title: Inferring parental genomic ancestries using pooled semi-Markov processes.
abstract: BACKGROUND
  A basic problem of broad public and scientific interest is to use the DNA of an individual to infer the genomic ancestries of the parents. In particular, we are often interested in the fraction of each parent's genome that comes from specific ancestries (e.g. European, African, Native American, etc). This has many applications ranging from understanding the inheritance of ancestry-related risks and traits to quantifying human assortative mating patterns.
  
  
  RESULTS
  We model the problem of parental genomic ancestry inference as a pooled semi-Markov process. We develop a general mathematical framework for pooled semi-Markov processes and construct efficient inference algorithms for these models. Applying our inference algorithm to genotype data from 231 Mexican trios and 258 Puerto Rican trios where we have the true genomic ancestry of each parent, we demonstrate that our method accurately infers parameters of the semi-Markov processes and parents' genomic ancestries. We additionally validated the method on simulations. Our model of pooled semi-Markov process and inference algorithms may be of independent interest in other settings in genomics and machine learning.
authors.affiliation: Microsoft Research, One Memorial Drive, Cambridge, MA 02142, USA, Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel, Department of Bioengineering and Therapeutic Sciences and Department of Medicine, University of California, San Francisco, CA 94158 and Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.
authors.forename: James Y
authors.initials: JY
authors.lastname: Zou
authors.affiliation: Microsoft Research, One Memorial Drive, Cambridge, MA 02142, USA, Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel, Department of Bioengineering and Therapeutic Sciences and Department of Medicine, University of California, San Francisco, CA 94158 and Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.
authors.forename: Eran
authors.initials: E
authors.lastname: Halperin
authors.affiliation: Microsoft Research, One Memorial Drive, Cambridge, MA 02142, USA, Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel, Department of Bioengineering and Therapeutic Sciences and Department of Medicine, University of California, San Francisco, CA 94158 and Department of Genetics, Harvard Medical School, Boston, MA 02115, USA Microsoft Research, One Memorial Drive, Cambridge, MA 02142, USA, Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel, Department of Bioengineering and Therapeutic Sciences and Department of Medicine, University of California, San Francisco, CA 94158 and Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.
authors.forename: Esteban
authors.initials: E
authors.lastname: Burchard
authors.affiliation: Microsoft Research, One Memorial Drive, Cambridge, MA 02142, USA, Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel, Department of Bioengineering and Therapeutic Sciences and Department of Medicine, University of California, San Francisco, CA 94158 and Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.
authors.forename: Sriram
authors.initials: S
authors.lastname: Sankararaman
mesh_terms: D000465:Algorithms; D002648:Child; D005260:Female; D005828:Genetics, Population / Q000379:methods; D023281:Genomics / Q000379:methods*; D060005:Genotyping Techniques; D006801:Humans; D008297:Male; D008390:Markov Chains; D008800:Mexico; D010290:Parents; D011647:Puerto Rico; D044469:Racial Groups / Q000235:genetics*