scholarly journals Signatures of selection analysis using whole-genome sequence data reveals novel candidate genes for pony and light horse types

Genome ◽  
2020 ◽  
Vol 63 (8) ◽  
pp. 387-396 ◽  
Author(s):  
Siavash Salek Ardestani ◽  
Mehdi Aminafshar ◽  
Mohammad Bagher Zandi Baghche Maryam ◽  
Mohammad Hossein Banabazi ◽  
Mehdi Sargolzaei ◽  
...  
Keyword(s):  
Sequence Data ◽  
Whole Genome Sequence ◽  
Fixation Index ◽  
Whole Genome ◽  
Evolutionary Trend ◽  
Body Type ◽  
Genomic Signatures ◽  
Selection Strategies ◽  
Signatures Of Selection ◽  
Genetic Signatures

Natural selection and domestication have shaped modern horse populations, resulting in a vast range of phenotypically diverse breeds. Horse breeds are classified into three types (pony, light, and draft) generally based on their body type. Understanding the genetic basis of horse type variation and selective pressures related to the evolutionary trend can be particularly important for current selection strategies. Whole-genome sequences were generated for 14 pony and 32 light horses to investigate the genetic signatures of selection of the horse type in pony and light horses. In the overlapping extremes of the fixation index and nucleotide diversity results, we found novel genomic signatures of selective sweeps near key genes previously implicated in body measurements including C4ORF33, CRB1, CPN1, FAM13A, and FGF12 that may influence variation in pony and light horse types. This study contributes to a better understanding of the genetic background of differences between pony and light horse types.

46 Footprints of Selection in Angus and Hanwoo Beef Cattle Using Imputed Whole Genome Sequence Data

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 25-25
Author(s):  
Muhammad Yasir Nawaz ◽  
Rodrigo Pelicioni Savegnago ◽  
Cedric Gondro
Keyword(s):  
Beef Cattle ◽  
Genome Sequence ◽  
Sequence Data ◽  
Whole Genome Sequence ◽  
Fixation Index ◽  
Whole Genome ◽  
Extended Haplotype Homozygosity ◽  
Extended Haplotype ◽  
Genome Sequence Data ◽  
Genomic Regions

Abstract In this study, we detected genome wide footprints of selection in Hanwoo and Angus beef cattle using different allele frequency and haplotype-based methods based on imputed whole genome sequence data. Our dataset included 13,202 Angus and 10,437 Hanwoo animals with 10,057,633 and 13,241,550 imputed SNPs, respectively. A subset of data with 6,873,624 common SNPs between the two populations was used to estimate signatures of selection parameters, both within (runs of homozygosity and extended haplotype homozygosity) and between (allele fixation index, extended haplotype homozygosity) the breeds in order to infer evidence of selection. We observed that correlations between various measures of selection ranged between 0.01 to 0.42. Assuming these parameters were complementary to each other, we combined them into a composite selection signal to identify regions under selection in both beef breeds. The composite signal was based on the average of fractional ranks of individual selection measures for every SNP. We identified some selection signatures that were common between the breeds while others were independent. We also observed that more genomic regions were selected in Angus as compared to Hanwoo. Candidate genes within significant genomic regions may help explain mechanisms of adaptation, domestication history and loci for important traits in Angus and Hanwoo cattle. In the future, we will use the top SNPs under selection for genomic prediction of carcass traits in both breeds.

148 Multiple Dysregulated Novel Pathways and Genes in Aleutian Mink Disease Revealed by Selection Signatures and Gene Network Analyses Using Whole-genome Sequence Data

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 76-76
Author(s):  
Seyed Milad Vahedi ◽  
Karim Karimi ◽  
Siavash Salek Ardestani ◽  
Younes Miar
Keyword(s):  
Sequence Data ◽  
American Mink ◽  
Enrichment Analysis ◽  
Whole Genome Sequence ◽  
Fixation Index ◽  
Pathway Enrichment Analysis ◽  
Whole Genome ◽  
Nucleotide Polymorphisms ◽  
Network Analyses ◽  
Genome Level

Abstract Aleutian disease (AD) is a chronic persistent infection in domestic mink caused by Aleutian mink disease virus (AMDV). Female mink’s fertility and pelt quality depression are the main reasons for the AD’s negative economic impacts on the mink industry. A total number of 79 American mink from the Canadian Center for Fur Animal Research at Dalhousie University (Truro, NS, Canada) were classified based on the results of counter immunoelectrophoresis (CIEP) tests into two groups of positive (n = 48) and negative (n = 31). Whole-genome sequences comprising 4,176 scaffolds and 8,039,737 single nucleotide polymorphisms (SNPs) were used to trace the selection footprints for response to AMDV infection at the genome level. Window-based fixation index (Fst) and nucleotide diversity (θπ) statistics were estimated to compare positive and negative animals’ genomes. The overlapped top 1% genomic windows between two statistics were considered as potential regions underlying selection pressures. A total of 98 genomic regions harboring 33 candidate genes were detected as selective signals. Most of the identified genes were involved in the development and functions of immune system (PPP3CA, SMAP2, TNFRSF21, SKIL, and AKIRIN2), musculoskeletal system (COL9A2, PPP1R9A, ANK2, AKAP9, and STRIT1), nervous system (ASCL1, ZFP69B, SLC25A27, MCF2, and SLC7A14), reproductive system (CAMK2D, GJB7, SSMEM1, C6orf163), liver (PAH and DPYD), and lung (SLC35A1). Gene-expression network analysis showed the interactions among 27 identified genes. Moreover, pathway enrichment analysis of the constructed genes network revealed significant oxytocin (KEGG: hsa04921) and GnRH signaling (KEGG: hsa04912) pathways, which are likely to be impaired by AMDV leading to dams’ fecundity reduction. These results provided a perspective to the genetic architecture of response to AD in American mink and novel insight into the pathogenesis of AMDV.

scholarly journals Sequencing era methods for identifying signatures of selection in the genome

2018 ◽  
Vol 20 (6) ◽  
pp. 1997-2008 ◽  
Author(s):  
Clare Horscroft ◽  
Sarah Ennis ◽  
Reuben J Pengelly ◽  
Timothy J Sluckin ◽  
Andrew Collins
Keyword(s):  
Genome Sequence ◽  
Sequence Data ◽  
Large Data ◽  
Substantial Improvement ◽  
Data Availability ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Genome Sequence Data ◽  
Signatures Of Selection ◽  
The Impact

Abstract Insights into genetic loci which are under selection and their functional roles contribute to increased understanding of the patterns of phenotypic variation we observe today. The availability of whole-genome sequence data, for humans and other species, provides opportunities to investigate adaptation and evolution at unprecedented resolution. Many analytical methods have been developed to interrogate these large data sets and characterize signatures of selection in the genome. We review here recently developed methods and consider the impact of increased computing power and data availability on the detection of selection signatures. Consideration of demography, recombination and other confounding factors is important, and use of a range of methods in combination is a powerful route to resolving different forms of selection in genome sequence data. Overall, a substantial improvement in methods for application to whole-genome sequencing is evident, although further work is required to develop robust and computationally efficient approaches which may increase reproducibility across studies.

TIGER: inferring DNA replication timing from whole-genome sequence data

2021 ◽  
Author(s):  
Amnon Koren ◽  
Dashiell J Massey ◽  
Alexa N Bracci
Keyword(s):  
Dna Replication ◽  
Genome Sequence ◽  
Genomic Dna ◽  
Sequence Data ◽  
Replication Timing ◽  
Whole Genome Sequence ◽  
Supplementary Information ◽  
Whole Genome ◽  
Genome Sequence Data ◽  
Dna Replication Timing

Abstract Motivation Genomic DNA replicates according to a reproducible spatiotemporal program, with some loci replicating early in S phase while others replicate late. Despite being a central cellular process, DNA replication timing studies have been limited in scale due to technical challenges. Results We present TIGER (Timing Inferred from Genome Replication), a computational approach for extracting DNA replication timing information from whole genome sequence data obtained from proliferating cell samples. The presence of replicating cells in a biological specimen leads to non-uniform representation of genomic DNA that depends on the timing of replication of different genomic loci. Replication dynamics can hence be observed in genome sequence data by analyzing DNA copy number along chromosomes while accounting for other sources of sequence coverage variation. TIGER is applicable to any species with a contiguous genome assembly and rivals the quality of experimental measurements of DNA replication timing. It provides a straightforward approach for measuring replication timing and can readily be applied at scale. Availability and Implementation TIGER is available at https://github.com/TheKorenLab/TIGER. Supplementary information Supplementary data are available at Bioinformatics online

scholarly journals Whole genome sequence data of Mycobacterium tuberculosis XDR strain, isolated from patient in Kazakhstan

Data in Brief ◽  
2020 ◽  
Vol 33 ◽  
pp. 106416
Author(s):  
Asset Daniyarov ◽  
Askhat Molkenov ◽  
Saule Rakhimova ◽  
Ainur Akhmetova ◽  
Zhannur Nurkina ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Genome Sequence ◽  
Sequence Data ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Genome Sequence Data

scholarly journals Elucidating the genetic basis of an oligogenic birth defect using whole genome sequence data in a non-model organism, Bubalus bubalis

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Lynsey K. Whitacre ◽  
Jesse L. Hoff ◽  
Robert D. Schnabel ◽  
Sara Albarella ◽  
Francesca Ciotola ◽  
...  
Keyword(s):  
Genome Sequence ◽  
Birth Defect ◽  
Genetic Basis ◽  
Sequence Data ◽  
Model Organism ◽  
Bubalus Bubalis ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Genome Sequence Data

scholarly journals Whole genome characterization of strains belonging to the Ralstonia solanacearum species complex and in silico analysis of TaqMan assays for detection in this heterogenous species complex

2021 ◽  
Author(s):  
Viola Kurm ◽  
Ilse Houwers ◽  
Claudia E. Coipan ◽  
Peter Bonants ◽  
Cees Waalwijk ◽  
...  
Keyword(s):  
Ralstonia Solanacearum ◽  
In Silico ◽  
Species Complex ◽  
Sequence Data ◽  
In Silico Analysis ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Genome Sequences ◽  
Pcr Assays

AbstractIdentification and classification of members of the Ralstonia solanacearum species complex (RSSC) is challenging due to the heterogeneity of this complex. Whole genome sequence data of 225 strains were used to classify strains based on average nucleotide identity (ANI) and multilocus sequence analysis (MLSA). Based on the ANI score (>95%), 191 out of 192(99.5%) RSSC strains could be grouped into the three species R. solanacearum, R. pseudosolanacearum, and R. syzygii, and into the four phylotypes within the RSSC (I,II, III, and IV). R. solanacearum phylotype II could be split in two groups (IIA and IIB), from which IIB clustered in three subgroups (IIBa, IIBb and IIBc). This division by ANI was in accordance with MLSA. The IIB subgroups found by ANI and MLSA also differed in the number of SNPs in the primer and probe sites of various assays. An in-silico analysis of eight TaqMan and 11 conventional PCR assays was performed using the whole genome sequences. Based on this analysis several cases of potential false positives or false negatives can be expected upon the use of these assays for their intended target organisms. Two TaqMan assays and two PCR assays targeting the 16S rDNA sequence should be able to detect all phylotypes of the RSSC. We conclude that the increasing availability of whole genome sequences is not only useful for classification of strains, but also shows potential for selection and evaluation of clade specific nucleic acid-based amplification methods within the RSSC.

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