Literature DB >> 29637265

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

Candelaria Vergara1, Margaret M Parker2, Liliana Franco3,4, Michael H Cho2,5, Ana V Valencia-Duarte4, Terri H Beaty6, Priya Duggal7.   

Abstract

Genotype imputation estimates unobserved genotypes from genome-wide makers, to increase genome coverage and power for genome-wide association studies. Imputation has been successful for European ancestry populations in which very large reference panels are available. Smaller subsets of African descent populations are available in 1000 Genomes (1000G), the Consortium on Asthma among African ancestry Populations in the Americas (CAAPA) and the Haplotype Reference Consortium (HRC). We compared the performance of these reference panels when imputing variation in 3747 African Americans (AA) from two cohorts (HCV and COPDGene) genotyped using Illumina Omni microarrays. The haplotypes of 2504 (1000G), 883 (CAAPA) and 32,470 individuals (HRC) were used as reference. We compared the number of variants, imputation quality, imputation accuracy and coverage between panels. In both cohorts, 1000G imputed 1.5-1.6× more variants than CAAPA and 1.2× more than HRC. Similar findings were observed for variants with imputation R2 > 0.5 and for rare, low-frequency, and common variants. When merging imputed variants of the three panels, the total number was 62-63 M with 20 M overlapping variants imputed by all three panels, and a range of 5-15 M variants imputed exclusively with one of them. For overlapping variants, imputation quality was highest for HRC, followed by 1000G, then CAAPA, and improved as the minor allele frequency increased. 1000G, HRC and CAAPA provided high performance and accuracy for imputation of African American individuals, increasing the number of variants available for subsequent analyses. These panels are complementary and would benefit from the development of an integrated African reference panel.

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Year:  2018        PMID: 29637265      PMCID: PMC6209094          DOI: 10.1007/s00439-018-1881-4

Source DB:  PubMed          Journal:  Hum Genet        ISSN: 0340-6717            Impact factor:   4.132


  76 in total

1.  Practical Consideration of Genotype Imputation: Sample Size, Window Size, Reference Choice, and Untyped Rate.

Authors:  Boshao Zhang; Degui Zhi; Kui Zhang; Guimin Gao; Nita N Limdi; Nianjun Liu
Journal:  Stat Interface       Date:  2011       Impact factor: 0.582

2.  Imputation of coding variants in African Americans: better performance using data from the exome sequencing project.

Authors:  Qing Duan; Eric Yi Liu; Paul L Auer; Guosheng Zhang; Ethan M Lange; Goo Jun; Chris Bizon; Shuo Jiao; Steven Buyske; Nora Franceschini; Chris S Carlson; Li Hsu; Alex P Reiner; Ulrike Peters; Jeffrey Haessler; Keith Curtis; Christina L Wassel; Jennifer G Robinson; Lisa W Martin; Christopher A Haiman; Loic Le Marchand; Tara C Matise; Lucia A Hindorff; Dana C Crawford; Themistocles L Assimes; Hyun Min Kang; Gerardo Heiss; Rebecca D Jackson; Charles Kooperberg; James G Wilson; Gonçalo R Abecasis; Kari E North; Deborah A Nickerson; Leslie A Lange; Yun Li
Journal:  Bioinformatics       Date:  2013-08-16       Impact factor: 6.937

3.  Imputation and quality control steps for combining multiple genome-wide datasets.

Authors:  Shefali S Verma; Mariza de Andrade; Gerard Tromp; Helena Kuivaniemi; Elizabeth Pugh; Bahram Namjou-Khales; Shubhabrata Mukherjee; Gail P Jarvik; Leah C Kottyan; Amber Burt; Yuki Bradford; Gretta D Armstrong; Kimberly Derr; Dana C Crawford; Jonathan L Haines; Rongling Li; David Crosslin; Marylyn D Ritchie
Journal:  Front Genet       Date:  2014-12-11       Impact factor: 4.599

4.  A new statistic to evaluate imputation reliability.

Authors:  Peng Lin; Sarah M Hartz; Zhehao Zhang; Scott F Saccone; Jia Wang; Jay A Tischfield; Howard J Edenberg; John R Kramer; Alison M Goate; Laura J Bierut; John P Rice
Journal:  PLoS One       Date:  2010-03-15       Impact factor: 3.240

Review 5.  10 Years of GWAS Discovery: Biology, Function, and Translation.

Authors:  Peter M Visscher; Naomi R Wray; Qian Zhang; Pamela Sklar; Mark I McCarthy; Matthew A Brown; Jian Yang
Journal:  Am J Hum Genet       Date:  2017-07-06       Impact factor: 11.025

6.  A combined reference panel from the 1000 Genomes and UK10K projects improved rare variant imputation in European and Chinese samples.

Authors:  Wen-Chi Chou; Hou-Feng Zheng; Chia-Ho Cheng; Han Yan; Li Wang; Fang Han; J Brent Richards; David Karasik; Douglas P Kiel; Yi-Hsiang Hsu
Journal:  Sci Rep       Date:  2016-12-22       Impact factor: 4.379

7.  Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk.

Authors:  Helen R Warren; Evangelos Evangelou; Claudia P Cabrera; He Gao; Meixia Ren; Borbala Mifsud; Ioanna Ntalla; Praveen Surendran; Chunyu Liu; James P Cook; Aldi T Kraja; Fotios Drenos; Marie Loh; Niek Verweij; Jonathan Marten; Ibrahim Karaman; Marcelo P Segura Lepe; Paul F O'Reilly; Joanne Knight; Harold Snieder; Norihiro Kato; Jiang He; E Shyong Tai; M Abdullah Said; David Porteous; Maris Alver; Neil Poulter; Martin Farrall; Ron T Gansevoort; Sandosh Padmanabhan; Reedik Mägi; Alice Stanton; John Connell; Stephan J L Bakker; Andres Metspalu; Denis C Shields; Simon Thom; Morris Brown; Peter Sever; Tõnu Esko; Caroline Hayward; Pim van der Harst; Danish Saleheen; Rajiv Chowdhury; John C Chambers; Daniel I Chasman; Aravinda Chakravarti; Christopher Newton-Cheh; Cecilia M Lindgren; Daniel Levy; Jaspal S Kooner; Bernard Keavney; Maciej Tomaszewski; Nilesh J Samani; Joanna M M Howson; Martin D Tobin; Patricia B Munroe; Georg B Ehret; Louise V Wain
Journal:  Nat Genet       Date:  2017-01-30       Impact factor: 38.330

8.  Fast and accurate short read alignment with Burrows-Wheeler transform.

Authors:  Heng Li; Richard Durbin
Journal:  Bioinformatics       Date:  2009-05-18       Impact factor: 6.937

9.  An integrated map of genetic variation from 1,092 human genomes.

Authors:  Goncalo R Abecasis; Adam Auton; Lisa D Brooks; Mark A DePristo; Richard M Durbin; Robert E Handsaker; Hyun Min Kang; Gabor T Marth; Gil A McVean
Journal:  Nature       Date:  2012-11-01       Impact factor: 49.962

10.  Comparing performance of modern genotype imputation methods in different ethnicities.

Authors:  Nab Raj Roshyara; Katrin Horn; Holger Kirsten; Peter Ahnert; Markus Scholz
Journal:  Sci Rep       Date:  2016-10-04       Impact factor: 4.379

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  18 in total

Review 1.  Evaluating the promise of inclusion of African ancestry populations in genomics.

Authors:  Amy R Bentley; Shawneequa L Callier; Charles N Rotimi
Journal:  NPJ Genom Med       Date:  2020-02-25       Impact factor: 8.617

Review 2.  Accurate Imputation of Untyped Variants from Deep Sequencing Data.

Authors:  Davoud Torkamaneh; François Belzile
Journal:  Methods Mol Biol       Date:  2021

3.  Genotype imputation for Han Chinese population using Haplotype Reference Consortium as reference.

Authors:  Yuan Lin; Lu Liu; Sen Yang; Yun Li; Dongxin Lin; Xuejun Zhang; Xianyong Yin
Journal:  Hum Genet       Date:  2018-05-31       Impact factor: 4.132

Review 4.  The Future of Genomic Studies Must Be Globally Representative: Perspectives from PAGE.

Authors:  Stephanie A Bien; Genevieve L Wojcik; Chani J Hodonsky; Christopher R Gignoux; Iona Cheng; Tara C Matise; Ulrike Peters; Eimear E Kenny; Kari E North
Journal:  Annu Rev Genomics Hum Genet       Date:  2019-04-12       Impact factor: 8.929

5.  Genetic model of MS severity predicts future accumulation of disability.

Authors:  Kayla C Jackson; Katherine Sun; Christopher Barbour; Dena Hernandez; Peter Kosa; Makoto Tanigawa; Ann Marie Weideman; Bibiana Bielekova
Journal:  Ann Hum Genet       Date:  2019-08-08       Impact factor: 1.670

6.  Genotype imputation and polygenic score estimation in northwestern Russian population.

Authors:  Nikita Kolosov; Valeriia Rezapova; Oxana Rotar; Alexander Loboda; Olga Freylikhman; Olesya Melnik; Alexey Sergushichev; Christine Stevens; Trudy Voortman; Anna Kostareva; Alexandra Konradi; Mark J Daly; Mykyta Artomov
Journal:  PLoS One       Date:  2022-06-28       Impact factor: 3.752

7.  A comparative analysis of current phasing and imputation software.

Authors:  Adriano De Marino; Abdallah Amr Mahmoud; Madhuchanda Bose; Karatuğ Ozan Bircan; Andrew Terpolovsky; Varuna Bamunusinghe; Sandra Bohn; Umar Khan; Biljana Novković; Puya G Yazdi
Journal:  PLoS One       Date:  2022-10-19       Impact factor: 3.752

8.  Chromatin Accessibility of Human Mitral Valves and Functional Assessment of MVP Risk Loci.

Authors:  Sergiy Kyryachenko; Adrien Georges; Mengyao Yu; Takiy Barrandou; Lilong Guo; Patrick Bruneval; Tony Rubio; Judith Gronwald; Hassina Baraki; Ingo Kutschka; Kedar K Aras; Igor R Efimov; Russel A Norris; Niels Voigt; Nabila Bouatia-Naji
Journal:  Circ Res       Date:  2021-01-28       Impact factor: 17.367

9.  A polygenic score for acute vaso-occlusive pain in pediatric sickle cell disease.

Authors:  Evadnie Rampersaud; Guolian Kang; Lance E Palmer; Sara R Rashkin; Shuoguo Wang; Wenjian Bi; Nicole M Alberts; Doralina Anghelescu; Martha Barton; Kirby Birch; Nidal Boulos; Amanda M Brandow; Russell John Brooke; Ti-Cheng Chang; Wenan Chen; Yong Cheng; Juan Ding; John Easton; Jason R Hodges; Celeste K Kanne; Shawn Levy; Heather Mulder; Ashwin P Patel; Latika Puri; Celeste Rosencrance; Michael Rusch; Yadav Sapkota; Edgar Sioson; Akshay Sharma; Xing Tang; Andrew Thrasher; Winfred Wang; Yu Yao; Yutaka Yasui; Donald Yergeau; Jane S Hankins; Vivien A Sheehan; James R Downing; Jeremie H Estepp; Jinghui Zhang; Michael DeBaun; Gang Wu; Mitchell J Weiss
Journal:  Blood Adv       Date:  2021-07-27

10.  High-throughput framework for genetic analyses of adverse drug reactions using electronic health records.

Authors:  Neil S Zheng; Cosby A Stone; Lan Jiang; Christian M Shaffer; V Eric Kerchberger; Cecilia P Chung; QiPing Feng; Nancy J Cox; C Michael Stein; Dan M Roden; Joshua C Denny; Elizabeth J Phillips; Wei-Qi Wei
Journal:  PLoS Genet       Date:  2021-06-01       Impact factor: 6.020

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