Polydactyly is one of the most common congenital limb dysplasia in many animal species. Although preaxial polydactyly (PPD) has been comprehensively studied in humans as a common abnormality, the genetic variations in other animal species have not been fully understood. Herein, we focused on the pig, as an even-toed ungulate mammal model with its unique advantages in medical and genetic researches, two PPD families consisting of four affected and 20 normal individuals were sequenced.ABCC4 and it encodes a transmembrane protein involved in ciliogenesis. We found that the affected and normal individuals were highly differentiated at ABCC4, and all the PPD individuals shared long haplotype stretches as compared with the unaffected individuals. A highly differentiated missense mutation (I85T) in ABCC4 was observed at a residue from a transmembrane domain highly conserved among a variety of organisms.Our results showed that the PPD in the sampled pigs were not related to previously reported variants. A strong association was identified at ABCC4 as a new candidate gene and identifies a missense mutation for PPD in pigs. Our results illustrate a putative role of ciliogenesis process in PPD, coinciding with an earlier observation of ciliogenesis abnormality resulting in pseudo-thumb development in pandas. These results expand our knowledge on the genetic variations underlying PPD in animals.This study reports  Polydactyly is one of the most commonly observed congenital limb malformations and ciliopathies. This abnormality is characterized with additional digits in fingers or toes and has been reported to be in association with dozens of genes and complicated diseases . PolydacSHH gene in the anterior part of limb bud is the main causative agent of PPD. Normally, SHH signal is only expressed at the posterior portion of ZPA region of limb bud [SHH located in the fifth intron of LMBR1 gene, results in the ectopic expression of SHH responsible for PPD. Many single nucleotide polymorphisms (SNPs) in ZRS have been reported to be in association with PPD [Previous studies have shown that the vast majority of polydactylies are associated with Sonic Hedgehog (SHH) signaling pathway and ciliogenesis process . The SHHlimb bud , 9. The limb bud . Letticelimb bud –14 have with PPD .Gli3 polarizes the limb into anterior-posterior axis through the antagonism with HAND2 [SHH mediates the limb patterning by regulating Gli2 and Gli3, which act as full-length transcriptional activators (GliA) in the presence of SHH and are cleaved into a short form as a truncated repressor (GliR) in its absence [Gli3 mutant limbs are characterized by severe polydactyly and associated with ectopic anterior expression of Hoxd gene [In the early stages of embryonic development, th HAND2 , 17. SHH absence , 19. Glioxd gene , 21.Gli gene family [DYNC2H1 in retrograde intraflagellar transport (IFT) have caused the short-rib polydactyly syndrome in human [CYLD (cylindromatosis) mediates ciliogenesis by deubiquitinating Cep70 and inactivating HDAC6, and CYLD knockout mice exhibit polydactyly [EN2 [MIPOL1 [TWIST1 [PITX1 [Additionally, as an important signal transduction and sensory center in eukaryotic cells, cilia play a crucial role in SHH signal transduction and regulation of its downstream target genes –24, suche family . The dise family , 26. Abnin human . Moreovein human . CYLD  (NCBI gene access ID: 100152536) strong association with PPD phenotype. Furthermore, a missense mutation in ABCC4 was detected but not in large normal samples. These findings highly prompt us in hypothesizing that ABCC4 is probably a new candidate gene for PPD through the regulation of ciliogenesis.In this study, four pigs from two pedigrees of Large White pig breed were identified with PPD deformity. We aimed at screening for the genomic variants of PPD phenotype through whole genome association studies based on high quality resequencing data. Genetic differentiations between PPD affected and normal groups were calculated and identification of ATP Binding Cassette Subfamily C Member 4  were euthanized to get the hooves for radiograph analysis. The detailed phenotypes and radiograph information of F1-a1 and F1-a3 are shown in Fig. Sus scrofa 10.2) is 88.79%  to 38.34× (F0–4) and on average 34.10×, and the average coverage with respect to the pig reference genome sequence  sequences, transcription factor binding sites and other highly conserved sequences surrounding the PPD affected individual’s candidate genes. Variants in intergenic region (IGR), intron and other regions were not significantly differentiated between PPD affected and normal pigs, so we ruled out the possibility of the previously reported candidate genes as the causing loci for this PPD phenotype in our study.ABCC4 gene on SSC11 (Sus scrofa chromosome 11) was highly associated with PPD phenotype . We calculated the Cross Population Extended Haplotype Homozygosity (XP-EHH) value  between ype Fig. a. We furST) [ABCC4 gene on SSC11 was highly differentiated between the affected and normal groups based on both SNPs  based on whole-gnome SNPs  of INDELs   analysesNPs Fig. b and INDNPs Fig. c. All hiNPs Fig. b are lisABCC4 on SSC11, including the missense mutation . The top ten highly associated SNPs (P ≥ 1.19 × 10− 5) are listed in Table Furthermore, whole genome association study was performed to further analyze the effective SNPs of this deformity based on the basic case-control association test model of PLINK  Fig. 2dd2d. We aABCC4 gene as a new candidate gene for PPD abnormal. Moreover, previous findings revealed that Iguana/DZIP1 (DAZ Interacting Zinc Finger Protein 1) is an important protein coding gene located in around 800Kb upstream of ABCC4  and long-range structural variants (SV) analysis to explore putative association between PPD and CNVs/SVs. The genome coverage, sequencing depth and mapping quality at the identified putative loci were analyzed in all individuals using the Integrative Genomics Viewer (IGV) . The resSHH and LMBR1 (ZRS region was covered)) and ABCC4, pairwise FST analysis was carried out based on SNPs and INDELs  on the third exon of ABCC4, which resulting in the change of isoleucine to threonine (I85T). Firstly, we captured the 60 bp sequence around this SNP and aligned it to the Sus scrofa 10.2 and Sus scrofa 11.1 reference genomes to further ascertain the position of this missense mutation and eliminate the mapping error. The results showed that the identity of sequence alignment between 11:70439350–70,439,409 of Sus scrofa 10.2 and 11:64267163–64,267,222 of Sus scrofa 11.1 is up to 100%  identified by association study was located in the intron region, and interestingly, all the affected individuals were homozygous wild-type at this locus, while the normal individuals were mutant genotypes at this locus. Besides, we have verified this SNP in the large normal population  and found that the majority (82.97%) of these no-missing normal individuals were homologous for ancestral allele (0/0), and some of them were heterozygous  or homozygous  for the derived allele  in the large unpublished samples of normal individuals  from our lab , functional similarities  and the availability of disease models  . Pig hasSHH signal of anterior-posterior axial plays an important role in the early limb development and patterning. The downstream transduction of SHH signal is received and transported by cilia through a complex network . As an iABCC4, a member of the ATP-binding cassette transporter family is also known as multidrug resistance-associated protein 4 (MRP4) [ABCC4 encodes an important transmembrane transporter known to transport PGE2 and other molecules across cellular membranes. Further, it is also involved in ciliogenesis by mediating PGE2 signaling acts through a ciliary G-protein-coupled receptor, EP4, to upregulate cAMP synthesis and increase anterograde IFT [ABCC4 results in the disorder of PGE2 transmembrane transport and further results in the misfunction of the stimulation of anterograde IFT through EP4 and protein kinase A (PKA). Jin et al. [ABCC4 showed cilium loss and cilium-associated phenotypes in zebrafish, including ventrally curved body axis, hydrocephalus, abnormal otolith number and laterality defects of the brain and other organs.4 (MRP4) . Most imrade IFT –53. As an et al.  reportedABCC4 were highly associated with PPD phenotype. Derived allele frequency analyses around ABCC4 revealed that there were significant differences between affected and normal groups. Furthermore, a homozygous missense mutation was identified in all affected individuals but not in normal groups. More interestingly, we did not detect this mutation in large unpublished samples. Based on the important role of cilia in SHH single transduction and the function of ABCC4 in cell ciliogenesis, we firmly suggest ABCC4 as a new candidate gene for polydactyly in pigs. As the sample size in this study is relatively small and there are no additional expression data to further validate our results, we therefore suggest additional experimental verification in future studies.In our study, based on two uncorrelated families’ association analysis showed that some SNPs and INDELs in ABCC4 as a new candidate gene involved in PPD regulation possibly through ciliogenesis process. Our analysis detected a highly associated missense mutation in all affected individuals but not in normal groups. Prediction of protein structure and function with different methods showed that the mutated residue is located in an important domain for binding of other molecules. Mutation of the residue might have disturbed this function, resulting in the inactivation of ABCC4 and further into the disorder of ciliogenesis.In this study, we identified To the best of our knowledge, this study is the first to report on the genetic variation identification of PPD in artiodactyls, and these results expand our understanding of PPD in further studies of limb malformation and enrich our knowledge on cilium as an important signaling center during vertebrate development. Finally, this study serves an example to study human diseases through the whole genome sequencing of pig as an animal model.Three sibling Large White pigs from one litter were detected having preaxial polydactyly in a farm in Hebei province of China Fig. , and allEar tissue samples were collected from all the 17 available individuals and stored at − 80 °C. For each sample, 30 mg ear tissue was used to extract genomic DNA with the standard phenol-chloroform method. Quality and quantity were assessed by Nanodrop spectrophotometer 2000 and gel electrophoresis experiment. Library construction for re-sequencing was performed according to the Illumina library prepping protocols  with the insert size of 380 bp. Pair-end (PE) reads length of 150 bp were generated from the resequencing libraries on the Illumina Hiseq X Ten platform , and all individuals were re-sequenced above 30× depth of coverage.Sus scrofa 10.2 [http://broadinstitute.github.io/picard/). Finally, read depth and coverage of each individual were estimated based on the results of SAMtools ver. 1.9 [Clean reads were trimmed from raw reads that were pre-processed to remove index adaptors and low-quality reads. Quality control for removing the low-quality reads was done based on the following criteria: Up to 10% of the read bases include “N” content in each sequenced read, up to 50% of the read bases include low quality (Q < = 5) bases content in any sequenced reads. After trimming, clean reads of each sample were aligned to the pig reference genome ofa 10.2  using BWofa 10.2  with defofa 10.2  were usever. 1.9 .RealignerTargetCreator, IndelRealigner, and BaseRecalibrator tools in the Genome Analysis Toolkit (GATK) (ver. 3.7–0-gcfedb67)  were useftp://ftp.ensembl.org/pub) with a custom Perl script. According to the genomic position, SNPs were classified into protein coding (synonymous or nonsynonymous) regions, introns, UTR and IGR. Moreover, to further annotate the SNPs in putative regulatory regions, we downloaded human genome annotation data from the ENCODE (Encyclopedia of DNA elements) [All called variants were annotated with GTF file downloaded from the Ensembl website (lements)  project lements) . Haplotylements) .Based on the identified SNPs and INDELs in this study, the candidate loci which reported previously were graphically showed with Integrative Genomics Viewer (IGV)  to scan ST [ST of the three candidate genes  were calculated based on every SNP and INDEL loci.XP-EHH  scores wST  was estiST . In addiFor analyzing of SNP effects related to the deformity of this two families, four affected and 13 available normal pigs from two families were compared using the basic case-control association analysis and family-based association test of PLINK v1.07 . Meanwhihttp://gpcr.biocomp.unibo.it/cgi/predictors/I-Mutant2.0/I-Mutant2.0.cgi) to predict the protein stability changes upon the mutation of rs791053563, the parameter of temperature was set to 38°Cand pH was 7.0. In addition, we used PROVERAN web server [http://provean.jcvi.org/index.php) and HOPE [http://www.cmbi.ru.nl/hope/) to predict this amino acid substitution’s impact on the biological function and structural effect of the protein. PROVERAN [ABCC4 with STRUM [https://zhanglab.ccmb.med.umich.edu/STRUM/). All the prediction was based on the protein sequence of UniPortKB (ID: A0A287A6F6_PIG) or Ensembl (Transcript ID: ENSSSCT00000037963.1).In order to further confirm the amino acid substitution (I85T) impact on protein structure and function, we used I-Mutant2.0   with annotation.Additional file 4: Table S4. The highly differentiated windows based on whole-genome SNPs. Chr: Chromosome; Win_Start: Window’s Start; Win_End: Window’s End; N_SNPs: Number of SNPs.Additional file 5: Table S5. The highly differentiated SNPs within the highly differentiated windows. The bold italic highlighted rows (*) represent the SNPs which identified in association test and for DAF analysis.Additional file 6: Figure S1. The distribution of the highly differentiated SNPs.Additional file 7: TableS6. The highly differentiated regions based on INDELs between PPD affected and normal pigs. Chr: Chromosome; Win_Start: Window’s Start; Win_End: Window’s End; N_SNPs: Number of INDELs.Additional file 8: Figure S2. Plots of whole-genome screening of putatively loci which associated with PPD in the large family. (A) Genome wide SNPs plot of XP-EHH between the 3 affected (excluded F0–4) and 13 normal individuals. (B) FST plot of the whole selected SNPs between 3 affected and normal individuals. (C) FST plot between the 3 affected and normal individuals based on whole selected INDELs. (D) Manhattan plot of whole-genome association analysis based on whole SNPs of PPD phenotype.Additional file 9: Figure S3. Schematic diagram of the relative position of ABBC4 and DZIP1. Arrows indicate the direction of gene transcription.Additional file 10: Figure S4. The FST comparison between three candidate genes based on all SNPs and INDELs. (A-C) FST plot based on all SNPs. (D-F) FST plot based on all INDELs.Additional file 11: Table S7. Phased genotyping results of the top ten highly associated SNPs. The bold italic highlighted columns represent the affected individuals. The bold italic highlighted rows (*) represent the missense mutation SNP which located in the third exon of ABCC4.Additional file 12: Table S8. The genotyping of the highly differentiated INDELs between PPD affected and normal pigs. Pos: Physical position; Ref: Reference allele; Alt: Altered allele; GT_A: Genotypes of cases; GT_U: Genotypes of controls; The number “0” represent reference allele and “1” represent altered allele; The number behind the genotype in brackets represent the number of this genotype’s individuals; DAF_A: Derived allele frequency in cases; DAF_U: Derived allele frequency in controls; IGR: intergenic region.Additional file 13: Table S9. The allele frequency of the top ten highly associated SNPs between population in this study and unpublished data. Chr: Chromosome; Pos (Ssc10.2): Position in Sus scrofa 10.2; Ref: Reference allele; Ref_Freq_A: Reference allele frequency of population in this study; Ref_Freq_B: Reference allele frequency of population in unpublished data; Alt1: Alternative allele 1; Alt1_Freq_A: Alternative allele 1 frequency of population in this study; Alt1_Freq_B: Alternative allele 1 frequency of population in unpublished data; Alt2: Alternative allele 2; Alt2_Freq_A: Alternative allele 2 frequency of population in this study; Alt2_Freq_B: Alternative allele 2 frequency of population in unpublished data.Additional file 14: Table S10. The genotype of the top ten highly associated SNPs between population in this study and the 559 unpublished data set. Pos: Physical position; Ssc: Sus scrofa; Ref: Reference allele; Alt: Altered allele; GT_A: Genotypes of affected cases; GT_U: Genotypes of unaffected controls; GT_C: Genotypes of validation data set; The number “0” represent reference allele and “1” represent altered allele; The number behind the genotype in brackets represent the number of this genotype’s individuals; IGR: Intergenic region. The bold italic highlighted rows (*) represent the SNPs which identified by genome scanning (Additional file


Triticum aestivum) single nucleotide polymorphisms (SNPs) and genotyping data. The CerealsDB website has been designed to enable wheat breeders and scientists to select the appropriate markers for research breeding tasks, such as marker-assisted selection. We report a large update of genotyping information for over 6000 wheat accessions and describe new webtools for exploring and visualizing the data. We also describe a new database of quantitative trait loci that links phenotypic traits to CerealsDB SNP markers and allelic scores for each of those markers. CerealsDB is an open-access website that hosts information on wheat SNPs considered useful for both plant breeders and research scientists. The latest CerealsDB database is available at https://www.cerealsdb.uk.net/cerealgenomics/CerealsDB/indexNEW.php.CerealsDB  is an online repository of mainly hexaploid wheat ( T. aestivum) is a cereal crop grown worldwide and is a major source of nutrition for both humans and livestock. Developing novel varieties of wheat that result in increased yield combined with other important traits such as disease and drought resistance that could potentially contribute to its long-term sustainability is necessary to guarantee the continued supply of this economically important crop.The rapid expansion in human population size has driven food security up the global political agenda, making it one of the primary issues that governments are keen to address. Bread or hexaploid wheat (Single nucleotide polymorphisms (SNPs) are defined as single base pair (bp) changes in a DNA sequence and are the most common form of sequence variation between individuals within a species. As a result of their high frequency in the wheat genome, SNPs have been used to develop markers such as KASP assays or array probes for use in marker-assisted selection of important agronomic traits that breeders are attempting to introgress into future elite wheat cultivars .CerealsDB initially hosted a dataset of wheat expressed sequence tag sequences and evolved to include a database of Diversity Arrays Technology markers, validated SNPs and links to a draft genome of the wheat variety ‘Chinese Spring’. The second iteration of the site aimed to provide a more intuitive browsing experience and displayed the underlying data in a format that was easy to understand and could be manipulated by non-expert users .The third version of CerealsDB was released in 2016, following an extensive redesign to accommodate new SNP datasets validated on a range of genotyping platforms. These included KASP and array-based platforms , 4 and mHere, we present the latest features and data updates to the CerealsDB site, including webtools that allow users to search for information on wheat accessions, tools that cross-reference SNPs that have been validated on multiple genotyping platforms and a new database that allows users to search for pre-identified quantitative trait loci (QTL), which link the genotypic information to the phenotypic data. We also present online phylogenetic trees, based on Axiom® array genotype scores, which allow an exploration of the genetic relationships of the scored accessions, and tools to explore putative introgressions between elite wheat varieties and wheat relative and progenitor species.SNPs from the Axiom® genotyping platform are currently the most abundant category of SNPs in CerealsDB with 819 571 held in the database. These SNPs are located on 91 848 wheat genome contigs that were assembled from 454 sequence data from normalized and non-normalized cDNA libraries prepared from RNA from wheat variety ‘Chinese Spring’ line 42  that contained SNPs and chromosome 6B contained the least number of contigs (4775).Axiom® SNPs have been derived from wheat exonic sequences and are therefore strongly associated with the gene content in the wheat genome. We have improved the utility of these SNPs by annotating their surrounding sequence by homology searches against common protein databases using BLASTX .The number of SNP sequences that could be annotated with a top BLASTX hit with Embryophyta protein database was 513 847 (62.7%) and the number that returned a ‘No hit’ was 305 725 (37.3%). There were 139 791 (17.1%) of these SNP sequences that produced an alignment to a ‘hypothetical protein’, and 98 778 (12.1%) to ‘predicted protein’, making these the most common annotations.https://www.ebi.ac.uk/QuickGO/api/index.html). This script parses the UniProt protein accession and searches QuickGO. The GO results were then parsed to extract the GO accession.The contig sequences in which each SNP is located  were aligned to the NCBI non-redundant (nr) database using default BLASTN parameters and the top hits were parsed. The putative protein sequences derived from the top BLAST hits were parsed for their GenBank accession codes, which were then used to query the UniProt REST API using custom Perl scripts to enable programmatic access to Gene Ontology (GO) information. A custom Perl script was written to automate the submission of accession codes to the QuickGO REST web services . There were 274 569 (33.5%) SNP contigs that could not be annotated as they did not produce an alignment to the nr database. Of these remaining contigs, a significant proportion (171 392 or 20.9%) were categorized as coding for hypothetical proteins.Genes encoding disease resistance proteins were well represented with 10 619 sequences. Other common annotations of gene functions included subtilisin-like proteases (1058), secologanin synthases (905), pentatricopeptide-repeat-containing proteins (951), SRG1 proteins (717), peptide transport proteins (703), peroxidase (572), receptor protein kinases (547) and MYB-related proteins (495) and cytochrome p450 proteins (3681). The top 25 represented proteins are displayed in et al.  and their probability of affecting protein function and thus phenotype. SNPs that have a FATHMM score less than −3 are considered to have a deleterious effect on protein function and are highlighted in red . The procedure for assigning FATHMM scores to SAVs was the following: if the contig the SNP belonged to was at least 15 amino acids in length, this allowed building a reliable hidden Markov model (HMM) for the sequence; for this purpose, the JackHMMER program from the HMMER 3 package  was run et al. . A searcAegilops mutica, A. speltoides, A. tauschii, T. timopheevii, T. urartu, Secale cereale, Thinopyrum bessarabicum, T. elongatum, T. intermedium and T. ponticum). The wheat-relative array was used to produce genotyping results for 278 wheat relative and progenitor species, which are held in CerealsDB. The total number of accessions genotyped for each of the Axiom® arrays and mapping populations is displayed in https://www.cerealsdb.uk.net/cerealgenomics/CerealsDB/array_info.php).CerealsDB hosts a range of genotyping datasets that were generated using different genotyping platforms. Many of these genotyping datasets are from mapping populations that consist of 3973 genotyped individuals across 25 mapping populations. The largest of these is the Axiom® 820K HD array SNPs with 476 varieties, and 458 individuals from two mapping populations  are currently genotyped. The Axiom® 35K breeders array has 1962 varieties genotyped along with 460 individuals from 23 mapping populations. There are also genotyping data for the 36 711 SNPs included on the Axiom® Wheat-Relative Genotyping array , based oA large proportion of the CerealsDB genotyping datasets were produced for 25 bi-parental mapping populations consisting of 3973 genotyped individuals across them. These populations are described in more detail in Genotyping data have been converted into variant call format (VCF) files for the 820K array, 35K wheat breeders and 35K wheat-relative arrays and are hosted on CerealsDB.CerealsDB now includes a varietal database providing descriptions on all wheat varieties and related species currently genotyped on the 820K HD array and 35K wheat-relative and breeders’ arrays. These data are especially useful to wheat breeders and consist of information on alternate names for the varieties, species ploidy, country of origin and the source of the material. The varietal database also includes information on the sample type, for example categorization of the variety as a ‘cultivar’—a wheat variety that has been generated via a breeding programme—in contrast to a ‘landrace’ that is locally adapted germplasm that has not been systematically bred. The ‘sample type’ field in the database includes information on specific wheat research or breeding programmes where applicable.According to the Axiom® genotyping array used, genotype data are stored in the ‘820K array’ or the ‘35K array’ tables in the database. Each table contains 12 columns and there are currently 975 varieties in the 820K array table, compared with 6144 varieties in the 35K array table. The varieties come from a total of 51 or 57 countries, respectively, with UK varieties being the most highly represented in both tables (512 in the 820K array table and 1971 in the 35K array table). The distribution of varieties by country is displayed in T. ponticum). The majority of the 35K array genotyping data are derived from hexaploid wheat varieties (6080); however, there are also data for 37 tetraploid species and 11 diploid species. We were unable to assign a ploidy level to some species (5 on the 820K array and 16 on the 35K array) and have categorized them as ‘unknown’. There are 820K array data for 86 wheat relative and progenitor species  compared with the 35K array that has data for only 11 wheat relatives, all of which are varieties of A. tauschii.The 820K array data come from 855 hexaploid wheat varieties, 65 tetraploid wheat and wheat relatives, 47 diploid species and a single decaploid species  programs, field trials were conducted on a set of bi-parental populations . The seget al. . Populatet al. . Due to et al.  , without nitrogen treatment. The populations were phenotyped for plant height (PH) in centimeter as measured from the soil surface to the highest point of the plant and for heading date (HD) in days after 1 May, when in half of the plot, half of the ears had emerged from the flag leaf. QTL mapping was performed for trait data and genotype and map information, using the package QTL  SNPs that could be accurately assigned to a location on the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v1.0 whole genome assembly, compared with 31 926 (90.86%) for the 35K array.A VCF file was generated for SNPs in accordance with version 4.1 specification with allelic information included. Genotypes were encoded with the Chinese Spring reference allele assigned to 0 and the alternative allele assigned to 1. There were 475 wheat varieties and wheat relative/progenitor species for the 820K genotyping dataset and 1962 wheat varieties that had been genotyped using the 35K array.The VCF file was then submitted to the SNPhylo analysis pipeline, which excluded 664 280 (85.9%) SNPs due to their monomorphic nature, having a minor allele frequency less than 0.1 or a missing data rate greater than 0.1 (missing data are where a specific variety has failed to genotype IE encoded as −/− in the VCF file) compared with the 35K array genotyping data where 12 739 (39.9%) SNPs were excluded. The number of SNPs for each chromosome for the 820K array data is displayed in The Newick strings generated by SNPhylo were imported into the Phylocanvas  package The assignment of a physical map position to the SNP markers was achieved by BLAST searching the probe sequences to the IWGSC whole genome assembly v1.0 (https://wheat-urgi.versailles.inra.fr/Seq-Repository/Assemblies). The identification of putative introgressions was performed by comparing the genotype calls for each of the 339 hexaploid lines with those of the 116 wheat relatives and progenitors over a 10 SNP window and calculating a percentage match. Analysis of control introgression lines indicated that a relative match of 0.4 or higher was indicative of an introgression in a hexaploid background. This threshold was chosen based on the screening of known introgressions such as 1B/1RS.The next part of the introgression prediction pipeline takes the predicted scores for each chromosome from the previous step and uses a sliding window of 10 SNPs along the chromosome and calculates the average similarity scores for consecutive 10 SNP windows for each of the varieties, to identify putative regions of introgression along each chromosome. A summary file was then generated for each variety to identify regions of introgression for each chromosome. The summary file reports the chromosomal location and size of putative introgressions above 0.4 for each wheat-relative comparison. The analysis pipeline achieved this by looking for consecutive SNPs that had a relative match of 0.4 or higher and if the next SNP along did not have a relative match of 0.4 or greater, then the preceding SNP was classified as a small introgression. If successive SNPs all had a relative match of 0.4 or greater, then the distance between the first and last SNP with a match ≥0.4 was calculated to identify the approximate size of the introgression. For example, if five consecutive SNPs have a similarity score greater than 0.4, then the distance between the first and the fifth SNPs would be used to calculate the size of the introgression. In this way, it was possible to identify potential introgressions/deletions along each chromosome for every combination of hexaploid wheat variety and wheat relative/progenitor species.Copy number variation (CNV) analysis was performed using the Affymetrix CNV Tool software. CEL files from the Axiom® Wheat HD Genotyping Array were processed using Axiom® Analysis Suite as described above. The annotation file was generated using the Affymetrix Annotation Converter. CNV analyses were visualized in BioDiscovery Nexus Copy Number .Introgression and CNV data can be visualized using the ‘Introgression Plotter’ tools located in the right-hand menu on the axiom genotyping page.https://www.cerealsdb.uk.net/cerealgenomics/CerealsDB/webtools.phpAn explanation of updated webtools and their current URLs are displayed in While the previous iteration of CerealsDB included the genotyping data as downloadable Excel spreadsheets, the updated site now has a webtool that allows users to download genotyping information for a specific Axiom® SNP or for a specific wheat variety or wheat relative/progenitor species.In the first step, the users must decide if they want to get genotype information for a variety or for a SNP. If the latter has been selected, users can search for allelic scores for any SNP on an Axiom® array by selecting one of the two arrays (either 820K or 35K) in the top selection box and providing the Axiom® SNP code (e.g. AX-94381147). Once the SNP code has been submitted, the webtool will list all varieties scored for the selected array with both the variety code generated by the Affymetrix array analysis pipeline and the genotype called. In addition, users can download the data as a tab-delimited text file.The genotyping data can also be retrieved by searching for a specific variety in the first step mentioned above. This is accomplished by selecting an array or mapping population and then choosing a variety from a drop-down list; alternatively, users can enter a partial variety name and the field will autocomplete if a variety is present on that array (or mapping population). Users can then select genotyping results for probes on all chromosomes or a combination of chromosomes, and the webtool will display the genotype calls for the selected chromosomes, with the option of downloading the data in tab-delimited text format.The varietal database can be searched via a simple webtool that allows users to select varieties genotyped on either the 820K or the 35K Axiom® array. Varieties can be selected from a drop-down list or the name can be typed into the entry field that auto-completes if the variety is present on the array. Alternatively, a general search term can be entered in this field, such as the country of origin or the source of the germplasm and the tool will retrieve varieties annotated with this information. For example, typing ‘Australia’ and searching the 820K array currently retrieves six varieties that were sourced from this country.CerealsDB varieties have been linked to the Germplasm Resource Unit (GRU), a UK seed bank with a focus on crops, including wheat, brassica and peas. A recently developed API now connects CerealsDB to SeedStor, a database hosted at the GRU . This SeThe QTL database, currently holding QTL data identified in bi-parental populations of the DFW landrace NAM panel (wisplandracepillar.jic.ac.uk/results_resources.htm) can be searched online via a web interface. Currently, users can type in one of two phenotypes, ‘Plant height’ or ‘Heading time’, and information on all QTL present for either of these phenotypes is displayed in a table, including information on chromosome location, LOD score, QTL confidence interval start and end markers and the QTL peak marker, along with positions on the genetic map . ClickinRht24 on chromosome 6A -reducing locus osome 6A  as ‘Rht2 locus.’ .Rht24. Predicted alleleic effects are 2.6–5.2 centimorgans, with the increasing effect coming from the landrace parents. Depending on the combination with other Rht genes, and on the target environment of the breeding programme, these alleles could be useful to achieve an optimum PH. The usefulness of the QTL database will grow with the number of QTL for different populations, traits and environments deposited here.It seems reasonable to hypothesize that users can find different alleles for this locus in a QTL database as it has been speculated  that thiBrachypodium, oryzae (rice), Sorghum and of wheat (IWGSC RefSeq v1.0 whole genome assembly). This information can be useful for homology-based cloning approaches and rapid identification of candidate genes.A webtool has been developed to find wheat SNP marker sequences on the genome sequences of related species. This webtool allows users to obtain the physical location of an SNP marker on the reference genomes of Users must enter an Axiom® SNP ID and can select a range in bps to retrieve all stored Axiom® marker SNPs within that range on either side of the query SNP. The Axiom® SNP marker sequences have been mapped to the reference genomes using BLASTN, and an e-value cut-off can be defined to exclude less reliable alignments. Users can choose a genome reference sequence and the SNP along with upstream and downstream flanking SNPs are displayed with positional information and the e-value score for that BLAST SNP alignment to the reference sequence. Other useful information includes the SNP status, i.e. is it a synonymous or non-synonymous SNP, and a FATHMM score  if this If alternate SNP codes are present, these are also displayed, allowing users to quickly cross-reference for SNPs on other genotyping platforms. Clicking on the Axiom® code displays the sequence surrounding the SNP and links out to UniProt resources  if the sThe ability to extract all stored SNPs that are located between two known SNPs of interest has been requested by several CerealsDB users. In response, we have created a webtool that can take two SNP IDs or a single SNP plus a specified distance in bps upstream or downstream of that SNP and the database will return a list of SNPs with the query SNP highlighted in red. Chromosomal location and physical positions are also indicated along with any annotations associated with the surrounding sequence. Many of the CerealsDB SNPs will have a putative annotation based on BLASTN alignments to the nr database. Clicking on the Axiom® probe names allows users to cross-reference this SNP with SNPs on other genotyping platforms that are hosted on CerealsDB.Located within the Axiom® SNP section of CerealsDB (accessed via the top menu bar) is a tool embedded in the right-hand vertical menu bar  that allows users to search for Axiom® probes that have been annotated by BLASTN alignment to the nr database. Users can enter the Axiom® code for a SNP, a GO term , a general search term such as ‘rust_resistance’ or a gene name, e.g. ‘spo11’. If the search term is found in the annotation table, the respective Axiom® SNPs are displayed along with the BLAST and GO terms (if available) and a weblink provided to the UniProt website   and users can search for names of wheat varieties using the search field. Found names or text patterns will be immediately shown as blue tree nodes, with the names of the nodes given a blue background, which helps to locate varieties of interest in the tree. A subtree containing these selected varieties can be redrawn. Downloadable circular dendrograms for both datasets in high-resolution portable network graphic (png) format are also available and were generated using the R package ggtree . A screeTwo online webtools were created for viewing and interrogating putative introgressions/deletions. The first generates a heatmap of putative introgressions/deletions from all wheat relatives for each hexaploid wheat accession. The heat map is coloured according to the size of the putative introgression/deletions and allows users to determine which wheat relatives are possible donors. All heatmaps were pre-computed in R and exported as png images, which are embedded into the webpage. An associated interactive table details the total number and size of introgressions/deletions (in bps) detected for each relative comparison and allows users to sort and select a comparison to view in more detail.A. sharonensis that was used to introgress stem rust resistance to bread wheat genomes  running on a Linux server (Ubuntu OS version 18.04) and hosted using the Apache2 web server (version 2.4.7) with Perl, Python and PHP scripts used for all data retrieval and output. Genotyping data are also hosted in a MongoDB database to speed up retrieval of large genotyping datasets. The aforementioned software and architecture were used as they are platform independent and open source.http://www.cerealsdb.uk.net/cerealgenomics/CerealsDB/indexNEW.php. The site is free from IP restrictions and access is not password protected.The CerealsDB website is freely available and can be accessed at API indicates application programming interface; BC, Bristol contig; BLAST, basic local alignment search tool; BS, Bristol SNP; CSS, cascading style sheets; DArT, diversity array technology; DFW, designing future wheat; EST, expressed sequence tag; GO, Gene Ontology; HTTP, hypertext transfer protocol; ID, identification; IWGSC, International Wheat Genome Sequencing Consortium; IWYP, International Wheat Yield Partnership; JSON, Javascript object notation; MAS, marker-assisted selection; NCBI, National Center for Biotechnology Information; NGS, next generation sequencing; nr, NCBI non-redundant database; OS, operating system; PHP, hypertext pre-processor; QTL, quantitative trait locus; RDMS, relational database management system; REST, representational state transfer; SNP, single nucleotide polymorphism; SNV, single nucleotide variant; SSD, single seed descent; TGbyS, targeted genotyping by sequencing; URGI, Unité de Recherche Génomique Info; URI, uniform resource identifier; URL, uniform resource locator; VCF, variant call format.Datasets contained in CerealsDB are available on the site to download as Excel spreadsheets or VCF format. SNP data can be downloaded from the right-hand menu for each of the different genotyping platforms.The authors declare that they have no competing interests.P.A.W., A.M.A., L.W., J.Z. and S.T. wrote the manuscript. P.A.W., G.L.A.B. and M.O.W. built CerealsDB version 3.0; P.A.W., S.T., X.B. and R.D. contributed to the web services; L.W. and S.G. provided the QTL data; A.M.A. and A.B. carried out the SNP validation experiments; D.S. and A.B. collated the varietal data; J.Z. predicted FATHMM scores; and K.J.E. and G.L.A.B. conceived of the study, participated in its design and coordination and helped to draft the manuscript. All authors have read and approved the final manuscript.CerealsDB_Supplementary_Data_revised_baaa060Click here for additional data file.


Modern potato breeding methods following a genomic-led approach provide means for shortening breeding cycles and increasing breeding efficiency across selection cycles. Acquiring genetic data for large breeding populations remains expensive. We present a pipeline to reduce the number of single nucleotide polymorphisms (SNPs) to lower the cost of genotyping. First, we reduced the number of individuals to be genotyped with a high-throughput method according to the multi-trait variation as defined by principal component analysis of phenotypic characteristics. Next, we reduced the number of SNPs by pruning for linkage disequilibrium. By adjusting the square of the correlation coefficient between two adjacent loci, we obtained reduced subsets of SNPs. We subsequently tested these SNP subsets by two methods; (1) a genome-wide association study (GWAS) for marker identification, and (2) genomic selection (GS) to predict genomic estimated breeding values. The results indicate that both GWAS and GS can be done without loss of information after SNP reduction. The pipeline allows for creating custom SNP subsets to cover all variation found in any particular breeding population. Low-throughput genotyping will reduce the genotyping cost associated with large populations, thereby making genomic breeding methods applicable to large potato breeding populations by reducing genotyping costs. Potato is the world’s third most important food crop, with an annual production of more than 300 million ton fresh-weight worldwide ,2. PotatSolanum tuberosum L.) is the heterozygosity of the plants being used as parents, as it leads to difficulties in predicting the outcome of a cross [Potato has been actively improved through breeding since the 19th century . Despite a cross . The tet a cross . Each brPhytophthora infestans—and early maturity.In a small to medium-sized breeding program, the number of new potato clones generated each year may vary between 10,000 and 40,000 . SelectiModern plant breeding techniques, such as marker-assisted selection (MAS) are increasingly used in potato breeding. MAS can be used for certain traits, and may lead to a decrease in time spent on phenotyping clones, thus, reducing the time from cross to variety release. Genetic markers are available for a number of phenotypic traits, mostly related to host-plant resistance . DespiteWith a decline in grant funding, public breeding programs are in demand of more precise and cheaper breeding methods, such as genomic-assisted breeding. A complement, and alternative to traditional MAS using markers linked to a single trait, is genomic selection (GS) by using genomic estimated breeding values (GEBVs) ,15. DeveMore genomic resources have become available following the sequencing and assembly of the potato genome in 2011 . Arrays n = 1882) consisting of eight bi-parental crosses from a field experiment in southern Sweden during the spring and summer of 2016. Analysis of variance for the 25 reference clones indicated a higher intra-block error than the inter-block error for all phenotypic traits; hence, no adjustments were required across the field (p < 0.001). A subset of the breeding population was selected for high-throughput genotyping. The subset consisted of 11 individuals from each of the eight crosses, which were selected through a principal component analysis (PCA) based on four phenotypic traits, namely: average tuber weight, per plant total tuber weight, per plant total tuber number, and host-plant resistance to late blight, and four parents or grandparents (n = 92).Phenotypic data were collected for a breeding population (he field . Host-pl2) within a set frame of SNPs on each pseudomolecule for LD pruning , host plant resistance to late blight (n = 1), tuber weight (n = 1), and tuber number (n = 10) (A genome-wide association study (GWAS) was conducted on each of the nine subsets of SNPs obtained from the LD pruning at different thresholds. The population structure was accounted for by including two principal components in the subsequent analysis of the data. Significant QTL were obtained from the GWAS for all phenotypic traits: flowering date ((n = 10) . The tot(n = 10) . For mos2 equal to 0.1. No significant differences were found when comparing the r2 values between the SNP subsets using the prior with the highest prediction accuracy Bayesian lasso.Five priors for controlling shrinkage in genomic prediction were included in this study: Bayesian ridge regression, BayesA, BayesB, BayesC, and Bayesian lasso. For the nine SNP subsets, each prediction model was fitted twice: with and without including family relations as a fixed effect. The highest obtained correlation between GEBVs and observed phenotypic values was 0.24 for host-plant resistance to late blight and 0.20 for tuber number per plant. These values were found in the subsets including 500 and 1500 SNPs respectively . In bothWe have utilized estimations of LD between adjacent genetic markers as an approach to reduce the marker number for genotyping large potato breeding populations. The proposed stepwise pipeline can be seen in n = 1500) was able to identify seven QTL, while 12 QTL were found using the biggest subset (n = 5000). The number of QTL detected by GWAS stagnated when using a larger set of SNPs, thus indicating that a larger number of SNPs might not be necessary to capture the complexity needed to detect useful markers for MAS in potato. The SNP markers related to QTL found in this study might be of interest for potato breeding. Previous work has described a QTL for host-plant resistance to late blight found on potato pseudomolecule 9  [P. infestans on chromosome V [In this study, we investigated two genomic-based analyses as validation for a reduced set of SNPs. First, we used a genomic-based association analysis (GWAS). It was recently estimated that 40K SNPs are necessary for successful QTL detection in potatoes . Nonethesome IX) . The higmosome V  as the cn = 92), and not the set of genetic markers.The second genomic-based analysis we used for validation of SNPs reduced by LD pruning was the prediction accuracy of GEBVs. We included two phenotypic traits of high importance to potato breeders—one with a complex genetic background, namely tuber number, and one that appears to be determined by a few, major QTL; i.e., host-plant resistance to late blight . The preMarker pruning by LD seems to be a safe approach to reduce the number of markers used for genotyping potato, thus lowering the costs related to generating genotypic data. Looking at the spread of SNP markers across the potato pseudomolecules, it is obvious that the harshest pruning has taken place close to the centromeres. This decrease of LD further out on the chromosome arms is to be expected . PruningThere are alternative methods for marker reduction, where SNPs are filtered on criteria other than LD correlation. For example, ranking SNPs based on p-values obtained from a GWAS, minor allele frequency or limiting to a single SNP per haplotype block . In thisIn this study, we show that a genomic-based analysis (GWAS) or breeding approach (GS) for potato can be performed with a marker set reduced by LD pruning. For a small to medium-sized potato breeding program , engaging in genomic-led genetic enhancement may be costly. We have showed that genotyping with a selected subset of the breeding population, and subsequent SNP pruning for LD, is an effect approach to reduce the number of genetic markers while not losing any of the complexity in the genetic information. We think this pipeline would increase the availability of genetic-enhanced breeding techniques for potato genetic enhancement, without limiting the size of the potato breeding population that today appears affordable to genotype.P. infestans was defined by the area under disease progress curve (AUDPC) following the EucaBlight protocol [This experiment included 1882 potato breeding clones (a sample of T1 generation of SLU Potato Breeding Program) representing eight biparental crosses along with some of their parents or grandparents. The experimental layout followed an augmented quadruple lattice design with 25 reference clones and cultivars to ensure uniformity across the field. Such a field design allows accounting for environmental differences across plots. Germplasm was evaluated for four phenotypic traits in the 2016 growing season at field in Mosslunda  in Southern Sweden. Tuber number and total tuber weight per plant data were recorded at harvest. Flowering time was noted throughout the growth season as number of days until floral emergence. Susceptibility to protocol . This hoA total of 88 breeding clones along with four of their ancestors (N = 92) were genotyped using a SNP array. From each of the eight crosses, 11 breeding clones were selected through a principal component analysis (PCA). The PCA was based on tuber number per plant, tuber weight per plant, the estimated individual tuber weight, and AUDCP. Flowering time was excluded due to missing data. The clones were selected based on an even spread across the axis representing the variation in AUDPC  between independent pairwise markers on each pseudomolecule. The window frame for r2 was 50 SNPs, and each new window was set by a move of 10 SNPs. The process of LD pruning also ensures an even spread of SNP markers across the genome. This software is not able to handle five cluster data; thus, the data was “diploidized” reducing the number of heterozygote loci from three  to one (Aa). The largest and smallest of the nine SNP subsets contained approximately 5000 SNPs and 500 SNPs, respectively. Population structure was estimated through a principal coordinate analysis (PCoA) on the genotypic data.Genotypic data from 92 clones of tetraploid potato was obtained from a high-throughput array including 12,000 SNPs. In total, 9180 SNPs with 10 or less missing genotypes were represented in the 92 individuals. These SNPs were mapped to the potato genome sequence of olecules . SNPs wiolecules . SNPs wiolecules  to filteA GWAS was carried out using the R package GWASpoly  for eachModels for GS were created for two of the phenotypic traits—per plant tuber number and tuber weight, and AUDPC for host-plant resistance to late blight, for each of the nine SNP subsets using the R package BGLR . For eac


The guppy sex chromosomes show an extraordinary diversity in divergence across populations and closely related species. In order to understand the dynamics of the guppy Y chromosome, we used linked-read sequencing to assess Y chromosome evolution and diversity across upstream and downstream population pairs that vary in predator and food abundance in three replicate watersheds. Based on our population-specific genome assemblies, we first confirmed and extended earlier reports of two strata on the guppy sex chromosomes. Stratum I shows significant accumulation of male-specific sequence, consistent with Y divergence, and predates the colonization of Trinidad. In contrast, Stratum II shows divergence from the X, but no Y-specific sequence, and this divergence is greater in three replicate upstream populations compared with their downstream pair. Despite longstanding assumptions that sex chromosome recombination suppression is achieved through inversions, we find no evidence of inversions associated with either Stratum I or Stratum II. Instead, we observe a remarkable diversity in Y chromosome haplotypes within each population, even in the ancestral Stratum I. This diversity is likely due to gradual mechanisms of recombination suppression, which, unlike an inversion, allow for the maintenance of multiple haplotypes. In addition, we show that this Y diversity is dominated by low-frequency haplotypes segregating in the population, suggesting a link between haplotype diversity and female preference for rare Y-linked color variation. Our results reveal the complex interplay between recombination suppression and Y chromosome divergence at the earliest stages of sex chromosome divergence. Sex chromosomes diverge from each other once recombination between the X and the Y chromosomes is halted . DespitePoecilia reticulata)  , helped iculata) , and obsiculata) , 2017 haiculata) . Curiousiculata)  which isiculata) .P. reticulata shows evidence of early Y degeneration at the distal end of Chromosome 12, in a region that is also ancestral to P. wingei, its sister species (Stratum I)  . Stratumratum I) , and thiratum I) , and a gratum I) .P. reticulata and between P. reticulata and P. wingei  between males and females. We observe significant increases in FST in replicate upstream compared with downstream populations along the length of Chromosome 12  in Stratum I based on its total length and SNP content  compared with females (XX). With a high number of Y haplotypes, explained below, we do not expect all Y-linked SNPs to be present in all males, and we therefore examined SNPs absent in all females but present in a subset of males. We observe an excess of such male-linked SNPs on Chromosome 12  online,  content . Within  content  online.P < 0.001, 2 test), with either all (Quare downstream) or most (>62% in Quare upstream and both Yarra populations) of fully male-linked SNPs present in Stratum I. Interestingly, genetic diversity on the sex chromosome and autosomes, estimated as the proportion of segregating sites (Watterson’s theta), was highest in Aripo compared with the other watersheds , 1.9× (Quare), and 15.6× (Yarra) more male-linked SNPs in Stratum I than their corresponding downstream populations. This difference was even more striking when considering only fully male-linked SNPs, with the upstream populations showing 21.5× (Quare) and 178.8× (Yarra) more full sex-linked SNPs than the respective downstream populations . Altoget. wingei , coupled. wingei . InsteadInversions are often implicated as a major driver of recombination suppression in sex chromosomes , and cana, If an inversion has formed between the X and the Y chromosomes, we would expect males to be heterozygous for this structural variation and that it would be absent in females because the reference genome is also from a female. Although we could detect distinctive barcode overlaps between distant positions of Stratum I across the three rivers, we failed to identify any consistent difference between females and males in any of the populations  online. supplementary fig. S1To further investigate the genetic diversity of the guppy Y chromosome, we used our linked-read sequencing data to computationally phase all genotypes in our populations . We estimated a phase switching error of <5% for all linked-read samples, with the exception of one Quare male and one Yarra female  online, P. reticulata populations in Trinidad and most Y haplotypes are monophyletic, and it is also consistent with our previous work showing that recombination was suppressed in this region in the common ancestor with P. wingei , and was significantly lower in Quare and Yarra   c. Under is test) . Both thTo further explore the diversity and evolutionary history of the guppy Y chromosome, we built haplotype networks for the Y-linked regions for which Y haplotypes were shared across both populations  online. The number of mutational steps separating haplotypes of the same population was considerably higher in downstream populations with an average of 10.67, 17.14, and 14.5 mutations in Aripo, Quare, and Yarra, respectively. In contrast, in the upstream populations, there were 2.8 and 1.67 mutations for Quare and Yarra, respectively (the three upstream haplotypes observed in Aripo are not directly connected). These findings indicate higher haplotype diversity in the downstream populations, which is in line with the distribution of male-linked SNPs between the two populations. Some of the downstream Y haplotypes branch directly from the upstream population and may represent episodes of downstream migration.P. reticulata, we searched for 21-bp k-mer sequences unique to males and absent in all females (hereafter Y-mers) in each of our populations. We have previously used a Y-mer-based approach to identify a small ancestral Y region across several Poecilia species  have been previously associated with the guppy SDR and mapped to a duplicated segment between Chromosome 9 and Chromosome 12 (We extracted 10x Genomics Y haplotypes (megabubbles) enriched for Y-mers  to further characterize Y-specific sequence. The density of transposable elements (TEs), measured as base pairs of TE per 50-kb nonoverlapping windows, was significantly higher in the candidate Y scaffolds, in comparison with both the PAR and the X-linked region of the sex chromosome  as well as by haplotype networks  in the Northern Range Mountains of Trinidad in December 2016. A description of the habitats can be found in We collected wild P. reticulata (∼700 Mb) compared with the human genome, for which the protocol was optimized. The libraries were sequenced on an Illumina HiSeqX with 2 × 150-bp cycles using the v2.5 sequencing chemistry, resulting in an average of 346 M reads per sample (range between 159 and 494 M), representing an estimated average coverage of 68× (32×–98×)  and assessed molecular weight with the Agilent Femto Pulse system prior to library preparation. We then selected ten males from each population with the highest molecular weights for 10x Genomics Chromium Sequencing. Because we were focused on phasing the X and the Y chromosomes, linked reads were not required from female samples, however, we still selected three females from each population with the highest molecular weights for 10x Chromium Sequencing. We used the 10x Genomics Chromium Genome Library Preparation Kit according to the manufacturer’s protocol (No. CG00043 Chromium Genome Reagent Kit v2 User Guide), reducing the amount of starting DNA from the recommended 1.25–0.6 ng to account for the smaller genome size of 32×–98×)  online.For the remaining seven female samples in each population with lower molecular weight DNA, we generated additional Illumina sequencing data after extracting genomic DNA from ∼25 mg of head tissue using the DNeasy Blood & Tissue Kit (Qiagen) following the standard protocol. We prepared sequencing libraries from 1 μg DNA with the TruSeq PCRfree DNA Sample Preparation Kit, targeting an insert size of ∼350 bp according to the manufacturers’ instructions (Guide No. 1000000039279). We sequenced resulting libraries on a Illumina HiSeqX with 2 × 150-bp cycles using the v2.5 sequencing chemistry, resulting in an average of 137 M reads per sample (93–218 M), representing 29× coverage on an average (20×–40×)  online.To construct a reference genome specific for each river, we assembled linked reads de novo for all our females with linked-read sequencing using Supernova v2.1.1 (10x Genomics). We then chose the best female assembly in each river based on scaffold N50 and phase block N50. Because Supernova can generate nearly identical haplotypes for the same sequence, we created a nonredundant assembly by removing smaller scaffolds with evidence of sequence overlap with longer scaffolds. For this, we aligned each assembly to itself with LAST v926 , using tWe then used ARKS v1.0.2  to increFinally, we scaffolded each of our best ARKS assemblies, using the guppy reference genome (NCBI accession GCF_000633615.1)  as backbb and c, In order to verify the scaffolding made by RaGOO, we performed pairwise synteny analyses between the assemblies and the NCBI guppy reference genome used in the scaffolding procedure. We made alignments with LAST v926  using thsupplementary fig. S1We used FastQC v0.11.5  to assess read quality and used BBTools v38.34 “bbduk”  to remove adapter sequences, trim regions with average quality scores <Q10 and remove PhiX-174 spike-in control reads. After filtering, we excluded read-pairs from downstream analyses if either read had an average quality score <Q10 or was <35 bp. In order to make both 10x Genomics and Illumina data sets comparable, we also preprocessed 10x Genomics reads as above after first trimming barcodes and performing barcode error correction with the Long Ranger v2.2.2 Basic pipeline.We aligned all preprocessed reads with BWA v0.7.15-r1140 using the MEM algorithm  and defaWe called genotypes within each river using Freebayes v1.3.1-16-g85d7bfc  with theUsing our female genome assemblies, we expect that any regions of Y degeneration will result in female-biased read depth, as highly diverged Y reads will not map to the X chromosome, an approach previously implemented for guppies . We therP. reticulata across a large proportion of the sex chromosome compared with downstream populations. To investigate the extent of male to female divergence, we used Hudson’s method . Prior to FST calculation, we excluded singleton sites and sites with ≥20% of missing data in each sex.For regions of the X and the Y chromosomes that have diverged, but still show little evidence of Y degeneration, we expect an increase in male compared with female diversity, as Y-linked reads will map to the female genome assembly, but will carry male-specific SNPs . We prevs method , as implWe used the phasing information from linked-reads sequencing of ten males and three females from each population to aid in phasing all our samples. 10x Genomics samples were assembled and phased using the 10x Genomics Long Ranger v.2.1.2 suite with the wgs option and using FreeBayes for variant calling. Briefly, reads that share the same barcode were sequenced from the same original long input DNA molecule, and this information is used to link the reads into large haplotype blocks. This is expected to generate a high-quality phasing for each individual sample. To make the variation data between the 10x Genomics single-sample VCF and the pooled variants from each watershed as close as possible, we regenotyped all individuals using the BAM files generated by Long Ranger. Genotyping was performed with FreeBayes and filtered for SNPs as detailed above.The full phasing of all genotypes was then performed in two steps. First, we used WhatsHap v0.19.dev156+g1564a9f  for readY haplotypes can be identified phylogenetically using gene trees from the phased genotypes if they form a single monophyletic clade. For this, we divided the genome into nonoverlapping windows of 100 SNPs and generated outputs in fasta format for the two inferred haplotypes of each individual with the BCFtools v1.9 consensus command. We used SNP windows for better resolution of regions with elevated SNP density. We then converted the fasta alignments at each window to the phylip format and built gene trees using FastME v 2.1.6.1  with theNucleotide diversity was estimated using the Watterson’s theta estimator for the male Y haplotypes and the correspondingly alternative haplotypes (inferred as the X haplotypes). To obtain an autosomal diversity estimate, we used a single arbitrarily chosen male haplotype from 1,000 randomly sampled autosomal gene trees (excluding Chromosome 12). To compute haplotype networks, we used the haploNet method with default parameters from the package pegas .k-Mer refers to all the possible substrings of length k that are contained in a genome, and have been useful in identifying sex-specific (Y chromosome) sequence in a range of organisms , includiTo identify scaffolds enriched for Y-specific sequence, we used Bowtie v1.2.3, without allowing for mismatches and reporting all alignments , to map Y-mers to the Supernova assemblies of each male genome. We used the megabubbles output in Supernova because in this output style Supernova generates an individual FASTA record for each homologous phased haplotype without mixing maternal and paternal alleles in the same sequence. Scaffolds with ≥100 Y-mers and more Y-mers than the homologous haplotype (if identified) were selected for further analysis. To remove possible redundancy in the resulting scaffolds, as the same region could be identified in scaffolds from different males, we clustered scaffolds within watersheds with a sequence identity threshold ≥90% and ≥50% of sequence overlap as calculated from BlastN v2.5.0+ with parameters -dust yes -evalue 0.000001 -max_target_seqs 100,000. The longest scaffold of each cluster with at least five samples, assuming that a Y-linked region could be absent from one sample due to insufficient coverage for assembly, was used for annotation.Danio rerio (GRCz11), Gasterosteus aculeatus (BROADS1), Oryzias latipes (ASM223467v1), P. latipinna (1.0), P. mexicana (1.0), P. reticulata (1.0), Takifugu rubripes (FUGU5), and Xiphophorus maculatus (5.0). For EST, we used 10,664 tags from Candidate Y-linked scaffolds from the three watersheds were pooled together for annotation. Annotation was performed with MAKER v2.31.10 . We ran Molecular Biology and Evolution online.msaa257_Supplementary_DataClick here for additional data file.


HD1 was simultaneously associated with the productive tiller ratio and heading date, indicating that the photoperiodic heading gene directly controls the productive tiller ratio. Multiple linear regression models of lead SNPs showed coefficients of determination (R2) of 0.49, 0.22, and 0.41 for the tiller number at the maximum tillering stage, productive tiller number, and productive tiller ratio, respectively. Furthermore, the model was validated using independent japonica rice collections, implying that the lead SNPs included in the linear regression model were generally applicable to the tiller number prediction. We revealed the genetic basis of the tiller number in rice plants during growth, By GWASs, and formulated a prediction model by linear regression. Our results improve our understanding of tillering in rice plants and provide a basis for breeding high-yield rice varieties with the optimum the tiller number.A tiller number is the key determinant of rice plant architecture and panicle number and consequently controls grain yield. Thus, it is necessary to optimize the tiller number to achieve the maximum yield in rice. However, comprehensive analyses of the genetic basis of the tiller number, considering the development stage, tiller type, and related traits, are lacking. In this study, we sequence 219 Korean rice accessions and construct a high-quality single nucleotide polymorphism (SNP) dataset. We also evaluate the tiller number at different development stages and heading traits involved in phase transitions. By genome-wide association studies (GWASs), we detected 20 significant association signals for all traits. Five signals were detected in genomic regions near known candidate genes. Most of the candidate genes were involved in the phase transition from vegetative to reproductive growth. In particular,  LAX PANICLE1 (LAX1), which encodes a basic helix-loop-helix (bHLH) transcription factor, is the main regulator of axillary meristem formation in rice [lax1 mutant plants show reduced branching in both vegetative and reproductive growth, with suppressed tillering and panicle branches. MONOCULM1 (MOC1) encodes a transcription factor in the GRAS family and is also involved in axillary bud regulation and rice tillering. moc1 mutants show completely defective tillering, producing only one main culm [Tiller number in rice is the major trait determining plant architecture. An excessive tiller number causes too many unproductive tillers, a reduced leaf area, and a reduction in photosynthetic efficiency by mutual shading. By contrast, too few tillers lead to low biomass production and a deficiency in the grain filling capacity and carbohydrate production . It is i in rice . lax1 muain culm .OsPIN1 is involved in polar auxin transport. Transgenic plants with low OsPIN1 expression show a reduced number of adventitious roots and a significantly increased number of tillers [Cytokinin oxidase/dehydrogenase 2 (OsCKX2) encodes a cytokinin oxidase in rice. The suppression of OsCKX2 expression by RNAi enhances growth and productivity by increasing the tiller number and grain weight [DWARF 53 (D53) is a substrate of the SCFD3 ubiquitination complex and acts as a repressor of strigolactone signaling [d53 mutants, strigolactone signaling is blocked, leading to the dwarf and high-tillering phenotype. Receptor-like cytoplasmic kinases 57 (OsRLCK57) is involved in the modulation of brassinosteroid signaling. The transgenic lines silenced for OsRLCK57 expression by RNA interference results in significant reductions in tillers and panicle secondary branching [Rice tillering is also influenced by genes involved in phytohormone signaling, such as auxins, cytokinins, strigolactone, and brassinosteroids. The auxin efflux carrier gene  tillers . Cytokinn weight . DWARF 5ignaling . In d53 ranching .Heading date 1 (HD1), an ortholog of Arabidopsis CONSTANS (CO), is a photoperiod-responsive flowering gene [HD1 acts as an activator of HEADING DATE 3a (HD3a) under short-day conditions, resulting in heading [TIMING OF CAB EXPRESSION 1 (OsTOC1), an ortholog of TOC1 in Arabidopsis, is an important circadian clock component and photoperiodic heading regulator [Hairy meristem 1 and Hairy meristem 2 (OsHAM1 and OsHAM2) regulate the vegetative to reproductive phase change [sHAM1 and OsHAM2 are regulated by osa-miR171. The down-regulation of these genes delays heading, thus increasing tiller numbers.In addition, growth phase transitions are a major determinant of rice tillering patterns. Axillary buds produce tillers during the vegetative stage. After the phase transition to the reproductive stage, the shoot apical meristem becomes an inflorescence meristem, and tiller development is delimited . Late heing gene ,16. HD1  heading . TIMING egulator . Hairy me change . OsHAM1 Genome-wide association studies (GWASs), using high-quality single nucleotide polymorphisms (SNPs), can be used to dissect the genetic basis of complex agronomic traits . Loci asIn this study, we sequenced 219 Korean rice accessions and constructed a high-quality SNP dataset. Then, we investigated natural variation in the tiller number at different developmental stages and heading traits. By a GWAS, we identified the complex genetic regulation of tillering and interactions between tiller numbers and phase transitions in rice plants. We also revealed the contribution of each trait-associated loci to the maximum tiller number, productive tiller number, and ratio by a linear regression analysis. Lastly, using independent japonica accessions, we verified the applicability of the detected loci to molecular breeding aimed at optimizing tiller numbers.A total of 266 rice accessions, including 219 Korean rice accessions for GWAS and 47 japonica accessions for the verification of linear regression models, were used. Korean rice accessions included 78 landraces, 130 modern cultivars, and 11 Tongil-type cultivars . Modern Total DNA was extracted from 90-day-old leaves of each accession by the CTAB method . DNA wasLinkage disequilibrium (LD)-based SNP pruning was performed using PLINK v1.9  with thep-value thresholds for genome-wide significance were calculated by dividing the significance level 0.05 by the effective number of independent SNPs  [r2). Lead SNPs were defined as the SNPs with the lowest p-value in loci, including significant SNPs. Haplotypes were constructed using all variants, including SNPs and InDels, without consideration of MAF. Individuals containing at least one missing or/and heterozygous genotype were excluded from the haplotype analysis.Only 1,509,362 SNPs with MAF > 0.05 and bi-allelic genotypes were included in the GWASs. All GWASs were performed using linear mixed-models (LMM) implemented in FaST-LMM v2.07 . The gen × 10−6) . LD patt × 10−6)  with theR > |0.3| using the corrr package [All statistical analyses were performed using R studio v1.2.5033 . Pearson package .Multiple linear regression models for TNM2, PTN, and PTR were estimated using phenotypes as dependent variables and lead SNPs in the GWAS as independent variables. Independent variables consisted of lead SNPs not only for TNM2, PTN, and PTR, but also for earlier traits with correlations. Since these three terminal traits are affected by traits at the previous growth stage, the directions of relationships between traits were determined based on the sequence of growth stages k. VariabGCTA v1.93  was usedAn SNP subset obtained by LD-based pruning was used for analyses. An MDS analysis showed that population genetic structures of Korean rice accessions could be classified into two major groups, japonica and Tongil-type accessions, developed from inter-subspecific crosses between indica and temperate japonica a. JaponiThe modern japonica cultivar group showed lower TNE and TNM1 than those of japonica landraces and Tongil-type rice d,e. TNM2Phenotypic relationships among all traits were investigated by correlation analysis k. Three p ≤ 1.35 × 10−6) for all traits (p = 3.52 × 10−16) was detected for HD   b. Howevep = 3.52 × 10−16) and chr06:8365187 (p = 1.23 × 10−20), respectively (r2 > 0.94) and were separated by about 25 kb. Thus, these lead SNPs were considered the same association signal. HD1 was located within a region in moderate LD (r2 > 0.6) with lead SNPs approximately 1 Mb from association signals  and nonfunctional haplotypes   c. In Koronal HD1 d,e.p = 9.65 × 10−7). We detected two candidate genes, OsHAM1 and OsHAM2, approximately 62 and 52 kb from chr02:26906676 with r2 > 0.8 , at 42.9 Mb on chromosome 1. A candidate gene, OsRLCK57, in strong LD (r2 > 0.97) was located about 23 kb from chr01:42957568 , was significantly associated with TNM2. OsTOC1 was located about 113 kb from the lead SNP and was in LD with r2 > 0.65  (R2), chr04:31232808 (23.6%) showed the largest contribution to the linear regression model for TNM2  . As detefor TNM2 b. In a lfor TNM2 . Chr02:2for TNM2 c. chr04:TNM2  involved in the developmental phase transition near lead SNPs associated with TNM1 and TNM2  indicated that a relatively small portion of the variance in PTN could be explained by genetic factors associated with the tiller number and heading traits (HD1, and chr01:34991240, which were associated with HD, were major contributors to PTR, accounting for 36.9%, 16.9%, and 14.6% of R2, respectively (To obtain a comprehensive understanding of the genetic basis of TNM2, PTN, and PTR, multiple linear regression models were estimated. The low g traits . This reg traits b. For PTg traits c. Chr06:ectively d. These In a previous study of progeny populations derived from various crosses, the heritability of tiller numbers was 37.8–97.7%, depending on the genetic background of the parents . The herOsRLCK57 involved in tiller development was associated with the tiller number at the early tillering stage. OsHAM1, OsHAM2, and OsTOC1, which were related to the developmental phase transition, were associated with the tiller number at maximum tillering stages. HD1 controlling flowering time was associated with the productive tiller ratio. Taken together, these results suggest that genes involved in developmental phase transitions, along with gene modulating tiller development, could also determine the rice tillering pattern at different growth stages. Our results provide insight into the genetic basis of overall tillering dynamics.In this study, we dissected the basis of rice tiller numbers by analyzing relationships with heading traits. We also revealed the genetic basis of tiller alterations at different growth stages, using GWASs. Several candidate genes were detected in loci significantly associated with tiller number.


ROH) and phenotypes were observed for 23 out of 32 traits analyzed. An overlap between ROH islands and significant markers from genome-wide association analyses was observed. Previously published quantitative trait loci for fruit traits and disease resistances also overlapped with some of the ROH islands. A prominent ROH island at the bottom of linkage group 17 overlapped with a recombination-supressed genomic region harboring the self-incompatibility locus. The observed ROH patterns suggested that systematic breeding of European pears would have started earlier than of Asian pears. Our research suggest that FROH would serve as a novel tool for managing inbreeding in gene-banks of self-incompatible plant species. ROH mapping provides a complementary strategy to unravel the genetic architecture of complex traits, and to evaluate differential selection in outbred plants. This seminal work would provide foundation for the ROH research in self-incompatible plants.Runs of homozygosity (ROH) have been widely used to study population history and trait architecture in humans and livestock species, but their application in self-incompatible plants has not been reported. The distributions of ROH in 199 accessions representing Asian pears (45), European pears (109), and interspecific hybrids (45) were investigated using genotyping-by-sequencing in this study. Fruit phenotypes including fruit weight, firmness, Brix, titratable acidity, and flavor volatiles were measured for genotype-phenotype analyses. The average number of ROH and the average total genomic length of ROH were 6 and 11 Mb, respectively, in Asian accessions, and 13 and 30 Mb, respectively, in European accessions. Significant associations between genomic inbreeding coefficients (F Systematic genetic improvement of outbred plants for economically important traits such as yield, consumer acceptance and nutritional value, has led to the loss of genetic diversity within and among accessions in domesticated gene pools . Mating As recombination interrupts long chromosome segments over time, the length of ROH segment depends in part on the number of generations since the parents shared an ancestor in common . In an iROH) derived from ROH, were significantly associated with deleterious effects in humans.With the development of cost-effective genome sequencing technologies, large numbers of single nucleotide polymorphisms (SNPs) can be generated at a relatively low price. This facilitates ROH analysis to capture the genomic regions contributing to inbreeding, and thus to assess the breeding history and to identify the genetic components for trait selection. ROH were first recorded in humans by using 8,000 short tandem-repeat polymorphisms . Using ∼The ROH patterns were shown to differ markedly among cattle breeds , and theInbreeding usually results in the loss of vigor and reduced reproductive fitness of offspring in outbred plant species . Lander Pyrus spp.), which exhibits gametophytic self-incompatibility, is among the important temperate fruit tree species, with at least 3,000 years of cultivation history. The genus Pyrus is believed to have originated in the mountainous regions of western China , 108 of European pear (P. communis), and 45 hybrids between Asian and European species  equivalents released cm–2 fruit surface area per hour.The flavor volatile analysis procedure using GC-MS, was used as described by P. × bretschneideri (cultivar “Suli”)  . SNPs wiA procedure to discover ROH in PLINK software  using a ns is the number of SNPs per individual, ni is the number of individuals and het is the mean SNP heterozygosity across all SNPs. Following Equation 1, the minimum number of SNPs constituting an ROH was set to 48 in this study. ROH segments were determined using PLINK v.1.7 based on the following settings: one heterozygous genotype and two missing SNP were allowed per window of 48 SNPs; minimum SNP density was set to one SNP per 50 kb, with a maximum gap between consecutive SNPs was set to 1 Mb to avoid low SNP density affecting ROH length; a minimum ROH length of 500 kb. The adjacent SNPs having a proportion of ROH occurrences over the adopted threshold formed ROH islands. In this study, putative ROH islands were determined based on overlapping ROH regions, shared by at least 15% of studied accessions. The adjacent SNPs were merged into genomic regions corresponding to ROH islands.where α is the percentage of false-positive ROH (set to 0.05), ROH, FSNP, and FGRM. FROH is the fraction of each genome in ROH > 0.5 Mb. For example, in a sample for which n ROH of length li (in Mb) were identified, then FROH was calculated as:For each accession, three estimates of the genomic inbreeding coefficient (F) were calculated, FSNP, which is a method of moment based measure of inbreeding in the most recent generation  is the observed number of homozygous SNPs, E(HOM) is the expected number of homozygous SNPs, and N is the total number of genotyped SNPs. FZ is an n × m matrix  representing genotypes at each locus. The coefficient of the ith column of the Z matrix are (0–2pi), (1–2pi), and (2–2pi) for genotypes AA, AB, and BB, respectively, pi is the allele frequency of allele A at the ith SNP. G was calculated using p = 0.5, which is the same as the method used by the USDA-ARS Animal Improvement Programs .where Programs . The valFor each trait, the phenotypes were modeled in two steps. First, a mixed linear model (MLM) was fitted accounting for fixed effects and random effects:b is a vector of unknown fixed covariate effects , X and Z are the known design matrices for the fixed and random effects, respectively; u is an unknown vector of additive genetic effects with a normal distribution N, where G is the genomic relationship matrix (GRM); and e is an unknown vector of residuals. In the second step, estimates of random additive effects (u′) from Equation 5 were regressed on FROH as follows:where y is a vector of measured trait values, ROH on the trait, FROH is a known vector of individual FROH, and ε is an unknown vector of residuals.where μ is the overall mean, β is the unknown scalar effect of FMarker-trait genome-wide association (GWA) analysis were also conducted for each trait using unified MLM as implemented in R package GAPIT . PrincipGRM, FSNP, and FROH, respectively, with means of 0.52, 0.67, and 0.03 in Asian accessions; 0.69, 0.80, and 0.08 in European accessions; and 0.57, 0.71, 0.04 in the hybrid population. The accessions with smaller FROH were considered as the least inbred, whereas accessions with larger FROH were considered as the most inbred accessions. Correlations between the three measures of genomic inbreeding were large, with correlations between FSNP and FROH of 0.64, FSNP and FGRM of 0.94, and FROH and FGRM of 0.74.After quality controls , about 8500 SNPs distributed across the genome  were retThe relationship between the total genomic length (Mb) covered by ROH and the total number of ROH per accession demonstrates separation between the European and Asian pear species . The aveClassification of ROH by length showed that the majority of ROHs were shorter than 4 Mb in all three pear genetic groups . For ROHROH to estimate the effect of inbreeding/selection on each of the 32 traits considered in this study, with 23 reaching experiment-wise significance threshold (0.01/32 = p < 3.1e-04). Effect size, in phenotypic standard deviation units (pσ), corresponding to FROH = 0.15  are shown in ROH was associated with 0.70pσ and 1.8pσ increase in fruit weight and fruit firmness, respectively. Non-volatile compounds (Brix and TA), which partly influence sensory flavor intensity, increased by about 1.0pσ at FROH = 0.15. Volatile compounds, alcohols and non-ethyl esters (esters not derived from esterification with ethanol), increased with increasing FROH. Skin bitterness and ethyl esters decreased significantly with increases in FROH  and PpAIV3 (controlling conversion of sucrose to hexose in mature fruit) resided within ROH islands on LG1. A 3.2 Mb long ROH island at the bottom of LG17 overlapped with the self-incompatibility gene (SI-locus). Using conventional GWA, with a genome-wide significance threshold of p < 0.05, a total of 294 SNPs were found to be significantly associated with at least one of the 33 traits considered in this study. Thirty-seven out of 294 significant SNPs resided within the significant ROH islands  improves mating decisions and gene conservation efforts. The average inbreeding coefficient (FROH) in the inter-specific hybrid population was similar to the Asian accessions, but lower compared with the European accessions. The first generation (F1) hybrid population is expected to display lower FROH than the parental species. Most of the hybrids used in this study were second-generation accessions selected from the F2 or BC1 crosses. The clustering patterns from PCA analysis also showed greater resemblance of many hybrid accessions with either the Asian or European species. Therefore, the artificial selection and the mating scheme in the inter-specific breeding program would have led to the observed higher level of FROH in the studied hybrid accessions. Use of genome sequence information has been advocated for monitoring and utilization of gene-banks of plant species , but the substantially higher value of FGRM compared to FROH  was higher compared with that of the Asian (0.03) accessions. There are no reports of FROH in self-incompatible plant species, but the much higher FROH (often > 0.15) for livestock species reflects stronger artificial selection ; Pyrus, this would suggest that Asian and European species would have shared common ancestors at least 2.8 million years ago, which is supported by a study on the domestication history of pears  haplotype shared by both the Asian and European species resided on LG15. The small size of the shared ROH haplotype would indicate these two species shared ancestry many thousands of generations ago. Assuming an average genetic map size 1,350 cM and a genome size of 527 Mb , one Mb of pears .ROH compared with commercial livestock species  of negative selection for traits, including skin bitterness and ethyl esters, with an increase in FROH. Flavor volatiles have not directly been the target of artificial selection in pear breeding  gene family which includes S-RNase and S-locus F-Box Brothers (SFBB) genes . It has I system . Wu et a species .Selection would result in selective sweeps, which refers to the genomic regions that have reduced nucleotide diversity compared with randomly evolving regions. ROH. Studies on humans  would serve as a novel tool for breeding and management of gene-banks lacking reliable pedigree information. Association between FROH and phenotypes provides a simple mechanism to evaluate the direction of phenotypic change because of increased inbreeding levels. Co-localization of ROH islands and GWA signals agreed with results from studies in humans and livestock populations, which suggested that ROH mapping offers a complementary strategy to understand the genetic architecture of complex traits. Distribution of ROH islands in different species or populations of fruit crops can effectively be used to evaluate signatures of differential selection.In summary, this first application of the ROH approach in self-incompatible fruit crop species enabled us to compare genomic inbreeding coefficients between https://zenodo.org/ repository, digital object identifier https://zenodo.org/record/4302655, https://zenodo.org/record/4308154, and https://zenodo.org/record/4304904.The genotyping-by-sequencing data presented in the study are deposited in the SK conceived the study, conducted ROH and GWA analyses, and wrote the first draft. SK and LB designed the study. CW and CK led leaf collection, DNA extraction, and GBS library preparation. CD conducted bioinformatics analysis. JW conducted QTL and candidate gene searches. DR, MH, and LB led the phenotyping. All authors helped to edit the manuscript, and read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.