Name
Title | ||
|---|---|---|
Algorithms to Distinguish the Role of Gene-Conversion from Single-Crossover Recombination in the Derivation of SNP Sequences in Populations |
Abstract | ||
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Meiotic recombination is a fundamental biological event and one of the principal evolutionary forces responsible for shaping genetic variation within species. In addition to its fundamental role, recombi- nation is central to several critical applied problems. The most impor- tant example is "association mapping" in populations, which is widely hoped to help find genes that influence genetic diseases (3,4). Hence, a great deal of recent attention has focused on problems of inferring the historical derivation of sequences in populations when both mutations and recombinations have occurred. In the algorithms literature, most of that recent work has been directed to single-crossover recombination. However, gene-conversion is an important, and more common, form of (two-crossover) recombination which has been much less investigated in the algorithms literature. In this paper we explicitly incorporate gene-conversion into discrete methods to study historical recombination. We are concerned with algo- rithms for identifying and locating the extent of historical crossing-over and gene-conversion (along with single-nucleotide mutation), and prob- lems of constructing full putative histories of those events. The novel technical issues concern the incorporation of gene-conversion into re- cently developed discrete methods (20,26) that compute lower and upper- bound information on the amount of needed recombination without gene- conversion. We first examine the most natural extension of the lower bound methods from (20), showing that the extension can be computed efficiently, but that this extension can only yield weak lowerbounds. We then develop additional ideas that lead to higher lower bounds, and show how to solve, via integer-linear programming, a more biologically realistic version of the lower bound problem. We also show how to com- pute effective upper bounds on the number of needed single-crossovers and gene-conversions, along with explicit networks showing a putative history of mutations, single-crossovers and gene-conversions. We validate the significance of these methods by showing that they can be effectively used to distinguish simulation-derived sequences generated without gene-conversion from sequences that were generated with gene- conversion. We apply the methods to recently studied sequences of Ara- |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/11732990_20 | Research in Computational Molecular Biology |
Keywords | DocType | Volume |
meiotic recombination,higher lower bound,historical recombination,algorithms literature,historical crossing-over,weak lower bound,needed recombination,lower bound problem,discrete method,single-crossover recombination,lower bound method,snp sequence,genetic variation,association mapping,lower bound,upper bound,gene conversion,nucleotides | Journal | 14 |
Issue | ISSN | ISBN |
10 | 1066-5277 | 3-540-33295-2 |
Citations | PageRank | References |
9 | 0.76 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yun S. Song | 1 | 165 | 18.06 |
| Zhihong Ding | 2 | 64 | 4.45 |
| Dan Gusfield | 3 | 3803 | 477.36 |
| Charles H. Langley | 4 | 192 | 15.60 |
| Yufeng Wu | 5 | 74 | 8.85 |