Selection for desirable traits and breed-specific phenotypes has left distinctive footprints in the genome of pigs. As representative of a breed with strong selective traces aiming for robustness, health and performance, the Mangalitza pig, a native curly-haired pig breed from Hungary, was investigated in this study. Whole genome sequencing and SNP chip genotyping was performed to detect runs of homozygosity (ROH) in Mangalitza and Mangalitza-crossbreeds. We identified breed specific ROH regions harboring genes associated with the development of the curly hair type and further characteristics of this breed. Further analysis of two matings of Mangalitza with straight-coated pig breeds confirmed an autosomal dominant inheritance of curly hair. Subsequent scanning of the genome for variant effects on this trait revealed two variants potentially affecting hair follicle development and differentiation. Validation in a large sample set as well as in imputed SNP data confirmed these variants to be Mangalitza-specific. Herein, we demonstrated how strong artificial selection has shaped the genome in Mangalitza pigs and left traces in the form of selection signatures. This knowledge on genomic variation promoting unique phenotypes like curly hair provides an important resource for futures studies unraveling genetic effects for special characteristics in livestock. In that respect, the need for specific phenotypes has led to similar selective pressures on different domesticated breeds, whereas the genes under selection varied significantly among them2. Thus, various studies on SNP data have been performed within or across pig breeds in order to determine regions harboring these genes under potential selection for favored traits10. It was demonstrated that particularly the detection of homozygous stretches along the genome, designated as runs of homozygosity (ROHs), displayed an effective approach to target functionally important regions as well as to elucidate demography of populations and genetic diversity5. ROH regions (ROHR) were found to give evidence for regions of strong directional selection for biological pathways related to desirable phenotypes like coat color and type, fertility, muscle and fat development, growth and metabolism14 significantly characterizing pig breeds.Intense human-mediated selection has driven the process of domestication and left footprints in the genome of pigs15. In particular, a curled hair shape was proposed to exhibit a beneficial property for thermal insulation in cold temperature conditions to retain body heat16 as well as for cooling under hot temperature17.Among these traits, the development and shape of hair stands out as a particularly differential and breed defining characteristic of high functional importance. It was proposed that hair coat and its shape has a considerable impact on conduction, convection, water vapor diffusion and radiative transfer between skin surface and the external environmental temperature19. The special appearance of its hair coat results from the existence of two curled hair types, namely bristles and additionally pronounced wool hair, which can only be found for this degree in wild boars21. Besides Mangalitza, curly hair exists only in few other pig breeds including the Turopolje pig22, Canastrao pig from Brasilia23 or the Mexican Cuino pig25 expressing a curly and/or hairless type there and was in earlier times exhibited in the extinct Lincolnshire curly-coated pig26.In pigs, a curly hair shape is found as breed characteristic in the Mangalitza, a fatty-type, robust and adaptive endangered pig breed from Hungary32, but has not been determined in Mangalitza pig or other pig breeds so far. It is well known that keratin (KRT) and keratin-associated-protein (KRTAP) genes, as main structural components of hair fiber33 are frequently affected by variants associated with curly or woolly hair among humans and mammals38. Further genes harboring variants for curly hair are lipase H gene (LIPH), lysophosphatidic acid receptor 6 gene (LPAR6), transcription factor (SP6) and serum- and glucocorticoid-regulated kinase gene (Sgk3)41. Furthermore, an influence on hair curvature and follicle formation was suggested to be derived from epidermal growth factor receptor (EGFR) and fibroblast growth factor (FGF)-related pathways as well as WNT/ß-catenin-signaling pathway43. Several of these involved genes and mutations were proposed to be under strong selection in domestic animals promoting such a desired and special phenotype32.The genetic background of the determination patterns for the hair shape, in particular curly hair, has been studied widely in domestic animals like mice, rabbits, cats, dogs, cattle and horsesIn our study, we aimed at investigating the genome of the Mangalitza as a representative for a European pig breed with a particular and well-defined phenotype. The objective was to demonstrate how strong targeted selection among breeds has influenced the genomic architecture and promoted selection signatures. As a particular example, we investigated the special type of curly hair in Mangalitza contributing to robustness as well as accounting for adaption as a genetic resource with potential economic value for future breeding programs. Information inferred from ROH analysis and variant annotation were used to target variant effects as result of artificial selection within Mangalitza breed.1-crossbreeds  as well as further variant calling collectively with 22 controls resulted in 23,875,519 SNPs for ROH analysis. In total, an average number of 25,790.50 ROHs with a total mean length of runs of 827,472 kb were called. The Mangalitza exhibited an average of 27,522.78 ROHs per individuum with total mean length of 935,739 kb. Genomic inbreeding coefficients (FROH) based on ROH were estimated to be 0.41 in Mangalitza. In control pig breeds, FROH ranged from 0.24 (Landrace) to 0.47 (Iberian pigs).Whole genome sequencing (WGS) of nine Mangalitza and three Mangalitza FFurthermore, ROH detection additionally performed in SNP chip genotyping data  based on 51 individuals including 19 Mangalitza and 15 Mangalitza-crossbreeds resulted in an average number of ROHs of 97.24 with total mean length of 112,667 kb. In Mangalitza, the mean number of ROHs per individuum was 139.26 with a total mean length of 164,599 kb.Subsequently, in order to identify Mangalitza specific homozygosity regions, ROHs were filtered for common ROH regions (consensus ROHR) found in Mangalitza in WGS as well as SNP chip genotyping data. We identified 25,508 consensus ROHR in WGS data with a total size  of 164,367 kb and with an average size of 6444 kb per ROHR. In SNP chip genotyping data, 38 consensus ROHR were detected exhibiting a total size of 40,773.10 kb and a mean size of 1072.98 kb per consensus ROHR.To narrow down consensus ROHR specific for Mangalitza, we reduced these identified regions for those ROHR absent in Mangalitza-crossbreeds. By this approach, consensus ROHR found in WGS data were reduced to 3401 ROHR with a total size of 15,244 kb and a mean size of 4.48 kb (see Supplementary Table )  and metabolic processes (GO:0008152), biological regulation (GO:0065007), cellular component organization or biogenesis (GO:0071840), localization (GO:0051179), response to stimulus (GO:0050896), multicellular organismal (GO:0032501) and developmental processes (GO:0032502) or signaling (GO:0023052), was found. In addition to that, we identified genes enriched in immune system processes (GO:0002376) or in processes affecting behavior , calcium-activated chloride channel protein (P = 4.60E−02 (WGS), P = 3.30E−04 (SNP chip), e.g. TCHHL1), kinesin protein 1B (P = 3.9E−02 (WGS); e.g. KIF13A), cadherin and cadherin-like , e.g. CDH10, CDH13) or S100/CaBP-9k-type calcium binding subdomain (P = 1.10E−04 (SNP chip), e.g. TCHHL1 among others  and P = 1.8E−02 (SNP chip), e.g. 1-piglets developed curly hair and wool hair up to the age of 12 months. Individuals of MAxAS litter (born in November) showed a clear curly hair development at the age of four to five months, whereas all individuals from MAxMI litter (born in April) were straight coated at the same age. During the first year of life of F1-generation, we observed the degree of curliness and the occurrence of wool hair to be strongly reduced in summer and the main hair of a straight coated appearance.To further investigate the genetic background of curly hair in Mangalitza as an example on how selection on special traits impact on the genome of pig breeds, we extended the WGS dataset to in total ten Mangalitza, three Mangalitza-crossbreeds and 56 controls. Furthermore, we studied two resource families produced for targeted investigation of curly hair trait. These families comprised a mating of a curly-haired Mangalitza boar and a straight-haired Angeln Saddleback sow (MAxAS), resulting in a litter of eleven piglets. As well, a curly-haired Mangalitza sow was mated with a straight-haired miniature pig boar (MAxMI), yielding a litter of nine piglets. Both families segregated curly hair in an autosomal dominant manner resulting in curly hair in all 20 progeny . Furthermore, curly hair was observed in all 12 progeny in a backcrossing to curly-haired Mangalitza  × Mangalitza). However, in one crossing  × Pietrain), a segregation of curly and straight-haired progeny within a litter was observed.A similar segregation was observed in four external breeding and fattening pig farms with distinct filial and backcrossing generations of Mangalitza (n = 42) and straight-haired pig breeds/populations  and 9,941,843 INDELs  to extract those variants with high or moderate effects exclusively found in a minimum of three Mangalitza and a minimum of one Mangalitza-crossbreed but not in the straight-haired controls. In total, 90 SNPs and 10 INDELs were obtained in this step (see Supplementary Table TXLNB (NC_010443.5:g.25231855G>A—rs336541123), CYP4F3 (NC_010444.4:g.61866070T>C), RYBP (NC_010455.5:g.53728542T>C—rs343014677), SERPINI1 (NC_010455.5:g.106612073C>G—rs333198858), TRPM2 (NC_010455.5:g.207222334G>C—rs335212953), LARP1 (NC_010458.4:g.68450902G>A), which segregated with curly hair phenotype in Mangalitza and did not appear in any other straight-haired pig breed  on SCA2 as well as NC_010455.5:g.207222334G>C (TRPM2) on SCA13 were found in a few straight-haired individuals , one Russian Livenskaya) (Table CYP4F3) in a high linkage disequilibrium (LD).To further narrow down variants associated with curly hair development in Mangalitza, we subsequently used a large data set of imputed SNPs based on SNP chip genotyping results from a total of 1275 controls as well as 61 Mangalitza and Mangalitza-crossbreeds. Error rate estimation for imputed data ranged from 0.00 to 0.26 per candidate SNP and mean error rate for five candidate SNPs was 0.11 (see Supplementary Table cytochrome P450 4F3 (CYP4F3) variant, resulting in a substitution of histidine to arginine (p.His488Arg) was predicted to be benign (score 0.00), the same as transient receptor potential cation channel subfamily M member 2 (TRPM2) variant, a glutamic acid to glutamine (p.Glu110Gln) substitution in both transcripts . However, both variants were found to be located in a highly conserved region.Evaluation of co-segregation of the two coding SNPs on SCA2 and 13 revealed that curly hair phenotype could be explained in all curly-haired individuals except for two Mangalitza and two Mangalitza-crossbreeds descendent from crossing  × Pietrain (Table https://igv.org/) displayed this variant to be present in all ten Mangalitza as well as Mangalitza-crossbreeds. However, further validation revealed no copy number variation within the predicted region in seven curly and three straight-haired individuals suggesting the occurrence of a translocation instead of a tandem duplication. A gene fusion, as it was predicted, could be confirmed by a PCR-amplicon comprising the gene fusion at suggested breakpoints in these Mangalitza samples but not in straight-haired controls. Sanger sequencing of this PCR product confirmed the gene fusion, but revealed a distal shift of the first predicted breakpoint from 143,207,109 to 143,207,203 bp. Finally, in further testing we found the PCR product to be amplifiable in 163 of 189 Mangalitza samples, but also in ten (European wild boars) of 114 straight-haired controls.Positional evaluation of structural variants in WGS data found exclusively in all Mangalitza and Mangalitza-crossbreeds but not in straight-haired controls within the range of 40 Mb proximal and distal to the two candidate SNPs did not reveal any results. However, we identified one region on SCA13 at 143,207,109 to 143,457,136 bp, which was predicted to be a tandem duplication resulting in a gene fusion of the two novel genes ENSSSCG00000042345 and ENSSSCG00000045503  of 0.41 is presumably a result from the strong decrease of population size of this by now endangered breed, which was nearly extinct in the nineteen-seventies due to the changing demand in pig production for lean meat and productivity19. Previous studies also detected quite low diversity in Mangalitza in comparison to other European as well as Asian pig populations44. As well, similar (0.415) or lower FROH (0.09–0.14) were reported in previous study in SNP chip genotyping data of Mangalitza pigs46. In contrast to that, only Iberian pigs were found with higher FROH in the breeds investigated in our study.Scanning for ROHs in the genome of Mangalitza has advanced the understanding of the important role of selection signals significantly marked as footprints in the genome of pigs. The high number of ROHs and subsequent high estimated inbreeding coefficient  and phospholipase A2 group IID (PLA2G2D) influencing fatty acid composition of meat49 or phospholipase A2 group V (PLA2G5), hydroxysteroid 11-beta dehydrogenase 1 (HSD11B1) and diacylglycerol kinase delta (DGKD), which have a crucial role in insulin resistance and the susceptibility for obesity52.However, although today not widely used for meat production, Mangalitza exhibit particular features, which make this breed special and of particular interest in genomics. On the one hand, it is known as a fat type pig, producing fine quality meat as a delicacy with a considerably high proportion of intramuscular fat and a high amount of unsaturated fatty acids compared to other pig breeds19. Thus, interestingly, we noticed that some genes identified in Mangalitza specific ROHR were associated with disease resistance and immune system response as well as with behavioral traits. In addition to that, we detected leucine rich repeats and calponin homology domain containing 1 (LRCH1) within a ROHR associated with leg and body conformation as well as soundness traits in pigs54.In addition to that, the Mangalitza is known as a specifically robust breed due to the protective layer of fat, but also to its generally observed strong disease resistance, strong motherliness and above all, its characteristic curly hairKRT and KRTAP, currently annotated in the reference genome Sscrofa11.1 , were found to be within or close to consensus ROHR. Moreover, we identified selection signatures in WNT signaling, EGFR and FGF signaling pathways, which play a crucial role in hair follicle differentiation and formation43. Similar to that, other studies on ROH in pigs also revealed hair morphologic traits fixed in potential selection signatures. For example, a previous analysis in Chinese pigs displayed ROH islands in genes encoding fibroblast growth factor 3 and 4 (FGF3 and FGF4) related to hair ridge10.Above all, we found strong evidence in the genome of Mangalitza pigs for targeted selection for curly hair in this study. Notably, all five clusters of genes coding for hair fiber components 1-piglets57 with a uniform and non-variable degree of hair curliness. In addition to that, we confirmed the occurrence of wool hair in Mangalitza crossbreeds as found in Berkshire, Cornwall and Lincolnshire x Mangalitza crossbreeds58 but rejected in other observational studies57. We assume that these inconsistent findings are due to varying manifestation ages and different seasonal time points under study in these publications or might be due to differential gene effects. Similar to observations in curly coated horses59, we found evidence for an enhanced and accelerated development of curly hair and wool hair in colder seasons in comparison to warmer seasons. The reason for these temperature dependent developmental differences remains unclear, although an influence of slick hair on improved regulation of body temperature in hot climate was suggested60.Thus, targeting curly hair developmental genes and potential causative variants for curly hair in Mangalitza, we found evidence for an autosomal dominant inheritance in our resource families. These results contrast with previous predictions from crossings of Mangalitza with Cornwall, Middle White and Large White, which suggested an intermediary hair type for FCYP4F3 and TRPM2 jointly segregating with the curly hair phenotype in all but four individuals. In both genes we found evidence for their role in skin and hair development and differentiation68.Scanning of the Mangalitza genomes for causative variants for curly hair rejected the hypothesis of such variants in known candidate genes from other species but instead revealed two breed-specific coding variants in CYP4F3, encoding a member of the cytochrome P450 enzymes, was found to be expressed in human and rodent skin62 as well as in a reconstructed human epidermal model from adult hair follicles63. It was proposed, that cytochromes P450 are up-regulated during keratinocyte differentiation and thus play a fundamental role for the development of these cell types, which in turn promote hair follicle morphogenesis61. Similar to that, various TRP subfamilies were also identified in epidermal keratinocytes66 and in the inner root sheet and infundibulum of hair follicles67. A wavy hair type with curly whiskers observed in TRPV3 knockout mice was assumed to be a result from TRP channel dependent regulation of epidermal growth factor receptor signaling orchestrating keratinocyte terminal differentiation68. Due to these potential functional effects on hair morphogenesis of the candidate genes, we propose that the two missense variants might play an important role in shaping the Mangalitza pig’s hair into a curly hair type. Particularly strong evidence can be found in our validation on DNA samples, which revealed the two variants to be Mangalitza-specific. This was underlined by the large imputation dataset. Only very few individuals from other breeds with a described straight hair coat were predicted to harbor a mutant allele, which is presumably the result of a false-positive variant calling we could predict according to our evaluation of imputation accuracy based on validated genotypes. Regarding the functional effects of these two variants, we assume that the alteration of the amino acid sequence might affect protein function in these conserved regions despite the prediction of a potential benign effect, similar as it has been found in other studies on curly coated species32.19. Both, the Bakony and Alföldi breed, were curly coated19, presumably due to different variant effects. Crossings of these breeds might have resulted in an intermixture of these variants. Similar findings were reported in curly coated horses, whose dominant curly hair phenotype was associated with either KRT25- or SP6-variant or both32. Moreover, in cats, different breed-specific variants have been identified to be causative for curly hair69.In addition to that, we suggest that more than one variant is causative for the specific hair type of Mangalitza pigs. This evidence is supported by the origin of Mangalitza developed by crossbreeding of Alföldi, Szalonta and Bakony breeds with the Serbian Sumadia pigKRT and KRTAP genes are annotated, whereas in human reference (GRCh38.p13) 161 KRTs and KRTAP as well as further 175 pseudogenes can be found (https://www.ncbi.nlm.nih.gov/). It was reported that Ensembl (release 93) harbors half the number of annotated genes in the pig genome to what has been reported for human70. Therefore, we suppose that a further curly-associated variant might have stayed undetected due to annotational limitations in the pig genome so far.Subsequently, in Mangalitza pigs, we expect an additional variant to play a role in curly hair development, which has not been identified yet. In Sscrofa11.1, in total 110 In conclusion, the present study demonstrated how strong selection for defined phenotypes has shaped genomic structure in Mangalitza, a native and endangered Hungarian pig breed. Artificial selection left traces in the genome in the form of selection signatures for breed specific features with special regard to curly hair phenotype in pigs. These investigations will support the preservation of unique phenotypes often found in endangered breeds with potential economic value as genetic future resource.71, 12 Bentheim Black Pied 71, 16 Husum Red Pied, ten Husum Red Pied-crossbreeds, nine German Landrace-crossbreeds, one Miniature pig, one Iberico, three Kune Kune, one Pietrain and one Pot-bellied pig. All work on animals has been performed following the national and international guidelines for animal welfare. EDTA-blood and hair sampling was approved by the animal ethics committee of the Lower Saxony state veterinary office Landesamt für Verbraucherschutz und Lebensmittelsicherheit, Oldenburg, Germany (registration number 33.9-42502-05-17A217). Furthermore, tissue samples were collected at the slaughterhouse. All individuals were phenotypically characterized for their hair type classified as straight or curly and presence of under wool was also recorded. Further 347 samples, including 107 German Landrace, 108 German Large White, 18 Goettingen Minipig, 30 Leicoma, 11 Mini-Lewe, one Meishan, one Pietrain, one Bentheim Black Pied, one Husum Red Pied, two Mangalitza and 67 European wild boars were obtained from the bio-bank of the Institute of Animal Breeding and Genetics, University of Veterinary Medicine Hanover.Hair, blood or tissue samples were collected from 115 Mangalitza, 62 Mangalitza-crossbreeds, 3 Angeln Saddleback 1 progeny and in the straight-haired Angeln Saddlebback sow and the Miniature pig boar used for mating. Genomic DNA of these animals was isolated from of 1000 µl EDTA-blood or 50 hair roots using chloroform extraction36. DNA fragmentation was done with M220 focused-ultrasonicator  using the following settings: duty factor 20%, peak incident power 50, 200 cycles per burst for 45 s. Library preparation was performed using the NEBNext Ultra II DNA Library Prep Kit for Illumina  including phosphorylation, dA-tailing, adaptor ligation and size selection using AMPure XP beads . The quality of the libraries was estimated on an Agilent 2100 Bioanalyzer  using the Agilent High Sensitivity DNA Kit (Agilent). Subsequently, the libraries were adjusted to a concentration of 4 nmol and sequenced paired-end for 2 × 150 bp on an Illumina NextSeq500 . All data were submitted to sequence read archive . Adapter trimming and low complexity filters were applied using the fastq processing tool fastp, version 0.20.073 . Finally, all files were mapped to the reference genome Sscrofa11.1 (ftp://ftp.ensembl.org) using Burrows-Wheeler Alignment tool (BWA), version 0.7.1374. Variant data were called using GATK software, version 3.775, tools Base Recalibrator, Haplotype Caller76, Base Quality Score Recalibrator (BQSR) and Calculate Genotype Posteriors and underwent variant effect prediction using SNPEff 77. For further analysis of the obtained variants, only sites with mean depth values  greater than or equal to two (-min-meanDP 10) and a maximum read depth of 1000 (-max_depth 1000) were admitted and SCA X, Mt and Un were excluded using VCFtools, version 0.1.1678.WGS was performed in five Mangalitza of which two individuals were also used for the production of the resource family  as well as in samples from three curly-haired Mangalitza × Angeln Saddleback F36. These samples included 20 curly-haired Mangalitza, 18 curly-haired Mangalitza-crossbreeds, one straight-haired Mangalitza-crossbreed and 17 individuals from ten straight-haired pig breeds/populations  remaining for further analysis.In addition to WGS, we performed genotyping on the Genomic Profiler Porcine 50 K SNP chip (Illumina), including approximately 51,000 SNPs, for 56 EDTA-blood and hair samples as well as for genomic DNA, isolated from 1 ml EDTA-blood, hair roots and tissue samples using chloroform extractionFor ROH analysis a reduced WGS dataset of 23,875,519 SNPs with a minimum read depth of 3, a maximum read depth of 60 and a minimum mean read depth of 6 among 34 individuals  was used. ROHs were defined as homozygous regions in sliding windows of a minimum of 20 SNPs within at least 2.4 kb with a density of 1 SNP per 0.12 kb . Within the sliding window, five SNPs with missing genotypes and three heterozygous SNPs as well as a distance between two homozygous SNPs not exceeding 100 kb were admitted . Detected ROHs were scanned for ROHR specifically found in all nine Mangalitza (for consensus ROH detection). In a next step, these ROHR were further narrowed down to those specific for Mangalitza by excluding ROHs present in the three Mangalitza-crossbreeds.1-generation were excluded) was analyzed for ROHR specifically present in Mangalitza following similar steps like ROH analysis in WGS data. First, homozygous regions were defined as ROH by following conditions: a sliding window of a minimum of 20 SNPs with a density of 1 SNP per 1000 kb within at least 10 kb. Within sliding windows five SNPs with missing genotypes and three heterozygous SNPs as well as a homozygosity gap of a 100 kb were admitted for ROH detection. Identified ROHs were scanned for common ROHR specifically found in all Mangalitza (consensus ROH) or in at least eight of nineteen Mangalitza. Furthermore, detected regions were narrowed down by excluding those present in a minimum of five Mangalitza-crossbreeds.In addition to that, Porcine 50 K SNP chip genotyping data including 45,285 SNPs in 51 individuals .ROH detection in WGS and SNP chip data was done using PLINK, version 1.09ROH were calculated for WGS data per individual as size of all ROH divided by the size of the autosomal genome captured with SNPs . Breed specific FROH were indicated as mean of individual FROH.Additionally, Fhttp://pantherdb.org/)80 was applied to functionally assign genes in ROHR. Furthermore, we applied functional annotation for the detected genes based on Sscrofa11.1 background list using DAVID Bioinformatics Resources 6.881. Fisher Exact test relying on the hypergeometrical distribution to compute P-values was performed. The EASE score threshold (maximum probability) for gene-enrichment analysis was set to 0.1. Gene enrichment analysis was based on 273 DAVID IDs (out of 668 genes) in WGS data and 184 (out of 483 genes) DAVID IDs for SNP chip genotyping data.Functional annotation of genes in consensus ROHR exclusive for Mangalitza (SNP chip and WGS data) was carried out to detect biological processes and pathways potentially associated with breed-specific characteristics. An overlap of these ROHR with Ensemble genes was used to create a list of genes located in ROHR using SAS/Genetics, version 9.4 . PANTHER gene list analysis  as well as mating of a curly-haired Mangalitza sow with a straight-haired miniature pig boar (MA × MI) was performed. The Mangalitza boar and sow, used for the two matings, were derived from a litter of totally six pure-bred Mangalitza . The development of curly hair was recorded in all progeny of both matings during their first year of life.82 applied by the Ensemble Variant Effect Predictor Toolset83. Furthermore, PolyPhen-284 predictions were performed for the two variants of interest.In order to identify potential candidate variants for the curly hair type in Mangalitza pigs, we used the total dataset of 69 pigs . A generated VCF file including regions of 40 Mb distance to the two candidate SNPs for curly hair was screened for variants harboring one or two mutant alleles exclusively in Mangalitza and one mutant allele in Mangalitza-crossbreeds but not in the straight-haired controls using SAS .Structural variants were identified using LUMPY software, version 0.2.13Twelve candidate SNPs were validated in a first sample set of 85 individuals, including 23 curly-haired Mangalitza, 14 curly-haired Mangalitza-crossbreeds and 48 straight-haired individuals from twelve pig breeds/populations using restriction fragment length polymorphisms (RFLP) or competitive allele specific PCR (KASP) genotyping assays  according to the manufacturer’s protocol (see Supplementary Table https://igv.org/), obtained by the search for structural variants exclusively in all Mangalitza and Mangalitza-crossbreeds but not in straight-haired controls was investigated using five TaqMan Copy Number assays  distributed across the predicted region to screen for copy number variation between curly and straight-haired individuals. We used GCG as housekeeping gene86  spanning the fusion at the two expected breakpoints. A positive result represented the mutant allele whereas a negative result suggested the wild type allele . Each breed was reviewed for its hair phenotype according to common literature. Breeds with unsecure curly phenotype were discarded, resulting in a total of 1336 individuals . This dataset was imputed using BEAGLE, version 5.190 specifying effective population size at 1000. Imputed data set of 44,737,773 SNPs was narrowed down due to a required MAF of 0.05 using PLINK, version 1.0979. Subsequently, a total of 20,208,017 SNPs remained for screening genotype frequencies of imputed SNP alleles and genotypes applying SAS/Genetics, version 9.4  procedures case–control and proc freq. An error rate for estimating the imputation accuracy was calculated as frequency of imputed genotypes differing from genotypes resulted from KASP genotyping.For validation of the five candidate SNPs in a larger sample set, we used in addition to our genotyping data of 55 individuals run on the Genomic Profiler Porcine 50 K SNP chip (Illumina), further 2093 samples from pigs genotyped on 60 K SNP chip publicly available, representing 122 local and commercial breeds, 215 wild boars and 39 out-group suidsSupplementary Information 1.Supplementary Table S1.Supplementary Table S2.Supplementary Table S7.Supplementary Table S11.


Bemisia tabaci (sensu latu) is a group of >40 highly cryptic whitefly species that are of global agricultural importance, both as crop pests and plant-virus vectors. Two devastating cassava diseases in East and Central Africa are spread by abundant populations of one of these species termed Sub-Saharan Africa 1 (SSA1). There is a substantive risk that these whitefly-borne pandemics will continue to spread westwards and disrupt cassava production for millions of smallholder farmers in West Africa. We report here, therefore, the first comprehensive survey of cassava B. tabaci in eastern Nigeria, a West African region likely to be the first affected by the arrival of these whitefly-borne pandemics. We found one haplotype comprising 32 individuals with 100% identical mtCO1 sequence to the East African SSA1 populations (previously termed SSA1-SG1) and 19 mtCO1 haplotypes of Sub-Saharan Africa 3 (SSA3), the latter being the most prevalent and widely distributed B. tabaci species in eastern Nigeria. A more divergent SSA1 mtCO1 sequence (previously termed SSA1-SG5) was also identified in the region, as were mtCO1 sequences identifying the presence of the MED ASL B. tabaci species and Bemisia afer. Although B. tabaci SSA1 was found in eastern Nigeria, they were not present in the high abundances associated with the cassava mosaic (CMD) and cassava brown streak disease (CBSD) pandemics of East and Central Africa. Also, no severe CMD or any CBSD symptoms were found in the region. Bemisia tabaci Gennadius (sensu latu) is a group of more than 40 morphologically indistinguishable (cryptic) putative whitefly species [Bemisia tabaci is required [B. tabaci species cause considerable economic damage to crops through, (i) direct leaf damage, (ii) promotion of saprophytic fungal growth by their excretion of honeydew on to plant leaves and (iii) the transmission of more than 200 plant viruses [Manihot esculenta, Crantz) is an important food security crop in sub-Saharan Africa (SSA) because it is resilient to harsh climatic conditions and is a rich source of carbohydrates [ species –10. Partrequired ,3,11. B. viruses , which i viruses  and cass viruses . Cassavahydrates ,16. Casshydrates –20.B. tabaci and the SSA1  population, in particular [A pandemic of severe cassava mosaic disease (CMD) began in Uganda in the 1980s  and quicrticular ,6. Thererticular ,27.B. tabaci species diversity in East Africa, the situation in West Africa is relatively unknown. Of the sixteen West African countries, B. tabaci survey reports are available only for Burkina Faso, Benin, Côte d'Ivoire, Liberia, Mali, Nigeria, Sierra Leone and Togo [et al. [B. tabaci populations in Côte d'Ivoire was published [B. tabaci species naming system, which is based on partial mtCO1 gene-sequence divergence [In comparison to the information documenting the and Togo –32. Leusand Togo , at the  [et al.  then surublished , where tublished , Benin [ublished  and Nigeublished ,38 a decet al. [Abelmoschus esculentus (L.) Moench) biotypes among sympatric whitefly populations in central and southern Côte d'Ivoire, which differed in their iso-enzyme electrophoresis patterns and host-plant preferences. The okra biotype was collected from all of the plant hosts they surveyed, except cassava. The cassava biotype, however, was only present on cassava and eggplant (Solanum melongena L.) [et al. [Burban et al.  conductegena L.) . Omondi  [et al.  also ideet al. [B. tabaci there. Out of 54 whitefly specimens used in the study, 20 originated from Ghana, Mali and Nigeria and the remainder came from Italy, Spain and Turkey [B. tabaci putative populations among specimens obtained from sweet potato (Ipomoea batata (L.) Lam), okra, cassava and cowpea (Vigna unguiculata (L) Walp). The MED species, referred to as biotype J in the study, were collected from okra and sweet potato in Ghana and cowpea in Nigeria. The study also identified the SSA2 species, which they referred to as Sub-Saharan VI, on cassava in Mali and Nigeria. The third population, SSA1-SG5, named “Sub-Saharan II” in the study, was found in Ghana and was only collected on cassava [et al. [De La Rúa et al.  was the d Turkey . They id cassava . Sartor  [et al.  also conet al. [B. tabaci populations in Burkina Faso that were named Q1, Q3 and African silver-leafing (ASL) based on their mtCO1 sequences. They also described Q1, ASL, African non-silver-Leafing 1 (AnSL1) and African non-silver-leafing 2 (AnSL2) populations from Benin and Togo. The first three ‘biotypes’  belong to the Mediterranean (MED) putative species group, while the AnSL1 and AnSL2 ‘biotypes’ belong to the SSA3 putative species. The MED species were found on sweet potato, tomato, okra, cotton and tobacco. The AnSL1 and AnSL2 biotypes (SSA3) were the only biotypes reported on cassava in the study [B. tabaci and B. afer. The reports, however, only identified the whiteflies based on their morphology and did not use mtCO1 markers to identify the members of the B. tabaci species complex present in the region.Gnankine et al.  identifihe study . More rehe study ,42 and net al. [B. tabaci populations, which were SSA1-SG1, SSA3 and SSA1-SG5. SSA3 individuals were found only in eastern Nigeria, while the SSA1-SG1 and SSA1-SG5 were collected from cassava plants in western Nigeria. So far, studies using mtCO1 for B. tabaci classification in West Africa have reported five main B. tabaci populations in the region, including (i) MED, (ii) SSA1-SG1, (iii) SSA1-SG5, (iv) SSA2 and (v) SSA3. Two recent reports have, however, attempted to reclassify some members of the B. tabaci sub-Saharan (SSA) based on single nucleotide polymorphism markers generated using NextRAD sequencing [Ghosh et al. , howeverquencing ,43. The quencing .B. tabaci SSA1 (‘-SG1’)-borne cassava-disease epidemics spreading from East and Central Africa. The pandemics have spread from their origin in East Africa and in the past decade spread to Central Africa [B. tabaci populations on cassava and other host plants in eastern Nigeria, or their distributions in the different agro-ecological zones. The survey-data presented here, therefore, provide a baseline data-set against which future changes to the whitefly populations and disease incidences can be assessed.The research presented here was carried out in eastern Nigeria because this West African region is likely to be the first affected by the arrival of l Africa ,27. Littc. 120,000 km2. The survey region included sub-humid and humid-tropic agro-ecological zones and was bordered in the north by Kogi and Benue states, to the west by the Atlantic Ocean and Ondo state and by the Atlantic Ocean in the south. Anambra, Ebonyi, Edo, Enugu and Imo states lay within the sub-humid tropics zone, with an annual rainfall of c. 1,682–2,217 mm. The rainy season there usually begins in mid-March, peaks in June-July and ends in early December [c. 1,700–4,700 mm and experiences rainfall throughout the year except for January and February. In general, rainfall increased with decreasing distance from the Atlantic Ocean [The survey region consisted of ten eastern Nigerian states, one of which is bordered with Cameroon to the east. These states were: (i) Abia, (ii) Akwa Ibom, (iii) Anambra, (iv) Ebonyi, (v) Edo, (vi) Enugu, (vii) Cross-river, (viii) Delta, (ix) Imo and (x) Rivers, which cover a total area of December . Cross Ric Ocean .Cucumis sativus), castor bean (Ricinus communis), morning glory (Ipomoea purpurea) and okra. This weed (I. purpurea) was chosen for collecting whiteflies, because it was observed to grow near or within cassava fields. The number of whiteflies on the top five leaves of each plant was counted. The second survey was conducted from March-December 2015, by the staff of the National Roots Crops Research Institute (NRCRI). The latter survey focussed primarily on collecting whitefly from cassava plants. Both surveys, however, sampled whitefly adults from plants selected at random from each field, using a mouth aspirator (Watkins and Doncaster pooter). Altogether, 119 plants in 119 locations were selected randomly and whitefly samples collected. The whiteflies were then transferred from the aspirator to 1.5 ml Eppendorf tubes and preserved in 70% ethanol. The location for each field was recorded using a GT-120 igotU GPS receiver. The GPS coordinates were entered into the QGIS software (v3.4) and used to produce a map of the sampled locations. The HarvestChoice sub-Saharan agroecological zones plugin was used to overlay the agro-ecological zones on the map [Two surveys were carried out in 2015. The first survey occurred in March-April 2015, at the start of the rainy season, when only a few farms had young crops and others remained fallow. Whiteflies were collected from cassava, cucumber  and C012/Bt-sh2 (5’-TTTACTGCACTTTCTGCC-3’) primers [5’- TTGATTTTTTGGTCATCCAGAAGT -3’) and TL2-N-3014 (TCCAATGCACTAATCTGCCATATTA -3’) primers [Partial mtCO1 gene sequences were amplified using 2 μL of the extracted DNA. Two sets of primers, with different degrees of sensitivity, were used to amplify the mtCO1 fragments, which were the: (i) African specific 2195Bt ( primers  and (ii) primers . PolymerB. tabaci mtCO1 reference sequences downloaded directly from GenBank. The resulting alignment was used to plot an initial phylogenetic tree that was used to identify the whitefly genetic groups, using the FastTree program in Geneious (v10.2.6) [The partial mtCO1 sequences obtained from sequencing two randomly selected whiteflies from each of the 119 locations were first screened using quality checks in Geneious (v10.2.6)  and thosv10.2.6) ,48.Aleurodicus dispersus Russell (GenBank code: AJ748380) was used as an outgroup for the analysis. The best model of nucleotide sequence evolution was evaluated using jModelTest (v2.1.10) [Twenty-nine unique haplotypes were identified in the 238 new sequences, which were extracted with Geneious 10.2.6 and used to generate the main phylogenetic tree. These unique sequences were combined with 18 selected reference sequences and aligned. A mtCO1 sequence of the spiralling whitefly v2.1.10) . MrBayesv2.1.10) ,51 and Gv2.1.10) . The treThe 29 unique haplotypes were used to generate a minimum spanning tree. The alignment file (.nex) obtained from Geneious was used to build the minimum spanning tree using Phyloviz , to idendx.doi.org/10.17504/protocols.io.bd6gi9bw.After the random selection and testing of two whitefly samples per field, a two-sided Fisher test and the chi-square test were carried out on the number of different populations identified in the two samples to ascertain whether or not more sampling was needed to capture the entire diversity of the genetic groups present in the survey collections. The protocols for whitefly DNA extraction, mtCO1 amplification, phylogenetic analysis and genetic network analysis have been submitted in the protocols.io repository and has a doi of B. tabaci mtCO1 groups found were: 29 SSA1 sequences (14 of SSA1-SG1 (11.8%), 15 of SSA1-SG5 (12.6%)) and three MED ASL (2.5%). Three of the adults (2.5% of the sample set), were Bemisia afer (Prisner & Hosny). The results obtained from the second sample were similar to the first, with minor variations. SSA3 was the predominant whitefly population in the second sample )  B. afer identified in the second sample, compared to only three identified in the first sample. The two-sided Fisher test showed that the sample results were not significantly different (P = 0.113). The less stringent chi-square test on the two samples also yielded a non-significant P-value of 0.116.Total DNAs were extracted and partial mtCO1 genes sequenced successfully for 238 whiteflies, representing two individual whiteflies from each of 119 locations. Phylogenetic analyses conducted on sequences from the first sampling showed that the SSA3 putative species was the most common, represented by 84 (70.6%) of the total number of adults in the first sample . The oth(70.6%)) . The indB. afer each had three unique haplotypes.The phylogenetic tree built using the 29 unique haplotypes obtained from the 248 sequences derived in this study  showed tB. afer, MED ASL, SSA1-SG1, SSA1-SG5 and SSA3, to be 3, 3, 1, 3 and 19, respectively  individuals. A nucleotide blast search of the three unique B. afer sequences revealed that NRCRI_12a and NRCRI_86b produced top hits (>99% sequence similarity) to sequences identified as B. afer. The sequence of the third B. afer (NRI_010b) which differed markedly from the others produced a top hit with another B. afer sequences [c 80% similarity to the sequence.The Minimum Spanning Tree (MST) showed the number of unique haplotypes amongst ectively . Circlesequences  but withB. afer  were only identified in the sub-humid tropics region . The secB. afer . The thrB. tabaci populations collected from them. Apart from MED ASL, the other four whitefly genetic groups were collected on I. purpurea. There were some differences in the species identified on the different plants for the two samples. In the first sample, the SSA3 was collected from all the host plants except cucumber. B. afer, SSA1-SG1 and SSA1-SG5 were only collected from cassava  SSA3, (ii) SSA1 (-SG1 and -SG5), and (iii) MED (-ASL). B. afer also made up 5% of the 238 individuals sequenced. SSA3 B. tabaci were the most ubiquitous, being uniformly distributed across the sampled region, occurring in all ten states. SSA3 B. tabaci have not been reported from East Africa to date, but previous surveys have reported SSA3 from eastern Nigeria, Benin, Cameroon, Togo and Central Africa Republic [This report is the first extensive sampling and molecular identification of Republic ,29,43.B. tabaci populations, fecundity and response to insecticides. Although the SSA3 is ubiquitous across the region we surveyed, due to their low numbers, they do not appear to pose a significant threat to their cassava host-plants in terms of direct damage. We did, however, observe some CMD incidence in the field, but it was not as severe or widely distributed as the current CMD epidemic affecting cassava production in East and Central Africa (ECA). This suggests that that the SSA3 population may not be as an efficient vector of CMVs as SSA1-SG1. Also, efforts are now underway to explore the mating compatibility of SSA3 with other cassava whiteflies in sub-Saharan Africa, with a view to resolving the complicated phylogeny of the B. tabaci species complex and to discover if gene flow might be possible between the SSA3 B. tabaci and potential B. tabaci invaders from ECA, such asSSA1-SG1,.The previous lack of research on SSA3 probably reflects its current non-pest status, so little is known about its virus transmission ability, mating compatibility with other I. purpurea, which was the same result as that for SSA1-SG1.The SSA1-SG5 genetic group was the least ubiquitous, but it still occurred in six out of the ten states and in the two agro-ecological zones surveyed. This population has also been reported on cassava in western Nigeria . SSA1-SGThe MED-ASL is polyphagous but does not survive on cassava . In thisB. tabaci is made up of up to four sub-groups called Q1, Q2, Q3, and ASL based on their partial mtCO1 sequences [MED equences . Recent equences . This suequences .B. afer identification was not the focus of this research, but the species was collected because under field-collection conditions it can be difficult to distinguish it morphologically from B. tabaci. Similar to the results for B. tabaci , B. afer was found on cassava and only once on I. purpurea. Of the three unique haplotypes of B. afer identified in this study, the minimum spanning network analysis revealed that one haplotype differed from the others by up to 144 mutational steps. The scientific articles which reported these B. afer reference sequences did not conduct any morphological taxonomical identifications, prior to generating the mtCO1 sequence. The species, therefore, were most probably named based solely on their sequences clustering with other reported B. afer sequences. A blast search using the sequences of the distinct B. afer (NRI_010b) produced top hits with another B. afer (GenBank code: JX416161) identified by van Brunschot et al. [B. afer whitefly were identified before crushing and amplification of mtCO1 sequences. The high nucleotide differences (>19%) between NRI_010b and the B. afer reported by van Brunschot et al. [B. afer species that has not been studied yet. Second, the population is likely to be an unidentified species that clustered together with B. afer. Without comparative morphological data, we cannot be certain which explanation is correct.t et al. . Fortunat et al.  may haveI. purpurea is a common weed in eastern Nigeria and we collected four of the whitefly populations, except MED-ASL, on this plant species. We propose, therefore, that this weed may provide a resting place for whiteflies and may also serve as an alternative host-plant to the cassava whiteflies when cassava is unavailable. The original host(s) for the African cassava whiteflies is currently unknown, since cassava was only introduced into Africa from Latin America c. 400 years ago [B. tabaci species have been evolving in the African continent for millions of years [I. purpurea is not a native host for the whiteflies, since it also originated from the New World [ears ago , whereasof years ,9. I. puew World . The pauB. tabaci populations identified in this survey were all present in the sub-humid tropical agro-ecological zone. The SSA3 populations were present in the two agro-ecological zones and were collected from all five plants. These populations appeared to be adapted to the environmental conditions in the two agro-ecological zones. A preference for the sub-humid agroecological zone was, however, observed among the SSA1-SG1 and MED ASL populations. Out of the 32 times the SSA1-SG1 was identified in the survey, it only occurred once in the humid agro-ecological zone. This suggests that the Nigerian SSA1-SG1 is adapted to the sub-humid zone, which experiences less heavy and persistent rainfall.The five B. tabaci diversity than is reported here. The aim of this study, however, was to identify the whitefly species colonising cassava in the region.Two samples were tested from each sampled site and a Fisher test between the results obtained from the two samples was not significant. This result reinforced our conclusion that we had identified the diversity of whiteflies in the collected samples. An increase in the number of plants and the sample locations may, perhaps, have revealed more et al. [M. esculenta, Ipomoea batatas, Solanum melongena, Solanum lycopersicum, Arachis hypogaea, Sida acuta and Gossypium sp. This abundant haplotype also occurred across the three agro-climatic zones surveyed by the study, which included Guinean Forest, Sudano-Guinea and Sudano Ubangian zones [I. purpurea. The Nigerian SSA1-SG1 was found predominantly in the sub-humid tropics with only one collected in the humid tropic agroecological zone. Tocko-Marabena et al. [The SSA1-SG1 population was shown to have expanded and evolved rapidly in the Central African Republic (CAR), where 47 unique SSA1-SG1 haplotypes were identified . More thet al.  identifian zones . The troan zones . In conta et al.  also linThe source of the introduction of the SSA1-SG1 into Nigeria or whether the population is indigenous to West Africa is not clear. Spread could have occurred by movement from the Central Africa Republic, where the haplotype P18F5 (GenBank code: LT707470) occurs in abundance. The haplotype could have also been introduced through the transport of ornamental plants to eastern Nigeria from neighbouring East African countries, although there are no records of such trades. The implications of this possible introduction are not readily evident. This survey did not, however, observe any CBSD infection nor a CMD pandemic in eastern Nigeria and there is no evidence that the SSA1-SG1-Ng occur in high abundance on cassava, as do their East and Central African (ECA) counterparts. The SSA1-SG1 has also been reported in western Nigeria by previous studies  nor the Abundance data collected from 50 randomly sampled plants  revealedB. tabaci populations on cassava plants [B. tabaci were identified  a plants  was seena plants ,62,63, ientified . This sut Africa ,27. ThisB. tabaci populations in West Africa. The SSA3 B. tabaci are the predominant population in eastern Nigeria, which is a key difference to the situation in East Africa. SSA3 B. tabaci have also been reported in the West African countries of Benin and Togo, as well as in the Central African Republic [B. tabaci SSA3 is indigenous to West Africa. The SSA1-SG1 population did not occur in high population density in eastern Nigeria and the population consisted of only a single mtCO1 haplotype. These data show that the SSA1-SG1 may not be indigenous to the sampled region and this single haplotype may have arrived from East and Central Africa (ECA). It is also possible that the nuclear genomes of the Nigerian SSA1-SG1 and ECA SSA1-SG1 have diverged, but this cannot be determined by use of the partial mitochondrial CO1 marker.These data provide a baseline for future surveys in eastern Nigeria and are valuable for tracking future changes in Republic ,29. The S1 TableInformation is displayed for the number of whiteflies on the top five leaves of each plant surveyed.(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


Mycobacterium bovis is a livestock disease of global economic and public health importance. There are currently no effective vaccines available for livestock and so control relies on animal level surveillance and pasteurization of dairy products. A new alternative control approach is to exploit the genetic variability of the host; recent studies have demonstrated that breeding European taurine cattle, such as Holsteins for increased resistance to bTB is feasible. The utility of such an approach is still unknown for African cattle populations. This study aims to assess genetic variation in bTB resistance and the underlying genomic architecture of cattle in Cameroon. We conducted a cross-sectional study of 2,346 slaughter cattle in Cameroon. Retropharyngeal lymph node samples were collected and cultured on Lowenstein Jensen media and the BACTEC MGIT 960 system, and M. bovis was identified using the Hain® Genotype kits. A total of 153 cattle were positive for M. bovis and were archived along with a random selection of negative samples. In this study, we genotyped archived samples from 212 cattle. Their genomic diversity was characterized using PCA, hierarchical clustering and admixture analysis. We assessed genetic variation in bTB resistance using heritability analysis and compared quantitative trait loci. Previous research on this study population have shown that Fulani cattle are more susceptible to bTB than mixed breeds. However, here we show that these apparent phenotypic differences in breeds are not reflected by clear genomic differences. At the genetic level, both the Fulani and mixed cattle show similar patterns of admixture with evidence of both taurine and indicine ancestry. There was little European taurine introgression within the studied population. Hierarchical clustering showed clusters of cattle that differed in their susceptibility to bTB. Our findings allude to bTB resistance being polygenic in nature. This study highlights the potential for genetic control of bTB in Africa and the need for further research into the genetics of bTB resistance within African cattle populations.Bovine Tuberculosis (bTB) caused by  Mycobacterium bovis is a major zoonotic livestock disease causing a chronic respiratory condition characterized by weight loss and poor welfare and eventually death. In low-and middle-income countries (LMICs) it is estimated that M. bovis is responsible for ~1.4% of human TB cases, which equates to an estimated 70,000 new human infections annually in Africa  caused by M. bovis has a wide host range, which includes domestic and wild bovids, small ruminants, swine and cervids; which means that control requires a strong coordinated multi-disciplinary, trans-boundary approach. Unfortunately, most LMICs have poorly resourced veterinary services and do not have the means to implement bTB control strategies, instead they rely on controlling the risk of exposure through abattoir surveillance. At the same time LMICs, encouraged and funded by many charities, have widely adopted dairy development strategies with the aim of aiding livelihood and community development  , these were incubated for 8 weeks on the BACTEC MGIT 960 automated culture system  following the manufacturer's instructions , 84 M. bovis culture positive and 128 M. bovis culture negative animals were included in the analyses.Out of the 2,346 animals in the cross-sectional study, 153 animals were cultured and confirmed positive for DNA was extracted from the frozen archived bovine lymph nodes using the Qiagen DNeasy® Blood & Tissue Kit. The “Purification of Total DNA from Animal Tissues (Spin-Column Protocol)” was used following the manufacturer's instructions.Frozen lymph node samples stored at −80°C were brought to room temperature and ~25 mg was sliced into tiny pieces on a sterile petri dish using sterile disposable surgical blades. The sliced tissues were transferred to a 1.5 ml screw cap microtube and heat-treated for 10 min at 95°C in a water bath. Tubes were retrieved from the water bath and 180 μl of Buffer ATL and 20 μl of proteinase K (provided with the kit) was added. Tubes were vigorously vortexed to mix and the tissues were completely lysed by incubating at 56°C for 3 h in a heater block. The rest of the procedure was done strictly following the manufacturer's instructions. Purified DNA was first eluted from the spin columns with 60 μl of the elution buffer to increase the DNA concentration. To maximize the DNA yield, a second elution with 180 μl of elution buffer was done. The concentration of the purified DNA were assessed using the Qubit.n = 63 and Jersey n = 36), African taurine breeds , Asian zebu Bos indicus breeds , and West African admixed breeds .The cattle were genotyped using the Illumina BovineHD 777K BeadChip, which included 777,962 SNPs, of which 735,293 were autosomal  to 144 reference animals that passed quality control checks  was performed using the SNPRelate package in R version 3.5.0  pairwise distances between individuals using the Bos indicus and African taurine ancestries at the genome-wide level. Variants in high linkage disequilibrium (LD) with each other were removed prior to analysis. The LD pruning criteria applied was to remove any SNP that had an r-squared >0.2 with another SNP within a 200-SNP window; for a sliding window of 10 SNPs at a time, which resulted in 55,361 markers  left for analysis. A 5-step expectation–maximization (EM) algorithm was used. In addition, 10-fold cross validation was performed with 200 bootstrap resampling runs to estimate the standard errors for each cluster level (K = 2–12). The output was plotted using the pophelper package for R  and Admixed groups pre-specified.To provide further insights into the Fulani and Mixed compositions related to the other groups, the ADMIXTURE analysis was also run in a supervised mode, with European taurine, African taurine, and Asian zebu  were calculated using the Weir and Hill  relativer2 and D′, was calculated between pairs of SNPs within a 50 SNP window using PLINK v1.90  and a second degree polynomial fitted to show the trend in r2 for each breed. The average r2 and D′ also calculated for each breed.Differences in the inbreeding coefficient may also be due to the ascertainment bias of the SNP array. To assess this ascertainment bias, linkage disequilibrium, Binomial generalized linear models with logit link functions were used to examine the association between breed and bTB infection status in R . We used the cut-offs from  and cattle with ≤ 1.56% ET background represented the “non-European” introgressed group (n = 183). There were no “substantially” ET introgressed cattle with >12.5% ET introgression in this population.European taurine (ET) introgression status was based on the proportion membership to the K value cluster representing European taurine breeds in the admixture analysis  ET introgression and (ii) the inbreeding coefficient using binomial generalized linear models with logit link functions in R  and b is a vector of fixed effects . The random effects, which determine the variance of the trait are the additive genetic (a) and residual effects (e). X and Z are all design matrices assigning individuals to their corresponding fixed and random effects. Both random effects were assumed to follow a normal distributions, a N is defined as the proportion of phenotypic variance explained by the additive genetic variance, The narrow-sense heritability of a trait (P < 1 × 10−5 was used in addition to a Bonferroni corrected genome-wide significance threshold of P = −log10(0.05/n) = 7.00 . Regions surrounding 100 Kbp of SNPs which had been identified as being associated with bTB resistance in cattle in the literature  = 5.96.A genome wide association study (GWAS) was carried out on the quality controlled Cameroon dataset using PLINK version 1.90  were positive for M. bovis compared to 42.3% of mixed breed animals . The proportion of individuals positive for M. bovis by age, gender and ET introgression and inbreeding status by abattoir is shown in A total of 212 animals from Cameroon passed quality control checks, of which 160 animals (75.5%) were Fulani and 52 animals (24.5%) were mixed breed. 84 animals (39.6%) were positive for PCA and hierarchical clustering analysis were used to show the population structure of the cattle breeds. Within the Cameroon cattle population, the PCA showed that the Fulani and mixed breed cattle cluster closely together along with the admixed Bunaji and Sokoto Gudali cattle . The AfrThe hierarchical clustering identified 10 large clusters of animals, shown by the gray and white bands and similarly colored nodes in K = 2 to K = 7 shown in K = 2 the cattle are split into the indicus and taurine groups. At K = 3, the African taurine and European taurine cattle diverge. The Fulani and mixed cattle have similar levels of African taurine and Asian zebu ancestries at the K = 3 level. The same pattern is observed in the supervised version of the admixture analysis , however focusing on just the mixed and Fulani breeds there were low levels of genetic differentiation  indicating that there are high levels of shared genetic ancestry between the Fulani and mixed breed animals .The observed inbreeding coefficient, F is shown in r2 between pairs of SNPs in a 50 SNP window against window size is shown in r2 follows a different trend for each of the breeds. Whilst there is a high level of ascertainment of SNPs from the European and African taurine breeds, the ascertainment of SNPs was a lot lower from the Asian (Bos indicus) zebu and admixed breeds . Yet, in a separate generalized linear models after accounting for age and sex there was no association between European taurine introgression and M. bovis   who are currently genotyping hundreds of cattle to generate a new SNP chip for African cattle populations which prioritizes SNPs for key traits, such as bTB resistance.Alternatively, as reflected from our linkage disequilibrium analysis, the marker diversity of the SNP chip does not accurately reflect the genetic diversity of the African admixed or There was very little European taurine introgression from Holstein and Jersey breeds in both the Fulani and mixed cattle. Unlike other African countries, there has been very limited introduction of European breeds into Cameroon  than local cattle breeds . The small sample size used for this heritability calculation has resulted in the large standard error.Lastly, we investigated the role of additive genetic effects and individual SNPs on bTB resistance. We found that the heritability of bTB after accounting for age, sex, and breed in this study was M. bovis positive samples. In European taurine cattle there is strong evidence for genetic variation in resistance to bTB . This SNP is in a region previously highlighted as being possibly associated with bTB resistance in cattle  can be found below: The animal study was reviewed and approved by University of Edinburgh Ethical Review Committee.BB, RK, AM, FE, and AD-W conceived and designed the study. RK, FE, LB, SM, and BB performed the field work. RC analyzed the data. AM, MS, LB, EC, and AD-W contributed the expertise and analysis tools. RC, AM, and MS wrote the first draft paper. All authors read and contributed to the final draft of the paper.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Perca fluviatilis L.), a widespread fish that goes through ontogenetic niche shifts and can have high individual niche specialization. Diet explained most of the variation in the FA composition of perch dorsal muscle over early ontogeny (28%), while the total length explained 23%, suggesting that perch significantly regulated FA composition over early ontogeny. Condition explained 1% of the remaining variation. 18:3n-3 (ALA) and 18:4n-3 (SDA) indicated planktivory; 18:1n-7, benthivory; and 22:6n-3 (DHA), piscivory in perch diet. Conversely, perch regulated long-chained polyunsaturated fatty acids (PUFAs), such as 20:5n-3 (EPA), 20:4n-6 (ARA) and 22:6n-3 (DHA) over ontogeny, emphasizing the role of such FAs in early growth and sexual maturation. Adult perch increasingly retained 16:1n-7 and 18:1n-9 suggesting higher energy storage in older perch. Furthermore, differences in DHA availability in diet correlated with intra-cohort differences in perch growth, potentially hindering the overall use of benthic resources and promoting earlier shifts to piscivory in littoral habitats. Overall, this study indicates that in addition to diet, internal regulation may be more important for FA composition than previously thought. Differences between FA needs and FA availability may lead to life-history trade-offs that affect the ecology of consumers, including their niche.Fatty acids (FAs) are key nutrients for fitness which take part in multiple physiological processes over the ontogeny of organisms. Yet, we lack evidence on how FA nutrition mediates life-history trade-offs and ontogenetic niche shifts in natural populations. In a field study, we analyzed ontogenetic changes in the FAs of Eurasian perch (The online version of this article (10.1007/s00442-020-04668-y) contains supplementary material, which is available to authorized users. Organisms often undergo life-history trade-offs in which they compromise the expression of different traits in order to maximize fitness Stearns . ChangesFatty acids (FAs) are critical biochemical compounds for organisms, being a major component of membranes, energy reserves and hormonal regulation Tocher . Among tYet, the availability of PUFAs in food differ substantially among prey taxa from different habitats  FA composition of perch is linked to the FA composition of their diet, reflecting ontogenetic niche shifts as well as differences in individual resource specialization. We therefore predicted that abundant fatty acids in diet would also be more abundant in the consumer, reinforcing the role of FAs as trophic biomarkers. (2) FA composition of perch would change with body size irrespective of dietary inputs, indicating trends and shifts in FA regulation during ontogeny. Due to the high physiological importance of long-chained PUFAs for life-history processes, we expect that such fatty acids would be selectively retained in tissues as compared to other less important fatty acids. Finally, (3) we hypothesized that mismatches between dietary FA and FA requirements over ontogeny may affect life-history trade-offs in perch, and eventually promote ontogenetic niche shifts, for instance by promoting shifts to a better-quality food at the expense of less foraging efficiency or higher predation risk. These results may be applicable to other organisms with ontogenetic niche shifts and with changes in physiological demands for PUFAs over ontogeny, which are likely to be the norm rather than the exception in nature  were captured in 18 Aug 2015 (in the middle of the growing season 8 months before reproduction) using multimesh gill nets . We set two pelagic nets in the open water environment and two littoral nets in shallow, near-shore areas for 12 h overnight. To minimize post mortem degradation of FAs, we selected only fish with clear red gills and froze them at −20 °C for up to 37 days. In the laboratory, perch and prey fish were partially thawed. We measured the total length (LT) to the nearest millimeter, and wet weight (W) of perch to the nearest gram, to calculate perch condition factor: The study was performed in the lake Erken, a mesotrophic lake located in central Sweden 59°50′N, 18°33′E) where perch exhibits resource polymorphism with respect to littoral and pelagic habitats  as the increment of total length between two consecutive years  for each habitat, We dissected two pieces of the dorsal muscle of each perch; one was stored at −20 °C for FA analysis; the other was dried in the oven for 24 h at 60 °C for stable isotope analysis. We used the opercular bones for age analysis Le Cren . To inveo et al. .On the same day as fish sampling, we collected zooplankton prey by multiple vertical hauls using a 100 µm mesh-size zooplankton net and littoral macroinvertebrates prey either with a sweep net (2 mm mesh size) or manually from different littoral substrates. Subsequently, we separated them into major taxonomic groups , using a dissecting microscope if needed and dried in the oven at 60 °C for 24 h for stable isotope analysis. Fatty acid data of invertebrate prey were obtained from Scharnweber et al.  see “Fa”.13C differs between benthic and pelagic production and δ15N increases with trophic position . As C/N was low , no lipid normalization was performed , littoral planktivorous (n = 28), pelagic planktivorous (n = 55), pelagic benthivorous (n = 2) and littoral piscivorous (n = 16) perch. For more information about the mixing model and cluster analysis, please see Supplementary Material 1.To facilitate further analysis and visualization, we classified perch according to habitat use  and on the main three diets over the ontogeny of perch . Perch tend to have high habitat fidelity Eklöv  and ther2SO4 as a catalyst and FA methyl esters (FAMEs) were analyzed by gas chromatography. We calculated single FAMEs concentration using a calibration curve from standard solutions of known lipid FAME mixtures of 20 fatty acids commonly known to occur in fish muscle tissues  on the area under each FA peak in the chromatogram. We inferred FA composition of zooplankton and macroinvertebrates based published data from Scharnweber et al.  with the PERMANOVA add-on. Proportions of FAs were arcsine-square-root-transformed and all ordination and tests were based on Euclidean distance matrices. We used distance-based linear modelling (DistLM) to evaluate how much of the variation in perch FA composition was explained by changes in diet, body condition and internal ontogenetic changes. As total length is a common and relevant ontogenetic measure in fish . We tested the effect of habitat irrespective of main explanatory variables by including habitat as a fixed factor and significant explanatory variables from DistLM analysis as covariates. In the latter model, significance was determined by restricted permutation of residuals under a reduced model (9999 permutations), using type I sum of squares  for pairwise comparisons between benthivorous vs. planktivorous, piscivorous vs. non-piscivorous and consecutive age groups of perch. To avoid age dependence on diet comparisons and diet dependence on age group comparisons, we used a two-way crossed design, because it summarizes dissimilarities nested within each age or niche group, respectively   revealed that the total length, diet and perch condition factors were significantly related to changes in FA composition of perch , explained 23% of FA variation, while piscivory explained an extra 5%, meaning that overall dietary choices explained 28% of FA variation in 1+ to 3+ years old perch . However, once the total length, diet and condition factor were included as co-variables, habitat did not have a significant effect on FA composition .Planktivory, which was correlated negatively with benthivory . Planktivorous perch had higher SDA and ALA and lower DPA and 18:1n-7 than benthivorous perch . Overall, FA composition of young-of-the-year perch had high DHA, intermediate EPA and low ARA proportions . However, these differences did not occur in age 3+ perch , as piscivorous perch had higher growth than pelagic planktivorous perch during their third growing season . The perch groups did not differ in condition factor . The only significant inter-cohort differences within the niche groups was the higher first year growth of pelagic planktivorous perch during 2014 . This suggests that, overall, our data reflect ontogenetic trends rather than inter-annual variation in the perch population.Back calculation of length at age revealed that after the first growing season, during which perch reached 75 ± 9 mm, the total length of perch increased linearly, around 30 mm per year . Age 1+ and age 2+ pelagic planktivorous perch were the longest and the heaviest of their cohort, which was also extrapolated to the previous two growing seasons based on the length at age calculations  associated with changes in FA requirements over ontogeny. Proportions of EPA and ARA, which are highly bioactive FAs during sexual maturity and reproduction , using mass-balance modelling. However, the extrapolation of these results in perch to other consumer taxa may still be difficult as bioconversion abilities depend on the taxonomic identity of the consumer, being highest in invertebrates and lowest in terrestrial carnivorous vertebrates . Such results are consistent with the higher availability of high-quality zooplankton prey in pelagic versus littoral environments Below is the link to the electronic supplementary material.


The development of microhaplotype (MH) panels for massively parallel sequencing (MPS) platforms is gaining increasing relevance for forensic analysis. Here, we expand the applicability of a 102 autosomal and 11 X-chromosome panel of MHs, previously validated with both MiSeq and Ion S5 MPS platforms and designed for identification purposes. We have broadened reference population data for identification purposes, including data from 240 HGDP-CEPH individuals of native populations from North Africa, the Middle East, Oceania and America. Using the enhanced population data, the panel was evaluated as a marker set for bio-geographical ancestry (BGA) inference, providing a clear differentiation of the five main continental groups of Africa, Europe, East Asia, Native America, and Oceania. An informative degree of differentiation was also achieved for the population variation encompassing North Africa, Middle East, Europe, South Asia, and East Asia. In addition, we explored the potential for individual BGA inference from simple mixed DNA, by simulation of mixed profiles followed by deconvolution of mixture components. Microhaplotypes (MHs), defined as sets of SNPs in sequence segments of less than 200 base-pairs (bp), which define multi-allelic haplotypes, have been proposed as forensic markers in concert with the forensic adoption of massively parallel sequencing (MPS) technologies . MPS plaThree notable advantages of MHs are: a higher degree of polymorphism compared to single-site SNPs; the absence of stutter artifacts; and short amplicon lengths compared to STRs. Therefore, possible applications of MHs include a wide range of forensic scenarios: individual identification from degraded DNA , kinshipHere, we have made new evaluations of a previously published multi-platform (MiSeq and Ion S5) panel of 102 autosomal and 11 X-chromosome MHs validated for forensic identification  ] and Ion Xpress Barcode Adapters (TFS) optimizing the manufacturer’s recommendations to half-volumes. A total of 1 ng of input DNA was used, quantified with Qubit 3.0 Fluorometer (TFS) and Qubit dsDNA HS Assay Kit (TFS) following the manufacturer’s recommendations. The primer pool was described in Sequencing quality parameters including sequence coverage, strand bias, allele balance and misincorporation rates were evaluated using single SNP data produced with the HID Genotyper plugin v.5.2.2 (TFS) of Torrent Suite v. 5.6.0 (TFS) using default parameters of minimum coverage of six reads and minimum allele read frequency of 0.1.Microhaplotype calling was performed using the pipeline described in Genotyping and phase concordance with publicly available data from Simons Genome Diversity Project (SGDP)  and receST were not conducted.Population data for haplotype frequency estimation and BGA analysis was obtained from 1,000 Genomes project phase III public releases   and cumulative match probabilities (as the product of the sum of the squares of the genotype probabilities of each locus) were calculated and plotted using R v. 3.6.1  or Excel 3.5.1.2 .pegas  (n is the most convenient metric for assessment of BGA informativeness in different types of genomic markers.Population-specific Divergence (PSD) and simple pairwise Divergence values were calculated using infocalc v. 1.1 for obtaining Rosenberg’s informativeness-for-assignment metric (In) . For PSDThree mixed profiles including 102 autosomal MHs were simulated from single source profiles of known ancestry, comprising: (i) a 1:3 mixture of HG02922 unadmixed ESN (AFR) and NA18939 unadmixed JPT (EAS)–herein, mixture 1; (ii) a 1:5 mixture of HG00097 unadmixed GBR (EUR) and HG00096 unadmixed GBR (EUR)–herein, mixture 2; and (iii) a 1:7 mixture of HG01565 admixed PEL (AMR) and HG00096 unadmixed GBR (EUR)–herein, mixture 3.Two analysts conducted a blind deconvolution of each of the mixed profiles, instructed to separate two components (minor and major) assigning only the haplotypes that were unequivocally from one of the components when taking into account stochastic phenomena . Results from both analysts were merged maintaining the most conservative profile when interpretations differed, and BGA inference analysis comprising STRUCTURE and MDS were performed as described in section “Population Differentiation and BGA Inference Performance.”Details of the overall performance of the seven sequencing runs are collected in Regarding strand bias, represented in Allele read frequency balance is described in After MH component SNP genotype calling, six samples: HGDP00588, HGDP00627, HGDP00634, HGDP00637, HGDP00640, and HGDP00642 showed highly imbalanced profiles with more than two haplotypes for several markers, and were excluded from further analysis, as this was most probably due to reference DNA contamination.Concordance with SGDP phased data comprised a total of 3,220 comparisons for 92 markers–note that all X-chromosome loci plus 10 autosomal MHs are not listed by SGDP. Comparisons were made in 35 samples . In addition, 82 genotypes could not be compared due to the lack of results from genotyping, most of these in MHs that showed the lowest coverage values: 6pB, 17qC, and 16pB. Concordance rates reached levels of 99.01%, with 31 discordances in 3,138 genotypes confined to 10 MHs, as listed in All the discordancies were explored further in IGV in order to clarify possible causes. Most discordancies (21/31) we presume to be caused by the use of probabilistic software to phase the SGDP SNP genotype data  in the following two ways: (i) erroneous phasing of heterozygous alleles in MHs 13qD, 20pA, and 22qA; or (ii) the software does not account for multi-allelic SNPs –affecting MHs 1qC, 5qB, and 11qA. This supports the idea that more accurate phasing is obtained through applying MPS to short MHs sequenced as single strands, rather than inferring phase from individually genotyped SNPs.For MH 16qB, previously identified as underperforming, a total of seven discordancies were found, due to allele drop-out  during genotype calling. These genotypes were corrected for further analysis. Also, single discordancies were found for MHs 1qD, 7pC, and 11pA. In 11pA the discordancy was due to high allele imbalance of the sequence reads and was corrected; while the cause of the others remained unclear.For concordance with HGDP WGS, a total of 234 out of the 240 analyzed samples–i.e., all excluding HGDP01003, HGDP01006, HGDP01042, HGDP01051, HGDP01273, and HGDP01278–were compared in 113 loci, adding up to a total of 26,442 comparisons. A total of 1,321 comparisons were inconclusive due to: (i) lack of genotypes in either dataset; (ii) HGDP WGS does not list the first SNP of MH XqD, located in position 93531382 (GRCh37/hg19) or 94276383 (GRCh38/hg38) and (iii) HGDP WGS does not provide phase information for the loci located on the X, thus haplotype reconstruction was not possible for MH loci comprising two or more heterozygous SNPs.The concordance rate reached 99.75%, with only 62 discrepancies observed  and the EUR populations. Higher values were found in comparisons between the AFR populations and EAS, OCE and AMR populations with a low degree of admixture, following the known demographic histories of continental populations. Likewise, the average number of pairwise differences between populations ranged from 60.94 to 80.35 and showed similar patterns to FST–with the low values corresponding to comparisons inside the same major population group and high values in the comparisons involving an AFR population. The lowest value was recorded for the comparison of Native Americans (NAM) with the least admixed 1 KG AMR population of Peruvians from Lima (PEL). Average number of pairwise differences within populations ranged from 54.13 to 71.70 with the lowest values in NAM and OCE populations.Heterozygosity values for the autosomal MHs are listed Bio-geographical ancestry inference analyses were performed considering genetic information from the 102 autosomal MHs in the panel. In order to minimize possible sample size effects , a referK = 1 to K = 8 , or partially shared (EUR component in mixture 3). This is not unexpected as the panel was designed for identification purposes.In this study, designed to evaluate extended functionality of MH loci for mixed DNA analysis and compile the necessary population reference data for this purpose, a total of 240 reference HGDP-CEPH individuals of native populations from NAF, ME, OCE, and AMR were analyzed with the panel of 102 autosomal and 11 X-chromosome MHs. Most MHs (109/113) performed well in MPS tests, even when chips were loaded with ∼40 sample libraries. Moreover, 99% concordance was achieved between the MH alleles obtained through MPS and the SGDP phased data used for direct comparisons, while reaching 99.75% concordance with HGDP WGS data. The concordance study revealed some inconsistencies due to the probabilistic phasing algorithm used by both datasets, emphasizing the idea that the phase of the SNPs forming the haplotypes is more accurately derived when detected directly from sequence reads of individual strands, which will encompass all the SNPs in the MH in the same amplicon and using the pipeline developed for the forensic use of the panel. This pipeline outputs the depth coverage of each haplotype and produces profiles similar to those from STRs. Moreover, the pipeline allows for costumization of minimum allele frequency and minimum coverage parameters, analogous to the analytical and interpretation thresholds used in capillary electrophoresis analysis. These characteristics aid the interpretation of MH results by forensic experts, especially for mixture analysis, and enhances the utility of the MHs panel we have developed.Despite the fact that some populations had limited numbers of samples, MHs showed similar degrees of polymorphism to those encountered in the extensive 1 KG dataset. This endorses the use of the panel for individual identification or kinship testing in the additional worldwide populations analyzed. For this purpose, one of the major advantages of the panel is the small size of the amplicons, that previously outperformed standard STR analysis when dealing with degraded DNA . CompareAt the same time, the new population data we report is a valuable addition to BGA analyses using the panel. The results demonstrate the ability of the panel to differentiate the five major continental groups  and, to a lesser extent, the main sets of populations within Eurasia . Populations NAF, ME, and SAS are sited in the middle of variation clines and therefore their differentiation is challenging, especially for NAF and ME regions. To address such challenges, MPS capabilities support much bigger multiplex scales than a typical SNaPshot multiplex assay for SNP genotyping while mantaining forensic sensitivity, allowing a more fine scale geographic resolution in BGA analyses. The MHs panel takes advantage of the higher multiplex capabilities while of MPS using highly polymorphic markers giving high heterozygosity values within populations  and high between population differentation . For these reasons, although not an original criterion for the selection of the component MHs of the panel, the degree of BGA information is similar or superior to that achieved with other custom  or commeFinally, in this study we began to explore the scope for BGA inference from deconvoluted mixed DNA contributors. Preliminary studies by Deconvolution of mixed MH profiles is simplified by the abscense of stutter artifacts and probabilistic genotyping software can be readily adapted and used for individual identification of the mixture contributors. The ability of the panel to identify the contributors is supported by the fact that, assuming a similar level of informativeness for all MHs [and as shown by the consistent gradient of the RMP slope from Figure 4 in The MHs panel we have previously developed is found to be even more of a multi-purpose tool for forensic applications than originally proposed. It is applicable in those forensic cases in which regular STR analysis by itself does not provide an answer or supplementary information is needed. The same component loci of the MHs panel prove to be highly informative for: individual identification with a focus on highly degraded DNA, especially since all amplicon sizes are less than 175 bp; kinship testing; mixed DNA analysis and BGA inference–with indications from our studies that the latter two functions can be combined in simple mixtures. With this in mind, the panel could help to improve identifications in disaster victim identification programs that involve multiple nationalities, where BGA can assist in the first triage of the victims and the selection of the correct allele frequencies for identification through comparisons to surviving relatives. The panel has been fully validated for forensic purposes and can be implemented with both the two main MPS platforms in common use in forensic laboratories: MiSeq and Ion S5, with the latter allowing automated library construction.The data generated in this manuscript has been deposited at the following public repositories:–PRJEB39413raw reads as fastq were submitted to the European Nucleotide Archive (ENA) under accession number –PRJEB39574.vcf files from Torrent Suite were submitted to the European Variation Archive (EVA) under accession number All authors listed made substantial and direct contributions to the work and approved it for publication. MVL, WP, CP, ÁC, and MdlP designed the study, developed the ideas and obtained funding for the project. JR-R and AA-C conducted the DNA analysis. MdlP, CP, CX, JA, MÁCdC, and AG-T analyzed the results. MdlP and CP wrote the manuscript. All authors discussed the results and contributed to the revision of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Larimichthys crocea) is one of the most commercially important marine fish species in China. Although plenty of SNPs have been identified in large yellow croaker, no high-throughput genotyping array is available. In this study, a high-throughput SNP array named NingXin-I with 600K SNPs was developed and evaluated. A set of 82 large yellow croakers were collected from different locations of China and re-sequenced. A total of 9.34M SNPs were identified by mapping sequence reads to the large yellow croaker reference genome. About 1.98M candidate SNPs were selected for further analyses by using criteria such as SNP quality score and conversion performance in the final array. Finally, 579.5K SNPs evenly distributed across the large yellow croaker genome with an average spacing of 1.19 kb were proceeded to array production. The performance of NingXin-I array was evaluated in 96 large yellow croaker individuals from five populations, and 83.38% SNPs on the array were polymorphic sites. A further test of the NingXin-I array in five closely related species in Sciaenidae identified 26.68–56.23% polymorphic SNP rate across species. A phylogenetic tree inferred by using the genotype data generated by NingXin-I confirmed the phylogenetic distance of the species in Sciaenidae. The performance of NingXin-I in large yellow croaker and the other species in Sciaenidae suggested high accuracy and broad application. The NingXin-I array should be valuable for quantitative genetic studies, such as genome-wide association studies (GWASs), high-density linkage map construction, haplotype analysis, and genome-based selection.High-density single-nucleotide polymorphism (SNP) genotyping array is an essential tool for genetic analyses of animals and plants. Large yellow croaker ( Larimichthys crocea) is a commercially important marine fish species native to the northwestern Pacific, generally found in temperate waters. It is one of the traditional species favored by Chinese consumers. The large yellow croaker industry in China collapsed in the 1970s due to overfishing and recovered gradually since the establishment of artificial propagation in 1985  markers were identified in large yellow croaker since the application of high-throughput sequencing technology , 2016. TDissection of the genetic basis of these traits requires a large number of tools and resources such as genetic markers and high-throughput genotyping platforms. A major challenge for genetic research in large yellow croakers is the lack of a high-throughput genotyping SNP array. High-density SNP array and direct genotyping by sequencing (GBS) techniques, in particular, restriction site-associated DNA sequencing (RAD−Seq), are popular tools for population-level SNP genotyping . CompareCryptocaryon irritans in large yellow croaker , 10 big head croakers (Collichthys lucidus), eight brown croakers (Miichthys miiuy), nine yellow drums , and eight dusky roncadors . Fin clips were collected from the fish samples, and DNA was extracted by using DNeasy Blood & Tissue Kit . DNA concentration was quantified by using NanoDrop 2000 (Thermo Scientific), and the integrity was checked by 1.5% agarose gel electrophoresis stained with ethidium bromide. DNA samples of large yellow croaker that fulfilled the quality requirement for whole-genome sequencing (WGS) were kept for further analyses. Sequencing libraries with 250 bp insert size were constructed using NEBNext Ultra DNA library Prep Kit  according to the manufacturer’s instructions. The libraries were sequenced by using the Illumina HiSeq2500 platform with a paired-end read length of 150 bp.Tissue samples were collected after euthanasia by using tricaine methanesulfonate . A total of 82 individuals from five wild and one cultured population of large yellow croaker were collected from different locations of China . The fivThe adapters, low-quality sequences , and short sequences (length < 30 bp) in the raw reads were trimmed using Trimmomatic-0.38 . After tThe SNPs identified in different populations of large yellow croaker were pooled together. SNPs were filtered based on the 35 bp flanking sequence with the following criteria: contain less than four contiguous G or C and less than six adjacent A or T; GC content between 30 and 70%; and contain only one SNP and no insertion or deletion. SNPs were further selected by a custom-made algorithm  includinL. polyactis), 10 big head croakers (C. lucidus), eight brown croakers (M. miiuy), nine yellow drums , and eight dusky roncadors (M. fusca). Genomic DNA was extracted from blood and quantified by NanoDrop 2000 . High-quality DNA samples were genotyped by using the NingXin-I SNP array. The genotype data were converted to Ped/Map format using a python script and then imported into PLINK software (version 1.9)   to conduion 1.9)  with a nA total of 650 Gb raw reads were obtained from 82 large yellow croaker individuals by whole-genome re-sequencing. The average sequencing coverage for each large yellow croaker was 11.33 × . SNP identification was performed separately in each population. The SNP numbers identified in the six populations varied from 6.5 to 7.6M . Overallin silico analysis to predict the reproducibility. Based on the score of in silico analysis, the SNPs were classified into recommended, neutral, and not-recommended categories, with 0.21, 1.67, and 0.97 M SNPs, respectively. The SNPs in recommended and neutral groups were kept for further analyses.For quality control, SNPs were filtered based on the 35 bp flanking sequence according to the criteria described in “Materials and Methods” section. A total of 1.97M SNPs were retained after filtration. These SNPs were submitted to Affymetrix Axiom myDesign GW bioinformatics pipeline  for The SNPs were filtered by a custom-made algorithm  to keep The SNP array was evaluated in different large yellow croaker populations, including 18 large yellow croakers used in the previous genome re-sequencing and 78 large yellow croakers from other populations. The percentage of 81.49, 73.00, 69.40, 67.23, and 72.60% polymorphic SNPs was identified independently from Fufa, Fuding, Zhoushan, Xiamen, and Zhanjiang populations, respectively . The numWe also compared the genotypes generated from the whole-genome re-sequencing and SNP array. Only 0.35M genotypes (3.86%) that were genotyped using WGS were missing from the SNP array, while 1.25M (13.75%) genotypes were recovered by the SNP array, showing an overall better success rate of the SNP array than WGS on these 579K SNPs. These results suggested the genotyping accuracy of the NingXin-I SNP array in different large yellow croaker populations.Larimichthys polyactis), 10 big head croaker (Collichthys lucidus), eight brown croaker (Miichthys miiuy), nine yellow drum , and eight dusky roncador . The percentage of working SNPs  was 95.86, 95.01, 89.32, 88.82, 87.72, and 87.45% in large yellow croaker, little yellow croaker, big head croaker, brown croaker, yellow drum, and dusky roncador, respectively   . About 2 edulis) . A total edulis) . SNP num edulis) . A simil species . With 60 species ; 64.5 an species  and 57K  species  rainbow  species  and 65K  species  Nile til species ; 74.6% p species ; 67.5% p species ; 70.6% p species ; 79.6% p species ; 74.06%  species ; and 74. species . The pol species , and the species  proved tGenotype imputation from very low to medium density has been proved to be a cost-effective tool for GS in Atlantic salmon breeding programs, and reducing SNP density had little impact on prediction accuracy until 5,000 SNPs . In the The first high-throughput genotyping SNP array NingXin-I for large yellow croaker was developed. After an evaluation with numerous samples of large yellow croakers from multiple populations, 83.38% of the SNPs were polymorphic sites. Besides, the array was also assessed in other closely related Sciaenidae species, and the polymorphic SNP rate ranged from 26.68 to 56.23%. These results suggested the stability, accuracy, and potential broad application of the NingXin-I array. This study indicates that the NingXin-I SNP array will be valuable for genetic studies and breeding in large yellow croaker and related species.PRJNA629649. The genotype of large yellow croaker and code for SNP selection are available at FigShare (https://doi.org/10.6084/m9.figshare.12937079).The sequencing data is available at the NCBI under accession number: The animal study was reviewed and approved by the Laboratory Animal Management and Ethics Committee, College of Ocean and Earth Sciences, Xiamen University. All experimental procedures involving large yellow croaker and the other fishes were performed according to the Regulations for the Administration of Affairs Concerning Experimental Animals .PX and WZ conceived and supervised the study. TZ, BC, and PX designed, managed the experiments, and wrote the manuscript. QK, JZ, FP, YW, and LC conducted the experiment. ZZ, YB, YP, and JG performed the analysis and designed the charts and tables. All authors have read and approved the manuscript.QK, YP, and WZ were employed by the Ningde Fufa Fisheries Company Limited. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.