BackgroundThe history of African indigenous cattle and their adaptation to environmental and human selection pressure is at the root of their remarkable diversity. Characterization of this diversity is an essential step towards understanding the genomic basis of productivity and adaptation to survival under African farming systems.ResultsWe analyze patterns of African cattle genetic variation by sequencing 48 genomes from five indigenous populations and comparing them to the genomes of 53 commercial taurine breeds. We find the highest genetic diversity among African zebu and sanga cattle. Our search for genomic regions under selection reveals signatures of selection for environmental adaptive traits. In particular, we identify signatures of selection including genes and/or pathways controlling anemia and feeding behavior in the trypanotolerant N’Dama, coat color and horn development in Ankole, and heat tolerance and tick resistance across African cattle especially in zebu breeds.ConclusionsOur findings unravel at the genome-wide level, the unique adaptive diversity of African cattle while emphasizing the opportunities for sustainable improvement of livestock productivity on the continent.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1153-y) contains supplementary material, which is available to authorized users.
EAT and AAI, along eigenvector 1, which explains ~15% of the total variation. AFT Muturu and N'Dama are close to EAT along the eigenvector 1. Most of the AFH cattle cluster together regardless of their breed memberships, leaving only Ankole, Mursi and Sheko outside the main cluster toward the AFT Muturu and N'Dama. The PCA results also show that Muturu and N'Dama, our representative of AFT population, are separated from the other cattle groups (eigenvector 2, ~2.5% of total variation). Sheko positions close to the AFH, as similarly reported in other studies 5,43 . Genetic clustering analysis using ADMIXTURE 44 corroborates the pattern found in PCA (Fig. 2b and Extended Data Fig. 2). Most of AFH show a similar proportion of taurine ancestry, around 25% on average. Only a few AFH breeds have elevated taurine ancestry: Ankole (53.37 ± 1.49%), Sheko (46.28 ± 2.03%) and Mursi (35.90 ± 2.16%). (Fig. 2b).Genetic distance and diversity. Pairwise F st were calculated to estimate the genetic distances between populations (n = 38) (Extended Data Fig. 3). Taurine (EUT, AST and AFT) show F st values of 0.1568 and 0.3287 on average against AFH and AAI, respectively.Across AFH, pairwise F st between breeds is close to zero, regardless of their phenotypic classification as African Zebu, Sanga or Zenga. Muturu and N'Dama show F st value of 0.1769, 0.1847 and 0.3734 against AFH, EAT and AAI, respectively.The genome-wide autosomal SNPs show reduced levels of heterozygosity in the taurine (0.0021 ± 0.0005/bp) compared to all other populations (0.0048 ± 0.0008/bp). Heterozygosity values of AFH are similarly higher across populations (0.0046 ± 0.0003/bp). AAI shows a higher level of heterozygosity compared to AFH (0.0052 ± 0.0014/bp) (Extended Data Fig. 4). The degree of inbreeding measured by runs of homozygosity (ROH) shows that taurine, including Muturu and N'Dama, have a higher level of inbreeding compared to the other and Ethiopia), the University of Khartoum (Sudan), and the National Biotechnology Development Agency (NABDA) (Nigeria). The following institutions and their personnel provided help for the sampling of the African cattle: ILRI Kapiti Ranch, Ministry of Animal Resources, Fisheries and Range (Sudan), Ol Pejeta Conservancy (Kenya), Institute of Biodiversity (Ethiopia), the Directors of Veterinary Services and the cattle keepers from Ethiopia, Kenya, Uganda and Sudan. ILRI livestock genomics program is supported by the
Background: Abalones are large marine snails in the family Haliotidae and the genus Haliotis belonging to the class Gastropoda of the phylum Mollusca. The family Haliotidae contains only one genus, Haliotis, and this single genus is known to contain several species of abalone. With 18 additional subspecies, the most comprehensive treatment of Haliotidae considers 56 species valid [1]. Abalone is an economically important fishery and aquaculture animal that is considered a highly prized seafood delicacy. The total global supply of abalone has increased 5-fold since the 1970s and farm production increased explosively from 50 mt to 103 464 mt in the past 40 years. Additionally, researchers have recently focused on abalone given their reported tumor suppression effect. However, despite the valuable features of this marine animal, no genomic information is available for the Haliotidae family and related research is still limited. To construct the H. discus hannai genome, a total of 580-G base pairs using Illumina and Pacbio platforms were generated with 322-fold coverage based on the 1.8-Gb estimated genome size of H. discus hannai using flow cytometry. The final genome assembly consisted of 1.86 Gb with 35 450 scaffolds (>2 kb). GC content level was 40.51%, and the N50 length of assembled scaffolds was 211 kb. We identified 29 449 genes using Evidence Modeler based on the gene information from ab initio prediction, protein homology with known genes, and transcriptome evidence of RNA-seq. Here we present the first Haliotidae genome, H. discus hannai, with sequencing data, assembly, and gene annotation information. This will be helpful for resolving the lack of genomic information in the Haliotidae family as well as providing more opportunities for understanding gastropod evolution.
Many recent RNA-seq studies were focused mainly on detecting the differentially expressed genes (DEGs) between two or more conditions. In contrast, only a few attempts have been made to detect genes associated with quantitative traits, such as obesity index and milk yield, on RNA-seq experiment with large number of biological replicates. This study illustrates the linear model application on trait associated genes (TAGs) detection in two real RNA-seq datasets: 89 replicated human obesity related data and 21 replicated Holsteins’ milk production related RNA-seq data. Based on these two datasets, the performance between suggesting methods, such as ordinary regression and robust regression, and existing methods: DESeq2 and Voom, were compared. The results indicate that suggesting methods have much lower false discoveries compared to the precedent two group comparisons based approaches in our simulation study and qRT-PCR experiment. In particular, the robust regression outperforms existing DEG finding method as well as ordinary regression in terms of precision. Given the current trend in RNA-seq pricing, we expect our methods to be successfully applied in various RNA-seq studies with numerous biological replicates that handle continuous response traits.
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