Doctor of Philosophy, Carnegie Mellon University (2012)
Carlos Bustamante, Postdoctoral Faculty Sponsor
Population stratification is an important task in genetic analyses. It provides information about the ancestry of individuals and can be an important confounder in genome-wide association studies. Public genotyping projects have made a large number of datasets available for study. However, practical constraints dictate that of a geographical/ethnic population, only a small number of individuals are genotyped. The resulting data are a sample from the entire population. If the distribution of sample sizes is not representative of the populations being sampled, the accuracy of population stratification analyses of the data could be affected. We attempt to understand the effect of biased sampling on the accuracy of population structure analysis and individual ancestry recovery. We examined two commonly used methods for analyses of such datasets, ADMIXTURE and EIGENSOFT, and found that the accuracy of recovery of population structure is affected to a large extent by the sample used for analysis and how representative it is of the underlying populations. Using simulated data and real genotype data from cattle, we show that sample selection bias can affect the results of population structure analyses. We develop a mathematical framework for sample selection bias in models for population structure and also proposed a correction for sample selection bias using auxiliary information about the sample. We demonstrate that such a correction is effective in practice using simulated and real data.
View details for DOI 10.1534/g3.113.007633
View details for Web of Science ID 000336483900015
View details for PubMedID 24637351
Clustering of genotype data is an important way of understanding similarities and differences between populations. A summary of populations through clustering allows us to make inferences about the evolutionary history of the populations. Many methods have been proposed to perform clustering on multilocus genotype data. However, most of these methods do not directly address the question of how many clusters the data should be divided into and leave that choice to the user.We present StructHDP, which is a method for automatically inferring the number of clusters from genotype data in the presence of admixture. Our method is an extension of two existing methods, Structure and Structurama. Using a Hierarchical Dirichlet Process (HDP), we model the presence of admixture of an unknown number of ancestral populations in a given sample of genotype data. We use a Gibbs sampler to perform inference on the resulting model and infer the ancestry proportions and the number of clusters that best explain the data.To demonstrate our method, we simulated data from an island model using the neutral coalescent. Comparing the results of StructHDP with Structurama shows the utility of combining HDPs with the Structure model. We used StructHDP to analyze a dataset of 155 Taita thrush, Turdus helleri, which has been previously analyzed using Structure and Structurama. StructHDP correctly picks the optimal number of populations to cluster the data. The clustering based on the inferred ancestry proportions also agrees with that inferred using Structure for the optimal number of populations. We also analyzed data from 1048 individuals from the Human Genome Diversity project from 53 world populations. We found that the clusters obtained correspond with major geographical divisions of the world, which is in agreement with previous analyses of the dataset.StructHDP is written in C++. The code will be available for download at http://firstname.lastname@example.org; email@example.com.
View details for DOI 10.1093/bioinformatics/btr242
View details for Web of Science ID 000291752600040
View details for PubMedID 21685088
PNAS article classification is rooted in long-standing disciplinary divisions that do not necessarily reflect the structure of modern scientific research. We reevaluate that structure using latent pattern models from statistical machine learning, also known as mixed-membership models, that identify semantic structure in co-occurrence of words in the abstracts and references. Our findings suggest that the latent dimensionality of patterns underlying PNAS research articles in the Biological Sciences is only slightly larger than the number of categories currently in use, but it differs substantially in the content of the categories. Further, the number of articles that are listed under multiple categories is only a small fraction of what it should be. These findings together with the sensitivity analyses suggest ways to reconceptualize the organization of papers published in PNAS.
View details for DOI 10.1073/pnas.1013452107
View details for Web of Science ID 000285050800013
View details for PubMedID 21078953
Traditional methods for analyzing population structure, such as the Structure program, ignore the influence of the effect of allele mutations between the ancestral and current alleles of genetic markers, which can dramatically influence the accuracy of the structural estimation of current populations. Studying these effects can also reveal additional information about population evolution such as the divergence time and migration history of admixed populations. We propose mStruct, an admixture of population-specific mixtures of inheritance models that addresses the task of structure inference and mutation estimation jointly through a hierarchical Bayesian framework, and a variational algorithm for inference. We validated our method on synthetic data and used it to analyze the Human Genome Diversity Project-Centre d'Etude du Polymorphisme Humain (HGDP-CEPH) cell line panel of microsatellites and HGDP single-nucleotide polymorphism (SNP) data. A comparison of the structural maps of world populations estimated by mStruct and Structure is presented, and we also report potentially interesting mutation patterns in world populations estimated by mStruct.
View details for DOI 10.1534/genetics.108.100222
View details for Web of Science ID 000270213900016
View details for PubMedID 19363128
Functional turnover of transcription factor binding sites (TFBSs), such as whole-motif loss or gain, are common events during genome evolution. Conventional probabilistic phylogenetic shadowing methods model the evolution of genomes only at nucleotide level, and lack the ability to capture the evolutionary dynamics of functional turnover of aligned sequence entities. As a result, comparative genomic search of non-conserved motifs across evolutionarily related taxa remains a difficult challenge, especially in higher eukaryotes, where the cis-regulatory regions containing motifs can be long and divergent; existing methods rely heavily on specialized pattern-driven heuristic search or sampling algorithms, which can be difficult to generalize and hard to interpret based on phylogenetic principles. We propose a new method: Conditional Shadowing via Multi-resolution Evolutionary Trees, or CSMET, which uses a context-dependent probabilistic graphical model that allows aligned sites from different taxa in a multiple alignment to be modeled by either a background or an appropriate motif phylogeny conditioning on the functional specifications of each taxon. The functional specifications themselves are the output of a phylogeny which models the evolution not of individual nucleotides, but of the overall functionality (e.g., functional retention or loss) of the aligned sequence segments over lineages. Combining this method with a hidden Markov model that autocorrelates evolutionary rates on successive sites in the genome, CSMET offers a principled way to take into consideration lineage-specific evolution of TFBSs during motif detection, and a readily computable analytical form of the posterior distribution of motifs under TFBS turnover. On both simulated and real Drosophila cis-regulatory modules, CSMET outperforms other state-of-the-art comparative genomic motif finders.
View details for DOI 10.1371/journal.pcbi.1000090
View details for Web of Science ID 000259786700004
View details for PubMedID 18535663