These attributes have specific geographical variations and may cause different adaptation traits. However, the adaptation profile of each attribute and the selective role of solar energy as a whole during human evolution remain elusive. Here, we performed a genome-wide adaptation study with respect to CAT, UVR, and SD using the Human Genome Diversity Project-Centre Etude Polymorphism Humain (HGDP-CEPH) panel data. We singled out CAT as the most important driving force with the highest number of adaptive loci . Five of the six genome-wide significant adaptation SNPs were successfully replicated in an independent Chinese population (N = 1395). The corresponding 316 CAT adaptation genes were mostly involved in development and immunity. In addition, 265 (84%) genes were related to at least one genome-wide association study (GWAS)-mapped human trait, being significantly enriched in anthropometric loci such as those associated with body mass index , immunity, metabolic syndrome, and cancer . For these adaptive SNPs, balancing selection was evident in Euro-Asians, whereas obvious positive and/or purifying selection was observed in Africans. Taken together, our study indicates that CAT is the most important attribute of solar energy that has driven genetic adaptation in development and immunity among global human populations. It also supports the non-neutral hypothesis for the origin of disease-predisposition alleles in common diseases.Solar energy, which is essential for the origin and evolution of all life forms on Earth, can be objectively recorded through attributes such as climatic  Solar radiation is an essential environmental factor in almost all ecosystems, and it is fundamental to the survival of the majority of life forms, including human beings Physiological studies have been intensively carried out to investigate the morphological adaptation to solar radiation in humans. Sunlight intensity is the most commonly researched, and it is found to correlate well with the worldwide variation in human skin pigmentation ASIP, ATRN, DCT, HERC2, IRF4, MC1R, OCA2, SLC24A5, SLC45A2, TYR, and TYRP1 have been identified to correlate with skin color SLC45A2 confirmed human adaptation to local variant levels of sunlight FADS region, which encodes fatty acid desaturases FADS region is closely associated with human body height and weight PER2 gene demonstrate a significant geographic distribution, which is suggestive of local directional selection MTNR1B gene encoding melatonin receptor 2 has undergone the natural selection of population residential SD Also, genetic studies have been increasingly used to help elucidate the mechanisms of human physiological adaptation to solar radiation. To date, genes including Although several aforementioned adaptive mechanisms have been suggested for single attribute—photosynthesis of vitamin D and/or photolysis of folate together with recently suggested protective mechanism of melanin for UVR; a thermal adaptation mechanism in response to CAT; and a biological rhythm adaptation in response to SD—none of the present studies has investigated the three attributes of solar radiation simultaneously. Thus far, the current physiological and genetic data are still insufficient to elucidate the overall selective role of solar radiation as an integrative environmental niche. In particular, it is unknown (1) which attribute of solar radiation played the dominant role, (2) which category of genes adapted most to these environmental attributes, and (3) what the underlying selection mechanism was.N = 764) i.e., CAT, SD, or UVR, has the strongest selective effect on human genome diversity. Genome-wide significant GWAdS SNPs were replicated by genotyping a cohort of Chinese individuals sampled from broad geographic ranges and by in-silico online analysis of dbCLINE database FST) To answer these questions, we conducted a series of analyses using an extensive genetic dataset generated from 40 subpopulations in the worldwide Human Genome Diversity Project-Centre Etude Polymorphism Humain (HGDP-CEPH) panel  than did UVR  and SD (one suggestive SNP) (ULBP3), keratin 31 (KRT31), long intergenic non-protein coding RNA 112 (LINC00112), dopamine receptor D3 (DRD3), and netrin G1 (NTNG1), with two hits at ULBP3. The two significant UVR SNPs were linked to NAD kinase (NADK) and insulin-like growth factor binding protein 3 (IGFBP3). All these SNPs were still significant after false discovery rate (FDR) corrections  . The sixP > 0.05).After using a haplotype tagging technique and a minor allele frequency (MAF) filter, five genome-wide significant CAT-associated SNPs and one UVR SNP were experimentally validated in 1395 Chinese individuals. Neither significant deviation from Hardy-Weinberg equilibrium (HWE) nor gender or age difference in allele frequencies was observed , whereas rs1444041T in NTNG1  and rs9825563A in DRD3 had marginally negative associations . For these four SNPs, rs12626864 in LINC00112 and rs13729 in ULBP3 were associated with an annual average maximum CAT and annual average minimum CAT, respectively. In addition, except rs12626864, the remaining three SNPs were associated with annual average extremely minimum CAT . However, no SNP was significantly associated with annual average extremely maximum CAT. On the other hand, the six genome-wide CAT SNPs, except rs9825563 in DRD3 being associated with intensive agriculture, were confirmed to be all significantly associated with minimum temperature (winter) in dbCLINE database  instead of annual average total UVR dose. However, the two UVR SNPs failed to be replicated in the dbCLINE. Only rs7531583 was found to be associated with absolute latitude (transformed rank statistic 0.002).For UVR-associated rs7531583 in In-silico analysis was used to annotate these SNPs in the GTEx project NTNG1) and both of the UVR-associated signals might have functional regulation on transcription.NTNG1 gene) are predicted to affect known transcription factor regulatory motifs: rs12202737 and rs13729 are putative cis-eQTLs for ULBP3 and RAET1L, while rs1107877 of KRT31 acts as a cis-eQTL for KRT32 and KRTAP3-2. For the two UVR signals, rs7531583 of NADK could cis-regulate itself and five nearby genes including CALML6, CDK11A, MMP23A, RP1-140A9.1, and TMEM52; rs1586360 from IGFBP3 could regulate IGFBP1. In addition, they both were predicted to affect transcription factor motifs for a given proximal gene.Five out of the six CAT signals (except rs11185115 from P ≤ 1 × 10−5). At this level, 316 CAT-associated genes, 191 UVR-associated genes, and a single SD-associated gene were identified. Among them, the SD-associated CSF2 was also targeted by UVR, while 23 other UVR-associated genes, including the genome-wide significant IGFBP3, were also targeted by CAT  in categories for immunoglobulin I-set domain (5.03), immunoglobulin C2-set domain (4.33), regulation of cell differentiation (2.58), and regulation of developmental process (2.14). To this end, we focused on the CAT-associated signals with respect to potential GWAS traits and evolutionary analyses to examine the underlying selective mechanisms.When examined separately, only the 316 CAT-associated genes showed a significant clustering with developmental processes and immunity after genomic control . More im2χ; P < 0.001). When examined separately, waist-hip ratio and obesity (early onset extreme) showed the most significant enrichment , weight (5), obesity (46), waist-to-hip ratio (8), fat (7), adipose (11), and body mass index , showing significant enrichment compared with the two null distributions of all GWAS genes and HGDP-CEPH genes , diisocyanate-induced asthma, inflammatory skin, and bowel diseases. For cancers, the main types involved are prostate cancer and colorectal cancer. For mental disorders, the main related types are referred to eating disorders (purging via substances), alcohol dependence, bipolar disorder and schizophrenia, major depressive disorder, schizophrenia, etc.Interestingly, other GWAS traits including immunity, cancer, mental function, and mental disorders also showed significant enrichment  were completely fixed in all four examined African populations, with additional SNPs partially fixed in at least one African population. All these SNPs were polymorphic in the Asian, European, and Middle Eastern populations, with the exception of Siberians .pq > 0.40, P < 0.01), with a significant scarcity of rare derived alleles  and high-frequency derived alleles  relative to control SNPs . Taken together, DAF distribution patterns propose balancing selection as the major evolutionary driver for both Asians and Europeans, and in Africans if considered the large proportion of fixed SNPs positive (complete sweeps) or purifying selection was suggested.The DAF spectrum of the CAT-associated SNPs in four continental populations, Asian, European, Middle Eastern, and Sub-Saharan African populations merged from HGDP-CEPH populations , was firrol SNPs . By contFor the 12 HGDP-CEPH constituent populations, those residing in China, Japan, Russia, and Siberia were also suggested to have experienced balancing selection, whereas all four African populations could have experienced positive or purifying selection events . For theFST was examined to measure the degree of population differentiation and to identify local adaptation signals. Among the four HGDP-CEPH continental populations, according to FST results derived from control SNPs, the population differentiation pattern was generally consistent with their speculated split time FST value. However, for CAT-associated SNPs, the pattern was distorted with Asian and European populations having the lowest differentiation . Additionally, distinct and shared positive selection signals were detected among different continental populations. For the HapMap populations, one SNP (rs174556 in FADS1 gene) was shared between the ASN and YRI populations, and two SNPs (rs657672 in CACNB1 and rs16823913 in CCL20) were shared between the ASN and CEU populations  were shared in ASN and CEU populations, while only FADS1 was shared between ASN and YRI populations (CDH10) shared among the East Asian, Middle Eastern, and South Asian populations; another SNP (rs16960758 in SLC12A1) among the European, Middle Eastern, and South Asian populations; and a SNP (rs11208527 in RAVER2) between the European and Middle Eastern populations   B. LastlyAs the major source of the Earth's energy, the Sun has played a key role in the development as well as maintenance of a rich and diverse biosphere. Therefore, it has long been proposed as a strong driving force for the origin and subsequent evolution of human beings de novo advantageous mutations with large effective size spread rapidly to fixation, usually agnostic to the underlying selective pressures. However, such ‘hard sweeps’ are rare. More adaptations may be expressed by the so-called ‘soft sweeps’ in which subtle allele frequency shifts occur in standing variation FST, iHS, and DAF were used to determine the selective scenario more specifically. By doing this, the signals and selection patterns revealed would be more accurate.Traditional selection analyses tended to identify ‘hard sweeps’ that Furthermore, population structure is an important and unavoidable confounding factor. Generally, principal component (PC)-based analyses are used for correction, although the number of PCs included can be arbitrary and subjective. In this study, we carefully controlled for potential population structure in three ways. First, we included geographic coordinates, which have been confirmed as a good representative for human demographic history Notably, CAT, especially an extremely minimum annual average temperature, is finally identified as the main force driving solar radiation selection of human genes involving development and immunity.ULBP3 and KRT31 are involved in hair development DRD3 and NTNG1 are involved in neuronal development KRT31, were successfully validated; five of them were significantly associated with the annual average CAT range and four were significantly associated with an annual average extremely minimum CAT. Previous studies on human skeletal variation have shown that the minimum temperature consistently had a stronger effect on shaping human morphology than maximum temperature Previous studies have described a close association between human morphology and CAT, in accordance with Bergmann’s and Allen’s rules ULBP3 is also a ligand for the NKG2D receptor, and it can activate multiple signaling pathways in primary NK cells to produce cytokines and chemokines DRD3-encoded dopamine receptor D3 is important for modulating T cell function by inhibiting the activated T cell receptor-induced cell proliferation and secretion of IL-2, IFN-γ, and IL-4 Global variations in CAT have affected the distribution and abundance of disease vectors; accordingly, the resultant difference in infectious disease patterns could exert selective pressure on the human immune system NADK is involved in most reductive biosynthetic reactions and antioxidant defense systems by controlling the NADPH concentration IGFBP3 is associated with osteoblast differentiation and bone development In addition, UVR-associated signal FST statistics for CAT SNPs, a selective scenario of positive selection upon several genes (by both hard sweep and soft sweep) together with events of purifying selection in other loci probably provide an optimal adaptation for African populations. As humans migrated out of Africa, the ancient heat-adapted genetic background encountered diverse CAT (average ranging from −10.0 °C in Siberia to 18.3 °C in Pakistan), particularly cold stress during the last glacial period FST, may also have contributed to this local adaptation to CAT sweeping variants from low to intermediate frequencies. Here, positive selection would tend to act on pre-existing variations (selection on standing variation – soft-sweeps), except for the Siberian population. The situation, for Israeli and Pakistani populations, however, is ambiguous and may reflect complex interactions between natural selection and demographic history. Further studies with larger sample sizes and from more subpopulations are needed to better elucidate their selection patterns.As described in the recently modified Out-of-Africa dispersal model with minor introgression from archaic humans KRT31, KRT32, KRT35, KRTAP4-1, COL22A1, CSMD1, and OVOL1), temperature homeostasis (ACOT11 and PPARGC1A), and body fluid balance (SLC12A1), were highlighted. Here, solute carrier family 12 member 1 encoded by SLC12A1 mediates Na+, K+, and Cl− reabsorption. It also plays a vital role in the regulation of ionic balance and cell volume associated with sweating SLC12A1 has been strongly suggested as a candidate gene for hypertension in a large-scale meta-analysis As for the associations between the morphological variations and CAT, all are related to efficient thermoregulation from heat conservation to dissipation, as described in Bergmann’s and Allen’s rules. Among the 316 CAT-associated genes, additional signals relevant to thermoregulation, including those involved in hair formation , hypertension and blood pressure (14), mental function and mental disorder (97), and cancer (49). This provides a new perspective for elucidating the genetic architecture and pathogenesis of these complex traits, as demonstrated by the examples of infectious diseases Homo sapiens; it also provides clues for future studies on humans and other species.In summary, this is the first systematic study of the evolutionary effects of solar radiation on human genome diversity. CAT, especially cold stress, was identified as the main driving force of selection by solar radiation. Together with two other attributes, CAT mostly selected for genes involved in human development and immunity, with thermal regulation being the major mechanism. Specifically, the enrichment in developmental genes provides direct genomic evidence for Bergmann’s and Allen’s longstanding rules, and the enrichment in immunity categories reveals the underlying environmental driving force for the pathogen selection hypothesis. Furthermore, human populations residing in different environments and with different demographic histories showed region-specific selective patterns. This study thus provides an initial genetic insight into the evolutionary role of solar radiation in N = 764, Population genotype data were retrieved from the HGDP-CEPH dataset, which included 952 unrelated individuals from 52 populations N = 424). The geographic origin of each individual was ascertained by self-reported precoded geographic tenancy for three generations, and only individuals without a history of family migration were used.To better control for the underlying population structure, the results from the HGDP-CEPH populations were validated in a replication sample set of Chinese populations . A totalFor HGDP-CEPH populations , environNTNG1 was replaced by a nearby SNP, rs1444041 (2r = 0.954), which was not in the original dataset, owing to a lack of the appropriate SNPstream primers; rs12202737 was represented by rs13729 from the same ULBP3 gene because of their complete LD in the CHB population (2r = 1). In total, four SNPs  were genotyped using the Beckman SNPstream genotyping system Six genome-wide CAT-associated SNPs  were experimentally confirmed in Chinese populations. Genomic DNA was extracted from whole blood using the standard phenol/chloroform method. Among them, rs11185115 within For each of the 516,663 common SNPs (MAF ≥ 0.1) of the whole 660,918 SNPs in the original HGDP-CEPH dataset http://www.ebi.ac.uk/gwas/search) through 20 May 2016 for potential phenotype effect. Similarly, the GWAS category distribution patterns for both the 12,456 total GWAS mapped genes and the 17,169 HGDP-CEPH genes were used as two separate null distributions.Associated genes were assigned according to the dbSNP database, and SNPs within an intergenic region were annotated with the closest gene symbol. Then, they were mapped to GO annotation terms FST values FST and iHS analyses.In addition to the HGDP-CEPH data, allele frequencies for the HapMap CHB, JPT, CEU, and YRI populations retrieved from HapMart (HapMap_rel27 dat) were analyzed. First, in DAF analysis, SNPs for CAT-associated intronic and flanking region SNPs were performed separately, with specific controls defined from the same Illumina 650Y dataset. For flanking region SNPs, controls should be from unassociated genes; the physical distance should be similar to that of the associated SNP, with matches made in a one-to-one manner. For intronic SNPs, we first carried out a similar one-to-one matching. Then we performed 100 simulations by randomly re-sampling the intronic SNPs from the remaining unassociated genes, regardless of physical distance. For all these SNPs, the ancestral allele information was retrieved from dbSNP. Second, pairwise 2χ tests . Environmental associations were assessed using Pearson bivariate correlation and multiple linear regression analysis as described for the worldwide populations .HWE was tested through the software PEDSTATS Blood samples from all 1395 Chinese individuals for the confirmation experiment were obtained with written informed consent. The protocol for this study was reviewed and approved by the ethics committee of the Kunming Institute of Zoology, Chinese Academy of Sciences.Lindan Ji: Investigation, Formal analysis, Writing - original draft, Writing - review & editing. Dongdong Wu: Formal analysis, Writing - original draft. Haibing Xie: Formal analysis, Writing - original draft. Binbin Yao: Formal analysis. Yanming Chen: Formal analysis. David M. Irwin: Writing - original draft, Writing - review & editing. Dan Huang: Formal analysis. Jin Xu:  Conceptualization, Supervision, Formal analysis, Writing - original draft, Writing - review & editing. Nelson L.S. Tang: Conceptualization, Supervision, Formal analysis, Writing - original draft, Writing - review & editing. Yaping Zhang:  Conceptualization, Supervision, Writing - original draft, Writing - review & editing. All authors read and approved the final manuscript.The authors have declared that no competing interests exist.


Traditional phenotypic selection is still the main method for genetic improvement of geese body weight. In this study, specific locus amplified fragment sequencing (SLAF-seq) with bulked segregant analysis (BSA) was conducted for discovering and genotyping single nucleotide polymorphisms (SNPs) associated with marketing weight trait in male geese. A total of 149,045 SNPs were obtained from 427,093 SLAF tags with an average sequencing depth of 44.97-fold and a Q30 value of 93.26%. After SNPs’ filtering, a total of 12,917 SNPs were included in the study. The 31 highest significant SNPs—which had different allelic frequencies—were further validated by individual-based AS-PCR genotyping in two populations. The association between 10 novel SNPs and the marketing weight of male geese was confirmed. The 10 significant SNPs were involved in linear regression model analysis, which confirmed single-SNP associations and revealed three types of SNP networks for marketing weight. The 10 significant SNPs were located within or close to 10 novel genes, which were identified. The qPCR analysis showed significant difference between genotypes of each SNP in seven genes. Developed SLAF-seq and identified genes will enrich growth performance studies, promoting molecular breeding applications to boost the marketing weight of Chinese geese.Growth performance is a complex economic trait for avian production. The swan goose  Growth performance is the most important economic factor in the poultry industry. In poultry breeding, males are usually selected for growth and meat production, while females are selected for reproduction . ChineseGenetic variations at candidate genes—associated with economic traits such as growth and meat production—have stimulated research in marker-aided selection (MAS) and evolutionary relationships studies . DNA-basSLAF-seq is a technique that uses an enhanced reduced-representation genome sequencing method for discovering de novo SNPs that can be used for large-scale genotyping . SLAF-seAccording to the lack of polymorphism information and incomplete geese reference genome sequence, GWA study on the body weight of male goslings at nine weeks of age (marketing weight) was conducted for the first time using a combination of SLAF-seq and BSA (SLAF–BSA) techniques followed by a replication association study to detect and identify significant SNPs related to marketing weight (MW) in Yangzhou goslings. Identification of genes related to variations in MW of male goslings could provide new insight into the genetic basis of this trait and utilize it in designing efficient early selection strategies in geese breeding programs to promote the production of geese with high marketing weight and earlier marketing age.All experimental protocols and animal care were reviewed and approved by Animal Care of Nanjing Agricultural University. The protocols were conducted in compliance with the Administration of Affairs Concerning Experimental Animals Regulations . All efforts were made during blood collection and geese slaughtering to minimize any discomfort.Anser cygnoides) of the first population (P1), belonging to Jiangsu Lihua Animal Husbandry Co. Ltd. commercial farm  were randomly selected and utilized for SNPs’ detection in the present study. Rice grains were used to feed goslings ad libitum and supplemented with green grass or water plants, whenever available. Goslings are fed on a diet containing 12 MJ/kg of energy and 18% of protein before three weeks of age and on a diet containing 10 MJ/kg of energy and 12% of protein after three weeks of age. Goslings were housed in a semi-enclosed building and raised under the same conditions according to the farm’s standard practice with stock density of 4.5 goslings/m2. Goslings were released into the barn yard during the daytime and fed at this time. Goslings were exposed to natural temperature and lighting throughout the study. Individual records of goslings’ body weight at nine weeks of age (marketing weight) were obtained from the farm. Marketing weight (MW) data were checked for normality before conducting any further analysis. Twenty individuals from each of the lowest and the highest male goslings groups of the P1 were used in the SLAF study based on best linear unbiased prediction (BLUP) of individual estimated breeding values (EBV) for MW trait, which had been assessed using full and half sibs’ information by the lme4 package of R software [A total of 167 male Yangzhou goslings (software  accordinsoftware .http://www.biomarker.com.cn/ accessed on 27 April 2018) [japonica) was used as a control for sequencing (http://rapdb.dna.affrc.go.jp/ accessed on 27 April 2018).Blood samples (2 mL) were collected from wing veins of 167 male goslings at nine weeks of age and immediately transferred into 5 mL tubes containing acid citrate dextrose and stored at −20 °C pending the DNA extraction. The conventional phenol/chloroform method was used to extract genomic DNA. The concentration and purity of DNA for each individual sample was assessed using the Thermo Scientific NANODROP2000 spectrophotometer . DNA concentration was adjusted to 100 ng/μL for each sample. Twenty samples from each of the low estimated breeding values (LEBV) and high estimated breeding values (HEBV) groups were used to prepare two DNA pools by mixing an equal amount of genomic DNA. The SLAF-seq technique was used to develop and obtain molecular markers across the whole genome data of the two goslings groups by Beijing Biomarker Biotechnology Co., Ltd.  was used to compare the sequencing reads of control with its reference genome . Two metp and sequence quality value is (Q-score = −10 × log10 p). If the sequence accuracy is 99.9%, the quality value for the base should be 30. Both the Euclidean distance (ED) and SNP index methods were then used to filter data for either LEBV or HEBV cohorts according to [HEBV, CHEBV, THEBV, and GHEBV are the depth of A, C, T, and G bases on a site in HEBV bulk, respectively. ALEBV, CLEBV, TLEBV, and GLEBV are the depth of A, C, T, and G bases on a site in LEBV bulk, respectively. The HEBV and PHEBV are the depth of the HEBV population derived from M and P, respectively. MLEBV and PLEBV are the depth of the LEBV population derived from M and P, respectively. The sequencing quality value (Q) was used to assess the quality of raw SLAF reads. The corresponding formula of base sequence error rate rding to ,30. GenoAll SNPs that met the condition ED ≥ 0.7, SNP index ≥ 0.5, and Q ≥ 30 were involved in the present analysis. After data filtering, 12,917 SNPs were used for further analysis. Allele frequency differences for each SNP were used to compare between LEBV and HEBV pools. SNPs with a highly significant difference in the allelic distribution of both pools were selected as candidate loci for further verification in the population.For the replication study, blood and MW of 114 male Yangzhou goslings were collected from individuals of the second population (P2) derived from the same farm. MW data were checked for normality before conducting any further analysis. P2 was kept under the same management system of P1. Blood samples (2 mL) were collected, reserved, and DNA was extracted and its quality was assessed with the same protocol described in In order to investigate if the candidate SNPs are gender-related, blood samples and MW of 323 and 345 female Yangzhou goslings belonging to the same farm were collected from individuals of the P1 and P2, respectively. Firstly, the discovered SNPs that showed significantly MW-related SNPs between HEBV and LEBV male goslings groups were genotyped using AS-PCR method in 24 individuals from each of the lowest and the highest female gosling groups of P1 based on EBV. The SNPs that showed a significant difference between female goslings of LEBV and HEBV were secondly genotyped using the AS-PCR method in 323 and 345 female goslings of P1 and P2, respectively. Female goslings of P1 and P2 were kept under the same management system of male goslings of P1 and P2. Blood samples were collected and reserved, and DNA was extracted and its quality was assessed with the same protocol described in https://www.ncbi.nlm.nih.gov/ accessed on 30 June 2018). The primers for AS-PCR were designed according to [2O. PCR amplification was carried out by preliminary denaturation at 94 °C for 5 min followed by 32 cycles of amplification  and a final extension at 72 °C for 7 min. All PCR products quantity were fractionated by agarose gel (2%) electrophoresis, visualized with gold view staining, and quantified with Tanon 3500 Gel Imaging system .Based on SLAF-BSA analysis and Chi square test, the 31 highest significantly candidate SNPs of different allelic distribution were selected for male individual-based genotyping in the LEBV and HEBV cohorts of P1. A total of 13 SNPs that showed significantly different allele distribution for MW in the LEBV and HEBV cohorts were further verified in all male individuals of P1 and P2 using the AS-PCR genotyping method. The 13 SNPs were also genotyped using the AS-PCR method in the lowest and the highest female gosling groups of P1 and the significant SNPs were further genotyped in all female individuals of P1 and P2. Primer Premier 5 software  was used to design each pair of primers to amplify fragments based on GOOSE genomic DNA sequence . SYBR® Green Master Mix  was used in a StepOne Plus Real-Time PCR system . The PCR reaction (20 µL) consisted of 1 µL cDNA, 0.4 µL from each primer (10 µmol), 0.4 µL ROX Reference Dye, 10 µL SYBR Green Master Mix, and 7.8 µL nuclease-free water. Amplification thermal conditions were as follows: pre-denaturation at 95 °C for 5 min, 40 amplification cycles (95 °C for 10 s and 60 °C for 30 s). Melting curve analysis was performed from 60 °C to 95 °C by reading plate every 0.1 °C. Each sample was analyzed three times. The house-keeping gene glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as internal control.Seven male goslings from each of the HEBV and LEBV groups of P2 were selected—according to their genotypes—and slaughtered to obtain brain tissues (pre-experiments of gene expression were performed to select the suitable tissue). Tissues were collected and immediately frozen in liquid nitrogen pending the expression analysis. Total RNA was extracted from the brain tissues using TRIZOL Reagent  according to the manufacturer’s protocol. RNA quality and quantity were assessed by NanoDrop spectrophotometer at 260/280 nm. The cDNA first strand was synthesized from 1 μg of purified total RNA using ProtoScript First Strand cDNA Synthesis kit . The gene specific primers of the identified 10 candidate genes harboring MW-related SNPs were designed using Primer Premier 5 software to determine the expression levels of the 10 genes in geese brain tissue by qPCR. The primers  were desijk = 1/2 (âj + âk) + ŵijk, where âijk = the BV ith progeny of parent j x parent k, âj = the BV of parent j, âk = the BV of parent k, and ŵijk = the within-family Mendelian additive effect of the individual ijk. ŵ was calculated as ŵijk = h2w (eijk), where eijk = the residual from the linear mixed model associated with progeny from parent j × parent k, and h2w = the within-family heritability = 1/2 σ2A/σ2e. The allelic frequency variation of SLAF tags between LEBV and HEBV male groups were tested using contingency tables and chi-square statistics. Two methods, false discovery rate and Bonferroni correction, were used to obtain adjusted p-values and estimate the significance threshold level at 5% overall Type I error rate [p < 0.05 were included in further multiple marker analysis. Multiple-marker associations were analyzed with two quantitative trait modes (additive mode: PAa (PAA + Paa)/2) and dominant mode: PAa either PAA or Paa) by the linear regression procedure of SAS software using forward or backward stepwise comparison [NCBI public database (https://blast.ncbi.nlm.nih.gov/Blast.cgi accessed on 20 March 2020) using the SLAF-taqs of 100 bp sequence was performed to retrieve orthologous sequences. The geese sequences in the whole-genome shotgun contigs (wgs) database of Anser cygnoides (taxid:8845) were used for alignment. The 2−ΔΔCT method was used to calculate relative quantification of gene expression. GAPDH was used as an internal control. Two tailed t-tests were used to analyze mRNA expression variation between different genotypes in each tested SNP.Function Shapiro test of R  was usedror rate ,33. One-ror rate . PLINK sror rate . All sinmparison  accordinmparison . All datGander had a higher marketing weight (MW) than goose. The average MWs were 4.12 and 4.09 kg for males and 3.54 and 3.47 kg for females in the first (P1) and second (P2) populations, respectively. The average estimated breeding values (EBVs) of males (0.01 and 0.003) were approximately equal to those of females (−0.006 and 0.002) in both populations. The heritability of MW was 0.29 and the environmental proportion of total variance was 0.71. p < 0.05), 382 SNPs reached a 5% false discovery rate (FDR), and only 68 SNPs reached 5% Bonferroni correction. The 31 highest significant SNPs (p < 2.26 × 10−6–9.22 × 10−33) were selected as candidate MW-related SNPs (p < 0.04–8.44 × 10−7) allele frequency differences between HEBV and LEBV male groups  and low (LEBV) estimated breeding value cohorts. A total of 3145 SNPs showed a significant effect in the Chi square test , and −0.453–(−0.073) for additive, dominant, and recessive effects, respectively. For P2, the values ranged between −0.198–0.030, −0.430–0.104, and −0.247–(−0.006) for additive, dominant, and recessive effects, respectively. In general, all tested SNPs in the present study showed significant effects of one or more genotypes in P1 or P2. Additive, dominance, and recessive values of males in both populations are shown in For more confirmation on SNP networks, two correlation matrixes based on coefficients of Spearman rank correlation were performed using SNPs’ genotype values. All SNPs of males from P1 and P2 showed a significant correlation with MW and, at the same time, most of them showed a significant correlation with themselves. The highest correlation coefficients were found between MW and SNPs of Record_1111, Record_7099, and Record_396 loci for P1 and SNPs Record_1056, Record_1102, and Record_396 loci for P2. The correlation coefficients between SNPs ranged between medium to unity in P1 and low to medium in P2. First and second populations heat map of all pairwise correlation between SNPs were constructed based on both correlation matrixes and visualized in p-values for females from P1 are summarized in p < 0.05) for only Record_1056. Goslings with GG genotype showed significantly higher MW than those with CC and CG genotypes in both populations  using the SLAF-taqs of 100 bp sequence was performed to retrieve orthologous sequences. The geese sequences in the whole-genome shotgun contigs (wgs) database of Anser cygnoides (taxid:8845) were used for alignment. The 10 significant SNPs were found to be located within or close to 10 genes that were concluded to be significantly associated with MW of male goslings. In general, the 10 SNPs are distributed in five different chromosomes including five SNPs  on KZ155846.1, one SNP (Record_2315) on KZ155852.1, two SNPs (Record_7099 and Record_7097) on KZ155908.1, one SNP (Record_8964) on KZ155945.1, and one SNP (Record_396) on KZ155843.1 chromosomes. As shown in HMX1) gene. SNP Record_1111 is located at 29.5 Kb upstream region of uncharacterized LOC106034756 gene. SNP Record_2315 is located at 10 Kb upstream region of LRR binding FLII interacting protein 1 (LRRFIP1) gene. SNP Record_1009 is located at 14.5 Kb upstream region of uncharacterized protein K02A2.6-like (LOC106035299) gene. SNP Record_1056 is located on intron one of protein phosphatase 2 regulatory subunit B γ (PPP2R2C) gene. SNP Record_1115 is located on intron one of uncharacterized LOC106034755 gene. SNP Record_7099 and Record_7097 are located at 3.3 Kb and 3 Kb, respectively, downstream region of ubiquitin C-terminal hydrolase L1 (UCHL1) gene. SNP Record_8964 is located at 28.3 Kb downstream region and 41.2 Kb upstream region of uncharacterized LOC106034143 and platelet derived growth factor D (PDGFD) genes. SNP Record_396 is located at 24.6 Kb upstream region of extracellular leucine-rich repeat and fibronectin type III domain containing 1 (ELFN1) gene.According to the 10 significant SNPs that were found to be associated with MW of males from P1 and P2, a BLAST analysis against the HMX1, LOC106034756, PPP2R2C, UCHL1, and PDGFD) and downregulated levels in five genes  (p < 0.05) between genotypes except LOC106034756, LOC106035299, and LRRFIP1 genes. For LOC106034756 and LRRFIP1 genes, the alternative homozygous genotype is difficult to detect and there is no significant difference between reference homozygous and alternative heterozygous genotypes for MW in the second population  . The mRNpulation .Goslings’ growth is a crucial factor for the whole life with a major impact on the efficiency of production and reproduction. Many factors influence body weight and growth performance of goslings; some are hereditary in origin  and others are environmental factors. China dominates the global geese industry by producing approximately 4.8 million tons of meat, owning more than 90% of the global goose population . ChineseOwing to the low average MW in Yangzhou geese, it was imperative to find a method for genetic improvement of growth, which can be utilized in selection programs. Achieving significant success using quantitative genetics tools depends on accurate data, a good management system, and unbiased distribution of ganders. Furthermore, it required several years to achieve the desired success. Genetic variations at candidate genes regarding economic traits have stimulated research interest . GWA stup < 0.05) in allele frequency between males of HEBV and LEBV groups. The 13 SNPs were genotyped in 167 males of P1. Ten out of the 13 SNPs reached 5% Bonferroni distributing. A follow-up replication study in 114 males of P2 was conducted for further verification of the SNP markers’ impact on MW. The results verified the significant association of the 10 SNPs with MW. Only Record_1056 showed a significant (p < 0.05) association with MW for 323 and 345 females of P1 and P2, respectively. These results suggested that these SNPs are associated with MW in Yangzhou geese, and this effect might relate to gender. The ti/tv value is an important property for evolution of the DNA-sequence where transition has less impact than transversion in changing amino acids in the protein, despite its higher frequency in the genome [In this study, an economic and effective method of SLAF-seq with BSA techniques (SLAF-BSA) was employed for discovering and genotyping MW-related SNPs. A total of 427,093 SLAF-tags were obtained with an average sequencing depth of 44.97-fold. A total of 149,045 SNPs were obtained from polymorphism of 84,594 SLAF tags. After quality control by SNPs index and ED corrections, 12,917 SNPs were included in this study. Based on Chi square test, the 31 highest significant SNPs that reached 5% Bonferroni correction were selected as candidate MW-related SNPs of male goslings. Thirteen out of 31 SNPs showed significantly variation  sequencing from a DNA pool to detect 2188 SNPs for Barnacle goose . Using tP method . Using rP method . In addiP method . RecentlP method .Regarding the 10 significant SNPs of male goslings in both populations—which were detected in the current study—five SNPs  are distributed in the ~4 Mb region (3215955–7261356) of KZ155846.1 chromosome; two SNPs (Record_7099 and Record_7097 loci) are distributed in the 250 b region (2481905–2482155) of KZ155908.1 chromosome; and the remaining three SNPs  are distributed in KZ155852.1, KZ155945.1, and KZ155843.1 chromosomes, respectively. Linear regression analysis procedure was conducted on the 10 significant SNPs to narrow down the genomic region of chromosome KZ155846.1, verify the impact of the SNP marker on MW, improve the accuracy of the analysis model, and detect SNP–SNP combinations (SNP networks). In the construction of SNP networks—related to quantitative traits—several unique features of linear regression procedure were shown such as determining a leading marker within a gene related to a particular trait of interest, improving the correlation between the actual and predicted network values and predicting the average values of genotypes substitution in each SNPs . Our linHmx1 gene causes a semi-lethal mutation in mouse called Dumbo, resulting in reducing body mass in survival mutants from ~3 days postpartum onwards with microphthalmic at puberty [Lrrfip1 gene were found to be associated with abdominal fat, body fat percent, and C-reactive protein levels [Lrrfip1 expression [GCF2/Lrrfip1 knockdown in human HepG2 cells [Ppp2r2c plays various biological roles in dynamics and mobility of cytoskeleton [Ppp2r2c is involved in the differentiation of bovine skeletal myoblast [Pdgfd gene is a member of the platelet-derived growth factor family that is important for several types of connective tissue cells in terms of survival, function, and growth. Pdgfd was found to be potentially differentially regulated in obese rats’ skeletal muscle by essential polyunsaturated fatty acids [Uncx and Elfn1 genes are encoding transcription factors that coordinate growth and innervation of somatic muscles in zebrafish [Uchl1 may have a potential role in energy metabolism, as the evidence showed its unique role in muscle differentiation and lipid deposition [Uchl1 was the most downregulated gene involved in oxidative stress during both short- and long-term weight loss [Uchl1 was also identified as a differentially expressed gene in the longest dorsal and semi-membranous muscles in two Polish pig breeds differing in fat and meat qualities [UCHL1 gene, which can alter transcriptional activity of UCHL1 gene and affect the growth performance of male Yangzhou goslings [The ten discovered genes in this study are novel and their association with growth in general and body weight of geese in particular has not yet been verified. Four out of the 10 genes are uncharacterized in geese genome. Despite there being some evidence indicating that the other six genes are associated with growth, their specific function has not yet been clearly defined in geese and further in-depth studies are needed. A nonsense mutation in the first exon of  puberty . Eight Sn levels . Micro Rpression . AdditioG2 cells . Ppp2r2cskeleton , and conskeleton . A recenmyoblast . Pdgfd gty acids . A pair ebrafish . Uchl1 mposition . Uchl1 wght loss ,63. Uchlualities . Based ogoslings .In conclusion, the annotation analysis for the 10 genes and gene expression analysis emphasizes that these genes might be related directly or indirectly to growth performance. The mechanism by which the polymorphism in genes affects growth performance in geese has not been established. Whereas the polymorphisms evaluated in this study do not result in amino acid substitutions , these polymorphisms may be associated with other mutation(s) in other site(s) of the nucleotide sequence or other genes closely linked with these genes. This is the first case of discovery of novel genes that may be associated with growth performance in Yangzhou geese. The 10 SNPs can be recommended as potential genetic markers for growth traits in geese, but further verification studies are still needed to emphasize the current obtained results.SLAF sequencing with the BSA approach was applied for discovering and genotyping SNPs related to MW of male goslings. In general, 10 SNPs were confirmed to be associated with MW of males and only one SNP was associated with females. Ten genes located in five chromosomes were suggested to be associated with it. Interestingly, five genes are located at the ~4 Mb region of KZ155846.1 chromosome, which includes a ~56 Kb region containing three genes. We suggest this region with the 250 b region of KZ155908.1 chromosome to be related to the MW of male goslings, as shown by SNP network analysis. Moreover, all 10 SNPs can be potential target markers for marker-assisted selection to help identifying goslings with greater growth performance. Finally, our results confirmed that molecular methods such as SLAF sequencing with BSA can be used for determining molecular mechanisms underlying certain physiological cascade. To the best of our knowledge, the specific function of any of the 10 identified genes has not been clearly defined in geese and further in-depth studies are needed to explore the new functional role in MW of male goslings.


Citrus sinensis Osbeck) (VO) is a type of late-ripening sweet orange whose ripening occurs 4 to 5 months later than that of the mid-ripening common sweet orange (CO). Notably, the mastication trait of VO fruit is inferior to that of CO fruit. To date, how inferior pulp mastication trait forms in VO has not been determined. In this study, 13 VO varieties and 12 CO varieties were subjected to whole-genome resequencing. A total of 2.98 million SNPs were identified from 25 varieties, and a SNP molecular marker was developed to distinguish VO and CO. Moreover, 144 and 141 genes identified by selective sweep analysis were selected during VO and CO evolution, respectively. Based on gene functional enrichment analysis, most of the selected VO genes were related to the stress response and lignin biosynthesis. Simultaneously, we comparatively analyzed the transcriptome profiles of peel and pulp tissues among three VO varieties and three CO varieties, and the results demonstrated differences in lignin biosynthesis between VO and CO fruits. Furthermore, coexpression network analysis was performed to identify hub genes of lignin-related and variety-specific networks, which included CsERF74, CsNAC25, CsHSFB3, CsSPL4/13, etc. Overall, this study provides important insights into the mastication trait formation of Valencia orange fruit.Valencia orange ( Citrus sinensis Osbeck) is widely cultivated worldwide and has various varieties. Valencia oranges (VOs) constitute an important group of natural late-ripening sweet orange varieties, whose maturation stage is delayed by 4–5 months compared to that of other common sweet orange (CO) varieties1. Fruit mastication trait is an important index for evaluating citrus fruit quality and directly influences the quality of fruit commodities. It is known that the mastication trait of VO fruit is inferior, which greatly affects its quality. However, the regulatory mechanism underlying the formation of VO fruit mastication trait is unclear.Sweet orange , pectin methyl esterase (PME) and lignin biosynthesis-related genes 4. Many studies have shown that the accumulation of lignin in fruit can affect fruit texture and quality. Lignin is deposited in large amounts in the peel and flesh of pear, resulting in the formation of rough-textured flesh5. Under 0 °C storage conditions, the accumulation of lignin in peach fruit diminishes their taste6. In citrus, the accumulation of lignin can reduce the water in juice cells and further lead to fruit granulation, which causes inferior fruit mastication trait7. In addition, lignin plays essential roles in supporting plants, water transport, and resistance to external stress factors 11.The mastication trait of citrus fruit is related to dietary fiber properties of the fruit pulp, which are related to cell wall metabolism. Pectin, cellulose, and lignin are important components of the cell wall12. Several key metabolic genes have been identified in the phenylpropane metabolic pathway, such as PAL, C4H, 4CL, CCoAOMT, CCR, F5H, and CAD4. Plant lignin consists mainly of G, S, and H units, among which gymnosperm lignin is composed mainly of G and H units, and dicotyledonous angiosperm lignin is composed mainly of G and S units13. In general, cell walls undergo lignification during plant stress14. Several studies have shown that salt stress15, boron deficiency stress16, and cold stress17 enhance lignin production.Lignin is biosynthesized through the phenylpropane metabolic pathway, which includes three main processes: biosynthesis, transport, and polymerization of monoxylin19. A population differentiation (Fst) and nucleotide diversity level (θπ) based cross approach has been shown to be very effective for detecting selective elimination regions, especially when functional regions closely related to the living environment are mined, and the two approaches can jointly identify strong selection signals, which facilitates the screening of target genes20. This approach has been applied to numerous organisms  to study the footprints of positive selection in genomes. For instance, Fst and θπ have been used to detect selective signals during grapevine domestication, and nine selective sweep genomic regions were identified21. Similarly, selective sweep analysis revealed the genomic regions of Berkshire, a native European pig, related to disease resistance, pork production, fertility, tameness and body length22. Moreover, several studies have shown that the identification of genomic region selection signatures is an effective approach associated with horticultural traits of fruit species .Selective sweep analysis is used to identify footprints of species that have experienced strong positive natural or artificial selection during evolutionIn this study, 13 VO varieties and 12 CO varieties were sampled for genome resequencing, and three VO varieties and three CO varieties were subjected to RNA-seq analysis. Genomic and transcriptomic analyses were integrated to provide insights into VO fruit mastication trait formation.A total of 25 sweet orange varieties (13 VOs and 12 COs) were selected for genome resequencing. The 25 varieties are different among each other in terms of fruit size, fruit color, number of seeds, maturity, and place of origin, among other characteristics  representing the major varieties of VOs and COs were selected for population analysis. As shown in Supplementary Fig. Among the COs, 141 genes were contained in the 29 selected regions. Of these genes, 14 disease resistance proteins, 3 myrcene synthases, and 8 leucine-rich repeat- containing proteins were identified  and caffeoyl-CoA O-methyltransferase (Cs1g22450), were identified in VO  and three CO varieties , which are typical representative varieties of VO and CO, respectively. The fruits of these six sweet orange varieties were collected, which includes two tissue types (peel and pulp), at 220 days after flowering (DAF). Data for a total of 36 transcriptomes were obtained from 12 samples  , a total of 16,328 genes were categorized into 18 modules   Fig. . The gen-4) Fig. . In addi-4) Fig. . The two-4) Fig. . The gen-4) Fig. . In the -4) Fig. .Fig. 4FuCsERF74 (Cs1g16690) was the hub gene in the blue module network and had the highest number of edges (178 edges) , CsHSFB3 (orange1.1t02319), CsSPL4/13 (Cs2g23550/Cs7g10990), etc.  in the blue, red, and brown module networks, respectively. For instance, es) Fig. . In othetc. Fig. .PALs in this pathway were upregulated in the peel or pulp of VOs , a CAD gene (Cs2g10070), and an OMT1 (Cs5g16290), all of which are involved in lignin synthesis, through qRT-PCR verification  and three CO varieties . As shown in Fig. Cs8g01300 and Cs1g12840) and two PMEs (Cs7g19180 and Cs4g15560) were more highly expressed in COs than in VOs  is a harder and coarser late-ripening bud sport of FJ72-1, and the content of protopectin in FJWC is higher than that in FJ72-1, while the content of water-soluble pectin is lower than that in FJ72-135. In the present study, two PGs (Cs8g01300 and Cs1g12840) and two PMEs (Cs7g19180 and Cs4g15560) were identified as being more highly expressed in COs than in VOs , while the fruit development and ripening time of COs generally takes 7–8 months; their fruit generally is subjected to only 1–2 months of low temperature. Therefore, in long-term interactions with the environment, VOs are exposed to stronger environmental stress than COs, so unique quality characteristics have developed throughout evolution. In this study, the lignin contents in the VO fruits were significantly higher than those in the COs fruits  varieties, which included 12 CO varieties and 13 VO varieties  and the three CO varieties  harvested at 220 DAF were divided into peels and pulp for RNA-seq  following the Illumina protocol. The C. sinensis genome41 was used as the reference genome. The DNA resequencing data were aligned to the reference genome by BWA42. The alignment results were deduplicated by SAMtools43. The raw data have been submitted to the NCBI SRA database under accession number PRJNA687608.Leaves were used for genomic DNA extraction and sequenced on an Illumina HiSeq43. The Bayesian model was used to detect polymorphic loci in the population, and high-quality SNPs were obtained through the following methods: the ‘mpileup’ command was used to identify SNPs with the parameters ‘-q 1 -C 50 -t SP -t DP -m 2 -F 0.002’, and after excluding SNP calling errors caused by incorrect mapping or InDels, only high-quality SNPs  were retained for subsequent analysis. ANNOVAR44 was then used to annotate the SNPs  was performed using GCTA software46. VCFtools47 was then used to calculate the population differentiation (Fst), nucleotide diversity (π), and Tajima’s D values. Fst and θπ were subsequently calculated, using a 20-kb window with a 10-kb step size.The distance between populations was calculated on the basis of the obtained individual SNPs. TreeBEST-1.9.2 software was used to construct a distance matrix, and the neighbor-joining method was used to construct a phylogenetic tree48. The coefficient (r2) between pairwise SNPs was calculated and the parameters were set as: ‘-n -dprime-minMAF 0.05’. The average r2 value was calculated for pairwise markers within a 5000-kb window and averaged across the whole genome.The pattern of linkage disequilibrium (LD) was compared using high-quality SNPs and calculated with Haploviewst and θπ values. Both the threshold values of Fst and the θπ ratio used to identify selected regions were in the top 5%. The process was performed as described by Xia et al.49. Genome-wide distribution of fixation index (Fst) values and θπ ratios were used for the defined group pairs. The Fst values were Z -transformed as follows: Z(Fst) = (Fst−µFst)/σFst, in which µFst is the mean Fst and σFst is the standard deviation of Fst. The θπ ratios were then log2 transformed. Subsequently, we estimated and ranked the empirical percentiles of the Z(Fst) and log2(θπ ratio) values in each window. We considered windows with the top 5% of Z(Fst) and log2(θπ ratio) values simultaneously as candidate outliers under strong selective sweeps.Selective sweep analysis was performed on the basis of the F50. The data for a total of 36 transcriptome profiles were obtained by using the Illumina HiSeqTM 4000 sequencing platform at Personalbio   from six varieties were used for RNA-seq. Each sample was sequenced, which included three biological replicates. Total RNA isolation was performed, as described previously53. The endogenous reference gene used was CsActin54. The primers used for each gene in this study are listed in Supplementary Table Relative gene expression was detected by real-time quantitative PCR, which was performed according to the methods of our previous study29.Approximately 5.0 g of peel and pulp tissue from each variety was dried to a constant weight at 80 °C, ground into powder using a mortar, and passed through a 40-mesh sieve. The total lignin content of 20 mg of peel and pulp tissue was determined in accordance with the protocol of the Plant Lignin Content Kit , which was purchased from Suzhou Comin Biotechnology Co., Ltd. (China). Approximately 3.0 g of peel and pulp tissue from each variety were used for pectin determination, as described by Lei et al.Supplementary informationFigure S1. Statistical distribution of SNP mutation type (a), SNP support read number (b), SNP quality (c), and neighboring SNP distance (d)Figure S2. Tajima'D test of VO and CO based on intraspecies polymorphism (a) and decay of linkage disequilibrium of VO and CO (b)Figure S3. The GO term  enrichment analysis of selected genes from CO (a) and VO (b)Figure S4. Global analysis of the 36 fruit transcriptomesFigure S5. The GO term  enrichment analysis of DEGs among COs and VOs identified from peel tissue (a) and pulp tissue (b)Figure S6. Coexpression modules identified by WGCNAFigure S7. The GO term  enrichment analysis of the genes from blue (a), red (b), and brown (c) modulesTable S1. Overview of sample information and resequencing statisticsTable S2. Distribution of SNPs within various Citrus sinensis genomic regionsTable S3. Screened group-specific SNP molecular markersTable S4. Annotation results of part of genes screened in CO and VOTable S5. The results of GO term enrichment analysisTable S6. The results of KEGG term enrichment analysisTable S7. Overview of sample information and RNA-seq statisticsTable S8. A list of key genes identified by RNA-seqTable S9. The primers used in this study


The usefulness of genomic prediction (GP) has been widely proofed by breeding analysis in livestock, plants and aquatic populations. It is well known that ‘marker density’ is a critical factor that affects the accuracy of GP, however, how to properly measure ‘marker density’ in GP is yet to be determined. With population-level whole-genome sequence data or high-density single nucleotide polymorphism (SNP) data available, this question seems to be answered more convincingly. In this study, we investigated and discussed the impact of four ‘marker density’ measures that reflect genetic or physical distances between SNPs on the accuracy of GP in a Germany Holstein dairy cattle population. Our results showed that the degree of variation of physical distance between adjacent SNPs had significant effects on the accuracy of GP, while the genetic distance between SNPs had no relationship with the accuracy of GP. Therefore, for studies based on high-density SNP data, the default strategy of pruning SNPs based on genetic distance is detrimental to heritability estimation and genomic prediction. The results extended the communities knowledge of ‘marker density’ and provided useful suggestions for the application and research on genome prediction.With the availability of high-density single-nucleotide polymorphism (SNP) data and the development of genotype imputation methods, high-density panel-based genomic prediction (GP) has become possible in livestock breeding. It is generally considered that the genomic estimated breeding value (GEBV) accuracy increases with the marker density, while studies have shown that the GEBV accuracy does not increase or even decrease when high-density panels were used. Therefore, in addition to the SNP number, other measurements of ‘marker density’ seem to have impacts on the GEBV accuracy, and exploring the relationship between the GEBV accuracy and the measurements of ‘marker density’ based on high-density SNP or whole-genome sequence data is important for the field of GP. In this study, we constructed different SNP panels with certain SNP numbers  by using the physical distance (PhyD), genetic distance (GenD) and random distance (RanD) between SNPs respectively based on the high-density SNP data of a Germany Holstein dairy cattle population. Therefore, there are three different panels at a certain SNP number level. These panels were used to construct GP models to predict fat percentage, milk yield and somatic cell score. Meanwhile, the mean ( Implementing genomic selection can increase genetic gain, which has now been demonstrated in livestock ,2, plantCompared with low-density <10 k SNPs) and moderate-density (10–100 k SNPs) panels, high-density panels (>100 k SNPs) can improve the accuracy of GP ,13. Howe0 k SNPs Recently, several studies have found that compared with moderate-density panels, GEBV accuracy based on real or imputed high-density panels has decreased ,25. The It is known that GEBV accuracy is closely related to marker density, however, it is yet to be determined the key measurements of ‘marker density’. Therefore, we conducted this study with the aims of (1) evaluating the impact of ‘marker density’ measured by physical or genetic distance on the performance of genomic prediction in a dairy cattle population, and (2) exploring the effect of mean and variance of marker distances on the accuracy of GP. Our result extended knowledge of measurements of ‘marker density’ on the performance of GP.p-value < 0.01 from the Hardy–Weinberg equilibrium test were removed, after filtering, 335,753 SNPs were retained for further analyses  between SNPs. The RanD method was implemented using PLINK software. The detailed implementation of the methods is shown in the In this study, three methods, namely physical distance (PhyD), genetic distance (GenD) and random distance (RanD), were used to measure the marker distance. The PhyD method selects markers by limiting the number of base pairs (bp) between adjacent SNPs. The GenD method filters the SNPs by limiting the maximum r2 between each pair of SNPs in the window, (b) remove one of a pair of SNPs if the r2 is greater than the threshold, (c) shift the window a step length forward and repeat the procedure. The window size and step were set as 50 and 5 kb respectively, and the relation curve between the number of selected SNPs and the threshold of genetic distance (r2) is present in r2 is close to 1. As a result, the GenD method does not produce panels containing 100 or 200 k SNPs. According to the above three methods, a total of 40 panels were generated, which can be regarded as SNP chips with different SNP numbers and conduct according to different marker selection methods.In the random method, 0.3%, 0.6%, 0.9%, 1.2%, 1.5%, 2.1%, 3%, 4.5%, 6%, 9%, 15%, 22.5%, 30%, 60% of the 335,753 SNPs were randomly sampled, and panels with 1, 2, 3, 4, 5, 7, 10, 15, 20, 30, 50, 75, 100, and 200 k SNPs were constructed. We term the marker sets containing 1–10 k SNPs as the low-density panels, containing 15–75 k SNPs as moderate-density panels and containing 100–300 k SNPs as the high-density panels. For the PhyD method, we plotted the relation curve between the number of the selected SNPs and the threshold of the distance (bp) between adjacent SNPs in All genomic predictions in this study were based on the genomic BLUP (GBLUP) model, the model includes a single random genetic effect and was expressed as:software .G matrix was constructed using all SNPs in the panel [kth column, and kth SNP. The solution of G matrix separately.The additive he panel :(2)G=WW′The predictive performance of the GBLUP model was evaluated using the 10-fold cross-validation (CV) method. A 10-fold CV process is as follows: (1) Randomly split all bulls into 10 folds. (2) Build the GP model on nine folds of the bulls (training set), and the remaining fold  is used to test the accuracy of the prediction model. (3) Repeat (2) until each of the 10-folds has served as the validation set. (4) The Pearson’s correlation coefficient of EBV and GEBV for individuals in the 10 validation sets obtained above is called the CV accuracy and will serve as the prediction accuracy of the GBLUP model. This 10-fold CV process was repeated 10 times for each scenario. Therefore, the 10 × 10 fold CV was used to evaluate GEBV accuracy in our study. The variance component estimates calculated by the above CV process were also used for subsequent analysis.r2 (genetic distance) between adjacent SNPs. These four statistics were used as the measurements of marker density. For each of the 40 panels, the four statistics need to be calculated separately. In this study, the length of the chromosomes used to construct the various panels was fixed. At the same SNP number level, the To make it easier to compare the panels produced by different marker selection methods, four statistics were calculated for each SNP panel, i.e., the mean , rather than detect the influence of marker density-related measures on GEBV accuracy as the SNP number changes, so we first standardized the measurements and the GEBV accuracies in each SNP number level. Another purpose of our standardization (using standard deviation) is to make the information at each SNP number level contribute equally to the results of the correlation analysis.To further investigate the relationship between marker density-related measurements and GEBV accuracy, we tested the correlation between each measurement and GEBV accuracy. First, the three values of each marker density-related measure at each SNP number level were standardized as follows:p < 0.001)  A. Compar01)  repeat (2) until throughout the entire genome. For the construction of marker sets 2–4 (Scenario 2–4), just replace the 67 in step 2 with integer sampling from normal distributions with a mean of 67 and variance of 20, 40 or 80 respectively. The The above results seem to indicate that the variance of physical distance between adjacent SNPs in panels influences the GEBV accuracy, and we further verified this result. We generated four different marker sets regardless of the physical distance and genetic distance between the SNPs. The first marker set (Scenario 1) was constructed as follow: (1) The SNPs were numbered 1 to 335,753 in the order in which they are placed on the chromosome, and select the SNP numbered 1, (2) select the SNP numbered Marker density is an important factor affecting the results of genomic prediction and heritability estimation. In general, the low-density SNP chips for genome prediction were constructed by SNPs evenly distributed across the entire genome . High-der2 is usually used as a threshold to prune the SNPs before formal analysis [Previous studies have found that the marker selection methods have an impact on the distribution of SNPs on the chromosome and, in turn, the performance of GP . In this methods . The gen methods . Correlaeen them A, and thhem  constructed by the PhyD method performed in this study are recommended for genome prediction and SNP-heritability estimation. The relationship between measurements of ‘marker density’ on GEBV accuracy discovered in this study provides useful suggestions for future work based on high-density SNP or whole-genome sequence data.


Micropterus spp.) is well documented, including an extensive intergrade zone between the widespread northern Largemouth Bass  and the Florida Bass (M. floridanus). Phenotypic surveys have identified an estuarine population of Largemouth Bass  in the Mobile‐Tensaw Delta, with larger relative weight and smaller adult size compared to inland populations, suggesting a potential third lineage of largemouth bass. To determine the evolutionary relationships among these Mobile Delta bass populations, M. salmoides and M. floridanus, putative pure and intergrade populations of all three groups were sampled across the eastern United States. Phylogenetic analyses of 8582 nuclear SNPs derived from genotype‐by‐sequencing and the ND2 mitochondrial gene determined that Delta bass populations stem from a recently diverged lineage of Largemouth Bass. Using a novel quantitative pipeline, a panel of 73 diagnostic SNPs was developed for the three lineages, evaluated for accuracy, and then used to screen 881 samples from 52 sites for genetic integrity and hybridization on the Agena MassARRAY platform. These results strongly support a redrawing of native ranges for both the intergrade zone and M. floridanus, which has significant implications for current fisheries management. Furthermore, Delta bass ancestry was shown to contribute significantly to the previously described intergrade zone between northern Largemouth Bass and Florida Bass, suggesting a more complex pattern of secondary contact and introgression among these diverged Micropterus lineages.Hybrid zones between diverged lineages offer a unique opportunity to study evolutionary processes related to speciation. Natural and anthropogenic hybridization in the black basses ( Of over 150 described pairs of hybridizing species in the United States, 20% are found within Centrarchidae, including the black basses  used thousands of single nucleotide polymorphisms (SNPs) and mitochondrial sequencing to characterize the phylogenetic and population genetic relationships between Delta Largemouth Bass, northern Largemouth Bass, and Florida Bass; (2) developed a SNP assay for accurate and rapid identification of pure and hybrid individuals; and (3) applied this SNP assay to hundreds of samples across the eastern United States in order to characterize the geographic extent of hybridization and assess the role of stocking in driving the extent of Florida Bass introgression.The objectives of this study were to employ modern genomic techniques to analyze the 22.1PstI restriction enzyme was previously described in Thongda et al. , northern Largemouth Bass , Florida Bass , and putative intergrade largemouth bass , Coosa Redeye Bass , Shoal Bass , Smallmouth Bass , and Guadalupe Bass . FLB, NLB, and ILB individuals were identified using previously developed 25–38 SNP panels that are diagnostic for northern Largemouth and Florida Bass  using the a et al. . For the2.2M. floridanus genome reference  v0.7.17 with default settings  or within the same 1000‐bp window were pruned using BCFtools +prune plugin , and had a minor allele count greater than 3. The VCF output of stacks was converted to a phylip file using vcf2phylip v2.0 , and had <15% missing data across all individuals. SNPs that departed Hardy–Weinberg equilibrium with a 0 Ortiz, .2.3DNA from 58 samples was amplified with previously published primers targeting the mitochondrial NADH subunit 2 (ND2) gene , expected heterozygosity (He), overall FST, and FIS were calculated using the basic.stats function in the R package hierfstat v0.5.7  and individual sampling sites with at least 5 individuals. Based on equations from Nei and Chesser , observeockerham  was calcockerham . VariablThe model‐based Bayesian clustering method STRUCTURE v2.2.4  as previously described. To identify SNPs that had the greatest contributions to the PCA and therefore were the most diagnostic, we extracted 549 SNPs in the 90% quantile of loadings for PC1 and PC2. We then randomly sampled 80–100 SNPs from this set for analysis with STRUCTURE and compared the M. floridanus genome (the SNP and 100 bp flanking on either side) and imputed into the MassARRAY Assay Design Software to design two multiplex assays with a maximum of 60 SNPs per assay. This produced forward, reverse, and extension primer sequences for assays of 52 and 39 SNPs (File A MassARRAY System (Agena Bioscience) was used to develop, genotype, and evaluate SNP panels. For each of the 127 diagnostic SNPs, a 201‐bp sequence was extracted from the NPs File , which wNPs File . SNP genQ‐value >0.94 from STRUCTURE when using the full GBS SNP dataset were chosen to be references (14–24 per lineage). Other parameters: admixture model, K = 3, correlated allele frequencies, migration prior 0.05, and burn‐in of 20,000 followed by 150,000 MCMC iterations. A subset of 751 samples were also genotyped using a panel of 35 SNPs that had previously been developed for differentiating FLB and NLB, where ancestry proportions were determined by counting the number of FLB or NLB alleles using a custom R script  and all other sites sampled with our SNP panel using the R package geosphere v1.5‐10 . Mean length of GBS loci was 91 bp (SD = 0.02). Further filtering by missing data <15%, HWE, MAF >5%, and thinning by LD reduced the dataset to 8582 SNPs. Average read depth across SNPs per individual ranged from 3.3 to 29.2 (mean = 11.2 ± 4.6).3.2ST when separated by lineage group was 0.426. Pairwise FST between lineages was highest between FLB–NLB (0.749) and lowest between DLB–NLB (0.459). Fixed SNPs were detected between each pair of lineages, with the fewest between DLB–NLB (46 unlinked SNPs) and the most between FLB–NLB , with the highest within‐lineage FST observed between Sugar Lake, MN, and Hatchery NLB  and ultrafast bootstrapping Figure . When sa3.4Q ≥ 0.94 resulted in adequate separation between pure species and hybrid individuals . Triple hybrids had >90% performance. When fourth‐generation backcrossed hybrids (Bx4) were included, efficiency decreased to 82.8%–92.4% for hybrids and accuracy decreased to 58%–78.7% for pure species, as many Bx4 hybrids were assigned as pure . Detailed information on SNP panels, including SNP ID, genome position, and primer sequences , are listed in File 2 and 39.re Table .3.5Population structure analysis of 52 sites using the 73 SNP panel identified populations with a majority of pure DLB individuals extending north of the Mobile Delta, encompassing all tested populations below the Fall Line and outside of peninsular Florida, including those in remote areas with no history of stocking , where separate geographic clines were fit for River and Reservoir sites . The proportion of FLB ancestry in an individual was best described by a model that included stocking: FLB ancestry ~ distance * stocking , including the first thorough genetic characterization of a unique lineage of M. salmoides radiating out from the Mobile‐Tensaw Delta. To assess the geographic extent of hybridization, we developed a suite of 73 SNPs that can accurately classify advanced‐generation hybrids between all three lineages. This SNP panel will be a valuable tool for fisheries management and conservation of these economically and ecologically important species, while also facilitating future investigations of introgression and species boundaries. While stocking of Florida Bass in reservoirs for recreational fishing has contributed in part to observed hybridization patterns, the natural geographic extent of both pure M. floridanus and introgressed populations are nonetheless much larger than currently accepted.Using a combination of 8582 SNPs and targeted mitochondrial gene sequencing, we identified a complex hybrid zone involving three distinct lineages of largemouth bass  was high (FST = 0.459), with 46 unlinked nuclear SNPs fixed between the two groups and two fixed SNPs in the first 598 bp of the mitochondrial ND2 gene. While significant, this differentiation is still much lower than that observed between Florida Bass (FLB) and NLB . These values are higher than those observed using GBS SNPs for different species of Squalius chubs (FST = 0.126–0.414) and comparable to FST between diverged lineages of walleye  are an evolutionarily diverged lineage of Micropterus using either two mitochondrial genes or 16 nuclear genes dated the divergence of NLB–FLB at the end of the Pliocene . As our panel was shown to have an 8.4% false discovery rate Table , we hypoWhether the result of natural introgression, anthropogenic introductions, or a combination of the two, the large hybrid zone observed between these three diverged lineages provides an excellent opportunity to study the speciation process  DLB ancestry in the Mobile Delta were shown to have >25% FLB ancestry when using the previous assay , stable fisheries, and biodiversity concerns (Young et al., Current diagnostic markers in largemouth bass include a set of 35 SNPs designed for the MassARRAY Sequenom (Li et al., The observation of individuals with DLB mitochondria and ancestry extending outside of the Mobile Delta raises the question of whether phenotypic variation observed in fish from the Delta are due to adaptation or phenotypic plasticity from the environment. While Largemouth Bass populations of the Mobile Delta are currently stable, they may be at particular risk from climate change due to unique environmental challenges in coastal habitats, such as low dissolved oxygen or storm‐mediated shifts in salinity. Microcosm experiments and functional genomics with both inland and coastal Delta bass can help elucidate whether these fish have adapted or acclimated to the estuarine environment.The authors declare no conflict of interest.Supplementary MaterialClick here for additional data file.File S2Click here for additional data file.File S3Click here for additional data file.


We used four single-nucleotide polymorphism markers and 360 cotton accessions and found that two FMs, namely, D11_24030087 and A07_72204443, could effectively differentiate accessions of different genotypes with higher consistency to phenotype. The appeared frequencies of varieties harbored Hap2  with longer FL  and higher FS  were 100 and 72.7%, respectively, which was higher than that of varieties harbored only on a single elite allele , suggesting a favorable haplotype for selecting varieties with superior FL and FS. These FMs could be valuable for the high-throughput selection of superior materials by providing genotypic information in cotton breeding programs.Fiber length (FL) and fiber strength (FS) are the important indicators of fiber quality in cotton. Longer and stronger fibers are preferred for manufacturing finer yarns in the textile industry. Functional markers (FMs) designed from polymorphic sites within gene sequences attributing to phenotypic variation are highly efficient when used for marker-assisted selection (MAS) in breeding superior varieties with longer FL and higher FS. The aims of this study were to develop FMs  Gossypium hirsutum L.) holds a predominant position as a cash crop by supplying largely natural fibers for the global textile industry (GhFL2 encoding Kip-related protein 6 (KRP6) was identified as a key gene for fiber elongation in our previous study  derived from polymorphic sites within gene sequences affecting phenotypic variation is considered to be more accurate . FMs to d levels , and a sd levels , were idd levels ; three S and HY5 ; 10 SNP  and HY5 ; and fou and HY5 . All theGhFL2 and Gh_A07G1769; (2) to evaluate the efficacy of the markers for allele discrimination and accuracy; and (3) to assess the potential application value of the markers and screen out the more favorable haplotype for MAS.The kompetitive allele-specific PCR (KASP), a single-step genotyping technology, can type SNPs at specific sites relying on allele-specific oligo extension and fluorescence resonance . It has G. hirsutum L.) accessions were used in this study. These accessions were derived from various cotton-producing areas, including 146 from the Yellow River basin; 69 from the Yangtze River basin; 25 from the Northwest Inland, China; 55 from Cotton Research Institute, Chinese Academy of Agricultural Sciences ; 44 from abroad; and others from China with no detailed information   . We extrThe total genomic DNA of each accession was extracted from young leaves following a modified cetyltriethylammnonium bromide (CTAB) method . The quaThe genotyping assays were performed on a 384-well plate. The reaction volume of 384-plate format PCR system can be established as 3.04 μl reactions . The amplifications were performed using a GenePro™ Thermal Cycler (Hydrocycler) with the following cycling conditions: 94°C for 15 min, 10 touchdown cycles  and then followed by 26 cycles of amplification . After the run was completed, reaction plates were read and the data were analyzed using StepOne Software version 11.1 .The FL marker D11_24030087 (G/T) was used first to discriminate the genotypes of materials, and then the materials with favorable alleles were selected. Second, these selected materials were further genotyped using the FS marker A07_72204443 (C/T). Finally, the materials with both favorable alleles were screened out.t-test. The effects of haplotypes on phenotypes were determined using a one-way analysis of variance (ANOVA). The significance level was set at P = 0.05 or 0.01. Traits were treated as dependent variables, and haplotypes served as independent variables.Statistical analyses for phenotypic traits were conducted using SPSS 20.0. Allelic effects for a single KASP marker were tested for statistical significance using a –1, which showed abundant variations in the population  with this marker was observed, no heterozygous genotype was discovered , on the coding region of FL trait . Therefoscovered . To valiscovered . The aboGh_A07G1769 resulting in amino acid change, were verified for deployed markers for the FS trait using the validation population. The result showed that only the marker A07_72204443 (C/T) could clearly distinguish type alleles CC, TC, and TT in the accessions  included 11 accessions with an average FL of 29.71 mm and FS of 30.69 cN•tex–1, Hap3 (TC) included 55 accessions with an average FL of 28.42 mm and FS of 28.53 cN•tex–1, and Hap4 (TT) included 9 accessions with an average FL of 28.96 mm and FS of 29.23 cN•tex–1. One-way ANOVA indicated that the haplotypes based on these two markers had significant effects on FL and FS. The accessions with Hap2 showed the highest averaged values in FL and FS. The multiple comparison analyses showed that Hap2 genotypes had significant differences from Hap3 and Hap4, while FS genotypes exhibited significant differences from Hap1. Therefore, Hap2 with two elite alleles was considered as the most favorable haplotype  or FS higher than 26.90 cN•tex–1  was also more often observed (100.0 and 96.7%) than that of varieties harboring other haplotypes  was located on the coding sequences of GhFL2, resulting in the change of amino acid, and showed two different genotypes corresponding to differential FL  and A07_72204443) could clearly distinguish the accessions and showed a high consistency between the genotyping results and phenotypic evaluation   with an average FL of 29.19 mm was longer than those harbored with T-allele with an average of 28.50 mm, indicating the elite allele “G” for increasing FL. Similarly, the varieties that possessed “T” from A07_72204443 C/T with an average FS of 30.15 were higher than those that possessed “C” with an average of 29.06 cN•texectively , suggestectively . In contectively . Toward ctively) , 4. In actively) , and theIn this study, two functional SNP markers in two genes, namely, D11_24030087 and A07_72204443, were successfully developed using a rapid and cost-effective method available to date, the KASP assay. These two markers could effectively discriminate target genotypes that provided an improved accuracy. The Hap2 (GT) based on the two FMs to assist selection could be more efficient in the breeding of upland cotton.The raw data supporting the conclusions of this article will be made available by the authors and available from the corresponding author on reasonable request.ZM designed the study and revised the manuscript. LL completed genotyping the materials and wrote the manuscript. ZS, YZ, HK, JY, ZL, LW, and GZ performed phenotyping experiments. LL and XW analyzed data. All authors read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Bemisia tabaci taxon consists of an undefined number of morphologically identical genetic variants of which only a few, including the B, harbor invasive haplotypes. These haplotypes have potential to upsurge and become important pests and plant virus vectors in irrigated agroecosystems worldwide. In the 1980s, unprecedented outbreaks associated with the B variant were reported worldwide, however, the precise origin(s) of the invasive haplotypes has not been determined. In this study, available B. tabaci mitochondrial gene sequences were examined for patterns of conserved single nucleotide changes (SNPs). The whitefly sequence records represented North Africa-Middle Eastern habitats, the proposed B variant center of origin, and distant locales recently invaded by haplotype(s) of the B variant. Unexpectedly, the analysis revealed eight SNPs groups (haplotypes) demonstrating that the genetic architecture of the B mitoype is more complex than previously recognized. Also, the distribution patterns of the eight B haplotypes were tightly linked to well-defined eco-geographic regions, suggesting the different groups have diversified by geographic isolation. Contrary to claims that collectively, the B variant is invasive, only two of the eight haplotypic groups have established in geographical locations outside of their zone of endemism.The whitefly Bemisia tabaci cryptic species contains 39 known mitotypes of which the B and Q are best recognized for having established outside their extant endemic range. In the 1980s, previously uncharacterized haplotype(s) of the B mitotype rapidly established in tropical and subtropical locales distant from their presumed center of origin, leading to displacement of several native mitotypes and extreme damage to crops and other vegetation particularly in irrigated agroecosystems. To trace the natural and evolutionary history of the invasive B haplotypes, a phylo-biogeographic study was undertaken. Patterns of single nucleotide polymorphisms (SNPs) and signatures potentially indicative of geographic isolation were investigated using a globally representative mitochondrial cytochrome oxidase I gene (mtCOI) sequence database. Eight haplotype groups within the North Africa-Middle East (NAFME) region were differentiated, NAFME 1–8. The NAFME 1–3 haplotypes were members of the same population that is associated with warm desert climate niches of the Arabian Peninsula and east coastal Africa-Ethiopia. The NAFME 4 and 5 haplotypes are endemic to warm and cold semi-arid niches delimited by the Irano-Turanian floristic region, itself harboring extensive biodiversity. Haplotypes 6 and 7 co-occurred in the Middle East along eastern Mediterranean Sea landmasses, while NAFME 8 was found to be endemic to Cyprus, Turkey, and desert micro-niches throughout Egypt and Israel. Contrary to claims that collectively, the B mitotype is invasive, NAFME 6 and 8 are the only haplotypes to have established in geographical locations outside of their zone of endemism.The  Bemisia tabaci cryptic (or sibling) species group [The whitefly es group ,2,3 refees group ,5,6. Prees group ,7, colleB. tabaci as a pest of tobacco crops in Greece [B. tabaci as a sporadic pest has been documented since the 1930s, particularly in cotton-vegetable cropping systems in Egypt, India, Israel, Sudan, and in Central and South America [B. tabaci was shown to complete up to 15 generations per year due to the favorable hot climate there, and 11 generations in the coastal areas [Since the first identification of n Greece , it has n Greece ,10,11. L America ,13. In tal areas . During al areas . Among tal areas ,19,20,21al areas , the Asial areas ,24,25,26al areas ,28,29.B. tabaci colonizing poinsettia [Brassica spp. [B. tabaci endemic to the Americas [B. tabaci from Egypt and Israel, whereas the B1 and B2 electromorphs best matched B. tabaci endemic to Cyprus and Yemen, respectively [The discovery in the United States of insettia ,31,32 anica spp. , both noAmericas ,36,37,38Americas , and shoectively ,34. The ectively . By 1990ectively ,39, resuectively . Widesprectively . HoweverB. tabaci mitochondrial substitutions have begun to saturate compared to the nuclear genome [B. tabaci mitotypes [B. tabaci by a ‘DNA barcoding’ approach, large datasets are increasingly useful for studying large-scale species assemblages and populations, worldwide.Because mitochondrial genes evolve more rapidly compared to their nuclear gene counterparts, base substitutions accumulate that are valuable for studying patterns of geographic variation among populations ,43,44. Ir genome . Howeveritotypes ,45,46,47The objective of this study was to investigate the patterns of intraspecific genetic variation for representative populations of the B mitotype collected from the presumed native niche ranges, and from locales where it has been reportedly introduced. To determine whether the genetic architecture of the B mitotype can be explained phylo-bio-geographically, the congruence between extant B mitotype ecological niches and geographical placement of mtCOI phylogenetic breaks was investigated. To accomplish this, a mtCOI sequence dataset was compiled from records available in the GenBank database and for additional samples available in the Brown lab collection, School of Plant Sciences, The University of Arizona . The mtCOI sequences were scrutinized for ‘signature SNPs’, enabling classification of unique haplotypes, and subjected to phylogenetic analysis. The SNPs haplotype results were overlayed with the hypothetical native niche range(s) for the B mitotype cryptic species, which were categorized as ecogeographical units based on global Koppen-Geiger climate classification  and hotsBemisia tabaci’, ‘B biotype’, ‘B mitotype’, ‘MEAM 1′, and ‘Middle East Asia Minor’. The function efilter was used to select mtCOI sequences by length, which ranged from 650–1000 bp. The filtered nucleotide sequences in ‘fasta’ format were downloaded using the efetch function. Validated reference sequences  not yet available in the GenBank database were selected from the laboratory database . Forty mtCOI sequences from the Brown Laboratory collection were determined from archived field samples and added to the dataset. The mtCOI sequences were obtained using standard methods established in the Brown laboratory for PCR amplification, cloning, and Sanger sequencing of selected archived adult B. tabaci. The 3′-mtCOI gene fragment [E. coli DH5α cells were transformed using ligated plasmids. Colony PCR amplification [In total, 591 sequences were downloaded from the NCBI nucleotide database using the Entrez search and retrieval system EDirect . The esefragment ,50 was afication  was carr® 2021.1.1. Plasmid DNA sequences were removed from reads and at least 25 bp from each end were trimmed. Consensus contigs were manually edited as needed to resolve conflicts between forward and reverse reads. Consensus contigs were aligned in Mesquite v2.75 [Sanger reads were de-novo assembled in Geneious Primete v2.75  using MUte v2.75  with theThe alignment was trimmed to 725 bp to remove primer annealing sites and misaligned ends. The alignment was filtered to identify and remove potential nuclear mitochondrial DNA and chimeric sequences (NUMTS). In total, 127 sequences were removed based on the following criteria : sequencIn total, 503 sequences representing the 3′-end of the mtCOI gene were aligned and evaluated for informative single nucleotide polymorphisms. The Arizona B-prototype (AzB) sequence (Accession no. AY057123) was included for comparison. Variants were called using the ‘Find variations/SNPs’ option implemented in Geneious prime v2019.1.3  with customized parameters at minimum coverage of 7 and a minimum of 50% variant frequency.To determine whether the SNP-defined haplotypes belonged to a single or multiple populations, an exact test of population differentiation was carried out in Genepop v4.7  with Mar7 generations. Trees were sampled every 1000 generations and log-likelihood scores were visualized using Tracer v 1.6 [8 generations. The 70% majority-rule consensus tree was obtained with TreeAnnotator v1.8.4 and visualized using FigTree v1.4.2 .The best-fit model of evolution was determined using jmodeltest software v2.1.7  based oner v 1.6  to verifhttp://www.fluxus-engineering.com/sharenet.htm, accessed on 8 December 2019. Each site of the alignment was weighted uniformly with the epsilon (η) value set to zero.The aligned matrix, which contained 315 unique mtCOI-3′ haplotypes representing all SNP profiles, was used to build a median-joining network of haplotypes using Network v10.1.0.0 . Sequencwww.maps.google.com, accessed on January 2021). The ‘occurrence records’ of the B mitotype were drawn with the biological records tool plugin, available in QGIS 3.8.1 [The geographic coordinates associated with the aligned sequences were downloaded from NCBI using the Biopython module “Entrez” . When NCIS 3.8.1 , and oveIS 3.8.1 ,66 climaIS 3.8.1 .The 509 sequences were differentiated as eight haplotypes, designated NAFME 1–8 . HaplotyThe phylogenetic breaks of all B mitotype haplotypes, except NAFME 3 and 8, were well-supported (100% posterior probability), and were concordant with the SNP-mediated classification of haplotypes NAFME 1–8 . DespiteThe phylogeographic distributions of the haplotype were concordant with the network analyses  except fp < 0.05), hereon POP1–5. Haplotypes NAFME 1–3 comprised a single population (p = 0.08), the POP1, representing the warm deserts of the Arabian Peninsula and Ethiopia. By comparison, the NAFME 4 and 5 haplotypes were distinct populations, POP2 and POP3, respectively, which distributed across warm and cold semi-arid regions of the northern ranges of the Middle East, respectively, within the Irano-Turanian floristic region, characterized by extreme endemism of its many life forms [An ‘exact test’ of population differentiation showed that the eight haplotypes consisted of five well-differentiated genetic populations . Analogences in . In thisThe GenBank database houses an important archive of mtCOI sequences , which iB. tabaci haplotypes in the region.Phylo- and eco-geographic breaks are correlated and support the existence of several NAFME haplotypes, of which only NAFME 6 and 8 have been introduced to tropics and subtropics of the world in the last three decades ,73, whicUnexpectedly, NAFME 4 and 5 haplotypes were also found in southern desert Pakistan, a country where Asia II mitotypes predominate ,74 and BB. tabaci haplotypes has become an urgent short-term need to abate the gradual expansion of these populations across neighboring countries. An alternative explanation to the unprecedented urban outbreaks in large cities in Iran could be that invasive NAFME 8, the resilient haplotype, has only recently dispersed to northern Iran, where it has outcompeted NAFME 4 and 5; however, a larger sample size is needed to corroborate this interpretation.In Iran, NAFME 4 or 5 have been reported as important pests and plant virus vectors , and recB. tabaci B mitotype by impeding gene flow among these populations for a long time so that clear differences have also accumulated in their nuclear genome . In addition, the ecology of whitefly species in desert habitats might also explain the diversification of these haplotypes in the Middle East in the context of isolation by distance principles. In such habitats, host species are mainly available along flowing rivers and washes and contract or expand seasonally, which directly influence whitefly dispersal. These observations do not preclude the possibility that some haplotypes might have occupied ranges free of firm impediments to gene flow, such as biogeographic transition zones in Kuwait, south Iraq, and Syria, which could potentially lead to hybridization events that give rise to resilient NAFME hybrids capable of withstanding broader ranges of climatic conditions than parental haplotypes. The nuclear genomes of representative populations are currently being studied to test this hypothesis.The most parsimonious explanation for the intra-mitotype phylogenetic breaks and discontinuous genetic networks with a strong geographic orientation among haplotypes of the B mitotype is the existence of long-term geographic  barriers to gene flow . Here, wB. tabaci evolution. Based on the proposed scenario, NAFME founder populations from the Arabian Peninsula (extant) spread into Eurasia during the late Oligocene when the Arabian and Eurasian plates collided ~25 Mya. The uplifting of mountain ranges and plateaus between 15 to 5 Mya was favorable for allopatric diversification of B. tabaci, while populations in west Eurasia (Mediterranean) became isolated by the Taurus and Alborz Mountain ranges and the Anatolian plate, and eastern and western Eurasian populations were from counterparts in the Arabian Peninsula by the Zagros Mountain range. These geological events match previous divergence estimates that place the radiation of B. tabaci between 19.52 to 3.43 Ma [The delimitation and cohesiveness of NAFME haplotype assemblages within well-delineated biogeographical regions in the Middle East reinforce the ‘out-of-Africa’ migration hypothesis  of the B 3.43 Ma ,87,88. T 3.43 Ma  or more  3.43 Ma . Therefo 3.43 Ma ,90, endo 3.43 Ma , climate 3.43 Ma , sky isl 3.43 Ma  within MApplication of the B-haplotype-specific SNPs for the identification of NAFME 1–8  by PCR a