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Rahimmadar S, Ghaffari M, Mokhber M, Williams JL. Linkage Disequilibrium and Effective Population Size of Buffalo Populations of Iran, Turkey, Pakistan, and Egypt Using a Medium Density SNP Array. Front Genet 2021; 12:608186. [PMID: 34950186 PMCID: PMC8689148 DOI: 10.3389/fgene.2021.608186] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Accepted: 11/03/2021] [Indexed: 11/21/2022] Open
Abstract
Linkage disequilibrium (LD) across the genome provides information to identify the genes and variations related to quantitative traits in genome-wide association studies (GWAS) and for the implementation of genomic selection (GS). LD can also be used to evaluate genetic diversity and population structure and reveal genomic regions affected by selection. LD structure and Ne were assessed in a set of 83 water buffaloes, comprising Azeri (AZI), Khuzestani (KHU), and Mazandarani (MAZ) breeds from Iran, Kundi (KUN) and Nili-Ravi (NIL) from Pakistan, Anatolian (ANA) buffalo from Turkey, and buffalo from Egypt (EGY). The values of corrected r2 (defined as the correlation between two loci) of adjacent SNPs for three pooled Iranian breeds (IRI), ANA, EGY, and two pooled Pakistani breeds (PAK) populations were 0.24, 0.28, 0.27, and 0.22, respectively. The corrected r2 between SNPs decreased with increasing physical distance from 100 Kb to 1 Mb. The LD values for IRI, ANA, EGY, and PAK populations were 0.16, 0.23, 0.24, and 0.21 for less than 100Kb, respectively, which reduced rapidly to 0.018, 0.042, 0.059, and 0.024, for a distance of 1 Mb. In all the populations, the decay rate was low for distances greater than 2Mb, up to the longest studied distance (15 Mb). The r2 values for adjacent SNPs in unrelated samples indicated that the Affymetrix Axiom 90 K SNP genomic array was suitable for GWAS and GS in these populations. The persistency of LD phase (PLDP) between populations was assessed, and results showed that PLPD values between the populations were more than 0.9 for distances of less than 100 Kb. The Ne in the recent generations has declined to the extent that breeding plans are urgently required to ensure that these buffalo populations are not at risk of being lost. We found that results are affected by sample size, which could be partially corrected for; however, additional data should be obtained to be confident of the results.
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Affiliation(s)
- Shirin Rahimmadar
- Department of Animal Science, Faculty of Agricultural Science, Urmia University, Urmia, Iran
| | - Mokhtar Ghaffari
- Department of Animal Science, Faculty of Agricultural Science, Urmia University, Urmia, Iran
| | - Mahdi Mokhber
- Department of Animal Science, Faculty of Agricultural Science, Urmia University, Urmia, Iran
| | - John L Williams
- Davies Research Centre, School of Animal and Veterinary Sciences, University of Adelaide, Roseworthy, SA, Australia.,Department of Animal Science, Food and Nutrition, Università Cattolica Del Sacro Cuore, Piacenza, Italy
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Qanbari S. On the Extent of Linkage Disequilibrium in the Genome of Farm Animals. Front Genet 2020; 10:1304. [PMID: 32010183 PMCID: PMC6978288 DOI: 10.3389/fgene.2019.01304] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 11/26/2019] [Indexed: 11/13/2022] Open
Abstract
Given the importance of linkage disequilibrium (LD) in gene mapping and evolutionary inferences, I characterize in this review the pattern of LD and discuss the influence of human intervention during domestication, breed establishment, and subsequent genetic improvement on shaping the genome of livestock species. To this end, I summarize data on the profile of LD based on array genotypes vs. sequencing data in cattle and chicken, two major livestock species, and compare to the human case. This comparison provides insights into the real dimension of the pairwise allelic correlation and haplo-block structuring. The dependency of LD on allelic frequency is pictured and a recently introduced metric for moderating it is outlined. In the context of the contact farm animals had with human, the impact of genetic forces including admixture, mutation, recombination rate, selection, and effective population size on LD is discussed. The review further highlights the interplay of LD with runs of homozygosity and concludes with the operational implications of the widely used association and selection mapping studies in relation to LD.
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Affiliation(s)
- Saber Qanbari
- Leibniz Institute for Farm Animal Biology (FBN), Institute of Genetics and Biometry, Dummerstorf, Germany.,Animal Breeding and Genetics Group, Department of Animal Sciences, Center for Integrated Breeding Research, University of Göttingen, Göttingen, Germany
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Schmid M, Bennewitz J. Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs. Arch Anim Breed 2017. [DOI: 10.5194/aab-60-335-2017] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Abstract. Quantitative or complex traits are controlled by many genes and environmental factors. Most traits in livestock breeding are quantitative traits. Mapping genes and causative mutations generating the genetic variance of these traits is still a very active area of research in livestock genetics. Since genome-wide and dense SNP panels are available for most livestock species, genome-wide association studies (GWASs) have become the method of choice in mapping experiments. Different statistical models are used for GWASs. We will review the frequently used single-marker models and additionally describe Bayesian multi-marker models. The importance of nonadditive genetic and genotype-by-environment effects along with GWAS methods to detect them will be briefly discussed. Different mapping populations are used and will also be reviewed. Whenever possible, our own real-data examples are included to illustrate the reviewed methods and designs. Future research directions including post-GWAS strategies are outlined.
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Abstract
Gametic phase disequilibrium is the nonrandom association of alleles within gametes. Linkage disequilibrium (LD) describes the special case of deviation from independence between alleles at two linked genetic loci. Estimation of allelic LD requires knowledge of haplotypes. Genotype-based LD measures dispense with the haplotype estimation step and avoid bias in LD estimation. In this chapter, the most important measures for allelic and genotypic LD are introduced. The use of software packages for LD estimation is illustrated.
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Affiliation(s)
- Maren Vens
- Institut für Medizinische Biometrie und Epidemiologie, Universitätsklinikum Hamburg-Eppendorf, Martinistraße 52, Hamburg, 20246, Germany.
| | - Andreas Ziegler
- Institut für Medizinische Biometrie und Statistik & Zentrum für klinische Studien, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, Lübeck, 23562, Germany
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de Candia T, Lee S, Yang J, Browning B, Gejman P, Levinson D, Mowry B, Hewitt J, Goddard M, O’Donovan M, Purcell S, Posthuma D, Visscher P, Wray N, Keller M. Additive genetic variation in schizophrenia risk is shared by populations of African and European descent. Am J Hum Genet 2013; 93:463-70. [PMID: 23954163 PMCID: PMC3845872 DOI: 10.1016/j.ajhg.2013.07.007] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2013] [Revised: 06/03/2013] [Accepted: 07/02/2013] [Indexed: 12/25/2022] Open
Abstract
To investigate the extent to which the proportion of schizophrenia's additive genetic variation tagged by SNPs is shared by populations of European and African descent, we analyzed the largest combined African descent (AD [n = 2,142]) and European descent (ED [n = 4,990]) schizophrenia case-control genome-wide association study (GWAS) data set available, the Molecular Genetics of Schizophrenia (MGS) data set. We show how a method that uses genomic similarities at measured SNPs to estimate the additive genetic correlation (SNP correlation [SNP-rg]) between traits can be extended to estimate SNP-rg for the same trait between ethnicities. We estimated SNP-rg for schizophrenia between the MGS ED and MGS AD samples to be 0.66 (SE = 0.23), which is significantly different from 0 (p(SNP-rg = 0) = 0.0003), but not 1 (p(SNP-rg = 1) = 0.26). We re-estimated SNP-rg between an independent ED data set (n = 6,665) and the MGS AD sample to be 0.61 (SE = 0.21, p(SNP-rg = 0) = 0.0003, p(SNP-rg = 1) = 0.16). These results suggest that many schizophrenia risk alleles are shared across ethnic groups and predate African-European divergence.
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Affiliation(s)
- Teresa R. de Candia
- Department of Psychology and Neuroscience, University of Colorado, Boulder, CO 80302, USA
- Institute for Behavioral Genetics, University of Colorado, Boulder, CO 80302, USA
| | - S. Hong Lee
- The University of Queensland, Queensland Brain Institute, Brisbane, QLD 4072, Australia
| | - Jian Yang
- The University of Queensland, Queensland Brain Institute, Brisbane, QLD 4072, Australia
- The University of Queensland, Diamantina Institute, Brisbane, QLD 4072, Australia
| | - Brian L. Browning
- Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA 98195, USA
| | - Pablo V. Gejman
- Department of Psychiatry and Behavioral Sciences, Northshore University Health System and University of Chicago, Evanston, IL 60601, USA
| | - Douglas F. Levinson
- Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA 94305, USA
| | - Bryan J. Mowry
- The University of Queensland, Queensland Brain Institute, Brisbane, QLD 4072, Australia
- Queensland Centre for Mental Health Research, Brisbane, QLD 4076, Australia
| | - John K. Hewitt
- Department of Psychology and Neuroscience, University of Colorado, Boulder, CO 80302, USA
- Institute for Behavioral Genetics, University of Colorado, Boulder, CO 80302, USA
| | - Michael E. Goddard
- Department of Agriculture and Food Systems, University of Melbourne, Melbourne, VIC 3010, Australia
- Biosciences Research Division, Department of Primary Industries, Melbourne, VIC 3001, Australia
| | - Michael C. O’Donovan
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff CF14 4XN, UK
| | | | - Danielle Posthuma
- Complex Trait Genetics, Department of Functional Genomics, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University, Amsterdam 1081 HV, the Netherlands
- Department of Clinical Genetics, VU University Medical Center, Amsterdam 1081 HV, the Netherlands
- Department of Child and Adolescent Psychiatry, Erasmus University Rotterdam, Sophia Child Hospital, Rotterdam 3000 CB, the Netherlands
| | | | | | - Peter M. Visscher
- The University of Queensland, Queensland Brain Institute, Brisbane, QLD 4072, Australia
- The University of Queensland, Diamantina Institute, Brisbane, QLD 4072, Australia
| | - Naomi R. Wray
- The University of Queensland, Queensland Brain Institute, Brisbane, QLD 4072, Australia
| | - Matthew C. Keller
- Department of Psychology and Neuroscience, University of Colorado, Boulder, CO 80302, USA
- Institute for Behavioral Genetics, University of Colorado, Boulder, CO 80302, USA
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Abstract
Gametic phase disequilibrium (GPD) is the nonrandom association of alleles within gametes. Linkage disequilibrium (LD) describes the special case of deviation from independence between alleles at two linked genetic loci. Estimation of allelic LD requires knowledge of haplotypes. Genotype-based LD measures dispense with the haplotype estimation step and avoid bias in LD estimation. In this chapter, the most important measures for allelic and genotypic LD are introduced. The use of software packages for LD estimation is illustrated.
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Affiliation(s)
- Maren Vens
- Institut für Medizinische Biometrie und Statistik, Universitätsklinikum Schleswig-Holstein, Universität zu Lübeck, Lübeck, Germany
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