Direct maximum parsimony phylogeny reconstruction from genotype data.
BACKGROUND
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.


RESULTS
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.


CONCLUSIONS
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.
Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA. srinath@cs.cmu.edu
Srinath
S
Sridhar
Fumei
F
Lam
Guy E
GE
Blelloch
R
R
Ravi
Russell
R
Schwartz
D000465:Algorithms; D000818:Animals; D001483:Base Sequence; D017124:Conserved Sequence; D018742:DNA, Chloroplast / Q000235:genetics; D004272:DNA, Mitochondrial / Q000235:genetics; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D019143:Evolution, Molecular; D005163:Factor Analysis, Statistical; D023281:Genomics / Q000379:methods*; D005838:Genotype; D006239:Haplotypes / Q000235:genetics*; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D015233:Models, Statistical; D009154:Mutation; D010802:Phylogeny; D011382:Programming, Linear; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA; D017386:Sequence Homology, Amino Acid
D018742:DNA, Chloroplast; D004272:DNA, Mitochondrial