Literature DB >> 25293720

Rare variant genotype imputation with thousands of study-specific whole-genome sequences: implications for cost-effective study designs.

Giorgio Pistis1, Eleonora Porcu1, Scott I Vrieze2, Carlo Sidore1, Maristella Steri3, Fabrice Danjou3, Fabio Busonero4, Antonella Mulas5, Magdalena Zoledziewska3, Andrea Maschio4, Christine Brennan6, Sandra Lai3, Michael B Miller7, Marco Marcelli8, Maria Francesca Urru8, Maristella Pitzalis3, Robert H Lyons6, Hyun M Kang2, Chris M Jones8, Andrea Angius9, William G Iacono7, David Schlessinger10, Matt McGue7, Francesco Cucca5, Gonçalo R Abecasis2, Serena Sanna3.   

Abstract

The utility of genotype imputation in genome-wide association studies is increasing as progressively larger reference panels are improved and expanded through whole-genome sequencing. Developing general guidelines for optimally cost-effective imputation, however, requires evaluation of performance issues that include the relative utility of study-specific compared with general/multipopulation reference panels; genotyping with various array scaffolds; effects of different ethnic backgrounds; and assessment of ranges of allele frequencies. Here we compared the effectiveness of study-specific reference panels to the commonly used 1000 Genomes Project (1000G) reference panels in the isolated Sardinian population and in cohorts of European ancestry including samples from Minnesota (USA). We also examined different combinations of genome-wide and custom arrays for baseline genotypes. In Sardinians, the study-specific reference panel provided better coverage and genotype imputation accuracy than the 1000G panels and other large European panels. In fact, even gene-centered custom arrays (interrogating ~200 000 variants) provided highly informative content across the entire genome. Gain in accuracy was also observed for Minnesotans using the study-specific reference panel, although the increase was smaller than in Sardinians, especially for rare variants. Notably, a combined panel including both study-specific and 1000G reference panels improved imputation accuracy only in the Minnesota sample, and only at rare sites. Finally, we found that when imputation is performed with a study-specific reference panel, cutoffs different from the standard thresholds of MACH-Rsq and IMPUTE-INFO metrics should be used to efficiently filter badly imputed rare variants. This study thus provides general guidelines for researchers planning large-scale genetic studies.

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Year:  2014        PMID: 25293720      PMCID: PMC4463504          DOI: 10.1038/ejhg.2014.216

Source DB:  PubMed          Journal:  Eur J Hum Genet        ISSN: 1018-4813            Impact factor:   4.246


  25 in total

Review 1.  Genotype imputation for genome-wide association studies.

Authors:  Jonathan Marchini; Bryan Howie
Journal:  Nat Rev Genet       Date:  2010-07       Impact factor: 53.242

2.  Minnesota Center for Twin and Family Research.

Authors:  William G Iacono; Matt McGue; Robert F Krueger
Journal:  Twin Res Hum Genet       Date:  2006-12       Impact factor: 1.587

3.  A new multipoint method for genome-wide association studies by imputation of genotypes.

Authors:  Jonathan Marchini; Bryan Howie; Simon Myers; Gil McVean; Peter Donnelly
Journal:  Nat Genet       Date:  2007-06-17       Impact factor: 38.330

4.  Low-coverage sequencing: implications for design of complex trait association studies.

Authors:  Yun Li; Carlo Sidore; Hyun Min Kang; Michael Boehnke; Gonçalo R Abecasis
Journal:  Genome Res       Date:  2011-04-01       Impact factor: 9.043

5.  MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes.

Authors:  Yun Li; Cristen J Willer; Jun Ding; Paul Scheet; Gonçalo R Abecasis
Journal:  Genet Epidemiol       Date:  2010-12       Impact factor: 2.135

6.  A rare variant in MYH6 is associated with high risk of sick sinus syndrome.

Authors:  Hilma Holm; Daniel F Gudbjartsson; Patrick Sulem; Gisli Masson; Hafdis Th Helgadottir; Carlo Zanon; Olafur Th Magnusson; Agnar Helgason; Jona Saemundsdottir; Arnaldur Gylfason; Hrafnhildur Stefansdottir; Solveig Gretarsdottir; Stefan E Matthiasson; Gu Mundur Thorgeirsson; Aslaug Jonasdottir; Asgeir Sigurdsson; Hreinn Stefansson; Thomas Werge; Thorunn Rafnar; Lambertus A Kiemeney; Babar Parvez; Raafia Muhammad; Dan M Roden; Dawood Darbar; Gudmar Thorleifsson; G Bragi Walters; Augustine Kong; Unnur Thorsteinsdottir; David O Arnar; Kari Stefansson
Journal:  Nat Genet       Date:  2011-03-06       Impact factor: 38.330

7.  Heritability of cardiovascular and personality traits in 6,148 Sardinians.

Authors:  Giuseppe Pilia; Wei-Min Chen; Angelo Scuteri; Marco Orrú; Giuseppe Albai; Mariano Dei; Sandra Lai; Gianluca Usala; Monica Lai; Paola Loi; Cinzia Mameli; Loredana Vacca; Manila Deiana; Nazario Olla; Marco Masala; Antonio Cao; Samer S Najjar; Antonio Terracciano; Timur Nedorezov; Alexei Sharov; Alan B Zonderman; Gonçalo R Abecasis; Paul Costa; Edward Lakatta; David Schlessinger
Journal:  PLoS Genet       Date:  2006-07-10       Impact factor: 5.917

8.  An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data.

Authors:  Goo Jun; Mary Kate Wing; Gonçalo R Abecasis; Hyun Min Kang
Journal:  Genome Res       Date:  2015-04-16       Impact factor: 9.043

9.  Genetic loci linked to type 1 diabetes and multiple sclerosis families in Sardinia.

Authors:  Maristella Pitzalis; Patrizia Zavattari; Raffaele Murru; Elisabetta Deidda; Magdalena Zoledziewska; Daniela Murru; Loredana Moi; Costantino Motzo; Valeria Orrù; Gianna Costa; Elisabetta Solla; Elisabetta Fadda; Lucia Schirru; Maria Cristina Melis; Marina Lai; Cristina Mancosu; Stefania Tranquilli; Stefania Cuccu; Marcella Rolesu; Maria Antonietta Secci; Daniela Corongiu; Daniela Contu; Rosanna Lampis; Annalisa Nucaro; Gavino Pala; Adolfo Pacifico; Mario Maioli; Paola Frongia; Margherita Chessa; Rossella Ricciardi; Stanislao Lostia; Anna Maria Marinaro; Anna Franca Milia; Novella Landis; Maria Antonietta Zedda; Michael B Whalen; Federico Santoni; Maria Giovanna Marrosu; Marcella Devoto; Francesco Cucca
Journal:  BMC Med Genet       Date:  2008-01-20       Impact factor: 2.103

10.  Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits.

Authors:  Angelo Scuteri; Serena Sanna; Wei-Min Chen; Manuela Uda; Giuseppe Albai; James Strait; Samer Najjar; Ramaiah Nagaraja; Marco Orrú; Gianluca Usala; Mariano Dei; Sandra Lai; Andrea Maschio; Fabio Busonero; Antonella Mulas; Georg B Ehret; Ashley A Fink; Alan B Weder; Richard S Cooper; Pilar Galan; Aravinda Chakravarti; David Schlessinger; Antonio Cao; Edward Lakatta; Gonçalo R Abecasis
Journal:  PLoS Genet       Date:  2007-07       Impact factor: 5.917

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  42 in total

1.  Population-specific genotype imputations using minimac or IMPUTE2.

Authors:  Elisabeth M van Leeuwen; Alexandros Kanterakis; Patrick Deelen; Mathijs V Kattenberg; P Eline Slagboom; Paul I W de Bakker; Cisca Wijmenga; Morris A Swertz; Dorret I Boomsma; Cornelia M van Duijn; Lennart C Karssen; Jouke Jan Hottenga
Journal:  Nat Protoc       Date:  2015-07-30       Impact factor: 13.491

2.  Kinpute: using identity by descent to improve genotype imputation.

Authors:  Mark Abney; Aisha ElSherbiny
Journal:  Bioinformatics       Date:  2019-11-01       Impact factor: 6.937

3.  Whole-genome sequencing in French Canadians from Quebec.

Authors:  Cécile Low-Kam; David Rhainds; Ken Sin Lo; Sylvie Provost; Ian Mongrain; Anick Dubois; Sylvie Perreault; John F Robinson; Robert A Hegele; Marie-Pierre Dubé; Jean-Claude Tardif; Guillaume Lettre
Journal:  Hum Genet       Date:  2016-07-04       Impact factor: 4.132

4.  Guidelines for Large-Scale Sequence-Based Complex Trait Association Studies: Lessons Learned from the NHLBI Exome Sequencing Project.

Authors:  Paul L Auer; Alex P Reiner; Gao Wang; Hyun Min Kang; Goncalo R Abecasis; David Altshuler; Michael J Bamshad; Deborah A Nickerson; Russell P Tracy; Stephen S Rich; Suzanne M Leal
Journal:  Am J Hum Genet       Date:  2016-09-22       Impact factor: 11.025

5.  Methods for association analysis and meta-analysis of rare variants in families.

Authors:  Shuang Feng; Giorgio Pistis; He Zhang; Matthew Zawistowski; Antonella Mulas; Magdalena Zoledziewska; Oddgeir L Holmen; Fabio Busonero; Serena Sanna; Kristian Hveem; Cristen Willer; Francesco Cucca; Dajiang J Liu; Gonçalo R Abecasis
Journal:  Genet Epidemiol       Date:  2015-03-04       Impact factor: 2.135

6.  New insights into the pharmacogenomics of antidepressant response from the GENDEP and STAR*D studies: rare variant analysis and high-density imputation.

Authors:  C Fabbri; K E Tansey; R H Perlis; J Hauser; N Henigsberg; W Maier; O Mors; A Placentino; M Rietschel; D Souery; G Breen; C Curtis; L Sang-Hyuk; S Newhouse; H Patel; M Guipponi; N Perroud; G Bondolfi; M O'Donovan; G Lewis; J M Biernacka; R M Weinshilboum; A Farmer; K J Aitchison; I Craig; P McGuffin; R Uher; C M Lewis
Journal:  Pharmacogenomics J       Date:  2017-11-21       Impact factor: 3.550

7.  Genotype imputation performance of three reference panels using African ancestry individuals.

Authors:  Candelaria Vergara; Margaret M Parker; Liliana Franco; Michael H Cho; Ana V Valencia-Duarte; Terri H Beaty; Priya Duggal
Journal:  Hum Genet       Date:  2018-04-10       Impact factor: 4.132

8.  Improving power of association tests using multiple sets of imputed genotypes from distributed reference panels.

Authors:  Wei Zhou; Lars G Fritsche; Sayantan Das; He Zhang; Jonas B Nielsen; Oddgeir L Holmen; Jin Chen; Maoxuan Lin; Maiken B Elvestad; Kristian Hveem; Goncalo R Abecasis; Hyun Min Kang; Cristen J Willer
Journal:  Genet Epidemiol       Date:  2017-09-01       Impact factor: 2.135

9.  Next-generation genotype imputation service and methods.

Authors:  Sayantan Das; Lukas Forer; Sebastian Schönherr; Carlo Sidore; Adam E Locke; Alan Kwong; Scott I Vrieze; Emily Y Chew; Shawn Levy; Matt McGue; David Schlessinger; Dwight Stambolian; Po-Ru Loh; William G Iacono; Anand Swaroop; Laura J Scott; Francesco Cucca; Florian Kronenberg; Michael Boehnke; Gonçalo R Abecasis; Christian Fuchsberger
Journal:  Nat Genet       Date:  2016-08-29       Impact factor: 38.330

10.  Across-Platform Imputation of DNA Methylation Levels Incorporating Nonlocal Information Using Penalized Functional Regression.

Authors:  Guosheng Zhang; Kuan-Chieh Huang; Zheng Xu; Jung-Ying Tzeng; Karen N Conneely; Weihua Guan; Jian Kang; Yun Li
Journal:  Genet Epidemiol       Date:  2016-04-07       Impact factor: 2.135

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