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Haplotype mapping of a diploid non-meiotic organism using existing and induced aneuploidies.

ABSTRACT: Haplotype maps (HapMaps) reveal underlying sequence variation and facilitate the study of recombination and genetic diversity. In general, HapMaps are produced by analysis of Single-Nucleotide Polymorphism (SNP) segregation in large numbers of meiotic progeny. Candida albicans, the most common human fungal pathogen, is an obligate diploid that does not appear to undergo meiosis. Thus, standard methods for haplotype mapping cannot be used. We exploited naturally occurring aneuploid strains to determine the haplotypes of the eight chromosome pairs in the C. albicans laboratory strain SC5314 and in a clinical isolate. Comparison of the maps revealed that the clinical strain had undergone a significant amount of genome rearrangement, consisting primarily of crossover or gene conversion recombination events. SNP map haplotyping revealed that insertion and activation of the UAU1 cassette in essential and non-essential genes can result in whole chromosome aneuploidy. UAU1 is often used to construct homozygous deletions of targeted genes in C. albicans; the exact mechanism (trisomy followed by chromosome loss versus gene conversion) has not been determined. UAU1 insertion into the essential ORC1 gene resulted in a large proportion of trisomic strains, while gene conversion events predominated when UAU1 was inserted into the non-essential LRO1 gene. Therefore, induced aneuploidies can be used to generate HapMaps, which are essential for analyzing genome alterations and mitotic recombination events in this clonal organism.

SUBMITTER: Legrand M

PROVIDER: S-EPMC2174976 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe genetic etiology of complex diseases in human has been commonly viewed as a complex process involving both genetic and environmental factors functioning in a complicated manner. Quite often the interactions among genetic variants play major roles in determining the susceptibility of an individual to a particular disease. Statistical methods for modeling interactions underlying complex diseases between single genetic variants (e.g. single nucleotide polymorphisms or SNPs) have been extensively studied. Recently, haplotype-based analysis has gained its popularity among genetic association studies. When multiple sequence or haplotype interactions are involved in determining an individual's susceptibility to a disease, it presents daunting challenges in statistical modeling and testing of the interaction effects, largely due to the complicated higher order epistatic complexity.ResultsIn this article, we propose a new strategy in modeling haplotype-haplotype interactions under the penalized logistic regression framework with adaptive L1-penalty. We consider interactions of sequence variants between haplotype blocks. The adaptive L1-penalty allows simultaneous effect estimation and variable selection in a single model. We propose a new parameter estimation method which estimates and selects parameters by the modified Gauss-Seidel method nested within the EM algorithm. Simulation studies show that it has low false positive rate and reasonable power in detecting haplotype interactions. The method is applied to test haplotype interactions involved in mother and offspring genome in a small for gestational age (SGA) neonates data set, and significant interactions between different genomes are detected.ConclusionsAs demonstrated by the simulation studies and real data analysis, the approach developed provides an efficient tool for the modeling and testing of haplotype interactions. The implementation of the method in R codes can be freely downloaded from http://www.stt.msu.edu/~cui/software.html.

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Haplotype mapping of a diploid non-meiotic organism using existing and induced aneuploidies.

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