Whole genome scan reveals the genetic signature of African Ankole cattle breed and potential for higher quality beef
© The Author(s). 2017
Received: 6 July 2016
Accepted: 20 December 2016
Published: 9 February 2017
Africa is home to numerous cattle breeds whose diversity has been shaped by subtle combinations of human and natural selection. African Sanga cattle are an intermediate type of cattle resulting from interbreeding between Bos taurus and Bos indicus subspecies. Recently, research has asserted the potential of Sanga breeds for commercial beef production with better meat quality as compared to Bos indicus breeds. Here, we identified meat quality related gene regions that are positively selected in Ankole (Sanga) cattle breeds as compared to indicus (Boran, Ogaden, and Kenana) breeds using cross-population (XP-EHH and XP-CLR) statistical methods.
We identified 238 (XP-EHH) and 213 (XP-CLR) positively selected genes, of which 97 were detected from both statistics. Among the genes obtained, we primarily reported those involved in different biological process and pathways associated with meat quality traits. Genes (CAPZB, COL9A2, PDGFRA, MAP3K5, ZNF410, and PKM2) involved in muscle structure and metabolism affect meat tenderness. Genes (PLA2G2A, PARK2, ZNF410, MAP2K3, PLCD3, PLCD1, and ROCK1) related to intramuscular fat (IMF) are involved in adipose metabolism and adipogenesis. MB and SLC48A1 affect meat color. In addition, we identified genes (TIMP2, PKM2, PRKG1, MAP3K5, and ATP8A1) related to feeding efficiency. Among the enriched Gene Ontology Biological Process (GO BP) terms, actin cytoskeleton organization, actin filament-based process, and protein ubiquitination are associated with meat tenderness whereas cellular component organization, negative regulation of actin filament depolymerization and negative regulation of protein complex disassembly are involved in adipocyte regulation. The MAPK pathway is responsible for cell proliferation and plays an important role in hyperplastic growth, which has a positive effect on meat tenderness.
Results revealed several candidate genes positively selected in Ankole cattle in relation to meat quality characteristics. The genes identified are involved in muscle structure and metabolism, and adipose metabolism and adipogenesis. These genes help in the understanding of the biological mechanisms controlling beef quality characteristics in African Ankole cattle. These results provide a basis for further research on the genomic characteristics of Ankole and other Sanga cattle breeds for quality beef.
KeywordsAfrican cattle Ankole cattle Meat quality Sanga cattle XP-CLR XP-EHH
Africa, with its diverse agro-ecological zones, is a home to diverse cattle breeds adapted to their local environments. African cattle breeds are derived from Bos taurus and Bos indicus subspecies introduced to the continent at different times, and through interbreeding between them [1, 2]. Since the introduction, their diversity has been shaped by subtle combinations of human and natural selection. Selection in African cattle is mainly for sociocultural concerns and to survive the heterogeneous environment . African cattle have been evolved to adapt to the poor feed availability, high environmental temperature, and high prevalence of internal and external parasite and disease conditions of the continent. These cattle breeds display better heat tolerance, adaptability, tick resistance, reproductive longevity, and maternal characteristics such as fertility, low inter-calf periods and cow efficiency [1, 4–7].
African Sanga cattle, sometimes referred to as Bos africanus, are an intermediate type of cattle believed to be the result of interbreeding between Bos taurus and Bos indicus, which dwell in eastern, central and southern Africa [1, 8]. Generally, Sanga cattle can be identified by their long and slender horns, small cervicothoracic hump, and small and unfolded dewlap . There are 30 Sanga cattle breeds/strains in Africa subdivided into Sanga of eastern and Sanga of southern Africa based on geographical location . Recently, research outputs are asserting the potential of African Sanga and Sanga derived breeds to produce carcass and meat quality attributes that favorably compare to British and Continental breeds and are often better than those of the Bos indicus breeds [6, 10–13]. Sanga breeds in south Africa (eg., Bonsmara, Drakensberger and Nguni) were found to produce beef with lower shear force, shorter myofibrillar fragment length, larger rib fat thickness, and larger soluble collagen when compared with indicus (Brahman) cattle .
Meat quality is a general term used to describe the attributes of meat which include carcass composition and conformation, the eating quality of meat, health issues associated with meat, and production and environmental issues . Meat sensory characteristics such as tenderness, flavor, juiciness, and color are important meat quality parameters which are affected by biological characteristics and proteolytic activities of muscle [15, 16]. The biological characteristics of muscles such as fiber type, collagen, intramuscular adipose tissue and protease activities regulate meat tenderness and flavor and are known to be affected by genetic and rearing factors . The heritability of beef quality traits is low to moderate which varies between breed groups, methods of estimation, number of records, and other factors [17, 18]. The genetic variation within and between breeds is because of the positive selection of gene regions caused by beneficial polymorphisms in the genes affecting the traits. Identification of selection signatures in the genome provide information about the evolutionary processes involved in shaping genomes and functional information about genes/genomic regions .
Studies attempting to detect positive selection signatures in African cattle have reported several genes involved in immune system, reproduction, energy metabolism, coat coloration, thermoregulation and tick resistance [20, 21]. The detection of immune related genes might be related to the selective pressure that has been exerted by the long-term presence of pathogens in the continent , whereas signatures of selection associated with reproduction and thermoregulation is an adaptation to perform under heat stress conditions . However, there have been no previous studies attempting to identify genes affecting meat quality traits in African cattle in general and Sanga cattle in particular.
In this study, we reported genes that are positively selected in Ankole cattle population associated with meat quality traits. This was done by scanning the whole genome of four African cattle breeds (African Sanga cattle: Ankole; and three indicus breeds: Boran, Ogaden, and Kenana) [22, 23]. We employed cross-population extended haplotype homozygosity (XP-EHH) and cross population composite likelihood ratio (XP-CLR) statistics in order to detect selection signatures from different data patterns; two approaches were used as each has its own advantages. XP-EHH compares haplotype lengths of populations to detect selective sweeps when the allele has approached or achieved fixation in one population but remains polymorphic in the other population . XP-CLR is a statistic based on allele frequency differentiation across populations. It is not affected by ascertainment biases and has the advantage of being able to detect older signals and selection on standing variation .
Results and discussion
DNA samples extracted from whole blood samples of four African cattle breeds (Boran, Ogaden, Kenana and Ankole) were sequenced to ~ 11 × genome coverage each. Using a standard sample preparation and whole genome re-sequencing pipeline, an overall alignment rate of 98.84% covering 98.56% of the taurine reference genome was obtained. After filtering false positive calls using several filtering steps, a total of ~37 million SNPs were retained and used for detection of positive selection signature analysis.
While African indicus cattle have been present on the continent since 1500 BC , Sanga cattle were derived through hybridization between taurine cattle and zebu cattle around 700 AD . More than 30 breeds/strains of Sanga cattle can be found distributed throughout Eritrea, Ethiopia, southern Sudan, the Great Lakes region of East Africa and southern Africa. The Ankole group is one of the three groups of Sanga cattle representing Sanga cattle in Uganda, Rwanda, Burundi, Tanzania and Democratic Republic of Congo ; it is a valuable and widely used genetic resource in these regions.
Positive selective signature in Ankole cattle population
KEGG pathways obtained from DAVID gene enrichment (p < 0.05) analysis
KEGG pathway term
Leukocyte trans-endothelial migration
ROCK1, PIK3CB, CLDN10, TXK, RAPGEF4, RAPGEF3, CTNNA3
MAPK signaling pathway
MAP4K4, ACVR1B, FGF18, MAP3K5, MAP2K3, MRAS, PDGFRA, PLA2G2A, NR4A1, MAPKAPK2, NFATC2
FGF18, PIK3CB, MITF, PDGFRA, MDM2
Species, trait and reference
Meat color d
OR2D2, OR10A4, OR2D3
Biological process and pathways related to meat quality traits
Meat quality is a multifactorial and complex trait affected by different factors at different levels ranging from molecular to mechanical. Molecularly, genes involved in many cellular mechanisms such as muscle growth, glycolysis, muscle contraction, stress reaction, cell cycle, proteolysis, protein ubiquitination and apoptosis have been reported to be associated with meat quality characteristics [16, 31, 32]. Previous studies reported that, as compared with indicus breeds, Sanga breeds produce better quality beef [11–13] with lower shear force, shorter myofibrillar fragment length, larger rib fat thickness, larger soluble collagen, and higher percent drip loss . Additionally, Sanga cattle have better feed conversion efficiency, reproductive performances, and tick resistance in the tropics .
Genes affecting meat quality traits in Ankole cattle
Here, we described genes positively selected in Ankole Sanga cattle that are potentially associated with meat quality and feed conversion efficiency traits based on previous studies and their biological functions (Table 2; Additional file 6: Table S5).
Genes related to meat tenderness
Meat tenderness is an important meat eating quality trait. It is mainly affected by the quantity and solubility of connective tissue, composition and contractile state of muscle fibers, and the extent of proteolysis in rigor muscle [11, 31, 41]. Tender meat contains higher levels of soluble collagen, more fat, and lower water content. Myofibril fragmentation index also has a positive correlation with beef loin tenderness . Sanga breeds have a lower percentage of white muscle fiber and a higher myofibrillar fragmentation index [6, 11], which results in lower shear force and more tender beef compared to indicus cattle . In this study, we have identified genes (CAPZB, COL9A2, PDGFRA, MAP3K5, ZNF410, LIMA1, and PKM2) that may potentially affect muscle structure and development thereby affecting meat tenderness in Ankole cattle.
LIMA1 (called EPLIN) encodes a cytoskeleton-associated protein that inhibits actin filament depolymerization and cross-links filaments in bundles. It is associated in pigs with functions regarding muscle development and metabolism . ZNF410, also known as APA-1, is an essential component of the stress pathway involved in the meat tenderization process . In previous muscle transcriptome analyses, ZNF410 has been shown to be highly expressed in the longissimus muscle of Basque pigs that are known to produce pork with higher intramuscular fat and tenderness compared to Large White pigs . COL9A2, a fibrillar collagen, constitutes the largest component of extracellular matrix (ECM) to which its amount, type, and solubility present in muscle tissue have a strong effect on meat tenderness . This gene was found to be upregulated in the longissimus dorsi muscle of Jeju native piglets , whose meat is known for its preferable taste, tenderness and superior marbling . ROCK1, a gene that regulates actin cytoskeleton and cell polarity, is associated with body weight, carcass weight, shank length, shank circumference and other carcass weight traits in chicken .
Genes involved in MAPK signaling (MAP3K5, MAP2K3, MAP4K4, and MAPKAPK2) were also identified. MAPK signaling is one of the major intracellular signaling pathways affecting myogenesis  and is relevant to postmortem meat quality . MAP2K3 shows associations with loin muscle area and fat traits in pigs, implying roles in muscle differentiation and growth .
E3 ubiquitin ligase genes (WWP1, and PARK2) play an important role in the regulation of a wide variety of cellular functions such as protein degradation, transcription, and RNA splicing. These genes catalyze protein ubiquitylation resulting in the targeting of proteins toward various cellular fates, with proteasome-mediated proteolytic degradation . The ubiquitin–proteasome system is one of the proteolytic systems responsible for the majority of the protein degradation in muscle that is relevant for meat quality postmortem .
The expression of PKM2 (XP-EHH = 1.9696; p =2.00.E-03), a gene involved in energy metabolism, is positively correlated with WBSF and has been reported as a functional protein marker for meat tenderness in Thai indigenous chicken  and beef . PDGFRA also has an effect on shear force and Loin Eye Area in pig . The Tajima’s D and FST plot of PKM2 and PDGFRA gene regions is shown in Fig. 4b and c, respectively. PRKG1 is reported to be important in the conversion of muscle to meat . NFATC2 is a calcineurin substrate expressed in skeletal muscle which is responsible for activating new myotubes . Calcineurin is crucial for myocyte differentiation and determination of the slow oxidative fibre phenotype .
Genes related to meat intramuscular fat (IMF)
IMF is a heritable meat quality trait which affects flavor, juiciness, visual characteristics and meat tenderness. It is positively correlated with body fat and red muscle fiber . Steak from Ankole cattle has been found to be juicy than those Ankole-Boran crossbreds , and Strydom et al. showed higher levels of rib fat thickness in Sanga as compared to indicus cattle .
We identified several genes (PLA2G2A, PARK2, ZNF410, PKM2, MAP2K3, PLCD3, PLCD1, ROCK1, and AHSA1) which affect the fat content of meat in Ankole cattle. PLA2G2A (XP-CLR = 117.48) is a member of the phospholipase A2 family (PLA2), which is involved in the hydrolysis of phospholipids into fatty acids and phosphatidylinositol and phospholipid metabolism . Also referred to as Adipose-Specific Phospholipase A2 (AdPLA), it is involved in adipocyte metabolism and catalyzes the efficient release of free fatty acids and lysophospholipid from phosphatidylcholine . It has been reported in the literature that PLA2 has a positive effect on porcine fat deposition (IMF) and potentially regulates lipolysis and increases the MUFA deposition rather than the SFA deposition . It is also associated with intramuscular fat in beef cattle . Pla2g2a has been reported to be a candidate gene in relation to obesity in mice .
ROCK1 is involved in pathways relevant to muscle/adipose tissue function in pigs with divergent phenotypes for fatness traits . E3 ubiquitin ligase enzymes have been identified to be involved in the modulation of lipid biology [50, 62]. PARK2 is a strong positional candidate for adiposity in chicken and a positive regulator of fat metabolism . PRKG1 is involved in gap junction and is a candidate gene for intramuscular fat in the pigs . MAP2K3 has been shown to be associated with loin muscle and fat traits in pigs . MAP4K4 is involved in adipogenesis, triglyceride storage, fatty acid release, fatty acid oxidation and mitochondrial oxidative phosphorylation . PKM2 is significantly associated with back fat thickness, an economically important trait in pigs . APOL6 is one of the most important known genes involved in lipoprotein metabolism . Phospholipase C family genes (PLCD1 and PLCD3) generate diacylglycerol and are involved in phosphatidylinositol catabolism and phospholipid synthesis . The transcription of AHSA1 (AHA1) is related to Omega-3 fatty acids in skeletal muscle, which influence meat tenderness, juiciness, and flavor, and are beneficial to human health . The positive selection of the AHSA1 gene region is shown in the Tajima’s D and FST plot in Fig. 4d.
Genes related to meat color, drip loss, and feed conversion efficiency (FCR)
Meat color and water holding capacity of meat are among the quality parameters used as an indicator of freshness and wholesomeness [41, 67]. These characteristics are related to variations in the glycolysis rate and muscle temperature decline postmortem. Myoglobin (MB; XP-CLR = 129.86; XP-EHH = 1.9640; p = 5.00.E-03), a globular single chain protein located in the sarcoplasm, is the principle protein responsible for the red color of meat. MB serves as a reserve supply of oxygen and facilitates the movement of oxygen within muscles [41, 67]. Figure 4e shows the Tajima’s D and FST plot of MB gene region in Sanga and B. indicus populations. The Solute Carrier Family 48 (Heme Transporter), Member 1 (SLC48A1) is responsible for the transport of heme from endosome to the cytosol  and may also have a function in meat color. In general, beef from Sanga cattle breeds showed higher chroma than that of indicus cattle breeds .
The loss of reddish fluid mainly consisting of water and proteins from meat, called drip loss, is an important meat quality characteristics which is affected by several ante- and post-mortem factors . A small but significant difference in drip loss is reported between Sanga and indicus cattle breeds; meat from Sanga cattle showed higher drip loss [10, 11]. Higher expression of PKM2 and MAP4K4 suppresses the glucose content of muscle cells promoting the onset of anaerobic production of lactate post mortem, thereby facilitating the decline in pH resulting in higher drip loss [70, 71].
Feed intake and efficiency, measured as residual feed intake (RFI), are economically important traits affecting the cost of beef production . Variation in RFI (animals with lower RFI are more efficient) has a genetic component with moderate heritability . We identified positively selected genes (TIMP2, PKM2, PRKG1, MAP3K5, and ATP8A1) that are reported in the literature to be related to RFI and feed conversion efficiency. TIMP2 has been shown to be upregulated in low RFI animals in gene expression profiling studies on genes expressed differentially in cattle with high and low RFI . PKM2 was associated with average daily gain, and feed to body weight gain ratio, with a significant additive and/or dominance effects on these traits . PRKG1 is involved in gap junction and is also a candidate gene for RFI in cattle . MAP3K5, also known as apoptosis signal-regulating kinase 1 (ASK1), is a candidate gene for residual feed intake in pigs . ATP8A1 is also related to feed intake, feed conversion ratio, residual feed intake and weight gain . Olfactory receptor genes (OR2D2, OR10A4, and OR2D3) have been shown to affect the perception of taste and smell [77, 78] and therefore can be related to feed intake and feeding behavior . PIK3CB, and MRAS genes involved in the Akt/PI3K and MAPK signaling pathways, respectively, are important for high feed efficiency in chicken . The positive selection of these genes may provide clues as to why Ankole cattle are able to use and survive on poor quality feed and withstand severe droughts .
Implication of the results of this study on Ankole population
The Ankole group is one of the three groups of Sanga cattle representing Sanga cattle in east and central Africa . Ankole breed is a valuable and widely used genetic resource in the region due to its better adaptability. However, there have been no well-designed breed improvement programs for Ankole and other Sanga breeds of eastern Africa [80, 81]. Selective breeding efforts in other South African Sanga cattle breeds (e.g., Mashona, Tuli, and Afrikander) have resulted in local cattle showing higher beef productivity . As cattle genetic resources are being depleted [1, 3] and given the importance of this vital genetic resource, designing breeding programs that would help improve and conserve Ankole cattle is crucial . With this regard, the results provide a basis for further research on the genomic characteristics of Ankole cattle in relation to meat quality traits.
Limitations of the present study
As is typical in this kind of study, there is a possibility of obtaining false positive results. Therefore, validation with other methods such as GWAS, candidate gene approach and gene expression analysis are suggested. In addition, given the multifactorial nature of meat quality traits, limited published literature is available on genes affecting beef quality characteristics.
Results from the whole genome scan revealed several positively selected genes involved in different biological and cellular functions including those affecting meat quality characteristics. The genes identified in relation to meat quality characteristics are involved in muscle and lipid metabolism that affect tenderness and intramuscular fat content of meat; and help to improve our understanding of the biological mechanisms controlling meat quality traits in beef cattle production. These results provide a basis for further research on the genomic characteristics of Ankole and other Sanga cattle breeds for quality beef production.
Sample preparation and whole genome re-sequencing
The data used for this paper was obtained from a project: “The genome landscape of indigenous African cattle” [Kim et al., accepted]. DNA extracted from whole blood samples (10 ml) taken from four African cattle breeds (ten Ankole, nine Boran, nine Ogaden and ten Kenana) was used for this analysis. G-DEXTMIIb Genomic DNA Extraction Kit (iNtRoN Biotechnology, Seoul, Korea) was used to isolate DNA according to the manufacturer’s protocol. To generate inserts of ~300 bp, 3 μg of genomic DNA was randomly sheared using Covaris System. Using the TruSeq DNA Sample Prep. Kit (Illumina, San Diego, CA), we constructed the library following the manufacturer’s guidelines and whole genome sequencing was performed using the Illumina HiSeq 2000 platform. To check the quality of the raw sequence data, we used fastQC software . Pair-end sequence reads were mapped to the reference bovine genome (UMD 3.1) using Bowtie2  with default parameters except the “--no-mixed” option. The overall alignment rate of reads to the reference sequence was 98.50% with an average read depth of 10.8×. On average across the whole samples, the reads covered 98.51% of the genome.
We used open source software packages of Picard tools (http://broadinstitute.github.io/picard), SAMtools , and Genome Analysis ToolKit 1.4 (GATK)  for downstream processing and variant calling. Picard tools was used to filter potential PCR duplicates. SAMtools was used to create index files for reference and bam files. Genome analysis toolkit 1.4 performed local realignment of reads to correct misalignments due to the presence of indels (“RealignerTargetCreator” and “IndelRealigner” arguments). We used the “UnifiedGenotyper” and “SelectVariants” arguments of GATK to call candidate SNPs. To filter variants and avoid possible false positives, the “VariantFiltration” argument of the same software was adopted with the following options: 1) SNPs with a phred-scaled quality score of less than 30 were filtered; 2) SNPs with MQ0 (mapping quality zero; total count across all samples of mapping quality zero reads) > 4 and quality depth (unfiltered depth of non-reference samples; low scores are indicative of false positives and artifacts) < 5 were filtered; and 3) SNPs with FS (Phred-scaled P-value using Fisher’s exact test) > 200 were filtered since FS represents variation on either the forward or the reverse strand, which is indicative of false positive calls. BEAGLE  was used to infer the haplotype phase and impute missing alleles for the entire set of cattle populations simultaneously. After all the filtering processes, a total of ~37 million SNPs were retained and used for further analysis.
To understand the genetic distance between the breeds considered, we conducted phylogenomic analyses using neighbor-joining (NJ) and maximum likelihood (ML) methods. A total of 26,427,196 autosomal SNPs from the genomes of 38 individuals of four breeds were used for the phylogenic tree construction.
ML analyses  were performed using the program TREE-PUZZLE 5.2  with the GTR model. For the quartet puzzling method (1000 puzzling steps), nucleotide frequencies and Ts/Tv ratios (3.18) were estimated from the dataset. Quartet puzzling provided reliability values for maximum likelihood analysis .
NJ analysis  was performed using the PHYLIP package 3.69  based on Kimura’s  2-parameter distance. Ts/Tv ratios (3.18) were estimated from the dataset using TREE-PUZZLE 5.2  and were used as inputs for the SEQBOOT, DNADIST, NEIGHBOUR, and CONSENS programs of the PHYLIP package. A bootstrap test (with 1000 pseudoreplicates)  was performed to obtain statistical support for each node of the NJ tree.
Detection of positive selection signals
To detect genome-wide selective sweeps, we used the XP-EHH  and XP-CLR  statistics. XP-EHH assesses haplotype differences between two populations and is designed to detect alleles that have increased in frequency to the point of fixation or near fixation in one of the two populations being compared [24, 95].
We compared the genome of Ankole cattle, used as a test population, with indicus cattle (Boran, Ogaden, and Kenana grouped into one population), used as a reference population. XP-EHH compared the integrated EHH between the two populations for each SNP and the sign of the XP-EHH score determines the direction of selection with extreme values indicating selection in the test population genome. To facilitate comparison of genomic regions across populations, we then split the genome into non-overlapping segments of 50 kb and computed the maximum XP-EHH score in each segment. In order to define the empirical P-value, genomic windows were binned in increments of 500 SNPs (combining all windows ≥ 1000 SNPs into one) according to the method used previously . Regions with P-values less than 0.01 (1%) were considered strong signals in the Ankole population.
We also performed XP-CLR to identify potential regions differentially selected between the two populations . XP-CLR is a likelihood method for detecting selective sweeps that involve jointly modeling the multilocus allele frequency differentiation between two populations. XP-CLR scores were calculated using XP-CLR software package . We used non-overlapping sliding windows of 50 kb, maximum number of SNPs within each window as 600, and correlation level from which the SNPs contribution to XP-CLR result was down weighted to 0.95. The regions with the XP-CLR values in the top 1% of the empirical distribution (XP-CLR > 97.86) were designated as candidate sweeps and the genes that span the window regions were defined as candidate genes . Significant genomic regions identified from XP-EHH and XP-CLR were annotated to the closest genes (UMD 3.1).
In order to confirm the positive selection of detected genes using these two statistics, we calculated Tajima’s D and FST for the candidate gene regions. Detecting the same gene regions using different methods can provide cogent evidence for selective influences in the region . Tajima’s D is used to detect selective sweeps going to fixation in the population that makes rare alleles in excess in the population, which results in a negative Tajima’s D . Population differentiation (FST) is based on the principle that natural selection can change the amount of differentiation between different populations of a species. When populations are differentiated, the amount of genetic differentiation within the region that includes selected locus will increase during when the genetic differentiation in the genomic region is greater than the level expected under neutrality, which can be a consequence of natural selection . VCFtools was used in a window size of 50 kb at an interval of 5 kb steps to calculate the Tajima’s D and FST values of the candidate gene regions .
Characterization of candidate genes under selection
We used the Database for Annotation, Visualization, and Integrated Discovery (DAVID; version 6.7) gene ontology and annotation tool for gene enrichment analysis to further understand the biological functions and pathways of selected genes . Significant GO terms provide insight into the functional characteristics of annotated genes. The KEGG database was also cross-referenced within DAVID to identify significant pathways. R software (version 3.2.1) was used for hierarchical clustering of GO terms from DAVID. Additionally, Cytoscape software’s (version 3.2.0) ClueGO plugin was used to visualize the integration of Gene Ontology (GO) terms as well as KEGG pathways and create a functionally organized GO/pathway term network  with default settings.
- GO BP:
Gene Ontology Biological Processes
Kyoto Encyclopedia of Genes and Genomes
Cross population composite likelihood ratio
Cross population extended haplotype homozygosity
This work was supported by a grant from the Next-Generation BioGreen 21 Program (Project No. PJ01134905), Rural Development Administration (RDA), Republic of Korea. We acknowledge the support from different institutions and their personnel providing help for the sampling of African cattle and cattle keepers for their assistance and permission to sample their herds.
This work was supported by a grant from the Next-Generation BioGreen 21 Program (Project No. PJ01134905), Rural Development Administration (RDA), Republic of Korea.
Availability of data and material
The data sets supporting the conclusions of this article are included within the article and its additional files. The full genome sequence data has been deposited in GenBank under the Bioproject accession number PRJNA312138.
MT conceived and designed the study, analyzed the data, and wrote the paper; JK designed the study and helped analyzing the data; SHY analyzed part of data; WL helped analyzing the data; KCA helped writing the paper; OH, TD, SK, OAM, SC, SJO, HKL, & HK designed the project; HK organized and supervised the project. All authors read and approved the manuscript.
The authors declare that they have no competing interests.
Consent to participate
Blood samples from African indigenous cattle breeds were collected after consent from the local authorities and owners of the animals. No further specific permissions were required from the Ethics Committee of the International Livestock Research Institute at the time of the sampling.
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.
- Mwai O, Hanotte O, Kwon Y-J, Cho S. African indigenous cattle: unique genetic resources in a rapidly changing world. Asian-Aust J Anim Sci. 2015;28(7):911–21.View ArticleGoogle Scholar
- Rege J. The state of African cattle genetic resources I. Classification framework and identification of threatened and extinct breeds. Anim Genet Res Inf. 1999;25:1–25.View ArticleGoogle Scholar
- Hanotte O, Dessie T, Kemp S. Time to tap Africa's livestock genomes. Science. 2010;328(5986):1640–1.View ArticlePubMedGoogle Scholar
- Hansen P. Physiological and cellular adaptations of zebu cattle to thermal stress. Anim Reprod Sci. 2004;82:349–60.View ArticlePubMedGoogle Scholar
- Piper EK, Jonsson NN, Gondro C, Lew-Tabor AE, Moolhuijzen P, Vance ME, et al. Immunological profiles of Bos taurus and Bos indicus cattle infested with the cattle tick, Rhipicephalus (Boophilus) microplus. Clin Vaccine Immunol. 2009;16(7):1074–86.View ArticlePubMedPubMed CentralGoogle Scholar
- Strydom P, Naude R, Smith M, Scholtz M, Van Wyk J. Characterization of indigenous African cattle breeds in relation to carcass characteristics. Anim Sci. 2000;70(2):241–52.View ArticleGoogle Scholar
- Strydom P. Do indigenous Southern African cattle breeds have the right genetics for commercial production of quality meat? Meat Sci. 2008;80(1):86–93.View ArticlePubMedGoogle Scholar
- Rege J, Tawah C. The state of African cattle genetic resources II. Geographical distribution, characteristics and uses of present-day breeds and strains. Anim Genet Res Inf. 1999;26:1–25.View ArticleGoogle Scholar
- Grigson C. An African origin for African cattle?—some archaeological evidence. Afr Archaeol Rev. 1991;9(1):119–44.View ArticleGoogle Scholar
- Strydom P, Frylinck L, Van der Westhuizen J, Burrow H. Growth performance, feed efficiency and carcass and meat quality of tropically adapted breed types from different farming systems in South Africa. Anim Prod Sci. 2008;48(5):599–607.View ArticleGoogle Scholar
- Strydom P, Frylinck L, Smith M. Variation in meat quality characteristics between Sanga (Bos taurus africanus) and Sanga-derived cattle breeds and between Sanga and Brahman (Bos indicus). Animal. 2011;5(03):483–91.View ArticlePubMedGoogle Scholar
- Gazzola C, O'Neill C, Frisch J. Comparative evaluation of the meat quality of beef cattle breeds of Indian, African and European origins. Anim Sci. 1999;69(1):135–42.View ArticleGoogle Scholar
- Kamatara K, Mpairwe D, Christensen M, Mutetikka D, Madsen J. Sensory characteristics and tenderness of meat from Ankole bulls: Influence of crossbreeding and feeding system. S Afr J Anim Sci. 2013;43:107–10.Google Scholar
- Maltin C, Balcerzak D, Tilley R, Delday M. Determinants of meat quality: tenderness. Proc Nutr Soc. 2003;62(02):337–47.View ArticlePubMedGoogle Scholar
- Bernard C, Cassar-Malek I, Le Cunff M, Dubroeucq H, Renand G, Hocquette J-F. New indicators of beef sensory quality revealed by expression of specific genes. J Agric Food Chem. 2007;55(13):5229–37.View ArticlePubMedGoogle Scholar
- Mullen A, Stapleton P, Corcoran D, Hamill R, White A. Understanding meat quality through the application of genomic and proteomic approaches. Meat Sci. 2006;74(1):3–16.View ArticlePubMedGoogle Scholar
- Rios Utrera A, Van Vleck LD. Heritability estimates for carcass traits of cattle: a review. Genet Mol Res. 2004;3(3):380–94.Google Scholar
- Johnston D, Reverter A, Ferguson D, Thompson J, Burrow H. Genetic and phenotypic characterisation of animal, carcass, and meat quality traits from temperate and tropically adapted beef breeds. 3. Meat quality traits. Aust J Agric Res. 2003;54(2):135–47.View ArticleGoogle Scholar
- Nielsen R. Molecular signatures of natural selection. Annu Rev Genet. 2005;39:197–218.View ArticlePubMedGoogle Scholar
- Bahbahani H, Clifford H, Wragg D, Mbole-Kariuki MN, Van Tassell C, Sonstegard T, et al. Signatures of positive selection in East African Shorthorn Zebu: A genome-wide single nucleotide polymorphism analysis. Sci Rep. 2015;5.
- Flori L, Thevenon S, Dayo GK, Senou M, Sylla S, Berthier D, et al. Adaptive admixture in the West African bovine hybrid zone: insight from the Borgou population. Mol Ecol. 2014;23(13):3241–57.View ArticlePubMedGoogle Scholar
- Lee H-J, Kim J, Lee T, Son JK, Yoon H-B, Baek K-S, et al. Deciphering the genetic blueprint behind Holstein milk proteins and production. Genome Biol Evol. 2014;6(6):1366–74.View ArticlePubMedPubMed CentralGoogle Scholar
- Moon S, Kim T-H, Lee K-T, Kwak W, Lee T, Lee S-W, et al. A genome-wide scan for signatures of directional selection in domesticated pigs. BMC Genomics. 2015;16(1):130.View ArticlePubMedPubMed CentralGoogle Scholar
- Sabeti PC, Varilly P, Fry B, Lohmueller J, Hostetter E, Cotsapas C, et al. Genome-wide detection and characterization of positive selection in human populations. Nature. 2007;449(7164):913–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Chen H, Patterson N, Reich D. Population differentiation as a test for selective sweeps. Genome Res. 2010;20(3):393–402.View ArticlePubMedPubMed CentralGoogle Scholar
- Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009;37(1):1–13.View ArticleGoogle Scholar
- Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics. 2009;25(8):1091–3.View ArticlePubMedPubMed CentralGoogle Scholar
- Korneliussen TS, Moltke I, Albrechtsen A, Nielsen R. Calculation of Tajima’s D and other neutrality test statistics from low depth next-generation sequencing data. BMC Bioinformatics. 2013;14(1):289.View ArticlePubMedPubMed CentralGoogle Scholar
- Oleksyk TK, Smith MW, O'Brien SJ. Genome-wide scans for footprints of natural selection. Phil Trans R Soc B. 2010;365(1537):185–205.View ArticlePubMedPubMed CentralGoogle Scholar
- Qanbari S, Simianer H. Mapping signatures of positive selection in the genome of livestock. Livest Sci. 2014;166:133–43.View ArticleGoogle Scholar
- Koohmaraie M, Kent MP, Shackelford SD, Veiseth E, Wheeler TL. Meat tenderness and muscle growth: is there any relationship? Meat Sci. 2002;62(3):345–52.View ArticlePubMedGoogle Scholar
- Guillemin N, Bonnet M, Jurie C, Picard B. Functional analysis of beef tenderness. J Proteomics. 2011;75(2):352–65.View ArticlePubMedGoogle Scholar
- Schoeman S. Recent research into the production potential of indigenous cattle with special reference to the Sanga. S Afr J Anim Sci. 1989;19:55–61.Google Scholar
- Gao Y, Zhang Y, Jiang H, Xiao S, Wang S, Ma Q, et al. Detection of differentially expressed genes in the longissimus dorsi of Northeastern indigenous and large white pigs. Genet Mol Res. 2011;10(2):779–91.View ArticlePubMedGoogle Scholar
- Damon M, Wyszynska-Koko J, Vincent A, Herault F, Lebret B. Comparison of muscle transcriptome between pigs with divergent meat quality phenotypes identifies genes related to muscle metabolism and structure. PLoS One. 2012;7(3), e33763.View ArticlePubMedPubMed CentralGoogle Scholar
- Hamill RM, McBryan J, McGee C, Mullen AM, Sweeney T, Talbot A, et al. Functional analysis of muscle gene expression profiles associated with tenderness and intramuscular fat content in pork. Meat Sci. 2012;92(4):440–50.View ArticlePubMedGoogle Scholar
- Jiang C, Shi P, Li S, Dong R, Tian J, Wei J, et al. Gene expression profiling of skeletal muscle of nursing piglets. Int J Biol Sci. 2010;6(7):627.View ArticlePubMedPubMed CentralGoogle Scholar
- Ponsuksili S, Murani E, Phatsara C, Schwerin M, Schellander K, Wimmers K. Porcine muscle sensory attributes associate with major changes in gene networks involving CAPZB, ANKRD1, and CTBP2. Funct Integr Genomics. 2009;9(4):455–71.View ArticlePubMedGoogle Scholar
- Chang K. Key signalling factors and pathways in the molecular determination of skeletal muscle phenotype. Animal. 2007;1:681–98.View ArticlePubMedGoogle Scholar
- Rolf M, Taylor J, Schnabel R, McKay S, McClure M, Northcutt S, et al. Genome‐wide association analysis for feed efficiency in Angus cattle. Anim Genet. 2012;43(4):367–74.View ArticlePubMedPubMed CentralGoogle Scholar
- Joo S, Kim G, Hwang Y, Ryu Y. Control of fresh meat quality through manipulation of muscle fiber characteristics. Meat Sci. 2013;95(4):828–36.View ArticlePubMedGoogle Scholar
- Culler R, Smith G, Cross H. Relationship of myofibril fragmentation index to certain chemical, physical and sensory characteristics of bovine longissimus muscle. J Food Sci. 1978;43(4):1177–80.View ArticleGoogle Scholar
- Xu Y, Qian H, Feng X, Xiong Y, Lei M, Ren Z, et al. Differential proteome and transcriptome analysis of porcine skeletal muscle during development. J Proteomics. 2012;75(7):2093–108.View ArticlePubMedGoogle Scholar
- Pyle WG, Hart MC, Cooper JA, Sumandea MP, de Tombe PP, Solaro RJ. Actin capping protein an essential element in protein kinase signaling to the myofilaments. Circ Res. 2002;90(12):1299–306.View ArticlePubMedGoogle Scholar
- Schellander K. Identifying genes associated with quantitative traits in pigs: integrating quantitative and molecular approaches for meat quality. Ital J Anim Sci. 2010;8(2 Suppl):19–25.View ArticleGoogle Scholar
- Ghosh M, Sodhi S, Song KD, Kim J, Mongre R, Sharma N, et al. Evaluation of body growth and immunity‐related differentially expressed genes through deep RNA sequencing in the piglets of Jeju native pig and Berkshire. Anim Genet. 2015;46(3):255–64.View ArticlePubMedGoogle Scholar
- Cho I, Park H, Yoo C, Lee G, Lim H, Lee J, et al. QTL analysis of white blood cell, platelet and red blood cell‐related traits in an F2 intercross between Landrace and Korean native pigs. Anim Genet. 2011;42(6):621–6.View ArticlePubMedGoogle Scholar
- Lu Y, Chen S, Liu W, Hou Z, Xu G, Yang N. Polymorphisms in Wnt signaling pathway genes are significantly associated with chicken carcass traits. Poult Sci. 2012;91(6):1299–307.View ArticlePubMedGoogle Scholar
- Wu H, Zhao S, Fan B. Investigation of effects of the MKK3 and MKK6 genes on meat production traits in the pig (Brief Report). Archiv Tierzucht. 2010;53(2):242–5.Google Scholar
- Yin H, Gui Y, Du G, Frohman MA, Zheng X-L. Dependence of phospholipase D1 multi-monoubiquitination on its enzymatic activity and palmitoylation. J Biol Chem. 2010;285(18):13580–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Clark KA, McElhinny AS, Beckerle MC, Gregorio CC. Striated muscle cytoarchitecture: an intricate web of form and function. Annu Rev Cell Dev Biol. 2002;18(1):637–706.View ArticlePubMedGoogle Scholar
- Teltathum T, Mekchay S. Relationships between Pectoralis muscle proteomes and shear force in Thai indigenous chicken meat. Kasetsart J (Nat Sci). 2010.
- Wimmers K, Murani E, Ngu N, Schellander K, Ponsuksili S. Structural and functional genomics to elucidate the genetic background of microstructural and biophysical muscle properties in the pig. J Anim Breed Genet. 2007;124(s1):27–34.View ArticlePubMedGoogle Scholar
- Lonergan EH, Zhang W, Lonergan SM. Biochemistry of postmortem muscle—Lessons on mechanisms of meat tenderization. Meat Sci. 2010;86(1):184–95.View ArticlePubMedGoogle Scholar
- da Costa N, Edgar J, Ooi P-T, Su Y, Meissner JD, Chang K-C. Calcineurin differentially regulates fast myosin heavy chain genes in oxidative muscle fibre type conversion. Cell Tissue Res. 2007;329(3):515–27.View ArticlePubMedGoogle Scholar
- Nakamura Y, Kanemarum K, Fukami K. Physiological functions of phospholipase Cδ1 and phospholipase Cδ3. Adv Biol Regul. 2013;53(3):356–62.View ArticlePubMedGoogle Scholar
- Duncan RE, Sarkadi-Nagy E, Jaworski K, Ahmadian M, Sul HS. Identification and functional characterization of adipose-specific phospholipase A2 (AdPLA). J Biol Chem. 2008;283(37):25428–36.View ArticlePubMedPubMed CentralGoogle Scholar
- Wang W, Xue W, Jin B, Zhang X, Ma F, Xu X. Candidate gene expression affects intramuscular fat content and fatty acid composition in pigs. J Appl Genet. 2013;54(1):113–8.View ArticlePubMedGoogle Scholar
- Chan EK, Reverter A. Integrating whole-genome genetic-association studies with gene expression data to prioritise candidate genes affecting intramuscular fat in beef cattle traits. Proc Assoc Advmt Anim Breed Genet. 2007;17:81–4.Google Scholar
- Sung MK, Bae YJ. Linking obesity to colorectal cancer: application of nutrigenomics. Biotechnol J. 2010;5(9):930–41.View ArticlePubMedGoogle Scholar
- Cánovas A, Quintanilla R, Amills M, Pena RN. Muscle transcriptomic profiles in pigs with divergent phenotypes for fatness traits. BMC Genomics. 2010;11(1):372.View ArticlePubMedPubMed CentralGoogle Scholar
- Roux P-F, Boitard S, Blum Y, Parks B, Montagner A, Mouisel E, et al. Combined QTL and selective swe1ep mappings with coding SNP annotation and cis-eQTL analysis revealed PARK2 and JAG2 as new candidate genes for adiposity regulation. G3 (Bethesda). 2015;5(4):517–29.View ArticleGoogle Scholar
- Puri V, Virbasius J, Guilherme A, Czech M. RNAi screens reveal novel metabolic regulators: RIP140, MAP4k4 and the lipid droplet associated fat specific protein (FSP) 27. Acta Physiol (Oxf). 2008;192(1):103–15.View ArticleGoogle Scholar
- Cho E-S, Jeon H-J, Lee S-W, Park J-W, Raveendar S, Jang G-W, et al. Association of a Pyruvate Kinase M2 (PKM2) Polymorphism with Back Fat Thickness in Berkshire Pigs. J Anim Sci Technol. 2013;55(6):515–20.View ArticleGoogle Scholar
- Corella D, Ordovas JM. Single nucleotide polymorphisms that influence lipid metabolism: interaction with dietary factors. Annu Rev Nutr. 2005;25:341–90.View ArticlePubMedGoogle Scholar
- Perez R, Cañón J, Dunner S. Genes associated with long-chain omega-3 fatty acids in bovine skeletal muscle. J Appl Genet. 2010;51(4):479–87.View ArticlePubMedGoogle Scholar
- Mancini R, Hunt M. Current research in meat color. Meat Sci. 2005;71(1):100–21.View ArticlePubMedGoogle Scholar
- Khan AA, Quigley JG. Heme and FLVCR-related transporter families SLC48 and SLC49. Mol Aspects Med. 2013;34(2):669–82.View ArticlePubMedPubMed CentralGoogle Scholar
- Borchers N, Otto G, Kalm E. Genetic relationship of drip loss to further meat quality traits in purebred Pietrains. Arch Tierz. 2007;50(1):84.Google Scholar
- Ponsuksili S, Jonas E, Murani E, Phatsara C, Srikanchai T, Walz C, et al. Trait correlated expression combined with expression QTL analysis reveals biological pathways and candidate genes affecting water holding capacity of muscle. BMC Genomics. 2008;9(1):367.View ArticlePubMedPubMed CentralGoogle Scholar
- Shen L, Lei H, Zhang S, Li X, Li M, Jiang X, et al. The comparison of energy metabolism and meat quality among three pig breeds. Anim Sci J. 2014;85(7):770–9.View ArticlePubMedGoogle Scholar
- Sherman E, Nkrumah J, Moore S. Whole genome single nucleotide polymorphism associations with feed intake and feed efficiency in beef cattle. J Anim Sci. 2010;88(1):16–22.View ArticlePubMedGoogle Scholar
- Chen Y, Gondro C, Quinn K, Herd R, Parnell P, Vanselow B. Global gene expression profiling reveals genes expressed differentially in cattle with high and low residual feed intake. Anim Genet. 2011;42(5):475–90.View ArticlePubMedGoogle Scholar
- Fontanesi L, Davoli R, Costa LN, Beretti F, Scotti E, Tazzoli M, et al. Investigation of candidate genes for glycolytic potential of porcine skeletal muscle: Association with meat quality and production traits in Italian Large White pigs. Meat Sci. 2008;80(3):780–7.View ArticlePubMedGoogle Scholar
- Do DN, Ostersen T, Strathe AB, Mark T, Jensen J, Kadarmideen HN. Genome-wide association and systems genetic analyses of residual feed intake, daily feed consumption, backfat and weight gain in pigs. BMC Genet. 2014;15(1):27.View ArticlePubMedPubMed CentralGoogle Scholar
- Santana M, Kadarmideen H, Pant S, Alexandre P, Junior GO, Gomes R, et al. Systems genetics investigations for feed intake, feed efficiency and performance in Nellore (Bos indicus) Cattle. In: 10th World Congress on Genetics Applied to Livestock Production, August 17–22, 2014. Vancouver: ASAS; 2014.Google Scholar
- Do DN, Strathe AB, Ostersen T, Pant SD, Kadarmideen HN. Genome-wide association and pathway analysis of feed efficiency in pigs reveal candidate genes and pathways for residual feed intake. Front Genet. 2014;5:307.View ArticlePubMedPubMed CentralGoogle Scholar
- Choquette AC, Bouchard L, Drapeau V, Lemieux S, Tremblay A, Bouchard C, et al. Association between olfactory receptor genes, eating behavior traits and adiposity: results from the Quebec family study. Physiol Behav. 2012;105(3):772–6.View ArticlePubMedGoogle Scholar
- Zhou N, Lee WR, Abasht B. Messenger RNA sequencing and pathway analysis provide novel insights into the biological basis of chickens’ feed efficiency. BMC Genomics. 2015;16(1):195.View ArticlePubMedPubMed CentralGoogle Scholar
- Ndumu D, Baumung R, Hanotte O, Wurzinger M, Okeyo M, Jianlin H, et al. Genetic and morphological characterisation of the Ankole Longhorn cattle in the African Great Lakes region. Genet Sel Evol. 2008;40(5):467–90.PubMedPubMed CentralGoogle Scholar
- Kugonza D, Nabasirye M, Mpairwe D, Hanotte O, Okeyo A. Productivity and morphology of Ankole cattle in three livestock production systems in Uganda. Anim Genet Resour. 2011;48:13–22.View ArticleGoogle Scholar
- Rewe T, Herold P, Kahi A, Valle Zárate A. Breeding indigenous cattle genetic resources for beef production in Sub-Saharan Africa. Outlook Agric. 2009;38(4):317–26.View ArticleGoogle Scholar
- Andrews S. FastQC: a quality control tool for high throughput sequence data. Reference Source. 2010.
- Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9(4):357–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9.View ArticlePubMedPubMed CentralGoogle Scholar
- McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297–303.View ArticlePubMedPubMed CentralGoogle Scholar
- Browning SR, Browning BL. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am J Hum Genet. 2007;81(5):1084–97.View ArticlePubMedPubMed CentralGoogle Scholar
- Felsenstein J. Evolutionary trees from DNA sequences: a maximum likelihood approach. J Mol Evol. 1981;17(6):368–76.View ArticlePubMedGoogle Scholar
- Schmidt HA, Strimmer K, Vingron M, von Haeseler A. TREE-PUZZLE: maximum likelihood phylogenetic analysis using quartets and parallel computing. Bioinformatics. 2002;18(3):502–4.View ArticlePubMedGoogle Scholar
- Strimmer K, Von Haeseler A. Quartet puzzling: a quartet maximum-likelihood method for reconstructing tree topologies. Mol Biol Evol. 1996;13(7):964–9.View ArticleGoogle Scholar
- Saitou N, Nei M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol. 1987;4(4):406–25.PubMedGoogle Scholar
- Felsenstein J. PHYLIP (phylogeny inference package), 3.5 c ed. Seattle: Department of Genetics, University of Washington; 1993.Google Scholar
- Kimura M. A simple method for estimating evolutionary rate of base substitution through comparative studies of nucleotide sequence. Mol Evol. 1980;16(2):111–20.View ArticleGoogle Scholar
- Felsenstein J. Confidence limits on phylogenies: an approach using the bootstrap. Evolution. 1985;39(4):783–91.View ArticleGoogle Scholar
- Pickrell JK, Coop G, Novembre J, Kudaravalli S, Li JZ, Absher D, et al. Signals of recent positive selection in a worldwide sample of human populations. Genome Res. 2009;19(5):826–37.View ArticlePubMedPubMed CentralGoogle Scholar
- Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, et al. The variant call format and VCFtools. Bioinformatics. 2011;27(15):2156–8.View ArticlePubMedPubMed CentralGoogle Scholar