CBLB on BTA1, which causes arthritis in rats; CAV2 on BTA4, which affects skeletal muscles in mice; PTHLH on BTA5, which causes disease phenotypes related to the skeleton in humans, mice, and zebrafish; and SORCS2 on BTA6, which causes decreased susceptibility to injury in mice. Some of the significant SNPs  revealed allelic heterogeneity—i.e., different allele frequencies between Fleckvieh and Braunvieh. Some of the significant regions  correlated to inter-breed differences in linkage disequilibrium (LD) structure and may thus represent false-positive heterogeneity. However, positions on BTA6 (SORCS2), BTA14, and BTA24 mark Braunvieh-specific regions. We hypothesize that the observed genetic heterogeneity of hoof disorders is a by-product of different selection goals defined for the analyzed breeds—toward dairy production in Braunvieh and toward beef production in Fleckvieh. Based on the current dataset, it is not possible to unequivocally confirm or exclude the hypothesis of genetic heterogeneity in the susceptibility to hoof disorders between Fleckvieh and Braunvieh. The main reason for the problem is that the potential heterogeneity was explored through SNP–phenotype associations and not through causal mutations, due to a limited SNP density offered by the SNP-chip. The rationale against genetic heterogeneity comprises a limited power of detection of true associations as well as differences in the length of LD blocks and in linkage phase between breeds. On the other hand, different selection goals defined for the analyzed breeds accompanied by no systematic, genome-wide differences in LD structure between the breeds favor the heterogeneity hypothesis at some smaller genomic regions.Genetic heterogeneity denotes the situation when different genetic architectures underlying diverse populations result in the same phenotype. In this study, we explore the genetic background underlying differences in the incidence of hoof disorders between Braunvieh and Fleckvieh cattle in the context of genetic heterogeneity between the breeds. Despite potentially higher power of testing due to twice as large sample size, none of the SNPs was significantly associated with the total number of hoof disorders in Fleckvieh, while 15 SNPs were significant in Braunvieh. The most promising candidate genes in Braunvieh were as follows:  Genetic heterogeneity denotes the situation when different genetic architectures underlying diverse populations result in the same phenotype. In human genetics, for decades, the concept of genetic heterogeneity has been considered and investigated using genome-wide association studies (GWAS) . One of ® Genomic ProfilerTM HD panel consisting of 76,934 SNPs. After quality control based on a minor allele frequency (<0.01) and a per-individual call rate (<99%), for further analysis, 74,762 SNPs remained.The analyzed dataset was collected within the frame of the Efficient Cow project and comprised scores of hoof and leg disorders in Austrian 985 Braunvieh and 1999 Fleckvieh cows. In particular, the analyzed phenotype comprised the total number of hoof disorders scored by claw trimmers until the 100th day of lactation. The considered disorders comprised digital dermatitis, double sole, heel horn erosion, interdigital hyperplasia, sole hemorrhage, sole ulcer, and white line separation. In both breeds, the number of disorders varied between none and five, but the distributions differed. The fraction of diseased cows was higher in Fleckvieh . The cowThe GWAS was performed separately for each breed by applying a single-SNP mixed linear model implemented in the GCTA software  with psey is a vector of total number of hoof disorders scored until the 100th day of lactation; β is a vector of fixed effects comprising a general mean, breed, parity, calving year-season, percent of non-Holstein-Friesian genes, and a hoof status recording code; u is a random additive polygenic effect of a cow representing her breeding value; v is a random veterinarian effect; p is a random permanent environmental effect; and ε is a vector of random residuals. It was assumed that A is an additive relationship matrix among individuals calculated based on pedigree; vI, pI, and εI are identity matrices. Then, the single-SNP GWAS was based on the following model:where u is a vector of breeding values estimated as shown above; μ is a general mean; b is a fixed additive effect of a single SNP; X is a corresponding design matrix coded as 0, 1, or 2 for a homozygous, heterozygous, and the other homozygous genotype respectively; G) calculated based on SNP genotypes    to accouε1,ε2,…,ε10) were quantified using the Mahalanobis distance: B) and Fleckvieh (subscript F) and V representing the pooled covariance matrix of ε1 and ε2. The Hotelling test: nx is the number of cows representing breed x  and Braunvieh (MAFB) at significant SNP positions. For hypotheses testing, the natural logarithm of the ratio was used: The allelic heterogeneity between breeds was tested by calculating the ratio of minor allele frequencies in Fleckvieh  and the 1st eigenvector of the k-th breed. Following S quantifies differences in the variability of LD between two populations. Furthermore, the genome-wide pattern of LD decay with physical distance between pairwise SNPs was binned into nine intervals .where CBLB gene, known to cause arthritis in rats. The same SNP is located within a region of a QTL for hindquarter proportions. The two most significant SNPs were located on BTA4. Both were intergenic, but their closest downstream gene encodes caveolin 2 protein, which in the mouse is known to effect skeletal muscles. A SNP on BTA5 falls within four QTL regions responsible for rump conformation traits. One of the most interesting significant annotations points out at another SNP on BTA5, which is located 76,362 bp downstream of PTHLH. In humans and mice, this gene causes multiple disease phenotypes related to the skeleton. In zebrafish, mutations of this gene result in decreased bone mineralization, in humans—in brachydactyly as well as in numerous bone and calcium related disease phenotypes in the mouse including decreased length of long bones, premature bone ossification, and increased osteocyte apoptosis. Another interesting significant annotation points out at the intron of SORCS2 gene on BTA6, which was assigned to decreased susceptibility to injury in the mouse. The effect on muscles, albeit in zebrafish, was assigned to the protein encoded by PIP4K2A, which is located close to the significant SNP on BTA13. The same SNP is also located within a QTL region for rump angle. On BTA16, a significant association points out at ENSBTAG00000009943 involved in inflammatory response. Significant SNPs are summarized in Adapting the FDR threshold of 10%, despite potentially higher power of testing due to twice as large sample size, none of the SNPs was significantly associated with the number of hoof disorders in Fleckvieh, while 15 SNPs were significant in Braunvieh . One of P-values observed for SNPs in FLV and BSW, which was estimated to 0.00302 for all SNPs and −0.01494 (−0.08065) for SNPs with 100 smallest P-values in BSW (FLV). In addition, breed-specific SNP effect estimates also revealed a very low correlation of 0.02363.There was no correlation between S statistic (expressing differences in the LD decay pattern between breeds) were visualized in S statistics—rs110488513 and rs41661497 on BTA1 as well as rs110792762 on BTA13. Some other significant SNPs are located in windows adjacent to such windows—rs110811919 on BTA1, as well as rs29024589, rs110843300, and rs41579631 on BTA16. For eight significant SNPs , significant allelic heterogeneity was detected  and values of the detected .S statistics and/or by significant allelic heterogeneity . Polymorphisms on BTA6 (marking SORCS2), BTA14 (marking TMEM74 and EMC2), and BTA24 (marking CCDC178 and KLHL14) are good candidates for Braunvieh-specific associations. Although the number of published studies related to GWAS for hoof disorders is very limited, their common feature is the lack of overlap in significant results both between and even within the studies. Similarly to our study, Summarizing the obtained results, in general, heterogeneity on a genome-wide level between breeds was observed; however, it does not explain the differences in GWAS results. Inter-breed differences in SNP effect significance at some of the 15 positions can be explained by inter-breed differences in LD structure  indicated by high values of the From the genetic perspective, Based on the current dataset, it is not possible to unequivocally confirm/exclude the hypothesis of genetic heterogeneity in the susceptibility to hoof disorders between Fleckvieh and Braunvieh. The rationales against the hypothesis comprise the following: (i) limited power of detection of true associations if the effect size is not large and therefore high rate of spurious associations among detected SNP; (ii) differences in the length of LD blocks, which imply differences in power of detecting the associations; and (iii) differences in linkage phase between breeds, which may hamper the detection of causal sites in Fleckvieh or Braunvieh, based on the available SNP panel. On the other hand, (i) different selection goals defined for the breeds—toward dairy production in the case of Braunvieh and toward beef production in the case of Fleckvieh, (ii) no significant allelic heterogeneity, and (iii) no systematic differences in LD structure between the breeds remain in favor of the heterogeneity hypothesis at the significant sites on BTA6, BTA14, and BTA24.The dataset available for the analysis comprises only common SNPs selected for a commercial microarray, so that we can explore only associations and not the causal mutations; therefore, a final verification of the above hypothesis would require a higher resolution of genomic data. However, if the total number of hoof disorders is genetically a different trait in Braunvieh and in Fleckvieh, this should be taken into account in selection strategies and in genomic evaluation programs.The data analyzed in this study is subject to the following licenses/restrictions: The datasets analyzed for this study are available from the ZuchtData EDV-Dienstleistungen GmbH upon request. Requests to access these datasets should be directed to egger-danner@zuchtdata.at.BK-S edited data and performed GWAS and heterogeneity analysis. TS edited data and performed some statistical analyses. CE-D contributed to writing of the manuscript. HS edited raw data and contributed to writing of the manuscript. MF calculated pairwise linkage disequilibrium. JS provided the funding and concept of the study, performed some statistical analyses, and significantly contributed to writing of the manuscript. All authors read and approved the final manuscript.CE-D and HS were employed by the company ZuchtData EDV-Dienstleistungen GmbH. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Bemisia tabaci (Gennadius) is a polyphagous, highly destructive pest that is capable of vectoring viruses in most agricultural crops. Currently, information regarding the distribution and genetic diversity of B. tabaci in South Sudan is not available. The objectives of this study were to investigate the genetic variability of B. tabaci infesting sweet potato and cassava in South Sudan. Field surveys were conducted between August 2017 and July and August 2018 in 10 locations in Juba County, Central Equatoria State, South Sudan. The sequences of mitochondrial DNA cytochrome oxidase I (mtCOI) were used to determine the phylogenetic relationships between sampled B. tabaci. Six distinct genetic groups of B. tabaci were identified, including three non-cassava haplotypes (Mediterranean (MED), Indian Ocean (IO), and Uganda) and three cassava haplotypes (Sub-Saharan Africa 1 sub-group 1 (SSA1-SG1), SSA1-SG3, and SSA2). MED predominated on sweet potato and SSA2 on cassava in all of the sampled locations. The Uganda haplotype was also widespread, occurring in five of the sampled locations. This study provides important information on the diversity of B. tabaci species in South Sudan. A comprehensive assessment of the genetic diversity, geographical distribution, population dynamics, and host range of B. tabaci species in South Sudan is vital for its effective management. Manihot esculenta) and sweet potato (Ipomoea batatas (L.) Lam.) are key staple root crops that assure food security in sub-Saharan Africa. This is due to their high calorie content, low production inputs, adaptation to different soil types, and resilience to climatic change as compared to other major staple food crops [Cassava , East African cassava mosaic virus (EACMV), and East African cassava mosaic virus-Uganda (EACMV-UG) have been reported to be prevalent in South Sudan [Sweet potato chlorotic stunt virus  is the most important virus that affects sweet potato due to its ability to mediate severe synergistic disease with several other sweet potato-infecting viruses, which results in major yield losses [Sweet potato feathery mottle virus (SPFMV), an aphid-transmitted potyvirus [Virus diseases is one of the major biotic factors that constrains the production of cassava and sweet potato in sub-Saharan Africa ,11,12,13th Sudan ,19. Sweed losses ,21,22. Sotyvirus ,24,25,26otyvirus . The occotyvirus .Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is a polyphagous highly destructive pest of food, fiber, and ornamental crops globally [Capnodium spp.) on the surfaces of leaves and fruits and the contamination of cotton lint, resulting in low quality grading [B. tabaci causes its most severe crop damage through vectoring plant viruses. The insect is a vector of more than 350 plant virus species belonging to five genera, including begomoviruses (family Geminiviridae), criniviruses (family Closteroviridae), ipomoviruses (family Potyviridae), torradoviruses (family Secoviridae), and some carlaviruses (family Betaflexiviridae). Most of the viruses transmitted are begomoviruses [B. tabaci is a vector of cassava mosaic begomoviruses (CMBs) and cassava brown streak ipomoviruses (CBSIs) that devastate cassava crops in East and Central Africa [B. tabaci, B. afer sensu lato and Trialeurodes abutilonea (the banded-winged whitefly) transmit SPCSV [B. tabaci include Sweet potato leaf curl virus (SPLCV), Ipomoea leaf curl virus (ILCV) (genus Begomovirus), and Sweet potato mild mottle virus (SPMMV) (Genus Ipomovirus) [B. tabaci populations [B. tabaci populations, leading to high incidences of SPVD [globally ,30. It iglobally ,32,33,34oviruses ,37. B. tl Africa ,39,40. Bit SPCSV ,42,43. Omovirus) ,45,46,47ulations ,48. Rapi of SPVD ,50,51.Bemisia tabaci is genetically complex with at least 34 cryptic species that are morphologically indistinguishable. These have been mostly identified through the sequencing of a portion of the mitochondrial cytochrome oxidase I (mtCOI) gene [B. tabaci is an important vector for plant viruses that infect cassava, sweet potato, and other crops, including tomato, cucurbits, eggplant, cotton, and leguminous crops [B. tabaci are known to occur in sub-Saharan Africa. One group colonizes sweet potato, vegetables, and other crops, but does not colonize cassava (non-cassava types). This group includes several putative species, including: Indian Ocean (IO), Mediterranean (MED), Middle East Asia Minor 1 (MEAM1), and Uganda [OI) gene ,55,56,57us crops ,59,60,61d Uganda ,63,64,65B. tabaci groups. They include esterase isozyme polymorphisms [B. tabaci populations [B. tabaci. It has a high degree of variability and has played an important role in characterizing the genetic relationships between the B. tabaci cryptic species and haplotypes [B. tabaci in Africa [B. tabaci genotypes [B. tabaci in sub-Saharan Africa is available, but there are no data for South Sudan. Assessing the nature of the problem that is posed by B. tabaci and the viruses it transmits in South Sudan and developing appropriate control strategies are currently impeded by the instability that is caused by the ongoing civil war. Data are urgently required regarding the genetic groups, haplotype diversity, geographical distribution, and the phylogenetic relationships of B. tabaci in South Sudan. As a first step, this study sought to address this need by sampling and characterizing B. tabaci collected from cassava and sweet potato in Juba County, Central Equatoria State, South Sudan. Therefore, we aimed to provide the first description of the diversity of B. tabaci on sweet potato and cassava in South Sudan, despite the civil unrest in the country severely limiting safe collection sites. The south of Sudan has been experiencing civil war for the past four decades, which is the reason for the lack of data in agricultural research and lack of agricultural progress. The civil unrest commenced again in 2013 one month after this project was started in the new nation of South Sudan.Several molecular techniques have been used to characterize orphisms , and DNAorphisms . Improveorphisms ,56,67,68ulations ,70. Receulations ,72,73. Tplotypes ,56,74. Mn Africa ,60,62,75n Africa ,77,78. Henotypes . These wenotypes ,72. InfoBemisia tabaci) were collected from sweet potato and cassava fields across 10 locations in Juba County  between July and August 2018 . The mixture was then vortex shaken and spun down and immediately incubated on ice for 15 min. This was followed by incubation at 55 °C in a water bath for 30 min. and the lysate was stored at −20 °C for downstream use. For PCR use, the lysate was diluted while using sterile DPEC treated water in a ratio of 1:9.DNA was extracted from single whiteflies, which were either adults or fourth instar nymphs. The insects were added to 3µL of lysis buffer in a 1.5 mL Eppendorf tube then macerated. The lysis buffer contained 10 mM Tris-HCl . DNA bands were visualized while using a Gel Doc™ XR+ Gel Documentation System and only samples with intact bands were selected for sequencing. PCR products were sent to Macrogen Inc.  for purification and direct sequencing. DNA sequences were manually edited while using Ridom Trace Edit v1.1.0 software . The sequences were assembled into contigs using CLC Main Workbench 7.0.2 . Multiple alignment of edited sequences was performed while using Clustal W in MEGA version 7.0.26 [B. tabaci groups was examined. Estimates were obtained for the number of haplotypes, polymorphic sites (S), average number of nucleotide differences (k), nucleotide diversity (Pi), haplotype diversity (Hd), Theta per sequence and Theta per site, and significance values using the mismatch distribution procedure of Dna-SP 6.12.03 [DNA extracted from 228 individual whiteflies from all the sampled locations was used for PCR amplification. Two sets of primers were used for the amplification of a partial fragment of mtCOI, primer MM1:  (IITA lab) and primer set: 2195-Bt-F (5′-TGRTTTTTTGGTCATCCRGAAGT-3′) and C012-Bt-sh2-R (5′-TTTACTGCACTTTCTGCC-3′) . These pn 7.0.26  and the  6.12.03 . Tajima’B. tabaci whitefly samples were collected from sweet potato (Ipomoea batatas (L.) Lam.), cassava (Manihot esculenta Crantz), tomato (Solanum lycopersicum L.), and squash (Cucurbita pepo L. ‘Zucchini’) from 10 locations in Juba County, Central Equatoria State, South Sudan. The locations included Tokiman (TOK), Lologo 2 (LO2), Beitery (BET), Juba Na Bari-Jezira (JNB-JZ), Mouna Suk Hajer (MO-SH), Gudele 1 Block 5 (GU1-B5), Gudele 1 Block 7 (GU1-B7), Gudele 2 Jopa (GU2-JP), Lemon Gaba (LEG), and University of Juba (UOJ). B. tabaci colonizing sweet potato were collected from eight locations, except Tokiman and Gudele 1 Block 7. B. tabaci on cassava were sampled from five locations: Lologo 2, Juba Na Bari-Jezira, Gudele 1 Block 7, Gudele 2 Jopa, and Lemon Gaba. On tomato, B. tabaci were collected from three locations including Juba Na Bari-Jezira, Gudele 1 Block 5 and Gudele 1 Block 7, whereas whiteflies on squash were collected from Tokiman . The degree of variability subsequently revealed by the sequencing and phylogenetic analysis confirmed this point. Tokiman . B. tabaB. tabaci populations that were collected from the sampled crop plants. The sequences obtained from sweet potato, tomato, and squash were grouped into three phylogenetically distinct groups, which included (MED), Indian Ocean (IO) and Uganda. The sequences from cassava were grouped into three distinct groups, SSA1-SG1, SSA1-SG3, and SSA2  suggest that the population is undergoing balancing selection and has not undergone rapid recent expansion. Sixty-two of the 90 MED sequences (Haplotype 1) represent an important African MED haplotype, for which there are a further 17 sequences in GenBank from Cameroon, Uganda, and Nigeria. The samples in this haplotype were predominantly from sweet potato, although there were also individuals from tomato, squash, and cassava, which indicated that it could be sharing host plants. Haplotype 2 had eight sequences from sweet potato that were identical to 13 sequences from GenBank originating from Sudan, Cameroon, Uganda and Burkina Faso. Haplotype 3 had eight sequences from sweet potato and squash. These were most closely matched (99.7%) with a GenBank sequence from Uganda KX570768. Haplotypes 4 and 5 occurred on sweet potato and had eight and three sequences, respectively. These were most closely related (99.7%) to a sequence from China (MH908653). Haplotype 6, which was recorded from sweet potato, had one sequence sharing 99.9% homology with MH908653 from China. Currently GenBank hosts 944 MED sequences that comprise 168 haplotypes. 673 (71%) of the sequences cluster in three major haplotypes that are spread worldwide. Of the 168 haplotypes, 137 (81%) have only one sequence in GenBank, although it is possible that some of these may be erroneously considered as unique haplotypes due to the frequent occurrence of sequencing errors in mtCOI data submitted to this database. In some scenarios, mitochondrial bar-coding has been found to overestimate the number of species or scale of divergence, where there are nuclear mitochondrial DNA pseudogenes (NUMTs) that can be PCR-amplified with mitochondrial primers [Bemisia species that was given the name MEAM2 based on mtDNA COI data [B. tabaci revealed the existence of fewer putative species (five so far), as opposed to the much larger number reported with mtCO1 [The MED haplotype analyses revealed six haplotypes amongst the samples that were collected from South Sudan . Two of hina MH9053. HaploCOI data . AnotherB. tabaci Indian Ocean were collected from sweet potato and tomato, and their sequences were most closely related to Reunion 1 from Spain [B. tabaci Indian Ocean has also been widely reported from sub-Saharan Africa and the surrounding islands [In this study, om Spain , althoug islands ,65,89. H islands . However islands  reported islands  made simB. tabaci, the largest number of samples were SSA2, and these were distributed through all of the locations. Two SSA2 haplotypes were identified (p < 0.05) suggest that the population has not undergone recent expansion, but is instead experiencing balancing selection. SSA1-SG1 was less frequent, as it was only detected at two locations and comprised only one haplotype. These results differ from other recent findings from East and Central Africa, which have shown SSA1-SG1 to be the predominant B. tabaci haplotype on cassava [B. tabaci confirms that both of the haplotypes colonize cassava in South Sudan. A recent continent-wide assessment of cassava-colonizing B. tabaci in sub-Saharan Africa noted that SSA2 was the most widely distributed of the recorded haplotypes [In the studied cassava group of entified . Haploty cassava ,61,71. S cassava , has bee cassava ,91, and  cassava , althoug cassava ,79,92. Tplotypes . SignifiB. tabaci haplotype in areas that were affected by the severe CMD epidemic, which spread through Uganda in the 1990s. It is quite likely that there might have been a similar association between virus and vector in southern Sudan during this period. However, whilst SSA1-SG1 subsequently displaced SSA2 as the predominant B. tabaci haplotype on cassava in Uganda, this change might not have happened further north in southern Sudan, with the result being that SSA2 is currently the main cassava-colonizing B. tabaci haplotype in present day South Sudan. The reasons behind these contrasting patterns of population change in Uganda and South Sudan are not currently apparent, but would be a useful topic for future study.In the 1990s, CMD was reported to be highly destructive in the Western Equatoria Province of pre-independence southern Sudan . FurtherB. tabaci haplotype MED was collected from cassava. These rare occurrences have been reported elsewhere [B. tabaci whiteflies are unable to reproduce on and colonize cassava [B. tabaci haplotypes occurring on cassava are present as visitors, and are not colonizing the crop.In this study, a single individual of non-cassava lsewhere ,75,91. H cassava , partly  cassava . In eachB. tabaci whitefly populations collected from South Sudan. Six B. tabaci haplotype groups, which include three non-cassava groups  and three cassava groups , were identified. MED and SSA2 were the most prevalent and most widely distributed amongst the sampled locations. The Uganda haplotype is also widespread and it was identified from five of the locations. The discovery of six B. tabaci haplotype groups from the relatively small portion of South Sudan that was sampled does suggest that, like Uganda, this part of East Africa has a high level of whitefly diversity. This provides a strong indication that this part of Africa might have been a source for MED whiteflies that have had devastating global impacts as an invasive pest [B. tabaci includes some of the most insecticide-resistant populations of whiteflies. Therefore, any future management efforts will need to apply extreme caution in the application of chemical insecticides to preclude the development of whitefly resistance, although Bemisia whiteflies may not be present on sweet potato and other host plants at high abundance levels. Whitefly populations that were observed in South Sudan were associated with transmission of viruses causing damaging disease in cassava and sweet potato. Improving the understanding of the dynamic interactions between vector and virus will be important for each of these crop-virus-vector pathosystems. An essential first step in this task will be conducting a comprehensive assessment of the genetic diversity, geographical distribution, population dynamics, and host range of B. tabaci species in South Sudan. This new knowledge will then provide the basis for the development of effective whitefly management strategies.This study presents the first report on the genetic diversity of ive pest . It is a


Cervus elaphus) population of Reinhardswald in Germany, about 5% of animals have a white coat colour that is not associated with albinism. In order to facilitate the conservation of the animals, it should be determined whether and to what extent brown animals carry the white gene. For this purpose, samples of one white hind and her brown calf were available for whole genome sequencing to identify the single nucleotide polymorphism(s) responsible for the white phenotype. Subsequently, samples from 194 brown and 11 white animals were genotyped.Red deer with very pale coat colour are observed sporadically. In the red deer (Based on a list of colour genes of the International Federation of Pigment Cell Societies, a non-synonymous mutation with exchange of a glycine residue at position 291 of the tyrosinase protein by arginine was identified as the cause of dilution of the coat colour. A gene test led to exactly matching genotypes in all examined animals. The study showed that 14% of the brown animals carry the white gene. This provides a simple and reliable way of conservation for the white animals. However, results could not be transferred to another, unrelated red deer population with white animals. Although no brown animals with a white tyrosinase genotype were detected, the cause for the white colouring in this population was different.Cervus elaphus.A gene test for the conservation of white red deer is available for the population of the Reinhardswald. While mutations in the tyrosinase are commonly associated with oculocutaneous albinism type 1, the amino acid exchange at position 291 was found to be associated with coat colour dilution in  Genes associated with white coat colour and dilution were most extensively studied in mice . At leasIn addition to colour inheritance in cattle , informaSequencing of the hind and her calf resulted in a coverage of 12.41 and 12.96 fold, respectively. Resulting reads were aligned to the reference sequence of the bovine genome (UMD 3.1). A total of 34.24 and 35.77 Gigabases mapped 82.18 and 82.08% of the whole genome, respectively. Comparing hind and calf, around 9.9 million SNPs were identified.ASIP with 3 ns and 4 s SNPs, DCT with 19 ns and 35 s SNPs, endothelin receptor type b (EDNRB) gene with 6 ns and 14 s SNPs, KIT with 18 ns and 78 s SNPs, MC1R with 11 ns and 46 s SNPs, TYR with 17 ns and 43 s SNPs, and TYRP1 with 24 ns and 43 s SNPs. Synonymous SNPs were excluded from further processing. Following the hypothesis of a recessive inheritance of the white colour, we expected the genotype of the white hind to be homozygous for the white allele and the brown calf to be heterozygous. All genes and SNPs that did not correspond to this assumption were sorted out, resulting in 15 genes with 21 ns SNPs to be further examined . 2185 of them were non-synonymous (ns) and 6565 synonymous (s) SNPs that covered 115 genes. Among them were The sequence of the five exons of the red deer tyrosinase mRNA, spanning 1593 bases showed a genetic similarity with the sequences of the human and bovine tyrosinase of 86 and 97%, respectively.Sequencing of the hind and her calf with the reference genome CerEla 1.0 resulted in a coverage of 9.58 and 10.05 fold, respectively. A total of 32.36 and 33.94 Gigabases mapped 92.0 and 92.0% of the whole genome, respectively. Comparing hind and calf, around 11 million SNPs were identified.Cervus elaphus (CerEla1.0). Nineteen of the 21 SNPs from 14 of the 15 candidate genes could be verified with CerEla1.0. One SNP in HPSA4 on Cervus elaphus chromosome (CEL) 5 and the SNP in the tyrosinase gene (CEL 2) could not be detected because of a gap in CerEla1.0 at heat shock protein family A (Hsp70) member 4 (HSPA4) and because the respective region of the tyrosinase gene was not yet annotated in CerEla1.0.The results were verified by sequencing the same two individuals using the later available genome sequence for Cervus elaphus chromosome (CEC) 2. The SNP c.871G > A in the tyrosinase gene is located within a highly conserved region and results in an amino acid substitution of Glycine by Arginine. From 194 brown red deer of the Reinhardswald 86% were homozygous and 14% were carriers of the white allele. Considering the estimation by the forest officers of the Reinhardswald of 50 white animals among the total population of around 1000 red deer (approximately 5%), genotype frequencies for GG, GA and AA were estimated as 81.7, 13.3 and 5%, respectively. Under this assumption, the allele frequencies are estimated to be 88.4% (G) and 11.6% (A), respectively. Thus, the estimated genotype frequencies deviate significantly from the Hardy-Weinberg equilibrium (p < 0.001). The expected values are 78.1% (GG), 20.5% (GA) and 1.3% (AA), respectively. There was no obvious phenotypic difference between carriers of the GG and the GA phenotype.In the population of the Reinhardswald, there was no brown individual with genotype AA of TYR and none of the white phenotypes had genotype GG or GA. Thus, the inheritance of the white colour in the red deer of the Reinhardswald was established as autosomal recessive. The tyrosinase gene is located on C.871G > A was not associated with red and brown coat colour in the unrelated German red deer population Siegen-Wittgenstein. However, the TYR-genotype AA was never detected in a brown individual regardless of its origin.Cervus elaphus reference genome was not available at the time of sequencing, sequence reads of the red deer were aligned to the reference sequence of the bovine genome (UMD 3.1). After CerEla1.0, the complete genome sequence of the red deer was published [Cervus elaphus sequences proved to be superior than sequencing on the basis of Bos taurus sequences.Because a ublished , the seqCervus elaphus. Nevertheless, the high degree of agreement even of microsatellite sequences, between red deer and other ungulates, especially cattle [6 SNPs. We were confident that the coding sequence ranges in particular would show a good match between red deer and bovine genome. In fact, 8570 SNPs were extracted after variant calling as a subset based on a list of colour genes . Twenty one SNPs in 15 candidate genes corresponded exactly to the requirements of a homozygous white hind and its heterozygous brown calf. However, only one SNP, located in the TYR gene matched exactly to the total sample with 194 brown and 11 white animals of the Reinhardswald population. The probability of a random match between genotype and phenotype (0.5205) in this number of animals corresponds to 1.94*10− 62. Although the exact number of white individuals is not known, the responsible forestry authority assumes about 50 white animals, within a total population of about 1000 red deer. Using the prevalence of heterozygous brown red deer, this results in a significant deviation from the Hardy-Weinberg equilibrium with too high a proportion of homozygous white genotypes. This could be explained by the fact that no white red deer had ever been shot up to the time of the study (selection). The mixed-bred, brown animals, on the other hand, were hunted without any difference to the clean-brown red deer. Factors that could have led to the preferred reduction of white individuals, such as predators (e.g. wolf or lynx), were not present in the region studied. The selection for white red deer results in particular from the fact that the reference to its existence is used as a unique selling point and tourist advertising object for the region. In this context, citizens’ initiatives have repeatedly been campaigning for the preservation of white individuals.However, since the TYR gene was not annotated in CerEla1.0 the responsible SNP for the white phenotype in the Reinhardswald red deer population had no chance to be detected. This is not unexpected, since 21,880 genes are annotated for the bovine genome in contrast to 19,368 for the genome of y cattle , 24 justSince white animals were also sporadically the victims of traffic accidents, it was an important question to investigate whether the 50 estimated individuals were left to their own or whether they could be regarded as an integrated part of the total population. The present study showed with the proof of the heterozygous brown individuals that the white allele is deeply anchored in the population and that statistically, one to two new white calves can be expected from the mating of heterozygous brown animals per year.http://www.ifpcs.org/albinism/). This is an autosomal recessive disorder, associated in most cases with severe hypopigmentation of the skin, hair and eyes, most often accompanied by nystagmus, foveal hypoplasia and reduced visual acuity [Tyrosinase is the key enzyme in the synthesis of melanin. It catalyses the rate-limiting step, the hydroxylation of the amino acid tyrosine to dopaquinone  and subsl acuity . Only fel acuity . Besidesl acuity , cats [2l acuity , rats [3l acuity , ferretsl acuity , minks [l acuity , donkeysl acuity , humpbacl acuity  and cattl acuity .TYR gene.In addition to the extensive cases of albinism, mutations in mice have also been described in connection with coat dilution, particularly in connection with pheomelanin –37. Howehttp://pantherdb.org/tools/csnpScoreForm.jsp) predicted this amino acid exchange as benign, this chemical difference may alter the efficacy of tyrosinase without a complete failure. Vitkup et al. [White deer is found only sporadically. We only know of one single reference that deals with microsatellite analysis for the control of inbreeding and genetic diversity in a population of white red deer in Czech Republic . The caup et al.  and Khanp et al.  concludeThe extension of the study to a second, unrelated red deer population revealed no brown carriers of the AA variant; however, white animals without the AA genotype at position 291 of the tyrosinase protein were found. This indicates that in this population (Siegerland-Wittgenstein) another, unknown gene variant segregates, which leads to dilution of the coat colour. Thus, although the tyrosinase mutation is responsible for the white colouring of the deer of the Reinhardswald, other previously unknown mutations are to be expected in other populations of white red deer.In addition to the result of anchoring the white individuals of the Reinhardswald in the brown red deer population, the study can also serve to document dispersal paths and migration movements to neighbouring red deer areas and to distinguish red deer populations with white individuals from each other. For this purpose, more red deer populations need to be tested for the presence of the c.871G > A tyrosinase gene variant. The gene test can also be used to investigate the influence of the tyrosinase gene variant on physical development, fertility and adaptability within the segregating population. It is anecdotically assumed that the white deer of the Reinhardswald were imported from southeastern Europe in the sixteenth century, scattered throughout the region in the turmoil of the Thirty Years’ War in Europe and have survived to this day. By screening different Southeast European red deer populations, it could be possible to decipher the origin of the white red deer of the Reinhardswald in future studies.Furthermore, the results show an enormous potential for the use of well-established reference genomes of closely related species for genomic analyses  in species for which no reference genome is yet available.The identification of the gene variant responsible for the white coloration and the quantification of heterozygous animals provided evidence that the few white animals are not an independent population. Rather, the white allele is widespread throughout the entire population via the heterozygous, brown animals.2 and, according to the Reinhardswald red deer association, has a census size of about 1000 animals of which about 50 animals are white.The Reinhardswald is a part of the Weserbergland, one of the biggest coherent forest areas of Germany and is located in the North of the Federal State of Hesse . The forest covers an area of 183 kmThe white deer of the Reinhardswald are not albinos. The coat colour is very pale, stronger in summer than in winter. The dilution is qualitatively distinguishable by eye. Eyes and claws are normally pigmented or slightly lightened. Apart from the coat and eye colour, the white animals do not differ from the brown of the population in height, weight and habitus Fig. . Howevern = 194) and white (n = 3) red deer and samples from antlers of white red deer (n = 8) were collected. For sequencing, samples of two female animals  were available. Samples were taken from existing antlers and frozen tissue samples provided by those authorised to practise hunting. No animals were killed specifically for the study. No live animals were sampled and no dropping antlers were sought or collected for the study. All samples were accompanied with information about age, weight, colour, and hunting ground. In addition, the presence/absence of white animals in the deer pack from which a sample was taken was recorded.During hunting seasons 2013 to 2015 tissue samples from brown (n = 21) and white (n = 9) red deer were collected in exactly the same way in Siegen-Wittgenstein, another area with brown and white animals. Reinhardswald and Siegen-Wittgenstein are separated by 110 km, a fenced motorway, several country roads and a red deer-free area. Both populations were not related or linked with each other as shown by a population differentiation test implemented in Genepop (see below).Further samples from brown . This kit was thoroughly tested against DNA extraction kits and its ease of use and its efficiency in extracting DNA was found to be comparable or even superior. Antler drill cores (0.1 to 0.3 g) were treated in a beadmill  at a frequency of 25 Hz for 2 min. Tissue samples were suspended in 450 μl of lysis buffer and subsequently treated in the same way as the antler drill cores. All following steps were performed according to the manufacturer’s instructions. The extracted DNA was eluted with 60 μl of RNAse-free water.DNA concentration was measured photometrically with the Nanodrop 2000 spectrophotometer  and the Qubit 2 system .The DNA of the hind and calf were provided for genomic sequencing. The amount of DNA was quantified through qPCR with the Kapa Library Quantification Kit  and diluted to 20–30 ng/μl for library preparation . Fragment sizes of the libraries were visualized with a BioAnalyzer 2100 .Quality controlled libraries were sequenced using the HiSeq 2500 instrument . Paired-end libraries (2 × 126 bp reads) were sequenced with a mean coverage of ten times.http://www.bioinformatics.babraham.ac.uk/projects/fastqc/).Prior to further processing raw data were quality checked for overrepresented and duplicate sequences with FastQC (http://emea.support.illumina.com/downloads/bcl2fastq_conversion_software_184.html?langsel=/de/). Because a Cervus elaphus reference genome was not available at the beginning of the study, resulting reads were at first aligned to the reference sequence of the bovine genome . After processing of data, single files were merged and converted from the SAM to the BAM format with SAMtools [PICARDtools command MarkDuplicates (https://github.com/broadinstitute/picard/).Raw sequences were then converted from a base call file (bcl) to fastq files and mixed probes were demultiplexed through the program bcl2fastq Conversion Software from Illumina ((UMD 3.1 ) and in SAMtools . Duplicampileup algorhithm implemented in SAMtools [filter algorithm from PICARDtools (https://github.com/broadinstitute/picard/) called variants were filtered by excluding all SNPs within 3 basepairs of an INDEL and with lower QUAL score, and by excluding INDELs within 2 basepairs of another INDEL.To identify single nucleotide polymorphisms (SNPs) as well as short insertion and deletion polymorphisms (INDELs) in the annotated reads of the two sequenced red deer samples, we used the SAMtools . With thVariantEffectPredictor (VEP) from Ensemble [For the functional annotation of each called SNP we adapted the Ensemble .http://www.ifpcs.org/albinism/). Resulting VEP annotated files containing only genomic regions coding for coat colour were checked on the basis of a recessive genetic inheritance model for non-synonymous impacts of the mutations.Furthermore, we extracted a subset of SNPs based on a list of colour genes detected in mice, human and zebrafish  from the NGS data in combination with data from the Bos taurus reference genome (UMD 3.1). Later the SNPs were verified with CerEla1.0, the Cervus elaphus reference genome.SNPs were selected in a hierarchical procedure as candidate SNPs for further processing. First and foremost, they had to be within the range of colour genes specified by the International Federation of Pigment Cell Societies. The second prerequisite was that the SNP was non-synonymous. The SNP had to be homozygous for the hind and heterozygous for the calf. The responding 21 candidate-SNPs (15 different genes) were validated by Sanger sequencing . For this purpose, regions including the candidate SNPs were PCR amplified and sequenced. PCR primers were designed  and sequences were analyzed with the Pyro-Mark ID 1.0 Software .PCR was performed in a total volume of 40 μl consisting of 20 μl of Multiplex Mastermix , 4 μl of primer mix  with a final concentration of 0.4 μM, 12 μl of water and 4 μl of template DNA. Cycling conditions were as follows: initial activation of the DNA polymerase for 15 min at 95°C, followed by 35 cycles of denaturation at 94°C for 30 seconds, annealing at 52°C for 90 seconds and extension at 72°C for 30 seconds, followed by final extension at 72°C for 10 min. Quality and amount of PCR products was checked by electrophoresis on 1.5% agarose gels stained with Midori Green Advance . PCR products immobilized to streptavidin-sepharose beads were released in 40 μl of 5 μM sequencing primer (HW-TYRS 5’-ATGGTCCCTCAGACG-3′) and subjected to pyrosequencing.https://kimura.univ-montp2.fr/~rousset/Genepop.htm) was performed as an exact G test with the following Markov chain parameters: dememorisation length of 100,000 and 100 batches with 10,000 iterations per batch.To test the effect of the white gene in another population red deer from Siegen-Wittgenstein  were included. Phenotypically, no differences could be found between red deer originating from Reinhardswald and Siegen-Wittgenstein. Population genetic analysis using microsatellites  was cond


Brazil is the world's largest producer of common bean. Knowledge of the genetic diversity and relatedness of accessions adapted to Brazilian conditions is of great importance for the conservation of germplasm and for directing breeding programs aimed at the development of new cultivars. In this context, the objective of this study was to analyze the genetic diversity, population structure, and linkage disequilibrium (LD) of a diversity panel consisting of 219 common bean accessions, most of which belonging to the Mesoamerican gene pool. Genotyping by sequencing (GBS) of these accessions allowed the identification of 49,817 SNPs with minor allele frequency > 0.05. Of these, 17,149 and 12,876 were exclusive to the Mesoamerican and Andean pools, respectively, and 11,805 SNPs could differentiate the two gene pools. Further the separation according to the gene pool, bayesian analysis of the population structure showed a subdivision of the Mesoamerican accessions based on the origin and color of the seed tegument. LD analysis revealed the occurrence of long linkage blocks and low LD decay with physical distance between SNPs . The GBS technique could effectively characterize the Brazilian common bean germplasms, and the diversity panel used in this study may be of great use in future genome-wide association studies. Phaseolus vulgaris L.) is one of the five cultivated species of the Phaseolus genus and is one of the most consumed legumes worldwide. It is the most important legume grain for direct human consumption and the main source of protein and micronutrients in several countries1. Globally, around 31 million tons of bean grains are produced per year, with the Americas accounting for 32.4% of the total production. Brazil is the world's largest producer of common bean, and other countries that are among the largest producers are India, Myanmar, China, United States, and Mexico2.The common bean  technologies has resulted in an exponential increase in the number of genetic variants that can be discovered in a single experiment15. The publication of the complete genome sequence of the common bean by Schmutz et al.5 facilitated the discovery of single nucleotide polymorphisms (SNPs) and genetic mapping, further allowing the construction of maps from short reads of different genotypes using the genome sequence as a reference16.Molecular markers have been widely used in plant breeding programs. Several different types of markers are available; however, their applications have been restricted in the past due to limitations such as low density, labor intensity, technical requirements, and high cost of large-scale analysis17. GBS is a robust, high-performance, cost-effective, and simple technique for obtaining thousands of markers from a large number of individuals, and allows the identification of SNPs using a reduced representation library20. SNPs are the most abundant and universal sequence variations in all genomes, which makes them very useful markers for genetic analyses in plants21.Among the NGS methods, the genotyping by sequencing (GBS) technique has emerged as a new approach to mitigate the constraints of previously employed markers13. Genome-wide association studies (GWAS) are a powerful tool for identifying candidate genomic regions associated with traits of interest. Some of the most important parameters for successful GWAS are the representativity of the diversity panel, the size of the panel, the levels and genomic distribution of linkage disequilibrium (LD), and the population structure or genetic relationships among individuals24. The diversity panel should represent most of the available genetic and phenotypic diversity, and LD should be analyzed to determine the density of markers required for GWAS25.The GBS technique is often employed in plant breeding, and is frequently used in genetic diversity studies, mapping (linkage and association) studies, and genomic selection (GS)28, flaxseed29, pepper30 and rice31. Several diversity panels have also been developed for the common bean crop, including accessions from different regions of the world37. Based on these initial studies, several GWAS have further been conducted for different traits of interest, such as yield, plant architecture, nutritional content of grains, cooking time, resistance to diseases, and tolerance to abiotic factors47. Some GWAS have been conducted in Brazil48, however, panels consisting of different genotypes, can contribute to a better understanding about the genetic diversity and relationships of the germplasm available for genetic breeding.Studies on genetic diversity and population structure have already been conducted for several crops, including wheatIn view of the above, the objective of the present study was to analyze the genetic diversity, population structure, and LD of the Brazilian Diversity Panel (BDP), which is a common bean diversity panel representing a large proportion of the genetic diversity of Brazilian common bean populations. It is composed mainly of materials from the carioca and black bean commercial groups, which are the most consumed cultivars in the country, and is expected to be used for GWAS in the future.18, a total of 392,585,199 good barcoded reads were obtained from the sequenced accessions, of which 364,454,550 could be aligned with the Andean reference genome (G198335), resulting in an average mapping rate of 93%. Initially, 461,199 SNPs were obtained, of which 49,817 SNPs were retained after filtering. Eleven accessions had a low rate of genotyping (less than 10% of genotyped positions) and were excluded from the BDP, for this reason 219 accessions were used in the subsequent analyses.Using the GBS method optimized for common beans by Ariani et al.SNPs were unevenly distributed throughout the genome, and fewer SNPs were observed in regions near the centromere than in regions near the telomeres on the chromosome Fig. a. The mePhaseolus vulgaris v2.0 were tagged by at least one SNP (tagged genes) , 12.5% in coding DNA sequences, 1.2% in 5′ UTR regions, and 2.6% in 3′ UTR regions  were more frequent than transversions (36.9%), resulting in a transition/transversion rate of 1.71 . The results of the principal component analysis (PCA) showed that the accessions could be segregated into two distinct groups, based on the gene pools (Andean and Mesoamerican)  with the highest value of ΔK was three (K = 3), which demonstrated a subdivision of the Mesoamerican group  for each of these SNP groups was 0.39, 0.34, and 0.94, respectively  than the Andean group (π = 0.22). Regarding Tajima’s D, the Mesoamerican gene pool showed a positive value (D = 1.50), while the Andean gene pool showed a negative value (D = − 0.50) , Pv08 (1781 SNPs), Pv09 (1554 SNPs), Pv02 (1506 SNPs), and Pv10 (1387 SNPs) , with the highest value being observed between CIAT and IAC (0.12) and the lowest value between IAPAR and EMBRAPA (0.01) , Agronomic Institute of Pernambuco (IPA), and the more recent inbred lines of the Rural Development Institute of Paraná—IAPAR–EMATER (IAPAR) Fig. c. The nud IAC 0.1 and the r2 and population structure-corrected r2 (r2s) were small. Considering the whole genome , the half-decay distance was 1361 kb and 1180 kb for r2 and r2s, respectively. The r2 was remarkably different when compared with r2v (r2 corrected for relatedness) and r2vs (r2 corrected for population structure and relatedness) , whereas the lowest decay values were presented by chromosomes Pv05, Pv04, Pv07, and Pv09   and were not used in subsequent analyses. Polymorphisms were widely distributed across the 11 chromosomes and were highly correlated with the length and number of genes on each chromosome. The transition/transversion rate was consistent with that observed in other studies on common bean and other species56.Because LD may affect the inference of the population structure, an LD filter was further applied, which resulted in a decrease of the number of SNPs. This is due to the fact that the common bean is an autogamous plant with very long blocks of markers in LD60. The relationship between the genetic similarity of the Mesoamerican accessions and the color of the seed tegument was also observed by Valdisser et al.61 and Gioia et al.62. In Brazil, breeding programs for the carioca and black commercial groups have different objectives63. Moreover, genetic breeding of the carioca group is much more advanced than that of the black group, because of its greater importance in the country due to consumers and market preferences. Efforts to improve the carioca bean are directed towards the grain size traits, to satisfy the consumers’ preference for larger grains. However, the grain size is negatively correlated with yield, in case of the black group, selection is based mainly on yield, resulting in cultivars with smaller grains64.Genetic differentiation between common bean accessions based on the gene pool has been well documented in several previous studies66. The two gene pools differ in both phenotypic and molecular characteristics, which is supported by the high rates of genetic differentiation obtained in the present analysis and in other studies67. In addition, the Mesoamerican gene pool exhibits higher nucleotide diversity than the Andean, possibly because a strong bottleneck occurred during the dispersal of Southern Andean common beans from Mesoamerica, which drastically reduced its nucleotide diversity67.Several SNPs exclusive to either of the gene pools were observed, in addition to the differentiating SNPs between the two pools. Other authors have also reported that the proportion of polymorphic loci tends to be higher in populations composed of accessions from the two centers of origin, and it tends to decrease when they are studied separatelyFst was estimated for each SNP. The Fst of nucleotide positions that were polymorphic only when the two gene pools were studied together was close to 1, and these SNPs were therefore highly discriminating between the gene pools. The Fst of SNPs present only in the Andean group was also high, similar to that of the discriminating markers. This may be due to the small number of Andean accessions included in this study.To identify genomic signatures of selection between the Andean and Mesoamerican pools, the 5 identified 1835 candidate genes for domestication in the Mesoamerican group and 748 candidate genes in the Andean group. Of these, only 59 genes were common to both groups. These genes are mainly located on chromosomes Pv01, Pv02, Pv07, Pv09, and Pv10. In the present study, 11% of all the candidate genes for domestication harbored differentiating SNPs. These genes have also been identified in other studies aimed at finding selection signatures between the Andean and Mesoamerican35 accessions. The candidate genes for domestication are directly or indirectly associated with the main characteristics that distinguish the two gene pools, such as flowering time, plant size, and seed size.There is significant evidence supporting the independent domestication of the Andean and Mesoamerican gene pools. Schmutz et al.63. Because of significant exchange of germplasm between institutions, there was no formation of well-defined groups among the accessions from the different institutions of origin57, only a trend for clustering was observed for accessions belonging to CIAT, IPA, and the more recent inbred lines of IAPAR.The low rate of differentiation of the accessions based on the institution of origin may be related to the protocols of the breeding programs. Breeding programs tend to be conservative and almost always employ the Mesoamerican germplasm, with little exploration of exotic germplasms; in addition, they use a selected group of elite parents, which further narrows the genetic base68. These factors can affect different genomic regions in several ways, which can introduce heterogeneity of LD through the genome. This makes the resolution and power achieved in GWAS dependent on the species and the population under study. LD decay is slower in autogamous species, such as common bean and soybean, in which recombination is less effective than in allogamous species69.LD measurement is very important in association mapping studies for identifying loci associated with quantitative traits. Importantly, the population structure and relatedness between the analyzed accessions may cause a bias in LD estimation. Frequent selection, admixture of populations, and crossing of a small number of cultivars in breeding programs reduces genetic diversity and affects LD patternsr2s) was not significantly different from the conventional r2. However, r2v and r2vs exhibited a faster LD decay when compared with the conventional r2. The fact that r2v was considerably lower than conventional r2 demonstrates the need to remove the effect of relatedness to reduce the overestimation of LD. The similarity between the estimated r2 and r2s  shows that the BDP is not highly structured, which is consistent with the results of other studies on common bean diversity panels57. As observed by Diniz et al.57 in panels composed mainly of improved genotypes, the degree of relatedness between individuals was very high.In this study, the LD corrected for population structure .The BDP, including 230 common bean accessions that represent a large component of the common bean genetic diversity in Brazil, was used in this study Table . The div18. DNA was extracted from lyophilized leaves collected from a single plant of each accession grown in a green house. The extracted DNA was purified using the Genomic DNA Clean and Concentrator kit , according to the manufacturer's instructions. The DNA quality was checked using NanoDrop Lite (Thermo Fisher Scientific), and only samples with an absorbance ratio (A260/A280) greater than 1.7 were used for preparing the libraries. Genomic DNA was quantified using Quant-iT PicoGreen dsDNA Assay Kit (Thermo Fisher Scientific), and 100 ng of the DNA from each genotype was used for preparing the libraries.DNA extraction and the preparation of GBS libraries for sequencing was performed following the protocol developed for common bean by Ariani et al.CviAII (recognition site C'ATG); after the preparation process, the samples were multiplexed into two libraries with up to 144 accessions each, including as control a blank sample and the genotype of P. vulgaris used to construct the reference genome (G19833) in each of the two libraries67. The presence of adapter dimers in the sequencing libraries was checked using DNA High Sensitivity Kit .The genomic DNA was digested using the restriction enzyme The genomic libraries were sequenced using the Illumina HiSeq 4000 sequencer  with the 100-bp-single-end protocol, at the DNA Technologies and Expression Analysis Core Laboratory, located in the Genome Center, University of California, Davis, CA.70, with the standard software settings, except for the minimum quality score (-mnQs 20) and minimum count (-c 10) parameters. The obtained sequences were aligned with the reference genome of Phaseolus vulgaris v2.0 obtained from the Phytozome website , using the Burrows-Wheeler Alignment (BWA)  tool version 0.7.1071. Non-biallelic SNPs, and SNPs with indels, minor allele frequency (MAF) < 0.05, coefficient of inbreeding < 0.9, and those SNPs and accessions containing < 10% of genotyped positions were removed using VCFtools version 0.1.1572. Because common bean is an autogamous species, after the initial filtering, the occurrence of heterozygotes was insignificant, but heterozygous SNPs were treated as missing data, as they may indicate sequencing errors. After filtering, the SNPs were imputed using Beagle software version 573, and only SNPs anchored to chromosomes in the common bean reference genome were used.SNPs were called using the Tassel-5-GBS pipeline version 2https://phytozome.jgi.doe.gov, accessed on January 10, 2019), using a custom R74 script developed by Hu et al.75 .The SNPs were annotated according to the common bean genomic annotation  available on the Phytozome website  between 1 and 10 was used. The statistical parameter ΔK49 was used to determine the number of groups. Only the accessions with a membership coefficient equal or higher than 0.6 were assigned to a genetic group, and those with membership coefficient lower than 0.6 were clustered in the admixture group. The admixture model assumes that the markers are not strongly linked; hence, the SNPs were filtered based on LD, using the indep-pairwise option of the PLINK78 software, and only SNPs with LD ≤ 0.2 were retained for population structure analysis. These data filtered for LD were also used for PCA, using the snpgdsPCA function of the SNPRelate79 package in R.The 219 accessions belonging to the BDP that passed by the quality control mentioned above were included in the initial analyses. The population structure was inferred using the Bayesian clustering algorithm in Structure v2.3.472 and PLINK78. These data were then used for PCA and population structure analysis, as previously described. In addition, phylogenetic inference was estimated using TASSEL v580, based on identity-by-state (IBS) distance and using Neighbor-Joining as the clustering method. The generated tree was customized using FigTree v1.4.481.After verifying the center of origin, only individuals of Mesoamerican origin were retained, and the SNPs were again filtered to exclude monomorphics, SNPs with MAF < 0.05 and LD ≥ 0.2, using VCFtools version 0.1.1582. Fst index83, nucleotide diversity (π) and Tajima’s D84, were also calculated using VCFtools version 0.1.1572 and averaged on 100-kb genomic bins.To detect molecular differences in relation to the center of origin, color of the seed tegument, and institution of origin, new files were created from the initial file  containing the different groups, and only polymorphic SNPs and those with MAF > 0.05 were retained. Subsequently, a Venn diagram was constructed to detect the differentiating SNPs for each of the three parameters using the JVENN tool85 package in R. This package corrects for the bias due to population structure and relatedness while estimating LD. In addition to the conventional r2, r2 corrected for population structure (r2s), r2 considering kinship (r2v), and r2 including both population structure and kinship (r2vs) were calculated. Only individuals belonging to the Mesoamerican group were used for these calculations. The STRUCTURE result at K = 2 for common beans of Mesoamerican origin was used as the population structure, and for relatedness the kinship matrix was calculated using the rrBLUP86 package in R. LD decay was calculated using the nonlinear method proposed by Hill and Weir87, and adjusted with the nls function in R.LD between SNPs was estimated using the LDcorSVSupplementary Table S1.


Whiteflies are major insect pests on a global scale. The use of insecticides is the primary tool for controlling them, but there are many problems relying on this strategy. However, natural enemies like predators and parasitic insects that attack whiteflies can help provide a sustainable pest management approach. This paper focuses on predators that feed on whiteflies as well as other insect pests and are called generalist predators. We provide a comprehensive view of generalist predator contributions and review the currently recognized generalist predators of whiteflies. There are many generalist predators in agricultural cropping systems that help control whiteflies. We highlight the need for conservation biological control programs through habitat management strategies and the use of selective insecticides, with an aim for more sustainable management of whiteflies in crops.Bemisia tabaci, has developed resistance to many insecticides, renewing interest in the biological control of this global pest. Generalist predators might contribute to whitefly suppression if they commonly occur in infested fields and generally complement rather than interfere with specialized natural enemies. Here, we review literature from the last 20 years, across US cropping systems, which considers the impacts of generalist predators on B. tabaci. Laboratory feeding trials and molecular gut content analysis suggest that at least 30 different generalist predator species willingly and/or regularly feed on these whiteflies. Nine of these predators appear to be particularly impactful, and a higher abundance of a few of these predator species has been shown to correlate with greater B. tabaci predation in the field. Predator species often occupy complementary feeding niches, which would be expected to strengthen biocontrol, although intraguild predation is also common and might be disruptive. Overall, our review suggests that a bio-diverse community of generalist predators commonly attacks B. tabaci, with the potential to exert substantial control in the field. The key challenge will be to develop reduced-spray plans so that generalist predators, and other more specialized natural enemies, are abundant enough that their biocontrol potential is realized.The whitefly,  Bemisia tabaci Gennadius (Hemiptera: Aleyrodidae) is a major agricultural pest that causes substantial crop yield losses to cotton and vegetables [B. tabaci is a challenging pest to manage in part due to polyphagous feeding on greater than 1000 host plant species [B. tabaci [B. tabaci [Worldwide, the whitefly getables . This pe species  as well  species  and pyre species . Further. tabaci ,6,7. Giv. tabaci ,9, underB. tabaci. A recent review by Liu et al. [B. tabaci parasitoids, including aphelenids, Eretmocerus mundus Mercet, E. eremicus Rose and Zolnerowich, and Encarsia formosa Gahan, which are widely used for successful augmentative releases. Thus, the significance of parasitoids is well documented [B. tabaci, although very few have been studied in detail [B. tabaci control [B. tabaci biological control, except in rare examples, their contributions are not fully integrated into B. tabaci management.Comprehensive published reviews and books ,11,12 alu et al.  highlighcumented . Howevern detail . Understn detail . For exan detail . Conversn detail ,16. Enco control ,18,19,20B. tabaci [B. tabaci life stages, which makes observations of predation challenging, MGCA is an essential tool to unravel the range of generalist predators, contributing to its control in the agroecosystem.Generalist predators contribute to pest suppression in agroecosystems by altering the behavior of or consuming major arthropod pests . Several. tabaci ,24,25. I. tabaci ,23. MGCAB. tabaci life stages) and indirect  effects of predators on the density of B. tabaci in the laboratory, greenhouse, and correlative field studies conducted in the USA. Currently, the scientific community lacks a comprehensive guide that summarizes the role of predators and, in particular, generalist predators (broadly defined as those consuming multiple species or taxonomic groups) as biological control agents of B. tabaci in the US agricultural landscapes. Such information will help bridge the knowledge gap and bring our understanding of generalist predators up to par with parasitoid research. Therefore, the objective of our review is to identify and discuss known predator species that contribute to B. tabaci management in the United States. With evidence from studies over the past 20 years across the United States . Given that our review uncovers regional bias in research coverage , we summarize and discuss the contribution of known generalist predator species that feed on B. tabaci and offer directions for future research in the southeastern US.In this paper, we review direct , B. tabaci. Searches included: , , , (Sweetpotato whitefly AND biocontrol), (Sweetpotato whitefly*predation OR predator), (MEAM1*biocontrol), , (Bemisia argentifolii*predation OR predator), , (Bemisia tabaci*predation OR predators), (Silverleaf whitefly*predation OR predators), , (Silverleaf whitefly predation AND United States), and . After finalizing the literature search, we discarded studies that were conducted outside of the United States, as the goal of this review was to directly focus on important generalist predators of B. tabaci present in the United States. However, we recognized that there were studies that were conducted outside of the US concerning generalist predator species that are found in the US. To review the impact of insecticides on B. tabaci predation, we included studies that contained data for both B. tabaci and predators and the impact of insecticides on B. tabaci predation rate and population growth under greenhouse and field scales. We also discarded all studies conducted before 2000 for two reasons: (1) several comprehensive reviews [Our literature search was conducted in two main databases—Thompson Reuters’ ISI Web of Science and Google Scholar. We searched multiple combinations of keywords to isolate studies that focused on generalist predators and biological control of  reviews ,11,12 an reviews  include B. tabaci, first within greenhouses and laboratory studies, followed by evidence from field studies. We consider the effects of predators on B. tabaci populations, as well as predator preference for specific life stages, interactions among predators, and the combined effects of insecticides on both predators and B. tabaci. We end this section by reviewing how gut content analysis can further shape our understanding of the role that generalist predators might play in whitefly suppression.In this section, we review evidence of biological control by generalist predators on B. tabaci. Such fine-scale studies can give an initial indication of a generalist predator’s (1) voracity for and preference for whitefly life stages and (2) potential to engage in intraguild predation. However, this information includes the substantial caveat that species interactions might be substantially different in simplified laboratory arenas, compared to the much more complex environment of an open agricultural field.Laboratory and greenhouse studies shed light on the potential natural enemies of B. tabaci in simplified laboratory and greenhouse arenas. These include six Coleoptera (beetle species), 11 Hemiptera (true bug), two Neuroptera (lacewing), one Diptera (fly), eight spider species, and two mite species  led to a decline in the density of B. tabaci eggs and nymphs and up to 60% suppression of nymphal stages [Serangium parcesetosum significantly suppressed the population growth of B. tabaci on poinsettia (Euphorbia pulcherrima) plants within six weeks of their release [Amblyseius tamatavensis Blommers (Acari: Phytoseiidae) colonize nightshade plants (Solanum americanum) in southern Florida, and in a laboratory experiment, fed on B. tabaci, suggesting that this species can be a useful predator of B. tabaci in the southeast USA [Greenhouse experiments demonstrate that various predator species contribute to direct control of l stages . Similar release . Populateast USA .B. tabaci natural enemies and for helpful versus harmful effects of alternative foods, and we next describe each of these ecological complications in turn.Generalist predators often forage within diverse communities of other natural enemy species, including both other generalists and more specialized predators and parasitoids. Sometimes, generalist predators occupy different feeding niches than other sympatric natural enemy species, attacking different pest species or stages, foraging in different places or times or using different means of attack. That is, different species complement one another, which, in turn, can lead to broader-based overall attacks on the pest when several natural enemy species co-occur. Other times, however, generalists heavily attack other natural enemies, and this intraguild predation can disrupt the overall strength of biocontrol. Intraguild predation can be particularly harmful when generalist predators with relatively broad feeding niches focus their attacks on the specialized natural enemy that would otherwise be the key biocontrol agent, suppressing a particular pest species ,61. FinaD. catalinae, Collops vittatus , Hippodamia convergens Guérin-Méneville (Coleoptera: Coccinellidae), and D. pallidus differ in their preference for consuming B. tabaci eggs versus small (1–3rd instars) or large nymphs (4th instar) [C. vittatus and H. convergens, preferentially attack B. tabaci eggs but also fed on nymphs [Orius tristicolor  (Hemiptera: Anthocoridae) and Geocoris punctipes  (Hemiptera: Geocoridae), preferentially attack adult B. tabaci, while Lygus hesperus  (Hemiptera: Miridae) more commonly attacks nymphs [B. tabaci, and synergism appears possible when chewing and sucking predators are found together, which would enhance the suppression of B. tabaci  ,35,38. T. tabaci . CombineDelphastus catalinae (Horn), D. pallidus LeConte, and Nephaspis oculatus (Blatchley), have shown a type two functional response towards B. tabaci eggs; predators have consumed a lower proportion of eggs as the number of offered eggs increased [B. tabaci consumed by D. catalinae within tomato plants. Interestingly, despite uniform distributions of B. tabaci across the tomato plants, D. catalinae has primarily foraged on lower and middle nodal leaves that might have led to microsites of predator-free space for whitefly on higher plant nodes [For example, coccinellids, including ncreased ,35. Convncreased  observednt nodes .G. punctipes, O. insidiosus, and H. convergens) and the parasitoid, Eretmocerus sp. nr. emiratus (Hymenoptera: Aphelinidae) feeding on B. tabaci 4th nymphal stage, has been evaluated under laboratory conditions [B. tabaci 4th instar nymphs. This suggests a high probability of intraguild predation between B. tabaci predators and parasitoids. Under the no-choice condition, G. punctipes and O. insidiosus have consumed all types of given prey without a significant preference. H. convergens, however, has a more complex response, where it consumes significantly more parasitized pupa and fewer B. tabaci nymphs. Some predators show flexible responses and may not discriminate between parasitized and non-parasitized prey and, as a result, may outcompete more specialized whitefly natural enemies. Similarly, Zang and Liu. [D. catalinae fed on parasitized B. tabaci nymphs on cabbage plants and that Encarsia sophia (Girault and Dodd) (Hymenoptera: Aphelinidae) parasitized 1.2-fold fewer B. tabaci nymphs when D. catalinae was present. Overall, B. tabaci suppression was higher when D. catalinae was present , regardless of its potential effect as an intraguild predator, suggesting while intraguild predation does occur, in this case, the benefits outweigh the costs. Although preliminary, these results suggest a high probability for intraguild predation between B. tabaci predators and parasitoids under field conditions, but the benefits to biological control appear to outweigh the trade-offs of more complex interactions.Generalist predators pose trade-offs and are known for their negative effects via competition and intraguild predation, which diminish the efficacy of biological control . The occnditions . In a chand Liu.  found evB. tabaci movement within and between plants, which may indirectly impact the colonization of, and damage by, whiteflies. Of particular interest is when the presence of natural enemies influences the dispersal of B. tabaci from a cash crop to a trap crop. In a greenhouse study, the colonization rate and density of B. tabaci are significantly higher on cucumber (trap crop) relative to poinsettia (cash crop) in the presence of Amblyseius swirskii Athias-Henriot (Mesostigmata: Phytoseiidae), D. catalinae, and the parasitoid E. formosa [B. tabaci has delayed settling on cucumber leaves in the presence of D. catalinae, suggesting an avoidance behavior when D. catalinae is present [B. tabaci adults move towards the top of cucumber plants in response to the presence of D. catalinae [D. catalinae alters B. tabaci host choice and dispersal from cash to trap crops but also small-scale movement within a single plant [B. tabaci population growth directly through predation and indirectly via non-consumptive effects, such as dispersal.The presence of natural enemies can alter  formosa . Further present , and witatalinae . Thus, tle plant ,48. GiveB. tabaci, larger-scale field and landscape studies have the potential to test these interactions under realistic environmental conditions. In this section, we review work at field and landscape levels and show that additional research is needed to better integrate generalist predators into sustainable control of B. tabaci in the United States.Although greenhouse and laboratory studies have greatly contributed to our understanding of the direct and indirect effects of predators on B. tabaci. In cotton, fourth instar nymph mortality is primarily due to consumption by predators with piercing-sucking mouthparts and predators with chewing mouthparts [O. tristicolor (Hemiptera: Anthocoridae), C. vittatus, Chrysoperla carnea s.l. Stephens (Neuroptera: Chrysopidae), and most strongly with G. punctipes density [L. hesperus and Geocoris pallens Stal (Hemiptera: Geocoridae) [B. tabaci nymphs, followed by eggs and other nymphal stages [B. tabaci also suggest life stage-specific contributions in whitefly control [C. carnea larvae/B. tabaci, Drapetis. nr. divergens (Diptera: Empididae) and Misumenops celer (Hentz) (Araneae: Thomisidae)/B. tabaci has reduced the proportion of leaves, surpassing the economic threshold. Further, low density and B. tabaci infestation rate are most consistently associated with the ratio of D. nr. divergens, M. celer, and G. pallens/B. tabaci in cotton fields. The ratio of six predator species/B. tabaci is significantly associated with motility of various B. tabaci life stages, providing further support for potential synergism  among chewing and sucking predators. Consequently, results from the southwest provide substantial support for several predator species in cotton fields that readily feed on various life stages of B. tabaci and contribute significantly to its population suppression  ,45,46. Cl stages . Further control . In cottpression .A. swirskii [D. catalinae [B. tabaci [B. tabaci. Laboratory research with Orius insidiosus Say and G. punctipes when provided adult B. tabaci suggest that the interpretation of feeding impact based on MGCA could be skewed based on the half-life detection within a specific predator to prey combination [The density of herbivores in agricultural fields often depends on the density of various natural enemies. However, the extent to which natural enemies contribute to pest control services, especially for small insects, such as whiteflies, is often difficult to estimate with traditional laboratory, greenhouse, or field techniques. Laboratory and greenhouse studies often present unrealistic environments that may under or overestimate the contribution of predators to pest control services for fields . In agriswirskii ,49 and Datalinae ,35, whicatalinae ,46,47, aatalinae , cage statalinae ,64. MGCAatalinae  and can . tabaci . Below wbination . Yet, coB. tabaci by multiple predators [H. convergens. Using a similar approach, Hagler and Naranjo [B. tabaci eggs and adult females’ protein in the gut of several abundant predator species in Arizona cotton fields. In addition to being the most abundant in cotton fields, these species were frequently observed feeding on B. tabaci  for augmentative releases in fields and greenhouses maintain databases on the compatibility of pesticides with biological control agents  to account for the potential contribution of biological control in the management of o et al.  found thB. tabaci predators  and predation, reducing the effectiveness of biological control in cotton [B. tabaci populations relative to the untreated control in squash, but the 1-day-old residue of these bioinsecticides also suppresses the density of introduced D. catalinae and may diminish biological control [One of the most influential ways chemical management may impact the efficacy of whitefly biological control with generalist predators is through the selection of insecticides. It is generally recognized that broad-spectrum insecticides, such as carbamates, organophosphates, and pyrethroids, can have devastating consequences for predators . Applican cotton . Asiimwen cotton  also rep control .B. tabaci life-tables to assess the effect of three pest management strategies  on B. tabaci control in Arizona cotton fields. Overall, they found predator populations recovered rapidly following buprofezin and pyriproxyfen (both IGRs) treatments and contributed to B. tabaci control, while predator populations were significantly reduced after the use of broad-spectrum insecticides . Moreover, the effect of commonly used insecticides  became ineffective, requiring up to four additional insecticide applications to maintain whitefly density below the economic threshold [B. tabaci, and (2) the conservation of natural enemies in cotton fields, which resulted in higher levels of whitefly control (There appears to be a good support for the benefits of insect growth regulators (IGR) in maintaining predator abundance in field crops ,19,44,45hreshold . Convers control . The useD. catalinae abundance was low on squash plants with the 1-day-old residue of M-Pede and PyGanic, the number of beetles found on plants with the 5-day-old residue of the insecticides were similar to those on the untreated plants. Each insecticide has its effective residual longevity, which is influenced by the target insects , various environmental factors, and the inherent chemical properties of the active and inert ingredients. However, the interactions among pesticide degradation, predator tolerance for pesticide residue, and the level of whitefly suppression are poorly understood at this time.The negative impacts of pesticides on predators can be moderated with carefully planned treatments of applications. Razze et al.  demonstrB. tabaci in the US . In contrast, hemipterans  prefer B. tabaci nymphs and adults, and D. nr. divergens consume both adults and eggs  compared with higher humidity (up to 30.25 mb) is found to have an adverse impact on the populations of D. catalinae. In the field environment, this predator is demonstrated to survive the mild winter in the same field environment where the host survives year-round [Because there are different climates and environments across the US, one would expect some predators may be more effective against whiteflies in some environments than in others. Only a few reports document the impact of the environment on the populations of whitefly predators in the US. Several laboratory studies have been conducted, which define the temperature and humidity ranges for atalinae ,55,56,58ar-round .B. tabaci management.Currently, our knowledge regarding whitefly predation is limited to field studies in the southwest and primarily laboratory and greenhouse trials in the southeastern United States. The majority of research on whiteflies and predators is derived from southwestern cotton systems. Because the distribution of whitefly is expanding into the southeastern US, solutions for this region are of critical importance. Research has focused primarily on laboratory and greenhouse systems ; therefoB. tabaci natural enemies into fields and associated rates of control. Landscape simplification may negatively affect insect pest biological control due to a reduction in the richness and abundance of natural enemies in agricultural landscapes [B. tabaci abundance and its predation. Further, while several studies have reported the potential predators of B. tabaci [B. tabaci population dynamics has not been studied in the US agroecosystems. To build area-wide pest management strategies for B. tabaci, research is needed not only in cotton fields but also in vegetables. This is particularly important because generalist predator communities are likely unique to each cropping system, and in the southeast, crop systems overlap in space and time, which provides host sources for B. tabaci and generalist predator populations.Investigations that cover broad spatial scales are required to understand how agricultural landscape complexity around crop fields affect the recruitment of ndscapes . Agriculndscapes ,74,75). . tabaci ,19,20, tB. tabaci in the US agroecosystems [B. tabaci trophic interactions will benefit from studies conducted with a gradient of agricultural land-use practices with a broad spatial extent to model not only the movement of but also trophic interactions between cropping systems.Presently, there is a dearth of information available on predation levels on whiteflies at field or landscape scales. Molecular gut content analysis (MGCA) is an effective method to determine the links between trophic levels in agricultural fields ,76. Giveosystems ,59. CurrB. tabaci management strategies, to some degree in the southwest [Orius, are primarily a different species, and Thomisidae and Empididae have been observed less frequently in recent southeast studies [B. tabaci in agroecosystems.Few studies have successfully incorporated the effect of natural enemies into the economic threshold for herbivorous insect pests ,81. Althouthwest , the sam studies . Therefo studies . Moreove studies ), makingB. tabaci, from our review, working at field scales and across landscapes will help move biocontrol efforts forward.With the great diversity of predators observed for whitefly control and success of integration, this system is ripe for continued research to optimize integrated pest management approaches that better harvest these biocontrol services. Advances in the southwest  are exce