Fast and accurate long-range phasing in a UK Biobank cohort.
- Authors
- Loh, Po-Ru; Palamara, Pier Francesco; Price, Alkes L
- Year
- 2016
- Journal
- Nature genetics
- PMID
- 27270109
- DOI
- 10.1038/ng.3571
- PMCID
- PMC4925291
Recent work has leveraged the extensive genotyping of the Icelandic population to perform long-range phasing (LRP), enabling accurate imputation and association analysis of rare variants in target samples typed on genotyping arrays. Here we develop a fast and accurate LRP method, Eagle, that extends this paradigm to populations with much smaller proportions of genotyped samples by harnessing long (>4-cM) identical-by-descent (IBD) tracts shared among distantly related individuals. We applied Eagle to N β 150,000 samples (0.2% of the British population) from the UK Biobank, and we determined that it is 1-2 orders of magnitude faster than existing methods while achieving similar or better phasing accuracy (switch error rate β 0.3%, corresponding to perfect phase in a majority of 10-Mb segments). We also observed that, when used within an imputation pipeline, Eagle prephasing improved downstream imputation accuracy in comparison to prephasing in batches using existing methods, as necessary to achieve comparable computational cost.
Eagle algorithm and example phase calls after each stepWe show phase calls for ten trio children after each successive step of the Eagle algorithm (applied to phase the first 40cM of chromosome 10 in all Nβ150,000 UK Biobank samples except trio parents). At all trio-phased sites, red and blue indicate whether the first Eagle-phased haplotype for each child matches the maternal or paternal haplotype. (a) After the first step, a sizable proportion of each genome is covered by long segments of near-perfect phase; these segments are the regions in which long IBD is available from several relatives. (b) The second step, which uses both long and short IBD, fixes most of the phase switch errors in the first step. (c,d) The subsequent approximate HMM iterations further reduce the error rate.
Computational cost and accuracy of phasing methodsBenchmarks of Eagle and existing phasing methods (all run with default options) on Nβ15,000, 50,000, and 150,000 UK Biobank samples and M=5,824 SNPs on chromosome 10. Log-log plots of (a) run times and (b) memory consumption using up to 10 cores of a 2.27 GHz Intel Xeon L5640 processor and up to two weeks of computation. (c) Mean switch error rate over 70 European-ancestry trios; error bars, s.e.m. All methods except HAPI-UR supported multithreading. As the HAPI-UR documentation suggested merging results from three independent runs with different random seeds, we parallelized these runs across three cores. (For the Nβ150,000 experiment, HAPI-UR encountered a failed assertion bug for some random seeds, so we needed to try six random seeds to find three working seeds. We did not count this extra work against HAPI-UR.) Numeric data are provided in Supplementary Table 1.
| Name | Type |
|---|---|
| 1000 Genomes GBR local | cohort |
| 1000 Genomes Phase 1 integrated v3 release local | cohort |
| 1000 Genomes Project | cohort |
| 10Mb segment local | drug |
| 150,000 diploid probands local | cohort |
| 150,000 individuals local | cohort |
| 150,000-sample cohort local | cohort |
| 150,000 samples local | cohort |
| 197 trios local | cohort |
| Any other white background local | phenotype |
| Beagle | drug |
| blip (1-2 SNPs) local | variant |
| British ethnicity local | phenotype |
| British samples local | cohort |
| Caribbean ethnicity local | phenotype |
| Chromosome 10 local | anatomy |
| common variants | cohort |
| diploid proband local | cohort |
| Eagle local | drug |
| European ancestry | cohort |
| European-ancestry local | phenotype |
| European-ancestry trios local | cohort |
| European population | cohort |
| FastPCA | drug |
| future population cohorts local | cohort |
| gene conversion local | drug |
| Genomics England local | cohort |
| genotyping error | drug |
| GERA | cohort |
| GERA EUR chip local | drug |
| GWAS imputation local | phenotype |
| HAPI-UR | drug |
| haplotype | variant |
| Haplotype Reference Consortium | cohort |
| Haplotype Reference Consortium (r1) panel local | cohort |
| hidden Markov model | drug |
| HRC | cohort |
| IBD | phenotype |
| IBS | cohort |
| Iceland | cohort |
| Icelandic population local | cohort |
| imputation accuracy | drug |
| imputation R2 local | phenotype |
| imputation reference panels local | cohort |
| Indian ethnicity local | phenotype |
| Irish ethnicity local | phenotype |
| large outbred population local | cohort |
| large-sample data sets local | cohort |
| Lisa Genetic Cluster Computer local | drug |
| Li-Stephens model local | drug |
| LRP local | drug |
| LRP-based methods local | drug |
| LRP-based phasing local | drug |
| million-sample data sets local | cohort |
| mutation local | drug |
| non-European ancestry | cohort |
| paired-end reads local | drug |
| PBWT imputation algorithm local | drug |
| pre-phasing | drug |
| probands | cohort |
| rare variant | cohort |
| reference panel | cohort |
| samples | cohort |
| Sanger Imputation Service local | drug |
| SHAPEIT2 | drug |
| SHAPEIT3 local | drug |
| SNP | cohort |
| SNPweights local | drug |
| SNPweights v2.0 local | drug |
| surrogate parent 1 local | cohort |
| surrogate parent 2 local | cohort |
| switch error rate | phenotype |
| trio children local | cohort |
| trio parents local | cohort |
| UK10K | cohort |
| UK10K-imputed 1000 Genomes GBR samples local | cohort |
| UK10K panel local | cohort |
| UK Biobank | cohort |
| UK population local | cohort |
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| Genetic Consequences of the Transatlantic Slave Trade in the Americas. | Micheletti SJ et al. | β | 2020 | β |
| Genome reconstruction and haplotype phasing using chromosome conformation capture methodologies. | Xu Z et al. | β | 2020 | β |
| Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure. | Shah S et al. | β | 2020 | β |
| Genome-wide association studies and heritability analysis reveal the involvement of host genetics in the Japanese gut microbiota. | Ishida S et al. | β | 2020 | β |
| Genomic diversity revealed by whole-genome sequencing in three Danish commercial pig breeds. | Cai Z et al. | β | 2020 | β |
| Heritability estimates for 361 blood metabolites across 40 genome-wide association studies. | Hagenbeek FA et al. | β | 2020 | β |
| High-depth African genomes inform human migration and health. | Choudhury A et al. | β | 2020 | β |
| Identification of a novel polymorphism associated with reduced clozapine concentration in schizophrenia patients-a genome-wide association study adjusting for smoking habits. | Smith RL et al. | β | 2020 | β |
| Identity-by-descent detection across 487,409 British samples reveals fine scale population structure and ultra-rare variant associations. | Nait Saada J et al. | β | 2020 | β |
| Improving Imputation Quality in BEAGLE for Crop and Livestock Data. | Pook T et al. | β | 2020 | β |
| Inclusion of endophenotypes in a standard GWAS facilitate a detailed mechanistic understanding of genetic elements that control blood lipid levels. | Zhang Q et al. | β | 2020 | β |
| Insights into human genetic variation and population history from 929 diverse genomes. | BergstrΓΆm A et al. | β | 2020 | β |
| Insights into variation in meiosis from 31,228 human sperm genomes. | Bell AD et al. | β | 2020 | β |
| Linked-read sequencing enables haplotype-resolved resequencing at population scale. | Lutgen D et al. | β | 2020 | β |
| Mendelian Randomization Study on Amino Acid Metabolism Suggests Tyrosine as Causal Trait for Type 2 Diabetes. | JΓ€ger S et al. | β | 2020 | β |
| Monogenic and polygenic inheritance become instruments for clonal selection. | Loh PR et al. | β | 2020 | β |
| Population-specific and trans-ancestry genome-wide analyses identify distinct and shared genetic risk loci for coronary artery disease. | Koyama S et al. | β | 2020 | β |
| Predicting Benefit From Evolocumab Therapy in Patients With Atherosclerotic Disease Using a Genetic Risk Score: Results From the FOURIER Trial. | Marston NA et al. | β | 2020 | β |
| RICOPILI: Rapid Imputation for COnsortias PIpeLIne. | Lam M et al. | β | 2020 | β |
| Selection signatures in tropical cattle are enriched for promoter and coding regions and reveal missense mutations in the damage response gene HELB. | Naval-SΓ‘nchez M et al. | β | 2020 | β |
| Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations. | Chen MH et al. | β | 2020 | β |
| Use of gene expression and whole-genome sequence information to improve the accuracy of genomic prediction for carcass traits in Hanwoo cattle. | de Las Heras-Saldana S et al. | β | 2020 | β |
| Using Haplotype Information for Conservation Genomics. | Leitwein M et al. | β | 2020 | β |
| Accurate, scalable and integrative haplotype estimation. | Delaneau O et al. | β | 2019 | β |
| Characterizing rare and low-frequency height-associated variants in the Japanese population. | Akiyama M et al. | β | 2019 | β |
| Combined analysis of keratinocyte cancers identifies novel genome-wide loci. | Liyanage UE et al. | β | 2019 | β |
| Differential methylation of enhancer at IGF2 is associated with abnormal dopamine synthesis in major psychosis. | Pai S et al. | β | 2019 | β |
| Efficient haplotype matching between a query and a panel for genealogical search. | Naseri A et al. | β | 2019 | β |
| Evaluating the quality of the 1000 genomes project data. | Belsare S et al. | β | 2019 | β |
| Exploring effective approaches for haplotype block phasing. | Al Bkhetan Z et al. | β | 2019 | β |
| Gastroesophageal reflux GWAS identifies risk loci that also associate with subsequent severe esophageal diseases. | An J et al. | β | 2019 | β |
| Genetic risk for schizophrenia is associated with substance use in emerging adulthood: an event-level polygenic prediction model. | Mallard TT et al. | β | 2019 | β |
| Genome-wide association analysis of venous thromboembolism identifies new risk loci and genetic overlap with arterial vascular disease. | Klarin D et al. | β | 2019 | β |
| Genome-wide association study of peripheral artery disease in the Million Veteran Program. | Klarin D et al. | β | 2019 | β |
| GWAS of mosaic loss of chromosome Y highlights genetic effects on blood cell differentiation. | Terao C et al. | β | 2019 | β |
| Multiomic Profiling Identifies cis-Regulatory Networks Underlying Human Pancreatic Ξ² Cell Identity and Function. | Lawlor N et al. | β | 2019 | β |
| Off the street phasing (OTSP): no hassle haplotype phasing for molecular PGD applications. | Zeevi DA et al. | β | 2019 | β |
| Persistent C-peptide secretion in Type 1 diabetes and its relationship to the genetic architecture of diabetes. | McKeigue PM et al. | β | 2019 | β |
| RaPID: ultra-fast, powerful, and accurate detection of segments identical by descent (IBD) in biobank-scale cohorts. | Naseri A et al. | β | 2019 | β |
| Recapitulation of previously reported associations for type 2 diabetes and metabolic traits in the 126K East Asians. | Choi JY et al. | β | 2019 | β |
| Translating genotype data of 44,000 biobank participants into clinical pharmacogenetic recommendations: challenges and solutions. | Reisberg S et al. | β | 2019 | β |
| Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program. | Klarin D et al. | β | 2018 | β |
| Genome-wide association study in Japanese females identifies fifteen novel skin-related trait associations. | Endo C et al. | β | 2018 | β |
| Genome-wide association study of self-reported food reactions in Japanese identifies shrimp and peach specific loci in the HLA-DR/DQ gene region. | Khor SS et al. | β | 2018 | β |
| Genomic prediction of the polled and horned phenotypes in Merino sheep. | Duijvesteijn N et al. | β | 2018 | β |
| Genotype Imputation from Large Reference Panels. | Das S et al. | β | 2018 | β |
| Haplotype Sharing Provides Insights into Fine-Scale Population History and Disease in Finland. | Martin AR et al. | β | 2018 | β |
| High-throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability. | Palamara PF et al. | β | 2018 | β |
| Identification and functional analysis of glycemic trait loci in the China Health and Nutrition Survey. | Spracklen CN et al. | β | 2018 | β |
| Insights into clonal haematopoiesis from 8,342 mosaic chromosomal alterations. | Loh PR et al. | β | 2018 | β |
| Japanese GWAS identifies variants for bust-size, dysmenorrhea, and menstrual fever that are eQTLs for relevant protein-coding or long non-coding RNAs. | Hirata T et al. | β | 2018 | β |
| Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. | Kang HM et al. | β | 2018 | β |
| Strategies for phasing and imputation in a population isolate. | Herzig AF et al. | β | 2018 | β |
| The feasibility of using low-density marker panels for genotype imputation and genomic prediction of crossbred dairy cattle of East Africa. | Aliloo H et al. | β | 2018 | β |
| The UK Biobank resource with deep phenotyping and genomic data. | Bycroft C et al. | β | 2018 | β |
| Type 2 Diabetes-Associated Genetic Variants Regulate Chromatin Accessibility in Human Islets. | Khetan S et al. | β | 2018 | β |
| Using imputed genotype data in the joint score tests for genetic association and gene-environment interactions in case-control studies. | Song M et al. | β | 2018 | β |
| A phased SNP-based classification of sickle cell anemia HBB haplotypes. | Shaikho EM et al. | β | 2017 | β |
| Benchmarking Relatedness Inference Methods with Genome-Wide Data from Thousands of Relatives. | Ramstetter MD et al. | β | 2017 | β |
| Genetic determinants of HbF in Saudi Arabian and African Benin haplotype sickle cell anemia. | Shaikho EM et al. | β | 2017 | β |
| Genome-wide association analyses of sleep disturbance traits identify new loci and highlight shared genetics with neuropsychiatric and metabolic traits. | Lane JM et al. | β | 2017 | β |
| <i>CPT1A</i> Missense Mutation Associated With Fatty Acid Metabolism and Reduced Height in Greenlanders. | Skotte L et al. | β | 2017 | β |
| Identifying genetic variants that affect viability in large cohorts. | Mostafavi H et al. | β | 2017 | β |
| Improved imputation accuracy of rare and low-frequency variants using population-specific high-coverage WGS-based imputation reference panel. | Mitt M et al. | β | 2017 | β |
| Haplotype estimation for biobank-scale data sets. | O'Connell J et al. | β | 2016 | β |
| Population Structure of UK Biobank and Ancient Eurasians Reveals Adaptation at Genes Influencing Blood Pressure. | Galinsky KJ et al. | β | 2016 | β |
| Reference-based phasing using the Haplotype Reference Consortium panel. | Loh PR et al. | β | 2016 | β |