Spiteful behaviors occur when an actor harms its own fitness to inflict harm on the fitness of others. Several papers have predicted that spite can be favored in sufficiently small populations, even when the harming behavior is directed indiscriminately at others. However, it is not clear that truly spiteful behavior could be favored without the harm being directed at a subset of social partners with relatively low genetic similarity to the actor . Using mathematical models, we show that (1) the evolution of spite requires kin discrimination; (2) previous models suggesting indiscriminate spite involve scenarios where the actor gains a direct feedback benefit from harming others, and so the harming is selfish rather than spiteful; (3) extreme selfishness can be favored in small populations  because this is where the direct feedback benefit of harming is greatest. Spite is the hardest type of social trait to explain because it involves an individual harming itself (reducing its own Darwinian fitness) to inflict harm on others. It has always been thought that spite should be rare because organisms will usually harm others for some feedback benefit for themselves or their offspring —in other words, most harming traits are selfish rather than spiteful. It has been argued that truly spiteful harming can be favored if it is directed specifically at less‐genetically related group members (nonkin) and ultimately benefits more‐related group members (kin). However, there is also a persistent idea that spite directed indiscriminately at others could evolve in sufficiently small populations. For example, some have predicted that animals should hold “super‐territories” to spitefully exclude others from resources. Using mathematical models, we show that (1) the evolution of spite requires kin discrimination; (2) previous models suggesting indiscriminate spite involve scenarios where the harming individual gains a direct feedback benefit, and so the harming is selfish rather than spiteful; (3) extreme selfishness, like holding super‐territories, can be favored in small populations  because this is where the feedback benefit of harming is greatest. Overall, we examine how to model natural selection acting on harming traits in order to distinguish between selfishness and spite.C + RB > 0, spite represents the case where there is a fitness cost to the actor (positive C) and a fitness cost to the harmed recipient (negative B), which can only be favored if the genetic relatedness term, R, is negative. Understanding the meaning of negative relatedness is therefore crucial for explaining how and why spite evolves.Spite is the hardest type of social trait to explain. Spiteful behavior reduces the lifetime fitness of both the recipient and the performer (actor) of that behavior Hamilton . In termIt has been argued that the evolution of spite requires kin discrimination, allowing the actor to direct harm toward a subset of individuals with whom they share relatively low genetic similarity Wilson . SpecifiHowever, a number of theoretical studies have suggested the possibility for indiscriminate spite. Hamilton  originalB < 0) but provide a direct fitness benefit to the actor, because they reduce competition for the actor or its offspring. Consequently, the traits are selfish (–C > 0) rather than spiteful (–C < 0)  and on two categories of recipients: the harmed primary recipients and the unharmed secondary recipients who benefit from reduced competition . We define an individual's fitness as its number of offspring that survive to adulthood (not simply the number of offspring produced), which is consistent with other definitions used for classifying social traits −C+B Queller  in which Queller , male fi Queller , or bact Queller .Q1 and Q2 are probabilities of sharing identical genes between the focal actor and a random individual from the primary and secondary recipients, respectively. We note that the fitness effects in equation Ri=Qi−Q¯1−Q¯, where Q¯ is the average genetic similarity in the entire population, including the actor; Hamilton To predict the direction of natural selection acting on the harming trait, we considered the fate of a mutant harming allele in a population of individuals with a fixed, resident genotype. The success of the mutant allele depends on its “inclusive fitness effect” Hamilton : the sumC and RB, respectively), making it appropriate for classifying harming traits as selfish (–C > 0) or spiteful (–C < 0). We therefore propose that the first partitioning presented below—which may at first seem unconventional—is best for the purpose of classifying harming traits. In contrast, the second partitioning—which may be seen as the more conventional approach—actually obscures the separation of direct and indirect fitness effects, making it inappropriate for classifying harming traits.In the following sections, we examine two different ways of defining the category of secondary recipients and therefore partitioning the fitness effects of harming. Both methods correctly predict the direction of selection . In contrast, if the actor were harming others indiscriminately—for example, harming a random subset of a population or local economic neighbourhood—then its expected similarity to these primary recipients would be the same as to the set of potential secondary recipients (Q1 = Q2), and relatedness would be zero (R1 = 0). This implies that indiscriminate harming will be favored when it is a selfish trait with a positive direct fitness benefit (–C > 0).Equation b2 is now the benefit of reduced competition that may be experienced by all individuals in population (including the actor), and Q¯ is the probability of genetic identity between the focal actor and a random individual from the entire population (including itself). It follows that –c is not a total direct fitness effect because it excludes the secondary benefit of harming that feeds back to the focal actor . Eliminating the fitness effect on secondary recipients (using b2 = c – b1) shows that indiscriminate harming is favored whenN – 1) is the relatedness between actor and primary recipients, measured with respect to the entire population . This is the version of negative relatedness that has led to predictions of indiscriminate spite in small populations , it also incorporates the secondary fitness benefit of harming that feeds back to the focal actor. This can be made more explicit by deriving an analogue of Hamilton's rule from equation b1 = c – b2). For example, in a well‐mixed population of N individuals, indiscriminate harming is favored whenN is the relatedness between the actor and the entire population (including itself), measured with respect to primary recipients . The term (1/N)b2 accounts for the fraction of the secondary benefit (reduced competition) that feeds back to the focal actor, which gets larger as the actor makes up a larger fraction of the population (as N declines).However, although the term C > 0 or –C < 0), whereas spiteful behaviors are strictly costly to the actor (–C < 0). We showed that indiscriminate harming is always favored because it is beneficial to the actor—it has a positive effect on the actor's number of surviving offspring (–C > 0). Moreover, indiscriminate harming can be favored most in small populations  because this is where the focal actor can benefit most from the reduced competition that results from its harming behavior.Our key distinction here is that harming behaviors can be either beneficial or costly to the actor (–We next re‐examined the territory size model from Knowlton and Parker  and Parkd demes (assuming d > 1) and n individuals competing for territory in each deme . Individuals that secure a territory have offspring and then die before a fraction m of their offspring disperse independently to a random deme in the entire population. All individuals have a genetically determined strategy for the size of territory that they try to obtain. Taking over a larger territory has three effects: (1) it incurs a fecundity cost for the actor  with Knowlton  considerW, of a focal actor. This is a function of the focal actor's strategy, x ; the average strategy of the actor's deme mates (primary recipients), y; and the average strategy in all other demes (secondary recipients), z. We used this “neighbor‐modulated” fitness function to derive the inclusive fitness effect, by taking partial derivatives with respect to the strategies of the different categories of individuals (Taylor and Frank x = y = z). We derive expressions for Q1 and Q2 in the Appendix, and with these we determined the equilibrium of the model  by solving ΔWIF = 0. We also checked that the equilibrium is a convergence‐stable strategy, denoted z*, meaning that if the population is perturbed from the equilibrium then natural selection will push it back (dΔWIFdz|z=z^=z∗<0).We first assumed that the actor, primary recipients, and secondary recipients are mutually exclusive categories (as in eq. z* = 1/(aN), is identical to that originally predicted by Parker and Knowlton (Q1 ≥ Q2). Accordingly, the relatedness to primary recipients (measured relative to secondary recipients) is never negative (R1 ≥ 0), and so there is no indirect benefit of larger territory size. Moreover, when offspring dispersal is limited (m < 1) and deme mates are positively related (R1 > 0), there is no indirect benefit of smaller territory size (as a form of helping). This is because limited dispersal increases competition among offspring within the deme, which promotes harming and exactly cancels the effect of positive relatedness (Taylor a) and smaller population size . Specifically, the direct fitness effect at equilibrium (z = z*) ism < 1) or zero (when m = 1). In the case of full offspring dispersal (m = 1), the equilibrium is the point where the fecundity cost to the actor is exactly balanced by the feedback benefit experienced by its offspring (reduced competition for space in the next generation). As the population approaches this equilibrium, however, direct fitness is always positive (–C > 0), confirming that territory size evolves as a selfish trait , and all partial derivatives are evaluated at x = y = zp. As expected, solving for the equilibrium of equation z* = 1/(aN).We next assumed that the set of secondary recipients is the entire population, including the focal actor , the first term in equation m = 1 this feedback benefit should exactly balance the fecundity cost at equilibrium. Following equations N – 1])b1 or (1/N)b2 , which gives the expected result, aN/(N – 1). The partitioning in equation c + (–1/[N–1])b1 or –c + (1/N)b2, which could be misinterpreted as a direct fitness cost (–C < 0) and an indirect fitness benefit (RB > 0).This version of the model shows, however, how territory size could be misclassified as spiteful. For example, in a fully mixing population at the equilibrium (R1B1 > 0). Overall, we illustrate why indiscriminate harming traits are selfish rather than spiteful, and how to model harming traits to distinguish between selfishness and spite.We examined both an illustrative model of harming traits and a specific scenario for territory size. In both models, we found that (1) the evolution of spite requires kin discrimination, where the actor harms only a subset of other individuals (those with relatively low genetic similarity); (2) without kin discrimination, harming can be favored but only when there is a sufficient direct, feedback benefit to the actor (reduced competition for the actor or its offspring); (3) indiscriminate harming is more favored in small populations , where the direct feedback benefit to the actor is greatest; (4) previous studies have misclassified indiscriminate harming as spite, partly because they misinterpret the direct feedback benefit as an indirect (kin‐selected) benefit  and ensures that non‐zero relatedness will always be associated with an indirect fitness effect. For example, spiteful harming  requires that harm is directed at primary recipients to whom the actor is negatively related , resulting in a positive indirect fitness effect (R1B1 > 0) , and so harming can be favored as a selfish trait only .For the purposes of classifying harming traits, we found that it is easiest to treat the actor, primary recipients, and secondary recipients as separate categories. This makes it straightforward to separate the total direct and indirect fitness effects of harming  are considered secondary recipients, a fraction of the fitness effect on primary recipients (–1/[N – 1]B1) actually accounts for the direct feedback benefit of indiscriminate harming.We showed that misclassification of indiscriminate harming is due to an assumption that the secondary benefit of harming that returns to the focal actor (feedback benefit) is an indirect rather than direct benefit , would be classified as spiteful, despite the feedback benefit to the focal actor. We argue, however, that a classification based on total effects to the actor and primary recipients (–C and B1) is more useful  or mutually beneficial  . This is because harming primary recipients leads to reduced competition for all individuals in the population or group, and a focal actor receives a larger fraction of this secondary benefit when it makes up a larger fraction of the population or group. Indiscriminate harming can therefore be thought of as producing a type of public good for secondary recipients Tullock , analogoMelittobia parasitoids; Griffin and West So where can we expect to find the most extreme examples of selfish harming? As recognized by Hamilton , very smAssociate Editor: A. Gardner


Conversion of SNP chip assays into locus-specific KASP markers requires adapted strategies in polyploid species with high genome homeology. Procedures are exemplified by QTL-associated SNPs in hexaploid wheat.Kompetitive allele-specific PCR (KASP) markers are commonly used in marker-assisted commercial plant breeding due to their cost-effectiveness and throughput for high sample volumes. However, conversion of trait-linked SNP markers from array-based SNP detection technologies into KASP markers is particularly challenging in polyploid crop species, due to the presence of highly similar homeologous and paralogous genome sequences. We evaluated strategies and identified key requirements for successful conversion of Illumina Infinium assays from the wheat 90 K SNP array into robust locus-specific KASP markers. Numerous examples showed that commonly used software for semiautomated KASP primer design frequently fails to achieve locus-specificity of KASP assays in wheat. Instead, alignment of SNP probes with multiple reference genomes and Sanger sequencing of relevant genotypes, followed by visual KASP primer placement, was critical for locus-specificity. To identify KASP assays resulting in false calling of heterozygous individuals, validation of KASP assays using extended reference genotype sets including heterozygous genotypes is strongly advised for polyploid crop species. Applying this strategy, we developed highly reproducible, stable KASP assays that are predictive for root biomass QTL haplotypes from highly homoeologous wheat chromosome regions. Due to their locus-specificity, these assays predicted root biomass considerably better than the original trait-associated markers from the Illumina array.The online version of this article (10.1007/s00122-020-03608-x) contains supplementary material, which is available to authorized users. Today, marker-assisted selection (MAS) for accelerating breeding progress predominantly uses single nucleotide polymorphism (SNP) markers. Commercially available SNP detection technologies for crops include array-based hybridization approaches (predominantly Illumina Infinium arrays and Affymetrix GeneChip/Axiom arrays), PCR-based amplification approaches  or genotyping-by-sequencing approaches like SeqSNP (LGC Biosearch Technologies) or DArT-seq   (Supplementary Table S1). The primer mixture comprised 46 μl water, 30 μl common primer (100 μM), and 12 μl of each allele-specific primer (100 μM). KASP assays were performed in 384-well format by using the ViiA7 real-time PCR system . One KASP reaction volume consisted of a total volume of 5 μl including 2.5 μl DNA (15–25 ng/μl), 0.08 µl primer mixture, and 2.5 µl of KASP master mix with a low ROX level  or PACE-IR Genotyping Master Mix with a low ROX level . On each SNP reaction plate, at least one water sample was included as a no-template control. All experiments were repeated at least three times. The thermo-cycling conditions were chosen according to protocols provided by LGC Biosearch Technologies and 3CR bioscience . If clear genotyping clusters were not obtained, the plate was subjected to further thermal cycling and re-read until tight genotyping clusters were obtained. The recycle protocol comprised three cycles of denaturation at 94 °C for 20 s and annealing/elongation at 57 °C for 60 s. See also Supplementary File S1 for specific recommendations not addressed in the manufacturer’s manuals which we consider particularly important to design and run locus-specific KASP assays in polyploid species.® Hot Start Colorless Master Mix, , 1.5 µl of each primer (10 µM), and 3.5 µl (15–25 ng/μl) genomic DNA. PCR reactions were performed in a T100 Thermal Cycler  using the following program: primary denaturing at 94 °C for 4 min, followed by 35 cycles of 94 °C for 40 s, 64–60 °C annealing temperature for 40 s and 72 °C for 45 s, with a final extension step at 72 °C for 10 min. Agarose gel electrophoresis was used to separate fragments of PCR products; then, single strong bands of correct size were sequenced from one or both directions using the same forward and reverse primers used for PCR amplification by Microsynth AG using the Sanger sequencing method.Locus-specific primers were designed to sequence the target region on each of three homeologous wheat subgenomes, using the program Primer3 v0.4.0 . BLAST hits which high similarity  were maintained. Sequence similarities were calculated using the Sequence Identity and Similarity tool SIAS (http://imed.med.ucm.es/Tools/sias.html). Based on these multiple alignments, KASP primers were visually placed to unique binding sites. Alternatively, the online tool PolyMarker which uses the IWGSC wheat reference was used ].The obtained hits were aligned with the multiple sequence alignment tool MUSCLE  and used for SNP chip data clustering and SNP calling from raw data. In addition, we used pre-processed SNP calls processed by the service provider TraitGenetics .The Genotyping Module and Polyploid Genotyping Module of the software package GenomeStudio 2.0.4 was downloaded from the Illumina webpage  from 5.6% to 12.5% for haploblock Hap-5B-RDMa and from 2.6 to 9.5% for haploblock Hap-5B-RDMb  and from nine to four haplotypes within haploblock Hap-5B-RDMb  , genotype data for Illumina wheat Infinium array SNP calls are reported either as AA/AB/BB allele patterns (representing cluster positions) or as predicted nucleotide allele patterns  . A summary of the data and the derived SNP alleles associated with high root dry biomass on the respective DNA strands are presented in Table Conversion of SNP chip markers used in GWAS and biparental QTL mapping into breeder-friendly marker systems requires determination of the nucleotide allele identity associated with the increase/decrease in the trait of interest, sourced either from published studies or from SNP chip databases. However, allele identification is not always simple due to the use of different formats applied for reporting the SNP and allele identities. For example, in the CropSNP database . An example is shown in Supplementary File S2 (example 2). BLASTn analysis of the 101 bp SNP flanking sequences against the wheat reference genome IWGSC RefSeq v1.0 cv. Chinese Spring confirmed that 5 out of 9 SNP-containing sequences in haploblock Hap-5B-RDMa and 0 out of 6 SNP-containing sequences in Hap-5B-RDMb were interrupted by introns  in haploblock Hap-5B-RDMa could be successfully converted into KASP assays producing the expected clusters when applying the second approach (Supplementary Table S4). From the 7 remaining KASP assays designed for haploblock Hap-5B-RDMa using this approach, 5 produced clearly separated clusters. However, although they were able as expected to call all 213 homozygous reference samples, they failed to call heterozygous samples correctly. To explore why the seven KASP assays designed using the 2nd approach for haploblock Hap-5B-RDMa failed to call genotypes correctly, 5B subgenome-specific primer pairs for Sanger sequencing of the SNP flanking regions (300–650 bp) were designed using the Chinese Spring reference genome and software package Primer3. These primers were used for PCR amplification from 40 lines harboring haplotypes Hap-5B-RDMa-h1 and -h2 (Table To explore why Sanger sequencing failed for genotypes exhibiting haplotype Hap-5B-RDMa-h2 seven subgenome-specific primer pairs were redesigned flanking the SNP using scaffold sequences from accession Paragon (Earlham Inst. v1) which also harbors haplotype 5B-RDMa-h2. PCR amplification products were obtained for all genotypes exhibiting haplotype Hap-5B-RDMa-h2 Table . Based oWhen testing initially designed KASP primer combinations with a set of 213 reference genotypes with known allele composition, 5 out of 9 assays for haploblock Hap-5B-RDMa and 3 out of 6 for haploblock Hap-5B-RDMb produced unexpected KASP clusters. Sanger sequencing of a set of 80 affected genotypes revealed different reasons that some genotypes were assigned to the wrong clusters . To test these assays for use in a breeding program, extensive validation in breeding material was applied to evaluate the robustness and limitations of the assays. The KASP assays were evaluated in a backcrossing program and applied for marker-assisted selection for increased root dry biomass. Of the 11 KASP assays, 3 were predicted to be sufficient to distinguish the haplotype combination Hap-5B-RDMa-h2 and Hap-5B-RMDb-h3 associated with high root biomass from other haplotype combinations associated with low root biomass . In the backcrossing program, two parents with high root biomass originating from China, 2 parents from the Netherlands with low root biomass and 4 other parents representing adapted cultivars from Australia were used. In total, approximately 400 offspring were tested with the 3 selected KASP assays. Two of the assays produced stable and robust data . However, one KASP assay, HapA6-1, produced unexpected clustering results in the F1 and BC1F1 generations, revealing a homozygous cluster including a high number of genotypes with high root biomass in F1 and BC1F1 Fig. b. Sangerhttps://www.biosearchtech.com/support/faqs/kasp-genotyping-assays/what-are-the-typical-call-rates-for-kasp-genotyping). However, call rates alone are of limited use to judge marker quality. This can be seen in our study, where although the call rates were 100% for both SNP technologies, the allele calls using these two different technologies were conflicting for 1.1% of data points, indicating that technical accuracy of SNP calling varies between the two methods. Thus, we suggest adding the criterion ‘calling accuracy,’ or ‘accuracy based on independent assessment,’ to the set of technical metrics of Platten et al. . In addition, exclusion of incorrectly predicted ‘Null’ alleles will lower the number of required KASP markers necessary to distinguish genotypes carrying favorable combinations of haplotype variants for application in MAS.Compared to diploid species, the highly similar structure of homeologous subgenomes in relatively recent polyploid crop species like hexaploid bread wheat is expected to limit the development of broadly applicable locus-specific SNP assays. However, although many reports exist on the validation of SNP markers in different polyploid crops, very different criteria have been used for judging the quality of markers and these are mostly insufficiently defined. Also, the comprehensiveness of technical validation and terms used to describe the quality of markers can vary substantially. Platten et al.  suggesten et al.  for judgWe found that the percentage of KASP markers giving a clearly scorable and accurate result with known genotypes can vary substantially when using different approaches to design KASP primers. The simplest approach to design KASP markers from SNP chip data using only 101 bp SNP flanking sequences revealed a low conversion rate of 20%, and therefore is not recommended for polyploid species. This approach is also applied when using the commercial LGC KASP-by-Design service. Furthermore, the second approach, using a single genome reference for alignment of SNP flanking regions before primer design, is not recommended as it increased the success rate for valid KASP marker design for one haploblock, Hap-5B-RDMa, to only 22%. The reason for this low success rate was identified by Sanger sequencing of a large number of genotypes with different haplotype composition to be a high sequence similarity of haploblock Hap-5B-RDMa on chromosome 5B to homeologous and paralogous genomic regions on chromosomes 5A and 5D and polymorphism between genotypes in the targeted genome region. This indicates that the wheat reference genome IWGSC v1.0 is sufficient to cover the most common genome composition for some regions, e.g., for haploblock Hap-5B-RDMb in the wheat gene pool, but is not sufficient to cover the diversity in other regions, e.g., haploblock Hap-5B-RDMa of the wheat gene pool. Thus, it is highly recommended for polyploid species to apply our third tested approach for KASP marker conversion from SNP chip data using a reference genome plus multiple genome assemblies (and if necessary Sanger sequencing from selected genotypes of the population) for alignment of SNP flanking regions and placement of KASP primers to unique binding sites.Following multiple genome alignments and identification of unique sequence regions flanking a SNP of interest, KASP primer design might be performed with a primer design program, like Primer3, using the target region as input. However, Primer3 and other primer design programs often failed to design appropriate primers at the intended unique genome positions, because they use a short single input sequence even when masking common regions. Such programs are generally optimized to base primer selection primarily on restrictive thermodynamic calculations, predictions of self-complementarity and other parameters rather than positional restrictions. In contrast, many primer pairs which are excluded by these primer design programs worked perfectly under laboratory conditions in KASP assays after visual design and exclusive selection for unique primer binding sites . However, quality validation for SNP and other markers especially with respect to accuracy is very rarely reported in the literature. A common problem we identified when converting SNP chip into KASP assays in hexaploid wheat was the production of unexpected clusters with the reference genotypes. We also realized that within 9 out of 15 cases where KASP markers produced unexpected clusters, the original SNP probes from the array also created segregation clusters typical for hemi-SNPs indicated by homo- and heterozygous cluster positions in allelic color discrimination plots for homozygous genotypes. Hemi-SNP segregation patterns in chip genotyping do not limit the use of this data for locus-specific genetic mapping in homozygous mapping populations. In this case, the raw data can be processed by calling the inter-varietal polymorphism  and/or removing the ambiguous SNP calls known from genetic mapping studies and reference genome alignment . However, applying SNP chip genotyping data from SNP probes creating hemi-SNP type segregation patterns cannot be applied for heterozygous genotypes, as differences in varietal variation cannot be distinguished from homeologous variation. This prevents the use of this SNP type for MAS. For this reason, researchers usually try to avoid using hemi-SNPs for KASP marker conversion for breeding. Instead, single-copy regions of the wheat genome can be addressed by using only markers with unique genome positions, which avoids difficulties in distinguishing between heterozygous and homozygous genotypes . SNP detection by both technologies is based on the same principle, the hybridization by complementary base pairing and signal intensity capture of fluorescent probes. Hybridization specificity on Illumina arrays is based on 50 bp probes, whereas on Affymetrix arrays it is based on one or two 20 bp probes and thus the Affymetrix Axiom technology is more tolerant to any additional variants present in the probe sequence compared to Illumina Infinium technology . Sanger sequencing of primer target sites for these assays suggested that preferential amplification of one allele over the other in KASP assays is highly sensitive to incomplete binding of one of the allele-specific primers, due to polymorphisms (including deletions) between genotypes in primer binding sites or due to size difference between the two allelic PCR products KASP assay recommendations for polyploid species (PDF 121 kb)Examples for stepwise conversion of SNPs from the 90k Illumina wheat SNP array into locus-specific KASP assays (PDF 385 kb)90k SNP chip discrimination plots for SNP probes located in haploblocks Hap-5B-RDMa and Hap-5B-RDMb for 213 genotypes of a wheat diversity panel (PDF 3639 kb)KASP discrimination plots for KASP assays located in haploblocks Hap-5B-RDMa and Hap-5B-RDMb for 213 genotypes of a wheat diversity panel (PDF 193 kb)Below is the link to the electronic supplementary material.


Sequencing data enable the detection of causal loci or single nucleotide polymorphisms (SNPs) highly linked to causal loci to improve genomic prediction. However, until now, studies on integrating such SNPs using a single-step genomic best linear unbiased prediction (ssGBLUP) model are scarce. We investigated the integration of sequencing SNPs selected by association (1262 SNPs) and bioinformatics (2359 SNPs) analyses into the currently used 54K-SNP chip, using three ssGBLUP models which make different assumptions on the distribution of SNP effects: a basic ssGBLUP model, a so-called featured ssGBLUP (ssFGBLUP) model that considered selected sequencing SNPs as a feature genetic component, and a weighted ssGBLUP (ssWGBLUP) model in which the genomic relationship matrix was weighted by the SNP variances estimated from a Bayesian whole-genome regression model, with every 1, 30, or 100 adjacent SNPs within a chromosome region sharing the same variance. We used data on milk production and female fertility in Danish Jersey. In total, 15,823 genotyped and 528,981 non-genotyped females born between 1990 and 2013 were used as reference population and 7415 genotyped females and 33,040 non-genotyped females born between 2014 and 2016 were used as validation population.With basic ssGBLUP, integrating SNPs selected from sequencing data improved prediction reliabilities for milk and protein yields, but resulted in limited or no improvement for fat yield and female fertility. Model performances depended on the SNP set used. When using ssWGBLUP with the 54K SNPs, reliabilities for milk and protein yields improved by 0.028 for genotyped animals and by 0.006 for non-genotyped animals compared with ssGBLUP. However, with the SNP set that included SNPs selected from sequencing data, no statistically significant difference in prediction reliability was observed between the three ssGBLUP models.In summary, when using 54K SNPs, a ssWGBLUP model with a common weight on the SNPs in a given region is a feasible approach for single-trait genetic evaluation. Integrating relevant SNPs selected from sequencing data into the standard SNP chip can improve the reliability of genomic prediction. Based on such SNP data, a basic ssGBLUP model was suggested since no significant improvement was observed from using alternative models such as ssWGBLUP and ssFGBLUP. A large number of causal loci or single nucleotide polymorphisms (SNPs) highly linked to causal loci have been discovered from sequencing data through association analyses , 2 and bOne of the critical factors that impacts the performance of genomic prediction is the statistical model used. The GBLUP and ssGBLUP models assume that all SNPs have effects, which are drawn from a normal distribution assuming they all have the same variance –12, wherG matrix . Many efu et al.  comparedu et al.  as the wu et al.  comparedIntegrating SNPs selected from sequencing data into the currently used 54K-SNP chip has the potential to include causal loci or SNPs in high linkage disequilibrium with causal loci. Therefore, a model that allows the inclusion of some SNPs with larger effects would be closer to the real distribution of SNP effects. For protein content in French dairy goats, which is a quantitative trait influenced by a major gene (explaining 40% of the genetic variance), the increase in accuracy was up to 6 percentage points from using a weighted ssGBLUP model compared to a basic ssGBLUP model when integrating the genotypes of the major gene into the genotype data of the 54K-SNP chip . For staOur primary objective was to investigate the effects of integrating SNPs selected from sequencing data in genomic prediction of Danish Jersey cattle using ssGBLUP models. In addition, we compared a basic ssGBLUP model with alternative weighted/featured ssGBLUP models. Furthermore, for the ssWGBLUP model, we investigated the optimal region size for selecting region-specific weights.In this study, we used two sets of SNPs selected from sequencing data included in the EuroGenomics customized Illumina Bovine low-density chip  . One SNPIn total, 5480 Danish Jersey bulls, 1161 US Jersey bulls, and 48,039 Danish Jersey cows were genotyped with versions 1, 2, or 3 of the Illumina Bovine SNP50 chip , the standard Bovine low-density Chip  , or the In order to investigate traits with various genetic architectures, three milk production traits including milk, fat, and protein yields and three female fertility traits including the interval from first to last insemination in heifers (IFLh) and cows (IFLc), and the interval from calving to first insemination (ICF) were analysed. Phenotypic data were provided by Nordic Cattle Genetic Evaluation . For milk, fat, and protein yields, corrected 305-day yields , which wBreeding values were predicted by using single-trait models, including a pedigree-based BLUP (PBLUP) model, a basic single-step genomic BLUP (ssGBLUP) model, a so-called featured ssGBLUP (ssFGBLUP) model, and a weighted ssGBLUP (ssWGBLUP) model.In the PBLUP model, the relationship matrix for the mixed model equation  was consThe model for IFLh is:and the model for milk, fat, and protein yields, IFLc, and ICF is:software .In the ssGBLUP model, the relationship matrix for the mixed model equation was constructed using both pedigree (without genetic group) and genomic information. The pedigree used for the ssGBLUP model was exactly the same as that used for the PBLUP model. The effects in the ssGBLUP model are also exactly the same as those in the above PBLUP model, but this model assumes ucted as , 11:\docbased on . The \doescribed .\documenFurthermore, mentclasspt{minimasoftware , but becmentclass2pt{minimIn the ssFGBLUP model, two The ssFGBLUP model for IFLh is:and the ssFGBLUP model for milk, fat, and protein yields, IFLc, and ICF is:software , but becsoftware  cannot ementclass2pt{minimmentclass2pt{minimIn the ssWGBLUP model, the genomic relationship matrix was weighted by the SNP variances estimated from a Bayesian whole-genome regression model, which used genotyped animals in the reference population. Here, four SNP sets were used to construct the relationship matrices: (i) 54K, (ii) 54K + DFS, (iii) 54K + FRA, or (iv) 54K + DFS + FRA. The effects of the ssWGBLUP model were exactly the same as those in the above ssGBLUP model, but the BayesN0) , where eith genomic region; The BayesN0 model used for all traits is:software . Each ofsoftware . The presoftware . The varmentclass2pt{minimFor the prediction of breeding values, 15,823 genotyped and 528,981 non-genotyped females born between 1990 and 2013 were used as the reference population. To avoid close relationships between reference and validation populations, 7415 genotyped females and 33,040 non-genotyped females born between 2014 and 2016 that did not have any sib or daughter born before 2014 were used as the validation population. For each trait, the numbers of genotyped and non-genotyped animals in reference and validation populations are in Table With the BayesN0_WG model   and protein yield , a small improvement for fat yield , but no improvement for female fertility traits. For milk and protein yields, the different SNP sets including SNPs selected from sequencing data had similar performances in terms of reliability. For example, for genotyped animals, the improvements of reliabilities from integrating SNPs selected from sequencing data ranged from 0.026 with the 54K + FRA SNP set to 0.032 with the 54K + DFS + FRA SNP set.When using the 54K SNP sets, different single-step genomic prediction models yielded significant differences in reliabilities for milk and protein yields, but no significant difference for fat yield and female fertility traits. When using the 54K + DFS + FRA SNP set, no significant difference in reliability was observed between all the ssGBLUP models for both genotyped and non-genotyped animals, except for fat and protein yields for which the ssWGBLUP models outperformed the basic ssGBLUP model. The average reliabilities over all traits and all SNP sets for the ssWGBLUP models with a region size of 1, 30, 100 SNPs, or considering the whole genome as one region were equal to 0.361, 0.363, 0.360, and 0.358 for genotyped animals, and 0.149, 0.148, 0.147, and 0.148 for non-genotyped animals. Although the differences were very small between region sizes, a region size of 30 SNPs was generally optimal for the ssWGBLUP models, which was consistent with the predictions using a BayesN0 model with different region sizes. For the ssFGBLUP models, the three SNP sets that included SNPs selected from sequencing data were used and compared to the basic ssGBLUP model, no improvement in prediction reliability was observed except for milk production traits with the 54K + DFS SNP set, but this improvement was not significant.Regression coefficients from the Bayesian models for genotyped animals are in Table S4  gene is already well-accounted for by the 54K-SNP chip (ARS-BFGL-NGS-4939 is the top SNP for fat yield in the 54K-SNP chip and is located within the DGAT1 gene). For milk production traits, the magnitude of improvement in reliability from integrating SNPs selected from sequencing data was smaller in our study than that reported in [Integrating SNPs selected from sequencing data improved reliabilities for milk production traits, which have heritabilities of about 0.37 but not for female fertility traits, which have heritabilities lower than 0.05. This is in line with a previous study for genotyped Danish Jersey animals ). Since orted in . In addiThe strategy used to select SNPs from sequencing data can highly impact their role in genomic prediction. The DFS and FRA SNPs were selected by association analyses and bioinformatics analyses , 20. TheThe performance in terms of genomic prediction models depends on the assumptions made about the effects of SNPs, the genetic architecture of the trait, and the SNP set used. In our study, when we used the 54K SNP set, the ssWGBLUP model outperformed the ssGBLUP model for milk and protein yields but not for fat yield and three female fertility traits. This is in line with a previous study that also used the 54K SNP set on genotyped Danish Jersey animals, which showed that a Bayesian R model  outperfodatasets . Comparedatasets  that useWhen SNPs selected from sequencing data were integrated in the SNP set, the reliability reached by the best ssWGBLUP model was slightly higher than that from the basic ssGBLUP model for milk and protein yields, but the difference was not statistically significant. In other words, adding SNPs selected from sequencing data to the 54K-SNP chip increased prediction reliability, but no further improvement was obtained by using the weighting strategy. This result does not agree with that of the simulation study by , which s10 [In the ssWGBLUP models, we used the posterior SNP variances estimated from the BayesN0 models to weight SNPs in the construction of the ariances , 42–44, ariances , 41, 45,-values) , 41, 45.-values)  proved tulations  and analulations .In our study, the weights on SNPs within each region were generated from region-specific variances, which were estimated by a BayesN0 model  that proSimilar to the ssWGBLUP model, the ssFGBLUP model, which we introduce for the first time here, was allowed to place more emphasis on the SNPs selected from sequencing data than on the SNPs of the 54K-SNP chip. One difference between the ssFGBLUP and ssWGBLUP models is that ssFGBLUP assumes that all the SNPs within a same genetic component share the same variance, whereas ssWGBLUP allows each SNP or each SNP region to have its own variance and therefore does not prioritize all SNPs selected from sequencing data. In our study, no significant difference was found between the reliabilities from ssFGBLUP and a basic ssGBLUP model. On the one hand, the basic ssGBLUP model automatically places more emphasis on the QTL regions because of the increased marker density in QTL regions due to the addition of SNPs selected from sequencing data, which were more likely to be causal SNPs or highly linked to causal SNPs. On the other hand, in the ssFGBLUP model, the proportions of variances accounted for by the two genetic components (54K SNPs vs. selected sequencing SNPs) were approximately derived from variance components estimated using a FGBLUP model. Such an approximation may be not optimal, because FGBLUP uses Regarding regression coefficients, with the same model, there was little difference among different SNP sets for both genotyped and non-genotyped animals. This was consistent with the finding from a previous study on stature in US Holsteins, where no difference in regression coefficients was observed between 54K and 54K plus 16,648 selected sequencing SNPs for both basic ssGBLUP and ssWGBLUP (with weights derived from squared SNP effects) models . In our In summary, we show that when using the genotype data of the 54K-SNP chip, a ssWGBLUP model with a common weight on the SNPs within a specific region (about 30 SNPs) can be a feasible approach for routine genomic evaluation. Integrating relevant SNPs selected from sequencing data by association and bioinformatics analyses into the standard SNP chip slightly improves genomic prediction reliability. With such a SNP set, we recommend the use of a basic ssGBLUP model since no significant improvement is observed from using alternative models such as ssWGBLUP and ssFGBLUP.Additional file 1: Table S1. Title: Variance components estimated from the pedigree-based BLUP (PBLUP) model. Description: The data provided variance components estimated from the PBLUP model. Table S2. Title: Variance components1 estimated from a featured genomic BLUP (FGBLUP) model, where the selected sequencing SNPs were considered as a feature component. Description: The data provided variance components estimated from the FGBLUP model. Table S3. Title: Reliabilities from Bayesian models for genotyped animals. Description: The data provided prediction reliabilities estimated from Bayesian whole-genome regression models using genotyped animals in the reference population. Table S4. Title: Regression coefficients of yield deviation (YD) on prediction from Bayesian models for genotyped animals. Description: The data provided regression coefficients of YD on prediction from Bayesian whole-genome regression models for genotyped animals in the reference population.


Dioscorea spp.) is a nutritional and medicinal staple tuber crop grown in the tropics and sub-tropics. Among the food yam species, water yam  is the most widely distributed and cultivated species worldwide. Tuber dry matter content (DMC) and oxidative browning (OxB) are important quality attributes that determine cultivar acceptability in water yam. This study used a single nucleotide polymorphism (SNP) assay from a diversity arrays technology (DArT) platform for a genome-wide association study (GWAS) of the two quality traits in a panel of 100 water yam clones grown in three environments. The marker–trait association analysis identified significant SNPs associated with tuber DMC on chromosomes 6 and 19 and with OxB on chromosome 5. The significant SNPs cumulatively explained 45.87 and 12.74% of the total phenotypic variation for the tuber DMC and OxB, respectively. Gene annotation for the significant SNP loci identified important genes associated in the process of the proteolytic modification of carbohydrates in the dry matter accumulation pathway as well as fatty acid β-oxidation in peroxisome for enzymatic oxidation. Additional putative genes were also identified in the peak SNP sites for both tuber dry matter and enzymatic oxidation with unknown functions. The results of this study provide valuable insight for further dissection of the genetic architecture of tuber dry matter and enzymatic oxidation in water yam. They also highlight SNP variants and genes useful for genomics-informed selection decisions in the breeding process for improving food quality traits in water yam.Yam ( Dioscorea, comprising over 600 species [Dioscorea alata, also known by the common names ‘water yam’ or ‘greater yam’, is one of the most important food yams. It is the world’s most widely distributed cultivated yam, though it is not cultivated on the same magnitude as D. rotundata due to traditional bias that overlooks its nutritional and agronomic potential [Yams are herbaceous perennial vine plants in the genus  species ,2. They  species , yam is  species . Dioscorotential .End-use quality significantly influences the acceptance of yam varieties by farmers and consumers . In effeDioscorea rotundata (white yam) is preferred to water yam for “fufu” and “amala” due to its ease of dough formation when pounded. However, some collections and advanced breeding lines of water yam at the International Institute of Tropical Agriculture  have displayed the capacity to form good dough, similar to or even superior to that of some genotypes of D. rotundata [Boiled yam, pounded yam , and “amala” (prepared from cooking and stirring of the fermented yam flour—“elubo”) are the three leading food forms of yams in West and Central African regions ,12. In totundata . Additiootundata .This highlights the potential of water yam for food and nutritional security in West and Central Africa, particularly as it has a very wide adaptation, high genetic potential to produce fairly high yield under low to average soil fertility, early vigour for weed suppression, ease of propagation through the production of bulbils resulting in a high multiplication ratio, low post-harvest losses, good processing quality and high nutritive value ,14.D. alata remains a key objective in yam breeding programmes [Dioscorea species including D. alata (water yam) [Dioscorea spp. [An improvement in the food quality of ogrammes  and is cogrammes ,15. In ater yam) . The userea spp. .D. alata. The precision and speed of crop breeding have been improved by the evolution of genetic linkage and association mapping of the quantitative traits. This has been clearly demonstrated by Pétro et al. [D. alata are being rapidly developed [Dioscorea alata see release notes and assembly [The advancements in next-generation sequencing technologies have led to the rapid development of DNA-informed breeding techniques such as marker-assisted breeding and genomic selection through which many crops have recorded fast genetic gains. The increasing availability of molecular markers enables researchers to tag regions of the genome associated with specific phenotypes of interest in Quantitative trait locus (QTL) mapping and genome-wide association studies . The geno et al.  in watereveloped . These iassembly . This geTo elucidate the genetic factors for tuber dry matter content and oxidative browning, a diversity panel consisting of 100 water yam clones was genotyped by diversity arrays technology sequencing. While whole-genome sequencing provides the highest resolution, it still remains expensive for non-model species such as yams. A genome-wide association study was conducted to identify single nucleotide polymorphism loci or QTL regions and genes associated with tuber dry matter and oxidative browning. The SNP loci and associated candidate genes, when validated, would be a valuable resource for marker-assisted selection in the breeding process to develop new water yam varieties with acceptable end-user qualities.p < 0.05) for total genotypic and genotype × location effects. The variance estimate for the total genotypic value was higher than that of the genotype × location effects. Genotypic differences among the diversity panel for DMC and OxB were statistically significant (p < 0.05) in each of the three locations  was obtained between DMC and the OxB.We analysed phenotypic data from the three locations to estimate the variance for total genotypic value and genotype × location effect . The varocations . Mean DMD. alata chromosomes before and after filtering for missing data, allele frequency, and heterozygosity . The SNP calling pipeline yielded 22,140 highly polymorphic SNP markers, of which 18,067 were mapped onto the 20 D. alata chromosomes while 4073 were unmapped. Out of the mapped markers, 9687 SNPs that qualified the filtering criteria were used as input for GWAS analysis. The filtered SNPs were not proportionally distributed across the 20 chromosomes with highest counts of 923, 893, and 843 displayed on chromosomes 5, 4, and 19, respectively (ectively . The lowectively . Minor aectively . Geneticp < 0.001). In addition, 221 (3.10%) of the pairs were in complete LD (R2 = 1). Pearson’s correlation coefficients were negative (r = −0.035) between linkage disequilibrium (R2) and physical distance (bp), as well as between p-value and R2 (r = −0.40), showing the existence of linkage decay. Linkage disequilibrium (LD) decay differed across the chromosomes, ranging from 8289 bp for chromosome 1 to 58,562 bp for chromosome 11.Linkage disequilibrium analysis revealed 312,479 loci pairs within a physical distance that extends up to 998,066 bp. About, 6.04% (450) of the loci pairs were in significant linkage disequilibrium (LD)  toward the expected level for the dry matter , showing a relatively high correlation (2  < 0.6) (LD block heatmaps based on the LD of each identified SNP loci are shown in relation . For OxB  < 0.6) .D. alata panel warrant further analysis for the dissection of the genetic basis of variation for these two traits.Dry matter content and oxidation properties of yam tubers are very important quality traits that influence the rate of adoption of new clones for cultivation and consumption. Yam improvement efforts worldwide, especially in West Africa, have tested several clones within yam populations for DMC and none or minimal oxidation of fresh tuber using conventional selective breeding based on phenotypic records ,22,23, ap-values) toward the expected level for both traits on the quantile–quantile plots is a sign that the model successfully accounted for population structure and familial relationships in the GWAS analysis.Detailed knowledge of population structure and familial relationships (kinship) in the association panel is crucial to prevent sham associations in GWAS . PopulatThree QTLs were identified to be associated with DMC, 2 of which showed significant QTL-by-Environment interactions (QEI). In a study to identify QTLs associated with cassava brown streak disease, Kayondo et al.  reportedUsing the marker effect, we observed allele AA on chromosome 6 and allele TT on chromosome 19 to be responsible for high DMC in the diversity panel used in the study. Information on marker effect through the segregation pattern is fundamental for marker validation and deployment in a breeding programme ,33,34. WSolanum tuberosum) and wheat (Triticum aestivum), SnRK1 was reported to stimulate some enzymes in the starch biosynthesis pathways [Our study also identified putative candidate genes within the QTL regions of the targeted traits. A total of 26 putative candidate genes were detected upstream and downstream of the SNP associated with DMC, of which 3 genes  were reported to play important roles in DMC. Serine/threonine-protein kinase (SnRK1) was reported to participate in the process of starch and sugar biosynthesis in potatoes and stimulated glucose pyrophosphorylase ,36,37. Ipathways ,39. The pathways ,41. The pathways .Six putative candidate genes were identified within the QTL regions of the peak SNPs detected for OxB. Of these 6 genes, the Thioesterase domain (IPR006683) has been reported to play a major role in tuber oxidative browning pathway . The thiTuber dry matter had a negative correlation with OxB. This desirable correlation suggests that selection for high DMC is expected to reduce enzymatic browning following oxidation of the tuber flesh. The genotypic factors greatly contribute for trait linkages and the negative correlations between the two phenotypic traits may be an indication of pleiotropism, genetic coupling and/or linkage disequilibrium with population structure effects . HoweverD. alata.The functions and characteristics of some identified genes have not been explored. However, the identified genes from this study may provide new intuition into the genetic fundamentals of DMC and OxB in D. alata clones, made up of breeding lines and landraces, obtained from the Yam Breeding Unit of the International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria, were utilized for this study. The clones were planted at three locations in Nigeria in the 2018 growing season: Ibadan , Ubiaja  and Ikenne . A lattice design with two replications was used in each of the three locations.A total of 100 Healthy yam tubers were sampled in each replication for dry matter determination. After harvest, the fresh tubers of each clone were cleaned with water to remove soil particles. Thereafter, the tubers were peeled and sliced into small sizes for easy oven drying; 100 g of freshly grated tuber flesh sample was weighed, put into a Kraft paper bag and dried at 105 °C for 16 h. After drying to s constant weight, the weight of each samples was recorded, and the DMC was determined using the following formula:After harvest, one well-developed and mature representative tuber was sampled in each replication. The sampled tuber was peeled, cut and chipped with a hand chipper to get small thickness size pieces (5 cm and 0.5 mm thickness). A chromameter  was used to read the total colour of sampled pieces placed on a petri dish immediately after tubers cut and exposure to air (0 min) and 30 min after. The lightness (L*), red/green coordinate (a*) and yellow/blue coordinate (b*) parameters were recorded for each chroma meter reading for the determination of the total colour difference. A reference white porcelain tile was used to calibrate the chromameter before each determination .E*) between all the three coordinates was determined using the following formula [E* = the total colour difference, ΔL* = the difference in lightness and darkness  and Δb* = the difference in yellow and blue .The total colour difference (Δ formula :(2)ΔE*=[EF = the colour reader value at final time (30 min) and ΔEI = the initial colour reader value at 0 min.Oxidative browning was estimated from the total variation from the difference in the final and initial colour reading as:About one gram of fresh, healthy and young leaves was collected from a field-grown plant of each clone and placed on dry ice immediately. The leaf samples were lyophilized and kept at under room temperature. DNA was extracted from lyophilized leaf samples using the CTAB (cetyltrimethylammonium bromide) protocol  with sliijklY = phenotypic observation for a trait, µ = grand mean, E = environment effect (location), B(E) = replication effect nested in location, G = genotype effect, GE = genotype by environment interaction, e = random residual error.Phenotypic data obtained from the three locations were pooled and subjected to a linear mixed model analysis using the lme4 package implemented in R . The besDioscorea alata v2 reference genome assembly in preparation [Multiple sequences were generated by the DArTSeq platform using proprietary analytical pipelines  and mapped to the paration  (used wiparation . The SNPparation .X represents the SNP markers (fixed effect), Z represents the random kinship (co-ancestry) matrix, b is a vector representing the estimated SNP effects, u is a vector representing random additive genetic effects, and e is the vector for random residual errors. A co-ancestry matrix from ADMIXTURE and kinship were included as covariates in the GWASpoly to account for population structure and familial relatedness, respectively, to reduce spurious associations. Four different gene action models  and dominant reference (1-dom-ref)) were considered for the trait association study [p-value (p < 0.05) adjusted by the Bonferroni correction implemented in GwasPoly [A mixed linear model implemented in the GWASpoly package in R was used to compute associations using the mixed model =Xb+Zu+e , where yon study . The GWAGwasPoly .10) of the p-values against their expected p-values to fit the appropriateness of the GWAS model with the null hypothesis of no association and to determine how well the models accounted for population structure. The Manhattan plot was created for visualizing GWAS on the entire genome and zoom mapping was performed on a particular chromosome after identifying a significant SNP marker.Quantile–quantile (QQ) plots were generated by plotting the negative logarithms  was evaluated between the significant SNPs at 5 kb (downstream and upstream) using the LDheatmap library ; the genTuber dry matter and oxidative browning are key quality attributes that determine cultivar acceptability in water yam. The results of this study represent significant progress toward dissecting the genetic architecture of these two key traits in yam breeding. We reported three SNP markers associated with DMC and two SNP markers for OxB. Markers associated with these key traits showed significant phenotypic variation, which could be useful to breeders for marker-assisted selection (MAS) in the yam breeding program. This study provides the basis for a long-term collective exploration to determine valuable genes and alleles from the yam and consequently yam varieties’ improvement.


Plasmodium vivax parasites are the predominant cause of malaria outside of sub-Saharan Africa, they not always prioritised by elimination programmes. P. vivax is resilient and poses challenges through its ability to re-emerge from dormancy in the human liver. With observed growing drug-resistance and the increasing reports of life-threatening infections, new tools to inform elimination efforts are needed. In order to halt transmission, we need to better understand the dynamics of transmission, the movement of parasites, and the reservoirs of infection in order to design targeted interventions. The use of molecular genetics and epidemiology for tracking and studying malaria parasite populations has been applied successfully in P. falciparum species and here we sought to develop a molecular genetic tool for P. vivax. By assembling the largest set of P. vivax whole genome sequences (n = 433) spanning 17 countries, and applying a machine learning approach, we created a 71 SNP barcode with high predictive ability to identify geographic origin (91.4%). Further, due to the inclusion of markers for within population variability, the barcode may also distinguish local transmission networks. By using P. vivax data from a low-transmission setting in Malaysia, we demonstrate the potential ability to infer outbreak events. By characterising the barcoding SNP genotypes in P. vivax DNA sourced from UK travellers (n = 132) to ten malaria endemic countries predominantly not used in the barcode construction, we correctly predicted the geographic region of infection origin. Overall, the 71 SNP barcode outperforms previously published genotyping methods and when rolled-out within new portable platforms, is likely to be an invaluable tool for informing targeted interventions towards elimination of this resilient human malaria.Although  Plasmodium vivax is the most widespread parasite causing human malaria, with more than one-third of the world’s population being at risk of infection. P. vivax is resilient due to its dormant liver phase, and there are increasing reports of drug-resistance and life-threatening infections. Despite this, P. vivax malaria is not always prioritised by elimination programmes. New molecular tools are needed to inform elimination efforts, including through better understanding the geographical source and outbreaks of P. vivax, thereby leading to the halting of transmission and the targeting of reservoirs of infection. Our work describes a 71 genetic marker barcode for P. vivax that has high predictive ability to identify the geographic origin, and has the potential to distinguish local transmission networks. If the 71 genetic marker barcode is implemented within new portable molecular platforms, it is likely to be an invaluable tool for informing targeted interventions towards elimination of this resilient human malaria. Plasmodium vivax is the predominant cause of malaria outside of sub-Saharan Africa [P. falciparum malaria, but areas of co-endemicity have seen a corresponding rise in the proportion of P. vivax infections, which appear more resilient to control strategies [P. vivax has been observed in regions where malaria transmission had once been interrupted [P. vivax populations in regions with stable transmission [P. vivax epidemiological and surveillance investigations.n Africa ,2 and thn Africa –7. Elimirategies . In ordeerrupted , and in errupted  or placeerrupted . Furthersmission –16. Othesmission –20. The smission  but can P. falciparum [P. falciparum. A barcode based on 24 SNPs in the nuclear genome (“24-SNP barcode”) has been used to identify and track isolates from an endemic population in Senegal [Plasmodium populations and lead to low precision when estimating relatedness [P. falciparum has recommended the use of at least 200 barcoding SNPs for identity by decent (IBD) analysis in haploid eukaryotes [P. falciparum have demonstrated that SNP barcodes can potentially provide insights into the intensity of transmission, identify the geographical origin of the field isolates, and inform the dynamics of the diversity in a parasite population. These include outbreak identification, an event which has been shown to be more likely in low-transmission settings [Microsatellite markers have been used to reveal a spectrum of population structures in lciparum , as welllciparum . Comparelciparum . By levelciparum ,15,25,26 Senegal . This ba Senegal  using da Senegal , and has Senegal . This ap Senegal . Neverthatedness , therebyatedness . Anotheratedness . Simulatkaryotes . Overallsettings .P. vivax have been proposed, including one based on 42-SNP nuclear polymorphisms and another on mitochondrial genome markers, but both developed to ascertain the source of infection [P. vivax and other plasmodium parasites for inferring transmission and infection source has the potential to revolutionise global malaria surveillance. Here, using whole genome sequencing data for 433 P. vivax isolates across 17 countries, we applied machine learning and SNP tagging approaches from human genome-wide association studies (GWAS) to create a 71 SNP barcode with high predictive ability for geographic origin (91.4% accuracy) and the capability to infer transmission. We demonstrate that the barcode outperforms alternative approaches, including microsatellite genotyping, for global geographical profiling and inferring outbreak events within a low-transmission setting in Malaysia. Further, we validate the barcode by analysing the 71 SNPs in P. vivax DNA sourced from 132 recent UK travellers to East Africa, Asia and South America. Our work demonstrates that the 71 SNP barcode has the potential to be an invaluable tool to help elimination efforts of this resilient neglected Plasmodium species.SNP barcodes for nfection ,33. One nfection –16,34. Tnfection . Ultimathttp://genedb.org) reference genome, and identified 1,522,046 SNPs. Isolates with high levels of missing genotype calls (> 30%) and high multiplicity of Infection (> 20% heterozygous genotypes) were excluded from analysis. The final dataset was formed of 433 isolates (left), consistent with previous findings [We aligned raw sequence data from 867 isolates –16,34,36isolates , spanninisolates , left, cright), and for each we constructed neighbour-joining trees and correlated the pair-wise genome distance with the estimate based on all 720k SNPs (r2 correlation of 0.94 with the full 720k SNP set) compared to the subsets with lower MAF (Pearson’s r2 correlations of 0.25 and 0.27). A MAF cut-off (> 0.3) was determined (right), leading to a subset of 16,110 SNPs used as the starting point for barcode building for country classification.A principal component analysis (PCA) revealed that the isolates clustered broadly by regional groups: Southeast Asia , South Asian/East Asian/Southeast Asia (China), Americas  and Oceanian/Southeast Asia  and others located around the Arabian Sea . The gen20k SNPs . These ctermined , right, TAGster [r2 metric across windows of size 500 kbp. The genetic variability can be captured by tagging SNPs due to the strong LD structure found in P. vivax populations [top). The correlation between genome distances based on the 1,173 SNPs and whole genome sequencing (720k SNPs) was high (Pearson’s r2 = 0.96) (middle). Similarly, a Bland-Altman analysis revealed the genetic distances between isolates obtained using the subset of 1,173 tagging SNPs were similar to those based on whole genome SNPs (bottom), especially those with the highest MAF (see bottom right). We sought to show that the 1,173 SNPs could not only predict differences between geographical regions, but also estimate genetic distances within intra-border isolates and identify subclades in low transmission settings [r2 = 0.98) between distances based on the 1,173 tagging and 720k genome-wide SNPs. This analysis revealed that the 1,173 high frequency tagging SNPs can not only detect strong inter-border differentiation but potentially identify highly related isolates within the same country based on genomic distance.In order to further reduce number of markers used for barcoding construction, we applied the software TAGster  to identulations , and we  = 0.96) , middle.ome SNPs , bottom,settings . In partP. falciparum genome-wide analysis with success [B). The final model inside the training set performed with an overall out-of-bag error rate of 17.1%, where the main classification errors were found across Southeast Asian populations . These populations were identified as being highly related using the SNP-based genomic distance (right), supporting previous observations [B). A further 11 SNPs were chosen to summarise high between-country genetic differentiation based on the fixation index (FST > 0.7), leading to a final barcoding set of 71 SNPs , but some blocks of correlation are observed, which can be explained by an uneven geographic distribution of the isolates.Application of the random forest classification approach to the classification of country involved constructing 500 trees, partitioning the dataset randomly into 80% training (n = 346) and 20% for validation (n = 87), and using 34 variables at each nodal split. These default settings have been used previously in  success . Classifdistance , right, rvations . The ran 71 SNPs . Within  71 SNPs  (LD r2: top), and similar clustering patterns were observed to those using the genome-wide (720k) SNP set (r2 correlation (0.898), providing evidence for the potential of this barcode to not only identify geographical origin but also provide insights into the relatedness of intra-border isolates (middle). Similarly, a Bland-Altman analysis revealed little overall bias in the estimation of genetic distance between isolates using whole genome and the 71 barcoding SNPs . Using the 71 barcoding SNPs, there is a trend towards an underestimation of the genetic distance for closely related isolates, and an overestimation for distantly related ones. The potential use of the barcode to infer intra-border relatedness and provide insights into transmission dynamics, is supported by the observation that 98.2% (425/433) of the haplotypes obtained were unique in the dataset. Furthermore, the 71 SNPs were used to predict the geographical source of the 20% of the isolates (n = 87) not used to develop the random forest model. The model using only the 71 SNPs yielded an overall accuracy of 91.4% (8.6% out-of-bag error) in predicting the country source of these 87 isolates and a further 16 Brazilian newly sequenced isolates. The 71 SNPs outperformed a published 42-SNP barcode [top) and neighbour-joining tree (bottom). Furthermore, the correlation between genetic distances based on the SNP barcode and genome-wide (720k) SNPs was lower (Pearson’s r2 = 0.59).In order to assess the potential of the barcode for geographic classification, a PCA was generated using only the 71 SNPs , top, an SNP set . When coisolates , middle..2% 425/4 of the hing tree , bottom.P. vivax isolates, sourced from Sabah Malaysia, where the population dynamics have been extensively studied using microsatellite markers and whole genome sequencing [In silico characterization using the 71 SNP barcode in a PCA analysis identified two main populations, denoted previously as K1 and K2 (r2 = 0.88). The outbreak isolates shared the same haplotype across the barcoding polymorphisms in most cases, with only 3 isolates presenting a one SNP difference. There was one notable exception (ERR1475456) which presented a larger genetic distance , Bangladesh (n = 1), Eritrea (n = 11), Ethiopia (n = 6), Guyana (n = 3), India (n = 38), Pakistan (n = 35), the Philippines (n = 1), Sudan (n = 7) and Uganda (n = 4)). The genotypes were used to construct a combined PCA plot for the 132 prospective and 433 barcode-development isolates, which revealed clear clustering by geographic region , which allows greater scope for informing on intra-border haplotype diversity, including low diversity such as in an outbreak setting. A simulation study using P. falciparum genome sequencing and P. vivax microsatellite data has estimated that 200 biallelic markers should be used for IBD analysis [P. vivax we demonstrate the potential of a 71 SNP barcode to estimate genetic distance (SNP differences) as a measure of relatedness and to predict geographic origin to the country level. Further work could be aimed at assessing and expanding this set of markers and explore its potential use for IBD analysis specifically focusing on intra-border transmission. This potential utility for transmission characterisation was demonstrated by the use of intra-border highly related isolates, where we confirmed that the SNP distances based on the 71-SNP barcode and genome-wide 720k markers were highly correlated, and represent an improvement on comparative reported values for microsatellites (Pearson’s r2 = 0.70) [in silico using data from 60 P. vivax sourced from a low endemic and near-elimination setting in Malaysia, where it was possible to partition the population into highly structured subclades and confirm the presence of an outbreak cluster, previously identified using both microsatellite genotyping and sequencing. Also, by using the 71-SNP barcode we identified a misclassified “outbreak” isolate with an identical microsatellite haplotype, and evidence of regional clustering by the different administrative divisions, further supporting the utility of the tool and its superior performance to microsatellite genotyping. One of the challenges when studying P. vivax infections is the difficulty to differentiate recrudescent infections from reinfections or relapses [Our 71 SNP barcode is the first that shows such strong levels of accuracy in geographic prediction at a country level in analysis . In our  = 0.70) . Furtherrelapses . Althougrelapses . This isP. vivax endemic regions, revealing that the 71 SNPs have a strong regional discriminatory power, and achieved an 80.1% overall accuracy in classifying the isolates at a country level for the countries represented in the barcode development. The PCA performed using the 71 SNPs showed that isolates originating from geographically distant regions (e.g. South Asia) clustered, and demonstrated the ability of the barcode to identify regional signatures even for previously unstudied locations. Nevertheless, the next step in order to improve our barcode would be the incorporation of the whole genome sequencing data from these isolates and future collections. Further, it is possible to genotype the barcoding SNPs using standard TaqMan genotyping assays or PCR followed by high resolution melting analysis, as previously described for other malaria SNP barcodes [P. falciparum parasites [The barcode was also tested on 132 newly assessed isolates sourced from travelers to 10 different countries across the main barcodes , 33. It arasites .in-silico molecular barcode for P. vivax that can provide information on both geographical origin and identify highly related isolates within country borders to help infer transmission events and identify foci of infection. The 71 SNP barcode out-competes previous genotyping methods and is a powerful and potentially affordable solution that could enable the execution of large genomic epidemiological studies, with high throughput assessment of large numbers of parasites. By leveraging off growing and large datasets of whole genome sequencing data, and the power of machine learning algorithms, it will be possible to update the barcode, augment it with drug resistance markers, and implement it rapidly in field-based settings using portable technologies. Ultimately, insights into genetic diversity will assist the much-needed understanding of the dynamics of P. vivax populations and inform disease control decision making.In summary, we have presented a new P. vivax Genome Variation project. Each isolate data was mapped against the PvP01_v1 reference (obtained from http://genedb.org) using bwa-mem [samtools software suite [vir genes). The final dataset consisted of 720,340 high-quality SNPs and 433 isolates.Illumina sequenced data from previously published studies –16 was d bwa-mem . SNPs  was calculated using in house R scripts and a threshold of FST > 0.7 was used to determine population-level informative barcoding SNPs [The 720,340 high-quality biallelic SNPs and its subsets were used to infer distance matrices by calculating Manhattan distances (”identity-by-state”) between samples. These genetic distances where divided by the sum of the allele frequencies of the SNPs included in their calculation, in order to make the units comparable. These matrices were used to generate principal component analysis (PCA) plots and neighbour-joining trees (R  package ). The Pe TAGster  was useding SNPs ,34. A flmissForest package [RandomForest [LDcorSV package [The selected 1,173 highly informative SNPs obtained using a combination of minor allele frequency and SNP tagging approaches were extracted from the original dataset. Allele imputation was performed on the missing data points (4.2%) in the dataset using the R  package . This yiomForest  package  package  to calcuP. vivax PvP01_v1 reference genome using bwa-mem (see [P. vivax DNA.DNA was extracted from blood samples sourced from 132 returning travellers to the UK from endemic areas between 2017 and 2018. The 132 samples were sourced from travellers to Afghanistan (n = 26), Bangladesh (n = 1), Eritrea (n = 11), Ethiopia (n = 6), Guyana (n = 3), India (n = 38), Pakistan (n = 35), the Philippines (n = 1), Sudan (n = 7) and Uganda (n = 4). The blood samples are stored in the Public Health England (PHE) Malaria Reference Laboratory (MRL) at the LSHTM. The DNA underwent Illumina MiSeq 150bp paired-end sequencing at the LSHTM. The resulting data was aligned to the mem Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S1 Fig(TIFF)Click here for additional data file.S2 Fig(TIFF)Click here for additional data file.S3 FigThe red dashed line is the cut-off based on importance, which is the threshold used to determine SNP inclusion in the barcode.(TIFF)Click here for additional data file.S4 Figr2 = 0.15). LD blocks were observed and correspond to SNPs with geographic signal (i.e. Southeast Asian high frequency SNPs).An overall low correlation (LD) was found between the 71 SNPs (mean linkage (TIFF)Click here for additional data file.S5 FigP. vivax isolates. Geographical clustering by region was apparent, although a degree of overlap was observed and separation by country was not clear. This result is suggested by the low accuracy (77.5%) obtained when predicting geographical origin using a random forest model formed with the set of 42-SNPs.It shows ambiguous geographic clustering of (TIFF)Click here for additional data file.S6 FigThere is clustering by geographical region, including between Eastern Africa countries , South/Central Asia , Guyana (South America) and the Philippines.(TIFF)Click here for additional data file.S7 Fig(TIFF)Click here for additional data file.


Feed accounts for about 70% of the total cost of poultry meat production. Residual feed intake (RFI) has become the preferred measure of feed efficiency because it is phenotypically independent of growth rate and body weight. In this study, our aim was to estimate genetic parameters and identify quantitative trait loci (QTL) for feed efficiency in 3314 purebred broilers using a genome-wide association study. Broilers were genotyped using a custom 55 K single nucleotide polymorphism (SNP) array.NSUN3 and EPHA6 as candidate genes. The most significant SNP in this region, AX_172588157, accounted for 4.4% of the genetic variance of RFI. In addition, a genomic region containing the gene AGK on chromosome 1 was found to be associated with RFIa. The NSUN3 and AGK genes were found to be differentially expressed in breast muscle, thigh muscle, and abdominal fat between male broilers with high and low RFI.Estimates of genomic heritability for seven growth and feed efficiency traits, including body weight at 28 days of age (BW28), BW42, average daily feed intake (ADFI), RFI, and RFI adjusted for weight of abdominal fat (RFIa), ranged from 0.12 to 0.26. Eleven genome-wide significant SNPs and 15 suggestively significant SNPs were detected, of which 19 clustered around two genomic regions. A region on chromosome 16 (2.34–2.66 Mb) was associated with both BW28 and BW42, and the most significant SNP in this region, AX_101003762, accounted for 7.6% of the genetic variance of BW28. The other region, on chromosome 1 (91.27–92.43 Mb) was associated with RFI and ADFI, and contains the NSUN3, EPHA6, and AGK were identified as the most likely candidate genes for these QTL. These genes are involved in mitochondrial function and behavioral regulation. These results contribute to the identification of candidate genes and variants for growth and feed efficiency in poultry.We identified QTL regions for BW28 and BW42 (spanning 0.32 Mb) and RFI (spanning 1.16 Mb). The  Feed efficiency is the most important economic trait in poultry and livestock industries because up to 70% of the total production cost is due to feed . ImproveRFI-efficient animals tend to have leaner carcasses with lower subcutaneous fat thickness , 7. InclGallus gallus (GGA) chromosome 4 by de Koning et al. [https://www.animalgenome.org/cgi-bin/QTLdb/GG/index, accessed 07 Jan 2021). Among these, five QTL were identified for RFI in chickens, on GGA3 (51.8–64.9 Mb), GGA10 (19.1–20.3 Mb), GGA12 (16.3–17.3 Mb), GGA19 (0.0–2.0 Mb), and GGAZ (2.1–7.8 Mb) [Quantitative trait loci (QTL) for traits in animals have been studied for more than 20 years. In chickens, the first QTL associated with FCR and RFI were detected on g et al. . Compare–7.8 Mb) , 12.With the advent of high-density SNP genotyping arrays, genome-wide association studies (GWAS) have become a powerful tool for the detection of QTL in farm animals. GWAS for RFI have been performed in cattle –15, pigsCompared with cattle and pigs, generation of data on complex traits in a large population of chickens is relatively easy because of operational convenience, low unit cost of animals, short duration of the study, etc. The objectives of this study were to estimate genetic parameters, and identify significant loci and genes that affect feed efficiency traits in 3314 broilers, based on genotyping with a customized 55 K chicken single nucleotide polymorphism (SNP) array .The chickens used in this study were obtained from the pure line B of fast-growing chickens that has been selected for seven generations for increased body weight and growth rate traits by Foshan Gaoming Xinguang Agricultural and Animal Industrials Co., Ltd. . The statistics of selection pressure in male and female selection candidates for generations 2 to 7 are in Additional file 0.75). After slaughter at 42 days of age, abdominal fat was removed and weighed for each individual to obtain AbF. RFI was estimated as the difference between the observed and the predicted ADFI with (RFIa) or without accounting for AbF. RFI and RFIa were derived as the residuals from the following two models:The eight traits that were either measured or calculated consisted of two growth traits, five feed intake and efficiency traits, and AbF weight. Growth traits were body weight at 28 days (d) of age (BW28) and BW42. Feed intake and efficiency traits were average daily feed intake (ADFI), residual feed intake (RFI), residual feed intake adjusted for weight of abdominal fat (RFIa), average daily gain (ADG), and feed conversion ratio (FCR). Total feed intake for each broiler for the total test period was used to calculate ADFI. Average daily gain (ADG) was calculated from BW28 and BW42. FCR was obtained from total feed intake divided by total weight gain from 28 to 42 days of age. Metabolic weight at mid-test (MWT) was calculated for each bird as the average of BW28 and BW42 (MBW) raised to the power of 0.75 , which is designed based on the Gallus_gallus-5.0 assembly and includes 52,060 SNPs . The phyP < 0.01) based on Wald F statistics and were, therefore, included in the subsequent analyses. The following model with maternal genetic and maternal environmental effects was used:, with indices 1 for non-genotyped dams and 2 for genotyped animals, and Univariate and bivariate animal models were fitted by restricted maximum likelihood (REML), using the ASReml v4.1 software package . In prela et al. :\documenVanRaden . The samhttps://github.com/genetics-statistics/GEMMA/releases) [A GWAS was performed for each trait using a univariate linear mixed model implemented in GEMMA version 0.98.1 software (eleases) , one SNPM method [http://cran.r-project.org/package=qqman) in R (version 3.6.0). The genomic inflation factor (GIF) was calculated by the GenABEL R package [The Wald test was used as a criterion to identify SNPs that were significantly associated with the investigated traits. Genome-wide significance was assessed using the simpleM method . This me package . The per package  as: \docSignificant and adjacent SNPs for the analyzed traits were further investigated by region-based association analyses using the fast family-based sequence kernel association test (FFBSKAT) program (SNP effects fitted as random effects) implemented in the R package FREGAT . Region-ion 1.9) . BoxplotAdditive and dominance effects of SNPs on the analyzed traits were estimated using the ASReml v4.1 software package . To estiAllele frequencies at the most significant SNPs in three generations  were estimated using PLINK (version 1.9) .http://genome-asia.ucsc.edu/cgi-bin/hgGateway?hgsid=472768848_otkBtCHKhHMTV1xrxHuq737iivJ1). Expression of these candidate genes  was assessed in the relevant tissues using qPCR analyses. One hundred and seventy-five males from generation 6 with growth and feed efficiency between 28 and 41 d of age were slaughtered at 42 d of age to measure carcass traits. Tissue samples from birds with the highest (n = 15) and lowest (n = 15) RFI phenotypes were collected . First-strand cDNA was synthesized from 2 µg total RNA using the FastQuant RT Kit (with gDNAase) . Power SYBR® Fast qPCR Master Mix  was used to analyze mRNA expression of the selected genes. qPCR analysis was performed with an ABI Q7 Flex Real-time detection system . Specific primers [see Additional file 2O. The PCR cycle parameters were: 95 °C for 3 min, then 40 amplification cycles at 95 °C for 3 s and 60 °C for 34 s. The following housekeeping genes were used as reference genes: ribosomal protein L32 (RPL32) for breast muscle and thigh muscle [ubiquitin B (UBB) for liver tissue [glyceraldehyde-3-phosphate dehydrogenase (GAPDH) for AbF tissue [P < 0.05 (*) or P < 0.01 (**).Amplification was performed in a total volume of 10 µL, containing 5 µL of 2 × PCR Master Mix, 0.2 µL of 50 r tissue , and glyF tissue  was usedary, NC) . ThreshoThe descriptive statistics in Table Estimates of variance components and heritabilities using genomic and pedigree relationship matrices are in Table Estimates of genetic  and 3.81*10–5 , respectively.The number of independent tests was estimated to be 26,217. Based on this, genome-wide and suggestive significance thresholds were set equal to 1.91*10Manhattan and quantile–quantile (Q-Q) plots for BW28 and BW42 are shown in Fig. zinc finger protein 692 (ZNF692), tripartite motif containing 39.2 (TRIM39.2), and tripartite motif containing 27.2 (TRIM27.2).For BW28, seven genome-wide significant SNPs were identified on GGA16, and five suggestively significant SNPs were detected on GGA4 and 16. The genomic inflation factor (GIF) was 1.02 for BW28, which suggests that the population structure was well controlled. Eleven of the genome-wide significant SNPs were within a 316 kb region on GGA16 (2.34–2.66 Mb). The minor allele of the most significant SNP in this region, AX_101003762, had a negative effect estimate  and four SNPs were the same as those found for BW28. The leading SNP for BW42, AX_172583407, accounted for 5.0% of the genetic variance.Manhattan and quantile–quantile (Q-Q) plots for the three feed efficiency traits are shown in Fig. NOP2/Sun RNA methyltransferase family member 3 (NSUN3) gene and accounted for 3.4% of the genetic variance for ADFI.For ADFI, one SNP exceeded the suggestive significance threshold (GGA1: SNP AX_75546765 at 91.27 Mb). This SNP was located near the EPH receptor A6 (EPHA6), NSF attachment protein gamma (NAPG), and leucine zipper and CTNNBIP1 domain containing (LZIC) genes. The leading variant, AX_172588157, accounted for 4.4% of the genetic variance for RFI.For RFI, one genome-wide significant SNP and three suggestively significant SNPs were detected, two of which were located within a 457.9 kb region on GGA1 (91.97–92.43 Mb). This region was located about 700.6 kb downstream of the SNP AX_75546765 that was associated with ADFI. These SNPs were located within or near the acylglycerol kinase (AGK) gene. This SNP accounted for 6.3% of the genetic variance.For RFIa, one genome-wide suggestively significant SNP (GGA1: 57.28 Mb) was located in the first intron of the TRIM39.2 gene (P = 6.81e−10), and AX_172583407 for BW42, within the ZNF692 gene (P = 1.03e−8). LD analysis showed that the levels of LD were moderate for several haplotype blocks. For two regions on GGA1 (91.15–92.61 Mb and 56.78–57.82 Mb), AX_75546765, AX_172588157, and AX_172566874 were identified as leading SNPs in the vicinity of the genes NSUN3, EPHA6, and AGK, for ADFI, RFI, and RFIa, respectively. The two regions also included other genes, including TRIM27.2, receptor for activated C kinase 1 (RACK1), kinesin family member C1 (KIFC1), ADP ribosylation factor like GTPase 13B (ARL13B), syntaxin 19 (STX19), and transmembrane protein 178B (TMEM178B).The two region-based association plots for GGA16 and GGA1, which included multiple genes in the associated LD region, are shown in Figs. CC for AX_101003762 and TT for AX_172583407 showed significantly larger BW28, BW42, and ADFI than those that were homozygous TT and CC (P < 0.05), respectively. ADFI and RFI were significantly lower for individuals that were homozygous CC for AX_75546765 and CC for AX_172588157 than those that were homozygous AA and TT (P < 0.01), respectively. The homozygous AA individuals for AX_172566874 had lower RFIa, and higher BW42 than those with homozygous GG (P < 0.05). In addition, these five SNPs had significant additive effects for multiple traits (P < 0.05) and did not exhibit significant dominance effects  is shown in Table NSUN3, EPHA6, and AGK) within the QTL regions for ADFI, RFI, and RFIa were evaluated by qPCR analysis in males with high and low RFI phenotypes . In abdominal fat (AbF) tissue, the expressions of NSUN3 and AGK were significantly lower in HRFI than in LRFI individuals (P < 0.01). No significant differences between the HRFI and LRFI cohorts were found for the expressions of NSUN3 and AGK in liver. There was no detectable expression of EPHA6 in the liver, breast muscle, thigh muscle, or AbF tissues.Expression of the three nearest genes (The genomic heritability estimates for growth and feed efficiency traits were low to moderate 0.12 to 0.26) and lower than those reported by Bernon and Chambers  and Xu e to 0.26 Heritability estimates for BW28, BW42, ADFI, RFI, RFIa, ADG, and AbF based on the combined genomic-pedigree relationship matrix were lower than those based on the pedigree relationship matrix. Similar findings were previously reported for pigs  and dairThe estimate of the genetic correlation between RFI and RFIa was strong (0.83). Similar results for RFI and RFI adjusted for backfat thickness (RFIb) in young beef bulls have been reported by Schenkel et al. . The estSexual dimorphism, largely caused by differences in gene expression , has beeA 316.0-kb genomic region on GGA16 was associated with both BW28 and BW42. Previous studies have reported QTL on GGA16 for BW9, BW56, and BW84 in medium-growth broilers  and in ITRIM39.2 and ZNF692 genes, respectively. TRIM39.2 is a member of the RING-B-box-coiled-coil (RBCC)/tripartite motif (TRIM) subfamily of zinc finger proteins that are involved in many biological processes, including cell differentiation [GNIP1) is an isoform of GNIP/TRIM7 and a tripartite motif (TRIM) protein. In vivo overexpression of GNIP1 in mouse muscle activates the protein kinase B–glycogen synthase kinase-3–glycogen synthase cascade and subsequent glycogen synthesis in muscle, leading to decreased blood glucose levels, lactate levels and mouse body weight, without affecting whole-body insulin or glucose tolerance [ZNF652, a member of the zinc finger protein gene family is significantly associated with height [The SNPs, AX_101003762 and AX_172583407, were found to be within the ntiation . Glycogeolerance . ZNF692 olerance . In humah height . Shirai h height  suggeste–92.43 Mb) was associated with RFI and a suggestive significant SNP (GGA1: 91.27 Mb) was associated with ADFI. This finding indicates that the 1.16-Mb region (GGA1: 91.27–92.43 Mb) is an important QTL for feed efficiency. This region was previously identified as a feed efficiency QTL (GGA1: 90.35–123.03 Mb) in a meat-type X egg-type resource chicken population by Hansen et al. [NSUN3 and EPHA6.An important region on GGA1  family, which is closely related to mitochondrial function and is localized to the mitochondrial (mt) matrix, where it interacts with mt-tRNAMet to methylate cytosine 34 (C34) at the wobble position [NSUN3-knockout cells showed strongly decreased mt-tRNAMet methylation and formylation, as well as a reduction in mitochondrial translation, protein synthesis, and reduced oxygen consumption, leading to deficient mitochondrial activity [NSUN3 is significantly associated with anorexia nervosa in human. Common symptoms of anorexia nervosa are related to functional impairments of the mitochondrial oxidative phosphorylation system complex I, including poor feeding, neurodegeneration, and muscle weakness [The position . A studyactivity , 59. Recactivity  showed tweakness .EPHA6 knockout mice display behavioral deficits associated with learning and memory disabilities [EPHA6 was significantly associated with behavioral reactivity in cattle, which is a temperament trait. In humans, EPHA6 is strongly associated with blood pressure [EPHA6 plays key roles in a variety of biological functions, such as brain development and behavioral regulation. It is highly expressed in most regions of the adult mouse brain, including the hypothalamus [EPHA6 affects physical activity, and thus energy expenditure. bilities . Dos Sanbilities  found thpressure . In addipressure  reportedpressure . EPHA6 pthalamus , which pthalamus , 67.AGK gene. Previous studies found a region on GGA1 (57.0–58.0 Mb) to be significantly associated with FCR in a commercial broiler line and found another SNP, GGaluGA019865, located in the first intron of AGK, to be a leading SNP [AGK leads to the reduction of mitochondrial maximal respiration and tricarboxylic acid (TCA) cycle flux in cells [NSUN3 and AGK genes play an important role in the regulation of the mitochondrial function.Only one suggestively significant SNP (GGA1: 57.28 Mb) was identified for RFIa. This SNP was located in the first intron of the ding SNP . AGK, alding SNP , 70. AGKding SNP . PA actsding SNP , 73. On ding SNP , 75. Inain cells . In summNSUN3 and AGK in breast and thigh muscle of LRFI chickens may result from decreased mitochondrial energy metabolism, which is consistent with previous observations in the skeletal muscle of pigs from LRFI and HRFI lines [Mitochondrial function has previously been associated with feed efficiency in poultry and livestock, because mitochondria are responsible for producing approximately 90% of the energy of the cells . In our FI lines . It is pNSUN3, EPHA6, and AGK, are known to be involved in mitochondrial function and behavioral regulation. These results contribute to the identification of candidate genes and SNPs for feed efficiency traits in poultry.Heritability estimates for growth and feed efficiency traits in broilers from 28 to 41 days of age were low to moderate, ranging from 0.12 to 0.26. Narrow QTL regions with additive pleiotropic effects were associated with BW28 and BW42 (spanning 0.32 Mb) and RFI (spanning 1.16 Mb). The most likely candidate genes for these QTL, Additional file 1: Table S1. Statistics of selection pressure for males and females.Additional file 2: Table S2. Statistics of the number of male and female broilers, separately, slaughtered in hatches and generations 5 to 7.Additional file 3: Table S3. Distribution of SNPs after quality control.Additional file 4: Table S4. Primer sequences for qPCR analysis.Additional file 5: Table S5. Phenotypes for individual broilers.Additional file 6: Table S6. Descriptive statistics for male and female broilers.Additional file 7: Table S7. Estimates of variance components and heritabilities for males and females using the relationship matrix that blends genomic and pedigree information.Additional file 8: Table S8. Estimates of variance components and heritabilities for males and females using the pedigree relationship matrix.Additional file 9: Table S9. Estimates of genetic and phenotypic correlations among growth and feed efficiency traits based on the pedigree relationship matrix.Additional file 10: Table S10. Sequences of SNPs reported in Tables Additional file 11: Table S11. Additive and dominance effects of the five most significant SNPs on growth and feed efficiency traits.Additional file 12: Table S12. Means (+ SE) of growth and feed efficiency traits by genotype of the five most significant SNPs in males and females.