NRG3 and NBEA genes, which are involved in the development and functioning of the neural system, the PTPRM gene, which mediates cell–cell interactions and adhesion, and a region on chromosome 4 (chr4:28.7–28.9 Mb) that contained several loci affiliated with different classes of non-coding RNAs . NBEA and the region on chromosome 4 were novel selection targets that were identified for the first time in Western Siberian populations. Cross-population comparisons of EHH profiles suggested a particular role for the chr4:28.7–28.9 Mb region in the local adaptation of Western Siberians. The strongest selection signal identified in Siberian sequenced genomes was formed by six SNPs on chromosome 11 (chr11:124.9–125.2 Mb). This region included well-known genes SLC37A2 and PKNOX2. SLC37A2 is most-highly expressed in the gut. Its expression is regulated by vitamin D, which is often deficient in northern regions. The PKNOX2 gene is a transcription factor of the homeobox family that is expressed in the brain and many other tissues. This gene is associated with alcohol addiction, which is widespread in many Northern Eurasian populations.Natural selection of beneficial genetic variants played a critical role in human adaptation to a wide range of environmental conditions. Northern Eurasia, despite its severe climate, is home to lots of ethnically diverse populations. The genetic variants associated with the survival of these populations have hardly been analyzed. We searched for the genomic signatures of positive selection in (1) the genome-wide microarray data of 432 people from eight different northern Russian populations and (2) the whole-genome sequences of 250 people from Northern Eurasia from a public repository through testing the extended haplotype homozigosity (EHH) and direct comparison of allele frequency, respectively. The 20 loci with the strongest selection signals were characterized in detail. Among the top EHH hits were the  In the last decade, human genetic diversity has been extensively studied at both the global and regional level using genome-wide panels of single-nucleotide polymorphisms (SNPs) and whole-genome sequences. A large amount of information has been obtained on the genetic structure of different populations and several models of their relationships have been proposed, including possible routes and waves of human migrations –12. In aFrom this point of view, Russia is one of the most promising geographic areas. Its vast territory is peopled by 195 ethnically diverse indigenous human populations  living iThe densest modern SNP microarrays  allow the processing of up to 4.3 million polymorphic sites. However, they represent about 1% of all known human SNPs . Using tP ≤ 1 × 10−5) iHS values in populations from Russia , while the remaining two SNPs–rs7695045 on chromosome 4 and rs10502389 on chromosome 18 –with four  and five  genes, respectively  were processed bioinformatically. According to the protocol of Voight et al. , raw intm Russia , S1 Fig.m Russia . These welection . Many ofhis gene . SNP rs1ectively –S4 Fig.NRG3 and NBEA genes, as well as the region surrounding the MSTP3 locus. The NRG3 gene was the candidate region that was detected most frequently in the tested populations. NRG3-related SNPs with top iHS values were identified in all five populations from European Russia and two European samples from the 1000 Genomes Project (CEU and FIN). By contrast, MSTP3-related windows were only revealed in Siberian populations. The top SNPs that also occurred mainly among Siberians were located in the NBEA gene. Among other potential candidates for positive selection were the TMEM229B and SEMA6A genes, both of which contain SNPs with extreme iHS values in multiple populations  in two or more populations tested. The highest number of top SNPs was observed in windows that included the ulations .P ≤ 1 × 10−5) XP-EHH scores were identified in the regions of the MESTP3, NRG3, NBEA, and PTPRM genes . SNPs with significant XP-EHH values were also found in other chromosome regions  calculations demonstrated the importance of several genomic regions where significant iHS values were detected previously. For example, multiple SNPs with significant . PDE11A and PPARD seemed to be under selection in all populations with the exception of CHB. By contrast, the selected regions comprising the PCSK9, KCNN3, SULT1C2, CYP26A1, and NIP7P1 genes were common among CHB and West Siberians. The signals of selection related to WNT5B were exclusively associated with West Siberian populations. Concomitantly, West Siberian Nenets were a stand-alone sample in their sharing of selection signals in the EDAR and CDCP1 genes with the Chinese population.Because the results of XP-EHH tests substantially depend on the choice of the reference population, we performed three sets of computations using the YRI, TSI, and CHB reference samples from the 1000 Genomes Project. The use of several reference populations may provide insights into the specificity of the distribution of selection signals observed. The mosTo determine whether any biological processes were targeted by selection in the populations studied, a gene ontology (GO) analysis was applied to the top loci identified. No categories were found to be significantly enriched for genes associated with iHS hits. A similar situation was with the genes selected based on XP-EHH tests, excepting Khanty population in which several GO categories were significant in the case of YRI reference .In parallel with SNP microarray study, we explored public data on multiple sequenced genomes from North Eurasia and neighboring populations from the EGDP project. First, P-value of iHS was set to < 1 × 10−4). This computation identified 24 SNPs that fulfilled all the requirements and were located in six different loci  and suggested that positive selection at all six loci from SLC37A2 and PKNOX2 genes. Several other genes with unknown functions, including ncRNAs, are also present within this locus.We also examined genotype data of whole-genome sequences of 402 individuals from the EGDP database and identified frequent SNP alleles (MAF > 0.25) in populations from Northern Eurasia that occurred at least three times more frequently there than they did in the remaining 10 populations, including five neighboring ones. For this computation, our thresholds became stricter than in previous paragraph to select the strongest sites throughout the whole genome . In addition, we examined whether the regions of these SNPs were among the signals of positive selection detected in any of the eight Northern European and Western Siberian populations examined using XP-EHH or iHS statistics  MAF frequencies > 0.20 in Northern Eurasia, and 2) over-representation in Northern Eurasia . We lowered the thresholds in this analysis because the original ones did not reveal a sole SNP. With new threshold conditions we found seven such SNPs, all of which represented missense mutations, which are listed in MESTP3, NRG3, NBEA, and PTPRM genes) were also revealed in cross-population testing of EHH (XP-EHH test).In the current study, we performed genome-wide scans of EHH in populations from Western Siberia and Northern European Russia. Via comparisons of the two alleles of particular SNPs, the ratios of integrated EHH (iHS) provide valuable information on selection strength at each locus, and the subsequent analysis of corresponding EHH profiles allows outlying the regions and genes under selection . Eight sMESTP3 and NBEA loci were shown to be under selective pressure for the first time. This situation seemed to be the result of the absence of appropriate populations in the previous studies . NBEA encodes the brain-enriched scaffolding protein neurobeachin, which is involved in membrane trafficking and synaptic functioning [NBEA were inside the gene and intronic. The MESTP3 genomic region provided a more interesting case. MESTP3 is a non-coding gene (pseudogene) [RN7SL101P, MIR4275, and LINC02364, encoded proteins. Their products are affiliated with long non-coding RNA or microRNA classes. Pseudogenes themselves can also be a source of small interfering RNAs, another RNA class resembling microRNAs [The ctioning . It has ctioning . All seludogene) . MoreoveicroRNAs –39. One NRG3 and PTPRM genes have been listed as potential targets of natural selection [PTPRM encodes a protein tyrosine phosphatase with an extracellular region that functions as a receptor involved in mediating cell–cell interactions and adhesion. PTPRM expression has been shown to be negatively correlated with oncogenic cell growth and cancer prognosis [NRG3 and PTPRM were mainly detected in European populations. The signals associated with NRG3 were all located inside the gene. At the same time, our results showed that the signals related to PTPRM were represented by two distinct groups, one associated with an intergenic region located at 80 kb from the start of the gene, and the other located in the middle of the first intron. Because PTPRM is a large differentially expressed and alternatively spliced gene [PTPRM.The election ,40. Neurelection . NRG3 haelection . PTPRM erognosis ,43. In aced gene , one canEDAR. It is required for the development of different ectodermal derivatives, including hair, teeth and sweet glands, and is a well-known target of selection in Asian (East Asian) populations [CDCP1, which was established to be involved in cell adhesion. Summarizing the above, one can propose that stand-alone position of Nenets among Western Siberians is attributed to the greater proportion of East Asian ancestry in Nenets, which they additionally obtained from Eastern Siberians, after the split from Khanty and Mansi [In the strategy used by us for searching for selection signals, we paid particular attention to the significant SNPs that occurred in more than one population. Such SNPs were found in populations from the same geographical regions . Some of those detected in populations from the European part of Russia were also found in European populations from the 1000 Genomes Project. This was unsurprising in the context of the patterns of population structure commonly shared among Europeans ,46. Addiulations ,48. AnotThe group identified and the regional population specificity in the distribution of selected signals agree with the results of previous studies ,47,49, w−8) in the FIN population. The whole-genome analysis also detected the high occurrence of the alternative alleles of SNPs under selection in this locus  in three sequenced Finnish and three Saami people from the EGDP database. One Finnish person (GS0000018756) was homozygous for these four alternative alleles while two Saami (GS0000035024 and GS0000035026) were heterozygous. A better correspondence between the SNP-microarray and genome sequence analysis approaches was observed after lowering the thresholds for MAF and/or allele over-representation level. However, lowering the thresholds may compromise the statistical significance of the results; thus, we did not follow this path. Whole-genome sequencing finds all genetic variations in individuals. Hence, it should allow finding the SNPs that are prime targets for selection. Similar to the SNPs from the microarray part, all 57 SNPs with the strongest signals for selection from Tables The results of studies that searched for signals of local adaptations are more reliable if they are supported by different algorithms . To veriSLC37A2 and PKNOX2. PKNOX2 belongs to the highly conservative TALE (3-amino acid loop extension) class of homeodomain proteins that play fundamental roles in cell proliferation, differentiation, and death. Genome-wide associated studies revealed that PKNOX2 is associated with alcohol addiction in mice and humans [SLC37A2, is a member of endoplasmic reticulum-associated sugar-phosphate/phosphate (P(i)) exchangers. A recent study found that SLC37A2 contains a conserved vitamin D receptor (VDR)-binding site [SLC37A2 in human peripheral blood mononuclear cells depends on the circulating form of vitamin D3. Deficit of vitamin D is often observed in northern populations, where exposure to sunlight is limited for many months. Hypothetically, mutations in the VDR-controlled SLC37A2 gene may help northern populations adjust to vitamin D levels. At the same time, the same mutations could have effects on alcohol tolerance in these populations through the PKNOX2 gene, located on the opposite strand of DNA in the same locus. Further studies of the expression of these two genes in Northern Eurasian populations are required to shed light on this problem.The strongest selection signal detected by whole-genome sequencing came from chromosome 11 at position 124 Mb. The six SNPs with selection signals from this locus spanned over 211 kb and were located inside the introns of two genes: d humans –56. Althing site . This st−35) associated with metabolic syndrome in Japanese populations [We also found seven SNPs in protein-coding regions whose derived alleles occurred substantially higher in Northern Eurasia. These SNPs might be potential targets of selection. The effects of two of them on risk of some medical problems had been demonstrated. For example, the SNP rs147417448 was significantly  that confirmed the proposed great role of such genes in local adaptations of humans after they left Africa. Generally, the distribution of both these and many other selection signals demonstrated geographic and group  specificities, suggesting the contribution of population structure and ancestry to the adaptation. Information on regional specificity in the distribution of the signals combined with the results of epidemiologic and animal model studies can be useful for clarifying the functions of particular genes .In conclusion, our study provided novel data toward establishing a genomic map of human local adaptations in Northern Eurasia, particularly in Russia, one of the largest and most multifaceted countries of the world, regarding both environment and ethnicity. In total, it outnumbered all studies performed before and demonstrated the fruitfulness of inclusion of new populations (populations from new geographic regions) for uncovering the molecular ways used for adaptation. Many of the described adaptation SNPs seemed to result from the involvement of non-coding RNAs. Several loci affiliated with different classes of non-coding RNAs were found to be under selection, particularly in the region of chromosome 4 (chr4:28.7–28.9 Mb), which contained the largest amount of significant selection signals. Cross-population testing suggested a role for this chromosomal region in the local adaptation of Western Siberians. It was also interesting to find among the top targets of positive selection the genes with known roles in the functioning and development of the nervous system .EGAS00001003955. DNA was isolated from peripheral leukocytes according to standard techniques using proteinase K treatment and phenol–chloroform extraction [DNA samples of Russians from the Ustyuzhna district of the Vologda region N = 46) and of Nenets from the Yamal peninsula  (N = 48) were genotyped for the first time using Illumina OmniExpress-24 arrays, according to the manufacturer’s protocol. The genotype data has been uploaded to the European Genome-phenome Archive and is accessible with EGA accession number traction . Blood s and of Ntraction ; samplestraction ,66. The As we were interested in cross-population statistics , we subjected to further analysis the common SNPs for the two data sets that were genotyped by us and taken from the 1000 Genomes Project. The genomic coordinates of our samples were unified to those of 1000 Genomes. Thus, 672559 SNPs were common. The final sample sizes were as follow: Veps (N = 72), Izhemski Komi (N = 74), Priluzski Komi (N = 69), Russians from the Archangelsk region (N = 93), Russians from the Vologda region (N = 45), Khanty (N = 42), Mansi (N = 37), and Nenets (N = 41).−5, and missing rate per SNP > 0.05. All data manipulations at this stage were also made using the Plink tool.iHS and XP-EHH tests require haplotypes for computations ,28. The The iHS and XP-EHH tests were performed using the REHH2.0 package . For eacP value cutoffs can be obtained from a Gaussian fit to the resulting data [P values ≤ 1 × 10−5 were considered as the signals of selection. Such SNPs were further annotated using the snpEff program (v.4.3p) [The default arguments of the REHH package were used to estimate EHH, iHS, and XP-EHH values; 200 kb regions located near the centromere and chromosomal ends were excluded from the analysis because of possible artifacts caused by the marginal effects of these tests. In the case of XP-EHH computations, eight of our populations and four populations from the 1000 Genomes Project  were tested in pairs with YRI, CHB, and TSI. Only positive XP-EHH scores corresponding to the suggested signals of selection in non-reference populations were subjected to further analysis. In the case of iHS data, the absolute values of the scores were used. As iHS and XP-EHH are constructed to have an approximately standard Gaussian distribution the ing data ,71. To c(v.4.3p) . Additio(v.4.3p) ,73,74. W(v.4.3p) ,76.The lists of genes that resulted from the annotation were checked for over-representation in terms of Gene Ontology using the WebGestaltR package, which represents an R interface of the WEB-based GEne SeT AnaLysis Toolkit .The exploratory analyses of the results obtained at each stage of the study, as well as the visualization of the data sets obtained, were made with custom scripts in R.EGDPindividuals.txt describing all individuals and the corresponding population groups are presented in SNPs of whole-genome sequences of 402 individuals from the EGDP database were downloaded in a multi-sample VCF format from the site . To obtaTo study the SNP allele frequencies within each group, we wrote the Perl script AlleleFreqEGDP.pl, which is also available for public use from S1 File(GZ)Click here for additional data file.S1 Fig−5). Loci of interest are pointed with arrows.Horizontal red lines indicate P-value threshold applied (P ≤ 1 x 10(TIF)Click here for additional data file.S2 FigThe bottom of the figure illustrates the location of the SNP in the corresponding part of chromosome 3, as at the NCBI variation viewer (GRCh37.p13), and the distances at which EHH for the ancestral allele drops to the threshold limit.(TIF)Click here for additional data file.S3 FigLINC02364 is absent in GRCh37 (it appears in GRCh38).The bottom of the figure illustrates the location of the SNP in the corresponding part of chromosome 4, as at the NCBI variation viewer (GRCh37.p13), and the distances at which EHH for the ancestral allele drops to the threshold limit. Note: (TIF)Click here for additional data file.S4 FigThe bottom of the figure illustrates the location of the SNP in the corresponding part of chromosome 18, as at the NCBI variation viewer (GRCh37.p13), and the distances at which EHH for the ancestral allele drops to the threshold limit.(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 TableFile includes complete list of individuals whose genome sequences have been analyzed. The table contains the EGDP identifiers of each person (in the first column), the population name (in the second column), the country of origin (in the third column), and the name of the corresponding population group (one of our 12 groups) (in the fourth column). Columns representing SNPs from Tables (XLSX)Click here for additional data file.


Hydrangea macrophylla L.) and both are recessively inherited. Molecular marker-assisted selection (MAS) can greatly reduce the time necessary to breed cultivars with desired traits. In this study, a genome-wide association study (GWAS) using 5803 single-nucleotide polymorphisms (SNPs) was performed using a panel of 82 bigleaf hydrangea cultivars. One SNP locus (Hy_CAPS_Inflo) associated with inflorescence type was identified with general linear model (GLM) and mixed linear model (MLM) methods that explained 65.5% and 36.1% of the phenotypic variations, respectively. Twenty-three SNPs associated with remontancy were detected in GLM whereas no SNP was detected in MLM. The SNP locus (Hy_CAPS_Inflo) was converted to a cleaved amplified polymorphic sequence (CAPS) marker that showed absolute identification accuracy (100%) of inflorescence type in a validation panel consisting of eighteen H. macrophylla cultivars. The SNP was investigated in 341 F1 progenies using genotyping by sequencing (GBS) and co-segregated with inflorescence type . The SNP was subsequently used for breeding selection using kompetitive allele specific PCR (KASP) technology. Future directions for the use of genomics and MAS in hydrangea breeding improvement are discussed. The results presented in this study provide insights for further research on understanding genetic mechanisms behind inflorescence type and remontancy in H. macrophylla. The CAPS and KASP markers developed here will be immediately useful for applying MAS to accelerate breeding improvement in hydrangea.Inflorescence type and remontancy are two valuable traits in bigleaf hydrangea ( Hydrangea macrophylla L. is a popular cultivated horticultural crop widely grown throughout America, Asia, and Europe2. Famous for its appealing large corymbs and unique flower color changes, hundreds of named cultivars has been developed and selected throughout the world3. Hydrangea is a versatile ornamental plant that can be used as a florist, potted, and landscape plant. Breeding programs in hydrangea usually have specific goals depending on how the plants will be used. Breeding landscape hydrangeas focuses on whether the plant forms a rounded or mounded shrub composed of erect, unbranched stems, whereas florist hydrangea breeding programs target stem strength and durability4. However, flowering traits are always considered as a priority for any hydrangea breeding program, as flowers present the key horticultural attraction for hydrangea as for many other ornamental crops. Sepal coloration, inflorescence type, and the timing of flower bud initiation are the most important flowering traits in hydrangea. While flower color changes in hydrangea have been shown to be a result of internal detoxification of Al under low-pH conditions5, inflorescence type and the timing of flower bud initiation are largely under genetic control8.H. macrophylla cultivars. Two types of inflorescence, mophead, and lacecap, are categorized by the relationship between the small, fertile flowers and the large, showy sepals. The showy sepals of mophead hydrangeas completely surround the fertile flowers located on the pedicels below, leading to a rounded inflorescence. Lacecap inflorescences consist of a plane of fertile flowers surrounded by a ring of showy sepals leading to a flat inflorescence9. “Endless Summer” The Original (“Bailmer”) and “Veitchii” are two H. macrophylla cultivars that represent the different inflorescence types. “Endless Summer” is a pink- or blue-flowered (pH dependent) hydrangea with mophead inflorescences varying from 7.5 to 15 cm in diameter10, while “Veitchii” has lacecap flower heads with large white sepals. Breeding efforts have been primarily focused on producing cultivars with mophead inflorescences given customer preference.Inflorescence type is the most obvious horticultural trait in 11. Well-known remontant H. macrophylla cultivars, include “All Summer Beauty”, “Bailmer” (marketed as “Endless Summer”®), “David Ramsey”, “Decatur Blue” “Early Sensation” (marketed as “Forever and Ever”®), “Oakhill”, and “Penny Mac”9. New remontant cultivars are often introduced as collections within an established brand, such as the Endless Summer® series, Forever & Ever® series, and Let’s Dance® series. So Long® is the latest series of reblooming hydrangeas launched in 2017 by SAPHO , the exclusive licensing partner of INRA.Remontancy, or reblooming, is the ability to initiate floral buds on new vegetative growth which leads to continuous flowering throughout the growing season. Remontancy is a highly desired trait for modern landscape plants, as consumers consistently choose varieties with season-long floral display and reliable blooming in more northerly or frost-prone areas12. Compared to traditional molecular markers, SNPs are abundant, stable, and easily detectable by sequencing technologies such as genotyping-by-sequencing (GBS) even without a priori genome sequence information13. Thus, SNP markers have been widely used in genetic diversity assessments, molecular evolution studies, and genetic mapping for traits of interest in diverse horticultural crops such as carnation14, chrysanthemums15, roses16, lilies17, and tulips18.Breeding procedures for horticultural crops based on 4–6 years of field evaluation are costly and inefficient given that the majority of plants will lack desired trait combinations. Trait-associated molecular markers can be used to select desired plants at their seedling stage to shorten the breeding time and increase the speed of cultivar development. Single-nucleotide polymorphisms (SNPs) are third-generation DNA molecular marker technology defined as single-base changes at a specific nucleotide position across the entire genome of all organisms19. In the past few years, GWAS has been applied in many plant species to find functional alleles that can assist breeding programs20. In roses, key genes that responsible for prickle density and the number of flower petals has been found by using GWAS21. A key gene that controlling fruit shape was identified by exploiting natural variation in a panel of peach accessions22. Significant SNPs identified from GWAS can be converted into readily usable molecular markers, such as single-strand conformation polymorphisms23, high resolution melting24, kompetitive allele-specific polymerase chain reaction (PCR) (KASP)25, and cleaved amplified polymorphic sequences (CAPSs)26 or derived CAPSs (dCAPSs)27. Among them, CAPS is the most simple, efficient and economic technique for in-lab SNP detection, especially for frequent and small quantity breeding selection, whereas KASP is more suitable for a large population selection program.Genome-wide association study (GWAS) is a powerful tool for dissecting complex traits in plants by correlating large numbers of molecular markers distributed across the genome with phenotypic variationH.macrophylla cultivars directly helps the nursery industry because consumer interest is driven in part by the release of new, novel plants. To this end, we developed 5803 SNPs for a cultivated H. macrophylla panel using GBS technology. The goal of this research was to (1) identify SNPs associated with inflorescence type and remontancy in H. macrophylla via GWAS, and (2) convert trait-associated SNPs into lab-friendly molecular markers for MAS of hydrangea.Conventional breeding of woody ornamental nursery crops can be improved greatly by using molecular markers associated with traits of interest. Accelerated breeding of new H. macrophylla cultivars were included in this study (Table ™ 5G homogenizer. Genomic DNA was isolated from the lysed tissue using DNeasy Plant Mini Kit (Qiagen) followed by RNase treatment. Nucleic acid was evaluated in 1% agarose gel and quantified using a spectrometer . Genotyping of the hydrangea cultivar panel was carried out via GBS as described by Elshire et al.28. A total of 5803 high-quality SNPs discovered and described previously29 were used to perform genome-wide association analysis in the present study.A total of 82 dy Table . Hydrang30. The number of subpopulations, K, was set from 1 to 10 with 100,000 burn-in and 100,000 Markov Chain Monte Carlo with 10 times of iteration. The output of STRUCTURE was evaluated in STRUCTURE HARVESTER31 to determine the best K value based on Evanno test32. Phylogenetic study was carried out using neighbor-joining method in MEGA 733, with 500 bootstraps to estimate nodal probabilities.Population structure was investigated using STRUCTURE 2.3.0 software with admixture mode9 with multiyear observations. Flower inflorescence type was considered as a qualitative trait and categorized as lacecap (L) or mophead  to reduce false-positive associations. The proportion of the phenotypic variation explained (PVE) by each marker was estimated by the relevant R2. SNPs that were repeatedly detected by all the models were considered as high-confidence GWAS results.Based on the filtered SNP data, the GWAS analysis was conducted using TASSEL v5.0 software with two models: general linear model (GLM) and mixed linear model (MLM) + Q, a MLM considering population structure (Q) and kinship (K) as covariates. The significance threshold was set to 34. The specific primers were designed using DNASTAR35. Primer sequences are as follows: GCTACAGCATACTGATTATCTCC (forward) and TGGAGGTCTTAATGCTCATAGAA (reverse). Eighteen H. macrophylla cultivars or breeding accessions with determined inflorescence type were used to verify the CAPS markers . The digestion products were separated via 2% agarose gel.The SNPs that were suitable for conversion to CAPS markers and the corresponding restriction enzymes were selected using dCAPS Finder 2.027rs Table . The gen1 progenies from a cross between the lacecap cultivar “Veitchii” and mophead cultivar “Endless Summer” were used to verify the phenotype–genotype association. Genomic DNA was prepared and submitted for GBS following the same protocol as stated above. SNPs were called via the TASSEL UNEAK pipeline using the same parameters described by Wu and Alexander29. Inflorescence type (mophead or lacecap) was investigated for genotype–phenotype association using chi-squared analysis.In addition, a total of 341 F2 population [(“Veitchii” × “Endless Summer”) × (“Veitchii” × “Endless Summer”)] resulting from two heterozygous progenies of the F1 cross mentioned above was tested for selection purposes. The DNAeasy Plant Mini (Qiagen) extraction kit was used for genomic DNA isolation in F2 progenies according to the manufacturer’s kit instructions. The concentration and quality of the DNA samples was determined in NanoDrop2000 spectrophotometer (Thermo Fisher Scientific) and checked in 1% agarose gel. DNA samples were then diluted to the appropriate level for SNP genotyping (10 ng mL−1) as required.Large-scale MAS requires the application of a high-throughput genotyping system to minimize cost and maximize efficiency. For this purpose, a KASP-SNP markers were also developed for MAS. A recently developed F36. SNP alleles were determined with KlusterCaller software37.Sequences that were 100 bp long on each side of the SNP were submitted for primer design. KASP genotyping assay includes KASP master mix containing the common fluorescent reporting dyes FAM and HEX, along with Rhodamine X (ROX) as a passive reference background dye. The KASP assay mix and primers were developed and run by LGC Biosearch Technologies  using the KASP on demand platformserrata were clustered as clade I (represented by green). Cultivars from subspecies macrophylla formed into two subclades named as clade II and III . Structure analysis identified three populations in the cultivar panel based on Evanno test . Eighteen genotypes were included in the validation panel. A single fragment of 278 bp was obtained from the PCR product across the validation panel, indicating specific and stable PCR amplification. Two types of band combinations were observed in the digested PCR product. In lacecap hydrangeas, PCR fragments were digested into two bands of 148 and 130 bp (undetectable due to 21 bp difference), along with the undigested 278 bp PCR product. The PCR product was not digested in mophead hydrangeas, showing only one band of 278 bp. Two well-known hydrangeas “Endless Summer”  and “Veitchii”  showed different band types as expected. The CAPS marker derived from the SNP exhibited 100% efficiency in identifying mophead and lacecap inflorescence types in the first validation panel consisting of eighteen H. macrophylla cultivars and breeding accessions  had mophead flowers and T/T genotype, 181 (57.28%) had lacecap flowers and C/T genotype  × (“Veitchii” × “Endless Summer”)] was used to investigate the segregation of the discovered SNP locus as well as for trait selection. Out of 73 F2 progenies that were tested for inflorescence type by the KASP-SNP marker, 70 were successfully genotyped resulting in a 95.9% calling rate  produces mophead inflorescences often confused as lacecap due to the many, small flowers and the absence of large, showy sepals. Also, the F1 population may have contained triploids due to unreduced gamete formation in the parents45. The presence of triploids in an F1 population has been linked to skewed segregation ratios46. Finally, the mismatch of genotype and phenotype could be due to the basic methodology of GBS. GBS is a reduced representation genotyping method that does not produce a whole-genome sequence28. Even if the significant SNP is tightly linked to the causative gene, there still exists the possibility that recombination between the gene and the marker could occur.The validation of the SNP marker using PCR-based and GBS methods exhibited 100% identification efficiency in the validation panel and 98.47% identification efficiency in the F1 population did not support a 1:1 ratio between mophead and lacecap inflorescence types, the chi-square test did indicate that SNP marker Hy_CAPS_Inflo co-segregated with inflorescence type in the F1 population. However, the heredity of this particular SNP did fit the 1:2:1 ratio in a recently developed F2 population derived from two heterozygous F1 progenies . In the current study, non-remontant hydrangeas were characterized by a single flush of inflorescences on old wood, while remontant hydrangeas flowered on both old wood and new growth. However, a third phenotype was also observed: some cultivars flowered continuously on old wood. For these cultivars, floral buds initiated the previous fall did not open in a single flush, but opened individually throughout the growing season. These cultivars may be mistakenly identified as remontant if phenotyping consists of counting new inflorescences throughout the growing season without removing previous season’s growth or noting where the flowering occurs . Similar phenotypes were also observed in dwarf lilac where a non-complete and nonstable remontancy (semi-remontancy) was observed and a two-gene model was found to fit the observed phenotypic segregation ratio better than a one-gene model55. Segregation ratios that fit a two-gene model have not been tested in hydrangea given that semi-remontancy has not been treated as an individual phenotype. More rigorous phenotyping efforts are necessary for future studies in order to elucidate the genetic nature of remontancy in hydrangea.Twenty-three SNPs were detected to be associated with remontancy using GLM but none of them were detected with MLM. Possible reasons include, but are not limited to, small population size, lack of phenotypic variation, and the stringent requirements of MLM for qualitative traitsH.macrophylla. Inflorescence type was detected with 100% accuracy using the CAPS marker in a validation panel. KASP-SNP genotyping revealed that 76% of progenies in an F2 breeding population produced lacecap inflorescences. These plants could be discarded as seedlings before further container or field evaluation in a breeding program targeting mophead inflorescence type. Furthermore, the SNP markers developed here will be useful to understand the biological mechanisms behind inflorescence architecture as improved genome resources for hydrangea become available.Traditional breeding of hydrangeas for trait improvement takes at least two years before promising plants can be selected, cloned, and evaluated in large numbers. The time-to-market for new cultivars can be reduced greatly by identifying molecular markers associated with specific traits. In the present study, a SNP associated with inflorescence type was identified and converted to both CAPS and KASP-SNP markers for small and large population selection in H. macrophylla. Two statistical models identified a SNP locus that was tightly linked to inflorescence type of hydrangea. This particular SNP locus (Hy_CAPS_Inflo) was converted to a CAPS-SNP and KASP-SNP marker which will be useful for MAS in hydrangea breeding programs. Even though no SNP associated with remontancy was identified in two simulation models, insights have been provided to further the genetic study of remontancy in hydrangea.A large number of SNPs developed through GBS were used to perform GWAS to investigate the genetic control of inflorescence type and remontancy in Supplementary Information


Oreochromis niloticus) strains exist, such as the iconic Genetically Improved Farmed Tilapia (GIFT), and breeding programs typically follow classical pedigree-based selection. The use of genome-wide single-nucleotide polymorphism (SNP) data can enable an understanding of the genetic architecture of economically important traits and the acceleration of genetic gain via genomic selection. Due to the global importance and diversity of Nile tilapia, an open access SNP array would be beneficial for aquaculture research and production. In the current study, a ∼65K SNP array was designed based on SNPs discovered from whole-genome sequence data from a GIFT breeding nucleus population and the overlap with SNP datasets from wild fish populations and several other farmed Nile tilapia strains. The SNP array was applied to clearly distinguish between different tilapia populations across Asia and Africa, with at least ∼30,000 SNPs segregating in each of the diverse population samples tested. It is anticipated that this SNP array will be an enabling tool for population genetics and tilapia breeding research, facilitating consistency and comparison of results across studies.Tilapia are among the most important farmed fish species worldwide, and are fundamental for the food security of many developing countries. Several genetically improved Nile tilapia ( Oreochromis niloticus) is one of the most widely farmed freshwater fish species in the world, with 4.2 million tons being produced in 2016  (Cyprinus carpio) (Oncorhynchus mykiss) (Crassostrea gigas) and European (Ostrea edulis) oysters   (Tinca tinca)  strain  were genotyped. The broad utility and open-access availability of the array is anticipated to benefit both the academic and commercial communities to advance genomic studies in this species and support ongoing and emerging breeding programs.The aim of this study was to develop a publicly available, open access ∼65K SNP array for Nile tilapia based on the widely cultured GIFT strain, but that also contains informative markers in multiple tilapia strains across Asia and Africa. To achieve this, a large SNP database was generated by whole genome Illumina sequencing of pooled genomic DNA from 100 individuals from the WorldFish GIFT breeding nucleus from Malaysia. These newly discovered markers were cross-referenced with SNP panels previously identified in several populations, with the aim of prioritizing markers that are informative across strains. To test the performance of the SNP array, nine Nile tilapia populations of different geographical origins and genetic backgrounds  were used for DNA sequencing for SNP discovery. Caudal fin clips were sampled and preserved in absolute ethanol at -20° until shipment from Malaysia to The Roslin Institute  for DNA extraction, sequencing and genetic analysis.One hundred Nile tilapia broodstock samples from the 15Genomic DNA was isolated from the tilapia fin clips using a salt-based extraction method . The intOreochromis niloticus genome assembly published by preprint) if (i) at least three reads supported the alternate allele or (ii) the SNP allele frequency in the pool was above 0.02, whichever condition was met first. As a first filtering step, only SNPs that had no interfering variants within less than 40 bp on either side were retained. The resulting vcf file was then filtered to obtain a list of high quality variants with vcffilter v1.0.0 (https://github.com/vcflib/vcflib); bi-allelic SNPs meeting the following criteria were kept for further evaluation: (i) a minimum coverage of 50X and maximum coverage of 150X, (ii) presence of supporting reads on both strands, (iii) at least two reads balanced to each side of the site and (iv) more than 90% of the observed alternate and reference alleles supported by properly paired reads. To enrich the platform for variants located on or nearby genes, polymorphisms were annotated and classified using the software SnpEff v4.3   (ii) markers with a call rate > 0.95, (iii) individuals with a call rate > 0.90, and (iv) one SNP of a pair showing high linkage disequilibrium (r2 > 0.7). In addition, for individuals sharing greater than 80% of alleles identical-by-state (IBS) with another individual, only one was retained for further analysis. The structure of the 135 individuals genotyped with the SNP array was investigated using the R package LEA  using the Nile tilapia ∼65K SNP array. To perform a principal component analysis (PCA) on the genome-wide SNP data the following SNPs and samples were retained using PLINK v1.9  for each Nile tilapia strain / population were calculated, and 95% confidence intervals of Ho estimated based on 1,000 bootstrap replicates. To evaluate the informativeness of the SNPs on the array, markers were classified into five different categories depending on their average MAF: Common (MAF > 0.3); Intermediate (0.3 > MAF > 0.1); Low (0.1 > MAF > 0.05); Rare (MAF < 0.05); and Fixed (MAF = 0).2 (2 corrected for relatedness (r2vs) using the package LDcorSV v1.3.1  for each Nile tilapia chromosome. The LD decay was visualized using the R package ggplot2  against inter-marker distances, which extended from ten up to 10,000 kb.The estimate of linkage disequilibrium (LD) was based on a version of the SNP dataset in which all individual and SNP QC filters had been applied , except the removal of markers based on LD. As a pairwise measure of LD, r2  was chosV v1.3.1 . For com ggplot2  by plottIn Vivo Experiments (ARRIVE) guidelines.Data collection and sampling of the GIFT tilapia populations was performed as part of a non-profit selective breeding program run by WorldFish. The animals from this breeding population are managed in accordance with the Guiding Principles of the Animal Care, Welfare and Ethics Policy of WorldFish. Tissue sampling was carried out in accordance with the norms established by the Reporting https://www.ncbi.nlm.nih.gov/sra) under accession number PRJNA520791. Genome position and probes for all SNPs included in the ∼65K SNP array are given in File S1. The genome positions and allele frequencies of the SNPs included on the array can be found in the European Variation Archive  under accession number PRJEB38548. The tilapia SNP array is available for commercial purchase from ThermoFisher . Supplemental material available at figshare: https://doi.org/10.25387/g3.12472121.Raw sequence reads from the two pools analyzed for SNP discovery have been deposited in NCBI’s Sequence Read Archive . After additional filtering criteria related to allelic frequency thresholds  and the type of allele polymorphism , 351,188 SNPs were sent as 71-mer nucleotide sequences to ThermoFisher for in silico probe scoring. From the list of scored SNP probes provided by ThermoFisher, only those that were categorized as either ‘recommended’ or ‘neutral’ were selected.The pooled DNA sequencing resulted in 458M and 461M paired-end reads for the two DNA pools. The alignment of the quality control filtered reads against the Nile tilapia reference genome (Genbank accession GCF_001858045.2) led to the discovery of ∼20 million putative polymorphisms. Of the 1,166,652 bi-allelic SNPs that remained after applying post-alignment quality control (QC) filters, 694,348 fell within genes or in the neighboring regions of genes  7 sex determination markers, (ii) 6,883 SNPs discovered in our population that overlap with SNP panels identified in other strains / populations, (iii) 11,328 SNPs located in exons, and (iv) 47,239 SNPs occurring in genes or within < 1 kb of genes. The latter set of SNPs were selected to be evenly spaced according to physical distance along the 22 chromosomes  and 130 of the longest unplaced scaffolds of the Nile tilapia genome assembly.After QC of the genotyping data, seven, two and one fish were removed due low call rate from the Abbassa wild, Abbassa strain and BEST population, respectively. Therefore, 125 individual fish from across nine different strains / populations were used to validate the SNP array . The obti.e., the primary discovery population) having the highest value (0.35), and the Kenyan population the lowest (0.21) (vs. 0.24).The average observed heterozygosity of the genotyped populations was 0.29, with the GIFT strain from Malaysia (t (0.21) . OverallThe average MAF of all 42,460 successfully genotyped SNPs ranged from 0.23 to 0.26 across the genetically improved strains and the single wild population evaluated. The number of informative markers (MAF > 0) on the array was higher for samples from GIFT and GIFT-derived populations than for populations with non-GIFT genetic backgrounds . The priThe population stratification of the nine Nile tilapia strains / populations was visualized using a PCA to reduce the dimensions of the genotype data . The two2  was ∼0.2 (MAF > 0.1 dataset). Within the short-range distances (< 100 kb), there was a 10–23% decrease in pairwise LD when SNP pairs separated by ∼10 kb were compared to SNP pairs separated by ∼100 kb. Considering long-range distances, the average r2 dropped by 65% from that estimated at 10 kb compared to 10,000 kb in GIFT-Ma (0.21 vs. 0.08), 60% in GIFT-Ba (0.18 vs. 0.07), 61% in GIFT-Ph (0.19 vs. 0.07), 59% in GET-EXCEL (0.19 vs. 0.08), 54% in the BEST strain (0.19 vs. 0.09), 72% in FaST (0.27 vs. 0.08), 54% in the Kenyan strain (0.17 vs. 0.08) and 52% in the Abbassa-strain (0.18 vs. 0.09). In contrast, LD decayed much more slowly in the Abbassa-wild population. The reduction in r2 between pairs of SNPs at 10 kb vs. 10,000 kb distance apart from each other was only 22% (0.19 vs. 0.15)  .e.g., 89% for rainbow trout  . This pattern is consistent with a short period of artificial selection that has not yet led to significant shifts in allele frequencies. Additionally, the projection of the Kenyan cluster along a line in the PC plot may indicate the recent admixture of two populations, as suggested for this dispersion pattern by TH generation), the FaST strain (13TH generation), an Egyptian strain  and a Kenyan strain (coming from stock collected in Lake Turkana) . This observation may suggest that the GET-EXCEL strain has a limited Abbassa, Kenyan and FaST genetic component, which could be explained by an unequal contribution of parental lines during the establishment of the strain.A principal component analysis demonstrated that our 65K SNP array distinguishes the four major strains evaluated in this study , indicating clear independent clusters based on the first two principal components. While the purpose of this analysis was to test the utility of the SNP array to distinguish populations, a few interesting observations were noted. First, individuals from the Abbassa genetically improved strain clustered with wild fish from the same region (Turkana) . However2 ∼0.2) were accompanied by a moderate to slow decay with increasing distance. Despite the small number of animals used to assess LD decay , a similar pattern was found to that reported by e.g., population bottlenecks, selective breeding) may influence patterns of LD  showed the slowest rate of decrease and the highest LD at longer distances compared to all Nile tilapia strains evaluated. Because it is possible that this sampled Abbassa population may not be a good representation of wild individuals  or LD estimates are being biased by population structure (this hypothesis was not tested in this study), additional wild populations should be genotyped and evaluated. The general trend observed across strains of overall low levels of r2 suggests that patterns of LD in Nile tilapia are complex and likely associated with particular features of the process of domestication of this species (Linkage disequilibrium (LD) is the non-random association between the observed frequencies of a particular combination of alleles . Adequatns of LD . Contrar2 across autosomes was 0.2. According to the simulations performed by 2 of at least 0.2 is required to achieve a power above 0.8 to detect a QTL for a complex trait of low heritability (h2 ∼0.05). Although overall LD levels appear to be low in Nile tilapia, our preliminary results suggest that this array provides sufficient genomic resolution to capture association signals in different strains, and will therefore contribute to expand genetic research in this species and effectively support ongoing and future breeding programs.At the mean inter-marker spacing on the SNP array (∼16 kb), the average rA high quality Nile tilapia SNP array was created and validated in several strains. The SNP array was built by prioritizing markers that are evenly spaced across gene entities and their local neighborhood (within < 1 kb), thereby potentially increasing the chance of detecting variants that alter gene expression and / or protein function. The open-access nature of the SNP array together with demonstration of its utility across multiple strains will facilitate its use in genetic research in this species. This may include studies to assess the origin of farmed populations, to track introgression of farmed genomes into the wild, and to understand the genetic architecture of traits of interest. Further, this SNP array will contribute to the management of farmed tilapia populations, and enable accelerated genetic gain and better control inbreeding in breeding programs via genomic selection.


Vitvi01g00455, Vitvi08g01528, and Vitvi18g01237 associated with the recessive seedless-regulating loci, which showed high homology with genes that regulate seed development in Arabidopsis. This study provides fundamental insights relevant to variant calling from genome resequencing data of diverse interspecific hybrid germplasms such as grape and will accelerate future efforts aimed at crop improvement.The seedlessness of grape derived from stenospermocarpy is one of the most prized traits of table or raisin grapes. It is controlled by a complex genetic system containing one dominant gene and multiple recessive genes. Here, we collected dense variation data from high-depth resequencing data of seeded, seedless, and wild relative grape genomes sequenced to > 37x mean depth. Variant calls were made using a modified variant calling pipeline that was suitable for highly diverse interspecific grape accessions. The modified pipeline enabled us to call several million more variants than the commonly recommended pipeline. The quality was validated by Sanger sequencing data and subsequently supported by the genetic population structure and the phylogenetic tree constructed using the obtained variation data, results of which were generally consistent with known pedigree and taxonomic classifications. Variation data enabled us to confirm a dominant gene and identify recessive loci for seedlessness. Incidentally, we found that grape cultivar Rizamat contains an ancestral chromosomal region of the dominant gene in Sultanina, a predominant seedlessness donor cultivar. Furthermore, we predicted new candidate causal genes including  While most is processed into wine, a significant proportion (∼30%) is also destined for fresh consumption (table grape), dried into raisins, or processed into juice. However, ∼90% of grape produced from ∼14,000 ha in Korea is consumed as table grape (http://www.krei.re.kr/). Seedlessness is one of the most prized traits of table or raisin grape. Most of the seedless table grape cultivars with known pedigrees are derived from the stenospermocarpic variety Sultanina, also known as Sultanine or Thompson Seedless (SDI) (SDI locus is located on chromosome 18 (AGAMOUS-LIKE 11 (VviAGL11) has been proposed as a candidate for SDI , grape population genomics has lagged behind other major crop plants  decay due to introgression from local wild progenitor Vitis vinifera L. subsp. sylvestris (Gmel.) Hegi during domestication  company in Korea. Paired-end sequencing libraries were constructed with an insert size of 500 bp using TruSeq DNA PCR-Free kit  according to Illumina library preparation protocols. Libraries were then sequenced using Illumina HiSeq 4000 platform with 2 × 151-bp paired reads to a target coverage of 40×. Raw sequencing data were deposited in the Short Read Archive at NCBI (BioProject PRJNA485199). We also used Illumina raw reads with >37 coverage depths for five other seeded and one seedless cultivars . We then essentially followed procedures described in the Genome Analysis Toolkit (GATK) Best Practices for data pre-processing (V. vinifera Pinot Noir PN40024 reference genome (12X.v2) (http://picard.sourceforge.net/). We performed data pre-processing, including sorting operation and base recalibration, using GATK (version 4.0.1.2). A total of 457,245 known variants of grape genomes used in this study were release-40 data downloaded from https://plants.ensembl.org/index.html . Their coordinates were converted to 12X.v2 coordinates using python script provided by V. vinifera, in contrast to the simple assumption that distantly related accessions would have higher genetic variation than closely related accessions. After further processing by applying IndelRealigner, we called SNPs and indels with UnifiedGenotyper. Raw variant calling data were divided into SNPs and indels with SelectVariants function of GATK (v. 4.0.1.2). Hard-filtering was then performed for these raw SNP calls using VariantFiltration function of GATK (v. 4.0.1.2) according to the following threshold criteria: MappingQualityRankSum of < -12.5, polymorphism confidence scores (QUAL) < 30, genotype call quality divided by depth (QD) < 3.0, Phred-scaled P value of Fisher exact test for strand (FS) > 30.0, mapping quality (MQ) < 30.0, total depth of coverage (DP) < 200, and genotype-filter-expression depth of coverage (DP) < 15. Bi-allelic variants were then selected using VCFtools (version 0.1.15)  (https://samtools.github.io/bcftools/bcftools.html), a variant calling project split from SAMtools package  , two interspecific hybrid (Campbell Early and Cheongsoo), and one wild relative (V. amurensis) accessions. Primer sets were designed to amplify sequences in the genomic region of these genes (Table S1). In particular, three primer sets that would amplify overlapping gene fragments for the assembly of three contigs were designed to amplify sequences in the up-and down-stream and in exon/intron regions of VviAGL11 in order to sequence the whole gene. As a result, the contigs from the same haplotypes were identified by specific polymorphisms in overlapping sequences. PCR amplifications were performed with 20 ng of grape genomic DNA using TAKARA LA Taq (Cat No. RR002A) with annealing temperature of 53° for most of the genes except VviAGL11. Because gene fragments amplified for VviAGL11 are larger than 3 kbp, we performed PCR amplifications using TAKARA LA Taq (Cat No. RR002A) with annealing temperature of 65°. Because these called variants contained an appreciable rate of heterozygous variants, a given PCR product was subcloned into a plasmid for sequencing. At least three different clones for each haplotype were then sequenced. Sequences of both ends of a PCR amplicon were also determined directly from the amplified products to predict copy number of the amplicon based on sequence profiles.We validated candidate SNPs and indels called from genome resequencing data by Sanger sequencing of genomic DNA fragments PCR-amplified from twelve genes in four K) ranged from 1 to 12. Python script ChooseK incorporated with the FastStructure package was used to choose the number of subpopulations that could maximize the marginal likelihood. Results were graphically represented using STRUCTUREPLOT v1  was performed using SMARTPCA with default setting , and two wild relative grapes (V. amurensis and V. flexuosa) were excluded.Population groups were inferred using a Bayesian model-based clustering method, fastSTRUCTURE (version 1.0)   was used as reference protein set. Annotation of Vitis vinifera 12X.v2 was downloaded from URGI (https://urgi.versailles.inra.fr/files/Vini/Vitis%2012X.2%20annotations/Vitis_vinifera_gene_annotation_on_V2_20.gff3.zip). Gff3 format was converted to Ensembl GTF format. Grape SIFT 4G database was constructed using SIFT4G_Create_Genomic_DB implemented in SIFT 4G. Functional effects of variants in coding regions of 33 grapes were predicted using SIFT 4G annotator with default option.To predict functional effects of variants, Sorting Intolerant From Tolerant 4G (SIFT 4G) software  downloaded from URGI (https://urgi.versailles.inra.fr/download/vitis/Genetic_maps_Vitis_12X_V2.zip) (FST) to measure population differentiation between the seeded and seedless grape populations to investigate the selection signals for seedlessness across the genome. A 100 kb sliding window with 10 kb step approach was applied to quantify FST with VCFtools (version 0.1.15)   with defp-values were constructed using qqman (Seeded and seedless traits were encoded as binary traits of 1 (control) and 2 (case), respectively. Case-control logistic mixed model association test was performed using GENESIS (ng qqman  in R pachttps://figshare.com/projects/Grape_resequencing_seedlessness_project/63569). The Sanger sequencing data from this study have been deposited with the GenBank data library under Accession Nos. MN243829–MN243907. A supplementary material file in the online of this article contains Figures S1 to S10 and Tables S1 to S7. Supplemental material available at figshare: https://doi.org/10.25387/g3.12581324.Short read data were deposited in the Short Read Archive at NCBI (BioProject PRJNA485199). Large datasets including SNP and indel calls and SIFT data are available from figshare repository  for variant calling . Of thesV. vinifera divided into subspecies vinifera and its progenitor subspecies sylvestris was the only species of food grape until the end of the 19th century. However, since the outbreak of phylloxera at the end of the 19th century, interspecific hybrids between V. vinifera and other interfertile Vitis species were extensively introduced for disease resistance, different flavors, or adaptation to geographic areas other than the Mediterranean region . This stands in stark contrast to the simple assumption that distantly related accessions would have higher genetic variation than closely related accessions, when compared to the reference genome sequence. This phenomenon became worse after a VariantFiltration step . HaplotypeCaller calls SNPs and indels simultaneously via local de-novo assembly of haplotypes in an active region where it remains to be candidate variant loci on the basis of reads mapping through data pre-processing steps , as expected based on their phylogenetic relationships. We also attempted to use BCFtools for variant calling. However, the number of raw SNPs or the number of filtered SNPs in each grape accession was approximately ten-fold lower than that from UnifiedGenotyper or HaplotypeCaller . Therefore, we did not use it further.n region . After rjaponica , we obtavinifera . Thus, f species . MoreoveV. vinifera variation data from only 23 V. vinifera and from all 33 grape accessions called through GenotypeGVCFs, and found no difference at identical sites between the two data sets. We also compared variant data called through GenotypeGVCFs with data called through UnifiedGenotyper from all 33 grape accessions. Interestingly, genotypes of SNPs that had identical coordinates between the two data sets were more than 93% similar in each V. vinifera accession and less than 85% similar in each hybrid and wild relative accession . We also used AGL11 sequences from Chardonnay and Sultanina reported by V. vinifera . However, these SNP sites numbered less than 10,000 for each V. vinifera accession, and these may be considered basal level errors. This phenomenon might have occurred because the UnifiedGenotyper caller purged alleles from more diverse reads of two haplotypes. Thus, we decided to convert these homozygous SNP to heterozygous SNPs. After this conversion, UnifiedGenotyper SNP calls for hybrid and wild relative accessions turned out to be highly accurate: 97.6% (13.8% increase from before conversion) and 92.9% (5.6% increase) of UnifiedGenotyper SNP calls were validated by Sanger sequences for hybrids and wild relative accessions, respectively. In sum, we identified in all 33 accessions approximately 17.45 million candidate SNPs that went through the quality control filtering described in the Materials and Methods section below (Table S4). To obtain high-quality SNPs for population analyses, we excluded SNPs with < 10% minor allele frequency (MAF) and > 10% missing rate, yielding a total of 5,373,452 high-quality SNPs (Table S5). Relative to other accessions, Cannonau and two wild relative accessions consistently showed higher number of missing sites in all filtering methods and steps . In the case of Cannonau, this might occur due to its lowest mapping depth. For the two wild relatives, it might reflect some chromosomal regions that were too diverse for short reads to map properly.When we examined the variant call format (VCF) file from UnifiedGenotyper, we found that many sites with higher than 30% of allelic balance were called homozygous SNPs. Interestingly, total numbers of SNP sites that were supposed to be erroneously called based on allelic balance were several hundred thousand for each of the hybrid and wild relative species accessions, with the highest number of 1.3 million seen for K populations existed based on a maximum-likelihood method. The estimated marginal likelihood value plot in this analysis clearly supported the presence of three clusters . At K = 2, grape accessions were divided into two groups consisting of a group of V. vinifera and a group of interspecific hybrids and two wild relatives .The genetic population structure and relationships among these 33 resequenced grape accessions were further examined by constructing a neighbor-joining phylogenetic tree . Group IV. vinifera and interspecific hybrid groups, we calculated 2r , which is similar to that for both wild and cultivated V. vinifera previously reported , we were able to sequence several haplotypes of the full-length VviAGL11 gene including a Sultanina mutant haplotype of Rizamat Gs. The Rizamat Gs VviAGL11 sequence showed two SNPs in non-coding regions and only one SNP (arginine-to-leucine substitution site in VviAGL11) with several indels at the microsatellite repeat regions in non-coding regions. Moreover, the Rizamat Gs sequence grouped with seedless VviAGL11 mutant sequences in our phylogenetic tree . However, among the approximately 300 SNPs and indels detected, only eight SNPs were located in coding regions, suggesting that coding regions have been well conserved. Of the eight, only two including the 197 arginine-to-leucine substitution site  was obtained due to a problem with Rizamat Wj and a Thompson Seedless and Sultanina pair was generated after downloading Sultanina resequencing data that were publicly available. Besides the problem for phenotyping of seeds in Rizamat Wj, as Rizamat Wj grouped together with seedless accessions in the tree constructed from the analyses  and 3. TSDI-locus-residing chromosomal region, as a major dominant seedless-regulating QTL reported by numerous studies , which are commonly used for measure of population differentiation but may not be optimal for multilocus allele frequency differentiation . Interestingly, an SNP with the highest -log10 P value of 5.051 in the highest peak was the arginine-to-leucine substitution site in VviAGL11 identified as a causal mutation of the SDI locus. Comparison between XP-CLR and logistic association results showed that the majority of peaks were overlapping with each other. However, chromosome 19 contained two high peaks in logistic association scan while it did not contain a selective sweep and, vice versa, the end of chromosome 11 contained no significant peak in logistic association scan, however it did contain a high selective sweep peak. Those non-overlapping peaks between XP-CLR and logistic association scan results might be false positives generated by population structure or kinship. Thus, we focused on examining variants under the overlapping peaks in detail, with an expectation that we might pinpoint candidate causal genes for the postulated recessive genes.Although our population size was only 28, consisting of 15 seedless and 13 seeded grape accessions, seedless-regulating SNPs might be more strongly associated with the seedlessness trait than other SNPs. Thus, we attempted to detect SNPs associated with seedlessness using a case–control logistic mixed model association test implemented in GENetic EStimation and Inference in Structured samples (GENESIS) software . In their milestone inheritance study, SDI. When the SDI gene is heterozygous or homozygous dominant, expression of the seedless phenotype requires a minimum of two genes to be homozygous recessive. According to this model, most of the causal mutation sites at the SDI locus for seedless accessions should be homozygous or heterozygous non-reference alleles as Pinot Noir of the grape reference genome sequence accession is seeded, and most of causal mutations at the recessive seedless-regulating genes should be homozygous non-reference alleles.Our SIFT (Sorting Intolerant From Tolerant) analysis . Of the seven, genotype distribution of only one SNP  in the seedless and seeded grape population was consistent with that predicted by the inheritance model of seedlessness. This SNP was heterozygous in all 15 seedless accessions tested, reference homozygous in 11 of 13 seeded accessions tested, and not called in 2 of the 13. Interestingly, it was the arginine-to-leucine substitution site identified as a causal mutation in VviAGL11 encoding the SDI locus (http://www.vivc.de/)  . Thus, o10P values higher than 2.5 from our logistic association and were non-reference homozygous recessive in more than 10 of 15 seedless accessions tested. Group II included SNPs that showed -log10P values higher than 2.5 from our logistic association and were non-reference homozygous recessive in less than 10 of 15 seedless accessions. Group III, which was excluded from further consideration, included SNPs that showed -log10P values of lower than 2.5 from our logistic association test. Based on these criteria, 13 SNPs in four candidate selective sweeps were assigned to group I, whereas 21 SNPs in eight candidate selective sweeps were assigned to group II.To examine variation patterns in the candidate selective sweeps of recessive seedless-regulating genes, we classified all SNPs predicted to be deleterious into three groups using SIFT program in the candidate regions (Table S7). Group I included SNPs that showed -logVitvi01g00455 was annotated as a cytosolic phosphoglucomutase (cPGM), a regulator of seed development in Arabidopsis   functions as a genetic factor associated with seed dormancy. However, it is unlikely to be one of the recessive seedless-regulating candidate genes because the candidate Vitvi08g01528 with a group I SNP resides at the same candidate selective sweep. The 41 SNP sites selected tended to show homozygous non-reference genotype in none of 13 seeded accessions tested. Several SNPs were non-reference homozygous in only one or two of 13 seeded accessions tested. Interestingly, Rizamat Gs showed homozygous non-reference genotype in all six selected SNPs on chromosome 1. In the phylogenetic tree constructed from the chromosomal region (SDI-residing chromosomal region in Sultanina.Most of the group II SNPs appeared to be homozygous non-reference genotypes in less than five of 15 seedless accessions tested, thereby excluding the possibility of the SNPs-carrying genes for recessive seedless-regulating candidate genes. Five group II SNPs at five genes showed homozygous non-reference genotypes in seven or eight of 15 seedless accessions tested. Of the five genes, only l region , RizamatV. vinifera, which is the species used to obtain the reference genome sequence. Genome resequencing data of 472 Vitis accessions published while conducting this study included 108 wild relative grape species and 109 interspecific hybrid accessions. However, variants called using HaplotypeCaller were not validated by an experimental approach. In this study, we show that UnifiedGenotyper is better than HaplotypeCaller for SNP calling in hybrids and wild relative species. We also added an additional step to convert erroneously called homozygous SNPs to heterozygous SNPs based on allelic balance values. Therefore, our modified variant calling pipeline should provide insight for improvement of current variant callers to facilitate molecular genetic studies including marker-trait association studies for interspecific hybrids and wild relative species.Development of variant calling methods from genome resequencing data have typically revolved around humans, livestock animals, and major crop plants, most of which contain diploid genomes. Numerous variant callers including UnifiedGenotyper and SAMtools have been proposed for variant calling of polyploidy species that are prevalent in the plant kingdom. Although none of these software packages is definitively recommended over others, many studies have successfully used the called variant data to address important biological questions (SDI locus showed that the majority of peaks detected were overlapping. Trees constructed from peak-residing chromosomal regions tended to separate seeded and seedless grape accessions. Several of 30 peaks were located at the same physical locations as previously reported for minor QTL identified using seedless phenotypes in full-sibling F1 populations (Vitvi01g00455, Vitvi08g01528, and Vitvi18g01237, as associated with the recessive seedless-regulating genes. It is difficult to pinpoint a causal gene underlying a weak recessive gene even with a large segregating population. In this study, based on analysis of high-density genome-wide SNP data, we have pinpointed several good candidate seedless-regulating genes that can be tested using techniques such as mutagenesis, transformation, and gene editing in the near future. This was made possible due to millions of high-quality genetic variants detected using our modified variant calling pipeline.Several lines of evidence suggest that our predicted recessive genetic loci are likely real. First of all, both XP-CLR and logistic association peaks with a shared high peak at the well-characterized In this study, we have provided a large genome-wide variation dataset for seedless and seeded grape accessions with diverse genetic backgrounds. Because our initial variant calling efforts suggested that the current widely used variant calling pipeline had problems with interspecific hybrids and wild relative species, we modified the pipeline. Variation data from the modified pipeline were validated by Sanger sequencing. Our population structure and phylogenetic analysis using the resultant high-quality SNPs strongly supported known pedigree information as well as taxonomic grouping of these sequenced grape accessions, indicating that our modified pipeline was sound. The resulting millions of high-quality variations also provided an opportunity both to validate a dominant seedless-regulating gene and to predict recessive seedless-regulating genes. Investigation of variation patterns at significant peaks allowed us to predict candidate causal genes that could regulate the seedless trait. Taken together, data generated in this study represent such a diverse grape genome background. They can now be used as dense markers of genome variation for marker-assisted mapping of important grape traits as well as for pinpointing agronomically important genes in grapes.


Ascites syndrome is a hypertensive, multifactorial, multigene trait affecting meat-type chickens imposing significant economic losses on the broiler industry. A region containing the CPQ gene has been previously identified as significantly affecting ascites phenotype. The region was discovered through whole genome resequencing focused on chicken chromosome 2. The association was confirmed through further genotyping in multiple broiler populations.The whole genome resequencing analyses have now been extended to the current chicken genome assembly. DNA samples were pooled according to gender and phenotype and the pools subjected to next generation sequencing. Loci were identified as clusters of single nucleotide polymorphisms where frequencies of the polymorphisms differed between resistant and susceptible chickens. The chickens are an unselected line descended from a commercial elite broiler line. Regions identified were specific to one or both genders. The data identify a total of 28 regions as potential quantitative trait loci for ascites. The genes from these regions have been associated with hypertensive-related traits in human association studies. One region on chicken chromosome 28 contains the LRRTM4 gene. Additional genotyping for the LRRTM4 region demonstrates an epistatic interaction with the CPQ region for ascites phenotype.a priori assumptions.The 28 regions identified were not previously identified in a multi-generational genome wide association study using 60k Single Nucleotide Polymorphism panels. This work demonstrates the utility of whole genome resequencing as a cost effective, direct, and efficient method for identifying specific gene regions affecting complex traits. The approach is applicable to any organism with a genome assembly and requires no  AS is associated with accumulation of fluid in the abdominal cavity leading to death –6. AS isic rates –3, 6–8. ic rates . The priic rates , 11. AS ic rates , lightinic rates , temperaic rates , feed reic rates , and dieic rates –18. Howeic rates –27. Idenhttps://www.ncbi.nlm.nih.gov/gap/phegeni). Additional genotyping in a broiler research line, and three commercial broiler lines, demonstrated an association of homozygotes for the non-reference SNPs in this region with resistance in male birds. The CPQ gene region is the first demonstrated marker for resistance to AS in broilers [Multiple Single Nucleotide Polymorphism (SNP) panel based genome wide association studies (GWAS) were used to identify potential genetic markers on chromosome 1, 2, 4, 9, and Z for this disease –33. Unfobroilers . The WGRNext-generation WGR data was generated for eight pools of 10 individual DNA samples representing two biological replicates for each gender for both AS resistant and susceptible birds from the unselected, Relaxed (REL) line. The REL is descended from an elite commercial line . Each pohttps://www.ncbi.nlm.nih.gov/gap/phegeni) for phenotypes that had been identified in human GWAS as associated with traits possibly contributing to PHS or AS. The most frequent traits from the PheGenI output are presented in Fig. To identify potential QTLs for AS phenotype, the difference between the frequency of the SNP in resistant versus susceptible phenotypes were calculated (resistant SNP frequency - susceptible SNP frequency). The SNP frequency differences were visually inspected using the Integrative Genomics Viewer  accordThe region on chromosome 2 for CPQ has already been extensively analyzed for association with ascites phenotype and confirmed for association of the non-reference SNPs with ascites resistance in males . The regP-value=0.047). There was also a significant adjusted P-value (P=0.0083) for all samples, combining male and female data  which gave SNP frequency differences of approximately 50% in males and 15% in females. These SNPs are in the intergenic region, 3.5 kbp from the 3’ end of the LRRTM4 gene. The exonuclease assay was used to genotype more than 600 archived DNA samples from REL birds previously phenotyped for AS susceptibility in the hypobaric chamber , 33, 34.ta Table . In bothP=0.033), contrary to the LRRTM4 genotype alone. The RRCC genotype was the most abundant genotype in the samples (n=131). Whereas, for most combined genotypes the males were nearly 50:50, 85% of the 61 RRCC males were resistant to AS . The expression data indicates that LRRTM4 in broiler heart is expressed at about 0.016 (0.56) the level as TBP. The RNAseq summary in NCBI for human LRRTM4 (Gene- Full Report) suggests that LRRTM4 expression is primarily restricted to the brain. Human RNAseq RPKM values for LRRTM4 are brain 5.6±1.1, heart 0.05±0.03, liver 0.033±0.012, lung 0.24±0.14, and testis 0.024±0.011. Based on the RPKM values for human TBP and LRRTM4 for these tissues, and an average ΔCt of 15.2±1.5 for TBP in the RT-qPCR analyses from REL broilers, amplification for all these tissues for LRRTM4 was expected within the 25 qPCR cycles employed. The relative expression in human heart of LRRTM4 to TBP is 0.0154 which is in agreement with the ΔΔCt values in heart for chickens. Thus, LRRTM4 expression in lung, liver, brain, and testis, is apparently much lower than the expression observed in human. Further, the two alternative alleles of LRRTM4, distinguished based on SNP differences distal to the 3’ end of the gene, do not appear to significantly differ in expression levels. Therefore, the contribution of LRRTM4 to AS more likely result from differences in the polypeptide sequences, or post-translational processes.LRRTM4 gene expression for each of the homozygous genotypes was measured by reverse transcriptase qPCR (RT-qPCR) for heart, lung, liver, brain, and testis samples. TATA-binding protein (TBP) served as the reference, as TBP is recommended across multiple human tissues , recommeThe poultry industry has fueled the affordable production of chicken by selecting for economically important traits such as growth rate, feed conversion, and meat yield. This rapid growth and muscle deposition make chicken an excellent model for human idiopathic arteriole hypertension . About 3Multiple investigations have estimated high heritability of AS susceptibility which led our group to pursue the genetic basis of AS , 43, 44.The CPQ work led to the expansion of the WGR analyses to the rest of the assembled genome. This identified 27 additional regions for ascites phenotype Table . The 28 Further investigation of one of these regions on chromosome 22 associated with the LRRTM4 gene supported an association with AS phenotype. LRRTM4 is a member of the leucine-rich-repeat (LRR) transmembrane protein family, whose members are involved in synapse development and maintenance of the nervous system . The LRRNone of the current regions reported above have appeared in any previous GWAS studies on AS –33. IdenCPQ and LRRTM4 are gender-biased markers for ascites resistance in broilers. MAS studies are in progress to confirm the contributions of these two gene regions for ascites syndrome resistance. The additional 26 regions are available for further investigation as potential QTLs associated with ascites syndrome. The current findings may also be relevant to pursuit of the genetics underlying idiopathic pulmonary arteriole hypertension, as well as other forms of hypertension, in humans.Gallus gallus genome .All genomic positions presented are relative to the May 2018 assembly of the th generation of unselected descendants from an elite commercial broiler stock [All animal procedures were approved by the University of Arkansas Institutional Animal Care and Use Committee under protocols 15039 and 15040. Birds utilized originated from a research line (REL) representing the 18er stock . The RELer stock . MortalioC. DNAs for next-generation sequencing were further purified by phenol-chloroform extraction, chloroform extraction, and ethanol precipitation. DNAs were quantified by Hoechst 33258 fluorescence in a GloMax .Genomic DNAs were isolated using a rapid isolation protocol  and archDuplicate pools of equal weights of 10 DNAs from each phenotype were pooled for each gender to construct 8 total pools, which were then submitted for Next Generation Sequencing library preparation. Libraries were sequenced, 2x125 bp paired-end on an Illumina HiSeq 2500, to generate approximately 66 Gb per library. Library construction and sequencing were performed by the Research Technology Support Facility at Michigan State University .The adapter-trimmed FASTQ sequence reads were mapped onto the May 2018 chicken genome assembly  using SeqMan NGen . Specific settings in NGen were SNP filter high, diploid genome, whole genome pipeline, and Illumina adapter scan. All other parameters were the default settings. The pools were coded as resistant (R) or susceptible (S), male (M) or Female (F), and biological replicate pools as 1 or 2 . Separate templated alignments for each pool were used for SNP identification and tabulation in ArrayStar (Lasergene Suite). The SNP data were exported to Excel for further analyses. The difference in the averages of SNP frequencies of replicates for each phenotype and gender were calculated and exported to .seg files for viewing in Integrative Genomics Viewer 2.7.4  by chromhttp://bioinfo.ut.ee/primer3-0.4.0/primer3/), and synthesized by Integrated DNA Technologies . Conditions for qPCR exonuclease assays were optimized for each SNP locus and then used to genotype individual DNAs from additional birds phenotyped for ascites in the hypobaric chamber. DNAs used were the same as those used previously for genotyping for CPQ [Primers and probes for exonuclease genotyping Table  were des for CPQ .Table 42, 30 μg/ml BSA), 0.2 mM MgCl2, 0.2 mM dNTP, 0.2 μM each forward and reverse primers, 0.05 μM each probe, 2.5 units Taq polymerase, and 2 μl DNA. cycling was denaturation at 90°C 3 mins, 10 cycles of 90°C 15s, 55°C 15s, 72°C 1 min, followed by another 30 cycles of 90°C 15s, 55°C 15s, 72°C 1 min with plate read. To verify qPCR genotype calls, relevant qPCR products were purified using RapidTip  and quantified by Hoechst 33258 fluorescence as above. DNAs were submitted for capillary sequencing by Eurofins MWG Operon . Sequence data (.ab1 files) were aligned to the reference Jungle Fowl sequence using SeqMan Pro software (DNASTAR) for SNP scoring.Reactions (20μl) included 1x Taq buffer . The mixtures were incubated at 50°C for 50 min and the reaction was terminated at 65°C for 5 min. The first strand cDNA was then diluted into a qPCR mixture (as described above) containing 1x EvaGreen dye . Cycling was as for exonuclease assays (above) but were followed by a high resolution melt curve from 65 to 90oC at 0.1oC steps. RT-qPCRs were run in triplicate with TATA-box binding protein (TBP) gene as the reference, and all ΔΔCt values were relative to TBP [Specific gene expression was assayed by RT-qPCR for heart, liver, brain, lung, and testis RNA previously extracted from broiler tissue samples . Total Re to TBP . Fold che to TBP .P-values for allele/genotype frequencies greater than 10%. P-values were then multiplied by the number of alleles/genotypes to generate a simple Bonferroni adjusted P-value. Deviation from expected was considered statistically significant where adjusted P-values < 0.05. Gene expression differences for alleles were based on P-values from Excel t-tests .Genotype frequencies from exonuclease assays were separately calculated for each phenotype and gender. The expected genotype counts for each phenotype were computed from the observed genotype frequencies from the entire population. Microsoft Excel chi-square test function was performed on the count versus expected to calculate