Currently, there is no cure for HD, and the clinical care of individuals with HD is focused on symptom management. Previously, we showed allele-specific deletion of the expanded HTT allele (mHTT) using CRISPR-Cas9 by targeting nearby (<10 kb) SNPs that created or eliminated a protospacer adjacent motif (PAM) near exon-1. Here, we comprehensively analyzed all potential PAM sites within a 10.4-kb genomic region flanking exon-1 of HTT in 983 individuals with HD using a multiplex targeted long-read sequencing approach on the Oxford Nanopore platform. We developed computational tools (NanoBinner and NanoRepeat) to de-multiplex the data, detect repeats, and phase the reads on the expanded or the wild-type HTT allele. One SNP common to 30% of individuals with HD of European ancestry emerged through this analysis, which was confirmed as a strong candidate for allele-specific deletion of the mHTT in human HD cell lines. In addition, up to 57% HD individuals may be candidates for allele-specific editing through combinatorial SNP targeting. Cumulatively, we provide a haplotype map of the region surrounding exon-1 of HTT in individuals affected with HD. Our workflow can be applied to other repeat expansion diseases to facilitate the design of guide RNAs for allele-specific gene editing.Huntington’s disease (HD) is an autosomal dominant neurodegenerative disease caused by CAG trinucleotide repeat expansions in exon-1 of  We sequenced 1,056 individuals with Huntington’s disease (HD), a neurodegenerative disease caused by repeat expansions in exon-1 of the gene, to identify candidate SNPs for allele-specific CRISPR-Cas9-mediated targeting. The resultant high-resolution haplotype map allowed for gRNA-targeting of the disease allele in patient cells. Phase III studies were ended early because participants receiving active drug progressed more rapidly than placebo-treated participantsClinicalTrials.gov identifier: NCT03761849). In addition, two allele-selective ASOs were tested in individuals with HD  with results released recently. Unfortunately, neither ASO showed target engagement in cerebrospinal fluid. The PRECISION-HD2 core trial participants who received WVE-120102 (targeting rs362331) had a median reduction of 9.9% in mHTT in cerebrospinal fluid (p = 0.74) compared with the placebo group, who had a median decrease in mHTT of 0.8%. Results of the PRECISION-HD1 core trial were similar.Gene-silencing strategies using RNA interference (RNAi) or antisense oligonucleotides (ASOs) have been efficacious in mouse models.,,The recently discovered CRISPR-Cas9 system,mHTT allele in vitro and in vivo, taking advantage of SNPs identified for use with the CRISPR/SpCas9 system. However, these studies mainly screened common SNPs in the normal population and a detailed haplotype map surrounding exon-1 of the HD population is unknown. To more broadly adapt this approach to the HD population, we developed a robust, high-throughput pipeline to detect and phase all highly prevalent SNPs in the HD population that, when present, create a PAM cis to the expanded HTT allele that can be used together with the CRISPR systems. Notably, haplotype analysis of HD cohorts has been reported previously,,HTT gene is 170 kb in length, most SNPs analyzed were distant from exon-1, yet, for deletion of the CAG-repeat, a haplotype map surrounding exon-1 is required. Because we found earlier that editing efficacy reduces with increasing distance from exon-1,WeHTT allele.To address this problem, we used Oxford Nanopore long-read sequencing paired with two novel computational tools (NanoBinner and NanoRepeat) to de-multiplex data, detect repeat size, and haplotype the allele. This was applied to 319 samples from the French HD consortium and validated on 664 samples from the CHDI Foundation, which consists of DNA samples from multiple continents and ethnic groups. We identified all common SNPs in the French and CHDI HD cohorts, and analyzed all SNPs that provide PAMs to mediate CRISPR editing of the expanded HD subjects from the French cohort were recruited at the Pitié Salpetrière Hospital in Paris, France. All subjects gave written informed consent, and blood samples were collected in accordance with local French regulations . Genomic DNA samples from the CHDI cohort were generously provided by the participants in the Enroll-HD study and made available by the CHDI Foundation. Detailed information of the HD subjects  is shown in ,The barcode design workflow used in this study is shown in For the French HD cohort, the target region was covered by two overlapping amplicons DNA repair, end repair, and dA-tailing: 1 μg of the pooled and purified PCR products was used as input DNA, and the volume was adjusted to 47 μL with nuclease-free water. One microliter of DNA CS , 47 μL input DNA, 3.5 μL NEBNext FFPE DNA Repair Buffer , 2 μL NEBNext FFPE DNA Repair Mix , 3.5 μL Ultra II End-prep reaction buffer , and 3 μL Ultra II End-prep enzyme mix  were mixed in a PCR tube. The mixture was incubated at 20°C for 5 min and 65°C for 5 min. A 1× volume (60 μL) AMPure XP clean-up was performed, and the DNA was eluted in 61 μL nuclease-free water. A 1-μL aliquot was quantified by fluorometry (Qubit) to ensure ≥700 ng DNA was retained.(2)Adapter ligation: 60 μL DNA sample from the previous step, 25 μL Ligation Buffer , 10 μL NEBNext Quick T4 DNA Ligase , and 5 μL Adapter Mix  were mixed in order. The mixture was incubated for 10 min at room temperature. The adaptor-ligated DNA was cleaned up by adding a 0.4× volume (40 μL) of AMPure XP beads, incubating for 5 min at room temperature, and resuspending the pellet twice in 250 μL Long Fragment Buffer . The purified-ligated DNA was resuspended in 15 μL elution buffer . A 1-μL aliquot was quantified by fluorometry (Qubit) to ensure ≥300 ng DNA was retained. A total of 50 fmol (278 ng) of this final prepared library was loaded onto the GridION sequencer with an FLO-MIN106D (R9.4.1) flow cell. The sequencing was run for 48 h. Basecalling was performed using the super-accuracy model of Guppy (v.5.0.14).The barcoded PCR products were pooled and purified with Agencourt AMPure XP beads . The sequencing library was prepared using a ligation sequencing kit , according to manufacturer’s instructions.We developed NanoBinner, a tool for de-multiplexing of barcoded amplicons from long-read sequencing data. Given the moderate error rate in long reads, there might be random matches of barcodes inside the amplicon sequence. To avoid this potential issue, NanoBinner aligns the barcode sequence as well as the 256-bp amplicon sequence next to it. The 256-bp amplicon sequence acts as an anchor so that the matching of barcodes is at the correct position. The alignment is performed using minimap2HTT gene. The joint quantification process has two steps: fast estimation and refining. In the fast estimation step, NanoRepeat performs a quick analysis of each repeat and estimates the lower and upper bound of the repeat size independently. This analysis is done by aligning the reads to a decoy reference sequence with 1,000 repeat units (CAG or CCG) using minimap2.L1, L2 denote the lower bounds of the CAG and CCG repeats, and U1, U2 denote the upper bounds of the two repeats, respectively. In the refining step, NanoRepeat generates a batch of amplicon sequences with m (L1 ≤ m ≤ U1) CAG repeat units and n (L2 ≤ n ≤ U2) CCG repeat units  are distributed according to a mixture of N Gaussian models, where N is 1 or 2 as human is a diploid genome. Akaike information criterion or Bayesian information criterion are commonly used criteria to select the best value of N and prevent overfitting. As Gaussian distribution is a probability distribution of real-valued random variables, we found that these two methods are not able to prevent overfitting of the Gaussian mixture model (GMM) if the input random variables are rounded to integers. Therefore, we added a uniform distributed random noise (between −0.5 and 0.5) to each m and n before model selection using Bayesian information criterion. After the best N is selected, we use the original value of m and n (without the random noise) to train the GMM. The label of each read was predicted using the trained model. To make sure the subsequent SNP calling is accurate, a read is discarded if it is not within 95% equi-probability surface of a Gaussian model. A sample failed QC if only one allele was detected (N = 1) or one of the alleles had less than 50 reads.After the repeat number of each read is determined, NanoRepeat classifies the reads to alleles. First, we remove outlier reads with repeat sizes that are outside three standard deviations from the mean. Next, we assume that the CAG and CCG repeat sizes ,An SNP is considered to mediate the gain of a PAM if the alternative allele contains a high-efficiency PAM and the reference allele does not contain any high-efficiency PAM or low-efficiency PAM of the same CRISPR enzyme. Conversely, an SNP is considered to mediate the loss of a PAM if the reference allele contains a high-efficiency PAM and the alternative allele does not contain any high-efficiency PAM or low-efficiency PAM. TTTN is a high-efficiency PAM of AsCpf1. Both NGG and NAG are considered high-efficiency PAMs of the wild-type SpCas9, although the recognition for NAG is less efficient than NGG. For other Cas9 enzymes of which the post-selection PAM depletion values (PPVDs) are measured,A 526-bp region flanking SNP16  was amplified by a nested PCR as we were not able to design a specific primer that directly amplifies this region. In the first round of the nested PCR, we used the primers of amplicon-2 , which aThe TaqMan Genotyping Master Mix  and the probes for SNP16 (assay ID: C__27529960_10) were ordered from Thermo Fisher Scientific. A 5-μL PCR reaction system was used. Real-time PCR and data analysis were performed following the manufacturer’s instructions.2. Cells were cultured in 24-well plates and transfected at 80%–90% confluence using Lipofectamine 2000 transfection reagent, according to the manufacturer’s protocol. After DNA transfection, cells expressing SaCas9 and sgRNA sequences were enriched by puromycin selection (3 μM) for 24 h, and subsequently expanded for genomic DNA and RNA extraction. Human HD fibroblasts  were maintained on DMEM mediom supplemented with 10% fetal bovine serum, 1% MEM non-essential amino acids, 1% penicillin/streptomycin and 1% L-glutamine at 37°C with 5% CO2. DNA transfection was done by electroporation using an Invitrogen Neon transfection reagent using the electroporation conditions , following the guidelines provided by the manufacturer. Fibroblasts were selected with puromycin (2 μM) for 24 h and subsequently expanded for genomic DNA extraction. Cells were not authenticated or tested for Mycoplasma by the investigators since they previously passed the quality controls of CHOP Research Vector Core and the Coriell Institute for Medical Research cell repository. None of the cells used in the study were listed in the ICLAC database of commonly misidentified cell lines.Human embryonic kidney (HEK293) cells (obtained from CHOP Research Vector Core stock) were maintained in DMEM medium containing 10% fetal bovine serum, 1% L-glutamine, and 1% penicillin/streptomycin at 37°C with 5% COThe plasmid pX330 containing the SpCas9 and sgRNA expression cassettes used in our previous studyTotal RNA was extracted using Trizol (Life Technologies) according to the manufacturer’s protocol, with the exception of 1 μL of Glycoblue (Life Technologies) in addition to the aqueous phase on the isopropanol precipitation step and a single wash with cold 70% ethanol. RNA samples were quantified by spectrophotometry and subsequently cDNAs generated from 1 μg of total RNA with random hexamers . To determine human HTT expression levels in HEK293 cells, we used TaqMan probes for human HTT and glyceraldehyde 3-phosphate dehydrogenase mRNAs obtained from Applied Biosystems. Relative HTT gene expression was determined using the ddCt method.HTT exon-1. Genomic DNA (gDNA) was extracted from cultured HD fibroblasts using a QiaAMP DNA mini kit (QIAGEN) according to manufacturer’s instructions. The gDNA was quantified by fluorometry (Qubit) and then diluted to the same concentration (5 ng/μL). We used BIOLASE DNA polymerase (Bioline) to amplify the input DNA templates. As the CAG repeat and its flanking region has a high GC content (72.4%), we added betaine to the PCR reaction system to enhance amplification. The 50-μL PCR reaction system contains 8 μL gDNA (5 ng/μL), 5 μL 10× NH4 buffer, 2 μL dNTP mixture (2.5 mM each), 1.5 μL MgCl2 (50 mM), 1 μL BIOLASE DNA polymerase, 12 μL betaine (5 M), 1 μL primer mixture (10 μM each), and 19.5 μL ddH2O. The PCR thermal cycling program was 95°C for 2 min, followed by 25 cycles of 95°C for 15 s, 57°C for 15 s, 72°C for 45 s, and a final extension at 72°C for 5 min. The PCR products were separated by electrophoresis through a 1.5% agarose gel stained with ethidium bromide. The gel bands were quantified using the Image Lab software (Bio-Rad).Allele-specific editing was assessed by semiquantitative PCR amplification of the CAG repeat within Statistical analyses were performed using GraphPad Prism v.7 software. Outlier samples were detected using the Grubb’s test (a = 0.05). Normal distribution of the samples was determined by using the D’Agostino and Pearson normality test. Data were analyzed using one-way ANOVA followed by Bonferroni’s post hoc. Statistical significance was considered with p < 0.05. All results are shown as the mean ± SEM.The workflow of this study is shown in The sequencing data were de-multiplexed with NanoBinner, which was originally developed for this work. Given the moderately high error rate and the flexibility of alignment for long reads, there may be random barcode matches inside the amplicon sequence. To avoid potential random matching, NanoBinner compares the barcode sequence as well as the 256-bp amplicon sequence immediately next to the barcode. The 256-bp amplicon sequence acts as an anchor such that the matching of barcodes is at the correct position. NanoBinner also uses a new algorithm to assign barcodes, which is described in the HTT, there is a CCG repeat immediately 3′ of the CAG repeat, with (CCG)7 and (CCG)10 predominant although there are other variants.m (L1 ≤ m ≤ U1) CAG repeat units and n (L2 ≤ n ≤ U2) CCG repeat units, where L1, L2 are the lower bounds of the two repeats and U1, U2 are the upper bounds of the two repeats, respectively . For normal alleles, the two methods were identical for approximately 98% of samples 10. In mHTT chromosomes, (CCG)7 predominates (∼92%).The distribution of CAG repeat size is shown in omosomes B. For nomHTT chromosomes, it was easy to separate the reads derived from normal and mHTT alleles for most samples (Since there was often a large difference (>20) in CAG repeat size between the normal and  samples C. One sa samples D and 3E.After the reads were phased according to the CAG and CCG repeats, SNP detection was performed for each allele see  section,mHTT chromosomes, or the gnomAD database (non-Finnish European [NFE] population). The positions of the 19 SNPs are shown in mHTT chromosomes are dramatically different (2 = 0.9725) with their AFs in NFE population in the gnomAD database. However, the correlation between the AFs of SNPs in the mHTT chromosomes and those in the gnomAD database are low (R2 = 0.3936)  of ≥5% in the normal or ifferent . To veri 0.3936) C and 4D.Staphylococcus aureus.Streptococcus pyogenes. AsCpf1 is a class 2 CRISPR enzyme from Acidaminococcus, which is highly specific for the AT-rich motif (TTTN). Only highly effective PAMs recognized by these enzymes were analyzed (mHTT: (1) SNPs causing loss of a PAM in the normal chromosome; and (2) SNPs causing gain of a PAM in the mHTT chromosome. The effect of the 19 SNPs on different PAMs, and their AF, is shown in We analyzed the gain and loss of PAMs mediated by the 19 common SNPs as they comprise the majority of diversity in HD individuals and the general population, yielding putative targets for allele-specific genome editing. In addition to the canonical PAMs (NGG and NAG) recognized by SpCas9, we analyzed PAMs for five other native or engineered CRISPR enzymes, namely SaCas9, SpCas9_VQR, SpCas9_EQR, SpCas9_VRER, and AsCpf1. SaCas9 is a Cas9 ortholog from analyzed . An SNP mHTT chromosomes (mHTT chromosomes (2.82%). All six SNPs cause PAM loss. Thus, the bias in AF  allows HD individuals to benefit from this genome editing strategy. This is consistent with our previous observations that SNP1 mediated deletion of the expanded HTT in 9 of 11 cell lines . Both SpCas9_VQR and SpCas9_EQR recognize the PAM of NGAG, but SpCas9_VQR also recognizes the motif of NAAG, at lower efficiency.mHTT chromosomes. SNP17 disrupts the PAM of AsCpf1 , which has a strong selectivity for the TTTN PAMs and thus SNP17 is AsCpf1 specific.Sixteen of the 19 SNPs cause the loss or gain of at least one PAM. In addition to SNPs1-6, SNP16 and SNP17 mediate PAM loss in the normal allele, with an AF > 30% in the normal chromosomes. The AFs of SNP16 in normal and The specificity of the guide RNA sequence is critical for preventing off-target cleavage events. For the Cas9 enzyme and its variants, the guide sequence is on the 5′ side of the PAM. But for the Cpf1 enzymes, the guide sequence is on the 3′ side of the PAM. We used the Benchling website to design guide RNAs and predict their specificity. The guide RNA sequence for SNP16 is very specific with an off-target score of 98.4 . The guide RNA sequence (TAAAAATAAAAATAAGTTAACAC) for SNP17 is not specific, with an off-target score of 43.3. This sequence contains a poly(A) and may have multiple copies in the human genome. Therefore, SNP16 is a strong candidate with high AF and specificity.We validated SNP16 genotyping from long reads using two traditional methods. First, we designed Sanger sequencing primers for SNP16 and randomly sequenced 20 samples from plate-1 of the French cohort. The Sanger sequencing results were completely consistent with the SNP genotypes called from the nanopore long reads. We also used TaqMan real-time PCR assay to genotype three 96-well plates (288 samples) of the CHDI cohort. The TaqMan assay results were also completely consistent with the SNPs called from the nanopore long reads. This indicates that our SNP calls made from the nanopore long reads are of high accuracy.HTT intron-1, which could be used alone or together with SNP1 (rs2857935) to edit and terminate mHTT expression. We designed sgRNA sequences targeting SNP16 (sgHD16) and SNP1 (sgHD1) to mediate SaCas9 editing of the mHTT exon-1. The sgHDi3 (targeting intron-1) and sgCtl guides used in our previous studyHTT genomic locus remained intact on cells co-expressing SaCas9 and sgCtl (negative control group), whereas a band resulting from HTT exon-1 deletion was observed on cells transfected with the sgHD1/i3 SaCas9 or the sgHD1/16 SaCas9 cassettes (Our screen identified SNP16 (rs3856973) as a novel prevalent SNP within assettes B. Notablassettes C. ReductmHTT allele  were analyzed. The 1KG dataset provides phased SNP/indel calls of 2,504 individuals, of whom 404 individuals are from the NFE population. In the following haplotype analysis, we used all SNPs listed in To better understand the percentage of HD individuals who could benefit from our editing approach, the haplotypes of normal and mHTT chromosomes) and individuals of NFE population in the phase 3 dataset. The SNPs carried by each haplotype are shown in mHTT chromosomes. It is also the most abundant haplotype in normal chromosomes (frequency = 48.39%). The frequencies of minor haplotypes (haplotypes 2–37) are dramatically different between normal and mHTT chromosomes. The frequencies of the minor haplotypes in the normal chromosomes are highly correlated (R2 = 0.8036) with those in the 1KG dataset. In contrast, the haplotype frequencies in the mHTT chromosomes have no correlation (R2 = 0.0031) with those in the 1KG dataset  compared with the intergenic region.  in hap1 chromosomes is 12.4 kb away from exon-1  from our sequencing data. However, all common Indels/SVs are located in tandem repeat regions where the guide RNAs are not specific. Next, we analyzed SNPs within a greater range than our earlier 10-kb limit using the 1KG dataset (NFE population). Since the 1KG dataset provides phased SNPs, we can extract the SNPs from chromosome 4 with different haplotypes. We found that, in hap1 chromosomes, there are fewer SNPs in the  region. . In regim exon-1 , which im exon-1 , similarHTT gene and applied this approach to two independent HD cohorts for the purpose of allele-specific editing for HD therapeutic development. We called genomic variants from the sequencing data and systematically analyzed potential gene-editing sites that could mediate allele-specific deletions of the mHTT allele. Our results showed that 22% of HD individuals can be edited by targeting SNP1, to which the guide RNA had been developed and tested earlier.mHTT cell lines. Overall, our haplotype analysis reveals that up to 57% HD individuals of European ancestry can be potentially targeted in an allele-specific manner by combinatorial editing.In this study, we developed a targeted long-read sequencing approach to resolve a 10.4-kb genomic region flanking the CAG repeats in exon-1 of the mHTT chromosomes (82.58%). Due to the lack of diversity in the haplotypes, about 40% of HD individuals carry hap1 in both alleles and cannot be edited by targeting an SNP near exon-1. Unfortunately, further analyses using other strategies did not find high-frequency Indels or SVs that could mediate allele-specific editing; the nearest high-frequency SNP was at least 10 kb away. Despite this, deletion of other exons in an allele-specific manner is possible. Their deletion could induce an open reading frameshift causing a premature stop codon and loss of mHTT expression. However, toxicity from exon-1-derived transcripts would remain.43Hap1 is the predominant haplotype in both normal chromosomes (48.39%) and ,,HTT gene, most of which are distant from exon-1. Here, we used long-read sequencing. This has the advantage over SNP array genotyping and short-read sequencing in that it can perform repeat quantification, SNP detection, and haplotyping at the same time. With long reads, the haplotyping process is straightforward and does not require trio data. We were able to assemble the diploid genome sequence for each HD individual. However, we want to stress that, while the genetic details that we observe provide some potentially interesting genetic insights into how the various haplotypes might have arisen, we do not have evidence that this specific haplotype is responsible for the HTT expansion observed; instead, it is quite likely that the expansion arose on a specific haplotype, which is then overrepresented in the HD population across the continent. Cumulatively, our data provide a comprehensive analysis of allele-specific target sites for CRISPR-based gene editing, which relies on the ability of an SNP to provide a PAM site for targeted editing of the expanded allele. For effective editing, we focused on genomic regions within 5 kb of exon-1, because previous work showed that the distance between upstream and downstream guides influenced editing efficacy.Earlier work also analyzed SNPs and HD haplotypes.Long reads have a higher per-base error rate than short reads. However, the sequencing error tends to be random and the consensus sequence of high-coverage long reads can be very accurate.NanoBinner and NanoRepeat are novel computational tools developed for this work. NanoBinner is a demultiplexer for amplicon sequencing data. Existing tools, such as DeepBinnermHTT in ND33392 fibroblast cells. However, the efficacy of editing SNP1/16 (two SNPs) was not higher than editing SNP1/i3 (one SNP and one homozygous site), which may due to the longer distance between the SNP1/16 sgRNA-Cas9 complexes and sequence context at SNP16. Nonetheless, SNP16 is a promising candidate because it creates a PAM more frequently than SNP1 and editing at SNP16 would benefit HD individuals without SNP1.sgRNAs targeting SNPs1-6 were validated previously.HTT gene for allele specific editing. We applied this workflow to two HD cohorts and comprehensively analyzed potential sites for allele-specific deletion of mHTT for CRISPR-Cas systems. We also generated a detailed haplotype map for the region near HTT exon-1, which may be applied to other editing strategies and newly emerging editing enzymes. In addition, our workflow and novel computational tools can be applied to other repeat expansion disorders.In summary, we developed an experimental and computational workflow to resolve the SNP haplotypes near exon-1 of the This study was approved by the Institutional Review Board at the CHOP Research Institute. HD participants from the French cohort were recruited at the Pitié Salpetrière Hospital in Paris, France. All participants gave written informed consent, and blood samples were collected in accordance with local French regulations . Genomic DNA samples from the CHDI cohort were generously provided by the participants in the Enroll-HD study and made available by the CHDI Foundation.


Malus X. domestica Borkh) cultivars  is one of the most diverse collections of apples in the world, which was designed to enable genetic mapping. The ABC is located at the Agriculture and Agri-Food Canada (AAFC) Kentville Research and Development Centre in Nova Scotia, Canada. A comprehensive description of the ABC was recently published . BrieflyM. domestica, but also include Malus sieversii (Ledeb.) M. Roem., the primary progenitor species of M. domestica.Although there are currently only 1,119 accessions (planted in duplicated) in the ABC, initial grafting efforts included additional accessions in order to ensure backups were available. As a result, the dataset presented here includes some accessions that are not planted in the ABC. However, because sequencing data were available for these accessions, we have retained them. The accessions primarily belong to the cultivated apple, In addition to phenotypic descriptions of the ABC, sequencing the accessions in the collection provides a valuable resource not only for researchers working on the collection, but for those studying apples more broadly. With this in mind, we report and make publicly available genotyping-by-sequencing (GBS) data for over 1,000 apple accessions from the ABC.Young leaf tissue was collected from all accessions in the ABC and DNA was extracted using commercial kits. DNA was sequenced using GBS  with ApeSingle nucleotide polymorphisms (SNPs) were called using three different SNP calling pipelines: GATK (v3.7) , SAMtoolhttp://cancan.cshl.edu/labmembers/gordon/fastq_illumina_filter/). Next, the GBSX toolkit (v1.3) (https://sourceforge.net/projects/bbmap/) to remove nucleotides with low quality (<20) from the 5′ end of each read, and to remove any trimmed reads that were <30 nucleotides. Reads with full enzyme cut sites were removed using BBMAP, as they were likely chimeric sequences. Reads were then pooled across runs and enzymes into one file per accession.Failed raw reads were removed using Illumina’s CASAVA-1.8 FASTQ filter (t (v1.3)  was usedGATK/SAMtools pipeline reads were aligned to the reference genome separately using BWA (v0.7.12) . GATK’s SNPs were imputed for each caller separately using LinkImputeR  at a maxNAC18.1, PG1, ACO1, and ACS1 as previously described  on a LightScanner HR384 (BioFire). These markers included escribed . In addi acidity  was alsond Rvi15 . Primers. The final SNP set consisted of 278,231 SNPs across 1,175 unique accessions.Since NAC18.1 was genotyped using both GBS and HRM, the GBS SNP was removed using PLINK and replaced with the HRM genotype calls. The markers which had been genotyped using HRM were merged using the --merge function in PLINK (v1.07)  into theR2 > 0.5, before shifting the window by three SNPs and repeating (PLINK command: -indep-pairwise 10 3 0.5). This filtering resulted in a pruned set of 180,075 SNPs that were used for principal components analysis (PCA) with TASSEL  using PLINK (v1.07) . To do th TASSEL . Principh TASSEL  with theh TASSEL . MetadatBy using three SNP callers and imputation, we were able to genotype 278,231 SNPs from 1,175 diverse apple accessions from Canada’s Apple Biodiversity Collection. These SNPs are distributed fairly evenly across all 17 apple chromosomes  with theThe minimum inter-SNP distance between SNPs on the same chromosome was 1 bp, while the maximum distance was 1,469 kb . Since sM. domestica accessions from the USDA and those from Canada  data for genetic mapping and for identifying accessions of use in apple breeding. Lastly, by including genetic markers related to texture, fruit flavor, and disease resistance, these data may benefit those involved in genomics-assisted breeding of apples.


To conclude, the SNP profiles based on ultra-deep sequencing data extend current knowledge on metagenomics and highlights the importance of evaluating sequencing depth before starting SNP analysis. This study provides new ideas and references for future strain-level investigations.Intestinal bacteria strains play crucial roles in maintaining host health. Researchers have increasingly recognized the importance of strain-level analysis in metagenomic studies. Many analysis tools and several cutting-edge sequencing techniques like single cell sequencing have been proposed to decipher strains in metagenomes. However, strain-level complexity is far from being well characterized up to date. As the indicator of strain-level complexity, metagenomic single-nucleotide polymorphisms (SNPs) have been utilized to disentangle conspecific strains. Lots of SNP-based tools have been developed to identify strains in metagenomes. However, the sufficient sequencing depth for SNP and strain-level analysis remains unclear. We conducted ultra-deep sequencing of the human gut microbiome and constructed an unbiased framework to perform reliable SNP analysis. SNP profiles of the human gut metagenome by ultra-deep sequencing were obtained. SNPs identified from conventional and ultra-deep sequencing data were thoroughly compared and the relationship between SNP identification and sequencing depth were investigated. The results show that the commonly used shallow-depth sequencing is incapable to support a systematic metagenomic SNP discovery. In contrast, ultra-deep sequencing could detect more functionally important SNPs, which leads to reliable downstream analyses and novel discoveries. We also constructed a machine learning model to provide guidance for researchers to determine the optimal sequencing depth for their projects (SNPsnp,  Escherichia coli O157:H7 has caused water-borne and food-borne disease outbreaks in many countries  have been utilized to disentangle conspecific strains to facilitate strain-level investigations such as strain identification and tracking . As the children . Besideschildren .Although next generation sequencing data greatly accelerates metagenomic discoveries, few works have studied the impact of sequencing depth on the identified SNP profile and strain-level composition of the gut microbial community. This cannot be ignored because sequencing depth directly impacts the result of read assignments and SNP calling. Two sequencing strategies, ultra-deep sequencing and shallow sequencing, can be adopted in metagenomic SNP studies. Ultra-deep sequencing represents the sequencing strategy to obtain whole genome sequences at very high depths, whereas shallow sequencing represents the strategy to obtain genomes at relatively lower depths. A shallow sequencing depth may lead to miss of important information and result in biased results. The sufficient sequencing depth for strain-level analysis remains unclear.Hence, we explored the human intestinal metagenomic SNP profiles by using ultra-deep sequencing. We investigated the impact of sequencing depth on identification of metagenomic SNPs and downstream strain-level analysis. We hope to provide new ideas and references for future metagenomic SNP researches.PRJEB24152 , and the other two samples that named D2 (BioSample: SAMEA5669780) and D3 (BioSample: SAMEA5669781) from the United States were downloaded from the European Nucleotide Archive (ENA) with the accession ID JEB24152 . The seqMicrobial genomic DNA from stool of the Chinese sample was extracted using Tiangen Fecal Genomic DNA Extraction Kit  in line with the standard procedure, and was stored at −80°C. The purity and concentration of DNA was measured with agarose gel electrophoresis and an optical density (OD) analysis with NanoDrop spectrophotometer . The DNA concentrations were also measured with Qubit™ 4.0 Fluorometer . Shotgun DNA sequencing was performed on the Illumina NovaSeq 6000.1 was used to check the quality of raw data. We then implemented a customized quality control strategy by combining Trimmomatic (parameters: -phred33 ILLUMINACLIP:adaptors.fa:1:0:7 TRAILING:20 SLIDING-WINDOW:5:10 MINLEN:45 AVGQUAL:20)  Adaptors are removed; (2) Bases with qualities smaller than 20 are trimmed from 3’ end; (3) In a 5mer sliding window of a read, if the average quality within the window falls below 10, the read is clipped; (4) Reads containing less than 45 bases are dropped; (5) Reads are also dropped if the average quality is blow 20; (6) For each site of all reads in each sample, a mean number of base calls (f) and a standard deviation (SD) were calculated. Sites whose bases call number deviated from its 2 standard deviations were cut. Steps (1) – (5) are performed by Trimmomatic and step (6) is performed by our custom script.FastQC 1 was useQUAL:20)  with in-Each of the three samples was randomly down-sampled to 1,000, 10,000, 1,00,000, 1 million, 10 million, 20 million, 30 million, 40 million, 50 million, 60 million, 80 million, 100 million, 200 million, 300 million, 400 million, 500 million, 1 billion and 2 billion sequencing reads as subsamples using BBMap , respects with genome length ls has ns uniquely mapped reads assigned to its genome, we defined the relative abundance of strain rs as follows:If a strain where, S denotes the whole strains set.3 Genome sequences of these microbial reference genomes were downloaded from the NCBI assembly database. Samples were mapped to their corresponding reference genomes with BWA , StrainPhlAn2  was usedCornell et al. proposed a term “damped increase” to account for the rate of diversification decreases to approach its upper limit . Hence, iX represents the normalized depth of thi subsampling points, and iY represents normalized SNP numbers of thi subsampling points. For a strain to be considered saturated, two conditions must be met: (1) as the sequencing depth deepens, Rs between two adjacent sub-sampling points gradually decreases; (2) R between the last two points is less than 0.1.Where, In order to predict the SNPs number of strains at their SNP saturation state, the sequencing coverage, sequencing depth, relative abundance, genome length, SNP number, SNP density and saturated SNP number of strains in the aforementioned subsamples and ultra-deep samples were used to construct a data set. Only saturated strains in our data were used here. The data set is divided into training set and test set according to the ratio of 4:1, and the python package scikit-learn  was usedTo obtain highly reliable SNPs by ultra-deep sequencing, we established an accurate and effective framework to eliminate noisy and erroneous information when processing the data. The framework consists of three major parts, involving data pre-processing, preliminary processing and SNP analyses . The datIn total, three ultradeep sequenced fecal samples were utilized in this study. They were labeled as D1, D2, and D3, with sequencing sizes of 437 GB (Gigabases), 786 GB and 754 GB, respectively. We identified the dominant strains (relative abundance >1%) of each sample . Details of the taxonomic levels of these strains are shown in Then, we identified reliable metagenomic SNP profiles of the dominant strains, namely, 693,338 SNPs in sample D1, 1,558,472 SNPs in sample D2, and 1,258,485 SNPs in sample D3. To investigate whether there exists any different SNP preference among difference microbes, we counted SNP numbers for each microbe as well as for higher taxonomic levels. The SNP proportions of microbial genomes within different taxonomic levels, namely the percentage of SNPs at different taxonomic levels, are shown in p-value = 0.74), suggesting that the number of SNPs and abundances of microbes might be independent. Linear mixed effects models using sample as random effect also support the non-correlation .As the taxonomic classification level varies from phylum to species, the differences of SNP proportions in the three samples further expand, which shows the heterogeneity among samples. In addition, we also studied the relationship between the number of SNPs and relative abundances of the dominant strains. As shown in Roseburia intestinalis M50/1, Roseburia inulinivorans DSM 16841, Ruminococcus sp. SR1/5, Ruminococcus torques L2-14, Lachnospiraceae bacterium 5_1_63FAA, Eubacterium siraeum V10Sc8a, Ruminococcus bromii L2-63, Bacteroides pectinophilus ATCC 43243 and Coprococcus sp. ART55/1) with relatively high proportions of SNPs in non-coding regions, all originate from the order Clostridiales. Clostridiales are considered to be the most active microbial components in the intestinal environment of healthy adults, including polysaccharide-decomposing bacteria, which contribute greatly to the production of short-chain fatty acids in the intestine  or the non-coding region (non-CDS-SNPs) of genomes. As shown in ntestine .p-value = 0.75). Further, the cumulative SNPs from all dominant strains in the three samples were calculated. As shown in In order to further study the functional changes, SNPs were annotated and classified into non-synonymous variants, synonymous variants and other variants. The dN/dS ratio of each strain was also calculated, which is an indicator of the selective pressure acting on protein-coding DNA sequences . In addiSubsequently, we further investigate genes with enriched SNPs, namely genes have more SNPs compared to background. A word cloud of these gene functions is shown in Furthermore, we studied the allele frequencies of SNPs by ultra-deep sequencing . The allAlipipes putredinis DSM 17216, we conducted an in-depth study on this strain and compared SNPs of it in different samples. Among the SNPs of A. putredinis DSM 17216, synonymous variants accounted for the vast majority , and non-synonymous variants accounted for a small portion ; the dN/dS ratios of A. putredinis DSM 17216 in sample D1, D2, and D3 are 0.31, 0.31, and 0.35, respectively . The 490 SNPs are mainly located in genes with functions of catalytic enzymes, transferases, recombination integrases, and others. Note that these genes are related with DNA recombination (We divided the SNP sites of the strain into different categories according to whether they are shared among samples (at least two samples) or not. Interestingly, the dN/dS ratio of the shared SNPs is relatively high, especially the dN/dS ratio of the 490 three-samples-shared SNPs (0.49) greatly exceeds that of two-samples-shared  SNPs and each sample’s exclusive SNPs   from ultra-deep sequencing data than from conventional sequencing data. From the phylogenetic tree in Ruminococcus obeum  and Roseburia intestinalis  as examples, respectively. These results suggest that deep sequencing of metagenomic samples could lead to detection of more strains, which could be overlooked by conventional shallow sequencing.For strains with particularly high increases in SNPs in ultra-deep sequencing data, we suspected that additional strains are detected due to the increased sequencing content. Hence, we used StrainPhlAn2 to track the changes of the representative strain under different sequencing depths . For sample D1, we identified much more SNPs of p = 3.2e-3, Mann–Whitney U rank sum test). To further investigate the increased SNPs due to the deeper sequencing, we checked the functions of the genes where newly detected SNPs locate. More functional genes with SNPs detected in the deep sample were involved, as shown in We also found that the SNPs from ultra-deep sequencing data has a lower proportion of CDS-SNPs and a higher proportion of non-CDS SNPs, compared to those from conventional sequencing data . We first focused on SNP profile changes of single strains as the sequencing depth increases. As shown in Furthermore, we investigated whether we can use machine learning models to predict the overall SNP numbers of strains given sequencing depths and other parameters from an under-sequenced sample . We used current sequencing coverage, current sequencing depth, current relative abundance, genome length, current SNP number, and current SNP density of strains in subsamples to predict the final saturated SNP number for each strain. Data from subsamples were collected. First, the correlations between different variables were compared. The SNP saturation number has high correlations with current SNP number and current SNP density, while having low correlations with current sequencing coverage, current sequencing depth, current relative abundance, and genome length . Then we5Relative square error (RSE), relative absolute error (RAE), and coefficient of determination R-squared were used to evaluate the performance of models. The smaller the RSE and the RAE, the better the model predicts; the higher the R-squared, the better the model fits the data. As shown in This research is a deep study of metagenomic samples by ultra-deep sequencing in the human gut. We investigate the gut microbial communities of the shallow-sequenced and ultra-deep sequenced samples, provides a framework for reliable SNP identification by ultra-deep sequencing, conducts SNP analysis and obtains SNP profiles by ultra-deep sequencing. SNP profiles from ultra-deep sequencing and conventional sequencing data were compared. Also, SNP changes with sequencing depth increasing were investigated and a machine learning model was constructed to guide researchers determine the optimal and affordable sequencing depth for their projects.According to our results, the conventional sequencing sizes of 6 GB for metagenomics studies for the human gut microbiome is not enough to support a comprehensive study of metagenomic SNPs and related downstream analysis. We found that compared with that from conventional sequencing size, SNPs identified from ultra-deep sequencing involved more important genes and functions. The results by StrainPhlAn2 suggest that deep sequencing of metagenomic samples could lead to detection of rare strains, which might be overlooked by conventional shallow sequencing. Further work is needed to solve the limitation of our work. The limitations include the sensitivity reduction of SNP calling caused by unique mapping, and the cost-intensive preliminary sequencing requirement for sequencing depth’s determination.Nowadays, metagenomic research has moved from species level to the high-resolution strain level, in which SNP identification plays an important role. By using our predictive model, the appropriate sequencing depths could be estimated using test samples prior to metagenomic variations investigation. Sufficient depth of sequencing can promote accurate analysis of metagenomic SNPs and further facilitate discovering accurate associations between metagenomic SNPs and diseases. Though more experiments need to be conducted to validate our work, our findings could provide valuable perspectives for future studies on metagenomic variations of the human gut or other environments, and further promote strain-level researches.https://ngdc.cncb.ac.cn/gsa.The raw sequence data reported in this manuscript have been deposited in the Genome Sequence Archive  in NatioThe studies involving human participants were reviewed and approved by Ethics Committee of Yongkang First People’s Hospital. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.XY, YC, and PL: conceptualization. YC, PL, and ZH: methodology. PL, YC, and SA: software. YC, PL, and GD: validation. PL, YC, and CF: formal analysis. PL and YC: investigation. RL: data curation. FX: provision of samples. PL: writing—original draft preparation. XY and YC: writing—review and editing and supervision. PL and SH: visualization. YC and CF: project administration. XY and FX: funding acquisition. All authors have read and agreed to the published version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The number of significantly associated regions reported in genome-wide association studies (GWAS) for polygenic traits typically increases with sample size. A traditional tool for quality control and identification of significant regions has been a visual inspection of how significant and correlated genetic variants cluster within a region. However, while inspecting hundreds of regions, this subjective method can misattribute significance to some loci or neglect others that are significant.The GWAS quality score (GQS) identifies suspicious regions and prevents erroneous interpretations with an objective, quantitative and automated method. The GQS assesses all measured single nucleotide polymorphisms (SNPs) that are linked by inheritance to each other [linkage disequilibrium (LD)] and compares the significance of trait association of each SNP to its LD value for the reported index SNP. A GQS value of 1.0 ascribes a high level of confidence to the entire region and its underlying gene(s), while GQS values <1.0 indicate the need to closely inspect the outliers. We applied the GQS to published and non-published genome-wide summary statistics and report suspicious regions requiring secondary inspection while supporting the majority of reported regions from large-scale published meta-analyses.https://github.com/Xswapnil/GQS/). The analyst can use whole-genome summary statistics to estimate GQS for each defined region. We also provide an online tool (http://35.227.18.38/) that gives access to the GQS. The quantitative measure of quality attributes by GQS and its visualization is an objective method that enhances the confidence of each genomic hit.The GQS code/scripts can be cloned from GitHub (Bioinformatics online. For example, the GIANT consortium identified 3290 independent SNPs associated with the height of 700 000 individuals via a GWAS meta-analysis  have identified well over 300 000 single nucleotide polymorphisms (SNPs) associated with various traits , that exploits the relationship between test statistics and linkage disequilibria within an associated genomic locus in GWAS  (The principle guiding the GQS method is that non-associated SNPs that are in high linkage disequilibrium (LD) with a significantly associated ‘index’ SNP are suspicious. The GQS method calculates a quality score and flags suspicious loci for secondary inspection.P-value within a genomic region. The GQS then estimates the LD between each SNPs in the region and the index SNP using the appropriate population reference genome. SNPs in linkage equilibrium (r2 of <1%) are excluded to reduce noise in the statistics. Then, the GQS method delineates a straight line from the index SNPs to the origin in a 2D space of the negative log of P-value and LD-r2  or signal lost (a negative ratio) for each SNP above or below the regression. Thus, for the measure of association between a genomic region and a trait, S reflects the deviation of statistical significance of each SNP in that region beyond what would be predicted by its LD with the index SNP. In the calculation of GQS, we do not incorporate SNPs that gain signals since these likely reflect one or more additional, independent associations within the same genomic location. The existence of such additional association can be evaluated by a reanalysis of the data by choosing a different index SNP  the absence of suspicious SNPs above this linear threshold and (ii) how far the outliers SNPs are to the worst-case scenario. The GQS method estimates the ratio between the SNP with the maximal signal loss in the region  to the index SNP while without any significance.For the example in https://github.com/Xswapnil/GQS/). The analyst can use whole-genome summary statistics to estimate GQS for each defined region. It is also implemented in the Ricopili pipeline (http://35.227.18.38/). The application has a user-friendly interface and does not require an understanding of UNIX.The GQS code/scripts can be cloned from GitHub (The results from a GWAS of a hapgen (The GQS method was tested on GWAS summary statistics for 244 genomic regions associated with schizophrenia (SCZ) obtained from the PGC Schizophrenia workgroup .Two sets of summary statistics were obtained from the PGC analysts who were responsible for the SCZ Asian analysis. One set, shown in the left half of N ∼ 700 000) (N = 170 911) (N = 293 723) . Among these associated regions, 239 produced a GQS of 1  were inspected and found to be acceptable after scrutiny by We applied GQS on publicly available genome-wide significant summary statistics for adult height, education attainment and neuroticism obtained from the following GWAS studies:Height GWAS .Education attainment GWAS  and fiveNeuroticism GWAS  and two COVID GWAS .GQS on targeted regions from seven neurotransmitter receptors  and anotThe statistical tool described here, the GQS, provides a quantitative measure of whether the SNPs in a genomic locus are consistently associated with the phenotype, as predicted by their LD structure. GQS values range from 0 to 1.0, where 0 has no consistent association with phenotype, to 1.0, with a highly consistent association. GQS can automatically identify problematic regions among hundreds of associations that might not be evaluated correctly or easily by the traditional subjective, visual inspection. Flags for such regions warrant a secondary inspection. Conversely, GQS values of 1.0 indicate high confidence in the validity of a reported region because the statistical significance of SNPs is in reliable concordance with the underlying LD pattern. As shown in P-value. This can support the technical validity of the reported results, which can increase confidence in including an allele in a polygenic risk score analysis or gene set analysis. It can also help justify expanding the sample size to attain statistical significance for the index SNP and the GQS-defined locus and could systematically uncover multiple loci that could benefit from the increased sample size. The more robust analysis by the GQS method can also reduce the variants to be evaluated in replication studies, thus increasing statistical power by predicting SNP association outcomes that are more likely to be true.One advantage of the GQS is that it assigns an objective confidence evaluation on reported genomic regions. Even when there is no genome-wide significant association, GQS values close to 1.0 indicate that the alleles under consideration within the range were measured correctly since they show the expected association between LD and When applied to published GWAS meta-analyses summary statistics , the GQSpostmortem gene expression in neurons of schizophrenia patients. The transcriptomes revealed schizophrenia as a diabetes-like condition in the brain  and dopamine D2 receptor (DRD2) (SNPs within a locus that contains the muscarinic CHRM4 receptor are highly significant for schizophrenia risk . A locusThese findings are consistent with recent evidence for the muscarinic receptor as only the second validated target for treating .A comparison between GQS and a recently developed method for QC of GWAS summary statistics, DENTIST (GQS targets the analysis of whole regions, with an emphasis on the aggregate of genome-wide significant hits, replacing the manual need for visual inspection to support or flag disease risk genes. The GQS was designed, and its algorithm was adjusted to identify the problematic cohorts within a single cohort as well as in meta-analyses.DENTIST targets single SNPs in whole-genome summary statistics with the goal of improving fine mapping, conditional analysis, pathway analysis, heritability and genetic correlation. This method is specifically meant for single-cohort association analyses, e.g. not designed for sample size differences between SNPs.The GQS method is also available as a user-friendly web tool that does not require an understanding of UNIX, while at present, DENTIST does.A detailed analysis and results comparing the two methods can be found in the In summary, the GQS and DENTIST are useful algorithms that compare the significance of specific associations with the expected significance due to the underlying LD. Discrepancies between the expected and actual outcomes are used to identify suspicious association results.The GQS adds confidence to the SNPs in the associated genomic region and removes the need for subjective, visual inspection of associated SNPs. We exclude the incorporation of SNPs that gain signals since these likely reflect one or more additional, independent associations within the same genomic location. The existence of such additional association can be evaluated by a reanalysis of the data by choosing a different index SNP . A seconA perspective for future uses includes using GQS to find and evaluate GWAS results of borderline significance. Such discoveries may provide a rationale for increasing the sample size to attain sufficient power to test such loci. Finally, the GQS method differentiates reliable, significant GWAS regions according to whether they contain, or do not contain, several technical artifacts. Such artifacts could occur in various steps in GWAS meta-analyses, e.g. errors during QC, imputation or in the meta-analysis setup. This strongly suggests that if applied generally to GWAS results, the GQS method can screen for diverse forms of biases in GWAS and do so without the need for visual inspection. This will be especially relevant for modern multi-site GWAS meta-analyses reporting hundreds of associated regions, where the tedium and subjective nature of visual inspection can be supplanted by objective validation.btad004_Supplementary_DataClick here for additional data file.


Xian/indica accessions had generally higher tolerance to alkali stress than Geng/japonica accessions at the germination stage. Using genome-wide association analysis, 90 loci were identified as significantly associated with alkali tolerance. Eight genes  were selected as important candidate genes for alkali tolerance based on the gene functional annotation and gene-CDS-haplotype analysis. According to the expression levels of LOC_Os09g25060 (OsWRKY76), it is likely to play a negative regulatory role in alkali tolerance during rice germination. An effective strategy for improving rice alkali tolerance may be to pyramid alkali-tolerant haplotypes of multiple candidate genes to obtain the optimal haplotype combination. Our findings may provide valuable genetic information and expand the use of alkali tolerance germplasm resources in rice molecular breeding to improve the alkali tolerance at the germination stage.Salinity–alkalinity stress is one of the main abiotic factors limiting rice production worldwide. With the widespread use of rice direct seeding technology, it has become increasingly important to improve the tolerance to salinity–alkalinity of rice varieties at the germination stage. Although we have a more comprehensive understanding of salt tolerance in rice, the genetic basis of alkali tolerance in rice is still poorly understood. In this study, we measured seven germination-related traits under alkali stress and control conditions using 428 diverse rice accessions. The alkali tolerance levels of rice germplasms varied considerably during germination.  Based o by GWAS . Yu et a by GWAS . Li et aTL qAT11 . HoweverXian and Geng, were evaluated for alkali tolerance at the germination stage , mainly belonging to subspecies on stage . We usedon stage  to identGeng accessions, 278 Xian accessions, 13 Aus, 6 Basmati and 6 admix accessions (2CO3 solution), respectively. As a control, 10 ml of distilled water was added to each Petri dish, and 10 ml of 0.15% Na2CO3 solution was added to each Petri dish as alkali stress. After that, the seeds were grown in a growth chamber under a 14-h light/10-h dark photoperiod (28°C/26°C) with 70% relative humidity for 7 days. Throughout the test, the solution was changed regularly every day.We used 428 accessions from the 3 K-RG to evaluate the alkali tolerance of rice at the germination stage. The accessions contained 125 cessions . A totalt/N0, where Nt represents the number of germinated seeds at day t and N0 represents the total number of experimental seeds), respectively. The GI was calculated as follows: GI = ∑(Gt/Tt), where Gt is the accumulated number of the germinated seeds on day t and tT is the time corresponding to Gt in days , germination energy (GE), and germination index (GI). We used the seed germination rates on day 3 and day 7 as GE and GR , Xian (p = 2.76E-06) and Geng (p = 8.31E-06) subpopulations, respectively. Manhattan and quantile–quantile (Q–Q) plots of GWAS were created by the R package “qqman”  with each trait. In the case of an SNP located in the intergenic region, the downstream gene was chosen. LDBlockshow (n ≥ 10 rice accessions). The expression profiles of candidate genes in Nipponbare were obtained from the RiceXPro database  genes with significant SNPs and functional annotation related to abiotic stress based on the Nipponbare reference genome IRGSP 1.0  and the lockshow  was usedlockshow . There wlockshow  in R to database .2CO3, 100 seed embryos were sampled under alkali stress and control conditions, respectively. Total RNA was extracted from germinating seeds using plant RNA extraction kit (Tiangen Biotechnology) and reverse transcribed using reverse transcription kit (Invitrogen). Real-time qRT-PCR analyses were conducted with Taq Pro Universal SYBR qPCR Master Mix . All primers used for qRT-PCR are listed in -∆∆CT method  from each subspecies. After 24 h of alkali stress with 0.15% NaT method .We used one-way ANOVA to test the differences between subpopulations in the traits related to alkali tolerance by the “agricolae”  package 2CO3 (named “TraitName+S”) and control condition with distilled water (named “TraitName+C”) at the germination stage  and GIS (1.04), “Heimangdao” with the longest SLS (6.03 cm), “ARC 15091” with the longest RLS (4.53 cm), and “NCS 766” with the highest VIS (6.17).Seven traits including GR, GI, MGT, VI, RL, SL, and GE were measured for 428 rice accessions under alkali stress with 0.15% Naon stage . Furtheron stage . Under aon stage . We obseccession . Among tXian subpopulation (n = 278) exhibited significantly higher RVI, RRL, RGE, but significantly shorter RMGT than the Geng subpopulation  harboring lead SNPs (rs8_17550068 with p = 4.92E-07 and rs12_26284167 with p = 6.1E-06) associated with RSL in Geng subpopulation (OsWRKY76 was significantly associated with RRL in the whole population (p = 8.68E-11) in the whole population. The lead SNP of rs3_34261591 showed the strongest association signal (p = 2.15E-8) with MGTS in Xian subpopulation. The lead SNP rs8_24108555 with the most significant association (p = 9.42E-9) for GES was identified in Geng subpopulation  had a lead SNP rs9_14985169 (p = 2.09E-06) for RRL in the whole subpopulation, which is a member of the rice WRKY transcription factor gene family  and included 491 SNPs . LOC_Os0e family . Three mcessions . Hap1 ispulation . In comppulation .LOC_Os03g60240 that had the most significant SNP rs3_34257992 (p = 7.96E-08) for MGTS in Xian subpopulation. Using 22 SNPs (including five non-synonymous SNPs) in the coding region of LOC_Os03g60240, six haplotypes were detected, each of which was shared by at least 10 accessions (Xian accessions (The LD block region for locus 32 on chromosome 3 was predicted from 34.256 to 34.267 Mb (11.16 kb) and contained 37 SNPs , includicessions . In the cessions ,D.LOC_Os01g11990 and LOC_Os01g12000), was significantly associated with RGR, GRS, and RVI. We identified four haplotypes (n ≥ 10 accessions) based on eight SNPs in the coding region of LOC_Os01g12000, the downstream gene of rs1_6533274  and included 730 SNPs based on local LD analysis . One of _6533274 . Among tplotypes ,D.LOC_Os11g35350, was significantly associated with RRL in the Xian subpopulation  were identified (Xian accessions compared to the other haplotypes (The LD block region of locus 82 was estimated to be from 20.71 to 20.75 (38.1 kb) on chromosome 11 including 438 SNPs. The SNP rs11_20724735, located in the candidate gene pulation . Using sentified . Hap3 haplotypes ,D.LOC_Os09g25060, Hap3 of LOC_Os01g12000, Hap5 of LOC_Os12g13300, Hap4 of LOC_Os03g60240, Hap3 of LOC_Os04g41410, Hap6 of LOC_Os12g09350, Hap4 of LOC_Os11g35350, and Hap1 of LOC_Os03g08960 were detected as alkali-tolerant haplotypes. We used a combined haplotype analysis in order to determine the breeding potential of candidate genes for alkali tolerance. There remained five groups comprising three candidate genes after the removal of rare haplotype combinations (n < 15 accessions). The Groups I, II, III, and IV were enriched in the Xian subpopulation, whereas Group V was mainly found in the Geng subpopulation, which showed clear Xian-Geng differentiation  were also detected in previous QTL mapping studies , primariThe original contributions presented in the study are included in the article/FZ conceived and designed the experiments. SM analyzed the data and wrote the manuscript. SM, GZ, JJ, and JL performed the experiments. All authors contributed to the article and approved the submitted version.This work was supported by the Key Research and Development Project of Hainan Province (ZDYF2021XDNY171), and the Agricultural Science and Technology Innovation Program and the Cooperation and Innovation Mission (CAAS-ZDXT202001).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.https://www.frontiersin.org/articles/10.3389/fpls.2022.887239/full#supplementary-materialThe Supplementary Material for this article can be found online at: Click here for additional data file.Click here for additional data file.


Well-developed maize reference genomes and genotyping technology along with fast decreasing detection costs have enabled the chance of shifting essentially derived varieties (EDV) identification to high-throughput SNP genotyping technology. However, attempts of using high-throughput technologies such as SNP array on EDV identification and the essential baseline parameters such as genetic homozygosity and/or stability in EDV practices have not been characterized. Here, we selected 28 accessions of 21 classical maize inbreds, which definitely form a pedigree network from initial founders to derivatives that had made huge contribution to corn production, to demonstrate these fundamental analyses. Our data showed that average residual heterozygosity (RH) rate of these 28 accessions across genome was about 1.03%. However, the RH rate of some accessions was higher than 3%. In addition, some inbreds were found to have an overall RH rate lower than 2% but over 8% level at certain chromosomes. Genetic drift (GD) between two accessions from different years or breeding programs varied from 0.13% to 13.16%. Accessions with low GD level showed cluster distribution pattern and compared with RH distributions indicated that RH was not the only resource of GD. Both RH and GD data suggested that genetic purity analysis is an essential procedure before determining EDV. Eleven derivative lines were characterized with regard to their genome compositions and were inferred as their breeding histories. The backcross, bi-parental recycling, and mutation breeding records could be identified. The data provide insights of underlining fundamental parameters for defining EDV threshold and the results demonstrate the EDV identification process. The increasing population and consumption in the world are presenting unprecedented challenges to agriculture to meet food security and sustainability needs . Meanwhihttps://www.upov.int/ (accessed on 22th July 2022). Beyond the 1961, 1972, and 1978 UPOV Conventions, the 1991 UPOV Convention (hereinafter 1991 UPOV) had introduced and adopted “essentially derived varieties” (EDV), which expand the scope of a breeder’s right to derived variety that was selected on the basis of a minor difference, a mutation, a genetic modification, a backcross, or a selection. The 1991 UPOV was accepted by the mainstream as a rule to achieve the balance of variety rights protection and research efficiency; though, it is still being debated in some large developing countries including China. In the contemporary era of rapid technology development, it is urgent to protect the right of initial varieties due to new advent technologies, such as transgene, physical, chemical, and biological mutagenesis, which may introduce traits or mutations into a given variety. For instance, genome editing has enabled researchers to precisely modify a desirable genotype of a given gene in crops [To fulfill the mission of plant variety intellectual property protection, the International Convention for the Protection of New Varieties of Plants (UPOV Convention) was established and adopted in Paris in 1961, and it was revised in 1972, 1978, and 1991 . Items with unavailable data entries indicate that the actual application area may not met the statistical data inclusion criteria. All maize inbred lines were generously gifted from Maize Research Institute, Jilin Academy of Agricultural Sciences, which is the one of the most important research institutes and contributed major germplasm and varieties for China temperate maize.A total of 21 maize inbred lines with 28 seed bank accessions were used in the present study . All inbAbout 100 seeds of each accession were germinated cultured in sand until 2-week-old seedlings to verify the seed purity before sampling. A bulk of 15 plants for each accession were sampled for the young seedling leaves to isolate genomic DNA using a Plant Genomic DNA Kit , according to the manufacturer’s protocols.® Maize56K SNP Array, which contains 56,000 SNPs. The hybridization signals were detected using a GeneChip™ Scanner 3000 7G  to obtain raw CEL files. The CEL files were processed using the Axiom Analysis Suite 5.0.1 . The dish quality of samples was > 0.82. The SNPs in this array were distributed evenly across maize 10 chromosomes. The SNP array was designed based on huge, previewed SNPs and reliably high polymorphic loci and probes among Chinese commonly used maize germplasms. The genotyping and raw data filtering were obtained from a service provided by China Golden Marker Biotech. Co., Ltd. . The physical haplotype map was constructed according to B73 RefGen v4. The missing data were removed by pairwise analysis between accessions. The call rate criterion of SNP was set at 97%. The SNPs, which were categorized into PolyHighResolution, MonoHighResolution, or NoMinorHom, were used in the present study. The high stringent, filtered SNP data were generated for the specific analysis  donor, was verified with regard to its EDV identification (Ht1 was located approximately at 218,321,093 on chromosome 2 [To further explore the application scenarios of high-throughput genome-wide SNP technology in this field, we attempted to analyze the EDVs, of which we know only one of founder lines. The A619Ht, which is an EDV inbred of A619 without information on northern leaf blight (fication . Based omosome 2 , and a lRegarding adoption of whole-genome high-throughput SNP method, the EDV threshold is obviously a core concern, but this threshold is not just a technical issue. One of the most important objectives of the 1991 UPOV Convention is the introduction of the EDV concept extending the breeder’s right to a variety that was selected basis on a minor difference, a mutation, a genetic modification, a backcross, a selection within a variety, and so on. In principle, the EDV concept is also expected to protect the breeding of new varieties from an initial parental line and/or hybrids. However, both the complexity of breeding and technical reasons makes it difficult to establish a simple and effective technical solution to meet clearly defined variety rights boundaries. On the one hand, concerning breeding practice, Troyer et al.  suggesteThe genetic homozygosity of the ideal parental inbred line is 100%. This feature is beneficial for inbred breeding practices due to expected genetic transmission and selective advantage . HoweverAnother aim in the present study is to analyze the extent of genetic variation caused by GD. This issue is also an essential parameter that is highly correlated with stability of parental inbreds and, therefore, hybrids in maize. Advances in both high-throughput genotyping and the maize reference genomes now enable the fine DNA segmentation tracing across the whole genome. Some accessions of the same lines and sharing the same seed origins were analyzed. Our data showed the GD may result from RH and some genome instability elements. Since RH is important resource of GD, overlapping GD and RH on chromosomes 4, 6, and 8 of DH02 can be well-explained by this hypothesis . There wWith the developed maize reference genomes ,22,23 an


N = 55, Mage = 12.27) and their parents using a video-mediated recall task of 10-min conflict-topic discussions. Using multilevel modeling, we found evidence of significant level-2 effects (mean affect) and level-1 effects (affective synchrony) for parents and their adolescents. Level-2 and level-1 associations were differentially moderated by adolescent age and adolescent internalizing and externalizing symptoms. More specifically, parent–adolescent synchrony was stronger when adolescents were older and had more internalizing problems. Further, more positive adolescent mean affect was associated with more positive parent affect (and vice versa), but only for dyads with low adolescent externalizing problems. Results underscore the importance of additional research examining parent–child affect in adolescence.Interactions with parents are integral in shaping the development of children’s emotional processes. Important aspects of these interactions are overall (mean level) affective experience and affective synchrony (linkages between parent and child affect across time). Respectively, mean-level affect and affective synchrony reflect aspects of the content and structure of dyadic interactions. Most research on parent–child affect during dyadic interactions has focused on infancy and early childhood; adolescence, however, is a key period for both normative emotional development and the emergence of emotional disorders. We examined affect in early to mid-adolescents ( The recognition, understanding, and regulation of emotions is important for healthy development and impairments in these processes are associated with the risk of internalizing and externalizing problems e.g., . Althougcontent of dyadic interactions  is central to developmental psychopathology , and extchildren . In theschildren . Despitechildren .The nature of parent–child mean affect and affective synchrony may change over time and be particularly important over the course of adolescence. Adolescence is a critical transitional period. During childhood, a characteristic, predictable, and stable pattern of behavior develops between parent and child based on repeated interactions . In the Several disorders increase in prevalence during adolescence, including depression and some forms of disruptive behavior disorders . Deficiton a moment-to-moment basis is arguably the most powerful and sensitive method to capture affective synchrony. With regard to reporting, both self-report and observational coding have methodological benefits and drawbacks. Observational coding by independent raters captures expressed emotion rather than experienced emotion and a large body of research has shown that these constructs are distinct , which involves obtaining reports from members of dyads about their emotions while they view recordings of their face-to-face interactions . VMR canAim 1. We examined the association between parent and adolescent affect over the course of the conflict-topic discussion task. As predictors, we used moment-to-moment changes in each partner’s affect1 as well as each partner’s mean level of affect. In the context of multilevel modeling, these associations represent the relation of variables within person but across time (level 1) and the relation of variables after pooling across time (level 2), respectively. In hypothesis one, we expected mean parent and adolescent affect to be positively related to partner affect. In hypothesis two, we expected positive associations between partners’ concurrent, moment-to-moment affect; in other words, we expected to observe affective synchrony. Aim 2. We explored whether affective processes in parent–child interactions differ as a function of adolescent age. For hypothesis three, we expected that parent and adolescent mean affect would be inversely related to adolescent age. Considering limited research on affective synchrony in adolescence, analyses examining the association of age and moment-to-moment affect were exploratory. Aim 3. We were interested in the ways in which associations between parent and adolescent affect differed based on levels of adolescent internalizing and externalizing symptoms. For hypothesis four, we expected that mean affect would be more negative for both parents and adolescents when levels of adolescent internalizing and externalizing symptoms were higher. Again, due to scant research on affective synchrony in adolescence, analyses examining the interaction between adolescent symptoms and moment-to-moment affect were exploratory.The current study used VMR on a moment-to-moment basis to capture the dynamics of affect in interactions between parents and their children during a laboratory conflict-topic discussion task. Our sample of youth ranged from 10 to 15 years old, and accordingly we tested our hypotheses in early to mid-adolescence. We addressed the following three aims and four hypotheses. Parents and adolescents in a southern metropolitan area were recruited to participate in a study about stress and emotions in families from October 2018 to February 2020 (recruitment was suspended in March 2020 due to the COVID-19 pandemic). In order to enroll a sample that varied regarding adolescents’ emotional and behavioral problems, recruitment occurred primarily through listserv postings and outpatient clinic referrals. Parents who expressed interest in the study were screened on the phone prior to enrollment. Parent–adolescent dyads were excluded from participation if the adolescent was outside the age range of 10–15 years old, the parent was not the legal guardian of the adolescent, or the dyad did not live together at least 50% of the time for the past 6 months. Dyads were also excluded if mothers reported a diagnosis of schizophrenia in themselves or their adolescent or a diagnosis of autism spectrum disorder in the adolescent, as these disorders may impede an individual’s ability to complete some of the study tasks. In families with multiple adolescents meeting inclusion criteria, the older adolescent was invited to participate in the study.Fifty-six dyads were enrolled and participated in the study. One dyad did not complete the conflict-topic discussion task due to an abbreviated lab visit, and their data were excluded listwise from the current analyses. Thus, 55 dyads contributed partial or full data to the current analyses. VMR data were missing from two parents and three adolescents due to research assistant error during the study visit ; no families refused to complete the VMR task. Continuous VMR affect ratings from the 55 dyads were divided into sixty 10-s intervals , and 55% were female. Sixty-nine percent were White, 24% Black, 6% Asian, and 2% more than one race. Parents were primarily female (93%)2, married or living with a partner (69%), and ranged in education . See calm, 5 = angry). Research assistants selected one topic that was rated highly by both the parent and adolescent. If the parent and adolescent did not rate the same topic highly, research assistants selected the topic that was rated highest by the adolescent. Dyads participated in a videotaped discussion task, during which they were instructed to discuss the selected topic for 10 min. The dyad was asked to (a) describe the issue, (b) explain how they feel about it, (c) discuss why it has become a source of conflict, and (d) attempt to resolve the issue. After the discussion, parents and adolescents viewed the videotape in separate rooms and provided moment-to-moment ratings of their own in-task affect . The reliability and validity of the YSR are well established  and the other predicting parent affect as the dependent variable PAtd; henceforth referred to as the “Parent Affect Model”). The Adolescent Affect Model is displayed below. See We used SPSS Version 26 for all analyses. In preliminary analyses, we computed zero-order correlations to understand basic associations among the study variables. We used multilevel modeling to test all hypotheses. Full information maximum likelihood estimation was implemented in multilevel modeling analyses to account for missing data. First, intraclass correlation coefficients (ICC) were estimated in separate null models predicting parent affect and adolescent affect to establish need for multilevel modeling . See SupIn the Adolescent Affect Model, parent affect ratings for all 10-s intervals were person-mean centered. In other words, each parent’s mean affect score across the 10-min interaction was subtracted from their affect ratings at each 10-s interval M = 4.23, SD = 44.13; parent affect: M = 5.65, SD = 41.78). Distributions of adolescent and parent affect ratings were extremely similar. Approximately 10.1% of adolescent ratings and 10.1% of parent ratings were very negative (−100 to −50); 33.6% of adolescent ratings and 29.3% of parent ratings were moderately negative (−50 to −1); 9.6% of adolescent ratings and 9.2% of parent ratings were neutral (0); 31.3% of adolescent ratings and 36.3% of parent ratings were moderately positive (1 to 50); and 15.4% of adolescent ratings and 15.1% of parent ratings were very positive (51 to 100). Of the 43 participants for whom we were able to calculate symptom T scores , adolescent symptoms were approximately one-half standard deviation above the normative mean for internalizing symptoms  and externalizing symptoms .4Zero-order correlations are also presented in The ICC derived from the null univariate models predicting parent and adolescent affect indicated that 38% of the observed variation in parent affect and 48% of the observed variation in adolescent affect was due to differences among dyads . These vp = .001), and more positive mean adolescent affect ratings were associated with more positive parent affect ratings (p = .001). In support of hypothesis 2 , multilevel models showed that more positive parent affect ratings were associated with more positive concurrent adolescent affect ratings (p < .001), and more positive adolescent affect ratings were associated with more positive concurrent parent affect ratings (p < .001). That is, results provide evidence of parent–child affective synchrony in adolescence.Tests of hypotheses 1 and 2 (Aim 1) are presented in p = .001). As depicted in With regard to Aim 2, adolescent age moderated parent–adolescent synchrony, but only in the Parent Affect Model (p = .02) and Parent Affect (p = .05) models. As depicted in In Aim 3, we examined how associations between parent and adolescent affect, both in terms of mean affect and affective synchrony, may differ based on levels of adolescent internalizing and externalizing symptoms . In part12 = .01, p = .01) and Parent Affect Model . As depicted in Analyses examining the interaction of adolescent internalizing and externalizing symptoms and moment-to-moment affect  were exploratory. We found that internalizing symptoms, but not externalizing symptoms, moderated parent–adolescent synchrony. These results were consistent in both the Adolescent Affect Model  and level-1 (affective synchrony) relations in the context of a VMR task following a 10-min conflict-topic discussion. Psychological scientists have predominantly concerned themselves with elucidating sources of variation between individuals. Yet, only under stringent conditions do level-2 effects generalize to level-1 phenomena . In the In the current study, we collected self-reported affect ratings using a VMR procedure to minimize potential biases in retrospective recall during a laboratory conflict-topic discussion task that provided similar contexts for emotion generation across dyads. Feasibility of the use of this task with parents and adolescents was supported, considering that no families actively refused to participate in the interaction task or to provide ratings of their emotions using the VMR procedure. Data were obtained from 93% of adolescents and 95% of parents, with missing data largely due to research assistant error. Preliminary construct validity of this task was supported, as well, considering medium positive associations between parent and adolescent affect and small negative associations between adolescent in-task affect and their own internalizing and externalizing symptoms. VMR has been implemented in studies of adult couples , but thiAs our first aim, we sought to better understand the association between parent affect and adolescent affect over the course of the conflict-topic discussion task. We examined each partner’s mean affect across the interaction (level-2 effects), as well as moment-to-moment changes in each partner’s affect, or affective synchrony (level-1 effects). As hypothesized, mean parent and adolescent affect were positively related to the affect of the other member of the dyad. Further, as expected, we found positive associations between partners’ concurrent, moment-to-moment affect. In other words, we found both level-2 and level-1 effects in affective interactions between parents and adolescents. These findings indicate that the correspondence in overall affect and affective synchrony that has been documented during interactions between parents and infants and young children e.g.,  is also The second aim of the study was to examine how parent–child affective patterns may be related to adolescent age. Our sample consisted of 10- to 15-year-olds, and so we were able to explore associations in early to mid-adolescence, specifically. We found significant associations between adolescent age and parent–adolescent affective synchrony, with the level of affective synchrony strongest for the oldest adolescents in our sample. The pattern of increasing synchrony with older age, which corresponds to mid-adolescence in the current sample, reflects the transitional nature of adolescence. Patterns of dyadic exchange that become well established throughout childhood are disrupted in early adolescence and restabilize over time . To negoWe did not find significant associations between adolescent age and mean levels of either adolescent or parent affect. In their meta-analysis, Finally, we were interested in the ways in which associations between parent and adolescent affect may differ based on levels of adolescent internalizing and externalizing symptoms. We hypothesized that mean affect for both parents and adolescents would be more negative when levels of adolescent internalizing and externalizing symptoms were higher. We had no a priori hypothesis for the association between parent–child affective synchrony and adolescent internalizing and externalizing symptoms. An interesting pattern of findings emerged.p = .09) in a subsample of only mothers . As such, this finding should be interpreted with caution and replicated in future work.More positive adolescent mean affect was associated with more positive parent affect (and vice versa), but only for dyads in which adolescents reported low externalizing problems. For dyads in which adolescents reported average or high externalizing problems, there was no association between adolescent and parent mean affect (and vice versa). In adolescence, parents might react in several ways to youth with externalizing symptoms during a conflict discussion, such as through escalation of negative affect, permissiveness, or inconsistency . VariatiWe did not find support for the interaction of externalizing symptoms and affective synchrony. Levels of parent–adolescent affective synchrony were higher for dyads in which adolescents reported higher levels of internalizing problems. The presence of internalizing symptoms in youth may prompt both youth and parents to display greater emotional acceptance  or empatWe did not find support for our hypothesis that parent and adolescent mean affect would be more negative when levels of adolescent internalizing symptoms were higher. A study by These novel findings notwithstanding, the current study also had limitations which provide directions for future research. First, concurrent covariation may reflect the transfer or exchange of emotions between partners, but concurrent covariation may also be influenced by third variables such as shared experiences . For exaSecond, our rating scale incorporates affect valence and intensity together on a continuum ranging from very negative (−100) to very positive (100), and we were unable to test our hypotheses separately for positive and negative affect. As described by Third, we examined concurrent synchrony, which presupposes that parent and adolescent emotion channels are tightly coupled. In contrast, it could be that emotion channels are lagged, such that current emotions are most strongly influenced by the partner’s prior emotions . Further, synchrony (concurrent and lagged) is only one of several ways to understand dynamic interpersonal affective systems. The field would benefit from future research on possible lagged effects of partners’ emotions, as well as other structural aspects of affective exchange in parent–adolescent conflict discussions  see .Fourth, the magnitude of the multilevel model variance at levels 1 and 2 indicates that additional predictors can be added. Future research should examine parent and dyadic factors, in addition to adolescent factors, that may influence mean affect and affective synchrony in parent–adolescent dyads . For exaFinally, while otherwise informative, the current cross-sectional investigation cannot point to whether high versus low mean-level positive/negative affect, or synchrony versus asynchrony, in parent–adolescent interactions is developmentally adaptive or maladaptive. Future work incorporating longitudinal measurement burst designs will elucidate how affective components of parent–child interactions change and remain stable across development and predict long-term adjustment . Neverthand adolescents could be coached to observe and understand moment-to-moment changes in the emotion channels of themselves and their partner. Observation of dyadic interactions will provide an important context from which dyads can work together to build skills in emotion recognition and regulation.The current study supported the feasibility of using VMR with parents and adolescents to retroactively rate affect generated in conflict-topic discussions. Similar methods have been used to bring dyadic behavioral and affective dynamics into the awareness of caregivers of infants and young children in the context of interventions. Attachment and Biobehavioral Catch-up  and FilmParents and children are linked across time, as are their emotions. Affective exchanges that occur over countless short-term dyadic interactions are key to shaping long-term emotional processes and psychological functioning. The results of the current study are a window into the content and structure of affect in parent–child interactions during early to mid-adolescence.1


The yak is the most important livestock in the Qinghai-Tibet Plateau, and body weight directly affects the economic values of yak. Up to date, the genome-wide profiling of single-nucleotide polymorphisms (SNPs) associating with body weight has not been reported in yak. In the present study, the SNPs in 480 yaks from three breeds were analyzed using the commercial high-density (600 K) yak SNP chips.The results identified 12 and 4 SNPs potentially associated with body weight in male and female yaks, respectively. Among them, 9 and 2 SNPs showed significant difference in yak body weight between different genotypes at each locus in male and female yaks, respectively. Further exploration found 33 coding genes within the 100 kbp upstream or downstream to the SNP loci, which might be potentially affected by the variation of SNPs. Among them, G protein-coupled receptor kinase 4 (GRK4) might be potentially affected by the SNP AX-174555047, which has been reported to affect the functioning of two body-weight associated hormones . Determination of PTH and ADM levels in yak revealed positive relationship between PTH level and body weight, negative relationship between ADM level and body weight along with the variation of AX-174555047 mutation.These results suggested that the SNP AX-174555047 might potentially affect body weight through mediating GRK4 expression and then PTH and ADM functioning. Thus, the SNP AX-174555047 might be used as a biomarker for molecular breeding of yak. More investigations are required to validate this point.The online version contains supplementary material available at 10.1186/s12864-022-09077-4. Bos grunniens) is naturally distributed across the Qinghai-Tibetan Plateau (QTP) and adjacent highlands, and provides meat, hides, dung fuel and transport power to the local residents. Breeding of yak aims to obtain strains with high-quality growth traits, such as high body weight, growth speed and meat quality [The yak  was the commonly used molecular breeding markers. Using the SSR technology, Cai et al.  and Asma.  detectedtic yaks . HoweverCompared with high-throughput genome sequencing, gene chip is low cost, more accurate, and convenient to operate, and has been widely used in breeding. Based on the gene chip technique, dense SNP genotyping arrays (DSNPGA), which incorporate thousands of SNP loci in a single chip for testing, provide extensive information on polymorphic variations across the genome of species of interest. Such information can be used in studies of selective sweeps, phylogeny, population structure, copy number variations, and genome-wide association analysis (GWAS). Based on DSNPGA and GWAS, multiple genes related to little size trait in Pelibuey sheep , four caBody weight is an important index to evaluate meat production performance , 14. In A total of 480 individuals were analyzed using the yak high-density 600 K SNP Chip. Finally, 13 individuals were discarded since their Dish QC values  were less than 0.82, and 467 individuals passed the quality control. Further genotyping analysis revealed that the average call rate of the 467 individuals was 0.9615. The average number of SNP passing the quality control was 605,946.st) at each SNP locus between Jiali yak (JL), Pali yak (YD) and Sibu yak (SB) yak populations are shown in Fig. st values between YD and JL, YD and SB were relatively higher than that between JL and SB  by the significant SNPs was shown in Table Body weight-associated SNPs have not been reported in yaks, but have been reported in other livestock. For instance, in the Landrace × Korean native pig, 5 SNPs at the LOC100621652 and LOC100523510 genes on the chromosome 12 were identified to highly associate with carcass weight using the 60 K Porcine SNP chips . In the The results of linkage disequilibrium (LD) analysis between significant SNPs were shown in Table In male yaks, 9 of the 12 selected SNPs showed significantly different body weight between different genotypes. Among them, A/G type at AX-174702570, T/G type at AX-174961896, A/G type at AX-174407967, G/A type at AX-174402854, G/G type at AX-174734142, and A/A type at AX-174706158 revealed significantly higher body weight than the G/G, T/T, G/G, A/A, A/G and A/G type, respectively. In addition, the A/A type at AX-174783962 displayed significantly higher body weight than A/G and G/G types. A/A type indicated significantly body weight than A/G and G/G types at AX-174547362. At AX-174555047, T/T type showed significantly higher body weight than G/G and T/G types , nucleolar protein 14 (NOP14), afadin- and alpha-actinin-binding protein (SSX2IP) and cAMP-dependent protein kinase catalytic subunit beta (PRKACB) were related to cell proliferation, migration and differentiation. G protein-coupled receptor kinase 4 (GRK4) has been reported to associate with obesity and muscular dystrophy , 17. Nuc1) [r = -0.21, Table S The SNP locus AX-174555047 was localized to the scaffold699_1, and might potentially affect the downstream GRK4 gene Fig. A. GRK4 w1) . The ove1) , 24. In It has bene reported that body mass index (BMI) was significantly positively correlated with PTH level , and obeGWAS identified 12 and 4 SNPs potentially associated with body weight in male and female yaks, respectively. The SNP AX-174555047 might regulate body weight through affecting PTH and ADM functioning by mediating the GRK4 gene in yak, and might be used as a biomarker for molecular breeding of yak in future.In the present study, a total of 480 yaks aged from 4 to 9 years with the body weight lower than 90 kg or higher than 130 kg were included. These yaks were from three yak populations . The information of sampling locations is shown in Table For each yak, DNA was extracted from 200 μl of blood using the TIANamp Genomic DNA kit  according to the manufacturer’s protocol. The concentration of DNA was quantified using a NanoDrop ND-2000 spectrophotometer  and the DNA integrity was assessed by 1% agarose gel electrophoresis.SNP loci were determined using the commercial Yak high-density 600 K SNP Chip 630,209 SNP) produced by the Affymetrix company  and the Axiom 2.0 reagent kit . The speciation of the Yak high-density 600 K SNP Chip is presented in Supplementary file 09 SNP prThe hybridization plates were placed in a PCR instrument for denaturation. The denaturation program included incubation at 95℃ for 10 min, 48℃ for 3 min and finally 48℃ for hold. Next, the denatured samples were transferred from the Hybridization Ready plate into the Hybridization tray, and the hybridization was conducted using a GeneTitan MC Instrument for 24 h. The Stain, Ligation and Stabilization mix solutions were added for staining. Finally, the hybridization chips were scanned using a Genetitian Multi-Channel (MC) Instrument (Thermo Fisher), and analyzed using the GeneTitan software to obtain the SNP calls of each sample.The SNP calls were exported to Axiom Analysis Suite for genotype calling. Next, quality control was carried out using the PLINK program . SNPs wist values of each SNP were calculated by the Bayesian method [st value along all chromosomes. The EIGENSOFT (v6.0.1) software [The Fn method . A 100-ksoftware  was usedsoftware  was used2 were calculated. A value of zero implies independence, whereas 1.0 means complete co-transferring. Phenotypic variance explained (PVE) of each SNP was calculated using the PLINK software [The linkage disequilibrium (LD) between candidate SNPs were analyzed using the LDBlockShow software (v1.40) . To quansoftware .Based on the yak genome sequence , the chrTO validate the biological functions potentially affected by variation of the AX-174555047 SNP locus, PTH and ADM contents in yak sera were detected in 93 yaks, which were collected from another batch of yak samples, including Chawula yak, Jila yak, Leiwuqi yak and Shenzha yak, using enzyme linked immunosorbent assay (ELISA) kits produced by the Jingmei Technology Company  following the manufacturer’s protocols. The sample sizes of the T/T, T/G and G/G genotypes at the AX-174555047 locus were 3, 53 and 37 individuals, respectively.p value was corrected using the Benjamini-Hochberg (BH) method by setting the false discovery rate (FDR) < 0.05. However, following this criterion, only four significant SNPs were identified. To expand the screening scope, we manually reset the screening threshold using the uncorrected P value of 1.0 × 10–5. At the uncorrected P value of 1.0 × 10–5, the Q-Q plot revealed that the observed value started to greatly deviate from the predicted value.GWAS was performed using the mix linear model (MLM) and the GAPIT software . The firt-test (for SNPs having two genotypes). Values were considered significantly different with the P < 0.05.Differences in body weight, PTH and AMD levels were compared between different genotypes at each locus using one-way ANOVA (for SNPs having three genotypes) or Student’s Additional file 1.Additional file 2.Additional file 3.Additional file 4.