d-4237-bcda-4e28dbb05f05
                History
                : 18 January 2005
                : 18 January 2005
                Categories
                Review

                Genetics
                algorithm,genetic variation,phase,haplotype,software,haplotyping
                Genetics
                algorithm, genetic variation, phase, haplotype, software, haplotyping

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