Nelumbo nucifera Gaertn.) is one of the most economically and ornamentally important perennial aquatic plants. Plant architecture is an important trait for lotus classification, cultivation, breeding, and applications. In this study, traits representing plant architecture were measured in 390 lotus germplasms for 3 years. According to the phenotypic distribution, 21 large architecture (LA) and 22 small architecture (SA) germplasms exhibiting extreme phenotypes were selected as representatives of plant architecture. Microscopy analyses revealed that LA lotuses possessed far more vertical cells and longer cell lengths than SA lotuses, and there was a closer linear relationship between vertical cell number and plant architecture than cell length and plant architecture. Furthermore, based on whole genome re-sequencing data from 10 LA and 10 SA lotus germplasms, fixation index (FST) genome scan identified 11.02 Mb of genomic regions that were highly differentiated between the LA and SA lotus groups. Chi-square test revealed that 17,154 single nucleotide polymorphisms (SNPs) and 1,554 insertions and deletions (InDels) showed distinct allelic distribution between the LA and SA lotus groups within these regions. A total of 126 variants with distinct allelic distribution in the highly differentiated region were predicted to cause amino acid changes in 60 genes. Among the 41 genes with functional annotation, the expression patterns of six genes involved in cell division and cell wall construction were confirmed using quantitative reverse-transcription PCR (qRT-PCR). In addition, 34 plant architecture-associated InDel markers were developed and verified in the remaining 11 LA and 12 SA lotus plant architecture representatives. This study identified promising functional markers and candidates for molecular breeding and will facilitate further elucidation of the genetic mechanisms underlying plant architecture in the lotus.The lotus ( Nelumbo nucifera Gaertn.; 2n = 2x = 16) is a perennial aquatic plant belonging to the Nelumbonaceae  genome scan identified a 100-kb region that showed the largest FST on chromosome 4. This region exactly overlapped the genomic region previously detected by genome-wide association studies (GWAS) and quantitative trait loci (QTL) mapping  has been successfully applied in the identification of genome-wide variants in many plant species . In the  mapping . In the  mapping . In the  mapping . These s mapping .ST genome scanning and chi-square test following WGRS of 20 representative lotus germplasms. Expression analyses of candidates in highly differentiated genomic regions and validation of developed plant architecture-associated InDel markers in lotus supported the reliability of our results. Our discovery of highly differentiated genomic regions and genetic variants which might be related to plant architecture in the lotus will provide vital clues for unraveling the genetic basis of plant architecture, and promising functional markers in the lotus.The genome sequences of two lotus individuals have been released , enablinSupplementary Table 1) that showed extreme plant architecture were collected, flash frozen in liquid nitrogen, and stored at -70°C. Genomic DNA was extracted using the cetyltrimethyl ammonium bromide (CTAB) method  was measured from the mud surface to the joint of the petiole and leaf, and peduncle length (cm) was measured from the mud surface to the joint of the peduncle and flower. Five leaves and five flowers of each lotus germplasm were chosen randomly in each pool. Normality tests were performed on the phenotypic data of the two traits in each year using SPSS 19.0 software . Fresh young leaves from 43 lotus germplasms  method  and evalP < 0.05) was selected for comparisons among lotus germplasms. The data were further subjected to linear regression modeling using SPSS 19.0 software to evaluate the relationships between cell morphological traits and plant height.For cell morphological measurements, the middle regions of the petioles and peduncles were harvested at the maturation stage from 10 LA and 10 SA lotus germplasms. Three petioles and peduncles were collected from each germplasm and fixed in 70% formaldehyde-acetic acid-ethanol for 24 h. Samples were then dehydrated in an ethanol series  and embeSRP145546.Whole genome re-sequencing was then conducted separately on the 20 lotus germplasms used in the cell morphological analyses. The 20 libraries with a target insert size of 350 bp were prepared according to the Illumina protocol and sequenced using the Illumina HiSeq 4000 to generate 150-bp pair-end sequence reads. The raw sequence data are available in the National Center for Biotechnology Information sequence read archive with the data accession number Before aligning reads to the lotus reference genome, quality control of the raw data was performed following strict filtering criteria for removing paired reads when: (a) the percentage of unidentified nucleotides (N) was more than 10% in either of the paired reads; (b) there were more than 10-nucleotide adapters in either of the paired-end reads; (c) there were more than 50% low-quality bases (Phred score ≤ 5) in either of the paired reads; and (d) putative PCR-duplicated reads were generated by PCR amplification during the library construction process.N. nucifera reference genome1  were designed according to the flanking sequences of the loci using Primer32. PCR amplifications were carried out in a total volume of 15 μL containing 1 μL gDNA (50 ng/μL), 1.2 μL each of forward and reverse primers (10 μM), 7 μL 2× Es Taq Master Mix for PAGE , and 4.6 μL ddH2O. PCR amplifications were performed on a 9902 Thermal Cycler  with the following reaction conditions: (1) denaturation at 94°C for 5 min; (2) 30 cycles of denaturation at 94°C for 30 s, annealing at the corresponding melting temperature of each primer (Supplementary Table 2) for 30 s, and extension at 72°C for 30 s; and (3) final extension at 72°C for 5 min. The PCR products of the SNPs were separated by agarose gel electrophoresis, purified, recovered using PCR purification kits , and sequenced on an ABI 3730xl sequencer . At each SNP locus, the sequences of all 20 germplasms obtained by Sanger sequencing were aligned using BioEdit version 7.0.5 software3. The PCR products of the InDels were separated using 6% polyacrylamide gel electrophoresis (PAGE) and visualized by silver staining. Allele size was estimated using a 20-bp DNA ladder . The amplified fragments for each germplasm and primer pair were scored manually as present (1) or absent (0). Using the sequence alignment and electrophoresis results, the genotypes of each SNP and InDel were retrieved for the 20 re-sequenced lotus germplasms.To validate the reliability of variant calling, 35 SNPs (from 16 loci) and 35 InDels (at least 5 bp in length) were randomly selected. Primers  phylogenetic tree was constructed with the software TreeBestv1.9.2ST genome scan was used to screen the lotus genome for differentiated genomic regions between the LA and SA groups with the sliding window method  ≥ 3, considering the low genomic differentiation between LA and SA lotus groups and to avoid false positive results  correction  enrichment analysis was processed by GOSeq and P values were calculated in Wallenius method . Enricherrection . Only teP < 0.01. Among these variants, the SNPs and InDels located in the highly differentiated genomic regions were considered putative variants responsible for the regulation of lotus plant architecture. Genes showing at least one change at the amino acid level were considered candidates.The genome-wide genotype frequencies of the reference genotype and alteration genotype for each SNP and InDel were separately counted in the LA and SA groups, and then chi-square test was used to analyze the differences in genotype frequency distribution between the LA and SA groups for each SNP and InDel . SNPs anSupplementary Table 3). Actin was used as the internal control with forward and reverse primers GCGTTCTGCCGTCTTCTAAA and CCCTCTTGGATTGTGCCTC, respectively.The expression patterns of candidate genes were explored using quantitative reverse-transcription PCR (qRT-PCR) at four development stages during petiole growth in LA lotus ‘Chongtai’ and SA lotus ‘Honghehuan.’ Total RNA was extracted from petiole tissues using an OminiPlant RNA Kit . The quality and quantity of the RNA was examined using 1.2% (w/v) agarose gel electrophoresis and a NanoDrop 2000 spectrophotometer , respectively. Reverse transcription to cDNA was carried out with 1 μg total RNA using the All-in-One First-Strand cDNA Synthesis Kit . The specific primers of the candidate genes were designed using Primer3 software (see footnote 2) . Gene expression was presented as relative expression levels calculated using the 2-ΔΔCT method  , 0.4 μL of each of forward and reverse primers, 1 μL cDNA template, and 8.2 μL ddHT method .Supplementary Table 4. The chi-square test was used to estimate the allelic differentiation of the developed InDel markers between the lotus LA and SA groups. For clustering analysis, similarity coefficients were used to construct an unweighted pair group mean algorithm (UPGMA) dendrogram using the SAHN module in NTSYS-pc v.2.10 software (InDels (at least 5 bp in length) located in the highly differentiated genomic regions with distinct allelic distribution between lotus LA and SA groups were selected to develop molecular markers. The verification methods were as described in part 4 of this section. Among the 43 representative germplasms, 11 LA and 12 SA lotus germplasms that were not re-sequenced were used in marker screening. Primers for the InDel markers are listed in software .Figure 1). As shown in Figure 1, 21 LA lotus germplasms (blue bars) were located in the top 15% of the phenotypic distributions, while 22 SA lotus germplasms (red bars) were located at the bottom 15% of the phenotypic distributions in all 3 years. Details on the 43 lotus germplasms are listed in Supplementary Table 1. To further explore the morphological characteristics and genomic differences between LA and SA lotuses, the 21 LA lotus germplasms together with the 22 SA lotus germplasms were chosen for further analyses, as the two groups expressed divergent plant architecture phenotypes.In total, 390 representative lotus germplasms with diverse plant architecture were collected across China. The 390 lotus germplasms are modern cultivars with diverse plant architecture phenotypes. Because the plant architecture of the lotus was mainly determined by petiole and peduncle length, the two traits were further measured in these lotus germplasms for three consecutive vintages (2013–2015). The phenotypic data of the two traits followed a normal distribution across the 3 years based on the results of the normality test . Multiple comparative analyses of petiole cell lengths and widths indicated that the petiole cell length of LA lotuses was significantly longer than that of SA lotuses , whereas there was no significant difference in cell width between the LA and SA lotuses . Additionally, the number of petiole cells in the vertical direction was significantly higher in LA lotuses than in SA lotuses . The phenotypic characteristics of peduncle cells also showed similar patterns to those of petiole cells, as suggested in Figures 2B,D,F.As mentioned above, cell number and cell size are two vital factors that facilitate elongation growth in plants . A totalR2 = 0.991; Figure 3B) was stronger than that between cell length and petiole length . The linear relationship between cell number and peduncle length was strong  and slightly larger than that between cell length and peduncle length . The results indicated that petiole and peduncle length had strong linear relationships with cell number and cell length.Furthermore, to determine the main causes that may be responsible for variation in plant height, the relationships between verticle cell number and petiole/peduncle length and between cell length and petiole/peduncle length were evaluated using linear regression analysis. The linear relationship between cell number and petiole length (Supplementary Table 1) used in the cell morphological analyses. Whole genome sequencing of the 20 selected lotus germplasms yielded 197.29 Gb of raw data (Table 1). A total of 196.12 Gb of high-quality clean data was obtained from the 20 germplasms following appropriate quality filtering as described in the “Materials and Methods” Section. The amount of high-quality clean data varied from 8.70 to 11.59 Gb for each germplasm (Table 1). The average sequencing depth was 12.22×. On average, 97.76% of the high-quality reads were successfully mapped onto the lotus reference genome using BWA software (Table 1). Mapped reads of the 20 germplasms covered approximately 95.66–98.62% of the reference sequence (Table 1), indicating that the generated data set was highly relevant to the reference genome.To detect genome-wide genetic variations between LA and SA lotuses, WGRS was conducted in the 10 LA and 10 SA lotus germplasms (software  (Table 1Supplementary Table 5) with an estimated SNP and InDel density of 9.68 and 1.37 per Kb. Among these polymorphic SNPs and InDels, 4,948,436 (63.57%) SNPs and 654,238 (59.31%) InDels were detected in intergenic regions, 477,162 (6.13%) SNPs and 110,722 (10.04%) InDels were detected in upstream and downstream regions, and 2,355,772 (30.26%) SNPs and 336,134 (30.47%) InDels were detected in genic regions. Among the polymorphisms in genic regions, 264,445 SNPs and 7,994 InDels were in exonic regions, and 2,090,709 SNPs and 327,807 InDels were in intronic regions (Supplementary Table 5).Genome-wide SNPs and InDels were detected among the 20 re-sequenced germplasms by applying SAMtools software . To incrSupplementary Table 2. Of the 35 SNPs, 31 were successfully sequenced and 98.23% contained the predicted genotypes . Of the 35 InDels, 30 were confidently scored and 96.17% contained the predicted genotypes . These results revealed fairly high validation rates and confirmed the high quality of the SNPs and InDels detected in this study.To validate the quality of the identified SNPs, 35 SNPs (from 16 loci) and 35 InDels were randomly selected for experimental validation. The locations and primers of SNPs and InDels selected in each scaffold are presented in Supplementary Figure 4). A genetic differentiation can be observed between LA group and SA group of lotuses. Some LA lotuses were genetically closer with SA lotus group. The reason maybe that the 20 lotus germplasms are all modern germplasms, and there may be genetic introgression between the two lotus groups.The phylogeny tree of the 20 lotus germplasms was further conducted based on all the SNP sites . Figure 4 shows the ZFST distribution of the sliding windows across the lotus genome. Considering the low genomic diversity between the LA and SA lotus genotypes, a strict cutoff parameter ZFST ≥ 3 was applied to select highly differentiated genomic regions which putatively related to lotus plant architecture. In total, 1,388 windows were identified, distributed across 35 scaffolds (Supplementary Table 6). After integration of these windows, a total of 92 regions covering 11.02 Mb of the genome were obtained. The highly differentiated genomic regions accounted for 1.37% of the lotus genome and the largest region was located on scaffold NW_010729262.1, covering 635 Kb. A total of 238 genes were identified within these highly differentiated genomic regions (Supplementary Table 6).Ft groups . To deteP < 0.05) were detected. Interestingly, several cell development and cell wall construction related GO terms were enriched. For example, GO: 0008360 was related to regulation of cell shape, and GO: 0022604 was regulation of cell morphogenesis). GO: 0010393 referred to galacturonan metabolic process, with GO: 0022603 is regulation of anatomical structure morphogenesis. These Go terms were related to cell proliferation/elongation (Supplementary Table 7).To better understand the function of the genes in differentiated genomic regions, gene functional enrichment analysis was performed. In detail, 114 significantly overrepresented GO terms (corrected ongation . Meanwhiongation  (SupplemP < 0.01) between the LA and SA lotus groups . Among them, 17,154 SNPs and 1,554 InDels were located in the highly differentiated genomic regions (Table 2). Subsequently, we placed more emphasis on variants that had effects on amino acids, as these variants are more important in functional analyses. A total of 120 SNPs together with six InDels were predicted to cause amino acid changes in 60 genes (Supplementary Table 8).Additionally, chi-square test was used to detect the allelic differentiation of SNPs and InDels between the LA and SA lotus groups. In total, 34,055 SNPs and 3,828 InDels showed distinct allelic distribution  may be involved in the cell cycle as the target gene of BABY BOOM AP2/ERF domain protein, which causes cells to re-enter the cell cycle  may play a role in cell wall composition and structure and in the cross-talk between the auxin, SA, and abiotic stress signaling pathways , which are predicted to be involved in the cell cycle, exhibited much higher expression in ‘Chongtai’ than in ‘Honghehuan’ during the S1, S2, and S3 stages . In addition, expression levels of the cell wall-related gene LOC104598294 in the S1 and S2 stages were also significantly higher in ‘Chongtai’ than in ‘Honghehuan,’ whereas the expression difference was not significant in stage S3 . Similarly, expression of the carbohydrate metabolism-related gene LOC104598311 was significantly upregulated in the S2, S3, and S4 stages in ‘Chongtai’ .Six genes among these candidates were selected for qRT-PCR analyses to determine whether they have different expression modes in lotuses with opposite plant architecture. The expression profiles of the six candidate genes were analyzed in four petiole developmental stages in the LA lotus ‘Chongtai’ and SA lotus ‘Honghehuan.’ Four genes . Chi-square test further indicated that 34 of the 50 InDels differed significantly (P < 0.01) between the LA and SA lotus groups, indicating the stability of the significant association between these InDel markers and plant architecture type (Supplementary Table 4). Despite the close association with plant architecture, the 34 new InDel markers were also examined for their potential use in genetic diversity analyses among the 23 lotus germplasms. The UPGMA phylogenetic tree revealed two major clusters, with 11 LA lotus germplasms in one cluster and 12 SA lotus germplasms in the other , indicating that the 34 InDel markers can be applied to differentiate LA and SA lotuses.A total of 386 InDels (at least 5 bp in length) located in the highly differentiated genomic regions showing distinct allelic distribution between LA and SA lotus groups were screened out. To develop InDels markers related to lotus plant architecture, 70 InDels were randomly selected and verified in the 11 remaining LA and 12 remaining SA lotus germplasms that were not re-sequenced among the 43 representatives of extreme plant architecture. Following PAGE validation, three primer pairs that did not amplify any fragments, 16 primer pairs that amplified numerous non-target bands or smeared target bands, and one marker that was shown to be monomorphic were abandoned. The remaining 50 InDels markers were confidently scored and exhibited dimorphism , as did peduncle length (R2 = 0.994) . Additionally, the vertical cell numbers of the petiole and peduncle in LA lotuses were significantly higher than those in SA lotuses . The results were consistent with previous studies showing that petiole elongation in response to submergence and phytohormone treatment was mainly caused by increases in cell number . Although the cell length of the petiole and peduncle showed strong linear relationships with petiole length (R2 = 0.966) and peduncle length (R2 = 0.962), respectively , the linear relationship between cell length and plant height was relatively weaker than that between vertical cell number and plant height. In a previous study, cell length showed few changes or even decreased when petioles were elongated in response to submergence and treatment with different phytohormones  between the LA and SA lotus groups, indicating the tight relationship between these InDel markers and plant architecture. Furthermore, the UPGMA phylogenetic tree constructed with the 34 InDel markers completely distinguished the LA and SA lotus groups. These InDels are predicted to be promising markers linked to plant architecture and may be applied in the early selection of plant architecture in the lotus.The polymorphisms of the 70 InDels located at the highly differentiated genomic regions putatively related to lotus plant architecture were further detected in 11 representative LA and 12 representative SA lotus germplasms, and 50 InDel markers amplified clear target bands and exhibited dimorphism. A total of 34 InDel markers (68%) were still highly differentiated (ST genome scan identified 11.02 Mb of highly differentiated genomic regions between the two groups. Meanwhile, chi-square test revealed 34,055 SNPs and 3,828 InDels with distinct allelic distribution between the LA and SA lotus groups. Functional analyses revealed 11 candidate genes putatively associated with plant architecture management. In addition, we developed and verified 34 InDels markers putatively associated with plant architecture in the lotus. The candidate genes and trait-associated markers detected in this study will contribute to selective breeding and a greater understanding of the genetic mechanisms underlying plant architecture in the lotus.In summary, we detected 7,784,623 SNPs and 1,103,064 indels among 20 lotus germplasms with contrasting plant architecture. FL-QC and JZ conceived and designed the experiments. MZ and J-XY performed the experiments. JZ and MZ analyzed the data and wrote the paper. MZ, T-YM, H-HZ, and LX contributed reagents, materials, and analysis tools.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.


Photobacterium damselae subsp. piscicida (Phdp), which may cause high mortalities (90–100%) in sea bream. Selection and breeding for resistance against infectious diseases is a highly valuable tool to help prevent or diminish disease outbreaks, and currently available advanced selection methods with the application of genomic information could improve the response to selection. An experimental group of sea bream juveniles was derived from a Ferme Marine de Douhet  selected line using ~ 109 parents . This group of 1187 individuals represented 177 full-sib families with 1–49 sibs per family, which were challenged with virulent Phdp for a duration of 18 days, and mortalities were recorded within this duration. Tissue samples were collected from the parents and the recorded offspring for DNA extraction, library preparation using 2b-RAD and genotyping by sequencing. Genotypic data was used to develop a linkage map, genome wide association analysis and for the estimation of breeding values.Photobacteriosis is an infectious disease developed by a Gram-negative bacterium Phdp revealed moderate genomic heritability with estimates of ~ 0.32. A genome-wide association analysis revealed a quantitative trait locus (QTL) including 11 SNPs at linkage group 17 presenting significant association to the trait with p-value crossing genome-wide Bonferroni corrected threshold P ≤ 2.22e-06. The proportion total genetic variance explained by the single top most significant SNP was ranging from 13.28–16.14% depending on the method used to compute the variance. The accuracies of predicting breeding values obtained using genomic vs. pedigree information displayed 19–24% increase when using genomic information.The analysis of genetic variation for resistance against Phdp. Prediction accuracies obtained using genomic information were significantly higher than the accuracies obtained using pedigree information which highlights the importance and potential of genomic selection in commercial breeding programs.The current study demonstrates that SNPs-based genotyping of a sea bream population with 2b-RAD approach is effective at capturing the genetic variation for resistance against The online version of this article (10.1186/s12863-018-0631-x) contains supplementary material, which is available to authorized users. Sparus aurata) is a widely distributed highly important farmed fish species. Global production of gilthead sea bream has risen about 60% during the last decade with an increase of 116,858 to 175,232 metric tons [The gilthead sea bream  is a reduced representation high-throughput sequencing technique for the simultaneous detection of single nucleotide polymorphisms (SNPs) and genotyping of individuals for the detected SNPs . RAD-SeqThe use of recent available technological advancements (i.e. genome-wide sequencing and genotyping technologies) allows to perform high resolution studies to detect molecular markers that are linked to trait(s) of economic importance, which could prove potent tools to overcome challenging genetic correlations. Several studies have indicated that knowledge about genetic markers linked to genes affecting quantitative traits can increase the selection response of animal breeding programs, especially for traits that are difficult to improve by traditional selection , 24. SevMoreover, development of genomic resources using described advancements also provides the opportunity to apply advanced selection methods e.g. genomic selection (GS) with state of the art statistical models. The application of GS with genome-wide distributed molecular markers (e.g. SNPs) to breeding is particularly valuable for difficult traits like disease resistance, which is almost impossible to measure on the selection candidates. Breeding values estimated using genomic prediction with genome-wide optimally dense molecular markers can deliver significant improvements in selection accuracy compared to traditional pedigree-based approaches , 28, 29.The current study aimed at identifying genetic basis of resistance to photobacteriosis in the gilthead sea bream by estimating the genetic variation in explaining resistance to this infectious agent, and performing a GWAS to detect quantitative trait loci and estimate accuracy of pedigree vs. genomics based selection.A population of nearly 1300 gilthead sea bream juveniles originated from the Ferme Marine de Douhet  breeding nucleus derived from a cross of 109 parents . Parents were crossed by artificial fertilization using full factorial mating design in “blocks” with 8 block in total, and in each block 3–4 females were fertilized by 9–11 males. Finclip tissues were sampled from each parent for later parentage assignment and genomic analysis. All the experimental fish were created the same day and mixed in a single non-sorted batch.Photobacterium damselae subsp. piscicida  by intramuscular inoculation of 1000 CFU. The remaining 67 fish represented the biological control group and received intramuscular injection with PBS 0.01 M.The developed population of nearly 1300 individuals (body weight ~ 3–5 g) at ~ 120 days post hatching was transferred to the experimental aquarium of the Istituto Zooprofilattico Sperimentale delle Venezie  for the challenge test. The population was kept for 30 days in rectangular fiberglass 4000 L tanks supplied with re-circulating aerated seawater  to get acclimatized and fed with a commercial pellet diet . A total of 1233 fish were challenged with a virulent strain of PDS) and day to death (PD2D) post challenge. Individuals that survived until day 18 were considered to be survivors. Finclip tissue samples were collected from all dead and alive individuals and stored in ethanol 85% at 4 °C.The challenge test lasted for 18 days and mortalities were recorded twice a day within this duration. A random sample of 30 dead fish was analyzed using validated molecular assay detailed by Roberta et al. (2018) [Genomic DNA was extracted from ~ 20 mg of collected tissue samples of parents and progeny using a commercial kit , following the manufacturer’s instructions. The resulting DNA quantity was determined by using a Qubit fluorimeter with a dsDNA BR Assay  and DNA quality was checked at 1% agarose gel electrophoresis.AlfI  at 37° for 1 h, followed by enzyme heat inactivation at 65° for 20 min. The ligation reaction was performed by combining 6 μl of digested DNA with 20 μl of a ligation master mix containing 0.4 μM each of two library-specific adaptors with fully degenerate cohesive ends (5′ -NN- 3′), 10 mM ATP , and 1 U T4 DNA ligase . Ligation was carried out at 16° for 3 h, with subsequent heat inactivation for 10 min at 65°.A total of 1342 2b-RAD libraries (109 parents and 1233 juveniles) were prepared following the protocol reported by Wang et al.  with minhttp://comailab.genomecenter.ucdavis.edu/index.php/Barcode_generator). The PCR reaction were prepared in a volume of 50 μl containing mixes of 12 μl of ligated DNA product, 0.5 μM of each primer , 0.2 μM each primer , 25 mM dNTPs , 1× Phusion HF buffer, and 1 U TaqPhusion high-fidelity DNA polymerase (New England Biolabs). This PCR reaction of 50 μl was then divided into three independent reactions of 16.6 μl each to reduce the problem of PCR duplicates.Sample-specific barcodes were designed through a Barcode Generator program  . The quaIndividual libraries were pooled into equimolar amounts by adopting two different multiplexing strategies for parents (64 libraries per pool) and offspring (128 libraries per pool). The quality of each pool was verified on Agilent 2100 Bioanalyzer. Finally, pooled libraries were sequenced on an Illumina NextSeq500 platform  using 50 base single-end sequencing .Adapters trimming and filtering based on the quality score of the sequenced reads were performed using custom developed scripts from the 2bRAD pipeline v2.0 , with fiGenotypes were called for each animal with a minimum genotype quality of 20, a minimum read depth of 5, and a population wise observed minor allele counts for a particular site must be at-least 50. A minimum of 40 individuals in a population needed to have a genotype call that met this criterion at a specific position. A SNP that passed the above mentioned criteria were considered as a putative SNP for further analyses.n = 750) were then used to construct pedigree applying the likelihood ratio method implemented in CERVUS version 3.0 [The SNP-based genotypic data were available for both parents and offspring, which were further filtered based on polymorphism by applying a criteria of minor allele frequency MAF ≥ 0.35) and genotyping rate of ≥90%. The remaining selected set of highly informative SNPs (sion 3.0 . Parenta and genosion 3.0  with theP < 0.001), and Hardy-Weinberg equilibrium exact test (P < 1.0e− 7). Individuals were filtered out based on two criteria; (i) with more than 30% missing rate of genotype calls, and (ii) individuals which had less than 4 full-sibs in a family. The criterion “ii” was only used for the construction of linkage map to include only informative families for analysis and to avoid computational problems in building map. Linkage groups (LGs) were built using a minimum LOD threshold value of 46 in the “SeparateChromosomes” module of Lep-Map v2 [Quality controls (QC) were performed both at marker and individual level, SNPs were filtered out based on the criteria of locus specific missing rate > 30%, deviation from expected Mendelian segregation  and PD2D (day to death) was estimated by ASReml 4.0 [A) or genomic (G) relationship matrix with the following linear mixed model:A summary of phenotypic data was obtained from a generalized linear model in R. The heritability of Reml 4.0  using a y is a vector of ‘n’ records on PDS and/or PD2D, μ is an overall mean, u is a vector of additive genetic effects distributed as , or G and A are genomic and pedigree relationship matrices, respectively; Z is the corresponding incidence matrix to additive effects, and e is the vector of random residual effects with where piis the allele frequency of second allele and Nsnp is the total number of SNP markers, while pedigree relationship matrix was computed using algorithm of Meuwissen and Luo [The genomic relationship matrix was constructed using the VanRaden  method a and Luo . Heritab1) was used to perform genome wide association analysis, however an additional variable was added to estimate marker effects. The GCTA program [A genome wide association analysis was performed using a linear mixed model equation. The same model as described in . The genome-wide significant threshold used in this study was P ≤ 2.22 × 10−06, which is equivalent to −log10(P) = 5.65.SNPs were considered genome-wide significant when they exceed the Bonferroni threshold for multiple testing  of 0.05/Quantile-quantile plot with distribution of observed vs. expected p-values was checked, and the Inflation factor  was calculated using following equation.pi and qi are allele frequencies for the major and the minor alleles respectively, αi is the allele substitution effect, For the direct approach, variance explained by significant markers was computed as 2piqiαi2 . TherefoGWS) SNP(s) was estimated using the model (1) with the addition of a fixed effect of detected GWS SNP(s). However, the G matrix used in this model was constructed with all other SNPs except GWS SNP(s). The variance (genetic or phenotypic) explained by the GWS SNP(s) was expressed as a reduction in the total genetic or phenotypic variance.For the indirect approach, the proportion of the genetic or phenotypic variance explained by the genome-wide significant (Variances explained by the top significant SNP(s) were estimated using two following approaches (direct and indirect).PDS and PD2D were computed to quantify and compare the accuracy of the breeding values estimated using pedigree or genomic information. The same model (1) described under the genome-wide association study was applied and the predictions were performed using PBLUP, GBLUP, BayesB, BayesC [Pedigree vs. genomic breeding values for , BayesC , and Bay, BayesC  models u, BayesC  program.Accuracy of prediction was calculated using cross validation scheme by random masking ~ 20% of the population with 994 training and 248 validation animals.rcorr) of the estimated breeding value (pedigree/genomic) with the true phenotype, which were scaled by the square root of the heritability as The mean accuracy of 20 replicates were computed as correlation  are given in Table Descriptive statistics of the recorded traits  million per parent and 3.89 (±1.72) million per offspring. The quality filtering of raw reads slightly reduced the number of reads with a 0.50% (26.05 million reads) loss, which resulted in an average number of quality reads of 5.57 million per parent and 3.86 million per offspring.The catalogue of tags that was built from the quality reads of parents which consisted of 230,500 unique 2b-RAD loci which were used as reference sequence. The SNP calling revealed that 37,247 of the tags had at-least one SNP detected for this population of parents and offspring. However, in order to increase overall informativeness and minimize the amount of missing or erroneous information, SNP data were further filtered using criteria of minor allele frequency ≥ 0.05 per SNP and locus specific genotyping rate of ≥30%, which resulted in 28,330 quality SNPs left for further analyses. Individuals were also removed if genotyping rate of an individual was < 30%, and the level of heterozygosity > 65%. Out of total 1342 individuals, 46 individuals (~ 3%) did not meet this criterion and hence were filtered out. After the above described filtration steps, SNPs based genotyping data consisted of 28,330 loci typed on 1296 individuals (1187 offspring 109 parents).Pedigree construction using selected highly informative SNP markers produced 177 full-sib families with 1–49 sibs per family. Out of 177 full-sib families, 74 families had a minimum of 5 sibs per family which were used for the construction of linkage map. The comparison of assignment results using likelihood method vs. opposite homozygotes count showed concordance of about 97.79% in assignments.n = 24 LGs). The female genetic map was 2068.32 cM and the male genetic map was 1727.02 cM.The linkage map consisted of 22,544 SNPs, which were grouped into 24 linkage groups SA01-SA24) with a total sex-average map length of 1970.29 cM  in descending order as BayesB (accuracy = 0.577 and 0.554 respectively), Bayesian Lasso (accuracy = 0.572 and 0.547 respectively) and BayesC (accuracy = 0.567 and 0.537 respectively) with marginal differences in accuracies  for resistance as well as determine the consistency in accuracies for genomic vs. pedigree based prediction methods. Photobacteriosis is one of the major infectious diseases that leads to high economic losses, and it is highly important to find efficient strategies to combat this infectious disease.Phdp, which was different compared to the other two studies where fish were infected using bathe method [The observed mortality of ~ 37% in our challenged test was significantly lower than reported by Antonello et al.  and Palae method , 9. Obsee method  and PalaPhdp  had moderate heritability with values ranging from 0.308 to 0.332 varying with source of information (pedigree or genomic) and trait (PDS and/or PD2D) used. We did not find any significant difference in heritability estimates of PDS or PD2D, and the genetic correlation between these traits was ~ 1, giving the impression that these are the same trait, possibly because mortalities happened in a very small window of time and hence making both traits behave in the same way. The Moderate heritability illustrates that there is encouraging potential for improving resistance against Phdp through selective breeding. The moderate level of heritability estimates (h2 = 0.31 to 0.33) obtained in our study were very similar to those reported by Palaiokostas et al. (2016) [h2 = 0.22 and 0.28), and falling within the range of estimates reported by Antonello et al. (2009) [h2 = 0.18 to 0.45).In the current study, the variances were estimated using two sources of information separately i.e. genomic or pedigree information. Results showed that resistance against . (2016)   specific linkage maps showed an occurrence of sex-biased recombination with total female-specific map was 341.3 cM longer than the male specific map with female-to-male recombination rates of 1.2:1.0  genome-wide significant SNPs that had unfavorable effects on the trait  in the challenged population which could not be adjusted due to lack of individual specific records on body weight.The obtained λ values of GWAS for α and allele frequencies [P-values for all the SNPs at linkage group 17  should be the one accounted to avoid any possible inflations. The relatively large impact of single QTL on the total genetic variation  is an SRY-box (Sox) transcription factor that is important during development of different tissues and organs. Sox-17 has been recently reported to also control adult hematopoiesis [Sox-17 locus have a role in response to bacteria. The next gene encodes a mitochondrial protein that is part of the large (39S) ribosomal subunit, without clear association with immune response [The sequence (36 bp) of the highest significantly associated tag/SNP 248936_32) was aligned to the unpublished sea bream genome assembly (Saurata_v1), which resulted in significant  alignment with chromosome 19. The region of approximately ±50Kb surrounding the highest significant SNP “248936_32” was searched for underlying candidate genes that might affect the resistance against pathogen. Detected genes within the specified region are detailed in  is a key enzyme regulating an important post-translational protein modification, S-palmitoylation. Protein palmitoylation is crucial for functioning of key immune-related proteins . Bacteria might also high-jack the palmitoylation machinery of host cells to internalization, survival, and replication [Acyl- thioesterase 1 (lication .RGS) proteins, which are regulatory and structural components of G protein-coupled receptor (GPCRs) complexes [RGS proteins expressed in immune effector cell such as mast cells and lymphocytes as well as in their end-organ targets  represent an important regulatory component of the intracellular signaling pathways induced by GPCRs in allergic inflammation [Phdp) of this disease has ability to cause septicemia and inflammation in different organs . Variation in expression levels of RGS protein due to genetic variants at the locus may lead variation in resistance.Regulator of G protein signaling 20 belongs to a family of regulator of G protein signaling  used, we observed 29 to 52% higher accuracy of prediction compared to the reported results by Palaiokostas et al. (2016) . The obtPDS and PD2D, and the ranking was followed by Bayesian Lasso BayesC and GBLUP with estimates as an average of both traits (PDS and PD2D). Relative performance of the GS models depends on the genetic architecture of the trait, and the GWAS results of our analysis revealed that a few genes/loci with moderate to large effect and many loci with a small effect might be involved in total genetic variation of the trait. Thus, Bayesian prediction methods are expected to perform better or equally well as GBLUP method [The comparison of accuracy of prediction within genomic information based models  showed that BayesB fractionally outperformed all other models for both P method , 67 whicPhdp. A genome-wide significant QTL at LG17 was detected which explained 13.28 to 16.14% of genetic variation. The QTL region is encased by genes like SAP, Il6ra and 26S non-ATPase regulatory subunit which might be involved in variation for resistance against Phdp. Prediction accuracies obtained using genomic information were significantly higher (19 to 24%) than the accuracies obtained using pedigree information which highlights the importance and potential of genomic selection in commercial breeding programs.The current study demonstrates that SNP based genotyping of a sea bream population using 2b-RAD approach is effective at capturing the genetic variation for resistance against Additional file 1:P-value, LD among significant SNPs, genes at QTL region and Manhattan plots. This file contains information on average and sex-specific  linkage maps along with 36 bp sequences of 2b-RAD loci (XLSX 1344 kb).Information on inflation of Additional file 2:P-value, LD among significant SNPs, genes at QTL region and Manhattan plots. This file comprises supporting information which contains Q-Q plot, zoom in view of GWAS based P-values distributed at chromosome 2, information on genes around the highest significant SNP of chromsome2, LD values with heatmap for the top 5 significant markers of GWAS analysis, and Manhattan plots showing SNP positions corrected using linkage map information, and the shrinkage of p-values using model with the addition of top significant SNP used as fixed effect. Figure S2.1. Distribution of dead and alive sibs out of total sib count per full-sib family. Figure S2.2. Manhattan plot of –log10 P-values for P_D2D trait distributed across different LGs. Figure S2.3. (A-B): Manhattan plot of –log10 P-values for P_D2D trait distributed along the length (cM) of SA17. Figure S2.4. Q Q-Plot of –log10 P-values – days to death phenotype (P_D2D). Table S2.5. Variances explained by genome-wide significantly associated SNPs of P_D2D trait. Table S2.6. Summary for the functions of important candidate genes at QTL region (DOCX 718 kb).Information on inflation of


Increasing grain yield is a primary objective of maize breeding. Dissecting the genetic architecture of grain yield furthers genetic improvements to increase yield. Presented here is an association panel composed of 126 maize inbreds (AM126), which were genotyped by the genotyping-by-sequencing (tGBS) method. We performed genetic characterization and association analysis related to grain yield in the association panel.P < 1 × 10− 3 across two environments (Yangling and Yulin). Among those SNPs, two loci displayed overlapping regions in the two environments. Finally, 30 candidate genes were found to be associated with grain yield.In total, 46,046 SNPs with a minor allele frequency (MAF) ≥0.01 were used to assess genetic diversity and kinship in AM126. The results showed that the average MAF and polymorphism information content (PIC) were 0.164 and 0.198, respectively. The Shaan B group, with 11,284 unique SNPs, exhibited greater genetic diversity than did the Shaan A group, with 2644 SNPs. The 61.82% kinship coefficient in AM126 was equal to 0, and only 0.15% of that percentage was greater than 0.7. A total of 31,983 SNPs with MAF ≥0.05 were used to characterize population structure, LD decay and association mapping. Population structure analysis suggested that AM126 can be divided into 6 subgroups, which is consistent with breeding experience and pedigree information. The LD decay distance in AM126 was 150 kb. A total of 51 significant SNPs associated with grain yield were identified at These results contribute to the genetic characterization of this breeding population, which serves as a reference for hybrid breeding and population improvement, and demonstrate the genetic architecture of maize grain yield, potentially facilitating genetic improvement.The online version of this article (10.1186/s12863-018-0669-9) contains supplementary material, which is available to authorized users. Zea mays L.), the most widely grown crop in the world, plays an essential role in global food security and industrial products [Maize , metabolome features and kernel rows, has greatly increased because of the development of association analyses, including candidate gene association mapping and GWAS in maize and other species –12. Howeelopment . Later, elopment , 15. At nd maize –19, becaFor maize, a variety of SNP chips based on SNP genotyping platforms have been designed by sequencing known genes for genotyping. These chips include the Illumina® SNP1536 chip, the MaizeSNP50 BeadChip with a high resolution, and a higher-density 600 k SNP genotyping array based on 57 M SNPs and small indels determined from 30 representative temperate maize lines in comparison with B73 AGP_v3 –22. HoweIncreasing grain yield is the primary target for meeting the food demand of the growing population, and dissecting the genetic architecture of grain yield is helpful for achieving this goal. Due to the complexity of the genetic architecture of grain yield and the difference between association populations, some researchers have aimed to uncover the genetic architecture of grain yield through association mapping; however, these studies are far from sufficient. Therefore, in this study, 126 maize inbred lines from the Shaan A and Shaan B groups were selected for genotyping with tGBS sequencing technology. Our aims were 1) to perform detailed characterization of the association mapping panel, including relationships, population structure, and genetic diversity; and 2) to dissect the genetic basis of grain yield in the association mapping panel.The association mapping panel consisted of 126 diverse inbred lines (AM126) selected from Shaan A and Shaan B group inbreds cultivated at Northwest A&F University. According to the theory of domestic and international maize breeding, we simplified heterotic model and adopted the breeding strategy of two divergent heterotic groups to build Shaan A and Shaan B heterotic groups, in which superior varieties are employed as basic materials to adapt maize production to Shaanxi Province [Upon harvest, all ears were harvested by hand threshing, and corresponding data, including the grain water content, total grain weight and weight of ten panicles, were recorded to calculate the grain yield per mu (kg) by multiplying the grain yield per panicle by the total number of plants in the plot and adjusting to a 14% moisture content. Then, the mean grain yield of two replicates was calculated for subsequent analysis  of yield is estimated with the formula: g2 is the genetic variance; σge2 is the interaction variance between the genotype and environment; σз2 represents the residual error variance; and n and k represent the environment and number of replications, respectively.Phenotypic data analyses, which included basic descriptive statistical analyses, ANOVA and Pearson correlation analysis, were carried out using SPSS v.22 software . According to the method described by Knapp et al. , the broallele1 x Frequencyallele2 + 20%) from the TASSEL-GBS Pipeline [Total genomic DNA was extracted from leaf samples of each inbred line based on the CTAB procedure . FundamePipeline .The polymorphism information content (PIC) and MAF, which can be used to evaluate the genetic diversity of the population, were calculated with Powermarker v3.25  using 46where Plu and Plv refer to the frequency of the uth and vth alleles of marker l, respectively. A PIC value from 0 to 0.25 indicates low polymorphism; a PIC value from 0.25 to 0.5 indicates intermediate polymorphism; and a PIC value from 0.5 to 1 indicates high polymorphism. The MAF was used to quantify the degree of genetic differentiation in the maize population. The ratio of the number of SNPs with less variation to the total number of SNPs at each SNP locus was calculated. To avoid the influence of sample size, a re-sampling strategy was adopted in this study. The distribution of the SNPs unique to the Shaan A inbred lines or the Shaan B group inbred lines on ten chromosomes was determined with the ggplot R package.The relative kinship matrix between inbred lines i and j was calculated with TASSEL v.5.0 software to explore the pairwise relationships of the 126 inbred lines. The results were illustrated with the Genomic Association and Prediction Integrated Tool-R (GAPIT) package . All negIn addition, to rapidly investigate population structure, 31,983 high-quality SNPs were screened with stringent criteria  using TASSEL v.5.0 software and imported into Admixture software version 1.23 for cross validation . The optP < 1 × 10− 3 was chosen to determine significant SNPs for the trait. Thereafter, the LD decay distance in AM126 was estimated using TASSEL v.5.0 software. Finally, we confirmed the unique candidate genes underlying the association signals with SNP markers based on the LD decay distance of this population and annotated the candidate genes according to the information available in the Maize Sequence (http://ensembl.gramene.org/Zea_mays/Info/Index) and the MaizeGDB (http://www.maizegdb.org/gbrowse) databases. Because only a version 3 gene annotation file exists, all v4 gene IDs were converted to v3 gene IDs and then annotated.A total of 31,983 SNPs with MAF ≥0.05 and missing rate ≤ 50% were filtered for the genome-wide association study (GWAS). The GWAS of the grain yield data from the two locations was accomplished in the TASSEL v.5.0 software with a mixed linear model (MLM), controlling for population structure and relative kinship (K + Q) to avoid spurious associations . When ushttp://ensembl.gramene.org/Zea_mays/Info/Index, AGPV4). Ultimately, 1,133,188 sites were identified, among which 46,046 SNPs were polymorphic in AM126, with a missing rate of less than 50% and MAF of more than 0.01. For the 46,046 SNPs, the number of SNPs per chromosome ranged from 3235 SNPs on chromosome 10 to 6513 SNPs on chromosome 1  with yield. Only one of these loci was the same in the two environments. The Manhattan plots showed that the SNPs associated with yield were spread across the genome in Yangling and were distributed mainly on chromosome 4 in Yulin  using 31,983 high-quality SNPs. As shown in the quantile-quantile plots of grain yield Fig. , d, fewelin Fig. , c. Whenhttp://ensembl.gramene.org/Zea_mays/Info/Index), MaizeGDB (http://www.maizegdb.org) and InterPro (http://www.ebi.ac.uk/interpro), genes that may be associated with yield were identified and were illustrated in Table GBP14, encodes the GLABROUS1 enhancer-binding protein (GeBP) transcription factor, which belongs to the GeBP family (IPR007592) and is located on chromosome 4. However, we did not find genes of known function at the loci with the lowest p values; these genes may not be directly involved in the relevant pathway, or the SNP loci may be linked with nearby genes.According to the gene and protein annotations from Maize Sequence , discovered in Yulin, was annotated as a GeBP transcription factor belonging to the DUF573 family, which includes GeBP and GeBP-like proteins as well as storekeeper and storekeeper-like (STKL) transcription factors (http://pfam.xfam.org/family/PF04504.13). In Arabidopsis, GeBP and GeBP-like proteins play roles in cytokine hormone pathway regulation [Arabidopsis [zmgf14–4 and zmgf14–6, which exhibit prominent expression during maize kernel development [Arabidopsis [The main objective in maize breeding programmes is to increase grain yield . GWAS is soybean , 58. Ano soybean . Therefogulation . The STKgulation , and an bidopsis . Therefoelopment . The levelopment . The GF1bidopsis . Hence, AtERF53 increases drought tolerance by facilitating stress-responsive gene expression in Arabidopsis [ZmNAC111, ZmVPP1 and ZmDREB2A, were not identified in this population [The genetic improvement of maize yield not only improves yield potential, but also increases stress tolerance . Grain ybidopsis . A shortbidopsis . Zm00001bidopsis , 72. Thepulation –75; it iIn the present study, the genetic diversity, population structure, relationships and association maps related to the grain yield of 126 inbred lines (AM126) selected from the Shaan A and Shaan B groups cultivated at Northwest A&F University were assessed using high-quality SNPs. The MAF and PIC of AM126 were 0.164 and 0.198, respectively. The Shaan B group exhibited a higher MAF and PIC and more unique SNPs than did the Shaan A group. Therefore, the Shaan B group has a broader genetic basis than the Shaan A group. AM126 was divided into 6 subgroups according to genotype analysis and breeding experience, and the inbreds display low relative kinship. GWAS was performed on this population to identify regions associated with grain yield using a population structure and kinship (Q + K) model. A total of 30 candidate genes related to grain yield were identified, among which two were identified in both environments and the others in a single environment. The largest proportion of the genes identified in this study were associated with abiotic stress, which is consistent with the climate conditions. These results illustrate the genetic characteristics of the breeding population and provide favourable alleles related to maize yield, and they can serve as a guideline for hybrid breeding and genetic improvement in the future.Additional file 1:Table S1. Numbers, groups and names of the AM126 lines. (XLSX 14 kb)Additional file 2:Table S2. The grain yield data of the AM126 lines. (XLSX 17 kb)Additional file 3:The hapmap file of SNP genotyping data of the AM126 lines. (TXT 13628 kb)Additional file 4:Table S3. Pairwise kinship coefficients between AM126 lines calculated with 46,046 SNPs. (XLSX 57 kb)Additional file 5:Figure S1. Heatmap of AM126 based on the kinship matrix computed using 46,046 SNPs. (PDF 135 kb)Additional file 6:Table S4. Co-ancestral membership of AM126 when K = 6. (XLSX 57 kb)Additional file 7:Figure S2. Frequency map of grain yield measured in the samples from Yangling (a) and Yulin (b). (PDF 220 kb)Additional file 8:Figure S3. Picture illustrating LD decay distance in AM126. (PDF 96 kb)


Centrocercus minimus) is a sagebrush obligate species with a constricted range consisting of seven discrete populations, each with distinctly different habitat and climatic conditions. Though geographically close, populations have low levels of natural gene flow resulting in relatively high levels of differentiation. Here, we use 15,033 SNP loci in genomic outlier analyses, genotype–environment association analyses, and gene ontology enrichment tests to examine patterns of putatively adaptive genetic differentiation in an avian species of conservation concern. We found 411 loci within 5 kbp of 289 putative genes associated with biological functions or pathways that were overrepresented in the assemblage of outlier SNPs. The identified gene set was enriched for cytochrome P450 gene family members  and could impact metabolism of plant secondary metabolites, a critical challenge for sagebrush obligates. Additionally, the gene set was also enriched with members potentially involved in antiviral response (DEAD box helicase gene family and SETX). Our results provide a first look at local adaption for isolated populations of a single species and suggest adaptive divergence in multiple metabolic and biochemical pathways may be occurring. This information can be useful in managing this species of conservation concern, for example, to identify unique populations to conserve, avoid translocation or release of individuals that may swamp locally adapted genetic diversity, or guide habitat restoration efforts.Understanding the genetic underpinning of adaptive divergence among populations is a key goal of evolutionary biology and conservation. Gunnison sage‐grouse ( Groups within a species which can be used to guide management and conservation efforts, termed conservation units  is a sagebrush (Artemisia spp.) obligate avian species persisting as seven isolated populations with low gene flow and high genetic differentiation ; Crawford is dominated by big sagebrush and black sagebrush (A. nova); Dove Creek has patchy big sagebrush and black sagebrush cover throughout; San Miguel is dominated by lowbrush sage (A. arbuscula) at low elevations and more contiguous low, black, and big sagebrush cover at higher elevations; and Piñon Mesa is dominated by big and silver sagebrush (A. cana) at lower elevations and patchy big and silver sagebrush at high elevations.The Gunnison sage‐grouse (Although populations are close in proximity (33.34 to 203.72 km apart) relative to observed dispersal capabilities  are genetically distinct enough at neutral loci to warrant consideration for special protection  to remove adapter dimers present in the reaction. To amplify DNA fragments, we performed a 10 µl PCR using 2 µl of cleaned ligation for each sample, 1 µl 10× Buffer (Fisher Scientific), 1 µl dNTPs, 0.2 µl each of the forward and reverse primers, 0.2 µl AmpliTaq Gold (Fisher Scientific), and 5.4 µl ddH2O. The thermocycler protocol for the PCR consisted of 22 cycles of the following: 95°C for 30 s, 55°C for 30 s, and 72°C for 30 s. Each sample was amplified with 9 independent replicates, with all PCR replicates for a sample being pooled into a single sample in an effort to identify and reduce the effects of PCR error. A 16‐µl aliquot of the pooled PCR replicates for each sample was then pooled into a single Eppendorf tube creating a multisample pool, which was then cleaned with SPRI beads in a 1:1 ratio to remove PCR dimers and small‐size amplicons. We performed a final size‐selection step using the Pippen Prep (Sage Science) selecting for fragments between 300 and 500 base pairs. The final size‐selected library was sent to the Genomics and Cell Characterization Core Facility at the University of Oregon in Eugene, Oregon, and was sequenced on the Illumina HiSeq 4,000 platform (Illumina).We accomplished SNP identification using an adapted version of the ddRAD protocol as first described by Peterson, Weber, Kay, Fisher, and Hoekstra . The dou2.4Cmin_1.0 Gunnison sage‐grouse draft genome assembly , allowing at most two ambiguous bases. Reads were mapped to the mpileup function using the recommended map‐quality adjustment (“‐C” set to 50) and base‐alignment qualities recalculated from the combined data. Indels were called for the purposes of filtering nearby SNP sites (within 3 bp) that could be affected by local misalignment, but were not otherwise used. Genotype likelihoods were estimated with the bcftools call function using the multiallelic model, although only biallelic loci were retained. SNP loci were further filtered by requiring a minimum coverage of 960× across all individuals  and called genotypes for at least 50 of the total 64 individuals. Loci potentially located on sex chromosomes were removed using both coverage and homology information: SNPs on scaffolds putatively homologous with the sex chromosomes of Gallus gallus  of 0.01). We verified that the scaled covariance matrix of population allele frequencies estimated from the simulated data was close to the matrix estimated from our data  greater than 4 were considered significantly associated. Gautier  marginal constrained axes were retained for evaluation of candidate SNPs  as a cut off for candidacy.Correlation of environmental covariates with SNP genotypes was evaluated using the standard covariate model in BayPass. The model was implemented as in the core model, though including the population covariance matrix estimated with the core model and the addition of regression coefficients which had a uniform prior bounded between −0.3 and 0.3. Covariates with an empirical Bayesian  Gautier  recommen Gautier . RDA is  Gautier . However2.5.3NE to 1,000 as recommended to indicate our data are from a small and inbred population  decay to determine at what distance candidate loci could be considered physically linked to a putative gene region on the reference genome based on the position of SNPs on pseudo‐chromosomes. To phase our SNPs we used BEAGLE 5.0, setting p‐value (before FDR adjusted) is calculated as the proportion of simulations with more genes for a category with at least one candidate locus than the whole observed data set. We used the SNP mode (a gene region containing multiple SNPs was counted once for each SNP and assumed complete linkage equilibrium) to test against the GO categories, with 100,000 simulations to generate the null distribution. We also used gene lists derived from Gowinda for all analyses  to evaluate significantly overrepresented functional annotation terms in DAVID  enrichment analysis. We used Gowinda v1.12  and accounted for 31.2% of the total variation. A total of 602 SNPs were identified as outliers in the tails of the first 4 axes . The significant axes accounted for descending amounts of variation  and different numbers of candidate loci . Predictor covariates (highest loading) corresponded to proportion of big sagebrush cover (10 SNPs), spring precipitation (180 SNPs), fall precipitation (212 SNPs), and CTI (200 SNPs). Each of the axes generally displayed an environmental gradient of predictor variables: RDA1 corresponded to an environmental gradient of fall precipitation, RDA2 to a spring precipitation gradient, RDA3 to a CTI and fall precipitation gradient, and RDA4 to a spring and fall precipitation gradient. Plots of the first 4 RDA axes are included in the Appendix  were identified in a gene ontology enrichment analysis of outliers identified by the BayPass core model and 15 unique genes from gene sets associated with 161 GO terms (FDR 0.05) were identified with outliers from pcadapt Table . Outlierr2 < 0.1 at ~ 350 kbp , ANK sequence repeat (cluster 2 enrichment score: 1.14), cytochrome P450, E‐class, group 1 (cluster 3 enrichment score: 1.08), and short sequence motif: DEAD box (cluster 4 enrichment score: 0.90)  and three of the populations stand out as holding signatures of potential divergent selection  as a biological process potentially underlying adaptive divergence and identified the same four cytochrome P450 family genes in many of the gene sets Table . DiffereThe fourth enrichment cluster indentified by DAVID Table  containeThe reduced representation approach used here allowed us to break the entire genome down into smaller pieces and obtain higher confidence genotypes for more individuals than we would have been able to obtain with whole‐genome resequencing. However, this resulted in a low density of SNPs (~16 SNPs/Mb), and many regions of the genome were not sampled (Tiffin & Ross‐Ibarra, 4.3We have identified signals of adaptive divergence associated with potentially ecologically important genes and groups of genes which may underlie adaptive divergence among populations of Gunnison sage‐grouse. Populations with different functional genetic variants could potentially impact management and conservation decisions (Savolainen, Lascoux, & Merilä, Similarly, if different populations are adapted to different species of sagebrush, habitat restoration efforts may require location specific sagebrush species as a seed source. Guidelines on seed and plant transfer zones for sagebrush species and subspecies have been based on moisture and elevation gradients in the past (Mahalovich & McArthur, Captive‐rearing of sage‐grouse has been attempted in recent years (Apa & Wiechman, In conserving species with fragmented ranges and declining populations, restoration of gene flow between isolated groups is a common objective. Our findings suggest increasing gene flow between Gunnison sage‐grouse may require careful consideration of local adaptation. On the other hand, locally adapted variation might persist in the face of gene flow (Fitzpatrick, Gerberich, Kronenberger, Angeloni, & Funk, 5Our results are consistent with the hypothesis of adaptive divergence among populations of Gunnison sage‐grouse for potentially ecologically important metabolic phenotypes. This study takes the first step in understanding and characterizing local adaptation within populations of Gunnison sage‐grouse. The correlative approach we used assumes high‐frequency alleles in a population correspond to a higher fitness phenotype locally. This relationship could be confirmed or further probed through genomic methods that more directly evaluate fitness effects and function (Carneiro et al., The authors have no conflicts of interest to declare.S.J.Z, S.J.O.M., C.L.A., and K.P.O. designed research. S.J.Z. performed research. R.S.C. performed bioinformatics. K.P.O. contributed new reagents or analytical tools. S.J.Z wrote the manuscript and analyzed the data. All authors contributed to manuscript editing. Click here for additional data file. Click here for additional data file.


Eucalyptus globulus (Labill.) is one of the most important cultivated eucalypts in temperate and subtropical regions and has been successfully subjected to intensive breeding. In this study, Bayesian genomic models that include the effects of haplotype and single nucleotide polymorphisms (SNP) were assessed to predict quantitative traits related to wood quality and tree growth in a 6-year-old breeding population. To this end, the following markers were considered: (a) ~14 K SNP markers (SNP), (b) ~3 K haplotypes (HAP), and (c) haplotypes and SNPs that were not assigned to a haplotype (HAP-SNP). Predictive ability values (PA) were dependent on the genomic prediction models and markers. On average, Bayesian ridge regression (BRR) and Bayes C had the highest PA for the majority of traits. Notably, genomic models that included the haplotype effect (either HAP or HAP-SNP) significantly increased the PA of low-heritability traits. For instance, BRR based on HAP had the highest PA (0.58) for stem straightness. Consistently, the heritability estimates from genomic models were higher than the pedigree-based estimates for these traits. The results provide additional perspectives for the implementation of genomic selection in Eucalyptus breeding programs, which could be especially beneficial for improving traits with low heritability. The number of breeding programs that use the principles of genomic selection (GS) has increased considerably in recent years. Over the last decade, several investigations have illustrated how to incorporate the principles of genomic prediction in the genetic improvement programs of complex traits ,7,8,9,10The non-random association between two or more loci due to a low probability of recombination between them (linkage disequilibrium: LD) in a given population constitutes haplotypes ,25, whicEucalyptus and most species of forest interest, LD usually decays at short genomic distances [Several studies have emphasized the use of haplotypes (from high-density DNA arrays) to estimate the predictive ability of different GS models ,36,37,38istances ,44, whicEucalyptus globulus from a high-quality SNP array (EUChip60K), (ii) investigate the possible benefits of using haplotype information to predict complex traits in trees, and (iii) estimate genomic parameters  using Bayesian whole-regression models  that include the haplotype/SNP effect on a 6-year-old breeding population of E. globulus.The objectives of this study were to (i) determine and characterize haplotype blocks in n = 152), while a smaller amount was constructed by SNPs located on chromosome 4 (n = 71). Of the total blocks formed by the chromosomes, 300 haplotypes were obtained (on average) per chromosome, while each block had three haplotype variants on average. The number of SNPs within each haplotype block varied from two to 12. The smallest blocks had an extension of 36 bp, while the largest haplotypes had a length of 482 kbp (chromosome 8). About 24% of the total haplotype blocks had a size greater than 10 kbp, while 2% had a size above 100 kbp. Particularly, on chromosomes 4 and 9, no haplotype blocks with an extension higher than 100 kbp were detected. The genome-wide average linkage disequilibrium decay estimated across all chromosomes is shown in r2 = 0.14 .The final dataset used for the haplotype block construction and genomic predictions consisted of 14,422 SNPs. An average of 1356 SNPs per chromosome and an average frequency of one SNP every 4000 bp were found. Chromosome 8 contained 12.5% (1811 SNPs) of the total SNPs, while chromosome 4 presented only 6.2% (893 SNPs). Total values of 1137 haplotype blocks and 3279 haplotypes  were iden = 65).Estimates of variance components, heritability and genetic gains based on pedigree information are shown in The genomic prediction methods were compared using the average values of marginal posterior distributions of each estimated parameter. Genomic heritability, genetic gains and predictive ability (PA) values, obtained for each trait and prediction model, are shown in For DBH, PA values ranged between 0.17 and 0.46. The PA varied significantly among models based on SNP, HAP and HAP-SNP. In general, the models based on SNP had the highest PA values. Among the models based on SNP, BRR had the highest predictive ability for DBH (PA = 0.45), while HAP-SNP-based BC had the highest predictive ability (PA = 0.46). The genomic heritability estimates based on BC and BRR ranged between 0.26–0.32 and 0.12–0.16, respectively, and were significantly higher than the pedigree-based heritability  in most cases. BB and BC had the highest PA among the models based on SNP  and HAP-SNP (PA = 0.52 for BB and BC), while BRR had the highest predictive ability (PA = 0.58) among the HAP-based models. Consistently, all genomic heritability estimates based on BB, BC and BRR . For the HAP-based models, the highest PA was obtained by BC (PA = 0.28). In accordance with this, BC and BRR had the highest PA of BQ among the HAP-SNP-based models . All genomic heritability estimates based on BC and BRR . For HAP-based models, the highest predictive ability was obtained by BC and BRR . In addition, for HAP-SNP-based models, BRR had the highest predictive ability (PA = 0.44). The heritability of WD based on any genomic model (2) between EBVs and GEBVs for all traits were above 0.98.The regressions between pedigree-based Estimated Breeding Values (EBVs) and genomic-based EBVs (GEBVs considering models with the highest predictive ability for each trait) are shown in E. globulus decreases rapidly in the range of 1000–4000 bp. However, in this study, several genomic regions were detected in strong disequilibrium above 10,000 bp. About 24% and 2% of the haplotype blocks formed had an extension over 10,000 bp and 100,000 bp, respectively, with a disequilibrium coefficient (D′) value higher than 0.7. The construction of the haplotype blocks was based on the confidence interval algorithm of Gabriel et al. [Previously, Durán et al. , Thavamal et al. , which eIn breeding populations of self-pollinated plants and controlled crosses, it is expected that LD decays over long distances. For example, in soybean and rice, LD can be significant at a distance of 100,000 bp and 250,000 bp between loci, respectively , while iEucalyptus spp. can be greater than 0.1 in populations from 3 to 15 years of age [Eucalyptus hybrids at 6 years of age. In this study, SNP-based BRR and HAP-SNP-based BC had the highest predictive ability for DBH, but the genomic heritability estimated by BC (HAP-SNP) was two times higher than the BRR model based on SNP markers. In BC, the prior assumptions of all SNP effects have a common variance, and the method assigns a nonnull prior probability for the marker effect to be equal to zero [Eucalyptus nitens and hybrids of Eucalyptus urophylla × E. grandis, respectively. In another study, Müller et al. [E. benthamii. Interestingly, Müller et al. [According to the pedigree and genomic prediction models, the traits studied in this population were found to be weakly to highly heritable. The results indicated that DBH, ST and BQ are under relatively low genetic control. However, previous studies have reported that the heritability of DBH in s of age ,53,54,55s of age  determin to zero . Due to  to zero . Similar to zero . In cont to zero  and Reser et al.  reportedr et al.  performeE. globulus can be moderate [E. nitens increased approximately twice when using marker-based models. In the context of our study, the highest predictive ability for ST was obtained by BC based on SNP, BRR based on HAP, and BRR and BC models based on HAP-SNP. BC and BRR models had higher heritability than the estimate based on pedigree. However, BC models (based on either SNP or HAP) had a higher genomic heritability than other prediction models. There have been conflicting reports on the genetic architecture of stem straightness in trees. For example, Bartholomé et al. [Eucalyptus cladocalyx.The heritability of ST has been previously reported as low ,61, whicmoderate  and even2 > 0.3, ). Almost2 > 0.3,  found thé et al.  detectedé et al.  found QTé et al.  reportedE. globulus. For instance, Callister et al. [E. globulus at 4 years of age was less than 0.1. In other tree species, traits related to branch quality have been described to be moderately heritable [BQ has been a scarcely evaluated trait in breeding programs of r et al.  reportedr et al.  reportederitable ,67. In teritable . This coEucalyptus spp. [E. nitens. Consistent with our findings, Resende et al. [E. urophylla and E. grandis. In other tree species, Beaulieu et al. [Picea glauca is lower when estimated by genomics than pedigree. As expected, the highest value of genetic gain was obtained for WD, while the lowest value was obtained for DBH. In this sense, the selection based on genomics for traits such as DBH, ST and BQ is highly justified and particularly attractive because of the potential of enhancing selection accuracy for low heritability traits and increasing the genetic gains for these traits [Several studies have shown that WD is a highly heritable trait in tus spp. ,62,69, wtus spp.  reportedu et al.  reportede traits . On the e traits  stated te traits  reportedEucalyptus hybrids. However, the genetic gains estimated using the pedigree and BC methods were not statistically different. Previously, Lenz et al. [The genomic heritability for HT, estimated by a BC model (SNP and HAP-SNP), was higher than the pedigree-based estimate. However, the HAP-SNP-based BC model had a predictive ability lower than the model based on SNP markers. In accordance with this, Tan et al.  determinz et al.  and Beauz et al.  reportedz et al.  found noEucalyptus and other forest species, LD usually decays at short genomic distances, which allows the identification of smaller haplotype blocks and with fewer variants (haplotypes). In this study, we found haplotype blocks formed by SNPs in a strong LD (D′ > 0.7) with a size above 300 kbp. According to Cuyabano et al. [Several studies in animals have emphasized the use of haplotypes (from high-density DNA arrays) to estimate the predictive ability of different GS models ,36,37,38o et al. , haplotyEucalyptus globulus from a progeny trial consisting of a mix of half-sib and full-sib families located in the La Poza sector, Purranque, Chile [In 2018, 6-year-old trees of e, Chile , were eve, Chile . ST was E. globulus . The DNA extraction protocol followed the work of Ballesta et al. [Genomic DNA was isolated from the leaves of 646 randomly selected individuals of a et al. . Individa et al. . The genThe haplotype blocks were defined according to the confidence interval algorithm developed by Gabriel et al.  using thE. grandis [r2). The critical r2 value was calculated according to the method used by Breseghello and Sorells [The physical positions of each SNP were established according to the consensus map of the genome of  grandis . The ext Sorells .In this study, prediction models based on pedigree and genomic data from an array of SNP markers were used. In the pedigree-based model, individual breeding values were predicted using a Bayesian generalized linear model implemented in the MCMCglmm  library  of R 3.6The prediction models based on SNPs/haplotypes were the following: Bayesian Least Absolute Shrinkage and Selection Operator , Bayee Refs. ). ε corri-th marker , 1 represents the heterozygous genotype for the i-th marker, and 2 represents the homozygous genotype of the allele with the highest frequency for the i-th marker. In the case of haplotypes, since one haplotype block can have more than two allelic variants, the values of 0, 1 and 2 represent the number of copies for each variant (haplotype), in which a value of 0 was assigned for those individuals who did not present any copy of the j-th haplotype and i-th block, a value of 1 was assigned if they presented a copy of the j-th haplotype and i-th block, and a value of 2 was assigned if they presented two copies of the j-th haplotype and i-th block [The matrix of SNP markers or haplotypes was coded by the numbers 0, 1 and 2. In the case of SNP markers, 0 represents the homozygous genotype of the allele with the lowest frequency for the th block . The modThe BL method assumes that the marker effects are distributed a priori according to a double exponential (DE), n effect . The marsee Ref. ). The vasee Ref. (5)mi|σmi = 1, …, n), which is equal for all markers. In BC, all markers are considered to have a common variance   of R 3.6The prediction methods (genomic and pedigree-based) were also compared by the values of heritability and genetic gains. In the case of the pedigree-based method , the herarameter ,82,83. TFor BRR and BC:For BA and BB:ith marker, ith marker (For BL:The genetic gain (GG) was estimated for each prediction method using the following expression ,84:(10)Gn et al. .Eucalyptus. In general, genomic heritability estimates were higher than those based on pedigree information for most of the studied traits. On the other hand, the predictive ability values were dependent on the genomic prediction models and marker type. On average, the homocedastic methods (BRR and Bayes C) had the highest predictive ability for the majority of traits. Notably, genomic models that included the haplotype effect (either HAP or HAP-SNP) significantly increased the PA of traits with low heritability. The results of this study provide additional perspectives for the implementation of genomic selection in Eucalyptus breeding programs, which could be especially beneficial for improving low heritability traits.To our knowledge, this study is one of the first to examine the inclusion of haplotypes in genomic selection models of