Carlos D Bustamante

Publications (2)11.27 Total impact

• Article: SnIPRE: Selection Inference Using a Poisson Random Effects Model.

  Kirsten E Eilertson, James G Booth, Carlos D Bustamante
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  ABSTRACT: We present an approach for identifying genes under natural selection using polymorphism and divergence data from synonymous and non-synonymous sites within genes. A generalized linear mixed model is used to model the genome-wide variability among categories of mutations and estimate its functional consequence. We demonstrate how the model's estimated fixed and random effects can be used to identify genes under selection. The parameter estimates from our generalized linear model can be transformed to yield population genetic parameter estimates for quantities including the average selection coefficient for new mutations at a locus, the synonymous and non-synynomous mutation rates, and species divergence times. Furthermore, our approach incorporates stochastic variation due to the evolutionary process and can be fit using standard statistical software. The model is fit in both the empirical Bayes and Bayesian settings using the lme4 package in R, and Markov chain Monte Carlo methods in WinBUGS. Using simulated data we compare our method to existing approaches for detecting genes under selection: the McDonald-Kreitman test, and two versions of the Poisson random field based method MKprf. Overall, we find our method universally outperforms existing methods for detecting genes subject to selection using polymorphism and divergence data.
  PLoS Computational Biology 12/2012; 8(12):e1002806. · 5.22 Impact Factor
• Article: Successful computational prediction of novel imprinted genes from epigenomic features.

  Chelsea M Brideau, Kirsten E Eilertson, James A Hagarman, Carlos D Bustamante, Paul D Soloway
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  ABSTRACT: Approximately 100 mouse genes undergo genomic imprinting, whereby one of the two parental alleles is epigenetically silenced. Imprinted genes influence processes including development, X chromosome inactivation, obesity, schizophrenia, and diabetes, motivating the identification of all imprinted loci. Local sequence features have been used to predict candidate imprinted genes, but rigorous testing using reciprocal crosses validated only three, one of which resided in previously identified imprinting clusters. Here we show that specific epigenetic features in mouse cells correlate with imprinting status in mice, and we identify hundreds of additional genes predicted to be imprinted in the mouse. We used a multitiered approach to validate imprinted expression, including use of a custom single nucleotide polymorphism array and traditional molecular methods. Of 65 candidates subjected to molecular assays for allele-specific expression, we found 10 novel imprinted genes that were maternally expressed in the placenta.
  Molecular and cellular biology 07/2010; 30(13):3357-70. · 6.06 Impact Factor