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      Detection of genomic regions underlying resistance to gastrointestinal parasites in Australian sheep

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          Abstract

          Background

          This study aimed at identifying genomic regions that underlie genetic variation of worm egg count, as an indicator trait for parasite resistance in a large population of Australian sheep, which was genotyped with the high-density 600 K Ovine single nucleotide polymorphism array. This study included 7539 sheep from different locations across Australia that underwent a field challenge with mixed gastrointestinal parasite species. Faecal samples were collected and worm egg counts for three strongyle species, i.e. Teladorsagia circumcincta, Haemonchus contortus and Trichostrongylus colubriformis were determined. Data were analysed using genome-wide association studies (GWAS) and regional heritability mapping (RHM).

          Results

          Both RHM and GWAS detected a region on Ovis aries (OAR) chromosome 2 that was highly significantly associated with parasite resistance at a genome-wise false discovery rate of 5%. RHM revealed additional significant regions on OAR6, 18, and 24. Pathway analysis revealed 13 genes within these significant regions ( SH3RF1, HERC2, MAP3K, CYFIP1, PTPN1, BIN1, HERC3, HERC5, HERC6, IBSP, SPP1, ISG20, and DET1), which have various roles in innate and acquired immune response mechanisms, as well as cytokine signalling. Other genes involved in haemostasis regulation and mucosal defence were also detected, which are important for protection of sheep against invading parasites.

          Conclusions

          This study identified significant genomic regions on OAR2, 6, 18, and 24 that are associated with parasite resistance in sheep. RHM was more powerful in detecting regions that affect parasite resistance than GWAS. Our results support the hypothesis that parasite resistance is a complex trait and is determined by a large number of genes with small effects, rather than by a few major genes with large effects.

          Electronic supplementary material

          The online version of this article (10.1186/s12711-019-0479-1) contains supplementary material, which is available to authorized users.

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          Most cited references50

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          A new approach for efficient genotype imputation using information from relatives

          Background Genotype imputation can help reduce genotyping costs particularly for implementation of genomic selection. In applications entailing large populations, recovering the genotypes of untyped loci using information from reference individuals that were genotyped with a higher density panel is computationally challenging. Popular imputation methods are based upon the Hidden Markov model and have computational constraints due to an intensive sampling process. A fast, deterministic approach, which makes use of both family and population information, is presented here. All individuals are related and, therefore, share haplotypes which may differ in length and frequency based on their relationships. The method starts with family imputation if pedigree information is available, and then exploits close relationships by searching for long haplotype matches in the reference group using overlapping sliding windows. The search continues as the window size is shrunk in each chromosome sweep in order to capture more distant relationships. Results The proposed method gave higher or similar imputation accuracy than Beagle and Impute2 in cattle data sets when all available information was used. When close relatives of target individuals were present in the reference group, the method resulted in higher accuracy compared to the other two methods even when the pedigree was not used. Rare variants were also imputed with higher accuracy. Finally, computing requirements were considerably lower than those of Beagle and Impute2. The presented method took 28 minutes to impute from 6 k to 50 k genotypes for 2,000 individuals with a reference size of 64,429 individuals. Conclusions The proposed method efficiently makes use of information from close and distant relatives for accurate genotype imputation. In addition to its high imputation accuracy, the method is fast, owing to its deterministic nature and, therefore, it can easily be used in large data sets where the use of other methods is impractical.
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            Repeat instability: mechanisms of dynamic mutations.

            Disease-causing repeat instability is an important and unique form of mutation that is linked to more than 40 neurological, neurodegenerative and neuromuscular disorders. DNA repeat expansion mutations are dynamic and ongoing within tissues and across generations. The patterns of inherited and tissue-specific instability are determined by both gene-specific cis-elements and trans-acting DNA metabolic proteins. Repeat instability probably involves the formation of unusual DNA structures during DNA replication, repair and recombination. Experimental advances towards explaining the mechanisms of repeat instability have broadened our understanding of this mutational process. They have revealed surprising ways in which metabolic pathways can drive or protect from repeat instability.
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              Drug resistance in nematodes of veterinary importance: a status report.

              Ray Kaplan (2004)
              Reports of drug resistance have been made in every livestock host and to every anthelmintic class. In some regions of world, the extremely high prevalence of multi-drug resistance (MDR) in nematodes of sheep and goats threatens the viability of small-ruminant industries. Resistance in nematodes of horses and cattle has not yet reached the levels seen in small ruminants, but evidence suggests that the problems of resistance, including MDR worms, are also increasing in these hosts. There is an urgent need to develop both novel non-chemical approaches for parasite control and molecular assays capable of detecting resistant worms.
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                Author and article information

                Contributors
                malkala2@une.edu.au
                jgibson5@une.edu.au
                Hong.Lee@unisa.edu.au
                gondroce@msu.edu
                jvanderw@une.edu.au
                Journal
                Genet Sel Evol
                Genet. Sel. Evol
                Genetics, Selection, Evolution : GSE
                BioMed Central (London )
                0999-193X
                1297-9686
                3 July 2019
                3 July 2019
                2019
                : 51
                : 37
                Affiliations
                [1 ]Cooperative Research Centre for Sheep Industry Innovation, Armidale, NSW 2351 Australia
                [2 ]ISNI 0000 0004 1936 7371, GRID grid.1020.3, School of Environmental and Rural Science, , University of New England, ; Armidale, NSW 2351 Australia
                [3 ]ISNI 0000 0000 8994 5086, GRID grid.1026.5, Australian Centre for Precision Health, University of South Australia Cancer Research Institute, , University of South Australia, ; Adelaide, SA 5000 Australia
                [4 ]ISNI 0000 0001 2150 1785, GRID grid.17088.36, Present Address: College of Agriculture and Natural Resources, , Michigan State University, ; East Lansing, MI 48824 USA
                Author information
                http://orcid.org/0000-0002-3206-6421
                Article
                479
                10.1186/s12711-019-0479-1
                6609385
                31269896
                78edab11-3878-4407-a125-ed409b78fead
                © The Author(s) 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 21 October 2018
                : 19 June 2019
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2019

                Genetics
                Genetics

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