Proceedings of the 25th international conference on Machine learning
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
ISBRA'10 Proceedings of the 6th international conference on Bioinformatics Research and Applications
| Hi-index | 3.84 |
Motivation: While genetic properties such as linkage disequilibrium (LD) and population structure are closely related under a common inheritance process, the statistical methodologies developed so far mostly deal with LD analysis and structural inference separately, using specialized models that do not capture their statistical and genetic relationships. Also, most of these approaches ignore the inherent uncertainty in the genetic complexity of the data and rely on inflexible models built on a closed genetic space. These limitations may make it difficult to infer detailed and consistent structural information from rich genomic data such as populational single nucleotide polymorphisms (SNP) profiles. Results: We propose a new model-based approach to address these issues through joint inference of population structure and recombination events under a non-parametric Bayesian framework; we present Spectrum, an efficient implementation based on our new model. We validated Spectrum on simulated data and applied it to two real SNP datasets, including single-population Daly data and the four-population HapMap data. Our method performs well relative to LDhat 2.0 in estimating the recombination rates and hotspots on these datasets. More interestingly, it generates an ancestral spectrum for representing population structures which not only displays sub-structure based on population founders but also reveals details of the genetic diversity of each individual. It offers an alternative view of the population structures to that offered by Structure 2.1, which ignores chromosome-level mutation and recombination with respect to founders. Contact: epxing@cs.cmu.edu