Genetic diversity and relatedness of accessions for coconut growing in Colombia was unknown until this study. Here we develop single nucleotide polymorphisms (SNPs) along the coconut genome based on Genotyping by Sequencing (GBS) with the goal of analyze the genetic diversity, population structure, and linkage disequilibrium (LD) of a diverse coconut panel consisting of 112 coconut accessions from the Atlantic and Pacific coasts of Colombia. A comprehensive catalog of approximately 40,000 SNPs with a minor allele frequency (MAF) of > 0.05 is presented. A total of 40,614 SNPs were found but only 19,414 anchored to chromosomes. Of these, 10,338 and 4606 were exclusive to the Atlantic and Pacific gene pools, respectively, and 3432 SNPs could differentiate both gene pools. A filtered subset of unlinked and anchored SNPs (1271) showed a population structure at K = 4, separating accessions from the Pacific and Atlantic coasts that can also be distinguished by palm height, as found in previous studies. The Pacific groups had a slow LD decay, low Fixation Index (Fst) and low nucleotide diversity (π), while the Atlantic group had slightly higher genetic diversity and faster LD decay. Genome-wide diversity analyses are of importance to promote germplasm conservation and breeding programs aimed at developing new cultivars better adapted to the region. Cocos nucifera) is cultivated extensively in all tropical countries around the world and considered one of the most important plant species to guarantee the survival of humankind1. Origins of cultivated coconut are difficult to trace strictly by morphology because coconuts lack clear domestication traits due to the widespread nature of admixed populations2. Two distinctively different forms of the coconut fruit, known as "Niu kafa” and “Niu vai”—Samoan names for traditional Polynesian varieties—are known. The Niu kafa form is triangular and oblong with a large fibrous husk. The Niu vai form is rounded and contains abundant sweet coconut “water” when unripe. Coconut palms are also classified by their height, “tall” type palms have evolved naturally and were disseminated by either the Pacific or the Atlantic Ocean currents. Tall palms come from the Indian and Pacific Oceans and are generally cross-pollinated, having long stems and late bearing fruit, and can be of the “Niu kafa” or “Niu vai” type3. The “dwarf” type palms are recognized for being self-pollinated, and displaying important domestication features such as short stems, high productivity in terms of fruit production, and low genetic variability4. Both palm types gave rise to a vast number of coconut populations of pantropical distribution that are poorly characterized at the genomic level, and that are generally identified based on variable morphological and agronomic traits, which are not apparent in juvenile phases7.Coconut (Cocos. Draft genomes have been published suggesting a genome size of about 1.93 Gb (dwarf coconut) to 2.4 Gb  and a complex genome structure composed of 50–70% of repetitive sequences, chromosome fractionation and duplication followed by rearrangements9. Many studies have been conducted to characterize the genetic diversity of coconut collections, and to understand coconut cultivation history11. However, northern South America is an underrepresented region in these studies. RFLP12, microsatellite13, and AFLP15 were used to understand coconut genetic diversity. High levels of genetic differentiation between populations of Pacific and Indo-Atlantic coastal regions have been proposed by most authors, suggesting coconut have a long-standing evolutionary history and were brought into cultivation independently in each of these regions. Evidence coming from sources such as microsatellites1 and written historical records16 suggest C. nucifera was introduced to Colombia. Coconut Pre-Columbian introduction from Southeast Asia to the central and south American Pacific coast covered a region spanning from Costa Rica to Peru16. Atlantic coconut populations probably came from India1 and were introduced directly or indirectly to Colombia after Vasco da Gama17. Dwarf coconut has been introduced to Colombia more recently and on both coasts1.Coconut palms are monoic, having both male and female flowers on the same inflorescence, perennial diploids (2n = 32) and the sole species of the genus 19. Genotyping by sequencing (GBS) is considered an efficient and economical method to quickly discover SNPs among several individuals simultaneously20, allowing plant breeders to use these resources for crop improvement. Such an approach has benefited many crops like watermelon21, cowpea22, rice23, and spinach24, among many others. The development of SNPs markers in coconut represents potential for application of molecular breeding techniques through marker-assisted selection (MAS) and association mapping.High-throughput sequencing has enabled the discovery of single nucleotide polymorphisms (SNPs) throughout the genome, greatly increasing power for detecting neutral and adaptive patterns of variationDespite the central importance of coconut as a widely distributed and cultivated palm around the world, little is known about the genomic diversity of this species in Colombia because coconut is not a central part of agriculture in the country. Here we tested if there is genetic differentiation within the country between samples from the Pacific and Atlantic coast. This study aims to: (1) identify SNPs at the genome level, (2) investigate the genetic diversity and population structure, and (3) characterize the linkage disequilibrium (LD) in coconut growing in Colombia. We present a comprehensive catalogue of approximately 40,614 SNPs in coconut, based on the GBS method of 112 accessions of unknown genetic origin. Our work provides a better understanding of the genetic diversity and population structure of coconut populations in Colombia.A total of 112 coconut mature individuals were selected from the main coconut producing departments on the Atlantic and Pacific coasts of Colombia. Most sites in both coasts covered some production sites, only a natural site in the pacific coast was screened. Two sampling sites in the Atlantic coast included: (1) 26 accessions from Córdoba, municipalities of Puerto Escondido  and Moñitos . (2) 27 accessions from Antioquia, municipalities of San Juan de Urabá  and Arboletes . Three sites were in the Pacific coast: (1) 25 accessions from Nariño, municipality of Tumaco . (2) 27 accessions from Cauca, municipality of Guapi  and (3) only seven natural accessions from Chocó, municipality of Nuquí (location: Coquí). Coconut palms are a combination of adventive admixed populations and perennial crops with a worldwide distribution. Selection of accessions was made trying to cover all different cultivated coconut varieties and adventive coconut palms present in each zone. There was not available information about specific cultivars grown in each zone, so individuals were geo-referenced and marked in the field Table .25 with Epoch Life Science  purification columns were used to extract and purify DNA. DNA quality was determined by spectrophotometry using Nanodrop 2000  and DNA concentration through fluorometry using Qubit 3.0 .A sample of fresh leaf tissue was collected and preserved in silica gel for each palm. DNA extraction was performed from leaf tissue collected in the field. A combination of CTAB (Hexadecyl Trimethyl Ammonium Bromide) extraction methodMslI restriction enzyme  and using one unit of NEB4 buffer in 20 μl of volume, incubated for two hours at 37 °C. The restriction enzyme was inactivated by incubation at 80 °C for 20 min. The same procedure was used for double digestion with the PstI-MspI enzymes . Enzyme MslI was chosen for this work because many more SNPs were recovered . Initially a pilot experiment using 12 samples (three accessions and four populations: two from the Pacific coast and two from the Atlantic coast) was performed; to find out which set of restriction enzymes work better to recover more Single Nucleotide Polymorphisms (SNPs) in coconut. Between 100 and 200 ng of genomic DNA was digested using 2 units of the ed Table .For the ligation reaction, 10 μl of each restriction digest were transferred to a new 96 well PCR plate, mixed on ice first with 1.5 μl of one of 96 inline-barcoded forward blunt adaptors , followed by addition of 20 μl ligation master mix containing 15 μl NEB Quick ligation buffer , 0.4 μl NEB Quick Ligase , and 7.5 pM pre- hybridized common reverse blunt adaptor. Ligation reactions were incubated for 1 h at room temperature, followed by heat inactivation for 10 min at 65 °C. For library purification, all reactions were diluted with 30 μl TE 10/50  and mixed with 50 μl Agencourt® XP beads , incubated for 10 min at room temperature and placed for five min on a magnet to collect the beads. The supernatant was discarded, and the beads were washed two times with 200 μl 80% ethanol. Beads were air dried for 10 min and libraries were eluted in 20 μl Tris Buffer (5 mM Tris/HCl pH: 9.0).For library amplification, 10 μl of each of the 96 Libraries were separately amplified in 20 μl PCR reactions using MyTaq™  and standard Illumina TrueSeq™ amplification primers . Cycle number was limited to 14 Cycles. Pooling and cleanup of GBS libraries was done using 5 μl from each of the 96 amplified libraries. PCR primer and small amplicons were removed by Agencourt® XP bead purification  using one volume of beads. The PCR enzyme was removed by an additional purification on MinElute® columns . The pooled Library was eluted in a final volume of 20 μl Tris Buffer (5 mM Tris/HCl pH: 9).Normalization was done using Trimmer Kit . One μg pooled GBS library in 12 μl was mixed with a four μl 4 × hybridization buffer, denatured for three min at 98 °C and incubated for five hours at 68 °C to allow reassociation of DNA fragments. Twenty μl of 2 × DSN master buffer was added, and the samples were incubated for 10 min at 68 °C. One unit of DSN enzyme (1 U/μl) was added and the reaction was incubated for another 30 min. Reaction was terminated by the addition of 20 μl DSN Stop Solution, purified on a Qiagen ® Column  and eluted in 10 μl Tris Buffer (5 mM Tris/HCl pH: 9). The normalized library pools were re-amplified in 100 μl PCR reactions using MyTaq™ . For each pool, a different i5-Adaptor primer was used to include i5-Indices into the libraries, allowing parallel sequencing of multiple libraries on the Illumina NextSeq 500 sequencer . Cycle number was limited to 14 cycles. The GBS libraries were size selected on Blue Pippin , followed by a second size selection on an UltraPure™ Low Melting Point Agarose (LMP) agarose gel , removing fragments smaller than 300 bp and those larger than 400 bp. Sequencing was performed on an Illumina NextSeq® 500  using V2 Chemistry (300 cycles).https://github.com/TheAriasLab/POPULATIONS_GENOMICS.For data pre-processing, filtering of restriction enzyme site at 5' end of reads was performed and reads with 5' end not matching the restriction enzyme site were discarded. Quality trimming was performed by removing adapter sequences, reads containing Ns, and trimming reads at 3'-end to get a minimum average Phred quality score of 20 over a window of ten bases. Reads with final length < 20 bases were discarded. A subsampling (evenly across the complete FASTQ files) of quality-trimmed reads was performed to 1.5 million read pairs per sample. FastQC reports (Andrews 2008) were prepared for all FASTQ files and read counts were recorded. All pipelines used here can be found at 26, Bowtie2 version 2.4.227 was used to index the “Hainan Tall Coconut” draft reference genome28 and to align reads to the reference genome index. Samtools version 1.1129 was used to compress SAM files and to convert them to their binary version (BAM). The Stacks script “populations” was implemented to filter and identify Single Nucleotide Polymorphisms (SNPs) according to a minimum percentage of individuals (80%) in a population required to process a locus, a minimum allele frequency (0.05) of a SNP to be considered, and a maximum observed heterozygosity (0.70) of a SNP to be considered. Total recovered SNPs included those anchored to chromosomes and those in scaffolds. The software Structure30 requires unlinked markers, to avoid misleading results, linked SNPs were filtered (LD > 0.2). Only SNPs anchored to chromosomes were included in this analysis.For the Stacks pipeline version 2.531 with pairwise Fst on a per site basis. For SNPs anchored to chromosomes, Fst values for each site correspond to the mean Fst value between the site and all the other sites across the genome. Nucleotide diversity (π)32 was calculated on a per site basis and averaged for each data set (Atlantic and Pacific) and Tajima’s D33 was calculated on a 100 k sliding window on each data set (Atlantic and Pacific). All calculations were carried out using vcftools version 0.1.1534.The Fst index  was calculated between the Atlantic and Pacific coast gene pools using the method of Weir and Cockerham30 was used to identify genetic structure of populations with an admixture model, a burn-in of 25,000, and several MCMC replicates after burn-in of 250,000 . Runs were performed from k = 1 to k = 6, assuming coconut populations from Colombia could show population substructure based on a global divide between Atlantic and Pacific populations1 and further substructures within each coast. Each run was repeated 10 times for a total of 60 runs per test. The method of Evanno et al.35 and associated R scripts were used to calculate delta K as a measure that best describes the number of clusters in the data. Permutation of runs was done using Clumpp 1.1.236 and visualization of the data was done using R scripts. Recent hybrids were detected using Snapclust from the Adegenet R package V2.1.3.37 and the kinship coefficient Identity By Descent (IBD) was calculated using PLINK v1.90b5.2, Method of Moment (MoM)29.A Bayesian method in Structure version 2.3.438 was used for each group found in the genetic Structure analysis. Set parameters were -Het 0.9 -Miss 0.1 -MAF 0.1. SNPs around 120 Mb were exclusively used for LD calculations. Mean LD for genomic bins was calculated using PopLDdecay with the following parameters -bin1 2000 -bin2 1,000,000 -break 600 for Pacific group 1, -bin1 2000 -bin2 3,000,000 -break 2000 for Pacific group 2 and -bin1 2000 -bin2 1,000,000 -break 600 for group “both coasts” and Atlantic. To calculate LD decay and half decay we fit the data to the Hill and Weir39 model using R.To calculate LD PopLDdecay version 3.4140 to infer a phylogenetic network with the Neighbor-net algorithm.To understand relationships and distances between samples, we used SplitsTree version 5A previous version of this paper has been published as a preprint here: . This past version differs from the current version because a new and updated coconut genome assembly to chromosomes was published before the past version was published. While the main core of results is very similar, we have done a series of analysis and figures that differ significantly from the previous version.We have all collection permits required by the Colombian government to collect leaves samples of mature individuals for DNA extractions: Autoridad Nacional de Licencias Ambientales (ANLA)- 8 de Octubre 2015, Resolución Número 1263. All methods were carried out in accordance with relevant guidelines and regulations.28), resulting in an average mapping rate of 92.47%. Initially, 40,614 SNPs were obtained with a frequency of depth above three  and Pacific  Colombian coasts. In total, approximately 182 million of raw reads were obtained in this study , from which 168 million could be aligned to the assembled coconut reference genome  showed that accessions from the Atlantic coast formed a group, whereas accessions from the Pacific coast were divided into two distinct groups . 1271 unlinked SNPs with 35 criterion. Structure analyses were performed using K values ranging from k = 2 to k = 6. The number of groups with the highest value of ΔK was four  analysis was done across all accessions. A pairwise IBD near zero was observed among most accessions from the Pacific coast of Colombia with several exceptions Fig. . While fUsing SNPs anchored chromosomes . Both gene pools shared 3,432 SNPs, whereas 4606 and 10,338 SNPs were unique to Pacific and Atlantic accessions , and population structure genotypes. LD decay values observed for Pacific coast accessions was slower (17 Mb) than for Atlantic coast accessions (24 Mb). LD < 0.1 for distances > 16 Mb for Atlantic coast accessions and > 71 Mb for Pacific coast accessions was observed Fig. .Figure 5A phylogenetic network also revealed accessions from the Atlantic coast to be separated from Pacific coast accessions. Within the Pacific coast group, a smaller well-defined cluster was identified Fig. .Figure 6C. nucifera genome, providing both neutral and putatively selected markers. Genotypes were successfully obtained for 112 samples from five different geographical regions in Colombia. A large genome variation dataset for coconut grown in Colombian coastal regions is provided here. The genetic population structure inferred in this study not only supports the hypothesis that there is a strong genetic break between Atlantic and Pacific coast accessions in Colombia but also provides finer structures within the Pacific coast group. This corroborates previous evidence for worldwide genetic diversity of coconut1. Results produced here will assist future genome-wide association studies for determining genomic regions or genes associated with several economically important traits.Reduced representation libraries from high-throughput sequencing allowed analysis of SNPs throughout the MslI restriction enzyme was found to be more effective in GBS of coconut than the double restriction enzymes (PstI-MspI) used in a pilot experiment. Coconut genome exhibited a higher number of MslI restriction sites, allowing the identification of thousands of markers spaced unevenly throughout the genome  genomes included 57,872 SNPs in intergenic regions, 21,066 in genic regions and 5552 in exonic regions9. This study using GBS recovered 70.18% of the total number of SNPs found in Xiao et al.9, Lantican et al.4 and Yang et al.28.Here the use of me Table . Palm st42. Transitions are usually more frequent than transversions and less likely to result in amino acid substitutions in protein-coding sequences and therefore they are more likely to persist in populations during natural selection43.Initially, 40,614 SNPs were identified. However, 53.97% of markers were not anchored to chromosomes and were not used in most subsequent analyses. However, once new versions of the coconut genome are improved, this larger set of SNPs can be used in future studies and represents a well of information available to breeders. Polymorphisms were widely distributed across the 16 chromosomes and were highly correlated with the length and number of genes on each chromosome. The transition/transversion ratio was consistent with that observed in other studies of palm species14. Zizumbo-Villareal and Arellano-Morin44 found genetic differences between Atlantic Tall and Pacific Tall coconuts, and a differentiation within Pacific tall coconuts. A study from the Philippines was able to cluster populations with similar genetic distances for morphological traits. A distinction between morphological and molecular markers was found for the Atlantic and Pacific gene pools45. Our results suggest that similarities at the phenotypic level do not imply close genetic relationship.Because LD may affect the inference of the population structure, an LD < 0.2 filter was applied, which resulted in a decrease of the number of SNPs to 1,271. All approaches used in this study revealed there is a genetic structure in coconut populations in Colombia even though both coasts are geographically close to each other in northern south America. Structure analyses, principal component analysis and phylogenetic networks are all consistent with four groups explaining the genetic structure of these populations Figs. , 6. Gene3  is the non-random association of alleles at different loci and is influenced by various factors. For instance, domestication, population subdivision, and selection can enhance LD in the genomePhylogenetic networks represent conflicting signals in non-treelike processes, such as hybridization followed by introgression. The network displays relative evolutionary distances between taxa as well as uncertainty in the groupings in the form of “splits” of internal branches. Our results suggest high levels of hybridization, gene flow or incomplete lineage sorting among cultivars in Colombia Fig. .Further work could incorporate more wild individuals from the Pacific coast of Colombia and examine whether cultivated samples from the Pacific Islands present a continuum or a hard genetic discontinuity as might be expected with multiple domestication events.Genotyping by sequencing data has proved useful and reliable for the identification of high-quality SNPs in coconut. We investigated genetic diversity, and structure of Colombian coconut populations. The pattern of variation found by genomic wide analysis indicates the existence of four groups of coconut populations in northern South America: the Pacific groups with a slow LD decay with low Fst and π, and the Atlantic one with slightly higher genetic diversity, fast LD decay and phenotypic similarity to the wild type of coconut populations. Information generated in this study will contribute to the knowledge of coconut genotypes and genetic diversity present in Colombia. Red ring disease in coconut and other cultivated and native palms in the Neotropics have reached epidemic levels. Knowledge of the genetic structure of Colombian coconut, including regions with augmented levels of genetic diversity, may ultimately prove useful in targeting source populations for disease resistance and other crop improvement traits. This work is a first step towards future genome-wide association mapping studies and the identification of SNP markers able to enhance the precision breeding for horticultural traits in cultivated coconut palms.Supplementary Information.


Ambystoma barbouri, is an endemic amphibian with a small geographic range that predominantly inhabits small, ephemeral streams. As A. barbouri is listed as near‐threatened by the IUCN, we describe range‐wide patterns of genetic differentiation and adaptation to assess the species’ potential to respond to environmental change. We use outlier scans and genetic‐environment association analyses to identify genomic variation putatively underlying local adaptation across the species’ geographic range. We find evidence for adaptation with a polygenic architecture and a set of candidate SNPs that identify genes putatively contributing to local adaptation. Our results build on earlier work that suggests that some A. barbouri populations are locally adapted despite evidence for asymmetric gene flow between the range core and periphery. Taken together, the body of work describing the evolutionary genetics of range limits in A. barbouri suggests that the species may be unlikely to respond naturally to environmental challenges through a range shift or in situ adaptation. We suggest that management efforts such as assisted migration may be necessary in future.Understanding spatial patterns of genetic differentiation and local adaptation is critical in a period of rapid environmental change. Climate change and anthropogenic development have led to population declines and shifting geographic distributions in numerous species. The streamside salamander,  A total of four libraries were sequenced on an Illumina HiSeq 2000 sequencing system at the University of Oregon Genomics Core Facility (gc3f.uoregon.edu) using single‐end, 100 bp reads.We performed double‐digest restriction site‐associated DNA sequencing , allow three mismatches between read stacks within individuals (M = 3), and allow four mismatches between read stacks across individuals (n = 4). These parameter values were optimized by following Paris et al.  using an iterative filtering procedure modified from O’Leary et al. . BrieflyA. mexicanum genome. We first downloaded BED files containing gene model annotations for each chromosome of the June 2018 A. mexicanum genome assembly from the Table Browser of the UCSC Genome Browser (www.genome.ucsc.edu) and then used the BEDTools closest command  before using a successive K‐means clustering algorithm to evaluate support for different values of K. We assessed results for K = 1–10 and selected the value of K with the lowest value of the Bayesian Information Criterion. We also employed PCA and discriminant analysis of principal components (DAPC) in Adegenet  for pairwise FST based on 1000 bootstraps using the bias‐corrected bootstrapping method implemented in the diffCalc function; we carried out bootstrapping across SNPs, as recommended by Weir and Cockerham (FST after removing SNPs determined to have significant signatures of selection in univariate tests (see below). We further characterized genetic diversity for each locality by calculating individual observed heterozygosity and per‐SNP nucleotide diversity (π) in VCFtools  for each locality using the linkage disequilibrium‐based method in NeEstimator v2.1 ; Rousset, For genetic‐environment association (GEA) tests, we reduced the number of highly correlated environmental variables using a two‐stage approach. In the first stage, we grouped logically related variables  and retained only those variables correlated at 2.6We carried out several tests for selection that broadly belong to two categories: differentiation outlier scans and GEA analyses. Outlier scans identify loci showing unusual differentiation among populations relative to background genetic differentiation, while GEA analyses identify loci with allele frequencies that correlate significantly with environmental variables while accounting for neutral population structure  to assess the significance of each SNP  of neutral SNPs based on the empirical covariance matrix estimated in Bayenv2  to account for neutral genetic structure when testing for associations between alleles and environmental factors. We accounted for multiple comparisons using a Bonferroni‐corrected significance threshold of 0.01/10,527 , we searched the mRNA sequences predicted for the gene models against the NCBI Basic Local Alignment Search Tools (BLAST) database .We used the BEDTools Xenopus frogs. We gathered additional information on candidate genes by searching the literature. We carried out GO enrichment analysis on the candidate genes using the PANTHER‐powered system  terms describing the molecular functions, biological processes, and cellular components associated with the candidate genes  of the de novo RAD loci mapped to at least one genomic location. After removing RAD loci with MAPQ < 10 and integrating alignment information with the de novo RAD catalog, 1,317,396 RAD loci remained for further filtering. The subsequent iterative filtering scheme . PC2 was also significantly correlated with both latitude and longitude  relative to other pairwise comparisons. Indeed, the highest pairwise FST calculated was between the north and south subpopulations , consistent with these subpopulations being separated by one of the greatest geographic distances between any pair  while retaining the same general patterns .Pairwise ns Table . EnvironFIS at 0.027 , −0.016 , and 0.025 , respectively. The southern population exhibited a higher FIS estimate of 0.079 . Individual observed heterozygosities were lowest in the northern and southern subpopulations, intermediate in the central subpopulation, and highest in the western subpopulation; per‐SNP nucleotide diversity showed a similar pattern  identified 732 unique SNPs with signatures of selection deemed significant at the thresholds described above, representing approximately 7.0% of the SNP dataset. Of these significant SNPs, 385 (approximately 52.6%) were identified by at least two tests for selection . Overlap among pairs of tests was also generally significantly greater than expected by random chance (p < 0.007), bar the overlap for the Bayenv2‐LFMM and Bayenv2‐PCAdapt comparisons , and 94.9% were invariant in at least one collection locality. This trend was also apparent among the 385 significant SNPs identified by at least two tests for selection, as many of these SNPs were invariant within collection localities , and 96.6% were invariant in at least one collection locality. Distributions of minor allele frequencies, observed heterozygosities, and missing data for the full dataset of 10,527 SNPs, full set of 732 significant SNPs, and the set of 385 significant SNPs found by at least two tests for selection are presented in Figure 3.4p < 0.001 for annual precipitation, mean annual temperature, and temperature seasonality). Models containing either latitude or longitude were also fit for each set of polygenic scores. 2R for all models  were >0.99. For each SNP set, polygenic scores appeared well explained by either the environmental factor of interest or latitude , with many SNPs occurring within A. mexicanum gene models but not necessarily exons. 130 SNPs corresponded to 123 genes that were uncharacterized or otherwise not assigned gene symbols in the A. mexicanum genome assembly; we obtained their predicted mRNA sequences from the UCSC Genome Browser and searched them against the BLAST database. Most of the uncharacterized genes (71/123) did not have significant similarity with BLAST database entries; those that did often had uninformative gene symbols . We therefore restricted our discussion to several genes that have associations with more specific ecological drivers of local adaptation; a full table of candidate genes, associated GO terms, and additional data can be found in Table Salamandra infraimmaculata  evidence for local adaptation; (2) evidence for strong range‐wide genetic differentiation, and (3) a synthesis of prior results suggesting that A. barbouri conforms to the central‐marginal hypothesis, which we discuss in tandem.Landscape genomics has emerged as a widely used framework for understanding the spatial distribution of adaptive variation , indicating the near‐absence of alleles putatively adaptive for alternative environmental conditions. To the extent that the polygenic scores may reflect the effects of selection, the northern population may therefore have a poor capacity to respond to changing environmental conditions through in situ allele frequency changes. Migration across the range may also be low enough that the spread of standing adaptive variants may be outpaced by environmental change. Together, potential strong local adaptation, low genetic diversity, and poor migration may contribute to evolutionary mismatch with the environment in future  and circadian rhythm (U2AF1L4) may reflect variation in the timing of developmental events, such as metamorphosis. Tests for selection in other species have also recovered genes involved in the circadian rhythm (e.g., Dall’Ara et al., A. barbouri through the reduced availability of dissolved oxygen in bodies of water. Numerous genes that mapped near SNPs associated with mean annual temperature have been implicated in adaptation to hypoxia in other species. Research in other ambystomatids has revealed a positive correlation between dissolved oxygen and hatching rate (Sacerdote & King, r = −0.52 across our four study sites (Table The repeated identification of hypoxia‐related genes associated with mean annual temperature but not elevation suggests that high temperatures toward the southern area of the range may impose selection on es Table . That mu4.4A. barbouri.Strong genetic differentiation among subpopulations and confounding between population structure and environmental conditions may increase the false positive rates of various tests for selection (e.g., Frichot et al., A. mexicanum, our set of candidate genes is sensitive to the level of structural conservation between the genomes of A. barbouri and A. mexicanum. Knowledge of the extent of linkage disequilibrium along the A. mexicanum genome is also lacking, although the recombination distance per cM is approximately 5 Mb (Smith et al., It is also important to note that because our procedure for identifying candidate genes near SNPs used a reference genome assembly of A. barbouri. Thus, the putatively adaptive genomic variation identified here provides incomplete insight into the genomic architecture underlying local adaptation in this species (Lowry et al., Given that our results are derived from reduced‐representation sequencing, we interrogated only a small fraction of the large genome of sensu Micheletti & Storfer, A. barbouri, as well as a number of hypotheses concerning the ecological pressures driving adaptation.Additional research should pursue functional validation of candidate loci, which is generally lacking in population and landscape genomics studies (Li et al., 4.5A. barbouri is listed as near‐threatened by the IUCN, owing to its limited geographic range, habitat alteration resulting from human land use, and climate change (Hammerson, A. barbouri suggests range shifts in response to climate change are unlikely. Consequently, our results could be used as preliminary data for the development of genotyping panels aimed at monitoring neutral genetic variation or a subset of adaptive variation, a method that has been suggested in relation to conservation of other species (e.g., Hohenlohe et al., A. barbouri is an endemic species with a small geographic range; such species are expected to be disproportionately threatened with extinction (Manne & Pimm, The authors declare no conflict of interest.Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.


The candidate gene identified within the linkage disequilibrium (LD) of this marker was shrunken2 , which encodes ADP-glucose pyrophosphorylase (AGPase), a 60 kDa subunit enzyme that affects starch metabolism in the maize endosperm. Several SNP markers specific to variants in sh2 were developed and validated. According to the validation in a set of 81 inbred, RIL, and popular corn varieties, marker Sh2_rs844805326, which was developed on the basis of the SNP at the position 154 of exon 1, was highly efficient in classifying sh2-based sweet corn from other types of corn. This functional marker is extremely useful for marker-assisted breeding in sh2-sweet corn improvement and marketable seed production.Sweetness is an economically important eating quality trait for sweet-corn breeding. To investigate the genetic control of the sweetness trait, we conducted a genome-wide association study (GWAS) in an association panel consisting of 250 sweet corn and waxy corn inbred and recombinant inbred lines (RILs), together with the genotypes obtained from the high-density 600K maize genotyping single-nucleotide polymorphism (SNP) array. GWAS results identified 12 significantly associated SNPs on chromosomes 3, 4, 5, and 7. The most associated SNP, AX_91849634, was found on chromosome 3 with a highly significant  Zea mays L. var. rugosa Bonaf.) is globally an economically important crop, and it is widely used for human consumption from both immature and mature kernels as fresh, canned, and frozen due to its high sugar content [Sweet corn , which increases sugar content, but has a high starch conversion rate. Supersweet corn carries a shrunken2 (sh2) mutant, which maintains high sugar level, has a steady rate of starch accumulation, and is suitable for shipping purposes. Shrunken2 (Sh2) encodes large regulatory subunits of the heterotetrameric ADP-glucose pyrophosphorylase (AGPase) present in the maize-kernel endosperm, which is upstream in the starch biosynthetic pathway [Sh2 locus causes corn to be supersweet and has thus been widely used for the development of extrasweet corn cultivars [sh2 mutant has three- and eightfold increases in sugar content (29.9% sucrose) compared with maize with su1 mutant (10.2% sucrose) and normal corn, respectively [se) sweet corn with an se mutant in an su1 background has a higher sugar level, more tenderness, and an extended shelf life compared with normal sweet corn [su-1, sh2, and se, gives a balance quality between high sugar content and tenderness [shunken2-Reference allele have been developed and used in marker-assisted sweet corn (Zea mays Sachharata) breeding program [Sweet corn can be classified on the basis of gene composition in the endosperm into five types: normal, sugary enhanced, super sweet, synergistic, and augmented shrunken . Normal  pathway . It is a pathway ,9. The multivars . Maize wectively ,11,12,13eet corn . Synerginderness ,12. Mole program .Quantitative trait locus (QTL) analysis is extensively used to improve the quality of numerous crops such as rice, wheat, tomato, and soybean ,17,18,19In this study, we identify the gene associated with the kernel sweetness trait through a GWAS approach using a collection of 250 sweet and waxy corn inbred and recombination inbred lines (RILs). The set of SNPs, obtained from an Axiom® Maize 600k Genotyping Array, were used to evaluate linkage-disequilibrium (LD) decays, population structure, and SNPs associated with kernel sweetness. Additionally, gene-based SNP markers were developed and validated. SNP markers are extremely useful for maize-breeding programs for sweetness traits.® Maize 600 K Genotyping Array  to obtain SNP data for the population study and GWAS analyses. Out of 616,201 variants, 426,858 high-quality SNPs were determined in the 250 lines. All of these SNPs had a minor-allele frequency (MAF) greater than 0.05, and maximal missing rate of less than 20%. These numbers of SNPs were further filtered to obtain a manageable SNP dataset. As a result, a subset of 159,201 SNPs were retained after filtering on the basis of the distance of at least 1 kb between each pair of adjacent SNPs  were used to evaluate the kernel trait . The pheent SNPs ; 19,565 ent SNPs . We performed principal-component analysis (PCA) on the basis of the 159,201 SNPs to characterize the population structure, and performed phylogenetic-tree analysis to determine the genetic diversity of the 250 maize lines. PCA revealed clearly separated sweet and waxy corn along the first principal component PC1; B. The ph2 = 0.1; the mean length of LD decay decreased rapidly to 76 kb at a cut-off of r2 = 0.2 , a gene involved in kernel starch biosynthesis  of the lead SNP. No starch- or sugar-related genes were annotated within the significant regions on the other detected chromosomes  on the basis of the 159,201 SNPs using a mixed linear model (MLM) implemented in the Genome Association and Prediction Integrated Tool (GAPIT version 3.0). SNP-based heritability for sweet corn versus waxy corn was 99.97%. The GWAS result identified 12 significant SNPs located on chromosomes 3, 4, 5, and 7 that passed the Bonferroni threshold of –loge = 7.20 ; Table 1omosomes . Based oSh2 and the sweetness trait in this maize panel, we developed SNP markers on the basis of variants in the Sh2 gene and genotyped in a subset of the maize lines used in GWAS analysis. Ten of the SNP variants on the gene were selected to develop the SNP markers; seven variants were retrieved from the Gramene  (gene id: Zm00001d044129), and three variants were obtained from the Affymetrix Axiom 600 K genotyping array   . This ma< 0.000) A. Among sh2-based sweet corn from waxy corn, field corn, and the sweet corn that was based on the other genes, i.e., su1, se1, and bt2 (sh2/se) exhibited a heterozygous genotype using this marker.The SNP marker was then used to genotype a set of 81 diverse maize composing of hybrids, inbred lines, and RILs . As expe and bt2 B. The swbrittle1 (bt1), brittle2 (bt2), shrunken1 (sh1), shrunken2 (sh2), sugary1 (su1), and sugary enhancer1 (se1) [shrunken2 (sh2), which causes a significant increase in endosperm sugar level, is extensively used in sweet-corn cultivar development. In this study, we performed GWAS analysis against the sweetness and waxy characteristics in a set of maize inbred and recombinant inbred lines. The used population in this study clearly showed distinct kernel phenotypes as sweet corn with a shriveled kernel, and waxy corn with an opaque kernel. Therefore, the two characteristics were simply determined as a qualitative trait using a binary code of 0 and 1 for sweetness and waxy, respectively, for GWAS analysis. Although GWAS is usually aimed at quantitative traits, GWAS based on qualitative traits were also reported [sh2, se1, su1, and bt2, and individuals in the waxy-corn group possibly contained a mutation in the waxy gene. However, the exact mutation in these individuals was not clearly characterized. Sweet corn is a globally popular crop for human food and the seed-production industry. Sweetness in corn kernels is controlled by six recessive mutant genes expressed in the endosperm: r1 (se1) ,4,5. Onereported . IndividSh2 on chromosome 3. However, no significant SNP was detected for other genes related to the maize endosperm, such as brittle2 (bt2), sugary1 (su1), sugary enhancer1 (se1), and waxy (wx). The lack of a significant association might be compressed by the amount of sweet-corn type in the genetic resource panel that is commonly controlled by the sh2 gene. This could be due to the fact that the maize lines were preferentially selected and maintained for sweetness as well as other agronomic traits, as these maize lines are used for genetic mapping and breeding purposes. Therefore, it is possible that the lines with sh2 background were more desirable and hence preferentially selected. Due to the strong population structure and a degree of individual relatedness present in our association panel, we performed association tests on the basis of the mixed linear model (MLM) that incorporated the PCA and kinship matrices. The GWAS results accordingly revealed that SNP AX_91849634 was significantly associated with kernel type and was located 67.5 kb away from Shrunken2 (sh2) is extensively used in sweet-corn cultivar development [Sh2 gene was previously reported to be involved in starch biosynthesis in the maize endosperm, which encodes the large subunit of the heterotetrameric enzyme, adenosine diphosphate glucose pyrophosphorylases (AGP), and a rate-limiting enzyme [sh2, is a loss-of-function mutation that causes a significant reduction in AGP activity [elopment . The Sh2g enzyme ,29. A naactivity . sh2 gene via bulk-segregant analysis (BSA) in three mapping populations [sh2 gene [sh2 gene on chromosome 3. One SNP was validated to differentiate sweet corn from other corn types by possessing a sh2 recessive allele (T). This SNP was located within the coding region in exon 1 at position 154 of the coding sequence, and represents protein sequence conversion from threonine to alanine at position 52 of the amino acid sequence. This might render an enzyme defective due to a change in polarity from a nonpolar, hydrophobic to a polar, uncharged amino acid, thereby affecting starch synthesis in the maize endosperm. The identified SNPs on the Sh2 gene could be used as selection markers for marker-assisted selection in maize-breeding programs. Moreover, the functional SNP identified on Sh2 would offer essential information for marker-based breeding in sweet-corn production and seed industry. The gene-based SNP markers identified thus far could effectively save breeders cost and valuable time from lengthy field trials, and aid in accelerating sweet-corn breeding programs.Molecular markers are used for the characterization of germplasms, identification and mapping of genes and QTLs for various agriculturally important traits in maize ,34,35,36ulations . Ideallysh2 gene . In thissu, se, sh2, bt, wx, btwx, and btsh2wx. Out of 250 lines, 60 were developed by Chai Nat Field Crops Research Center (CNFCRC) and 190 were developed by the Plant Breeding Research Center for Sustainable Agriculture, Faculty of Agriculture, Khon Kaen University  originally developed to contain different kernel-trait genes and gene combinations, such as, ity KKU; . The genity KKU; . Sucrose® Maize 600k Genotyping Array that contained 616,201 variants . SNPs with more than 10% missing data and a minor-allele frequency (MAF) less than 0.05 were removed, yielding a total of 426,858 quality SNPs. Resulting SNPs were then further pruned to retain SNPs of at least 1 kb apart between each pair of adjacent SNPs. Lastly, a total of 159,201 remaining SNPs were retained. These SNPs were used for the genome-wide association study (GWAS) for the kernel trait. Another set of pruned SNPs was performed on the basis of linkage disequilibrium (LD) to obtain a subset of unlinked SNPs using a variant pruning tool (-indep-pairwise 50 10 0.1) in PLINK [DNA from the 250 maize lines was extracted from young leaves using DNeasy Plant Mini Kit (QIAGEN) and then genotyped by the Axiomin PLINK . A totalin PLINK . The gen2 using the same software. Genetic distance was calculated using Nie’s standard distance [Principal-component analysis (PCA) was performed to determine the genetic population structure of the samples using TASSEL 5.0 software . The lindistance , and thedistance , where tdistance . Genome-distance  based onp ≤ 0.01/n, where n represents the number of SNP markers across the entire genome.The GWAS was performed for a simple qualitative trait of kernel structure between sweet versus waxy corn with a mixed linear model (MLM) that incorporated PC and kinship data using R package Genome Association and Prediction Integrated Tool (GAPIT) version 3 . The sig® Maize 600 k Genotyping Array were used as queries in a BLAST algorithm-based search against the reference genome sequence in MaizeGDB  to identify the positions in the maize genome. The genomic regions within the LD block of the significant SNPs (peak SNPs) were selected to identify candidate genes. Gene annotation was based on the B73 reference genome (B73_RefGen_v4). SNP probe sequences of ~150 bp on the Axiomhttps://ensembl.gramene.org; accessed on 1 May 2021). The primer set for high-throughput MassARRAY was designed and analyzed according to the manufacture of the MassARRAY® iPLEX system protocol. The primers for the SNP sites were designed to contain 1 bp mismatch at the second base closest to the 3’ end of the forward primer. All markers were validated in the 81 maize accessions, including popular commercial maize varieties (Genomic sequences for primer design were obtained from B73 sequence version 4 at Gramene (arieties .


A significant proportion of perinatal losses in pigs occurs due to congenital malformations. The purpose of this study is the identification of genomic loci associated with fetal malformations in piglets.The malformations were divided into two groups: associated with limb defects (piglet splay leg) and associated with other congenital anomalies found in newborn piglets. 148 Landrace and 170 Large White piglets were selected for the study. A genome-wide association study based on the gradient boosting machine algorithm was performed to identify markers associated with congenital anomalies and piglet splay leg.Forty-nine SNPs (23 SNPs in Landrace pigs and 26 SNPs in Large White) were associated with congenital anomalies, 22 of which were localized in genes. A total of 156 SNPs  were identified for piglet splay leg, of which 79 SNPs were localized in genes. We have demonstrated that the gradient boosting machine algorithm can identify SNPs and their combinations associated with significant selection indicators of studied malformations and productive characteristics.http://www.compubioverne.group/data-and-software/.Genotyping and phenotyping data are available at  The mortality of piglets represents a severe loss for the pig farming industry . The losses predominantly occur during the last days of pregnancy, delivery, and the first three days of life . Many faThe profitable production of pigs requires that a farm consistently produces healthy piglets . HoweverCongenital diseases often pose a diagnostic problem, having unknown etiology, pathology, and prognosis. This is also true beyond animal husbandry: up to 79% of human congenital diseases have unknown etiology .The frequency of occurrence of specific malformations varies from farm to farm. Breeds with small populations have smaller genetic diversity and are prone to more defects. The most common defect, affecting between 2.3% and 5.0% of all newborn piglets, is the piglet splay leg (PSL) . PSL is Genetic factors are believed to play an essential role in the development of congenital malformations in piglets. Studies found genetic drivers of PSL  and clefWhen analyzing extensive genomic data with traditional parametric models, the problem of a small number of observations and a large number of predictive variables  is challenging to solve due to the high dimension or highly correlated data structure . TraditiWe have conducted a pilot GWAS to identify potential genomic regions and genes associated with congenital diseases and anomalies in newborn piglets. We used two common pig breeds for our study: Landrace and Large White. The Landrace pigs originated from the free-breeding, non-pedigreed stock of swine, the regional landrace native to Denmark. The Large White pig was produced in the 18th century by crossing the large indigenous white pig of North England with the smaller, fatter, white Chinese pig. These breeds have different genetics but share the problems with congenital malformations. We use the gradient boosting machine (GBM) approach since it is flexible and relatively easy to implement. Moreover, GBM has demonstrated considerable success in practical applications and various machine learning and data extraction tasks . We haveWe have an ongoing collaboration with pig breeding farms in Russia. The tissue samples were collected at the same breeding farm with a closed breeding system by the farm employees over the last decade. Ear clipping and phenotyping were performed following the standard EU monitoring procedures and guidelines (2010/63/EU) at the farm. The piglets that were assessed as healthy were kept alive, and the affected piglets were culled, and the ear clipping was performed post-mortem. Per 2010/63/EU guidelines, ear clipping and pig slaughter do not require a separate IRB approval or anesthesia. We have collected tissue samples, and phenotypic information on all available piglets at the pig farm determined to have birth defects by specially trained farm employees. We have also collected samples from healthy piglets from the same farm to form control groups.Among the 148 Landrace piglets, 75 were healthy, 27 had piglet splay legs, and 46 had other congenital anomalies. Among the 170 Large White piglets, 108 were healthy, 38 had piglet splay legs, and 32 had other congenital anomalies. All sampled piglets were female.Genomic DNA was extracted from the tissue samples using a set of DNA-Extran-2 reagents  per the manufacturer’s instructions. The quantity, quality, and integrity of DNA were evaluated using a Qubit 2.0 desktop fluorometer  and a NanoDrop8000 spectrophotometer .p-value <10−7, 45,218 SNPs for the Landrace pigs and 44,324 SNPs for the Large White pigs were retained for further analysis.The animals were genotyped using the GeneSeek Genomic Profiler (GGP) BeadChip for Porcine HD , comprising 68,516 SNPs. According to the recommendations, genotype quality control was performed using Plink 1.9 . After eSignificant SNPs were identified separately for the two breeds and two congenital disabilities.The malformations detected in newborn piglets were divided into two groups by assessed qualities: leg defects  and other congenital anomalies (CA). The analysis was carried out using a case-control design . The depFor the gene-phenotype association analysis, a manifestation of congenital anomalies in piglets and PSL genotypes was used as a predictor in the model. We used the Plink 1.9 0∕1∕2 encoding for genotypes . The datAll scripts were written in R and executed on the H2O platform .princomp in R. Plots of top 10 principal components were done in R. Admixture analysis was performed using the ADMIXTURE algorithm  using the script from  and Prinlgorithm  for varylgorithm  were useGPS algorithm was used for predicting the provenance of all genotyped individuals. If no geographic information is supplied for the reference samples, GPS identifies the closest reference populations to the tested sample. If geographic information is available, GPS also finds a global position where the individuals with the genotype closest to the tested one live. If there is no geographic information, GPS can find the nearest individuals among the reference dataset. GPS is not suitable to analyze recently admixed individuals. GPS works by calculating the Euclidean distance between the sample’s admixture proportions and the reference dataset.pi is the alternative allele frequency in the ith locus. Calculation of matrix Z requires several steps. At first, matrix P, containing (2pi − 1) in each cell, is calculated. It has dimension LxN, where L is the number of SNPs, and N is the number of animals. Next, genotype matrix M is calculated, where genotypes are coded as −1/0/1 . Matrix Z is the difference between P and M, and Z′ isthe transposition of matrix Z.We also used the genomic relationship matrix (GRM), which is calculated by the formula  \documenSVD is a valuable tool for characterizing the genetic structure to detect and extract small signals even if the data is noisy . We perfGBM can provide an accurate estimate of the response variable by creating new base learners maximally correlated with the negative gradient of the loss function associated with the entire ensemble. Using the classical quadratic error loss function (L2 norm), the training procedure produces a sequence of error-fitting steps. AUC (”Area Under the receiver operating characteristic Curve”) is used to assess classifier performances: better classifiers have larger values of AUC. AUCPR  - Logistic Loss –is a logistic loss function that penalizes the classifier’s confidence in an incorrect response; Gini index was used for evaluating the quality of classification, Mean Per-Class Error is the average value for a class error.−3), maximum tree depth (max_depth = 10), and number of cross-checks (nfolds = 10).We have selected the following parameters of GBM to minimize the validation error: the number of trees (ntree = 1000), learning rate (learn_rate = 10We stratified the samples by breed and type of defect. All samples were randomly divided into training (80%) and testing (20%) sets. In each iteration of the training, hyperparameters were set up using a search within a 10-fold cross-checking structure on a random 80% of the training set. The most effective hyperparameters were used to train the model and tested on the test set. GBM algorithm was used to select the most “important” predictors among SNPs. GBM approach uses relative importance to rank the SNPs. At each split in each tree, GBM computes the mean squared error (MSE) for regression (the tree split criterion). GBM averages the MSE improvement (decrease) made by each variable across all the trees that the variable is used. The variables with the largest average decrease in MSE are considered the most important. The values of the relative importance of variables vary from 0 to 1 . BecauseGBM algorithm can potentially identify too many SNP with positive relative importance when complex traits are analyzed. To reduce the false-positive rate, functional annotation of SNPs is conducted, intersection with other methods is determined, and only the highest scoring SNPs are considered.Ensembl! browser (Sus scrofa 11.1) (http://uswest.ensembl.org/Sus_scrofa/Tools/VEP/) (https://www.ebi.ac.uk/gwas/home) and a manual search in the literature for data on their associations with any human or animal traits.SNPs with positive relative importance were used to generate a list of associated SNPs; their positions and corresponding genes were examined with the fa 11.1) . The funfa 11.1)  and the ls/VEP/) . To findWe compared the performance of GBM with rrBLUP  and Plinmin.maf =0.05 and P3D =FALSE  and selected SNPs with marker scores −log10p >2, which is equivalent to the condition of p-value <10−2.We used the function GWAS from the rrBLUP R package using flags –all-pheno –allow-no-sex –bfile <...>–model –pheno <..>. This command conducts five tests: the tests offered here are : basic allelic test, Cochran-Armitage trend test, Genotypic test, Dominant gene action test, and Recessive gene action test. We chose SNPs with asymptotic p-value <10−2 in at least one of these five tests.Plink was used with the following options: All procedures were performed under the guidelines approved by the LK. Ernst Federal Research Center for Animal Husbandry (Russia) and with the rules for conducting laboratory research (tests) in the implementation of veterinary control (supervision) approved by Council Decision Eurasian Economic Commission No. 80 .K = 2...11. Both breeds appear to be genetically diverse. Both Admixture and PCA  kmeans procedure in R to partition the samples using 26 different scores to define the optimal number of clusters. The number of clusters for K>3 was between 6 and 8.As a function of the number of admixture components, the cross-validation error drops to a plateau value at K = 2 and 3, nearly 10% of the samples were misclassified (the rest was placed to correct clusters). For the higher values of K, GPS was placing 100% of samples into correct clusters. The fraction of affected animals per cluster ranged from 11% to 42% for splay leg phenotype and 13% to 83% for other congenital anomalies. For example, for K = 11, in cluster LA_2, all six piglets were affected; one had a splay leg and five other congenital anomalies. In cluster LW_3, having 23 members, three piglets were born with splay legs, and no other anomalies were detected.GPS  leave-onK = 18, in both breeds, the presence of congenital anomalies is negatively correlated with the admixture component K8 . This effect is especially pronounced in Large White piglets: 3% of piglets with K8>0.3 (1 out of 38) have congenital defects, as compared to 31% of piglets with K8<0.3 (29 out of 94). Cluster LW_7, where K8 is the main ancestry, is the healthiest. Congenital anomalies in Large White piglets are positively correlated with the admixture component K5 . 41% of piglets with K5>0.3 (15 out of 37) have congenital defects, as compared to 16% of piglets with K5<0.3 (15 out of 95). Therefore, it appears that certain pig lines are more susceptible to the development of congenital anomalies.We have calculated Pearson’s correlation coefficient between piglets’ status and values of admixture components. For example, for st between Landrace and Large White pigs was estimated by the method of SNPRelate package in R  and also by rrBLUP (rs80912121) was found significant by two Plink tests for piglet splay leg . It is an intron variant for CRISP3 (cysteine-rich secretory protein 3) on chromosome 7. For other congenital anomalies of Large White pigs, rrBLUP has identified two SNPs, intergenic variant ALGA0086405 and downstream gene variant WU_10.2_13_216912741, the first of each was significant in four out of five tests, according to Plink. SNP ALGA0086405 (rs81454026) is located on Chromosome 15 position 92,733,414. There are three genes near this SNP: GULP, TFPI, and CALCRL. Gene GULP PTB (domain-containing engulfment adaptor 1) is located on Chromosome 15: 92,838,715–93,251,019. Gene TFPI (tissue factor pathway inhibitor) is located on Chromosome 15: 92,345,075–92,409,905. Gene CALCRL  is located on Chromosome 15: 92,233,841–92,332,363. These genes may be associated with the disease since, due to artificial selection and small effective population size in pigs, the LD in pigs is much larger than in humans  was not significant according to Plink. Plink has identified four SNPs , that were not significant according to rrBLUP. ALGA0097461 is in an intron of a lncRNA on Chromosome 18: 23,230,190–23,234,168, forward strand. ASGA0009945 is an intron variant of gene growth arrest-specific 2 gene GAS2 on chromosome 2: 36,713,106–36,840,407 reverse strand. WU_10.2_2_80100147 is an intron variant of TBC1 domain family member 9B, located on Chromosome 2:78548370 (forward strand). WU_10.2_6_139448200 is an intergenic variant on Chromosome 6:151776796 (forward strand).Ensembl Variant Effect Prediction tool classified the SNPs into the following categories: intron variant: 71%; non coding transcript variant: 9%; intergenic variant: 8%; downstream gene variant: 5%; upstream gene variant: 4%; 3-prime UTR variant: 2%; non coding transcript exon variant: 1%; synonymous variant: 1%. To compare the GBM and Plink performance, we have calculated the confusion matrices  linked with congenital anomalies. Twenty-two of these SNPs were located in genes . In totaThe overlap between GBM, Plink (selecting SNPs significant in at least one of the five Plink tests), and rrBLUP identified SNPs is shown in https://www.ebi.ac.uk/gwas/variants/snp:rs2221894).ALGA0075476, significant in Landraces for splay leg, is on the 14, position 12790416 in the intron of homeobox-containing gene 1, HMBOX1. According to the EBI GWAS catalog, SNPs in this gene’s human ortholog are associated with obesity-related traits  is on chromosome 1, position 112210717. This SNP is located in the Intron 5–6 of lncRNA ENSSSCG00000048340. In general, lncRNAs serve as molecular signals to regulate transcription.WU_10.2_6_138715609 (significant in Large White pigs for the splay leg) is an intergenic variant on chromosome 6, position 150780123. Two more intergenic variants (CASI0008977 at chr2:107190210 and ALGA0021723 chr3:129009157) were identified as significant in Landrace pigs for the splay leg.SNP WU_10.2_18_48204887 was identified as significant by GBM for the splay leg in Large White pigs, for other congenital anomalies in Landrace pigs by Plink and rrBLUP. WU_10.2_18_48204887  is in the intron of gene TRIL (TLA4 interactor with leucine-rich repeats). In humans, SNPs in this region are associated with anomalies in the urogenital system.The reason for the small number of SNPs in the overlap between the three methods is in the approaches used by the programs: the first two programs use parametric methods , while in the GBM algorithm, a nonparametric approach is taken.β-oxidation and phospholipids, and triacylglycerol synthesis. Changes in ACSL enzymes can contribute to various physiological processes, including apoptosis, gene transcription, signaling, and vulnerability to oxidative stress  encoded a metabolite interconversion enzyme from the long-chain fatty acid coenzymes A-ligases. All members of this family convert free long-chain acids into esters but differ in tissue-specific expression. Complex fatty acyl-COA esters are substrates for numerous fatty acid metabolic pathways, including mitochondrial e stress .rs344559304 in BTB16 gene (zinc finger and BTB domain-containing 16 as PLZF and ZFP145) received the top score in PSL in Large While pigs. The protein it encodes belongs to the Krüppel-like family of zinc finger proteins. It has been previously shown that ZBTB16 acts as an essential transcriptional repressor or activator , to be of the highest significance for the congenital anomalies in Landrace pigs. Polymorphisms in the UBAC2 gene are associated with a genetic predisposition to Behcet’s disease in humans  from diseased and healthy piglets. As a result, they proposed four genes  related to splay legs in piglets. The etiology and pathogenesis of the splay leg are complex and still poorly understood. Histomorphological studies have described PSL as myofibrillar hypoplasia, but this condition has also been detected in clinically normal piglets . UltrastOur work has identified 79 genes that could be considered candidate genes for the splay leg. Even though the genes identified by Our pilot study showed that machine learning approaches could identify genomic loci associated with congenital malformations in piglets. We have identified lists of SNPs and candidate genes associated with congenital anomalies and piglet splay leg for Large White and Landrace pigs. We have also highlighted the need to study farm animals to determine population characteristics and identify genotypes associated with significant selection indicators of malformations and productive qualities.The small sample size used here is a limitation of our pilot study. We will extend this approach to larger datasets since it is vital for ensuring efficient and ethical handling of farm animals.10.7717/peerj.11580/supp-1Supplemental Information 1(1) PCA (2) Overlap between SNP listsClick here for additional data file.10.7717/peerj.11580/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.11580/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.11580/supp-4Supplemental Information 4FAM, BIM, and BED files.Click here for additional data file.


FST and Hp approaches. When we combined previous functional studies with our genomic analyses we identified 26 genes  that potentially regulate feather color in geese. These results substantially expand the catalog of potential feather color regulators in geese and provide a basis for further studies on domestication and avian feather coloration.During the domestication of the goose a change in its feather color took place, however, the molecular mechanisms responsible for this change are not completely understood. Here, we performed whole-genome resequencing on three pooled samples of geese , with two distinct feather colors, to identify genes that might regulate feather color. We identified around 8 million SNPs within each of the three pools and validated allele frequencies for a subset of these SNPs using PCR and Sanger sequencing. Several genomic regions with signatures of differential selection were found when we compared the gray and white feather color populations using the  Anser cygnoides)  . Domestignoides) . Wild swgnoides) , while dgnoides) .Since feather coloration and patterns are prominent features in birds, and play essential roles in their survival, mechanisms that regulate the differentiation of feather color has been intensively studied . FeatherAnser caerulescens caerulescens) and arctic skuas (Stercorarius parasiticus) correlate with changes in the copy number of variant MC1R alleles (Cygnus atratus and C. melanocoryphus), independently derived nucleotide substitutions in MC1R, which cause amino acid changes at important functional sites, have been identified that are consistent with increased MC1R activity and melanism pigment synthesis  that was associated with white feather color.Investigations in some avian species have identified a limited number of genes involved in the mechanisms controlling feather coloration, however, only a few studies have focused on changes in feather color in geese or swans . Melanic alleles . In the ynthesis . In the  plumage . RecentlTYR, MITF and KIT have been found to be associated with differences in feather coloration in domestic geese  on three populations of swan geese with wild type and white-colored feathers. By identifying genomic regions that experienced selective sweeps, we aimed to identify genes that have experienced artificial selection and thus might explain the change in feather color in domesticated geese.Anser cygnoides, 60 females, 57 males, group gray; Anser cygnoides, 10 females, 15 males, group White_1; Anser cygnoides domesticus, 52 females, 35 males, group White_2; A total of 117 feral gray  following the manufacturer’s instructions. Equimolar quantities (3 μg/ml) of DNA from each individual were pooled to establish the three sequencing libraries. The first pooled sample was from 117 feral gray geese, the second from 25 feral white geese and the third from 87 domestic white geese. The concentrations and purity of genomic DNA were checked before library construction. Libraries were generated via adapter ligation and DNA cluster preparation and subjected to 150 bp paired-end sequencing on an Illumina HiSeq 4000 platform. Sequencing depth of each library was at least 30×. Library construction and genome sequencing was conducted by the Beijing Genomics Institute Co., Ltd. .1 was used to obtain clean data by trimming primer dimers and adapter sequences from the reads, discarding bases with quality lower than Q20 and reads with lengths less than 25 bp. BWA-MEM  tool using Ensembl database version 103 with the input VCF file . AnnotatP-values in the comparison of the Gray and White_2 groups by Fisher’s exact test based on read depth of alleles. Eight SNPs  were also selected as these genes had previously been reported to be associated with the regulation of feather or coat color  and feather color phenotype. Primers used for the amplification of the selected SNPs are listed in SNP AFs were calculated from the read depths of each allele in the Pool-Seq data. To confirm the accuracy of AFs estimated from the Pool-Seq data, we performed a Kendall W’s coordination coefficient test on a subset of the SNPs (28 loci) . Of thesat color . The SNPFST) and pooled heterozygosity (Hp) approaches  were calculated using a custom python3 script in 10-kb non-overlapping sliding windows. The Hp approach determines, for each pool and SNP, the numbers of reads corresponding to the most (nMAJ) and least (nMIN) abundant alleles. For each window in each breed pool, the heterozygosity score of the pool was calculated as:To accurately detect genomic regions in geese that had experienced selection during domestication and to estimate the patterns of genetic diversity across the goose genome, we conducted selective sweep analyses including the fixed index  windows in the top 3% of the −ZHp distributions of both the White_1 and the White_2 populations were identified; (3) the intersection of region identified in (1) and (2) were considered to have experienced a selective sweep. Genes located in these overlapped regions might be involved in the change of goose feather color.Genomic regions that might have experienced selective sweeps were identified through three steps: (1) windows in the top 3% of the 5. The orthologous genes were then uploaded into the DAVID online tool to test for enrichment in gene ontology (GO) terms  terms . KEGG paO) terms . A FisheA total of 148.26 Gb clean data was obtained from the three Pool-Seq libraries . MappingP < 0.05), respectively, showing that there is a good concordance between the results obtained using the two different methods.To assess the reliability of estimating allele frequencies of SNPs using the population genomic sequencing (Pool-Seq) data, we genotyped 28 SNPs from an average of 210 individuals using Sanger sequencing , 3. AFs KITLG, MITF, TYRO3, and KIT) previously reported to be associated with feather or coat color (KITLG (NW_013185706.1: G232853A and NW_013185706.1: C232854T), one in the 5′ UTR of MITF (NW_013185692.1: G4400553C), one in the TYRO3 (S772G) coding region and one in the KIT (T887A) coding region (P < 0.001) for these SNPs.Among the 28 SNPs examined above, five were SNPs that are located in four genes  and the Gray vs. White_2  comparisons and (2) 253 regions in the top 3% of the −ZHp distributions of the intersection of the White_1  and White_2  populations  have previously been associated with animal coloration  were previously associated with animal coloration  and one by only Hp (CCDC112) in the selective sweep regions of geese with different feather colors (or in the 10 kb region upstream and downstream of the sweep regions)  .P < 0.05) and in three pathways, “RNA polymerase,” “SNARE interactions in vesicular transport” and “cytosolic DNA-sensing pathway”  alleles potentially involved in regulating feather color.In this study, we performed whole-genome Pool-Seq on three populations of geese with two different colors of feathers to identify SNPs, and genes that might be responsible for these differing phenotypes. The Kendall W’s coefficients for the AFs calculated from the Pool-Seq and Sanger sequencing data indicated a good correlation between them, which suggests that our Pool-Seq data is adequate for identifying loci that are differentiated between the goose phenotypes. Selective sweep analyses of this SNP data was used to identify genomic regions that show signatures of selection during the domestication of geese. This lead to the identification of 17 genes located in candidate regions identified by both the VAMP7, SLC16A2, and LOC106047519) have previously been associated with the regulation of coat color in animals , and has been described as an EDNRB-like gene  that have been reported to be associated with animal coloration  that were previously reported to be associate with feather color  and TYRO3 (S772G) that are significantly associated with feather color phenotypes, indicating that they may regulate goose feather color.We also focused on four genes  are associated with this term. We also identified three pathways  that are significantly enriched, where VAMP7 is also involved with SNARE interactions in vesicular transport. Although the enriched GO term and the pathways do not seem to directly correlate with animal coloration, it is still possible that the genes involved in them could regulate feather coloration in geese.GO and KEGG enrichment analyses of the 17 candidate genes in the most significant sweep areas are significantly enriched in the GO term late endosome membrane . Two of FST and Hp approaches, five by either FST or Hp and four previously reported color-related genes) from our genomic Pool-Seq data that might be responsible for the change in feather color that occurred during the domestication of geese (Anser cygnoides). Among these 26 genes, 12 have previously been found to be associated with animal coloration in other studies. The roles of the other genes in feather coloration requires further investigation. Additional studies, including functional experimentation, are needed to confirm the roles of these genes, and the consequence of the mutations caused by the SNPs, on phenotypic variation in feather color in geese.In conclusion, we identified 26 genes (17 detected by both the PRJNA532466 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA532466/).Whole-genome sequencing data reported in this study were deposited into the NCBI Sequence Read Archive under the accession number The animal study was reviewed and approved by the Animal Care and Use Committee of Shenyang Agricultural University. Written informed consent was obtained from the owners for the participation of their animals in this study.SR, XL, CF, and RL performed the experiments. SR and GL analyzed the data. JZ, YS, and SZ collected the samples. SR, GL, SS, DI, and ZW wrote the manuscript. SZ and ZW designed the study and supervised the work. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Thrips tabaci) is a major pest in organic onion production and effective integrated pest management strategies are lacking. Our objective was to evaluate pest management programs consisting of several different tactics: (1) two onion plant cultivars with semi-glossy leaves (“Rossa di Milano” and B5336AxB5351C) and one with waxy leaves (“Bradley”), (2) silver reflective and white plastic mulches, and (3) with or without an application of a biopesticide (spinosad + neem oil tank mix). Thrips densities were counted weekly and bulbs weighed at harvest. The application of the biopesticide had the most significant reduction in thrips densities and increase in yield. The cultivar “Rossa di Milano” had lower thrips densities compared with “Bradley” and B5336AxB5351C, but also had the lowest yield. Reflective mulch had lower thrips densities than white mulch but had no effect on yield. None of the other tactics provided any significant additional benefits to thrips management. While biopesticides will still be a key component to onion thrips management programs, their application frequency should be further optimized.Onion thrips (Thrips tabaci Lindeman) is a major pest in organic onion production and effective integrated pest management strategies are lacking. Our objective was to evaluate combinations of semi-glossy (“Rossa di Milano” and B5336AxB5351C) and waxy (“Bradley”) onion cultivars with reflective mulch, with or without biopesticides (spinosad + neem oil tank mix), to manage T. tabaci in organic onion production. Thrips densities were assessed weekly and bulbs graded and weighed at harvest. Onions sprayed with spinosad + neem oil had fewer T. tabaci (adults: 74% (2019); larvae: 40% (2018), 84% (2019) and produced higher yields (13% (2018), 23% (2019)) than onions that were unsprayed, regardless of mulch type or onion cultivar. “Rossa di Milano” had relatively fewer adult and larval thrips populations compared with “Bradley” (21% (2018), 32% (2019)) and B5336AxB5351C. However, “Rossa di Milano” had the lowest marketable yield in both years. Reflective mulch reduced densities on certain dates in both years compared to white mulch, but the largest and most consistent reduction only occurred in 2019. Reflective mulch had no impact on bulb yield. While spinosad + neem oil reduced thrips numbers and increased yield alone, none of the treatment combinations were effective at suppressing populations of thrips. Future T. tabaci management in organic onions will require optimization of the available effective biopesticides.Onion thrips ( Thrips tabaci Lindeman (Thysanoptera: Thripidae) is one of the most economically important insect pests of onion (Allium cepa L.) worldwide [Tospovirus spp. (IYSV)), which can kill onion plants before they fully mature as well as reduce bulb sizes [orldwide ,2. Adultorldwide . Feedingorldwide ,5,6,7. Tlb sizes ,9,10.T. tabaci on onion relies on the use of insecticides in both conventional and organic production systems. Although resistance in T. tabaci populations has not been documented to the most commonly used insecticide, spinetoram [T. tabaci is crucial to reduce the risk of resistance developing in organic onion production due to repeated use of a limited number of effective active substances. Additionally, non-chemical strategies also can help minimize adverse side effects of pesticides on non-target beneficial organisms, human health, and the environment.Management of inetoram , resistainetoram ,13 and Oinetoram  and resiinetoram . Unlike inetoram , organicinetoram . The neeFrankliniella occidentalis (Pergande), F. tritici, and F. bispinosa in tomato [F. occidentalis and T. palmi in pepper [T. tabaci in onion. In two previous studies, silver reflective mulches reduced numbers of T. tabaci in onion early in the season with differences fading as the season progressed [Plastic mulches are used in vegetable production for weed suppression ,18, soiln tomato  and F. on pepper ,32. Lessogressed ,34.Thrips tabaci has shown affinity toward certain types of onion phenotypes. Phenotypes with blue-gray, “waxy” leaves tend to be more attractive and support higher densities of T. tabaci and have more feeding damage compared with those that have lighter-green (“glossy” or “semi-glossy”) leaves [T. tabaci, although increased bacterial disease incidence also occurred [) leaves ,36,37,38) leaves ,40. Glos) leaves . Onion poccurred ,43,44. S®, Corteva Agriscience, Indianapolis, IN, USA), produced from a soil-inhabiting actinomycete bacterium, Saccharapolyspora spinosa [®, Corteva Agriscience, Indianapolis, IN, USA), spinosad’s conventional counterpart, is arguably one of the most effective active ingredients for managing T. tabaci in conventional onion production throughout the northern US [T. tabaci densities in onion [Organic farms that are certified by the United States Department of Agriculture (USDA) must adhere to strict rules regarding production, food safety, and pest management . Many bi spinosa , is one  spinosa ,47,48. Sthern US ,2,49. Fein onion ,51,52.T. tabaci in onion is crucial due to the lack of available active ingredients that successfully reduce T. tabaci populations and the development of insecticide resistance in organic production systems. The objective of our study was to evaluate combinations of UV-reflective mulch and semi-glossy onion cultivars, with and without biopesticides, to determine the most effective integrated management approach for T. tabaci in organic onion production. We hypothesized that each management tactic would provide some reduction in thrips densities, but that the most effective strategy would be a combination of UV-reflective mulch, semi-glossy cultivars, and biopesticide applications. We also hypothesized that bulb yield would be greatest following strategies that were most effective against T. tabaci.An integrated approach for managing ® Nitrogen Plus, SQM Organic North American Corp., Atlanta, GA, USA) plus 5-5-3 (N-P-K) at 504 kg/ha . The beds were fitted with one line of drip irrigation down the center of the beds. Onions (Allium cepa L.) were irrigated when necessary to keep the soil moist [Trials were conducted at Cornell University’s AgriTech Gates West Organic Research Farm in Geneva, New York (USA)  during the summers of 2018 and 2019. The site was in the process of transitioning to USDA-certified organic land, so virtually all practices followed USDA National Organic Program standards . The lanil moist .Plot beds were 1.4 m wide and separated from adjacent beds by 1.8 m. Plots consisted of two 4.6 m long rows spaced 0.5 m apart and had a total of 90 onions at 0.1 m spacing. Plots within a bed were separated by 0.9 m unplanted buffer. The beds were covered with either white-on-black  or silver-on-black (1 mm thickness) plastic mulch .The experimental design was a split-split-plot with 12 treatments (2 mulches × 3 cultivars × 2 insecticides) arranged in a randomized complete block design with six replicates . Mulch type was considered as the main plot factor, cultivar as the sub-plot factor, and insecticide as the sub-sub-plot factor. Nutrient, water, weed, and disease management followed recommendations for organic onion production in New York . InsectiWhen >30% plants in the experimental area had naturally lodged, onions were pulled from the soil and allowed to cure in the field for two weeks prior to harvest. Lodging in onions occurs when plants are nearing final maturity, which is characterized by the cessation of leaf production, softening of the neck, and collapsing of the upright leaves . DetailsPythium and Fusarium spp. while being propagated in the greenhouse; however, this was determined unnecessary as the level of soil pathogens was low, and seeds were left untreated in 2019. No insecticides were applied to seeds. Onion seeds were planted in potting mix  and fertilized via fertigation once per week as a foliar feed with a 2-3-1 nitrogen-phosphorus-potassium (N-P-K) fertilizer at 11.3 mL/L per 10,000 onion transplants . Plants were propagated in 96-cell plastic trays in glass greenhouses for 6–8 weeks until they reached the 2- to 3-leaf stage. Greenhouse conditions were 21.1:15.6 °C day:night and 14:10 h light:dark cycle. Plants were then hardened off outdoors for 1–3 weeks before they were transplanted into raised beds on 1 June 2018 and 7 June 2019. Hardening plants is the process of preparing greenhouse-grown seedlings for transplant by exposing them to ambient temperatures and conditions for 1–3 week(s) to minimize transplant plant shock [The three onion cultivars evaluated in the study included the waxy cultivar “Bradley” (control), and two semi-glossy cultivars previously shown to suffer less feeding damage by thrips: “Rossa di Milano” and B5336AxB5351C . In 2018nt shock .® SC) with neem oil (Trilogy®) and (2) unsprayed  spaced 0.5 m apart and a walking speed of 1.2 m/s calibrated to deliver 337 L/ha at 276 kPa. An OMRI-Listed fungicide copper octoanate  was applied at a rate of 1% v:v to protect the crop from foliar fungal diseases. Fungicides were applied weekly immediately after the insecticide applications and began when signs of foliar disease were observed.The OMRI-Listed insecticide treatments included (1) a tank mix of spinosad . Numbers of onion leaves also were counted weekly to calculate thrips density per leaf. Thrips densities (adults/leaf and larvae/leaf) were calculated by dividing either the total number of adults or larvae in each plot by the number of sampled plants and then by the mean number of leaves per plant in that plot. In New York, Thrips tabaci is the dominant species of thrips that infests onion .Marketable bulb yield was assessed at season end in September in 2018 and 2019. After onions were allowed to dry in the field, all onions from each plot were harvested and leaves trimmed. The bulbs were counted and weighed, and then separated into four grades based on bulb diameter: boiler (<4.8 cm), standard (4.8–7.3 cm), jumbo (7.3–9.5 cm), and colossal (>9.5 cm) . A subsap ≤ 0.05) using LSMeans with the Tukey Honestly Significant Differences (HSD) adjustment. Data from each year were analyzed separately because environmental conditions were different between years (New York State Integrated Pest Management Program Network for Environment and Weather Applications (NEWA)). All models included mulch, cultivar, mulch × cultivar, insecticide, insecticide × mulch, and insecticide ×cultivar as the fixed effects. The error terms for the split-split-plot design were included as random variables . For the repeated measures analysis, date Date was modeled using a repeated measures structure when analyzing thrips densities and was included as a fixed effect in interaction with all the above listed variables.All analyses were conducted using a generalized linear mixed model  with post hoc analysis conducted where appropriate  were analyzed using models fit to a negative binomial distribution. The mean bulb yield (metric tons/ha) model was fit to a Gaussian distribution and residuals were evaluated for normality. Residuals were found to be normal and did not require a transformation. The proportion of the total bulbs harvested within each size grade was analyzed using a binomial distribution .To simplify coverage of these results, only the highest order of significant treatment interactions are included in the main text. All significant lower order interactions and main effects are included in T. tabaci were lower in 2018 than in 2019  of 0.67 ± 0.02 adults/leaf in 2018 vs. 1.74 ± 0.11 adults/leaf in 2019). Weather conditions were hotter and drier in 2019 than in 2018, which likely contributed to the higher thrips infestation in 2019.Densities of adult Year 2018: Adult densities peaked during the second week (17 July) and gradually declined until the end of the season . Adult tYear 2019: Adult densities gradually increased at the beginning of the season and did not peak until early August week 5, . The intThe interactions of date × mulch and date × biopesticide were also significant . Fewer aT. tabaci larvae were also lower in 2018 than in 2019  of 3.62 ± 0.09 larvae/leaf in 2018 vs. 11.9 ± 0.95 larvae/leaf in 2019). As mentioned previously, these results were likely a reflection of hotter and drier weather in 2019.Densities of larval Year 2018: Larval densities were highest at the start of the season and had a second peak on week 4  . The intThe interactions of date × mulch and date × cultivar were also significant . Fewer lFinally, all three main effects significantly impacted larval densities . The denMarketable yields (metric tons per ha) were similar in 2018 (mean ± SE = 6.1 ± 0.2) and 2019 (mean ± SE = 7.1 ± 0.2).Year 2018: Mean marketable yield was significantly affected by cultivar and insecticide, but not by mulch type . The semYear 2019: The cultivar × insecticide interaction was significant . The bioThe proportion of large onion bulbs (diameter > 7.3 cm) in 2018 (mean ± SE = 0.39 ± 0.03) was lower than in 2019 (mean ± SE = 0.61 ± 0.03).Year 2018: The interaction of mulch × cultivar was significant . The proYear 2019: The interaction of mulch × biopesticide had significant impacts on the proportion of large bulbs . BiopestThe main effects of cultivar and biopesticide were also significant while mulch type was not . SimilarT. tabaci is managed nearly exclusively with insecticides in organic systems. Because insecticides that effectively reduce populations of T. tabaci are limited and the potential for insecticide resistance is high in organic production systems, an integrated approach using a combination of non-chemical and chemical strategies is crucial for T. tabaci management. Thus, in this study, we examined a multipronged approach to determine the most effective combination of integrated management tactics for T. tabaci in onion. We hypothesized that reflective mulch combined with a semi-glossy cultivar and a biopesticide program would maximize onion thrips control. We also hypothesized that bulb yield would be greatest following management strategies that were most effective against T. tabaci. While a combination of the semi-glossy cultivar “Rossa di Milano” and the biopesticide treatment spinosad + neem oil significantly reduced thrips densities, reflective mulch had little impact on thrips densities. Despite improving T. tabaci management using a combination of “Rossa di Milano” and the biopesticide treatment, total yield and the proportion of large bulbs in this treatment were lower than those in the thrips-susceptible “Bradley” treated with the biopesticide treatment. Similarly, T. tabaci densities and marketable yield in the semi-glossy cultivar B5336AxB5351C combined with the biopesticide treatment were comparable to those in the “Bradley” and biopesticide treatment combination. The benefits of reducing T. tabaci densities using reflective mulch were slight and inconsistent. However, when reflective mulch was combined with the biopesticide treatment, yields were higher than in the treatment combining white mulch and the biopesticide treatment, regardless of cultivar. Our study highlights the complexity of selecting a combination of IPM tactics that effectively manage T. tabaci, while also optimizing yield.Few IPM programs in organic onions include multiple tactics for managing a single insect pest. For example, T. tabaci in our study. Onions sprayed with spinosad + neem oil had fewer T. tabaci and produced higher yields than onions that were unsprayed, regardless of mulch type or onion cultivar. Previous studies in onion production regions of the United States and Pakistan have shown that biopesticides are effective for reducing T. tabaci densities in onion [T. tabaci densities and improving bulb yield in New York and Wisconsin [T. tabaci control in onions. Azadirachtin, derived from the neem tree seeds or leaves (Azadirachta indica Juss), provided moderate reductions in T. tabaci densities in studies conducted in the Pakistan [Datura spp., respectively. Khaliq et al. [T. tabaci using tree tobacco (Nicotinia glauca Graham), as well as a similar yield to the pyrethroid synthetic insecticide lambda-cyhalothrin. Additionally, some of the botanical extracts may be more available and affordable in parts of the world where onions are grown, such as the United States and Canada [Biopesticide use was the most effective tactic for managing in onion ,58,59,60isconsin ,47,48,55T. tabaci in onion. In our study, “Rossa di Milano” had relatively consistent reductions in both adult and larval thrips populations, but this was not evident for B5336AxB5351C. Previous studies indicated that thrips prefer onion cultivars with higher levels of certain epicuticular waxes [T. tabaci and reduced thrips feeding damage [Leaf phenotype shows some promise as an approach for managing ar waxes ,39,61. Hg damage ,38. The g damage , which cT. tabaci. Ideally, resistant cultivars should reduce onion thrips densities below action thresholds, thereby reducing the number of insecticide applications applied throughout the season [T. tabaci infestations without the use of biopesticides.Semi-glossy cultivars must have additional properties that make them acceptable replacements for thrips-susceptible cultivars. “Rossa di Milano” consistently had lower thrips densities, but also had lower marketable total yield than “Bradley”. One potential reason for the lower yield in “Rossa di Milano” is that it simply has a lower yield potential than “Bradley” and such a comparison is not meaningful. The replacement of “Bradley” with a lower yielding thrips-resistant cultivar is not likely to be adopted by onion growers unless fewer insecticide applications are required to protect it against e season ,49. In oe season ,64, but T. tabaci in our study. Reflective mulch showed an inconsistent reduction of adults and larval populations on certain sampling dates throughout both seasons. This result is in contrast to our hypothesis that reflective mulch would significantly delay thrips infestations by reducing thrips colonization early in the season. Because silver reflective mulch works by reflecting shortwave ultraviolet (UV) wavelengths of sunlight, which confuses and repels incoming adults [T. tabaci pressure is highest. Although reflective mulches have been used successfully to reduce other insect pest densities including thrips [T. tabaci has been limited and the results are inconsistent. While van Toor et al. [T. tabaci densities among UV-reflective or silver reflective mulches compared with bare ground, Lu et al. [T. tabaci when shallots were spaced further apart. This result is consistent with the hypothesis that as the plant canopy shades the reflective mulch, the mulch becomes less effective. Even when combined with other IPM tactics, reflective mulch had little effect on reducing T. tabaci densities in our study.Reflective mulch was not a highly effective cultural management tactic for g adults , we expeg thrips ,28,31, rr et al.  saw no su et al.  found thBemesia tabaci (Gennadius)) in zucchini squash with the use of silver reflective mulch compared to bare ground. Incidence of Tomato Spotted Wilt Virus (TSWV) vectored by Frankliniella spp. was reduced in tomatoes planted in reflective mulch compared to black mulch [T. tabaci infestations or improve onion yield in New York, there is a possibility that they may reduce Iris Yellow Spot Virus (IYSV), which is transmitted by T. tabaci and can be an economically devastating problem in many onion-producing regions in the United States [Reflective mulches provide other benefits to vegetable growers. Silver mulches have improved yields of squash , peppersck mulch . Althougd States ,66.T. tabaci in organic onion production, the results from our study failed to show consistent and significant benefits of non-chemical tactics. The application of a tank mix of spinosad + neem oil was highly effective and consistent for T. tabaci management. Further research on relevant economic injury levels for T. tabaci in organic onion production is a necessary first step for measuring the effect of some of the evaluated cultural strategies. For example, the small reduction we saw in thrips numbers in onion grown in reflective mulch may not have translated to improved yield, but any reduction in the number of biopesticide applications during the season is unknown. Additionally, other effective biopesticides are needed for T. tabaci to prevent insecticide resistance in these systems. Action thresholds for spinosad + neem oil are also needed to optimize use of spinosad. Finally, similar research should be conducted in other regions where benefits of multiple IPM tactics, especially non-chemical tactics, may occur.As far as identifying an optimal combination of IPM tactics for managing T. tabaci pest management in all onion systems is complex, pest management in organic systems proves to be more restrictive and challenging. Our study indicates that biopesticides continue to be the most consistently effective tactic for managing T. tabaci in organic onions. Although this result is promising, the availability of only one effective tactic can be problematic. Thrips tabaci is susceptible to developing insecticide resistance and reduced effectiveness of conventional insecticides has been reported [T. tabaci would benefit from these same strategies and could sustain the effectiveness of the few products available to organic growers.While reported ,13,14,15reported ,46,49,63