Mangifera indica) cultivating areas located in Minas Gerais State (Brazil) to ascertain the causal agent of severe anthracnose infections and to evaluate disease susceptibility within a world collection of mango germplasm. Overall, 86 cultivars were monitored and 152 fungal isolates recovered from infected samples were identified by morphological characterization, DNA sequencing and phylogenetic analyses. All isolates were identified as Colletotrichum asianum. Under natural disease pressure, it has been possible to ascertain a variable tolerance degree within the germplasm collection. By applying a categorized classification, cultivars were classified as follows: 10 highly sensitive (11.6%), 13 sensitive (15.1%), 18 moderately sensitive (20.9%), 23 moderately tolerant (26.7%), 11 tolerant (12.8%), and 11 highly tolerant (10.4%). The most susceptible cultivars to anthracnose were Ubà, Quinzenga, Amarelinha da Sementeira followed by Aroeira and Correjo, whereas Mallika followed by Ourinho and Lita resulted in the least susceptible cultivars. To the authors’ knowledge, this is the first large-scale evaluation of mango susceptibility to C. asianum infections within a wide number of cultivars. Anthracnose is a serious threat to mango production and assessment of cultivar response to disease could be useful in breeding programs.During the spring of 2014, a wide survey was conducted in one of the most important mango ( Mangifera indica L.) is the predominant tropical fruit in the world being cultivated in more than one hundred countries and accounting for more than half of global major tropical fruit production [Colletotrichum genus represent the most serious threat for mango cultivation worldwide. Disease caused by these fungal pathogens can negatively impact both yield and fruit quality [Mango . Symptoms can sometimes be observed as twig dieback, stalk lesions and flower blight. Although many portions of the plant may be infected, the major losses occur during ripening and in postharvest when brown or black lesions developed on fruit surfaces ,25,26.C. asianum is one of the most common and representative species associated with mango anthracnose, being reported from Australia, Brazil, China, Colombia, Ghana, India, Japan, Malaysia, Mexico, Panama, Philippines, South Africa, Sri Lanka, Thailand and Florida [Among all these species  Florida ,30,31,32Colletotrichum spp. associated with mango anthracnose using morphological, molecular characterization and multi-gene phylogenetic analysis; (ii) to compare the susceptibility of cultivars in southeastern Brazil to anthracnose infections; and (iii) to identify the cultivars representative in each of the susceptibility groups. Phenotypic evaluation of susceptibility to the pathogen under field conditions could be very useful for the selection of mango cultivars by farmers, technicians, and breeders.The objectives of this study were: (i) to identify the Colletotrichum isolates were collected from all evaluated mango cultivars showing leaf symptoms different in disposal, size and shape on the leaf blade  with slight narrowing at the center  of the C. gloeosporioides clade musae, were not included in the tree loci phylogeny, since no HIS sequence was available  confirm that they were distinct from C. asianum. There were a total of 1255 positions in the final dataset of the combined three loci. The tree with the highest log likelihood (-2531.80) is shown in From the phylogenetic analysis of the tree loci  considered in this study, all of the 82 vailable . HoweverColletotrichum is also able to induce leaf anthracnose infections very variable in the number of spots, size and placement on leaf blade  were categorized as tolerant with DI ranging from 9.5% to 18.75% and mean SS higher than 2 . On the However, the frequency distribution of mango cultivars within the established categories is not perfectly balanced . As expected, it shows a prevalence of intermediate susceptibility categories (moderately tolerant and moderately sensitive) with a cumulative value of 47.7% on the total of examined germplasm whereas the distribution tails intercept 26.7% of susceptible (sensitive and highly sensitive) and 25.6% of resistant (tolerant and highly tolerant) accessions, respectively .C. asianum while Ouroporanga displayed the highest disease amount values  from Juazeiro and Roxa cultivars whereas Kent (among the most well-known cultivars), Irwin (with a low disease incidence) and remaining tolerant cultivars had intermediate leaf anthracnose severities (data not significant) . Since a great variability was investigated on mango orchard for DI and SS parameters within moderately tolerant and moderately sensitive groups respectively it was very difficult to discriminate significant differences among examined cultivars within these groups, respectively . In the moderately tolerant group, only Couquinho, Extrema, Fafá, Rosa Vila and Mamão showed DI values significantly lower than those of Umbigo and Imbú whereas the remaining ones, including the well-known mango cultivars Tommy Atkins and Haden .Otherwise, within the moderately sensitive group the only significant differences amongst cultivars were detected for SS parameter between well-known Kensington Pride  and Lira and Bourbon  .Within sensitive mango germplasm group , five cuOn the other hand, Rosa Astolfo Dutra exhibited a mean SS value significantly lower than those of Imperial, Torrinha and Dura whereas intermediate anthracnose sensitivity values were recorded for remaining cultivars including the well-known cultivar Keitt . In the highly sensitive germplasm group , mango cC. asianum. Other well-known mango cultivar Palmer fell in this highly sensitive germplasm group . This finding is not surprising since this latter species is worldwide reported as the most prevalent and virulent from the main mango production areas of Brazil and around the world [Colletotrichum species are involved in disease infections. Although the disease evaluation was done on mango plant canopies (leaves), these data are valuable since the fungus is polycyclic and the role exerted by leaf infections in enhancing disease amount and duration of epidemics is crucial [C. gloeosporioides s. lat. without considering accurate Colletotrichum species involved in anthracnose infections. It is also interesting note that none of the accessions examined here were resistant and none of the commercial cultivars cannot generally provide under environment humid conditions adequate qualitative and quantitative yields without scheduled fungicide spray applications [C. asianum attacks, while Kent and Irwin were categorized as tolerant accessions. On the other hand, the well-known cultivar Ubá for this Brazilian region revealed to be the most sensitive to fungal infections of C. asianum. The well-known cultivar Keitt revealed to be sensitive to anthracnose attacks.In this study, we present the first data on the evaluation of susceptibility to anthracnose leaf infections in an important mango germplasm collection of southeastern Brazil. The paper also represents the first report describing n Brazil ,24,34, whe world ,32,34,35 crucial . This is crucial ,38. Howe crucial ,40,41 as crucial ,43,44. A crucial ,49,50,51C. asianum. The establishment of susceptibility to C. asianum against which coming changes can be measured is a crucial and starting point for evaluating the tolerance of local, commercial and well-known mango germplasm under specific agronomic and phytosanitary conditions. For future studies, the methodology described here should also be used to evaluate the cultivar response to other aerial fungal pathogens affecting canopy of mango. Although host resistance alone is not resolutive, it can be considered a sustainable mean since pathologists and breeders can use it both to implement IPM strategies for mango anthracnose caused by C. asianum and to reduce number fungicide treatments.Although these findings should be confirmed under different conditions, the paper gained for the first time a preliminary insight about the susceptibility of several mango species to A wide disease survey was performed in 2014 using the most important mango germplasm collection located in Southeastern Brazil . The entire collection constitutes about two hundred accessions of mango cultivars from different countries and areas of origin. The accessions are conserved as a live collection at the Sementeira Farm located in a wide orchard subdivided into several areas. The entire collection , belonging to the Universidade Federal de Viçosa in the municipality of Visconde do Rio Branco, MG , had in the recent past growing seasons a history of severe anthracnose infections. For most cultivars tested in this paper, the degree susceptibility (with particular reference to strongly tolerant and highly sensitive cultivars) versus anthracnose was very similar to one presented here.Colletotrichum species were incubated at 25 °C in the dark on PDA. Conidia were examined after 10 days of incubation. Conidial shape and color were determined by mounting fungal structures in clear lactic acid. Measurements of 50 conidia for each representative isolate were determined at 400 and 1000× using an optical microscope with interference contrast illumination. The mean measurements of conidia were calculated. Colony characteristics were determined after seven-day period growth at 25 °C on PDA.Isolations were made from symptoms of infected leaves of all cultivars. Small sections from the edge of lesions were disinfested in 1% NaOCl (1 min), rinsed in sterile distilled water (SDW) and placed onto potato dextrose agar  with streptomycin sulfate (100 µg/mL) and incubated at 25 ± 1 °C with a 12-h photoperiod. At least two single-spore isolates were obtained from each mango sample. Morphological characterization of isolates was performed by using single conidial cultures prepared on PDA. In detail, monoconidial cultures of www.ncbi.nlm.nih.gov/blast) was queried after aligning, editing and trimming the sequences by Geneious R10 (Biomatters Ltd.). Sequences of the three loci  of reference type strains belonging to the musae clade of the C. gloeosporioides species complex, were retrieved from GenBank. The multi-locus alignment was performed using MEGA-X [The species-level identification was obtained by DNA sequencing and phylogenetic analyses of β-tubulin (TUB2), histone H3 (HIS3) and nuclear ribosomal internal transcribed spacer (ITS) region gene sequences of the 82 strains  tested in this study. Isolates were grown in potato dextrose broth  incubated at 25 °C in the dark, under shaking, for 4-6 days. Mycelial mats were collected, dried with sterile filter paper, frozen in liquid nitrogen and ground to a fine powder. Genomic DNA extraction was performed using Wizard Magnetic DNA Purification System for Food kit  according to the manufacturer’s protocol. The quality of genomic DNA was determined by agarose gel electrophoresis and quantified through a Nanodrop ND-1000 Spectrophotometer . Part of the β-tubulin gene was amplified using primers T1 ) and Bt2g MEGA-X  softwareg MEGA-X  and Tamug MEGA-X  were thei each class detected, n the number of leaves in each class, i (0-to-12) the numerical values of classes, N the total number of leaves examined. The definitive DI and SS data  was always confirmed by an adequate number of isolation attempts performed in the laboratory (at least from 10 to 20 attempts for each leaf sample with little clear symptoms) from symptomatic mango samples  at the same locations for their susceptibility to leaf anthracnose infections under natural anthracnose disease pressure. Since low disease level, data on fruit infections collected during other months are not detected. Tested mango cultivars located in four distinct areas of germplasm collection were scored for their susceptibility/tolerance to leaf anthracnose infection evaluating four plants (four replicates) by assessment of disease incidence (DI) and symptoms severity (SS) parameters. The former  was always referred to the assessment of average percentage of symptomatic leaves on entire canopy of each plant whereas SS (quantitative parameter) was accounted on 16 leaves  adopting an empirical 0-to-12 rating scale set up for evaluation of leaf anthracnose amount. This empirical scale takes into account the mean percentage of infected surface where 0 = no symptoms; 1 = up to 0.5% of infected leaf surface; 2 = 0.6 to 1%; 3 = 1.1 to 2%; 4 = 2.1 to 3%; 5 = 3.1 to 4%; 6 = 4.1 to 6%; 7 = 6.1 to 10%; 8 = 10.1 to 25%; 9 = 25.1 to 40%; 10 = 40.1 to 60%; 11 = 60.1 to 75%; 12 = more than 75% of infected leaf surface. To measure the infected leaf area, mango leaves were well extended and the relative images were captured using a scanner (HP Scanjet G2710) . From th samples F and con−1 square root x) to improve homogeneity of variances [2 value and associated p level < 0.05 indicate the significance) for experiment wise significance followed by all possible pairwise comparisons using the Mann-Whitney test  [STATISTICA package software  was used for statistical analyses according to parametric or nonparametric approaches for randomized complete block design (RCBD) with the different cultivars having four replicates. In the posthoc analysis the mean separation was conducted on DI data using post-hoc Fisher’s least significant difference test at α = 0.05. Prior to analysis, percentage values were transformed as arcsine square root  .


Glycine max), 345 wild (Glycine soja), and 18 natural hybrid (G. max/G. soja) accessions. We describe the enhanced detection of 183 domestication-selective sweeps and the patterns of putative deleterious mutations during domestication and improvement. This predominantly selfing species shows 7.1% reduction of overall deleterious mutations in domesticated soybean relative to wild soybean and a further 1.4% reduction from landrace to improved accessions. The detected domestication-selective sweeps also show reduced levels of deleterious alleles. Importantly, genotype imputation with this resource increases the mapping resolution of genome-wide association studies for seed protein and oil traits in a soybean diversity panel.Globally, soybean is a major protein and oil crop. Enhancing our understanding of the soybean domestication and improvement process helps boost genomics-assisted breeding efforts. Here we present a genome-wide variation map of 10.6 million single-nucleotide polymorphisms and 1.4 million indels for 781 soybean individuals which includes 418 domesticated ( The accumulation of recombination events in selfing species may lead to a rapid fixation of both beneficial and deleterious mutations. Here, the authors resequence 781 soybean accessions, show purging of deleterious mutation during domestication, and report genome-wide associations for seed protein and oil traits. Glycine max [L.] Merr.) is a globally important crop species, as it is a major source of seed protein and oil. Cultivated soybean (G. max) was domesticated an estimated 7000–9000 years ago from wild soybean (Glycine soja Sieb. & Zucc.) with distribution in East Asia2. The cultivation of soybean has been historically confined to East Asia and only recently expanded to North America, South America, and India, positioning it as one of the top crops in terms of growing area worldwide3. Both wild and domesticated soybean are predominantly selfing4. The accumulation of recombination events across generations in such selfing plant species may result in rapid fixation and weak selection of both favorable and deleterious mutations5. Furthermore, the reduced effective population size due to a genetic bottleneck during domestication will enhance the stochasticity of allelic fixation by genetic drift6. Deleterious mutations are hypothesized to be the genetic basis of inbreeding depression and heterosis in other major crops including maize and cassava that have outcrossing mating systems7. Thus, understanding the genome-wide patterns of deleterious mutations across the wild-to-domesticated continuum of soybean can help to better optimize soybean breeding and potentially other major crops.Soybean  array genotyping9 and whole-genome resequencing (WGS)14 have resulted in the global catalog of common and rare SNPs across the genome. However, those data have yet to be fully utilized in an integrated manner to impute marker genotypes at millions of SNP loci as performed for other plant species17. In addition, the genetic variation of wild soybean remains largely untapped and unexplored relative to that of domesticated soybean.After the release of the draft soybean genome sequenceG. max, 345 G. soja, and 18 hybrid (G. max × G. soja) accessions obtained through high-coverage (>13X) WGS data. We conduct the detection of presumed domestication-selective sweeps and the identification of putative deleterious mutations in soybean populations. We then show the usefulness of our data in genetic mapping by imputing millions of the identified SNPs to a panel of 8844 soybean accessions18 for enhancing genome-wide association studies (GWAS) of seed protein and oil traits.Here, we analyze the genomic variation of 781 soybean individuals consisting of 418 2  accessions. The G. soja and hybrid accessions were obtained from China, Korea, Japan, and the Russian Far East. The sequence data of the 781 accessions were mapped to the soybean Williams 82 reference genome ver. Wm82.a2.v18 with mean depths ranging from 14.09 to 61.27 after removing duplicate reads and covered over 95.2% of the reference genome by more than one read and over 85.4% by more than five reads for all accessions whose lower bound of coverage is >10% higher than those of rice and maize20. After variant calling and filtration steps, we retained 10,597,683 high-quality SNPs to perform most of the population analyses, with the exception of the mutation load analysis that used 30,753,511 SNPs without the 1% minor allele frequency (MAF) filter  in G. soja (0.0023) than G. max (0.0012) in our 781 soybean genome population with a G. soja level (0.0020) in hybrid samples. The results indicate that, consistent with previous observations22, roughly half of the genetic diversity has been lost during domestication from wild (G. soja) to domesticated soybean, which supports the occurrence of a bottleneck in the genetic pool during the soybean domestication process.The population structure of the 781 soybean set assessed using the 10.6 million SNPs  varied substantially along chromosomes, consistent with observations in other plants24. All chromosomes had lower recombination near the centromere repeat regions, which are presumed to be within pericentromeric regions spanning more than 10 Mbp, relative to that in euchromatin regions. This pattern of recombination frequency distribution has been well supported experimentally by studies of multi-parental maize mapping populations25, although recombination rates were detected to be almost entirely suppressed in pericentromeric regions in those mapping populations. With available estimates of the recombination rate (R) from four soybean inter-crossed bi-parental populations, which captured ~38,000 meiotic crossovers26, we compared our estimates of historical recombination rates with empirical estimates of the recombination rate. Overall, R and ρ were moderately correlated, suggesting that our historical recombination rate estimates inferred on the basis of the SNP distribution likely reflected naturally occurring recombination patterns .Genome-wide profiling of variants was performed on the Williams 82 reference genome to reveal diversity patterns in soybean , which is strongly influenced by the mutation and recombination history among many factors. LD (r2) decay was faster in G. soja than G. max , similar to those of a previous soybean study12 and outcrossing wild rice  including both perennial and annual forms29. In contrast, the LD decay was much slower than that of annual outcrossing wild maize 30. In domesticated soybean, LD increased to 97 kb similar to that of predominantly selfing cultivated rice 31 but much higher than outcrossing cultivated maize (r2 < 0.2 in 5.5 kb)19. We found that the local LD of pericentromeric (heterochromatic) regions with ~97 kb of half LD decay distance was much greater than that of euchromatic regions with ~7 kb in total population  scores were similar to those of recombination rates Fig. . A detai32. A total of 183 domestication-selective sweep regions were detected , we scanned genomic regions with extreme allele frequency differentiation over extended linked regions using a likelihood test 33, the comparison supported the selective sweeps identified in this study. Out of 42 chromosomal regions containing unique and overlapping QTL, about 70% corresponded to chromosomal regions detected by XP-CLR. Among 17 QTL that had more than 5% of phenotypic variance explained, 13 corresponded to the selective sweep regions that were detected by XP-CLR. However, because several QTL spanned more than 20 Mb around pericentromeric regions that have low recombination rates, these comparisons should not be considered conclusive but rather suggestive of findings for further study. Two (GmHs1-1 and Bloom1) of several genes implicated in having involvement in soybean domestication were supported by XP-CLR scores and frequency comparison of major domesticated alleles as weak domestication genes or hitchhikers  populations Fig. 33, the c36 showed that the process of domestication has resulted in an increased number of deleterious variants in the domesticated genome, supporting the cost of domestication hypothesis38. Here, to quantify the extent of purifying selection on deleterious alleles in the self-compatible, predominantly selfing plant soybean, we used GERP scores39. GERP scores were obtained by computing constraint for individual positions across the soybean genome on the basis of comparative genomic approaches using the whole-genome sequence alignment of 12 species including six species in the family Fabaceae. GERP identified 237.5 Mb of the soybean genome (24.3%) as evolutionarily constrained (GERP > 0), and 111.5 Mb (11.4%) as highly evolutionarily constrained with GERP > 2, which is frequently used as a cut-off GERP score to identify the deleterious mutations in constrained portions of the genome in previous studies40  of overall deleterious alleles in landraces relative to wild soybean accessions and 1.4% additional decrease (P = 0.0003) in improved lines  and 11.8% (improved lines) fewer 43. Because both domesticated and wild soybean are a predominantly selfing species, we adopted a strategy of creating pseudodiploid genomes from data for two individuals, similar to what has been done in other inbreeding species such as African rice44 and Caenorhabditis45. Using this approach, we found that domesticated soybean experienced a continual reduction of effective population size (Ne) starting ~15,000 years ago until its nadir from ~5000 to 9000 years ago  and rare in the wild population   with the imputed SNP dataset on the 8844 accessions were quite similar to our re-analysis results based on the existing SoySNP50K array genotype and phenotype dataset  analysis on the same dataset, none of the novel minor signals remained significant  gene provided an opportunity to examine the improvement of our imputed GWAS. GmSWEET39 was cloned as a gene controlling seed oil content by selection during soybean improvement and was suggested as the causal gene for the major association peak on chromosome 1556. The most significant SNP did not shift in a notable manner  gene is located in a newly observed association region (~7.0 kb) with 34 significant imputed SNPs  of 27.86) in the middle of the chromosome 15 peak, which was non-significant valley in the GWAS with the original unimputed data  Fig. , which waks Fig. . The numner Fig.  and the ata Fig. . Because58 (see ref. 42 for a review) except a recent report of sorghum28. However, those well-characterized major crops have different reproduction modes from soybean. Both wild and domesticated species of maize are predominantly outcrossing. Domesticated species of rice and sorghum tends to be selfing while their wild types are predominantly outcrossing. Both wild and domesticated species of cassava and grape are outcrossing; however, cultivated types are predominantly clonally propagated. Nonetheless, genome-wide patterns of soybean variation were similar to those of other major crops including the well-characterized maize, although the genome-wide nucleotide diversity and half LD distance estimates appeared to be unique in soybean. Moreover, soybean showed a similar demographic history as the other major crops investigated. Our results of total mutation burden comparison are in contrast to the previous studies from cassava, grape, maize, rice, and sunflower58. Interestingly, our results are similar to a substantial decrease of the homozygous-mutation burden in domesticated cassava and grape accessions, compared with progenitors36. In selfing taxa Arabidopsis, selective sweeps of extreme haplotype sharing were observed likely due to removal of variation59. Decrease of overall mutation burden from landraces to improved lines, which are inbreds, was also observed between inbred elite maize lines and their comparable landraces60. This decreasing trend of the homozygous-mutation burden somewhat reflects the behavior of recessive model for human populations that leads to a slight decrease in recessive load49. Taken together, our results suggest that the selfing reproduction mode of both the domesticated and wild forms contributed to reduction in mutation burden in domesticated relative to wild soybean accessions5.The discovery and characterization of extensive genome-wide genetic variation in the 781 diverse soybean accessions containing an enhanced number of wild soybean accessions provided us with an opportunity to find unique features of plant genomes that were largely due to both wild and domesticated species being predominantly self-pollinating. The most striking feature is that mutation burden was reduced in domesticated relative to wild soybean accessions. During the past decade, studies of deleterious alleles identified from genome-wide fine genetic variation data of major crops including rice, maize, sorghum, cassava, and grape as well as of dogs have revealed that more deleterious alleles have remained in their domesticated accessions62, which have the same reproduction mode as soybean but whose genome analyses have lagged behind due to their huge genome sizes. While there has been the lack of quantitative comparisons of deleterious alleles between domesticated and wild populations of these crops, deleterious alleles were identified from 8 soybean and 15 barley accessions and then their enrichment within genes associated with phenotypic traits were shown50. For wheat, exome capture sequence data of 890 diverse landrace and cultivar accessions were collected and the identified variants revealed the reduced deleterious allele burden by introgression and selection for improvement and environmental adaptation in cultivars compared with landraces63.The findings from this study may be extended to the characterization of wheat and barley64, a drastic reduction in nucleotide diversity (∼48%) was observed during the transition from wild to domesticated soybean. This likely reflects different reproduction modes between selfing soybean and outcrossing maize. The overall chromosomal distribution patterns of variation of several variables including recombination, gene density, and LD were also quite similar to those observed in other major crops.Of the originally resequenced 855 samples, we excluded 74 samples (8.65%), which showed high heterozygosity and low inbreeding coefficient, based on the presumed reproduction mode of soybean. The 781 soybean accessions were clearly divided into domesticated and wild accession groups with a distinct subgrouping of wild accessions according to geographic collection sites, in a similar fashion to other major crops. However, compared to maize landraces that showed only 17% diversity reduction from their wild progenitorA diverse collection of 345 wild soybean accessions were analyzed against 418 domesticated accessions to detect selective signals for soybean domestication. Although many canonical domestication genes have been cloned from major grass crop species, such knowledge has not been translated well to domestication research in eudicot seed crop species including soybean. In major crop species, cultivated species and their progenitors usually show distinct morphological and physiological differences in so-called domestication syndrome traits such as seed size, shattering, seed dormancy, flowering time, and viny growth habit. Soybean is not an exception. However, organs and tissues where several domestication traits are expressed differ between soybean and grasses. For example, shattering is related to the pod in soybean, but to pedicel in rice. Unfortunately, our analysis shows that although two of them might be regarded as weak domestication genes, none of the soybean domestication genes cloned thus far should be regarded as a canonical domestication gene. However, in this study, we reported many candidate canonical domestication genes whose alleles are almost fixed in domesticated soybean and are rare in wild soybean.28. Mutation burden was further decreased in improved lines from modern soybean breeding. The results are in stark contrast to the observations that deleterious alleles (the genetic load) that happen to be present in the neighborhood background of the strongly selected allele in the presence of selective sweeps may become more prevalent than those in other neutral backgrounds38. However, a direct comparison in terms of the accumulation of deleterious alleles may be difficult because of different reproduction modes between soybean and other well-studied crops. Purging of deleterious alleles from the domesticated soybean has been further enhanced in selective sweep regions. Unlike outcrossing species that maintained accumulated deleterious mutations in the heterozygous state, this predominantly selfing plant may have been less tolerable to the accumulation of deleterious alleles, eventually leading to the reduction of diversity. Introgression of untapped variation in wild soybean should be an important objective for the future breeding of soybean. Most of the deleterious alleles should be prioritized for elimination through breeding process, although a handful of deleterious alleles may provide beneficial effects for soybean growth or yield in crop fields. Information obtained here should help better design crossing and selfing efforts to efficiently eliminate deleterious alleles in a breeding program to select agronomically important untapped genes from wild soybean.In this selfing species, overall deleterious alleles among landraces relative to wild soybean accessions, which were defined by GERP scores, have been moderately reduced by up to almost 7%, similar to the observation in sorghumFinally, we have shown that our high-quality map of SNP variation in soybean could be used as a reference panel for the imputation of genotypes to improve GWAS of oil and protein traits. In addition to those unique genome variation features due to selfing and being a eudicot seed crop species that suggest soybean as a model for other such crops, our imputation results suggest that the soybean variation map and methods developed here can be used in a direct manner to accelerate genetic variation discovery in this economically important crop.® SoyaSNP array genotyping data of ~4400 diverse soybean accessions, most of which were collected from South Korea. These diverse soybean accessions also contained representatives from the worldwide distribution of soybean2. Soybean plants were grown in the Ochang field of the Korea Research Institute of Bioscience and Biotechnology, Cheongju, Korea. Although more than two times of single plant selection for the SoyaSNP array genotyping had been performed2, we collected young leaves from a single plant of each accession and then extracted genomic DNA using the cetyltrimethylammonium bromide (CTAB) method65. DNA sequencing was performed at LabGenomics (Seongnam) or Macrogen (Seoul) companies in Korea. Paired-end sequencing libraries were constructed with an insert size of 500 bp using a TruSeq DNA PCR-Free kit  according to Illumina library preparation protocols. Libraries were then sequenced using Illumina HiSeq 2500 or 4000 platforms with 2 × 151-bp paired reads to a target coverage of 10X. Some accessions that showed high heterozygous variant levels were sequenced multiple times. We also added resequencing data of 16 accessions determined in our previous studies66 except IT182932 that was newly sequenced in the present study. Consequently, resequencing data from a total of 855 samples were used for initial variant calling in this study . We then essentially followed procedures described in the Genome Analysis Toolkit (GATK) Best Practices for data pre-processing and variant calling68. We used BWA (version 0.1.12) with default parameters except for –M option69 to map genomic reads from each accession against soybean Wm82.a2.v1 reference genome assembly8. Alignments were further checked for PCR duplicates using Picard tools (version 1.134) (http://picard.sourceforge.net/). We performed sorting operation, base recalibration, per-sample and joint variant callings, and variant filtration using GATK (version 4.0.1.2). Known variant sites for soybean extracted from NCBI dbSNP Build 144 (https://www.ncbi.nlm.nih.gov/projects/SNP/snp_summary.cgi?build_id=144) were used for base recalibration. Raw variant calling data were divided into SNPs and indels with SelectVariants function of GATK (v. 4.0.1.2). A total of 62,987,283 SNPs and 8,567,041 indels were identified from the analyses of the genomes of 855 samples. Quality filtering of raw SNP calls was performed using VariantFiltration in GATK according to the following criteria: ReadPosRankSum of <−2.0, MQRankSum < −2.0, polymorphism confidence scores (QUAL) < 30.0, genotype call quality divided by depth (QD) < 3.0, Phred-scaled P-value of Fisher exact test for strand (FS) > 30.0, mapping quality (MQ) < 30.0, total depth of coverage (DP) < 100, genotype-filter-expression depth of coverage (DP) < 5, and genotype-filter-expression genotype call quality (GQ) < 10.0. Bi-allelic variants were then selected using VCFtools (version 0.1.15)70. To exclude erroneous variants in repetitive regions, variants with high mapping depth  in each sample were masked. Allele balance (AB) was calculated and variants with AB < 30 in heterozygous genotypes were masked71. The VCFtools was then used to remove markers that were monomorphic and markers with call rates <50%. Up to this stage of filtration, 36.8 millions of SNPs were defined as candidate variants. In the 62.9 million raw SNP calls, some samples showed more heterozygous than homozygous non-reference alleles. Those samples still showed high heterozygous rates in the 36.8 million candidate SNP set. Thus, 66 samples that contained higher than two-third heterozygous to homozygous non-reference SNPs ratios among the samples with more than 0.5 million heterozygous SNPs in the raw SNP call set were excluded from further analyses. The inbreeding coefficient per individual was then calculated as the difference between the expected and the observed heterozygosity standardized by the expected heterozygosity under Hardy–Weinberg. Based on the assumption that pure inbred lines would show inbreeding coefficients of near 1.0, we additionally excluded eight wild samples that had <0.8 inbreeding coefficients per individual in the 36.8 million candidate SNP set. Finally, 781 accessions were determined as our soybean genome variation study set. To perform population analyses using a set of 781 accessions, we further filtered these candidate SNPs by removing SNPs with >20% missing calls and >10% heterozygosity and monomorphic SNPs and then removing SNPs with minor allele frequency (MAF) < 1%. Finally, we retained 10,597,683 high-quality SNPs for population analyses of the genomes of 781 accessions except mutation load analysis, which used 30,753,511 SNPs without 1% MAF filtration. Filtering of raw indel calls was performed according to the following threshold criteria: ReadPosRankSum of <−20.0, QUAL < 30, QD < 2.0, and FS > 200. Bi-allelic variants were then selected using VCFtools (version 0.1.15)70. A resultant set of 5,717,052 indels was further filtered to obtain high-quality indels using the same cut-off criteria as the SNP filtration. From this analysis of the genomes of 781 accessions, a filtered set of 1,436,499 indels were defined. The indels were then divided into small indels and structural variants (SV) with a cut-off of sequence length of 50 bp.Short paired-end reads of 855 samples were quality checked using FastQC (version 0.11.3)  ranging from K = 2 to 12. Numbers of subpopulations were defined using the marginal likelihood function. We plotted the membership coefficient using DISTRUCT v1.176. A neighbor-joining tree was constructed by MEGA777 under the p-distances model.Principal component analysis (PCA) was conducted to summarize the genetic structure and variation present in the 781 accessions using smartpca function in Eigensoft v7.2π)78, SNP density, and Tajimas’s D79 for 100 kb were calculated with the 10.6 million SNPs using vcftools --window-pi 100000, --SNPdensity 100000, and --TajimaD 100000, respectively70. Indel and SV densities for the bi-allelic variants were calculated using vcftools --SNPdensity 100000. Population recombination rates  were estimated in the entire panel using the machine learning R package FastEPRR v1.080. Linkage disequilibrium (LD) decay was calculated using PopLDdecay v3.31 with -MaxDist 1000 -MAF 0.05 -Miss 0.1 parameters81. Measures of LD (r2) were calculated for each subpopulation using Plot_MultiPop.pl with -bin1 100 -bin2 1000 -break 5000 -keepR parameters implemented in PopLDdecay81. The r2 values were also calculated for the entire population for euchromatic and heterochromatic regions, which were previously defined by the regions with high and low recombination rate82. Pairwise LD scores were calculated from the unimputed 10.6 million SNP dataset using the genome-wide complex trait analysis (GCTA) suite (version 1.92.1) with default settings84. Circos v0.69-685 was used to display distributions of estimated variables on the Williams 82 reference genome ver. Wm82.a2.v18.Nucleotide diversity  (version 1.0)88 to annotate the 30.8 million SNP dataset. To create a soybean database, uniref90  was used as a reference protein set. Annotation of G. max Wm82.a2.v1 was downloaded from EnsemblPlants (ftp://ftp.ensemblgenomes.org/pub/plants/release-44/gff3/glycine_max). Gff3 format was converted to Ensembl GTF format. Soybean SIFT 4G database was constructed using SIFT4G_Create_Genomic_DB implemented in SIFT 4G. SIFT scores ranged from 0 to 1, and any nonsynonymous position with a SIFT score <0.05 was considered to be putatively deleterious.To predict functional effects of variants, we used Sorting Intolerant From Tolerant 4G (SIFT 4G)39 for each site in the soybean genome. GERP score reflects the strength of purifying selection based on constraint in a whole-genome alignment of multiple plant species. For the whole genome alignment, we used the LASTz/MULTIz approach (http://genomewiki.ucsc.edu/index.php/DoBlastzChainNet.pl) described for the alignment of 20 angiosperm genomes to A. thaliana reference89 with the following minor modifications. We aligned 12 soft repeat-masked genomes of Arabidopsis thaliana (TAIR10.1), Cajanus cajan (V1.0), Lupinus angustifolius (v1.0), Medicago truncatula (4.0), Oryza sativa (IRGSP_1.0), Phaseolus vulgaris (1_0), Populus trichocarpa (v3), Prunus persica (v2), Vigna radiata (ver6), Zea mays (v4) from RefSeq database (https://www.ncbi.nlm.nih.gov/refseq/), and Vitis vinifera (V2) from URGI database (https://urgi.versailles.inra.fr/Species/Vitis) to the G. max (Wm82.a2.v1) genome using LASTz v1.04.00 and MULTIz v012109. Topology of the 12 species of interest was extracted from the whole phylogenetic tree using ete3 toolkit (v3.1.1)90. The phylogenetic tree was downloaded from Phylogenetic Resources files on Dryad database (10.5061/dryad.63q27.2)91. The branch length (substitution per site) of the phylogenetic tree was calculated using phyloFit v1.592 with four-fold degenerated sites of chromosome 1 in G. max. All alignment files (maf files) were merged using MULTIz and converted to fasta format using maf2fasta function. Alignment gaps (−) in the reference genome (G. max) and sequences of the same position in other genomes were removed. Finally, we calculated GERP scores using gerpcol with –j option from GERP++39. The -j option projected out the soybean reference sequence to prevent any bias in the estimates using GERP, a method that predicts functional consequences based on phylogenetic information40. Uncalculated positions were filled with 0 because neither GERP score of N nor n sequence position in G. max genome was calculated.We estimated the individual burden of deleterious alleles based on the genomic evolutionary rate profiling (GERP) scoresPhaseolus vulgaris (1_0) and Vigna radiata (ver6) genomes as outgroups. For each variant, the corresponding nucleotides in both the outgroup genomes were identified based on the whole-genome alignment for the GERP score calculation above. We then used the est-sfs (v2.03) software93 to infer the probability of the derived versus ancestral allelic state at a polymorphic site. For estimation of GERP-based mutation load, we categorized these mutations into four categories of deleterious variants: nonsynonymous SNPs (stop mutations were included in nonsynonymous mutations in this study) with moderately-conserved deleterious mutations (2 < GERP > 4), synonymous SNPs with moderately-conserved deleterious mutations, nonsynonymous SNPs with highly conserved deleterious mutations (GERP > 4), and synonymous SNPs with highly conserved deleterious mutations. The criterion of GERP > 2 to determine conservative site was proposed by previous studies40 based on the distribution of GERP scores. For most of the mutation load analysis, a combined dataset containing all nonsynonymous SNPs with GERP > 2 was used. For estimation of SIFT-based mutation load, we referred to nonsynonymous SNPs with SIFT score <0.05 as deleterious mutations. However, because we also observed a strong reference bias that sites where the reference soybean genome carries the derived allele are much more likely to be classified as tolerated than are sites where the reference is ancestral as reported in human genetics . We then polarized derived and ancestral alleles for the 1,187,829 CDS SNPs using 43, considering each soybean accession as a single genomic haplotype. Because both domesticated and wild soybean are predominantly selfing species, we adopted a strategy of creating pseudodiploid genomes from data for two individuals, similar to those performed in other inbreeding species such as African rice44 and Caenorhabditis45. Eight samples with >19X genome coverage after removing duplicate reads for each of domesticated (G. max) and wild (G. soja) soybean were used to generate pseudodiploid genomes. Each of SNPs in genomes was called using a SAMtools v1.995 and BCFtools v1.996 pipeline. The soft-masked Williams 82 genome sequence (version Wm82.a2.v1) was used to identify repetitive regions and mask genotype calls overlapping these repetitive regions. Pseudodiploid was generated using Seqtk v1.2 (https://github.com/lh3/seqtk). Heterozygous sites were randomly selected by randbase function and each haploid was merged by mergefa function. The eight samples for domesticated soybean consisted of four (Fu yang (30), No. 39 Green, Ji li huang dou, and PI 72227) from China, two (PI87631-1 and PI87630) from Japan, and two (PI 96786 and PI 458232) from Korea. The eight samples for wild soybean consisted of four  from China, two (B07162 and PI 378691) from Japan, and two (IT182932 and YWS1588) from Korea. Analysis employed default parameters for the PSMC program. Assuming a mutation rate of 1.5 × 10−8 mutations per nucleotide per year97 and a generation time of 1 year, we converted scaled population parameters into years and Ne. We constructed pseudodiploid genomes for all 28 possible combinations of the eight accessions for each of domesticated and wild soybean. We excluded 15 pseudodiploids of the 28 wild pseudodiploids from further analysis owing to spurious PSMC profiles. Most of the 15 pseudodiploids with spurious profiles were derived from samples within countries. No such spurious profiles were observed from the domesticated pseudodiploids.We conducted historical demographic analysis with PSMC (v0.6.5-r67)9 were downloaded from SoyBase as of 10 June 201998. In this dataset, we corrected the genotypes of 3494 reverse-oriented SNP sites in Glyma.Wm82.a2. We removed 96 SNPs presumed to be absent in Glyma.Wm82.a2 because they showed a base that was different from both reference and non-reference bases. We also removed 2 mitochondrial DNA SNPs. The resultant 42,193 then underwent further filtration. From the whole set, we selected a total of 12,116 accessions for GWAS of seed protein and oil content by Bandillo et al.18. Of the 12,116 accessions, 559 with heterozygous rate >0.05 or missing rate >0.05 were removed. We calculated identical-by-descent (IBD) values for all pairwise comparisons among 11,557 G. max accessions using PLINK v1.999. We considered pairs of accessions to be duplicated if they had an IBD > 0.98100. As a result, 3272 duplicates were removed, leaving 8844 non-duplicated accessions with high-quality genotype data. In this set of 8844 accessions, SNPs with heterozygous rate >0.02, minor allele frequency <0.02, and missing rate >0.10 were discarded from the genotype data, leaving a total of 36,489 high-quality SNPs for the imputation of soybean haplotype data and GWAS. Beagle v5.0 was used to impute the SoySNP50K data using 4,467,134 SNP data on chromosomes with MAF > 0.02 from 481 G. max genomes. The strategy that we used 481 G. max genome data instead of the whole 781 soybean genome is similar to imputation strategies in human genetics that use a part of reference haplotype panels closely related to subjects . A genetic map constructed from a population of 233 recombinant inbred individuals derived from a cross between Williams 82K and IT18293226 was used as the fine-scale recombination map input for imputation. Genotype imputation accuracy was assessed in terms of the squared correlation (r2) between the true allele dosage and the imputed posterior allele probability implemented in the Beagle program103. After genotype imputation, we filtered out poorly imputed (Beagle r2 < 0.3) and low MAF (<0.01) SNPs, resulting in 3,082,234 SNPs available for GWAS analysis.Genotype data in soysnp50k_wm82.a2_41317.vcf that consisted of 42,291 SNPs scored on 20,087 germplasm accessions using the Illumina Infinium SoySNP50K BeadChip18 was also conducted for a comparison using 37,142 SNPs located on the 20 soybean chromosomes with frequency >2%. Missing variants were imputed using Beagle v5.0104 with default option. We used GEMMA v0.98.1105 to infer the correlation between each variant and seed oil and protein content. We first estimated a relatedness matrix from genotypes using the -gk 1 option in GEMMA. Then, we assessed evidence for correlation in a univariate linear mixed model (LMM) framework using the -lmm 4 option. We also assessed evidence for testing marker associations between oil and protein content as well as for estimating genetic correlations between oil and protein content in a multivariate linear mixed model (mvLMM). The Benjamini–Hochberg procedure106 was used to account for multiple testing by controlling the false discovery rate (FDR) at 5%. Manhattan plots were constructed to display GWAS results using qqman v0.1.487 in R package. The GWAS procedure for seed protein and oil content on the 8844 accessions using genotype data that imputed 4.5 million SNPs into SoySNP50K array data of 8844 accessions were essentially the same as that for unimputed SoySNP50K data.For GWAS for seed protein and oil content on the 8844 accessions using the original SoySNP50K data, of the 36,647 high-quality SNPs, 36,498 SNPs located on 20 soybean chromosomes were used. GWAS on the 12,116 accessions originally used by Bandillo et al.107 for implementing a multi-locus mixed-model (MLMM)108 to resolve association signals involving large-effect genes was used to further identify SNPs potentially associated with the oil and protein traits. The MLMM method relies on a simple, stepwise mixed-model regression procedure with forward selection and backward elimination while re-estimating the genetic and error variances at each step of the regression. This method may well lead to higher detection power and a lower FDR relative to traditional single-locus approaches. Because the imputed data appeared to exceed the computing power available, we reduced the number of markers by linkage disequilibrium (LD)-based marker pruning in PLINK v1.9 software99. Briefly, we pruned markers from imputed data using the --indep-pairwise 100 25 0.99 option in PLINK. This option considers a window of 100 SNPs, calculates LD between each pair of SNPs in the window, and finally removes one of a pair of SNPs if the LD is >0.99. Next, overlapping SNPs between the imputed data and SoySNP50K data that were deleted during pruning were added back to the pruned data, resulting into 291,388 markers for MLMM models.A modified genome-wide approachFurther information on research design is available in the Supplementary InformationReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3


Single nucleotide polymorphism array (SNP-array) has been introduced for prenatal diagnosis. We aimed to evaluate the clinical value of SNP-array in the diagnosis of fetal chromosomal anomalies.A retrospective study was conducted on 5000 cases tested by SNP-array, and the results of 4022 cases analyzed by both karyotyping and SNP-array were compared.SNP-array analysis of 5000 samples revealed that the overall abnormality detection rate by SNP-array was 12.3%, and the overall detection rate of clinically significant copy number variations (CNVs) by SNP-array was 2.6%. SNP-array identified clinically significant submicroscopic CNVs in 4.5% fetuses with anomaly on ultrasonography, in 1.6% of fetuses with advanced maternal age (AMA), in 2.5% of fetuses with abnormal result on maternal serum screening, in 2.9% of fetuses with abnormal non-invasive prenatal testing (NIPT) results and in 3.0% of fetuses with other indications. Of the 4022 samples analyzed by both karyotyping and SNP-array, SNP-array could identify all the aneuploidy and triploidy detected by karyotyping but did not identify balanced structural chromosomal abnormalities and low-level mosaicism detected by karyotyping.SNP-array could additionally identify clinically significant submicroscopic CNVs, and we recommend the combination of SNP-array analysis and karyotyping in prenatal diagnosis. Standard G-banded karyotype analysis is the conventional cytogenetic technique used in prenatal diagnosis, which can detect chromosomal aneuploidies, polyploidies, mosaicism, and structural abnormalities such as balanced or unbalanced translocations or inversions, and deletions or duplications (>5–10 Mb). However, it has several disadvantages such as low resolution, long turnaround time and high rate of culture failure. The development of CMA allows us to identify chromosomal aneuploidy as well as submicroscopic imbalance on the whole genome with short reporting time and higher resolution, and it is recommended as a first-tier approach in prenatal diagnosis for detection of copy number variations (CNVs) in fetuses with structural anomalies observed by ultrasound  ID System , which could amplify 20 STR loci and the amelogenin gender marker simultaneously. In addition, 4022 pregnant women chose to perform SNP-array and G-banded karyotyping simultaneously, and 978 pregnant women chose to perform SNP-array only. G-banded karyotyping was performed for 4022 (80.4%) samples according to the principle of ‘An International System for Human Cytogenetic Nomenclature, ISCN2013’ as described previously of the 4941 amniotic fluid samples were cultured. Chorionic villi were rinsed by saline solution three times and separated using needles under a dissecting microscope. Then genomic DNA of chorionic villi and amniotic fluid were extracted by QIAamp DNA Mini Kit , and genomic DNA of cord blood was extracted by QIAamp DNA Blood Mini Kit . Maternal cell contamination were ruled out for all the 5000 samples by short tandem repeat (STR) profiling using Microreaderreviously.This was a selective cohort of pregnant women during the study period of Sep 2015 to Feb 2020 who received SNP-array ± karyotyping only. There were women who had karyotyping only during the study period which were not included. There was no detailed information on the number of patients who had maternal serum screening or NIPT in the cohort.1, ClinVar2, Database of Genomic Variants (DGV)3, DECIPHER4, Online Mendelian Inheritance in Man (OMIM)5, and literatures in PubMed6. Microdeletions less than 200 Kb or microduplications less than 500Kb in size and benign CNVs were not reported unless they contained genes known to be associated with disease. We also reported mosaicism greater than 30% and loss of heterozygosity (LOH) of more than 10 Mb in size. If SNP-array detected a CNV in prenatal samples, the parents may choose to perform SNP-array to identify the origin of the CNV. And if a VOUS CNV unrelated to the clinical features was confirmed to be inherited from an unaffected parent, the classification of this CNV will be changed to be likely benign after precluding special considerations listed by the standards and guidelines released by the American College of Medical Genetics  according to the manufacturer’s protocol. 250 ng genomic DNA was digested, ligated, PCR amplified, purified, fragmented, labeled and hybridized to the Affymetrix Cytoscan 750K array, which includes 550,000 CNV markers and 200,000 SNP markers. After washing, staining and scanning of arrays, raw data were analyzed by Chromosome Analysis Suite (ChAS) 3.2 . CNVs were called at a minimum length of 50 Kb containing at least 20 contiguous markers, interpreted and classified as pathogenic (P), likely pathogenic (LP), variants of uncertain significance (VOUS), likely benign (LB) or benign (B), according to the standards and guidelines released by the American College of Medical Genetics . Some daGenetics .de novo in origin. Of 389 cases with CNV, a total of 418 CNVs were identified for 2 CNVs were detected in 27 cases respectively and 3 CNVs were identified in 1 case. One hundred and forty-five CNVs  were classified as pathogenic (P) or likely pathogenic (LP), 157 CNVs  were classified as variation of uncertain significance (VOUS), and 116 CNVs  were classified as likely benign (LB). Moreover, the 5000 samples were categorized into five groups according to the indications for invasive prenatal testing and discussed below , including 207 cases with numerical chromosome anomalies , 21 cases with LOH  and 389 cases with CNV  . Among 2ed below .P = 0.09881).A total of 1055 samples showed fetal anomalies on ultrasound scan, and SNP-array analysis detected 165 samples with chromosome abnormalities , including 66 cases with numerical chromosome anomalies , 2 cases with LOH  and 97 cases with CNV  . The 105SNP-array analysis was performed for a total of 1784 pregnant women with AMA (≥ 35 years old), and the average age of this group is 37.65 years (range: 35–49 years) . 193 samIn our center, second trimester maternal serum screening was conducted by measuring AFP and free β-HCG levels in maternal serum. A total of 1199 samples with abnormal result on maternal serum screening were analyzed by SNP-array, and abnormal results were observed in 108 samples , including 21 cases with aneuploidy , 3 cases with LOH  and 85 cases with CNV  . The 119Currently, whole-genome sequencing-based NIPT could be used for detecting aneuploidy as well as genome-wide CNVs (> 10 Mb) . SNP-arrin vitro fertilization, medication use or toxic exposure during pregnancy, consanguineous marriages and parental anxiety (Table 1). The results of SNP-array revealed that 63 samples  had chromosomal abnormalities, including 1 case with aneuploidy , 3 cases with LOH and 59 cases with CNV (The rest (709 samples) had other indications for prenatal testing, such as history of adverse pregnancy, parental genetic abnormalities, with CNV .Traditional karyotype analysis were performed simultaneously on 4022 samples  of our cohort . Of thesIn this study, we investigated the clinical value of SNP-array in prenatal diagnosis in a cohort of 5000 pregnancies. The detection rate of abnormalities by SNP-array was 12.3%, including 4.1% of cases with numerical chromosome anomalies, 0.4% with LOH and 7.8% with CNV . And theP = 0.55)  clinically significant submicroscopic CNVs in fetuses with abnormal prenatal ultrasound findings , which i = 0.55) .In the group of AMA, clinically significant submicroscopic CNVs were detected by SNP-array in 1.6% (29/1784) of fetuses with AMA , which iIn the group of abnormal result on maternal serum screening, SNP-array identified clinically significant submicroscopic CNVs in 2.5% (30/1199) of fetuses with abnormal result on maternal serum screening , which iIn the group of abnormal NIPT results, SNP-array analysis detected clinically significant submicroscopic CNVs in 2.9% (15/515) of fetuses with abnormal NIPT results, while in the group of other indications, clinically significant submicroscopic CNVs were detected by SNP-array in 3.0% (21/709) of fetuses with other indications . In thisWe also compare the results of karyotype and SNP-array analysis on 4022 samples  of our cohort. As SNP-array can identify submicroscopic CNVs and LOH not detected on karyotyping, 286 abnormal results  were additionally revealed by SNP-array analysis over G-banded karyotyping. Apart from 2 cases of mosaic 45, X, SNP-array analysis detected clinically significant submicroscopic CNVs in 2.9%  of fetuses with a normal karyotype. And the discrepant results between karyotyping and SNP-array analysis for samples with mosaicism or structural rearrangement detected by karyotyping could be due to direct (uncultured) analysis by SNP-array, and structural rearrangement  and low-level mosaicism (<10–15%) not detectable by SNP-array. As conventional karyotyping is still valuable in the identification of balanced structural chromosomal abnormalities and low-level mosaicism, we recommend the combined application of karyotyping and SNP-array analysis in prenatal diagnosis.In this cohort, the detection rate of VOUS CNVs by SNP-array was 2.9% , which iIn summary, a retrospective analysis was performed on a cohort of 5000 pregnancies, and the detection rate of chromosome abnormalities by SNP-array was 12.3%, including 4.1% of cases with numerical chromosome anomalies, 0.4% with LOH and 7.8% with CNVs. The overall detection rate of clinically significant CNVs by SNP-array was 2.6%, and we recommend the combined application of karyotyping and SNP-array analysis in prenatal diagnosis. According to the indications for invasive prenatal testing, SNP-array identified clinically significant submicroscopic CNVs in 4.5% fetuses with anomaly on ultrasonography, in 1.6% of fetuses with AMA, in 2.5% of fetuses with abnormal result on maternal serum screening, in 2.9% of fetuses with abnormal NIPT results and in 3.0% of fetuses with other indications.biowt@njmu.edu.cn.The genotyping data for this article are not publicly available to assure patient confidentiality and participant privacy. Requests to access the datasets should be directed to TW, The studies involving human participants were reviewed and approved by the institutional Ethics Committee of The Affiliated Suzhou Hospital of Nanjing Medical University. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.JX, QZ, and TW were responsible for testing strategy design and manuscript preparation. XS and CH carried out the SNP-array analysis. JX, JM, ML, and YL performed data analysis and interpretation. YD and QZ conducted genetic counseling. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Anopheles funestus, which plays a major role in transmitting malaria in sub-Saharan Africa, carries several common chromosomal inversions. Three of these inversions have been implicated in traits such as indoor resting behavior, which affects their rate of contact with both humans and insecticide-based interventions. Inversions therefore have relevance for malaria epidemiology and control. Inversions are traditionally identified by examining chromosomes under a microscope, but this method is difficult and time-consuming, and only applicable to a subset of female adult mosquitoes. To overcome this limitation, we developed high-throughput DNA-based diagnostic methods to predict the presence of inversions. The availability of these methods will allow scientists to more easily identify inversions in larger samples of mosquitoes, from all life stages and both sexes, which will help us determine how inversions are affecting malaria transmission.Chromosomal inversions occur when a segment of DNA breaks in two places, rotates 180 degrees, and reattaches. Inversions can protect sets of genetic variants, including those adapted to local conditions, from being split up in the random shuffling that occurs when genetic information is transmitted from one generation to the next. For this reason, inversions can play a role in local adaptation and range expansion. Like many malaria mosquitoes, Anopheles funestus. We ascertain and validate tag single nucleotide polymorphisms (SNPs) using high throughput molecular assays that allow rapid inversion genotyping of the three most common An. funestus inversions at scale, overcoming the cytogenetic karyotyping barrier. These same inversions are the only available markers for distinguishing two An. funestus ecotypes that differ in indoor resting behavior, Folonzo and Kiribina. Our new inversion genotyping tools will facilitate studies of ecotypic differentiation in An. funestus and provide a means to improve our understanding of the roles of Folonzo and Kiribina in malaria transmission. Polymorphic chromosomal inversions have been implicated in local adaptation. In anopheline mosquitoes, inversions also contribute to epidemiologically relevant phenotypes such as resting behavior. Progress in understanding these phenotypes and their mechanistic basis has been hindered because the only available method for inversion genotyping relies on traditional cytogenetic karyotyping, a rate-limiting and technically difficult approach that is possible only for the fraction of the adult female population at the correct gonotrophic stage. Here, we focus on an understudied malaria vector of major importance in sub-Saharan Africa,  Drosophila, Simulium, and Anopheles mosquitoes—whose giant polytene chromosomes [Paracentric chromosomal inversions result from the breakage and end-to-end reversal of a segment of one chromosome arm. This type of chromosomal rearrangement is ubiquitous across plant and animal species ,2,3, butomosomes  form disomosomes ,6,7. Theomosomes . If an iDrosophila, Simulium, and Anopheles share characteristics that might make them prime candidates for paracentric inversion-influenced speciation, among them low chromosome number, active dispersal, and high vagility relative to environmental heterogeneities that can be perceived by these insects. The observations underpinning his model came from arguably the most extreme dipteran group in this regard, Anopheles mosquitoes, with high dispersal ability and only three chromosomes. His model envisioned a species such as Anopheles gambiae distributed across a spatially heterogeneous environment, with recurring “boom-bust” demographic cycles associated with temporal  heterogeneities. Inversions arising in populations locally adapted to conditions at the ecological or geographic margins of the species range would capture allelic variation that could facilitate range expansion, while also permitting more efficient utilization of spatial and temporal heterogeneities in the central range.Although lacking a basis in formal genetic modeling, Coluzzi’s 1982 theory of ecotypification ,8. In hiAnopheles gambiae complex, in which at least 31 fixed and polymorphic inversions have been described [Plasmodium susceptibility [Among anophelines, paracentric chromosomal inversions have been most extensively studied in the Afrotropical sibling species group known as the escribed ,10. The escribed . These mescribed ,12. The escribed ,13. Fielescribed ,18,19,20tibility ,22,23, aAn. gambiae complex, Anopheles funestus is another primary African malaria vector of equal or sometimes greater efficiency, but one that remains relatively understudied [An. funestus extends malaria transmission into the dry season, peaking in abundance at this time after the populations of other vector species have waned. Cytogenetic studies have revealed at least 17 paracentric chromosomal inversions segregating in An. funestus [An. gambiae complex [An. funestus inversions in local adaptation and assortative mating [An. funestus, referred to as Folonzo and Kiribina chromosomal forms, are marked by contrasting degrees of inversion polymorphism [An. funestus requires cytogenetic analysis.Broadly co-occurring throughout sub-Saharan Africa with the primary vectors in the rstudied ,25,26. Ifunestus ,29,30,31 complex . Investie mating ,33, as we mating . In the morphism ,36. Theymorphism ,38, but An. funestus in particular, these difficulties are compounded because its polytene chromosomes are relatively difficult to work with [An. gambiae and An. coluzzii in the An. gambiae complex [Traditional cytogenetic karyotyping relies on observation of banding patterns in polytene chromosomes under a phase-contrast microscope. This approach is restrictive for any anopheline species, as the most favorable polytene chromosomes  develop in the ovarian nurse cells after blood feeding. Accordingly, cytogenetic karyotyping is limited to adult females at a specific gonotrophic stage, and only those whose chromosomes have been adequately fixed in the appropriate solution and preserved at the correct (cold) temperature . Furtherork with . Recentl complex . Tag SNP complex ), and hi complex . We founAn. gambiae s.l. to achieve high throughput genotyping of three of the most highly polymorphic and geographically widespread inversions in An. funestus, notably those used to discriminate the Folonzo and Kiribina ecotypes . In the absence of a database comparable to Ag1000G for An. funestus, we performed deep Illumina sequencing of 186 previously cytogenetically karyotyped An. funestus from Burkina Faso, where Folonzo and Kiribina co-occur, and 15 unkaryotyped An. funestus from six other countries. After calling variants in the rearranged regions with respect to the recent chromosome-based An. funestus assembly [An. funestus from Burkina Faso. We achieved relatively high concordances between molecularly and cytogenetically assigned karyotypes for each of the targeted inversions in An. funestus. Our study provides tools to advance the study of inversion-influenced ecotypification in this important but neglected vector species.Here, we implement a strategy similar to that used in assembly , we compAn. funestus samples used for whole genome sequencing and candidate tag SNP ascertainment were from historical collections, conducted indoors by pyrethrum spray catch from 11 villages in the Sudan-Savanna or Guinean-Savanna ecoclimatic zones of Burkina Faso, between 2000 and 2002   . These hPantoea sp., Asaia bogorensis, Enterobacter asburiae, Klebsiella oxytoca, K. variicola, and Pseudomonas aeruginosa) and PhiX genomes using BWA v.0.7.15 [An. funestus AfunF3 reference assembly [Genomic DNA was individually extracted from a combined total of 202 specimens from seven African countries , followiv.0.7.15 , with onassembly  using BWhttp://lh3lh3.users.sourceforge.net/snpable.shtml).Variants were called separately for each individual mosquito using GATK v.3.5  and HaplAlthough cytogenetic karyotype information was available for the mosquito sample from Burkina Faso, this information was lacking for the specimens collected from other countries. Fortunately, independent of knowledge (or lack thereof) about cytogenetic karyotypes, it is possible to infer inversion genotypes from population-based high-density SNP genotype data, owing to the population substructure created by suppressed recombination in the inverted region. Ma and Amos  demonstrAn. funestus cytogenetic photomap [An. gambiae and An. coluzzii [As a first step toward that goal, we exploited the local PCA approach just described to impute inversion genotypes at 2Ra, 3Ra, and 3Rb computationally. We used the high-density population genomic SNP data of specimens from seven African countries , limitinphotomap . Followicoluzzii , we conscoluzzii , we compcoluzzii . We reprAn. gambiae and An. coluzzii specimens with previous cytogenetic karyotype assignments [This approach depends upon the observation of a three-stripe pattern in plots of PC1 and PC2. Specimens not conforming to this expected pattern (28 of 201), presumably due to cryptic population structure or admixture with an unsampled population, could not be confidently genotyped by PCA and were thus excluded from tag SNP development , althougignments , these mIn the last step, we calculated the concordance between the PCA-based inversion genotype  and each candidate tag SNP genotype  for all mosquitoes in the sample that could be genotyped at the focal SNP and by PCA. SNPs capable of accurately predicting inversion genotype should have allelic states that are strongly correlated with inversion status. Given that the Fumoz reference represents an un-inverted karyotype across the genome, reference alleles at candidate tags are expected to be associated with standard arrangements, while alternate alleles should be associated with inverted arrangements. For each candidate tag SNP in a focal inversion , we measured the proportion of mosquitoes in the sample whose PCA-based inversion genotype, expressed as the number of chromosomes carrying the inversion in question, matched the SNP genotype, expressed as the number of alternate alleles. Candidate tags were defined as those whose genotypes agreed with the PCA-based genotype in at least 80% of mosquitoes.Our ultimate goal was to ascertain biallelic SNPs whose alternative alleles were strongly (>80%) correlated with opposite orientations of the inversions of interest . The association between alleles at a SNP and inversion orientation in a population polymorphic for the inversion is maintained by suppressed recombination inside a heterozygous inversion region and potentially also by selection, but the association is typically not absolute because of double crossover events and gene conversion. Importantly, no individual candidate tag SNP need be perfectly deterministic of inversion orientation for successful genotyping, because the intended high throughput molecular genotyping approaches , below aAn. funestus collection was used for validation of candidate tags  SNP genotyping assays were designed at an early stage in the assembly of the chromosome-based AfunF3 reference genome , thus foAn. gambiae genomic DNA, assuming a haploid genome size of 0.27 pg; [Quantification of genomic DNA extracted from the validation specimens  was cond0.27 pg; ), 28 thrWe used the multiplexed amplicon sequencing (AS) approach called GT-seq . As a fThe final libraries prepared by the GBCF were constructed without optional exo-SAP treatment following Reference  except fAn. funestus individuals  using the Illumina Kapa Library Quantification Kit . The libraries were then normalized to a concentration of 4 nM and pooled for sequencing. The final pooled library containing 235 te study ) was seqhttps://github.com/GTseq), sequencing data were demultiplexed into single fastq files for each individual sample. Individuals were genotyped at each locus with a perl script (GTseq_Genotyper_v3.pl) that counts the occurrence of each allele at a locus within individual fastq files. The ratio of allele 1 to allele 2 counts was used to generate a genotype for each locus with total read counts > 10, following the methods and cut-offs of Reference [Using scripts described in the bioinformatics pipeline of Reference  and avaieference .For both OA and AS approaches, data quality was checked using two measures. First, we assessed the tag SNP call rate, a tag-specific value representing the percentage of mosquito specimens in the sample with a genotype call at the focal tag. Any tag with a call rate < 80% was eliminated from the genotyping panel. Second, we calculated the specimen call rate, a specimen-specific value representing the percentage of tag SNPs that could be confidently genotyped in a focal mosquito. If the specimen call rate was <80%, that mosquito specimen was excluded from further analysis.Following data quality filtering, a multi-locus inversion genotype was calculated for each mosquito based on a custom python script. Genotypes at individual tag SNPs represent the count of alternate alleles at that tag. The multi-locus inversion genotype represents the average number of alternate alleles across all tag SNPs scored in a given inversion. Binning of this average  produces the predicted inversion genotype of 0, 1, and 2, respectively.https://figshare.com/projects/Anopheles_gambiae_An_coluzzii_and_An_funestus_molecular_inversion_karyotyping_raw_amplicon_sequencing_data/81128 and https://figshare.com/projects/Anopheles_funestus_SNPs_within_inverted_regions/89024, and in the Sequence Read Archive under BioProject ID PRJNA660016, accession numbers SAMN15932485 to SAMN15932719. Code used to generate the data can be found on Github (https://github.com/GTseq and https://github.com/rrlove/molec_karyo_notebooks).Data can be found at An. funestus from Burkina Faso . The tag SNP call rate was below the 80% threshold for eight tags , OA remains a cost effective option that is still widely used [Consistent with the performance of OA and AS methods in  gambiae , we founfunestus  that coufunestus , OA may ely used .An. gambiae [An. funestus, we were constrained both by small sample size overall and limited numbers of specimens from geographic regions other than Burkina Faso. This may be one factor responsible for the apparently lower performance of molecular genotyping in An. funestus observed in this study relative to An. gambiae [An. funestus analogous to Ag1000G [An. gambiae complex, we anticipate that chromosomal inversions are instruments of ecotypic differentiation rather than taxonomic boundaries [In contrast to the ascertainment of tag SNPs for inversion genotyping in  gambiae , for An. gambiae . A publi Ag1000G , broadly Ag1000G ,38,44,58undaries ,60, but An. funestus, we individually sequenced a collection of cytologically karyotyped mosquitoes and ascertained tag SNPs highly correlated with inversion genotype in inversions 2Ra, 3Ra, and 3Rb. We developed high throughput molecular assays that target these tags, both by probe hybridization and by amplicon sequencing, and validated these assays against an independent sample of cytologically karyotyped An. funestus. Both methods are more than 90% accurate, and because they can be performed at scale, they open up the possibility of studying the role of polymorphic inversions in the adaptive divergence of one of the most important malaria vectors in Africa.To overcome the serious constraints of cytogenetic karyotyping of chromosomal inversions in


PRL) gene is a single chain polypeptide hormone belonging to a family of growth hormone genes that are mainly synthesized in the anterior pituitary gland in all vertebrates. It has a significant effect on reproductive traits and EP. Single nucleotide polymorphisms (SNPs) present in PRL are a useful molecular marker for EP. This study aimed to identify the PRL polymorphisms based on these SNPs and to uncover the associations with reproductive traits in ILD.Reproductive traits play an important role in population increases and the egg production (EP) abilities of Indonesian local ducks (ILD). The prolactin  ducks and their reciprocal crosses, namely, Gallang (F1) and Maggal (F1), were maintained and specific variables were recorded, that is, age at first egg, body weight at first egg, first egg weight, and EP, for 90 days. Allele and genotype frequencies were used to determine the Hardy-Weinberg (H-W) equilibrium. The association between the SNP genotypes of PRL in ILDs is polymorphic. A SNP was found at position 164 nt (c.164G >A), consisting of three different genotypes, namely, GG, GA, and AA. The genotypes of Tegal and Magelang (F0), and Gallang (F1) populations were not in H-W equilibrium. The Maggal population (F1) was in H-W equilibrium. Significant associations were detected between the genotypes and EP in all ILDs (p<0.01), following a regression line of y=2.337x+64.605, with a determination coefficient of 0.0188 (r=0.14).This study successfully amplified a polymerase chain reaction product of 190 bp, which was used to identify the SNP. Results indicated that PRL can be recommended as a candidate gene for reproductive traits in ILD, especially EP. Indonesian local duck (ILD) germ plasma is an important Indonesian source of protein (eggs and meat) and income for rural communities, contributing 14.72% or 290.10 thousand tons of the national egg demand . Duck faPRL is a single chain polypeptide hormone belonging to a family of growth hormone genes that are mainly synthesized in the anterior pituitary gland in all vertebrates [PRL) has a significant effect on reproductive traits and EP [PRL is regulated by sequences in the 5′-flanking region through binding to specific transcription factors [PRL expression through certain promoter binding sites.Population genetic diversity and polymorphisms form the basis of breeding application technologies in livestock utilization. The phenotypic variation of a population expressed as a quantitative characteristic is the reflection of the population’s genetic makeup. tebrates ,6. Specitebrates . Prolacts and EP . Express factors . Other pPRL gene is associated with the reproductive traits of ducks, including egg shell strength, EP, and egg weight, the genes of which are located in exon 2, 4, and 5, respectively [PRL single nucleotide polymorphisms (SNPs) are located in exon 5, are significantly associated with parent behavior and EP [PRL SNPs with reproductive traits in ILD populations, especially Tegal and Magelang (F0), and the reciprocal crosses (F1), has not been done.The ectively . Chickenr and EP , and arer and EP ,10. HowePRL SNP and its association with the reproductive traits of Indonesia’s local duck populations. This research intends to obtain genetic markers based on PRL SNPs that can be used as a basis for early selection of local ducks and to provide important references for the improvement of biomolecular-based genetic quality in ILD breeding programs.This study aimed to identify the The experimental protocols were approved by the Animal Ethics Committee, Jenderal Soedirman University No. 158/UN.23/14/PN.01.00/2019.ad libitum. The reproductive traits observed included age at first egg (AFE), body weight at first egg (BW), first egg weight (FEW), and EP for 90 days. The AFE was obtained from individual records of the hatching dates up until the first egg laying. The duck body weights were measured when the first egg was laid to obtain BW. Eggs were also weighed to obtain the FEW. The EP was obtained by adding the number of eggs each duck laid within 90 days from the 1st day of egg laying.The study included 280 female Tegal (F0) and Magelang (F0) ducks, and their reciprocal crosses, namely, Gallang (F1) and Maggal (F1) each with equal numbers  and of the same age (4-months-old) were collected from March 2019 to February 2020. The population was reared at an experimental farm of Faculty of Animal Science, University of Jenderal Soedirman, Indonesia, under uniform management regarding ethical animal standards. The pen mating model with an open-sided house system was equipped with individual nesting chambers of 1.5×1.5 m/pen. Forty pens were used in the study, each of which contained one male and seven females. Laying ducks were given  a 160 g/PRL gene was based on data from GenBank , namely, Anas platyrhynchos. Primer pairs were identified in the conserved area and analyzed using Oligoprimer Design Software with an Online Primer3 program (http://www-genome.wi.mit. edu/cgi.bin/ primr3.cgi/results_from-primer3). The primer pairs used were forward PRL-AnasPF primers, namely: L 2294 5’- ATAACGCCTCTCCTTGCTGA-3’, and reverse PRL-AnasPR, namely: H 2463 5’- TTTTCCTCCCCCTCTGTCT-3’. A PCR product of 190 bp was amplified, sequenced, and analyzed. The amplification process was performed through PCR using the GeneAmpR PCR system thermocycler 2400 (Perkin Elmer). The PCR reagent mixture consisted of 12.5 μL KAPA (PCR Kit), 1μL (10 pmol/μL) of each primer, 9.5 μL dH2O free nuclease, and 1 μL DNA template, with a total volume of 25 μL. The PCR cycle consisted of four stages, namely: Pre-denaturation at 94°C for 5 min, denaturation at 94°C for 30 s, annealing at 55°C for 45 s, elongation (extension) at 72°C for 1 min, and post elongation at 72°C for 5 min, which was repeated for 35 cycles. The PCR product was separated by electrophoresis in low melting agarose gel 1% and 1×TBE buffer in the Submarine Electrophoresis device  at 50 V for 15 min. The PCR products were then visualized by UV illumination.DNA genomes were isolated from 280 4-month-old local ducks by drawing blood samples and using the DNA Isolation Kit High Pure PCR template preparation , according to the manufacturer’s protocol. Isolated DNA was used as a PCR template without the purification process. The primary design of oligonucleotides specific for the PRL-AnasPF and PRL-AnasPR). The sequencing process was performed by PT Genetics Science Indonesia. The sequencing product was read using Sequence Scanner v1.0 software, in the form of an electropherogram consisting of nucleotide sequences from the PRL gene samples of Tegal and Magelang ducks (F0), and their reciprocal crosses (F1). Each nucleotide produces peaks with different colors on an electropherogram, that is, adenine (A)=Green, guanine (G)=Black, cytosine (C)=Blue, and thymine (T)=Red.PCR products were sequenced using the same primers (PRL gene polymorphisms were determined using the BioEdit v7.2.0 program (https://bioedit.software.informer.com/7.2/) by aligning the sequencing results to those obtained from GenBank (JQ677091.1) using the ClustalW (accessory application) option. Alignment results were viewed on the electropherogram so that the SNP is obtained at a certain position, which was then used to determine the genotype of each individual, for example, on SNP c.164G >A, alleles G, and A were observed so that there were three pairs of genotypes, namely, GG, GA, and AA for the entire data population. Individuals with genotypes that have the same allele pairs are called homozygotes, while differing allele pairs are called heterozygotes., where FAn is frequency of gene A at the nth locus, which were then used to test population equilibrium based on the Hardy-Weinberg (H-W) principle. Pearson’s Chi-square test was used to verify that the samples did not deviate from H-W equilibrium. The test statistics were computed using the formula  where X2 represents the Chi-square value, Oi the observed frequency, Ei the expected frequency, and n the number of possible outcomes for each event [Genotype and gene frequencies were calculated based on Pichner  using thch event .The mean and standard deviations of reproductive traits were calculated for each breed and genotype. Genotypic associations with reproductive traits were determined using one-way analysis of variance and the GLM procedure of SAS Institute (2001), according to the following model:Y=μ + G + e2) representing the variance proportion (%), accounted for by a genotype for each reproductive trait, was also computed. The genotypic value was determined based on its dominance: GG is the dominant homozygote with a value of 3, heterozygote GA with a value of 2, and a recessive homozygous AA with a value of 1.where Y=dependent variables, μ=population mean, G=genotypes’ effect on reproductive traits, and e=error term. If a genotype or haplotype was found to act significantly on reproductive traits, then a Tukey test was performed using a significance level of p<0.05 and p<0.01. Linear regression analyses were conducted to assess the linear trend of the reproductive traits across genotypes. The coefficient correlation (r) obtained was then compared with the standard grouping according to , that isBased on the results from this study, the reproductive traits, that is, AFE, BW, FEW, and EP, obtained from 280 ILDs consisting of Tegal (F0) and Magelang (F0) ducks, and the reciprocal crosses, namely, Gallang (F1) and Maggal (F1), were relatively varied. The AFE of base (F0) and offspring generations (F1) differed significantly (p<0.01). FEW and EP traits were also significantly different (p<0.05). The BW of Tegal ducks (F0) differed significantly (p<0.01) from Magelang ducks (F0) and their crosses (F1). Magelang ducks tended to have higher means of reproductive traits compared to Tegal, Gallang, and Maggal ducks .A PCR product of 190 bp specific to the primer pair used was obtained after the optimization of the PCR process. The successful amplification was demonstrated by PCR fragments separated by electrophoresis in low melting agarose gel 1% and 1×TBE buffer in the Submarine Electrophoresis device , at 50 V for 15 min .PRL gene of ILDs were aligned with data obtained from GenBank (JQ677091.1), using the ClustalW and BioEdit programs (https://bioedit.software.informer.com/7.2/) and found mutations at position 164 nt. The sequencing alignment  test was used to test for H-W equilibrium in ILD populations. The results of the Chi-square test are presented in Based on the identification of the PRL gene fragment was found at SNP c.164G >A, which caused amino acid changes from position 121 nt (JQ677091.1), to 116-117 nt . Changes in position, weight, number, and percentage of amino acid molecules of the PRL gene in Tegal, Magelang (F0), Gallang, and Maggal (F1) ducks are presented in Variation in the PRL gene and reproductive traits are presented in PRL gene and reproductive traits . There was a significant correlation (p<0.05), with a positive correlation coefficient of 0.10, between GG, GA, and AA genotypes with BW traits in Tegal and Magelang duck populations (F0), as well as between the GG and GA genotypes in Gallang duck populations (F1). A significant association (p<0.05) was also observed between the GG, GA, and AA genotypes with the FEW trait in the Gallang duck population (F1), although with a negative correlation coefficient (−0.198). A highly significant association (p<0.01) with a positive correlation coefficient of 0.14 was observed between the GG, GA, and AA genotypes of the PRL gene related to the EP trait in Magelang (F0), Gallang (F1), and Maggal (F1) populations. Conversely, there was no significant association (p>0.05) (with a negative correlation coefficient of −0.22) between the GG, GA, and AA genotypes of the PRL gene related to the AFE trait in each ILD population.The results of the association analysis of the PRL gene on reproductive traits is presented in PRL gene affects the AFE, BW, FEW, and EP traits. The regression analysis indicated that the PRL gene affected 4.98, 0.97, 3.93, and 1.88 % of the variation in AFE , turkey (91.3%), and quail (91.3%) [PRL gene polymorphisms relating to production traits have also been reported in chickens [PRL gene SNPs exist, namely, C-2161G and C-2402T in Fars native chickens of Iran [As demonstrated in Figures-, and GG . Two mut, and GG . The PRL (91.3%) . PRL genchickens -21 and gchickens . The fac of Iran .PRL gene distribution in Tegal (F0), Magelang (F0), and Gallang (F1) duck populations is not in H-W equilibrium. However, Maggal ducks exhibited H-W equilibrium for the PRL gene. The different H-W equilibrium states in this study population provide support that the frequency of alleles and genotypes in the duck population is relatively diverse. This further indicates that the population underwent selection, mutation, migration, or non-random mating within the population [The Chi-square calculated value  was greapulation ,24,25. Tpulation ,27.PRL gene in Tegal (F0), Magelang (F0), Gallang, and Maggal (F1) ducks. Changes occurred in the alanine, cysteine, glycine, and threonine amino acids. Amino acids containing the SNP c.376A >G and c.409G >A from the Melanocortin 1 Receptor (MC1R) gene in Magelang ducks were altered. The mutation changed amino acid isoleucine to valine and valine/isoleucine (c.376A>G) and alanine to threonine and threonine/alanine (c.409A>G) [.409A>G) . Amino a.409A>G) .PRL gene with the AFE trait in any ILD population. However, there was a significant association (p<0.05) with BW and FEW traits. The association with the EP trait was highly significant (p<0.01). The highest EP in Tegal (F0), Gallang (F1), and Maggal (F1) ducks was obtained with the heterozygote genotype (GA), whereas the highest EP in Magelang duck (F0) was obtained with the dominant homozygote genotype (GG). Thus, it can be stated that the G gene is dominant for high EP and is associated with the relatively varied Quantitative Trait Locus in ILDs. This suggests that G gene population enrichment using molecular selection is useful for increasing the EP of ILD. Chinese local ducks carrying the GG genotype exhibited significantly higher EP and egg weights than those with the AG and AA genotypes (p<0.05) [As demonstrated in (p<0.05) .PRL gene positively correlates with the BW and EP traits, but has a negative relationship with the AFE and FEW traits , Magelang (F0), Gallang (F1), and Maggal (F1) duck populations was relatively small (0.14), it still had a significant positive (p<0.01) effect (PRL receptor (PRLR) genotypes AA, AB, and BB was 0.247, 0.386, and 0.367, respectively. Pigs that carry the AA genotype had significantly higher numbers of piglets (p≤0.01) than those who had the AB or BB genotypes [PRL gene in Wan-xi White (China) and Rhine (European) goose breeds. Geese with the AA genotype laid more eggs than the AB or BB genotypes [PRL gene and the PRLR gene [The correlation coefficient obtained can be used as the basis for a selection program. Genetic polymorphisms that have an effect on body weight or the production trait that plays a role in other economic improvements can be used as a selection tool . Althoug) effect . In a pienotypes . EP in penotypes . A signienotypes . There wenotypes . The genPRL is biologically mediated by the PRLR, which has structural similarities with the PRL Growth Hormone Receptor and belongs to the Class I cytokine receptor superfamily. A sharp increase of plasma PRL levels can cause a decrease in EP [PRL can cause inhibition of follicle formation and inhibited egg lying. Potential anti-gonadal effects of PRL through inhibition of Gonadotropin-releasing hormone I and II and Luteinizing Hormone have been demonstrated in in vitro testing in poultry [PRL in invivo testing of several species [PRL have not been found in other species [The action of se in EP . In some poultry ,39 and i species . However species ,41. The  species ,43 make PRL gene is polymorphic and is an important marker, and can be used to increase the economic characteristics of local ducks [PRL genes of domestic poultry can be used as genetic markers for further selection. Sequences of the PRL gene in various bird species can provide useful information to uncover the physiological and general functions as well as species-specific mechanisms for gene expression. The use of genetic markers has the potential to increase selection intensity and is supported to be most effective way of selection [The al ducks . Geneticelection .PRL gene are polymorphic and associate with positive genetic effects in reproductive traits, especially EP in ILDs. The GA genotype obtained (SNP c.164G >A) can be used as a marker-assisted selection candidate for the high EP trait.Based on the results of this study, it can be concluded that SNP genotypes of the DP compiled the research ideas, designed the main framework, and composed the manuscript. RSSS, SAS, AS, and DPC contributed in coordinating field data collections. SAS conducted the statistical analysis. AS compiled and revised the manuscript. II read, criticized, and revised the text according to its scientific content. All authors have read and agreed to the final draft.