DOCSLIB.ORG

Genotype Imputation Yun Li,1 Cristen Willer,1 Serena Sanna,2 and Gonc¸Alo Abecasis1 ANRV386-GG10-18 ARI 29 July 2009 15:32

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Genotype Imputation Yun Li,1 Cristen Willer,1 Serena Sanna,2 and Gonc¸Alo Abecasis1 ANRV386-GG10-18 ARI 29 July 2009 15:32 Genotype Imputation Yun Li,1 Cristen Willer,1 Serena Sanna,2 and Gonc¸alo Abecasis1 ANRV386-GG10-18 ARI 29 July 2009 15:32 INTRODUCTION haplotype mapping studies geneticists expect Identifying and characterizing the genetic vari- to identify long stretches of shared chro- ants that affect human traits, ranging from dis- mosome inherited from a relatively recent ease susceptibility to variability in personality common ancestor, in GWAS focusing on measures, is one of the central objectives of apparently unrelated individuals geneticists human genetics. Ultimately, this aim will be expect to identify only relatively short stretches achieved by examining the relationship between of shared chromosomes. Remarkably, geno- interesting traits and the whole genome se- type imputation can use these short stretches quences of many individuals. Although whole of shared haplotype to estimate with great genome resequencing of thousands of individ- precision the effects of many variants that are uals is not yet feasible, geneticists have long not directly genotyped. recognized that much progress can be made by In this review, we first attempt to provide measuring only a relatively modest number of the reader with an intuition for how geno- genetic variants in each individual. This type type imputation approaches work and for their of “incomplete” information is useful because theoretical underpinnings. We start with the data about any set of genetic variants in a group relatively intuitive setting of imputing missing of individuals provide useful information about genotypes for a set of individuals using informa- many other unobserved genetic variants in the tion on their close relatives. We then examine same individuals. how genotype imputation works when applied The idea that data on a modest set of ge- to more distantly related individuals. Next, we netic variants measured in a number of re- survey results of studies that have used genotype lated individuals can provide useful information imputation to study complex disease suscepti- about other genetic variants in those individu- bility. We attempt to provide the reader with als forms the theoretical underpinning of both critical information to assess the merits of geno- genetic linkage mapping in pedigrees and hap- type imputation-based analyses and to provide lotype mapping in founder populations (23, 24, guidance to analysts attempting to implement 50). These studies typically use <10,000 genetic these approaches. Finally, we survey potential markers to survey the entire human genome by uses of imputation-based analyses in the con- identifying stretches of chromosome inherited text of whole genome resequencing studies that from a common ancestor. The shared stretches we believe will soon become commonplace. usually span several megabases and include thousands of genetic variants. Both approaches GENOTYPE IMPUTATION have been highly successful in identifying genes IN STUDIES OF RELATED responsible for single-gene Mendelian disor- INDIVIDUALS ders (9). In contrast, they have had only lim- ited success in mapping genes that influence Family samples constitute the most intuitive complex traits, although success stories do exist setting for genotype imputation. Genotypes Annu. Rev. Genom. Hum. Genet. 2009.10:387-406. Downloaded from www.annualreviews.org Access provided by University of North Carolina - Chapel Hill on 05/08/18. For personal use only. (40, 42, 75, 83). for a relatively modest number of genetic More recently, technological advances markers can be used to identify long stretches have made genome-wide association studies of haplotype shared between individuals of (GWAS) possible (39, 67, 109). Rather than known relationship. These stretches of shared <10,000 variants, these studies typically geno- haplotype (or regions “identical-by-descent”, type 100,000–1,000,000 variants in each of the IBD) are typically used to evaluate the evidence individuals being studied. Since >10 million for linkage. Specifically, genetic linkage implies common genetic variants are likely to exist that family members who share a region IBD (104), even these detailed studies examine only will be more similar to each other than will fam- a fraction of all genetic variants. Whereas ily members with the same degree of relatedness in traditional genetic linkage and founder who do not share the region IBD. In the context 388 Li et al. ANRV386-GG10-18 ARI 29 July 2009 15:32 of genotype imputation, we characterize each 830,000 genetic markers in the first phase of of these stretches in detail by genotyping the International HapMap Project (103). Us- additional markers in one or more individuals ing genotypes for approximately 6500 genetic in the family. Genotypes for these markers can markers genotyped by the SNP consortium then be propagated to other family members in all three generations of the pedigrees (85), who are only typed at a minimal set of markers. Burdick and colleagues imputed genotypes for The approach is illustrated in Figure 1.In most of the HapMap Project markers in the the figure, all individuals have been genotyped third generation of these pedigrees (12). They for a set of genetic markers indicated in red; a showed that this imputation-based analysis was subset of individuals in the top two generations more powerful than the original analysis, which has been genotyped at additional markers indi- examined only directly genotyped markers for cated in black (panel a). Genotypes for the red each individual. markers, available in all individuals, can be used Several formal statistical descriptions of to infer the segregation of haplotypes through genotype imputation procedures for association the family (panel b). Finally, most of the missing analyses in families have now been published genotypes for individuals in the bottom gener- (15, 108), and the procedures to support geno- ation can be inferred by comparing the hap- type imputation are implemented in packages lotypes they inherited with copies of the same such as MERLIN (2, 3) and MENDEL (52, haplotypes that are IBD and present in other 53). In principle, these procedures can be imple- individuals in the family (panel c). mented using the infrastructure of the Lander- The idea that family members share long Green (48) or Elston-Stewart (29) algorithms, stretches of haplotype that are IBD underpins or one of the many other pedigree analysis algo- nearly all methods of linkage analysis. Further- rithms, including those that are based on Monte more, many early approaches for association Carlo sampling (38, 96). An important obser- analysis in pedigree data implicitly impute miss- vation from these more formal treatments of ing genotypes by considering the distribution of the problem is that even when genotypes can- potential genotypes of each individual jointly not be imputed with high confidence, partial with that of other individuals in the same pedi- information about the identity of each of the gree (35, 45). The extension of this idea to the true underlying genotypes can be productively imputation of missing genotypes (as outlined incorporated in association analysis (15, 108). above) was first described by Burdick and col- For example, when genotypes are measured di- leagues (12), who coined the term in silico geno- rectly, observed allele counts are often used in typing to describe the idea that computational regression analyses to estimate an additive ef- analyses could be used to replace laboratory- fect for each marker (1, 8, 34). These observed based procedures in the determination of in- allele counts indicate the number of copies of dividual genotypes. To illustrate the potential the allele of interest (0, 1, or 2) carried by each of the approach, they reanalyzed the data of individual. When genotypes are not measured Annu. Rev. Genom. Hum. Genet. 2009.10:387-406. Downloaded from www.annualreviews.org Access provided by University of North Carolina - Chapel Hill on 05/08/18. For personal use only. Cheung and colleagues (17). Cheung and col- directly, these discrete counts can be replaced leagues sought to identify genetic variants as- with an expected allele count for each marker sociated with regulation of gene expression by (a real number between 0 and 2) (15). examining RNA transcript levels and genotype The approach has been successfully used data for individuals in the top two generations of to study several quantitative traits in a sam- the Centre d’Etude du Polymorphism Humain ple of closely related individuals from four vil- (CEPH) pedigrees (21). The CEPH pedigrees lages in Sardinia (77). Among study partici- are three-generation pedigrees with a struc- pants, 1412 individuals were genotyped with ture similar to that of the cartoon pedigree in the Affymetrix 500K mapping array set (which Figure 1.The top two generations of several of assays ∼500,000 SNPs) and a further 3329 in- these pedigrees were genotyped at more than dividuals were genotyped with Affymetrix 10K www.annualreviews.org • Genotype Imputation 389 ANRV386-GG10-18 ARI 29 July 2009 15:32 a b A/A A/G A/A A/G AA GA A A G A A/T A/A A/T T/T TA AAA T T T T/T G/T G/G G/T T T T G G G T G G/G G/T G/T G/T GG TGGT TG A/G A/A G/G A/A AG AAG G AA T/T T/T T/T T/G T T T T T T T G C/G G/G C/C G/G CG GGCC GG A/G A/G AG A G A/T A/T TA AT T/T G/T T T G T G/T G/T G T G T A/A G/A AA GA T/T T/T T T T T C/G C/G CG CG A/G A/G A/A A/A G/G G/G AG AG AA AA G G G G ./.
Load more

Recommended publications
  • Impact of Imputation Methods on the Amount of Genetic Variation Captured by a Single-Nucleotide Polymorphism Panel in Soybeans A
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Purdue E-Pubs Purdue University Purdue e-Pubs Department of Agronomy Faculty Publications Department of Agronomy 2-2-2016 Impact of imputation methods on the amount of genetic variation captured by a single-nucleotide polymorphism panel in soybeans A. Xavier Purdue University William M. Muir Purdue University Katy M. Rainey Purdue Univeristy, [email protected] Follow this and additional works at: https://docs.lib.purdue.edu/agrypubs Recommended Citation Xavier, A.; Muir, William M.; and Rainey, Katy M., "Impact of imputation methods on the amount of genetic variation captured by a single-nucleotide polymorphism panel in soybeans" (2016). Department of Agronomy Faculty Publications. Paper 13. http://dx.doi.org/10.1186/s12859-016-0899-7 This document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. Please contact [email protected] for additional information. Xavier et al. BMC Bioinformatics (2016) 17:55 DOI 10.1186/s12859-016-0899-7 METHODOLOGY ARTICLE Open Access Impact of imputation methods on the amount of genetic variation captured by a single-nucleotide polymorphism panel in soybeans A. Xavier1, William M. Muir2 and Katy M. Rainey1* Abstract Background: Success in genome-wide association studies and marker-assisted selection depends on good phenotypic and genotypic data. The more complete this data is, the more powerful will be the results of analysis. Nevertheless, there are next-generation technologies that seek to provide genotypic information in spite of great proportions of missing data. The procedures these technologies use to impute genetic data, therefore, greatly affect downstream analyses.
    [Show full text]
  • Imputation-Based Assessment of Next Generation Rare Exome Variant Arrays
    IMPUTATION-BASED ASSESSMENT OF NEXT GENERATION RARE EXOME VARIANT ARRAYS ALICIA R. MARTIN* Department of Genetics & Biomedical Informatics Training Program, Stanford University Stanford, CA, 94305 Email: [email protected] GERARD TSE Department of Computer Science, Stanford University Stanford, CA, 94305 Email: [email protected] CARLOS D. BUSTAMANTE Department of Genetics, Stanford University Stanford, CA, 94305 Email: [email protected] EIMEAR E. KENNY* Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai New York, NY 10029 Email: [email protected] A striking finding from recent large-scale sequencing efforts is that the vast majority of variants in the human genome are rare and found within single populations or lineages. These observations hold important implications for the design of the next round of disease variant discovery efforts—if genetic variants that influence disease risk follow the same trend, then we expect to see population-specific disease associations that require large samples sizes for detection. To address this challenge, and due to the still prohibitive cost of sequencing large cohorts, researchers have developed a new generation of low-cost genotyping arrays that assay rare variation previously identified from large exome sequencing studies. Genotyping approaches rely not only on directly observing variants, but also on phasing and imputation methods that use publicly available reference panels to infer unobserved variants in a study cohort. Rare variant exome arrays are intentionally enriched for variants likely to be disease causing, and here we assay the ability of the first commercially available rare exome variant array (the Illumina Infinium HumanExome BeadChip) to also tag other potentially damaging variants not molecularly assayed.
    [Show full text]
  • Allele Imputation and Haplotype Determination from Databases Composed of Nuclear Families by Nathan Medina-Rodríguez and Ángelo Santana
    CONTRIBUTED RESEARCH ARTICLE 35 Allele Imputation and Haplotype Determination from Databases Composed of Nuclear Families by Nathan Medina-Rodríguez and Ángelo Santana Abstract The alleHap package is designed for imputing genetic missing data and reconstruct non- recombinant haplotypes from pedigree databases in a deterministic way. When genotypes of related individuals are available in a number of linked genetic markers, the program starts by identifying haplotypes compatible with the observed genotypes in those markers without missing values. If haplotypes are identified in parents or offspring, missing alleles can be imputed in subjects containing missing values. Several scenarios are analyzed: family completely genotyped, children partially genotyped and parents completely genotyped, children fully genotyped and parents containing entirely or partially missing genotypes, and founders and their offspring both only partially genotyped. The alleHap package also has a function to simulate pedigrees including all these scenarios. This article describes in detail how our package works for the desired applications, including illustrated explanations and easily reproducible examples. Introduction The knowledge about human genetic variation has been growing exponentially over the last decade. Collaborative efforts of international projects such as HapMap (Consortium and others, 2005) and 1000 Genomes (Consortium and others, 2012) have contributed to improving the discovery about human genetic diversity. Genotype imputation and haplotype reconstruction have achieved an important role in Genome- Wide Association Studies (GWAS) during recent years. Estimation methods are frequently used to infer missing genotypes as well as haplotypes from databases containing related and/or unrelated subjects. The majority of these analyses have been developed using several statistical methods (Browning and Browning, 2011a) which are able to impute genotypes as well as perform haplotype phasing (also known as haplotype estimation) of the corresponding genomic regions.
    [Show full text]
  • Identity-By-Descent Estimation with Population- and Pedigree-Based Imputation in Admixed Family Data Mohamad Saad
    Marshall University Marshall Digital Scholar Biochemistry and Microbiology Faculty Research 2016 Identity-by-descent estimation with population- and pedigree-based imputation in admixed family data Mohamad Saad Alejandro Q. Nato Jr. Marshall University, [email protected] Fiona L. Grimson Steven M. Lewis Lisa A. Brown See next page for additional authors Follow this and additional works at: https://mds.marshall.edu/sm_bm Part of the Genetic Phenomena Commons, Genetic Processes Commons, and the Genetic Structures Commons Recommended Citation Saad M, Nato AQ, Grimson FL, et al. Identity-by-descent estimation with population- and pedigree-based imputation in admixed family data. BMC Proceedings. 2016;10(Suppl 7):295-301. doi:10.1186/s12919-016-0046-5. This Conference Proceeding is brought to you for free and open access by the Faculty Research at Marshall Digital Scholar. It has been accepted for inclusion in Biochemistry and Microbiology by an authorized administrator of Marshall Digital Scholar. For more information, please contact [email protected], [email protected]. Authors Mohamad Saad, Alejandro Q. Nato Jr., Fiona L. Grimson, Steven M. Lewis, Lisa A. Brown, Elizabeth M. Blue, Timothy A. Thornton, Elizabeth A. Thompson, and Ellen M. Wijsman This conference proceeding is available at Marshall Digital Scholar: https://mds.marshall.edu/sm_bm/238 The Author(s) BMC Proceedings 2016, 10(Suppl 7):7 DOI 10.1186/s12919-016-0046-5 BMC Proceedings PROCEEDINGS Open Access Identity-by-descent estimation with population- and pedigree-based imputation in admixed family data Mohamad Saad1,2, Alejandro Q. Nato Jr2, Fiona L. Grimson3, Steven M. Lewis3, Lisa A. Brown1, Elizabeth M.
    [Show full text]
  • Genotype Imputation Via Matrix Completion
    Downloaded from genome.cshlp.org on September 27, 2021 - Published by Cold Spring Harbor Laboratory Press Method Genotype imputation via matrix completion Eric C. Chi,1,6 Hua Zhou,2 Gary K. Chen,3 Diego Ortega Del Vecchyo,4 and Kenneth Lange5 1Department of Human Genetics, University of California, Los Angeles, California 90095, USA; 2Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695-8203, USA; 3Department of Preventive Medicine, University of Southern California, Los Angeles, California 90089, USA; 4Interdepartmental Program in Bioinformatics, University of California, Los Angeles, California 90095, USA; 5Department of Biomathematics, Department of Human Genetics, and Department of Statistics, University of California, Los Angeles, California 90095, USA Most current genotype imputation methods are model-based and computationally intensive, taking days to impute one chromosome pair on 1000 people. We describe an efficient genotype imputation method based on matrix completion. Our matrix completion method is implemented in MATLAB and tested on real data from HapMap 3, simulated pedigree data, and simulated low-coverage sequencing data derived from the 1000 Genomes Project. Compared with leading im- putation programs, the matrix completion algorithm embodied in our program MENDEL-IMPUTE achieves comparable imputation accuracy while reducing run times significantly. Implementation in a lower-level language such as Fortran or C is apt to further improve computational efficiency. [Supplemental material is available for this article.] Modern genomics studies almost invariably deal with massive the spirit of Occam’s razor, it seeks the simplest matrix consistent amounts of data. Data sets collected on single nucleotide poly- with the observed entries. This criterion conveniently translates morphisms (SNPs), next-generation sequencing (NGS), copy num- into searching for a low rank matrix with a small squared error ber variations (CNV), and RNA-seq all fall into this category.
    [Show full text]
  • Impact of Imputation Methods on the Amount of Genetic Variation Captured by a Single-Nucleotide Polymorphism Panel in Soybeans A
    Xavier et al. BMC Bioinformatics (2016) 17:55 DOI 10.1186/s12859-016-0899-7 METHODOLOGY ARTICLE Open Access Impact of imputation methods on the amount of genetic variation captured by a single-nucleotide polymorphism panel in soybeans A. Xavier1, William M. Muir2 and Katy M. Rainey1* Abstract Background: Success in genome-wide association studies and marker-assisted selection depends on good phenotypic and genotypic data. The more complete this data is, the more powerful will be the results of analysis. Nevertheless, there are next-generation technologies that seek to provide genotypic information in spite of great proportions of missing data. The procedures these technologies use to impute genetic data, therefore, greatly affect downstream analyses. This study aims to (1) compare the genetic variance in a single-nucleotide polymorphism panel of soybean with missing data imputed using various methods, (2) evaluate the imputation accuracy and post-imputation quality associated with these methods, and (3) evaluate the impact of imputation method on heritability and the accuracy of genome-wide prediction of soybean traits. The imputation methods we evaluated were as follows: multivariate mixed model, hidden Markov model, logical algorithm, k-nearest neighbor, single value decomposition, and random forest. We used raw genotypes from the SoyNAM project and the following phenotypes: plant height, days to maturity, grain yield, and seed protein composition. Results: We propose an imputation method based on multivariate mixed models using pedigree information. Our methods comparison indicate that heritability of traits can be affected by the imputation method. Genotypes with missing values imputed with methods that make use of genealogic information can favor genetic analysis of highly polygenic traits, but not genome-wide prediction accuracy.
    [Show full text]
  • References for Haplotype Imputation in the Big Data
    ogy iol : Op r B e a n l A u c c c e l e s Li et al., Mol Biol 2015, 4:4 o s M Molecular Biology: Open Access DOI: 10.4172/2168-9547.1000143 ISSN: 2168-9547 Review Article Open Access References for Haplotype Imputation in the Big Data Era Wenzhi Li1,2#, Wei Xu1,2#, Qiling Li1, Li Ma2,3* and Qing Song1,2,3* 1Center of Big Data and Bioinformatics, First Affiliated Hospital of Medicine School, Xi’an Jiaotong University, Xi’an, Shaanxi, China 2Cardiovascular Research Institute and Department of Medicine, Morehouse School of Medicine, Atlanta, Georgia, USA 34DGenome Inc, Atlanta, Georgia, USA #Equal contribution Abstract Imputation is a powerful in silico approach to fill in those missing values in the big datasets. This process requires a reference panel, which is a collection of big data from which the missing information can be extracted and imputed. Haplotype imputation requires ethnicity-matched references; a mismatched reference panel will significantly reduce the quality of imputation. However, currently existing big datasets cover only a small number of ethnicities, there is a lack of ethnicity-matched references for many ethnic populations in the world, which has hampered the data imputation of haplotypes and its downstream applications. To solve this issue, several approaches have been proposed and explored, including the mixed reference panel, the internal reference panel and genotype-converted reference panel. This review article provides the information and comparison between these approaches. Increasing evidence showed that not just one or two genetic elements dictate the gene activity and functions; instead, cis- interactions of multiple elements dictate gene activity.
    [Show full text]
  • Accurate Genotype Imputation in Multiparental Populations
    Genetics: Early Online, published on July 25, 2018 as 10.1534/genetics.118.300885 1 Accurate genotype imputation in multiparental populations 2 from low-coverage sequence ∗ 3 Chaozhi Zheng , Martin P. Boer, and Fred A. van Eeuwijk 4 Biometris, Wageningen University and Research, Wageningen, The Netherlands 5 July 25, 2018 6 Running head: Genotype imputation 7 Key words: Genotype imputation, genotyping by sequencing, hidden Markov model, cross 8 pollinated (CP) population, multiparent advanced generation intercross (MAGIC) 9 *Corresponding author: 10 Chaozhi Zheng 11 Biometris 12 Wageningen University and Research 13 PO Box 16, 6700AA Wageningen 14 The Netherlands 15 Email: [email protected] 1 Copyright 2018. 16 Abstract 17 Many different types of multiparental populations have recently been produced to increase ge- 18 netic diversity and resolution in quantitative trait loci (QTL) mapping. Low coverage geno- 19 typing by sequencing (GBS) technology has become a cost effective tool in these populations, 20 despite large amounts of missing data in offspring and founders. In this work, we present a 21 general statistical framework for genotype imputation in such experimental crosses from low 22 coverage GBS data. Generalizing a previously developed hidden Markov model for calculating 23 ancestral origins of offspring DNA, we present an imputation algorithm that doesn’t require 24 parental data and that is applicable to bi- and multiparental populations. Our imputation al- 25 gorithm allows heterozygosity of parents and offspring as well as error correction in observed 26 genotypes. Further, our approach can combine imputation and genotype calling from sequenc- 27 ing reads, and it also applies to called genotypes from single nucleotide polymorphism (SNP) 28 array data.
    [Show full text]
  • 1000 Genomes-Based Imputation Identifies Novel and Refined
    European Journal of Human Genetics (2012) 20, 801–805 & 2012 Macmillan Publishers Limited All rights reserved 1018-4813/12 www.nature.com/ejhg SHORT REPORT 1000 Genomes-based imputation identifies novel and refined associations for the Wellcome Trust Case Control Consortium phase 1 Data Jie Huang1, David Ellinghaus2, Andre Franke2, Bryan Howie3 and Yun Li*,4 We hypothesize that imputation based on data from the 1000 Genomes Project can identify novel association signals on a genome-wide scale due to the dense marker map and the large number of haplotypes. To test the hypothesis, the Wellcome Trust Case Control Consortium (WTCCC) Phase I genotype data were imputed using 1000 genomes as reference (20100804 EUR), and seven case/control association studies were performed using imputed dosages. We observed two ‘missed’ disease- associated variants that were undetectable by the original WTCCC analysis, but were reported by later studies after the 2007 WTCCC publication. One is within the IL2RA gene for association with type 1 diabetes and the other in proximity with the CDKN2B gene for association with type 2 diabetes. We also identified two refined associations. One is SNP rs11209026 in exon 9 of IL23R for association with Crohn’s disease, which is predicted to be probably damaging by PolyPhen2. The other refined variant is in the CUX2 gene region for association with type 1 diabetes, where the newly identified top SNP rs1265564 has an association P-value of 1.68Â10À16. The new lead SNP for the two refined loci provides a more plausible explanation for the disease association. We demonstrated that 1000 Genomes-based imputation could indeed identify both novel (in our case, ‘missed’ because they were detected and replicated by studies after 2007) and refined signals.
    [Show full text]
  • The Use of Genomic Data and Imputation Methods in Dairy Cattle Breeding
    Review Czech Journal of Animal Science, 65, 2020 (12): 445–453 https://doi.org/10.17221/83/2020-CJAS The use of genomic data and imputation methods in dairy cattle breeding Anita Klímová1,2*, Eva Kašná2, Karolína Machová1, Michaela Brzáková2, Josef Přibyl2, Luboš Vostrý1 1Department of Genetics and Breeding, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague – Suchdol, Czech Republic 2Department of Genetics and Breeding of Farm Animals, Institute of Animal Science, Prague – Uhříněves, Czech Republic *Corresponding author: [email protected] Citation: Klímová A., Kašná E., Machová K., Brzáková M., Přibyl J., Vostrý L. (2020): The use of genomic data and im- putation methods in dairy cattle breeding. Czech J. Anim. Sci., 65: 445–453. Abstract: The inclusion of animal genotype data has contributed to the development of genomic selection. Ani- mals are selected not only based on pedigree and phenotypic data but also on the basis of information about their genotypes. Genomic information helps to increase the accuracy of selection of young animals and thus enables a reduction of the generation interval. Obtaining information about genotypes in the form of SNPs (single nucleotide polymorphisms) has led to the development of new chips for genotyping. Several methods of genomic comparison have been developed as a result. One of the methods is data imputation, which allows the missing SNPs to be calculated using low-density chips to high-density chips. Through imputations, it is possible to com- bine information from diverse sets of chips and thus obtain more information about genotypes at a lower cost.
    [Show full text]
  • Imputation Accuracy from Low to Moderate Density Single Nucleotide Polymorphism Chips in a Thai Multibreed Dairy Cattle Population
    464 Open Access Asian Australas. J. Anim. Sci. Vol. 29, No. 4 : 464-470 April 2016 http://dx.doi.org/10.5713/ajas.15.0291 www.ajas.info pISSN 1011-2367 eISSN 1976-5517 Imputation Accuracy from Low to Moderate Density Single Nucleotide Polymorphism Chips in a Thai Multibreed Dairy Cattle Population Danai Jattawa, Mauricio A. Elzo1, Skorn Koonawootrittriron*, and Thanathip Suwanasopee Department of Animal Science, Faculty of Agriculture, Kasetsart University, Bangkok 10900, Thailand ABSTRACT: The objective of this study was to investigate the accuracy of imputation from low density (LDC) to moderate density SNP chips (MDC) in a Thai Holstein-Other multibreed dairy cattle population. Dairy cattle with complete pedigree information (n = 1,244) from 145 dairy farms were genotyped with GeneSeek GGP20K (n = 570), GGP26K (n = 540) and GGP80K (n = 134) chips. After checking for single nucleotide polymorphism (SNP) quality, 17,779 SNP markers in common between the GGP20K, GGP26K, and GGP80K were used to represent MDC. Animals were divided into two groups, a reference group (n = 912) and a test group (n = 332). The SNP markers chosen for the test group were those located in positions corresponding to GeneSeek GGP9K (n = 7,652). The LDC to MDC genotype imputation was carried out using three different software packages, namely Beagle 3.3 (population-based algorithm), FImpute 2.2 (combined family- and population-based algorithms) and Findhap 4 (combined family- and population-based algorithms). Imputation accuracies within and across chromosomes were calculated as ratios of correctly imputed SNP markers to overall imputed SNP markers. Imputation accuracy for the three software packages ranged from 76.79% to 93.94%.
    [Show full text]
  • Odyssey: a Semi-Automated Pipeline for Phasing, Imputation, and Analysis of Genome-Wide Genetic Data Ryan J
    Eller et al. BMC Bioinformatics (2019) 20:364 https://doi.org/10.1186/s12859-019-2964-5 METHODOLOGY ARTICLE Open Access Odyssey: a semi-automated pipeline for phasing, imputation, and analysis of genome-wide genetic data Ryan J. Eller1* , Sarath C. Janga2,3 and Susan Walsh1 Abstract Background: Genome imputation, admixture resolution and genome-wide association analyses are timely and computationally intensive processes with many composite and requisite steps. Analysis time increases further when building and installing the run programs required for these analyses. For scientists that may not be as versed in programing language, but want to perform these operations hands on, there is a lengthy learning curve to utilize the vast number of programs available for these analyses. Results: In an effort to streamline the entire process with easy-to-use steps for scientists working with big data, the Odyssey pipeline was developed. Odyssey is a simplified, efficient, semi-automated genome-wide imputation and analysis pipeline, which prepares raw genetic data, performs pre-imputation quality control, phasing, imputation, post-imputation quality control, population stratification analysis, and genome-wide association with statistical data analysis, including result visualization. Odyssey is a pipeline that integrates programs such as PLINK, SHAPEIT, Eagle, IMPUTE, Minimac, and several R packages, to create a seamless, easy-to-use, and modular workflow controlled via a single user-friendly configuration file. Odyssey was built with compatibility in mind, and thus utilizes the Singularity container solution, which can be run on Linux, MacOS, and Windows platforms. It is also easily scalable from a simple desktop to a High-Performance System (HPS).
    [Show full text]