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Genotype Phasing and Imputation in 1x Sequencing Data

Genotype Phasing and Imputation in 1x Sequencing Data. Warren W. Kretzschmar. DPhil Genomic Medicine and Statistics Wellcome Trust Centre for Human Genetics, Oxford , UK Supervisor: Jonathan Marchini. Major Depression.

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Genotype Phasing and Imputation in 1x Sequencing Data

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  1. Genotype Phasing and Imputation in 1x Sequencing Data Warren W. Kretzschmar DPhil Genomic Medicine and Statistics Wellcome Trust Centre for Human Genetics, Oxford, UK Supervisor: Jonathan Marchini

  2. Major Depression • Commonest psychiatric disorder and the second ranking cause of morbidity world-wide. • Affects 1 in 10 people in their lifetime. • Estimates of heritability range between 30-40%.

  3. Top Ten causes of DALYs DALY : Disability adjusted life year : number of years lost due to ill-health, disability or early death

  4. Genetics of Major Depression Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium (2012). A mega-analysis of genome-wide association studies for major depressive disorder. Molecular Psychiatry18.4:497-511. • Study Design • Unrelated Europeans • 9240 cases • 9519 controls • 1.2 million SNPs • Hypotheses • Depression has heterogeneous environmental and genetic causes • Depression is a complex trait with genetic components of small effect size

  5. CONVERGE (China, Oxford and VCU Experimental Research on Genetic Epidemiology) 59 hospitals, 45 cities, 21 provinces. Genetically Homogeneous : All subjects are female and their grandparents are Han Chinese 6,000 cases : typically severe affected: 85% qualify for a diagnosis of melancholia by DSM-IV. >25% reported a family history of MD in one or more first-degree relatives 6,000 controls : patients undergoing minor surgical procedures. Extensive Phenotyping: primary disorder of major depression, common comorbid disorders (e.g. generalized anxiety disorder, panic disorder), within disorder symptoms (e.g. suicidal ideation), disorder subtypes (e.g. melancholia, dysthymia), possible endophenotypes (e.g. neuroticism) and a range of risk factors (e.g. child abuse, stressful life events, social and marital relationships, parenting, post-natal depression, demographics). Sequencing : mean depth 1.7X using llluminaHiSeqat Beijing Genomics Institute Current status Sequencing finished. We have data on 12,000 samples. For now we have only considered ~13M sites polymorphic 1000 Genomes Asian samples. Analysis ongoing…

  6. Sequence analysis pipeline Phase 1: genotype likelihood estimation One sample at a time Phase 2: phasing and imputation All samples together Raw reads Genotype likelihoods My focus! Mapping Stampy 48 TB 350 GB 2.7 CPU years Phasing and imputation Duplicate Picard marking Base quality GATK recalibration 5 CPU years Genotype probabilities Genotype likelihood SNPTools estimation 650 GB 4.6 CPU years Genotype likelihoods

  7. Genotype Phasing and imputation

  8. Genotype Phasing Example SNP chip data Unphased: G/G A/T A/A T/T G/T A/T T/T A/A G/G G/C After Phasing Hap 1: G A A T T T T A G C Hap 2: G T A T G A T A G G Phase-informative Sites

  9. Genotype Imputation from Haplotypes J Marchiniand B Howie. Nature Rev. Genet. 2010

  10. Genotype Likelihoods

  11. What is a Genotype Likelihood? Genotype likelihoods (aka GL) are defined on a site by site basis. GLs are conditional probabilities. Genotype Likelihood = Pr( R | G ) R = Reads; also known as the “observed data” G = Genotype; usually one of ref/ref, ref/alt, alt/alt

  12. How are Genotype Likelihoods Useful? Genotype likelihoods allow us to quantify how much the reads support each possible genotype independent of other information. To determine the most likely genotype call, we need a genotype probability. Genotype Probability = Pr ( G | R ) proportional to Pr( R | G ) * Pr( G ) Pr( G ) = prior probability of G. May be determined through haplotype phasing and imputation approaches.

  13. Genotype Likelihood Creation with SNPTools Three distributions observed reads Pr(R|G = ref/ref) = 0.06 Pr(R|G = alt/alt) = 10e-6 Pr(R|G = ref/alt) = 10e-3 Y Wang, J Lu, J Yu, RA Gibbs, FL Yu. Genome Research. 2013

  14. Genotype Phasing using Genotype Likelihoods Hap 1: G A A T T A C A G G Reference Haplotypes Hap 2: G T A T T A T A G G Hap 3: G T A T G A C A G G Hap 4: G T A T G A T A G C Example GL data Pr(ref/ref): G/G A/AA/A T/T G/G A/A T/T A/A G/G G/G Pr(ref/alt): G/AA/TA/GT/A G/T A/T T/C A/G G/C G/C Pr(alt/alt): A/A T/TG/GA/A T/TT/TC/C G/G C/C C/C Plausible Haplotypes after Phasing Hap 5: G A A T T A T A G C Hap 6: G T A T T A T A G G

  15. General MCMC Scheme for Phasing from GLs • When using GLs, haplotype estimation is currently done in an iterative Markov Chain Monte Carlo (MCMC) scheme • Initalize haplotypes for each sample randomly • for a predetermined number of iterations • for each sample • Find a plausible haplotype pair using its GLs and all other haplotypes as a reference panel • Update that sample’s haplotypes with the plausible haplotype pair • Return each sample’s current pair of haplotypes

  16. The Tools/Languages I use

  17. A Bioinformatician’s Best Practices according to Nick Loman & Mick Watson. Nature Biotechnology. 2013 see also: W. S. Noble. PLoS Computational Biology. 2009 • Understand your goals and choose appropriate methods • Be suspicious and trust nobody • Set traps for your own scripts and other people’s • Be a detective • You're a scientist, not a programmer • Use version control software • Pipelineitis is a nasty disease • An Obama frame of mind • Someone has already done this. Find them!

  18. Good Directory Structure according to W. S. Noble. PLoS Computational Biology. 2009

  19. Thank you. Questions?

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