Genome Research · 2007 · 287 citations · 42 references
Life SciencesBiologyGeneticsComparative GenomicsGenomicsMolecular EvolutionCancer Genomic InstabilityEvolutionGenome InstabilityGenome StabilityGenomic InstabilityMutation ProcessesDna Secondary StructureDna Secondary Structure StabilityGene Conversion IntermediatesGenetic InstabilityGenome-wide CharacterizationMicrosatellite EvolutionMicrosatellite StabilityNonallelic Gene Conversion
Mutation rates of microsatellites vary greatly among loci. The causes of this heterogeneity remain largely enigmatic yet are crucial for understanding numerous human neurological diseases and genetic instability in cancer. In this first genome-wide study, the relative contributions of intrinsic features and regional genomic factors to the variation in mutability among orthologous human-chimpanzee microsatellites are investigated with resampling and regression techniques. As a result, we uncover the intricacies of microsatellite mutagenesis as follows. First, intrinsic features (repeat number, length, and motif size), which all influence the probability and rate of slippage, are the strongest predictors of mutability. Second, mutability increases nonuniformly with length, suggesting that processes additional to slippage, such as faulty repair, contribute to mutations. Third, mutability varies among microsatellites with different motif composition likely due to dissimilarities in secondary DNA structure formed by their slippage intermediates. Fourth, mutability of mononucleotide microsatellites is impacted by their location on sex chromosomes vs. autosomes and inside vs. outside of Alu repeats, the former confirming the importance of replication and the latter suggesting a role for gene conversion. Fifth, transcription status and location in a particular isochore do not influence microsatellite mutability. Sixth, compared with intrinsic features, regional genomic factors have only minor effects. Finally, our regression models explain approximately 90% of variation in microsatellite mutability and can generate useful predictions for the studies of human diseases, forensics, and conservation genetics.
42
Applied Linear Statistical Models
Eric R. Ziegel, John Neter, William Wasserman et al. · Technometrics · 1992 · 10K citations
Tandem repeats finder: a program to analyze DNA sequences
Gary Benson · Nucleic Acids Research · 1999 · 9.9K citations
Applied Linear Statistical Models
Michael Kutner · Technometrics · 1997 · 7.3K citations · Full text
A Fine-Scale Map of Recombination Rates and Hotspots Across the Human Genome
Simon Myers, Leonardo Bottolo, Colin Freeman et al. · Science · 2005 · 1.2K citations
Slippage synthesis of simple sequence DNA
Christian Schlötterer, Diethard Tautz · Nucleic Acids Research · 1992 · 1.1K citations · Full text