title: Adapt-Mix: learning local genetic correlation structure improves summary statistics-based analyses.
abstract: BACKGROUND
  Approaches to identifying new risk loci, training risk prediction models, imputing untyped variants and fine-mapping causal variants from summary statistics of genome-wide association studies are playing an increasingly important role in the human genetics community. Current summary statistics-based methods rely on global 'best guess' reference panels to model the genetic correlation structure of the dataset being studied. This approach, especially in admixed populations, has the potential to produce misleading results, ignores variation in local structure and is not feasible when appropriate reference panels are missing or small. Here, we develop a method, Adapt-Mix, that combines information across all available reference panels to produce estimates of local genetic correlation structure for summary statistics-based methods in arbitrary populations.
  
  
  RESULTS
  We applied Adapt-Mix to estimate the genetic correlation structure of both admixed and non-admixed individuals using simulated and real data. We evaluated our method by measuring the performance of two summary statistics-based methods: imputation and joint-testing. When using our method as opposed to the current standard of 'best guess' reference panels, we observed a 28% decrease in mean-squared error for imputation and a 73.7% decrease in mean-squared error for joint-testing.
  
  
  METHODS
  Our method is publicly available in a software package called ADAPT-Mix available at https://github.com/dpark27/adapt_mix.
authors.affiliation: Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
authors.forename: Danny S
authors.initials: DS
authors.lastname: Park
authors.affiliation: Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
authors.forename: Brielin
authors.initials: B
authors.lastname: Brown
authors.affiliation: Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
authors.forename: Celeste
authors.initials: C
authors.lastname: Eng
authors.affiliation: Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
authors.forename: Scott
authors.initials: S
authors.lastname: Huntsman
authors.affiliation: Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
authors.forename: Donglei
authors.initials: D
authors.lastname: Hu
authors.affiliation: Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
authors.forename: Dara G
authors.initials: DG
authors.lastname: Torgerson
authors.affiliation: Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
authors.forename: Esteban G
authors.initials: EG
authors.lastname: Burchard
authors.affiliation: Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
authors.forename: Noah
authors.initials: N
authors.lastname: Zaitlen
mesh_terms: D000465:Algorithms; D003324:Coronary Artery Disease / Q000235:genetics; D003627:Data Interpretation, Statistical; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D006801:Humans; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D012984:Software