Complex age-associated phenotypes are caused, in part, by an interaction between an individual’s genotype and age. The mechanisms governing such interactions are however not entirely understood. Here, we provide a novel transcriptional mechanism-based framework–SNiPage, to investigate such interactions, whereby a transcription factor (TF) whose expression changes with age (age-associated TF), binds to a polymorphic regulatory element in an allele-dependent fashion, rendering the target gene’s expression dependent on both, the age and the genotype. Applying SNiPage to GTEx, we detected ~637 significant TF-SNP-Gene triplets on average across 25 tissues, where the TF binds to a regulatory SNP in the gene’s promoter or putative enhancer and potentially regulates its expression in an age- and allele-dependent fashion. The detected SNPs are enriched for epigenomic marks indicative of regulatory activity, exhibit allele-specific chromatin accessibility, and spatial proximity to their putative gene targets. Furthermore, the TF-SNP interaction-dependent target genes have established links to aging and to age-associated diseases. In six hypertension-implicated tissues, detected interactions significantly inform hypertension state of an individual. Lastly, the age-interacting SNPs exhibit a greater proximity to the reported phenotype/diseases-associated SNPs than eSNPs identified in an interaction-independent fashion. Overall, we present a novel mechanism-based model, and a novel framework SNiPage, to identify functionally relevant SNP-age interactions in transcriptional control and illustrate their potential utility in understanding complex age-associated phenotypes. Numerous traits, such as cardiovascular diseases and cancer, are associated with age. However, these associations vary across races and ethnicities, suggesting an interplay between age and the genetic background in determining the trait. Although previously studies have attempted to detect Age-Genotype interactions based on statistical models, they are mostly devoid of mechanism, thus limiting their efficacy and scope in informing therapeutic strategies. Here, we propose a novel framework to investigate such interactions, by incorporating a specific transcription-based mechanism in the model. More specifically, our model is based on the mechanistic scenario that an age-associated transcription factor (TF) binds to a regulatory polymorphism (SNP) in an allele-specific manner to regulate the transcription of the downstream gene in an Age- and Genotype-specific fashion. By analyzing 25 tissues in the GTEx consortium, we detected tissue specific SNP-TF-Gene interaction triplets and functionally validated the detected SNP based on epigenomic and functional data. What’s more, multiple lines of evidence link detected interactions to aging and to age-associated diseases. We expect our new methodological framework and the detected functionally relevant interactions will enhance understanding of the underlying mechanism of SNP-Age interaction and its contribution to age-associated diseases. Normal aging is a critical “environmental” risk factor for complex diseases such as hypertension, cardiovascular defects, macular degeneration, Parkinson’s disease, and cancers ,2. For iIn addition to aging, genomic variations also significantly contribute to complex diseases –11, inclIt is likely that aging and genome variations jointly contribute to complex diseases. That is, systemic molecular changes through aging may affect complex phenotypes in a genotype-dependent manner. Indeed, such genotype-environment  interactions have been previously investigated. For instance, by incorporating a SNP-age term in a regression model, Yao et al identified 10 age-dependent eQTL SNPs (eSNPs) in Whole Blood . In a meHere, based on established transcriptional mechanisms, we present a model and a pipeline, that we term “SNiPage”, to identify SNP-Age interactions in determining target gene expression. Our model is based on the hypothesis that age-associated transcription factors (TF) exhibiting allele-specific binding at a regulatory SNP will lead to age-associated expression changes in the target gene in an allele-specific manner . Thus, oApplication of SNiPage to 25 human tissues revealed ~637 significant TF-SNP-Gene triplets on average in each tissue. Multiple biological evidence, including chromatin marks indicative of active regulation, allelic imbalance, and SNP-Gene spatial proximity, suggest that our detected SNP-Gene pairs are highly likely to be functional. Functional enrichment analyses link the detected target genes regulated by SNP-age interactions to the aging process and complex diseases. Interestingly, aggregated SNP-age interactions significantly inform an individual’s hypertension state in six known hypertension-related tissues. Likewise, for several other phenotypes, including cancers, the age-interacting SNPs exhibit a significantly higher linkage to the reported phenotype-associated SNPs than the SNPs detected using standard interaction-independent model. Finally, the detected SNPs exhibit relatively higher derived allele frequencies in human, suggestive of their potential role in adaptive evolution.In summary, we have reported a novel framework, exploiting established transcriptional mechanisms, to identify SNP-age interactions driving gene expression, and the detected TF-SNP-Gene triplets may provide further insights into the mechanisms linking genotype and aging with complex age-related phenotypes.The SNiPage pipeline is illustrated in We tested a total of 2,997,407 TF-SNP -gene triplets involving 125 TFs, 8,377 SNPs, and 4,661 genes on average across each of the 25 tissues and identified ~637 significant TF-SNP-Gene triplets (parameters at various steps of the pipeline are mentioned in Methods) on average in each tissue; details in Tables MAX binds at SNP rs2295079 to regulate the expression of MTOR in Whole Blood. Adjusted expression of TF MAX significantly decreases with age  with MAX expression when the individuals have at least one G allele, while for ‘C’ allele homozygotes, there is no correlation between MAX and MTOR .A specific example of a detected TF-SNP-Gene interaction is illustrated in with age ; note thTo assess the statistical robustness of the detected Age-associated TFs as well as the interactions, we estimated their replication rate in random down-sampled  datasets in four tissues  with relatively large sample size. In addition, we also estimated the replication rate in the background dataset (randomly shuffled target gene expression profile) as control. We did not explicitly use race and ethnicity as covariates in our model because the GTEx data is biased (>80% of the samples) towards White race, and the annotation of ethnicity is sparse. However, we have included SNP-derived hidden variables as confounding factors in our model, which is expected to account for race and ethnicity. We have further assessed the effect of including race in our models as follows. We included race in our age-associated TF detection model in the three tissues  with large sample sizes, and observed that a very large fraction  of age-associated TFs detected by the original model were also detected by the race-controlled model. Additionally, in the three tissues we randomly selected 1% of candidate triplets  and again, a very large fraction  of the triplets detected by the original model were also detected by the race-controlled model.Overall, our analyses suggest that our detected Age-associated TFs and the interactions are not likely to be false positives and are statistically robust.In view of our hypothesis that age-associated TFs bind to a SNP locus and regulate target gene expression, the detected SNP loci are expected to be in cis-regulatory elements such as enhancers and promoters. We therefore checked whether the detected SNP loci are enriched for open chromatin signals (DHS) and other histone marks  characterizing regulatory regions. Even though DHS peaks were used as an inclusion criterion for the SNPs, we tested whether the 200 bps flanks of the detected interacting SNPs exhibit higher DHS intensity compared to other tested SNPs which, notably, also qualified under the initial DHS filter. Each of the 4 epigenomic marks was analyzed for the tissues in which the relevant data was available and there were at least 200 foreground SNP loci. As shown in Next, we checked whether our detected SNPs are under evolutionary selection by comparing their conservation  with those for three backgrounds: randomly selected SNPs, randomly selected SNPs after DNase filtration, and eSNPs. As shown in To further assess functionality of our detected SNPs, we estimated allelic imbalance in DNA accessibility in five tissues . More specifically, for each tissue we obtained DNase-seq reads from ENCODE/RoadMap  and thenK562, Hela, Nb4 and MCF7) from ENCODE, after which we quantified (Methods) and compared the spatial interaction of the detected SNP-Gene pairs with a background composed of interacting SNPs paired with randomly selected genes with the same distance distribution as the foreground. As shown in Finally, we tested the tendency of detected SNP locus and the target gene locus to spatially co-localize in the nucleus, which would further attest to transcriptional regulation of the gene by the SNP locus. Since tissue-specific ChIA-PET data are relatively rare and of low resolution, we therefore followed  and usedThe genes involved in our detected TF-SNP-Gene triplets are expected to exhibit age-associated expression in an allele-specific manner, which may have implication on ageing and age-associated complex diseases. To explore this further, first we performed functional enrichment analysis over target genes in a tissue-specific manner see ; we howeNext, using Hypertension as an exemplar age-associated complex disease, we assessed the association of detected TF-SNP interactions with Hypertension in eight tissues  that are implicated in hypertension. For these tissues, we used all samples that were annotated with hypertension status unambiguously. The number of samples in each tissue are provided in Previous studies have shown that tissue-specific eQTL SNPs (eSNP) exhibit significant overlap with those associated with various phenotypes. For instance, Higgins et al reported that eSNP rs43555985 (linked to gene GFRA2), which is shared with many other species, is associated with residual feed intake in beef cattle . Luo et Additionally, we assessed whether our detected SNPs segregate with ethnicity. Interestingly, our detected SNPs, pooled across all tissues, exhibit greater genomic proximity to 299 ethnicity-associated SNPs  Fig 7B)Fig 7B), Taken together, our results suggest that target genes regulated by SNP-age interactions are potentially linked to crucial aging-related biological processes, aggregated interactions could potentially contribute to age-associated diseases such as hypertension, and relative to eSNPs, which are detected in an interaction-independent manner, SNiPage-detected SNPs are more likely to be causally linked to complex age-associated phenotypes.We have reported a novel framework and a pipeline SNiPage, which incorporates a specific interaction mechanism to test the association between SNP-Age interactions and phenotypes (gene expression/diseases). We hypothesized that age-related TFs might preferentially bind to one of the alleles at a SNP, forming the basis for the interplay between the SNP and Age. We robustly detected numerous TF-SNP-Gene interaction triplets (average ~637) across 25 tissues. The detected SNP loci were enriched for epigenetic signals related to regulatory elements, exhibit allelic imbalance for chromatin accessibility, and exhibited spatial proximity to the putative target gene promoters, strongly supporting their functionality. Although our detected SNPs are under evolutionary conservation in human to the same extent as eSNPs, DAF analysis points to their evolution under positive (or relaxed purifying) selection during more recent human evolution, while at the same time, based on GO term enrichment and previously reported phenotype-associated SNPs, they are linked to aging and age-associated disease. This paradoxical result is, in fact, consistent with the “antagonistic pleiotropy” model of aging , which pAs noted above, although multiple previous studies incorporating intuitive SNP-Age terms in a regression framework have reported potential interactions ,21, theyvia gene regulation, which could be complex and diverse. Age-associated TFs represent just one specific agent of aging, and additional factors could be involved. For instance, age-associated splicing factor or epigenetic modification enzymes, both of which play essential roles in gene regulation and are linked to complex diseases, are potential agents of aging. We have previously observed age-associated splicing factor expression changes across tissues [As an “environmental” risk factor, aging is likely to affect various age-associated phenotypes  tissues . Analogo tissues  and more tissues –49. Thus tissues . CombinaTissue-specificity of gene regulation complicates our understanding of complex diseases. We expect that our analysis across 25 tissues would shed light on molecular basis underlying tissue-specific genetic regulation. In fact, we specifically observed a greater number of interactions in tissues such as whole blood, Brain–Cortex and Colon—Transverse, which might imply that the genotype-associated functional diversity of those tissues may be more age-dependent. What’s more, our data can be used to draw links between tissues and complex diseases, as has been suggested based on eQTL studies ,17. For Our results can inform the annotation and interpretation of GWAS and eQTL signals. In recent years, large scale GWAS and eQTL signals have been reported, however, it is still difficult to decode the observed associations into explicit pathways and mechanisms. Alignment of our detected interaction with reported associations by GWAS could suggest potential explicit links between environment, genetics, and phenotypes.Our main goal here was to explore SNP-Age interactions and thus our model first identifies Age-associated TFs with allele-specific binding and then assesses the allele-specificity of the target gene’s expression association with Age. However, our overall strategy can be easily adapted for other contexts, for instance, all TFs regardless of their association with Age, to explore general allele-specific regulatory effects of TFs, or to assess allele-specific effects of TFs whose activity are perturbed in a disease, such as cancer.We obtained the processed transcriptome data across 25 tissues from Genotype-Tissue Expression (GTEx) database version 6 . CorrespWe used the linear regression model from previous studies ,29 to degij is the expression of target gene i in jth sample and αi is the basal expression and the intercept on y axis for gene i. βi is the coefficient for covariates in the ith gene model. AGEj and SEXj are the age and sex for jth sample respectively. Sj is the covariate derived from SNPs for jth sample. The SNP-derived covariates are expected to control for the hidden race and ethnicity confounders, which we did not directly include in the model due to biases and sparsity of such information in the GTEx database. εij denotes the error term.Where We removed PEER factors, which significantly correlate with age  ,52. We sWe generated allele-specific sequence (~100 bps) around all the SNP loci and scanned the sequences for age-associated TFs’ binding sites based on published binding motifs in form of Positional Weight Matrix (PWM), using PWM-scan tool . Allele-We modeled the association between the interaction (TF-SNP) and potential target gene expression (within 1 Mb) as follows:gij is the expression of gene i in ith sample, αi is the basal expression and the intercept on y axis for gene i. βi is the coefficient for covariates in the ith gene model. AGEj and SEXj are the age and sex respectively for jth sample. SNPj is allele frequency for ith sample. Sj is the covariate derived from SNPs for jth sample and Eq  is the nTo select interactions consistent with our hypothesis and allele-specific TF binding prediction, we performed further filtering: we divided individuals into three categories: heterozygous , homozygous for binding allele, homozygous for non-binding allele. We retained the interactions only if the correlations between TF expression and target gene expression are significant for heterozygous and ‘homozygous for binding allele’ groups, but not for ‘homozygous for non-binding allele’ group. In addition, we require a greater correlation in ‘homozygous for binding allele group’ than for heterozygous group.We performed functional analysis on target genes involved in significant interaction across 25 tissues using R package “GoStats” . Then a We extracted ~200 bps windows centered at SNP loci and mapped raw DNase-seq, ChIP-seq signals for H3K27ac, H3K4me1, and H3K4me3  to estimate the replication rate for detected interactions and age-associated TF from the original dataset. The replicate rates were compared with that from the background dataset.We obtained 2–3 DNase-Seq samples . Then we quantified the support for spatial proximity for a SNP-Gene pair using”bedtools”. To test the significance, we generate a background by selecting a random pair of SNP and target gene within 1M bp distance while controlling for the distance to match those for the foreground SNP-Gene pairs; for each pair of detected SNP and target gene, we selected a random pair of SNP and gene with approximate distance (within 10% difference) as corresponding background). We then applied one-sided wilcoxon test to compare the signal difference bwteen the foreground and the background.We obtained CHIA-Pet data from , which mWe obtained PhastCons scores derived from multiple alignment across 100 species from UCSC browser . “bedtooThe variant calling files (VCF file) were obtained from 1000 genome consortium , then weWe obtained 299 race-specific SNPs  and GWASWe obtained a comprehensive published disease-tissue map to select potential causal tissues for all the diseases involved in our study . For eacS1 Fig(A) bar plot depicting the replication rates of interactions across 4 tissues. (B) bar plot depicting the replication rates of age-associated TFs across 4 tissues. The y-axis represents the replicate rate (%) relative to the original data, while x-axis denotes the tissues. The bars in four colors respectively represent the performance for the 70%, 80%, 90% down sampling and randomly shuffled background data.(TIF)Click here for additional data file.S2 FigThe y-axis denotes the conservation score, while the x-axis represents tissues. The green denotes the detection interaction relevant SNPs and the red denotes random SNPs without passing DNase filtration.(TIF)Click here for additional data file.S3 FigThe y-axis denotes the conservation score, while the x-axis represents tissues. The green bar denotes the detection interaction relevant SNPs and the red bar denotes random SNPs which passed DNase filtration.(TIF)Click here for additional data file.S4 FigThe y-axis denotes the conservation score, while the x-axis represents tissues. The green bar denotes the detection interaction relevant SNPs and the red bar denotes eSNPs.(TIF)Click here for additional data file.S5 FigEnrichment analysis was done independently in each tissue, and the tree-map view shows the GO terms that were enriched (FDR ≤ 5%) in at least three tissues.(TIF)Click here for additional data file.S6 Fig(A-Y) histograms depicting the age distribution in each of the 25 tissues.(TIF)Click here for additional data file.S7 Fig(A-Y) histograms depicting the sex distribution in each of the 25 tissues.(TIF)Click here for additional data file.S8 Fig(A-Y) histograms depicting the race distribution in each of the 25 tissues. For race, “1”: Asian, “2”: African American, “3”: white, “4”: American Indian, “98”: Not Reported, “99”: Unknown.(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.


The coat colour of fallow deer is highly variable and even white animals can regularly be observed in game farming and in the wild. Affected animals do not show complete albinism but rather some residual pigmentation resembling a very pale beige dilution of coat colour. The eyes and claws of the animals are pigmented. To facilitate the conservation and management of such animals, it would be helpful to know the responsible gene and causative variant. We collected 102 samples from 22 white animals and from 80 animals with wildtype coat colour. The samples came from 12 different wild flocks or game conservations located in different regions of Germany, at the border to Luxembourg and in Poland. The genomes of one white hind and her brown calf were sequenced.http://www.ifpcs.org/albinism/), a variant in the MC1R gene (NM_174108.2:c.143 T > C) resulting in an amino acid exchange from leucine to proline at position 48 of the MC1R receptor protein (NP_776533.1:p.L48P) was identified as a likely cause of coat colour dilution. A gene test revealed that all animals of the white phenotype were of genotype CC whereas all pigmented animals were of genotype TT or TC. The study showed that 14% of the pigmented (brown or dark pigmented) animals carried the white allele.Based on a list of colour genes of the International Federation of Pigment Cell Societies (MC1R gene provides a deeper insight into the mechanism of dilution. The gene marker is now available for the conservation of white fallow deer in wild and farmed animals.A genome-wide scan study led to a molecular test to determine the coat colour of fallow deer. Identification of the The online version contains supplementary material available at 10.1186/s12863-020-00950-3. White coat colour or dilution are commonly found within fallow deer in game farming and in the wild. It is important for the management of the white animals to identify the responsible gene variant and develop a gene marker. This is the only way to make informed statements about the distribution of the white gene allele in a population. However, up to now, no scientific articles have been available on the colouring of fallow deer and nothing was known about the genes that are responsible for the white coat colour in this species.http://www.ifpcs.org/albinism/). The most important proteins are formed in melanocytes where they are involved in pigmentation on five independent levels: melanocyte development and migration, melanosome biogenesis, melanosome transport, biosynthesis of melanin and control of melanogenesis.A list of 256 genes involved in the white colour or dilution is available from the International Federation of Pigment Cell Societies (Major proteins involved in melanocyte development and migration are the tyrosinase protein kinase KIT (KIT), the KIT ligand (KITLG), endothelin 3 (EDN3), endothelin receptor type b (EDNRB). Melanocytes contain melanosomes, subcellular lysosome-like organelles in which melanin pigments are synthesized and stored before distribution to the surrounding keratinocytes. Biogenesis of melanosomes is controlled by premelanosome protein (PMEL), silver (SILV), pink-eyed dilution protein (P) and adaptor related protein complex 3 (AP3). Melanophilin (MLPH), myosin Va (MYO5A) and RAS-related protein b27a (RAB27A) are involved in melanosome transport.Tyrosinase (TYR), tyrosinase-related protein 1 (TYRP1) and dopachrome tautomerase (DCT) are involved in the biosynthesis of the different kinds of melanin . TYRP1 aASIP  SNPs), DCT (21 ns + 33 s SNPs), EDNRB (6 ns + 15 s SNPs), KIT (18 ns + 85 s SNPs), MC1R (11 ns + 49 s SNPs), TYR (20 ns + 45 s SNPs) and TYRP1 (25 ns + 46 s SNPs). Synonymous SNPs were excluded from further processing. Following the hypothesis of a recessive inheritance of the white colour, we expected the genotype of the white hind to be homozygous for the white allele and the brown calf to be heterozygous. All genes and SNPs that did not correspond to this assumption were filtered out.After variant calling and annotation, 12,751 SNPs were extracted as a subset of SNPs based on a list of colour genes detected in mice, human and zebrafish  and brown (n = 3) individuals. Only the genotypes at one SNP in the MC1R gene were associated to the phenotype in these individuals. Results of the phenotype-genotype association are presented in Supplementary Table n = 102) were tested for the SNP at the MC1R gene revealing a 100% match between genotype and phenotype (Fisher’s exact test: P = 8.4− 23)  is predicted to result in an amino acid substitution from leucine to proline (p.L48P).Eleven out of 80 wildtype or dark pigmented individuals carried the TC genotype (14%). Any other wildtype or dark pigmented individual were homozygous for the T-allele. Any of the white individuals was homozygous for the C-allele. The Dama dama is not available, the high degree of sequence conservation, even of microsatellites, between cervides and other ungulates, especially cattle [Cervus elaphus [Cervus sequences were mapped based on the well-established bovine genome , as compared to 92% when mapped to the Cervus elaphus genome CeEla1.0 [TYR gene that is responsible for the white phenotype in the studied red deer population was not annotated in CerELa1.0, there was no chance that it would be detected based on the Cervus reference sequence. This is not unexpected, since 21,880 genes are annotated for the bovine genome in contrast to 19,368 for the genome of Cervus elaphus. Therefore, we decided to use the better annotated bovine genome UMD 3.1. In fact, more than 12,000 SNPs were extracted after variant calling as a subset based on a list of colour genes . Nineteen SNPs in sixteen candidate genes corresponded exactly to the requirements of a homozygous white hind and its heterozygous brown calf. However, only one SNP, located in the MC1R gene showed perfect genotype-phenotype association in the entire cohort with 22 white and 80 brown individuals, collected from 12 locations with unrelated fallow deer populations, mostly in Germany. However, we have to admit that the method used would probably not have found all types of variants, especially those in gene regions with lower agreement between Dama dama and Bos taurus, e.g. in non-coding regions. Furthermore, indels and large structural variants would not have been detected by our approach.Although a complete genome sequence of y cattle , 23 led  elaphus  82% of CCeEla1.0 . Since tMC1R gene is involved in a huge network of colouring genes (for overview see [MC1R variants with white coat colour are rare. They were found in black bears [The view see ), and thview see , mouse [view see  and seveview see , foxes [view see , dogs [3view see , rabbitsview see , chickenview see , alpacasview see , buffaloview see , sheep [view see , 35, goaview see , and catview see , 38. Varview see  and incrview see  and otheview see ). Howeveview see , associack bears , white lck bears , martensck bears , mice [4ck bears , Huskiesck bears  and the ck bears . MC1R haMC1R is a seven-pass transmembrane G protein  coupled http://www.ifpcs.org/albinism/). Genetic testing confirmed the expected genotypes in all 22 white and 80 brown animals from 12 different locations examined. The study showed that 14% of the brown animals carry the white allele. This genetic test provides a simple and reliable way of conservation and management for the white animals.The genomic sequencing of a white hind and her brown calf led to the identification of a non-synonymous variant with exchange of a leucine residue at position 48 of the melanocortin 1 receptor by proline as a likely cause of dilution of the coat colour. This variant was detected using a list of colour genes of the International Federation of Pigment Cell Societies  and white (n = 22) fallow deer were collected over the 2017/2018 seasons. Two female animals  were available for Next Generation Sequencing. All samples were accompanied with information about age, weight, colour, and hunting ground.Samples from pigmented . Antler drill cores (0.1 to 0.3 g) were treated in a bead mill  at a frequency of 25 Hz for 2 min. Tissue samples were suspended in 450 μl of lysis buffer and subsequently treated in the same way as the antler drill cores. All following steps were performed according to the manufacturer’s instructions. The extracted DNA was eluted with 60 μl of RNAse-free water.DNA concentration was measured photometrically with the Nanodrop 2000 spectrophotometer  and the Qubit 2 system .DNA samples of one white female and it’s brown offspring were used for library preparation with the TruSeq DNA PCR-free sample preparation kit . The protocol was adjusted to receive fragments with a 350 bp insert size according to the manufacturer’s instructions. Quantification and quality control of the libraries was carried out through qPCR with the Kapa Library Quantification Kit  and high-resolution electrophoresis with the Bioanalyzer 2100 . Paired-end sequencing with a read length of 2 × 126 bp was performed on a HiSeq2500  with HiSeq v4 chemistry.fastq files were generated with bcl2fastq Conversion Software . The quality of the sequence reads was observed by FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/).Raw data were demultiplexed and .All programs used in further processing of raw reads were embedded in python scripts to connect the different steps and programs.http://emea.support.illumina.com/downloads/bcl2fastq_conversion_software_184.html?langsel=/de/). Because a Cervus elaphus reference genome was not available at the time of sequencing, resulting reads were aligned to the reference sequence of the bovine genome . After processing of data, single files were merged and converted from the SAM to the BAM format with SAMtools56. Duplicated reads were marked by the PICARDtools command MarkDuplicates (https://github.com/broadinstitute/picard/).In a first step raw sequences were converted from a base call file (bcl) to fastq files and mixed probes were demultiplexed through the program bcl2fastq Conversion Software from Illumina ((UMD 3.1 ) using tmpileup algorhithm implemented in SAMtools [filter algorithm from PICARDtools (https://github.com/broadinstitute/picard/) called variants were filtered by excluding all SNPs within 3 base pairs of an INDEL and with lower QUAL score.To identify single nucleotide polymorphisms (SNPs) in the annotated reads of the two sequenced fallow deer samples, we used the SAMtools . With thVariantEffectPredictor (VEP) from Ensemble [For the functional annotation of each called SNP we adapted the Ensemble .http://www.ifpcs.org/albinism/). The resulting VEP annotated files containing only genomic regions coding for coat colour were checked on the basis of a recessive genetic inheritance model for non-synonymous impacts of the variants.Furthermore, we extracted a subset of SNPs based on a list of colour genes detected in mice, humans and zebrafish . For this purpose, regions including the candidate SNPs were PCR amplified and sequenced. PCR primers were designed from the NGS data in combination with data from the Additional file 1:Supplementary Table S1. Genotype-phenotype associations in white and brown fallow deer for all tested single nucleotide variants.


Specific adaptive features including disease resistance and growth abilities in harsh environments are attributed to indigenous cattle breeds of Benin, but these breeds are endangered due to crossbreeding. So far, there is a lack of systematic trait recording, being the basis for breed characterizations, and for structured breeding program designs aiming on conservation. Bridging this gap, own phenotyping for morphological traits considered measurements for height at withers (HAW), sacrum height (SH), heart girth (HG), hip width (HW), body length (BL) and ear length (EL), including 449 cattle from the four indigenous Benin breeds Lagune, Somba, Borgou and Pabli. In order to utilize recent genomic tools for breed characterizations and genetic evaluations, phenotypes for novel traits were merged with high-density SNP marker data. Multi-breed genetic parameter estimations and genome-wide association studies (GWAS) for the six morphometric traits were carried out. Continuatively, we aimed on inferring genomic regions and functional loci potentially associated with conformation, carcass and adaptive traits.PIK3R6 and PIK3R1 showed direct functional associations with height and body size. We inferred the potential candidate genes VEPH1, CNTNAP5, GYPC for conformation, growth and carcass traits including body weight and body fat deposition. According to their functional annotations, detected potential candidate genes were associated with stress or immune response  and with feed efficiency .SNP-based heritability estimates for the morphometric traits ranged between 0.46 ± 0.14 (HG) and 0.74 ± 0.13 (HW). Phenotypic and genetic correlations ranged from 0.25 ± 0.05 (HW-BL) to 0.89 ± 0.01 (HAW-SH), and from 0.14 ± 0.10 (HW-BL) to 0.85 ± 0.02 (HAW-SH), respectively. Three genome-wide and 25 chromosome-wide significant SNP positioned on different chromosomes were detected, located in very close chromosomal distance (±25 kb) to 15 genes (or located within the genes). The genes Accurate measurements contributed to large SNP heritabilities for some morphological traits, even for a small mixed-breed sample size. Multi-breed GWAS detected different loci associated with conformation or carcass traits. The identified potential candidate genes for immune response or feed efficiency indicators reflect the evolutionary development and adaptability features of the breeds.Supplementary information accompanies this paper at 10.1186/s12864-020-07170-0. In Sub-Saharan Africa, the cattle breeding sector comprises smallholder subsistence farms in extensive production systems, and only a few exclusively market-oriented productions. In consequence, there is a substantial gap with regard to organized breeding programs and routine performance testing, technical support and precise breeding policies. Against such a background, applying advanced breeding technologies combined with enhanced genomic statistical methods in order to conserve animal genetic resources remains a major challenge, as observed in many developing countries , 2. FirsHigh-throughput genotyping and genomic methods provide new opportunities for the genetic characterization and genetic management of African indigenous animal breeds. An elementary genetic and phenotypic evaluation is the prerequisite for future performance improvements. Such efforts might be the key to successful management and conservation of animal genetic resources in the light of policy developments, climate change, and diversifying market demands –11. In tA great variety of study designs and methods for GWAS as established in recent years are powerful tools to study the genomic architecture of both qualitative and quantitative traits. Consequently, series of GWAS have been performed for performance and functional traits in various livestock species and breeds in European, American and Asian countries, e.g. –19. In AThree main factors are decisive to design a reliable GWAS: i) accurate phenotype and genotype data, ii) sufficient sample selection and sample size, and iii) application of adequate statistical methods . With reSeveral GWAS associated single nucleotide polymorphism (SNP) and genes to cattle morphometric or conformation traits. Major and commonly investigated cattle body traits for GWAS were hip height (stature), height at withers, body length, hip (rump) width, chest width, and scrotal circumference –38. The As discussed by many scientists , 51, GWACattle in Benin are commonly kept in small herds under extensive production conditions. The indigenous cattle breeds of Benin consist of two taurine (Somba and Lagune) and two hybrid  breeds. These breeds were described for their adaptive potential to disease and harsh environmental conditions as well as for their importance in the livelihoods of poor farmers , 54, 55.The aim of the present study was to estimate genetic parameters and to perform genome-wide associations for morphometric traits in four indigenous cattle breeds from different agro-ecological zones (AEZ) of Benin using medium density SNP chip data. In addition, functional annotation and gene enrichment analyses were applied to identify genes and functional loci potentially associated with morphometric traits. Finally, the indigenous Benin breeds from smallholder farms were contrasted genomically with other African livestock and exotic breeds or crossbreeds raised under improved management conditions in research stations , 59.g) with all other morphometric traits (0.38–0.80). Overall, genetic correlations among all morphometric traits ranged from 0.14 (HW with BL) to 0.85 (HAW with SH). Most of the estimated genetic correlations had small standard errors below 0.11. The largest standard error (SE = 0.28) was estimated for the genetic correlation between HG and HAW. In general, HG had larger SE for genetic correlations and for the heritability in comparison to all other traits. The phenotypic correlations (rp) ranged between 0.25 ± 0.05 (HW with BL) and 0.89 ± 0.01 (HAW with SH). Heart girth and hip width were phenotypically and genetically highly correlated . In contrast, phenotypic and genetic correlations between EL and BL  were considerably lower. In addition, BL showed the smallest phenotypic and genetic correlations with other traits, especially with HW .SNP-based heritability estimates for the morphometric traits ranged between 0.46 ± 0.14 (HG) and 0.74 ± 0.13  identified four linear discriminant functions (LDF) validly representing the genetic structure in the sampled population . In addition, five chromosome-wide suggestively associated SNP were detected on chromosomes 3, 17, 19 and 21 (p = 6.44e-06 - 5.04e-05); see Table rs110369628 and rs109889052 on BTA19 were positioned in relative proximity to each other (7.41 Mb), within the PIK3R6 and SSH2 genes, respectively. The VEPH1 gene harbored the significantly associated SNP for HAW on BTA1, while another SNP, rs4163436, was positioned in CCDC117 on BTA17. No genes were annotated for the two remaining significantly associated SNP.Height at withers was significantly associated with the SNP p = 2.35e-05 - 3.86e-05) and each was located in close distance to a gene. The SNP rs111001850 was positionally linked to the LYPD8 gene on BTA7, and the SNP rs110441360 was located near the PIK3R1 gene on BTA20.Only two SNP were suggestively associated with SH (p = 1.17e-05 - 4.10e-05). Two of them were positioned on BTA2 and the two others were located on BTA19 and BTA22. The SNP rs41579167 on BTA2 was positioned near the two genes PTAFR and EYA3. The gene PBRM1 harbored the SNP rs41637645 on BTA22.No SNP surpassed the genome-wide significance threshold for associations with HG and HW. However, four SNP were suggestively associated with HG (p = 2.08e-05 - 2.99e-05). Two of the three SNP were positioned relatively near to each other (4 Mb) on BTA16. The third SNP was detected on BTA8. Only the ABL2 gene was mapped as a potential candidate gene for HW, harboring the SNP rs42843320 on BTA16.Hip width was associated with three suggestive SNP  along with two others suggestive SNP (p = 9.33e-06 - 1.38e-05). Similarly, BTA21 harbored the second significant SNP  and one suggestive SNP (p = 3.74e-05), but none of them was positionally linked to a gene. The other associated SNP variants were identified on chromosomes 3, 10, 17 and 27 (p = 1.91e-05 - 4.93e-05). On BTA2, the SNP rs110694334 was positioned within the CNTNAP5 gene, in relative proximity (2.32 Mb) to the SNP rs109186122 positioned in the GYPC gene. Two additional genes, SLC16A4 and MEGF11, were mapped as potential candidates for BL, harboring the SNPs rs135705191 and rs43616983 on BTA3 and BTA10, respectively.Body length presented the highest number of associated SNP with two significant and 7 suggestive SNP. BTA2 harbored one significant SNP (p = 1.08e-05 - 2.54e-05). The SNP rs110608572 SNP was positioned on BTA10 within the MEGF11 gene, which was also identified as a potential candidate gene for BL. The SNP rs109985119 was located in the ADAMTS12 gene on BTA20.Four SNP positioned on different chromosomes  were suggestively associated with EL  and HG 0.59 and 0.62) were slightly larger than those (0.20 to 0.50) reported in previous studies , 70. How and 0.62As a major difference to previous studies, our approach focused on multi-breed genetic parameter estimations. Meyer et al.  observedrs109126926 SNP significantly associated with HAW and the corresponding gene VEPH1, no direct association between the variant or the identified gene with cattle height were previously reported. However, rs109126926 was significantly associated with recoverability from mastitis in Holstein cows [VEPH1 influenced other cattle conformation traits such as udder cleft in Holstein and rump fat thickness in Nellore cattle, reflecting its role in lipid metabolism [. Furthermore, recent studies observed an association of VEPH1 with residual feed intake and antibody response to parasites in cattle [For the SNP ein cows . VEPH1 itabolism , 79. Furn cattle , 81.rs109889052 and rs110369628 were located on BTA19. These two SNP have not been directly associated with cattle height, but rs109889052 contributed to feed efficiency in cattle [rs109889052 is located in the PIK3R6 gene, affecting body size in sheep [rs110369628 positioned in the SSH2 gene, was linked to carcass traits in sheep [SSH2 is involved in the regulation of the actin cytoskeleton pathway and in several other molecular and biological functions such as the regulation of actin polymerization or depolymerization and DNA binding. In previous studies, SSH2 was associated with somatic cell score and semen traits in cattle [rs132693733 (19:21339030), rs109018020 (19:23816722) and rs137732346 (19:25980624). These markers are positioned in a distance between 0.45 Mb and 6.96 Mb from the SNP identified in our study, i.e., rs109889052 (19:20892297) and rs110369628 (19:28301046). Another variant rs42741630 (19:25439551) in close proximity (2.86 Mb) to rs110369628 (19:28301046), was detected in a recent GWAS for stature [rs41634361 on BTA17 is positioned within the CCDC117 gene, which is involved in feed intake and heat stress regulation in cattle [Among the five suggestive SNP associated with HAW, n cattle . Moreovein sheep . Likewisin sheep . Accordin cattle , 85. Boun cattle  identifiximity 2. Mb to rsn cattle , 88.rs110441360 on BTA20 is positioned in the PIK3R1 gene, which influenced beef fat content [PIK3R1 for its implication in the human short stature syndrome [PIK3R1 in protein stabilization, insulin resistance, and growth hormone receptor signaling pathways. The potential effect of growth hormone receptor genes on animal conformation traits has been previously reported [rs111001850) associated with SH is located in the LYPD8 gene. This gene is involved in biological processes of defense responses to gram-negative bacteria, and has been related to adaptive responses to environmental stimuli such as stress, infection and inflammation in cattle [The two SNP suggestively associated with SH were not associated with cattle height in previous studies. However, the SNP  content . More insyndrome , 91, consyndrome –44. Morereported . The secn cattle .PI3K regulatory subunit gene, i.e.PIK3R5, was identified in a region highly associated with body size in sheep [PIK3R6 and PIK3R1 are declared as potential candidates for feed intake and feed efficiency in cattle [PIK3R6 and PIK3R1 are involved in several animal immune system pathways or related biological processes, such as the B cell receptor signaling pathway, the AMPK signaling pathway and regulation of T cell differentiation. Moreover, they are members of different signal transduction pathways , which are also linked to inflammatory diseases [PIK3R6 and PIK3R1 in signal transduction and immunity pathways reflect the associations with resistance to Mycobacterium avium ssp. paratuberculosis (MAP) infections in cattle [The association of two PIK regulatory subunit genes with height traits in this study is remarkable. Another in sheep . Moreoven cattle , 94. Botdiseases , 96. Then cattle –29.PIK3R6 and PIK3R1 may play an important role in the interaction of adaptation to diseases and body size variability in African cattle, and potentially in cattle populations worldwide.Indigenous cattle breeds in Benin, especially the taurine breeds, are known to be resistant against diseases , 54, 55.rs41579167 SNP is located in direct proximity to the genes PTAFR and EYA3. These two genes are involved in inflammatory responses and both are associated with immune response in cattle [PTAFR is associated with intramuscular fat deposition in Nellore cattle [PTAFR affected body weight by controlling feed intake and obesity [EYA3 gene is involved in cell differentiation processes and in a pathway related to DNA repair mechanisms. This gene was also identified as a potential candidate gene in a GWAS for milk production in dairy sheep [rs41579167 SNP (2:125244296) is located at 0.15 Mb distance to another variant rs2083797338 (2:125093797), which has been identified in a meta-GWAS for cattle stature [The four SNP suggestively associated with HG were not detected in any previous GWAS for HG. However, two of them are positionally linked to three genes influencing dairy cattle traits. First, the SNP n cattle , 98. In e cattle  and was e cattle , 101. Fu obesity . Accordiry sheep . The SNPP 2:1252496 is locrs41637645 is located within PBRM1, a gene which was associated with heat stress regulation in tropical breeds [PBRM1 is also involved in the negative regulation of cell proliferation and in the RMTs methylate histone arginine pathway, while this pathway is involved in different diseases in mammals [Second, the SNP l breeds . PBRM1 i mammals .ABL2 is a potential candidate gene for HW, harboring rs42843320. ABL2 influenced feed intake in cattle and backfat thickness in pigs [ABL2 is involved in innate immune response processes through cell proliferation, migration and differentiation .We found no direct link between the four suggestively associated SNP for HW with HW in previous studies. Nonetheless,  in pigs , 107. Inrs41646754 on BTA27, and with rs110694334, which is located within CNTNAP5 on BTA2. Neither the two variants, nor the identified genes, have been previously associated with BL. Nevertheless, CNTNAP5 was detected in a chromosomal segment significantly associated with hip cross height in Brahman cattle [CNTNAP5 is a potential candidate gene for conformation traits in Sudanese goats and growth traits and pigs [CNTNAP5 was identified in a selective region for adaptation in cattle as well as in sheep [CNTNAP5 is an integral component of membranes and is related to the epidermal growth factor-like protein domain.Body length was significantly associated with n cattle . Furtherand pigs –111. In in sheep , 113. Acrs43616983 on BTA10 was associated with direct perinatal mortality in Holstein–Friesian dairy cattle [rs43616983 is positioned within the MEGF11 gene, which regulated daily gain and immune response to mastitis in cattle [MEGF11 in the musculus longissimus dorsi of two different cattle breeds in response to low energy diets [MEGF11 as a potential candidate gene for feed efficiency [MEGF11 was significantly associated with height in Buffalo [rs109186122 and rs43616983, are located in the genes GYPC and SLC16A4, respectively. According to its annotation in DAVID, GYPC is involved in oligosaccharide binding and is reported to be associated with intramuscular fat deposition and reproduction performances in cattle [SLC16A4 gene had effects on postweaning weight gain, feed efficiency and resistance to the bovine viral diarrhea virus in cattle [SLC16A4 in transmembrane transport functions and glucose import processes, as well as with the impact of the SLC16 gene family on health regulation [SLC16A4 gene is located in a genomic segment being under divergent selection in South African cattle breeds [Among the seven suggestive SNP associated with BL, n cattle , 114. Angy diets . This fificiency . Further Buffalo . Two othn cattle –119. Then cattle –122. Thegulation . The SLCe breeds . In addie breeds , 125.MEGF11, harboring the SNP rs110608572 on BTA10, overlap with our findings for BL. Secondly, ADAMTS12, harboring the SNP rs109985119 on BTA20, was associated with body weight and supernumerary teat in cattle [ADAMTS12 was involved in inflammatory responses and in the regulation of the hepatocyte growth factor (HGF) receptor signaling pathway. Indeed, HGFs are known to play an important role in the stimulation of epithelial cell proliferation, motility, morphogenesis and angiogenesis [ADAMTS12 gene was identified in pigs in a selection signature associated with genetic adaptation to high altitude [ADAMTS12 on EL variability, considered as an adaptive trait for heat tolerance in the indigenous breeds in Benin [None of the four SNP variants suggestively associated with EL are linked to EL in the literature, because of the trait relevance only for tropical production systems. Two potential candidate genes for EL are related to different traits in cattle and other mammal species. Firstly, n cattle –128. ADAogenesis . The ADAaltitude . These oin Benin , 131.The comparison with the literature gives convincing evidence for the validity of our results based on gene functions and associations as detected in other traits and species. The rather small number of detected SNP for the moderately to highly heritable polygenic traits is in line with our small sample size and its diversity (multi-breed), and is most likely due to a lack of power to detect variants with smaller effects. However, with regard to the functional annotations of the identified genomic regions, our study confirms the potential of multi-breed GWAS in detecting fewer variants, but more precise functional loci or causative mutation across breeds , 132. InOur findings reveal a close connection between genomic regions associated with morphometric traits and adaptive traits. For instance, two of the identified variants detected for morphometric traits in this study are directly linked to immune response, while several potential candidate genes have functional annotations for, e.g., signal transduction, metabolism, and immune response adaptation . The comOn the other hand, genomic regions associated with adaptive traits (immune response or feed efficiency) have been similarly detected in GWAS for linear body traits in more developed breeds such as Holstein and Angus , 92, 138Heritabilities for as well as phenotypic and genetic correlations among morphometric traits based on dense SNP marker data and a multi-breed approach were moderate to moderately high. Twenty-five SNP and fifteen genes potentially associated with the morphometric traits were detected. Comparisons with previous studies and the functional annotation of the genes revealed a clear association of loci identified in this study with conformation, growth and carcass traits in cattle or in other species. Moreover, the majority of the detected genes are associated with immune response and feed efficiency, or involved in related biological processes, suggesting a strong correlation between morphological and adaptive traits. This is in line with the evolutionary development and breeding history in these indigenous breeds mainly shaped by natural selection. Our findings suggest that accurate phenotyping (measurements) for morphometric traits combined with SNP marker data can be used for genetic evaluations, considering mixed-breed cattle populations.Following the FAO guidelines , we reco(p ≤ 0.001) across breeds with higher values for hybrid breeds (Borgou and Pabli). For instance for height at withers, Borgou and Pabli cattle measured 116.5 ± 5.67 cm and 111.4 ± 7.58, but Somba and Lagune are smaller with 96.37 ± 4.97 cm and 92.59 ± 7.18, respectively. The full description of the six morphometric traits for the four breeds are presented in Additional file The animals were selected from a larger dataset of 462 animals as described in a previous study . ThirteeThe 449 selected animals with phenotypes were genotyped using the Illumina BovineSNP50 BeadChip, and 51,278 SNP were available before quality control. Sample collection, DNA extraction and genotyping procedures are described in detail in Scheper et al. .p ≥ 10− 06). For individual animals, a genotype call rate larger than 95% was required. After quality control, 14,518 SNP and one animal were discarded, implying a genotype dataset with 36,760 SNP variants from 448 cattle.Genotyping quality control was performed using the PLINK software  to retaiSporadic missing variants were imputed with BEAGLE  after re(p ≤ 0.001), while sex had significant effect only on HG, HW and BL, and age on HG, HW and HAW , which combines the two variables AEZ and breed, was created. The factors age, sex, and AEZ_Breed were simultaneously included in a multi-factor linear model to test their effects on the respective morphometric trait via the Type III sums of squares from ANOVA, using the Car package in R . AEZ_Bre“--indep-pairwise” command in PLINK and defining 0.2 for the r2 threshold, was considered in this regard [The genetic structure in the dataset was evaluated applying a DAPC, using the R package ADEGENET , 144. LDs regard .grm method in GCTA, and afterwards considered for the estimation of the genetic parameters [y was a vector of morphometric traits; b was a vector of fixed effects including age, sex, and AEZ_Breed; u was a vector of polygenic effects with a variance-covariance structure of G was the genomic relationship matrix between individuals [e was a vector of random residual effects with I was an identity matrix of dimension n × n ; and X and Z were incidence matrices for b and u, respectively.A genomic relationship matrix between the animals was firstly generated with the -rameters , 74. Thex and y, the genetic correlation (rg) between x and y was estimated using the “--reml-bivar x y” option in bivariate genomic REML analyses. The phenotypic correlation (rp) between x and y was calculated from the bivariate genomic REML outputs using the following formula:x and y, respectively; and x and y, respectively. The standard errors of the phenotypic correlation were calculated based on the “deltamethod” function from the R-package “msm” [Considering two conformation traits ge “msm” .y was a vector of morphometric traits; b was a vector of fixed effects including Age, Sex, AEZ_Breed, and linear discriminant functions; g was a vector for the SNP effects; e was a vector of random residual effects with X, W were incidence matrices for b and g, respectively.GWAS were performed for the six morphometric traits using PLINK . A lineay, g, e and incidence matrices X and W were notations as defined in Eq. b was a vector of fixed effects including Age, Sex, AEZ_Breed; Z and u were notations as defined in Eq. For a verification of results from PLINK, we additionally performed GWAS applying the following model eq.  in the Gmentclass2pt{minimThe assessment of the models for population stratification based on the genomic inflation factor (lambda-λ) and on the quantile–quantile (Q–Q) plot. Manhattan and Q-Q-plots plots were generated by means of the ggplot2 package in R .p = 1.55 × 10− 06), i.e., calculated as p = 0.05 / m, with m = 32,185 (the effective number of SNP). In addition, chromosome-wide Bonferroni-corrected significance thresholds  with mc denoting the effective number of SNP for each chromosome  and for each chromosome (mc) were determined from the genetic type I error calculator (GEC) [Significantly associated SNP were detected according to the Bonferroni corrected significance threshold (or (GEC) .To identify potential candidate genes associated with the morphometric traits, the rs-accession numbers of the significant and suggestive SNP were retrieved from the Ensembl genome database (version 96), using the BioMart R package , 151. GeFurther, functional annotation was performed on the set of identified candidate genes per trait using the DAVID  included as covariates in GWAS model for population stratification in the four Beninese indigenous cattle breeds. The coloring represents the original breed assignment of samples.Additional file 2: Table S1. Characteristics and origins of sampled animals in four Beninese indigenous cattle breeds. Animal ID, sex, breed, year of sampling and origin: farm longitude and latitude, commune, agro-ecological zones (AEZ).Additional file 3: Figure S2. The variations of six morphometric traits from all (ALL) and from four respective Beninese indigenous cattle. Height at withers (HAW), sacrum height (SH), heart girth (HG), hip width (HW), body length (BL) and ear length (EL).Additional file 4: Table S2. Effects of AEZ_B, sex and age in multi-factor linear models on six morphometric traits in four Beninese indigenous cattle breeds. Analysis of variance (ANOVA) table presenting the significance of fixed effects on height at withers (HAW), sacrum height (SH), heart girth (HG), hip width (HW), body length (BL) and ear length (EL). Table S3. Genome-wide and chromosome-wide significant SNP associated with six morphometric traits from GWAS model including the first four principal components (instead of the linear discriminant functions as considered for main results).Additional file 5: Table S4. Chromosome-wide significance thresholds used in multi-breed GWAS for conformation traits in four Beninese indigenous cattle breeds.Additional file 6: Table S5. Functional annotation of candidate genes for six morphometric traits in four Beninese indigenous cattle breeds. Results retrieved from the database for annotation, visualization and integrated discovery (DAVID) for candidate genes associated with height at withers (HAW), sacrum height (SH), heart girth (HG), hip width (HW), body length (BL) and ear length (EL).


SLC25A13. Although previous studies have reported ketosis in CD, it was observed at the time of euglycemia or mild hypoglycemia. Blood ketone levels concomitant with symptomatic or severe hypoglycemia in CD have not been a topic of focus despite its importance in identifying the etiology of hypoglycemia and assessing the ability of fatty acid utilization. Herein, we describe a patient with CD who had repeated episodes of hypoglycemia with insufficient ketosis.Citrin deficiency (CD) is a recessive metabolic disease caused by biallelic pathogenic variants in A 1-year-old boy with repetitive hypoglycemia was referred to us to investigate its etiology. The fasting load for 13 h led to hypoketotic hypoglycemia, indicating the possibility of partial β-oxidation dysfunction. A genetic test led to the diagnosis of CD. The hypoglycemic episodes disappeared after switching to a medium-chain triglyceride-containing formula.This case report suggests that symptomatic or severe hypoglycemia in patients with CD could be associated with relatively low levels of ketone bodies, implying that β-oxidation in these patients might possibly be partially disrupted. When encountering a patient with hypoglycemia, clinicians should check blood ketone levels and bear in mind the possibility of CD because excessive intravenous administration of glucose can cause decompensated symptoms in patients with CD as opposed to other disorders presenting with hypoketotic hypoglycemia, such as fatty acid oxidation disorders. Further studies in a large-scale cohort are warranted to confirm our speculation. SLC25A13 (MIM 603859) that encodes citrin, a mitochondrial membrane protein that is mainly expressed in the liver [+ balance [+ ratios increase in the cytosol in patients with CD [+ imbalance after glycolysis. Individuals with CD have unique preferences for low-carbohydrate and high-fat foods, which are probably associated with energy production balance [Citrin deficiency (CD) is caused by biallelic pathogenic variants in  with CD , resulti5A13 MIM 03859 tha balance . Urine kHerein, we describe the case of a patient with CD who had repeated episodes of hypoglycemia with insufficient ketosis.SLC25A13 , both of which are reported as prevalent pathogenic variants [The patient was born at 37 weeks of gestation without adverse perinatal events and neonatal jaundice. His body weight and height at birth were 2530 g − 0.39 SD) and 47.0 cm − 0.63 SD), respectively. Newborn screening tests were negative. At the age of 1 year, he started presenting afebrile convulsions on days when he fell asleep without having milk at night. Laboratory examination indicated hypoglycemia . He was transferred to a previous hospital to investigate the cause of the hypoglycemic episode. His height and body weight were 73.1 cm (− 0.7 SD) and 9.6 kg (+ 0.3 SD), respectively. He had a plump face but no hepatosplenomegaly. After 12 h of fasting, blood samples revealed hypoglycemia without ketosis . Laboratory examination revealed mild liver injury and hypertriglyceridemia . The blood ACTH, cortisol, IGF-1, thyroid hormones, galactose, and amino acid levels were normal. Urine organic acid and serum acylcarnitine analyses with hypoglycemia showed nonspecific results. Glycogen storage disease (GSD) type Ia was suspected on the basis of these laboratory findings. The patient was given a high-glucose-containing formula for GSD to avoid hypoglycemia; however, he still presented repetitive hypoglycemia when fasting over 12 h. In total, three episodes of hypoglycemia with glucose levels below 2.5 mM were observed. At the age of 1 year and 1 month, he was referred to our institution to investigate the cause of repetitive hypoglycemia despite the consumption of a formula for GSD. Given the possibility of fatty acid oxidation disorders (FAODs) or GSDs, we measured sequential glucose and ketone levels in a fasting state as well as glucose loading  dysregulation; this hypothesis might have explained the phenotypic discrepancy between patients with CD and FAODs if these disorders commonly occurred due to errors in β-oxidation. PPARα, a subtype of PPAR, is enriched in the liver. It maintains lipid homeostasis and positively regulates multiple enzymes of β-oxidation . A previients CD . In PPARients CD ; in contients CD , 18. Ppaat in CD . Transgein FAODs , 21. Thuin FAODs , 23. Thein FAODs . AdditioIn summary, CD might possibly cause hypoketotic hypoglycemia due to partial impairment of ketogenesis, and physicians should measure blood ketone levels in cases of unexplained hypoglycemia. It is important to consider the possibility of CD during the differential diagnosis of hypoketotic hypoglycemia because high-glucose intake may cause hyperglycemia and exacerbate the metabolic state rather than prevent hypoglycemia.


P-value <10−3) on chromosomes 2D, 3B, 4D and 7B that were associated with KB resistance in individual years. In particular, two SNPs (chromosome 4D) had the maximum R2 values: SNP 1114200 | F | 0–63:T > C at 1.571 cM and R2 of 12.49% and SNP 1103052 | F | 0–61:C > A at 1.574 cM and R2 of 9.02%. These two SNPs displayed strong linkage disequilibrium (LD). An in silico analysis of SNPs on chromosome 4D identified two candidate gene hits, TraesCS4D02G352200  and TraesCS4D02G350300 (a rhomboid-like protein belonging to family S54), with SNPs 1103052 | F | 0–61:C > A and 1101835 | F | 0–5:C > A, respectively, both of which function in biotic stress tolerance. The epistatic interaction analysis revealed significant interactions among 4D and 7B loci. A pedigree analysis of confirmed resistant PBLs revealed that Aegilops species is one of the parents and contributed the D genome in these resistant PBLs. These identified lines can be crossed with any elite cultivar across the globe to incorporate novel KB resistance identified on 4B.This study was initiated to identify genomic regions conferring resistance to Karnal Bunt (KB) disease in wheat through a genome-wide association study (GWAS) on a set of 179 pre-breeding lines (PBLs). A GWAS of 6,382 high-quality DArTseq SNPs revealed 15 significant SNPs ( Triticum aestivum L.) is consumed worldwide as a key component of the human diet. The global wheat production for 2018–19 was 730.9 million tons, which is approximately 29 million tons lower than production in the preceding year (http://www.fao.org/worldfoodsituation/csdb/en/). With the global population estimated to be 9 billion in 2050, wheat demand is expected to increase by 60%. Increases in the annual wheat yield must grow from the current level of less than 1% to at least 1.6% to meet the demand (https://www.wheatinitiative.org/about-us/wheat-research-food-security).Wheat  has categorized N. indica as a quarantine pest (zero tolerance), which forced the adoption of strict laws under the Sanitary and Phytosanitary (SPS) agreement4.Rusts, smuts, bunts, mildews and foliar blights are major biotic factors that significantly affect wheat production and productivity. Karnal bunt (KB), which is caused by 5. As a seed-, soil- and air-borne fungus, the control of the disease through cultural practices and/or seed treatment is not very effective6. The chemical control of KB by spraying fungicides at the anthesis stage is possible but not cost-effective for farmers; thus, conventional methods are not sufficient to cope with KB7.The management of KB is challenging due to long-lived teliospores8. However, the lack of fast, simple and environment-independent methods for screening wheat germplasm that resist the pathogen is a bottleneck during the development of resistant varieties. Researchers are managing the problem with available screening methods, but because KB is a disease of the mature grain, the identification of resistant donors is only possible in the next generation, which also decelerates the breeding progress. Because of these limitations, marker-aided selection/screening (MAS) is another opportunity for efficient introgression of resistance into elite genotypes8.The identification and development of resistant genotypes is a viable option to manage KB9. The former relies on trait segregation in a bi-parental cross population and has been exploited for the identification of QTLs for KB resistance in a range of studies8. GWAS is a population-based method that exploits the linkage disequilibrium phenomenon between a trait and a genetic marker10. This mapping population-independent method relies on a population consisting of a set of unrelated accessions and has been successfully used to identify numerous traits in many crops, such as Arabidopsis thaliana11, rice12, barley13 and wheat14. However, only two GWAS studies have been reported to date with the aim of determining marker-trait associations (MTAs) for KB resistance in elite wheat lines16. Both these studies were carried out with wheat germplasm collection. Here, we report the very first association analysis of KB in the diverse wheat pre-breeding lines derived from exotic x elite crosses and elucidate the genetic components of KB resistance.Linkage analyses and genome-wide association studies (GWAS) are two powerful statistical genetic methods that have been successfully deployed for the genetic dissection of quantitative traits17. Thus, the identification and/or development of KB resistance genotypes(s) that minimize the probability of establishment or decrease the inoculum spread rate into newer areas are urgently needed17. The development of resistant cultivars through breeding programs is an appropriate strategy to achieve zero tolerance to the disease.KB is a quarantine disease (zero percent tolerance) that is indirectly responsible for economic losses in more than 40 countries by affecting the free import-export of wheat grains. Additionally, infected grains are not fit for human and animal consumption18. The severity of KB incidence in the germplasm of 179 pre-breedling lines from different environments ranged from 1.43–58.34%, whereas infection with KB-SUS (check) ranged from 53.38 to 98.84%  between KB infection and the year indicated that the disease pressure was substantial in both years and phenotypic data were suitable for the GWAS analysis   to 500 (7D)  population (0.31), which comprises many synthetically derived lines. The PIC  of the current study was comparatively higher than values  reported in previous studies32.The mean genetic diversity in current sets of germplasm was 0.32 (range 0.035–0.71), which is higher than previous studies examining SNP markers33. The largest delta K was observed at K = 6, suggesting the presence of six sub-populations  in the parentage dominated sub-population 1, as 80% of the population lines had these parents. Seventy percent of the lines in subpopulation 2 shared three elite parents, Wiblli, Kukuna and Tacupeto F2001, while the remaining lines shared two parents, Baj #1 and Super152. Similarly, sub-populations 3, 4, 5 and 6 were dominated by parents Baj#1, Super152/Villa Juarez F2009, Kachu and Kiritati, respectively. Based on these results, sub-populations largely corresponded to the elite parents in each cross of exotic/elite1/elite2.r2 = 0.1 has been reported to 2.5 cM for whole genome34, which suggests higher genetic diversity of the investigated panel as compared to the wheat panels used in previous studies37. This panel has been drawn from a large set of pre-breeding lines (PBLs) developed by a three way crossing scheme (exotic/elite1//elite2) among exotics and elites38. Each pre-breeding line acquired approximately 25% of the exotic genome and 75% of the elite genome at an early stage thus allowing recombination between exotic and elite genomes. Further, LD decay in the three genomes was at 2.5, 5.0, and 2.5 cM for A, B, and D genomes, respectively. These results indicate a faster LD decay in the D genome, almost comparable to A genome, than reported in previous studies. This might be due to the use of synthetics for developing PBLs, driving more recombination in the D genome.The extent of LD decay in this panel at cut off P-values <10−3 in the joint analysis , 15 significant SNPs on chromosomes 2D (1 SNPs), 3B (4 SNPs), 4D (7 SNPs), 5B (1 SNP), 6A (1 SNP) and 7B (1 SNP) were identified with 16. Hence, cultivar pyramiding/stacking of favourable alleles from multiple loci is a prerequisite to develop KB resistance22.Previous studies reported the presence of KB resistance loci scattered on many chromosomes in all three genomes of wheat15  at position 1.571 cM with an R2 of 12.49% and SNP (1103052 | F | 0–61:C > A) at position 1.574 cM with an R2 of 9.02%. The physical positions of SNPs, sequence reads of the SNPs were blasted to the reference genome of RefSeq V.1.0 with E value <10−8 and identity >95%38. These two SNPs were in strong LD with each other, confirming that they are part of the same genomic region on chromosome 4D   for multi-disease resistance.Chromosome 3B appears to be a hot spot for various disease resistance genes, as it is the location of QTLs for KB resistanceAegilops species are one parent used during the creation of synthetic wheat and contributed the D genome to these resistant PBLs. The analysis of these same lines for the SNP 1114200 marker (chromosome 4D) indicated that 6 of 7 PBLs contained the ‘T’ allele . The role of the D genome in KB resistance suggested that the D genome must be further enriched with additional polymorphic SNPs to exploit more QTLs for various diseases viewed as upcoming threats to wheat cultivation.During the detection of highly tolerant PBLs based on phenotypic data, a set of 7 lines were confirmed to be resistant Table . An analRht-D1 dwarfing locus, as the frequency of the dwarf allele was 0.1% , indicating that the Rht-D1 gene was fixed  in the current KB panel of PBLs and the KB resistance locus on chromosome 4D is novel  to obtain additional insights into the genes on chromosome 4D that are involved in KB resistance. Of the 8 associated SNPs located on chromosome 4D, three showed direct hits with candidate genes and three were located in the vicinity (2 Mb)  were mapped to chromosome 4DL. TaNOX8 is specifically present on the membrane and is co-expressed with TaNOX13AL on chromosome 5AL. Thus, we speculate that the marker 1103052 | F | 0–61:C > A might represent the TaNOX8 gene. The other candidate gene, TraesCS4D02G350300, is a Rhomboid-like protein, which belongs to a family of serine proteases (family S54) that cleave substrates within transmembrane domains44. Rhomboid protease family member S54 has proven to be active during fungal-plant interactions45.The identified markers were subjected to BLAST search in Ensembl Plants server  , and powdery mildew (Blumeria graminis f. sp. tritici (Bgt)), character and yield performance in Mexico. The pedigree information for these lines is presented in Supplementary Table The plant material used in the current investigation comprised 179 pre-breeding lines (PBLs). A detailed description of the development of these PBLs has been reported in Singh 48. Three milliliters of sterile distilled water were added to the bottom of the plates.The phenotypic screening was conducted at Ciudad Obregon, Mexico over two years (2016–17 was referred to as E-1 and 2017–18 was referred to as E-2) using a randomized complete block design (RCBD). For the preparation of the inoculation, one-year-old teliospores were scraped off infected grains with a dissecting needle and incubated in a water-Tween 20 solution for 24 h; then, the suspension was filtered through a 60 µM nylon sieve and centrifuged at 3000 rpm. After discarding the supernatant, 0.5% sodium hypochlorite was applied as the active ingredient for 2 min to disinfect teliospores and the suspension was centrifuged again. Teliospores were then rinsed twice with sterile distilled water, followed by centrifugation. Teliospores were resuspended in sterile distilled water in the centrifuge tube. One milliliter of the teliospore suspension was spread on Petri dishes containing 2% water-agar (AA) and incubated at 18–22 °C in the dark. After 6 to 9 days, teliospore germination was evaluated using a compound microscope at 10x magnification. Pieces of AA with germinated teliospores were removed and placed upside down on the lid of Petri dishes containing potato-dextrose-agar (PDA). After 10 to 14 days, 2 to 3 mL of sterile distilled water was added to the plates and the colonies were scraped gently using a sterile spatula. Hyphae and sporidia were inoculated onto other plates with PDA using a sterile syringe, and the plates were incubated at 18–22 °C in the dark for approximately nine days. After the incubation, pieces of PDA with the different fungal propagules were transferred and placed upside down on the lids of sterile glass Petri dishes to induce the production of allantoid secondary sporidia2.Water was collected from the plates every 24 h; secondary allantoid sporidia were collected and counted using a hemocytometer. Then, the concentration was adjusted to 10,000 sporidia per mL. Five heads of each experimental line at the boot stage were inoculated by injecting 1 mL of the allantoid sporidial suspension during the boot stage. Stems of the inoculated heads were identified with a piece of red plastic. An automatic mist irrigation system was used to water the plants during the period of inoculation (January-March) for 20 min five times per day, and the area was covered with nets to prevent bird damage. Grains were harvested manually, and the healthy and infected grains were counted by a visual inspection to calculate the percentage of infection (infected grains). Standard agronomic practices were used during the crop season. The mean disease incidence (%) was estimated from five inoculated spikes per plot. The phenotyping for KB screening was based on ratings reported as percentages to identify tolerant and susceptible genotypesThe mean for each PBL was calculated to reduce the environmental variance in the data recorded through the partitioning of phenotypic variance using the maximum residual likelihood (ReML) algorithm. The KB infection data were subjected to an ANOVA to reveal the effects of the genotype, year and their interaction.49. Leaves were collected from each PBL (TC1F5) 25 days after germination. The extracted DNA was quantified with a NanoDrop 8000 spectrophotometer V 2.1.0, while intactness was analyzed using agarose gel electrophoresis. The DNA concentration was adjusted to 50–100 ng µL−1 in a volume of 30 µL. Genotypic characterization used DArTseq technology (http://www.diversityarrays.com/dart-application-dartseq) at the Genetic Analysis Service for Agriculture(SAGA) service unit at CIMMYT headquarters . The main parameters to select markers were call rate (the proportion of samples with genotypic score and not recorded as missing data) and average reproducibility . More details on SNP filtering are given in Ledesma-Ramírez34. A total of 12,071 SNP markers belonging to 10,111 sequence tags were identified based on above criteria. Chromosome location, marker order and genetic distances were defined based on a 64,000-marker DArT-seq consensus map released by Diversity Arrays Technology Pty Ltd. (DArT) (http://www.diversityarrays.com/sequence-maps). The methodology described by Vikram et al.49 was followed to capture 6,382 high quality SNP markers on 179 PBLs. These high-quality DArTseq SNPs were used for the population structure and association analyses (Supplementary Table A modified CTAB (cetyltrimethylammonium bromide) method was used to extract genomic DNA from leaf samplesSTRUCTURE software51. Five independent analyses were performed for each K-value, with 100,000 set as the length of the burn-in time and number of Markov Chain Monte Carlo replications. The correct estimation of K was provided by an ad hoc statistic deltaK52 that was calculated using the program STRUCTURE HARVESTER53. The linkage disequilibrium analysis was conducted on this panel using GAPIT version 2.034. Birefly, a squared correlation coefficient r2 was estimated for all pairwise comparisons. Pattern of LD decay was then visualized by plotting pair-wise r2 values against the genetic distance for A, B, and D genomes separately and for whole genome. For the LOESS estimation of LD decay, genetic distance was estimated as the point where the LOESS curve first crosses the baseline r2 of 0.1.The structure was analyzed using K-values ranging from 1 to 8 for the entire population with 6,382 SNPs markers with 54 was used to calculate associations between the markers and KB score with the mixed linear model (MLM)55. MLM takes advantage of the population structure (Q) and kinship (K) matrix as covariates during a GWAS to avoid spurious associations56. Both of these matrices were also generated using TASSEL 5.01. To correct for multiple testing, the step up procedure of Benjamini and Hochberg29 which controls the false discovery rate was used with a cut-off value of 0.05. ANOVA analysisand estimates ofcorrelations and heritability were conducted in SAS 9.4. The KB scoing data of both environments separately and mean over the environments (Supplementary Table S9) was used for the association mapping.Phenotypic data from two years were analyzed separately and combined for the association analysis. TASSEL software v5.0150. A significant threshold of P < 0.001 represented significant marker-marker interactions.Two- and three-locus interactions among associated loci were studied using an in-house script executed in R, as described previouslyIn silico analysis of the significant loci was conducted in Ensembl Plants sequence database (https://plants.ensembl.org/Triticum_aestivum/Info/Index) by using BLASTN algorithm with functional annotations from IWGSC v1.1 RefSeq annotation. Precisely, to find the candidate genes, the physical starting point of the marker along with chromosome name was put in Ensembl Plants and around 300 bp were added before and after the SNP. For some markers, SNPs fell within gene sequences and hence were classified as direct gene hits in Supplementary Table Triticum aestivum genome and for such cases orthologous genes in related species were screened with known predicted functions using the comparative genomics tool in Ensembl.In the present study, an association mapping panel consisting of 179 PBLs genotyped with 6382 high-quality DArTseq SNP markers was utilized to obtain an understanding of the genetics of Karnal bunt resistance in wheat (Supplementary Fig. Supplementary material.Supplementary material 2.Supplementary material 3.Supplementary material 4.Supplementary material 5.Supplementary material 6.Supplementary material 7.Supplementary material 8.Supplementary material 9.Supplementary material 10.Supplementary material 11.Supplementary material 12.Supplementary material 13.Supplementary material 14.Supplementary material 15.Supplementary material 16.Supplementary material 17.Supplementary material 18.Supplementary material 19.figure legends


Oryza sativa L. and its relatives), they vary in their informativeness, cost, marker density, speed, flexibility, and data quality. In this study, we report the development and performance of the Cornell-IR LD Rice Array (C7AIR), a second-generation SNP array containing 7,098 markers that improves upon the previously released C6AIR. The C7AIR is designed to detect genome-wide polymorphisms within and between subpopulations of O. sativa, as well as O. glaberrima, O. rufipogon and O. nivara. The C7AIR combines top-performing SNPs from several previous rice arrays, including 4,007 SNPs from the C6AIR, 2,056 SNPs from the High Density Rice Array (HDRA), 910 SNPs from the 384-SNP GoldenGate sets, 189 SNPs from the 44K array selected to add information content for elite U.S. tropical japonica rice varieties, and 8 trait-specific SNPs. To demonstrate its utility, we carried out a genome-wide association analysis for plant height, employing the C7AIR across a diversity panel of 189 rice accessions and identified 20 QTLs contributing to plant height. The C7AIR SNP chip has so far been used for genotyping >10,000 rice samples. It successfully differentiates the five subpopulations of Oryza sativa, identifies introgressions from wild and exotic relatives, and is useful for quantitative trait loci (QTL) and association mapping in diverse materials. Moreover, data from the C7AIR provides valuable information that can be used to select informative and reliable SNP markers for conversion to lower-cost genotyping platforms for genomic selection and other downstream applications in breeding.Single nucleotide polymorphisms (SNPs) are highly abundant, amendable to high-throughput genotyping, and useful for a number of breeding and genetics applications in crops. SNP frequencies vary depending on the species and populations under study, and therefore target SNPs need to be carefully selected to be informative for each application. While multiple SNP genotyping systems are available for rice ( Single nucleotide polymorphisms (SNPs) occur at specific positions in the genome and are the most common form of genetic variation in eukaryotic organisms. They are the result of random mutations that occur every few hundred base pairs in most genomes . While tIn rice research communities, high-throughput sequencing and SNP genotyping assays over the last 20 years have generated large amounts of SNP data that can be used to calculate the frequency of SNP alleles in different populations, to track the inheritance of those alleles across generations, and to associate SNP variation with phenotypic variation [O. sativa [SNP assays for rice have been developed at different densities, with examples including the C6AIR , the RIC. sativa .O. sativa, and incorporating new SNPs that are informative for specific subgroups, particularly the US tropical japonica breeding community. The C7AIR targets a sweet spot between cost and informativeness for the global rice community. In contrast to de novo sequencing technologies that require a high level of computational and bioinformatics expertise, the C7AIR offers a high throughput genotyping assay with straightforward data analysis, and provides opportunities for immediate application in genebank management, genetic analysis, and applied plant breeding. Moreover, the C7AIR rice genotype data provides a valuable source from which to select validated SNP markers informative for specific germplasm groups for conversion to lower-cost assay systems for high-sample throughput applications.The Cornell-IR LD Rice Array (C7AIR) improves upon the C6AIR  by removOryza accessions were used for the genotyping analysis with the C7AIR  for DNA extraction and genotyping.A total of 544 he C7AIR . The 378The C7AIR is a beadpool manufactured in liquid phase and stabilized on chips that are commercially available as Illumina Infinium arrays. It was developed by the “International RiceLD Consortium”, a group of investigators from Cornell University, the International Rice Research Institute (IRRI), Texas A&M University, Louisiana State University (LSU), The Dale Bumpers National Rice Research Center (DBNRRC) in Arkansas, the University of Arkansas, and the University of York in the UK.tropical japonica breeding material. The selection of SNPs for the C7AIR was based on the following metrics using diversity information from prior work in the McCouch lab: no variants within 10 bp of a target SNP locus; no SNPs within 35 bp of a target SNP locus having a minor homozygote count > 4; no INDELS or repetitive sequences in the target region. The C7AIR included 4,007 SNPs from the C6AIR, 2,056 SNPs from the High Density Rice Array (HDRA) [tropical japonica rice varieties, and 8 SNPs in genes of interest  design represents an improved version of the Cornell_6K_Array_Infinium_Rice (C6AIR) . SNPs thinterest . Althougalk gene , three f[Wx gene , 13, oneene [Gs3 , and one3 [Pi-ta .Genotyping was performed following the manufacturer’s protocol for amplification of DNA, hybridizing to the Infinium II BeadChips, staining with fluorescent dye and scanning to measure the fluorescence intensity of the beadchip. Raw intensity values were sent to each collaborating rice lab and converted to SNP data using Illumina’s GenomeStudio software. The 551 varieties genotyped were then filtered down to 448 based on a call rate above 0.939 (less than 6.1% missing data per sample) and a P10 GC score above 0.45. P10 GC is a score developed by Illumina to identify samples which may have failed genotyping as described in the user manual for GenomeStudio. Upon compiling all data in GenomeStudio, SNPs were manually re-clustered in order to correctly sort the clusters as the correct genotype.O. glaberrima, 12 O. sativa, and 1 O. meridionalis. After filtering, the sample clusters were shifted to match three potential clusters (two homozygous and a heterozygous class) or two clusters, when heterozygotes were missing. SNPs were then classified as very high , high , poor , and fail quality  . These s quality . Failed Reclustered data was exported from GenomeStudio and imported into TASSEL GUI 5.2.43 where CenteredIBS kinship using a maximum of 6 alleles was calculated . KinshipA custom R script was used to estimate the number of polymorphic markers between pairwise combinations of lines genotyped with the C7AIR. Genome-wide SNP data from pairs of samples were compared for each individual locus and heterozygosity was calculated. Population subgroups were determined based on the fastStructure output included in O. sativa were grown in Beaumont, TX during summer 2017. These varieties were grown using standard agronomic practices in two replications of three-row plots. Five plants of each accession were randomly chosen from the middle row of each plot avoiding the border plants. The height (cm) of each selected rice plant was measured from the base to the neck of the panicle. The heritability of the trait was calculated as: H2 = σ2g / (σ2g + σ2e/r). Homogeneity of variance across the two replications was estimated using Levene’s test [A total of 189 varieties of e’s test . These aThe GWAS analysis was performed using Mixed Linear Model (MLM) of GAPIT . Both kiA significant QTL was defined as a single or a cluster of SNP markers that passed the significance threshold of p <0.001. In general, the extent of LD in rice on average ranges from 100 to 500 kb –23. Basetropical japonica rice varieties, and 8 SNPs in genes of interest (The Cornell-IR LD Rice Array C7AIR) design represents an improved version of the Cornell_6K_Array_Infinium_Rice (C6AIR) , and conAIR desiginterest . A summainterest . SubsequO. sativa), we masked accessions with low call rates and low quality values, as measured by p10 GC, an Illumina quality parameter . Low call rates on SNP arrays are typically due to sequence variation in the regions flanking the target SNPs. It is noteworthy that all O. rufipogon and O. nivara samples in this study had high call rates (> 0.94), similar to O. sativa, and 0.45 p10 GC, while O. glaberrima accessions had lower call rates (~0.88), consistent with the greater evolutionary distance between O. sativa and O. glaberrima as reflected in the kinship matrix  passed initial quality control, but the number of heterozygous calls for each locus was very low due to the highly homozygous nature of rice as an inbred crop. This is challenging when developing an automated cluster file with the Illumina GenomeStudio software because the algorithm often cannot correctly identify three clusters when there are few heterozgyotes. Therefore, GenomeStudio clusters were manually corrected and saved as a new cluster file, as described below in the Methods section. SNPs filtered out using this process may still be beneficial, especially for diversity analysis within wild species.O. sativa using Centered IBS  take advantage of historical LD in a collection of diverse lines in an effort to correlate regions of the genome with specific phenotypes . The avejaponica , 25. USDjaponica , 9. To tThe plant height data showed a wide distribution ranging from 32.9 to 150.35 cm with a population mean of 103.82 cm and standard deviation of 28.78 cm. The heritability was estimated to be 97.4; which showed that the trait was mostly genetically controlled. This was also further confirmed by the the Levene’s test result which shows that the variances across replications were insignificant ; consequently the plant height data were averaged across replications and used as representative phenotypic data for the GWAS study. A total of six subpopulations were obtained from FastStructure. This population structure and the kinship matrix obtained were used in the plant height GWAS.qPHT-10-1. This QTL was identified by a clustered of 5 SNPs having distances of less than 250 kb between each other, within a total region of 485.32 kb. Out of 20 QTLs, 8 of them were found in the vicinity of genes previously reported to be associated with plant height or dwarfism; while the rest of the QTLs are potentially novel, including the QTL on chromosome 12 (qPHT-12-3) that has the most significant SNP in our study. Six of these genes were located less than 250 kb to the nearest significant SNPs, while the gene pla1 was 269.8 kb and the gene OsGH3.1 was located 300.5 kb from its nearest significant SNP. Interstingly, two of the genes were detected within distances less than 15 kb to the nearest SNP peaks (as described below).A total of 20 QTLs were detected using the p-value of 0.001 significant threshold; among them 14 were detected with a FDR < 0.1 and 4 were detected with FDR<0.05 ; Fig 5. qPHT-1) which is in the vicinity of OsGH3.1 (35.067Mb). Overexpression of the OsGH3.1 gene in rice causes dwarfism and decreased free auxin content and cell elongation [qPHT-5-2, potentially colocalized with gid1 (19.878 Mb), which encodes for an unknown protein similar to lipases and may act as a receptor mediating GA signaling in rice which leads to dwarfism [qPHT-6-1, was located nearby the d3 gene, which causes tillering dwarf mutants and an increase in tiller number [qPHT-7-1, was detected only 10.2 kb away from oswrky78 (24.320 Mb)—a gene that may regulate stem elongation and seed development [OsBZR1 (24.145 Mb), and supression of this gene leads to dwarfism and alters brassinosteroids (BR) responses in rice [qPHT-7-2, was in the vicinity of bui1 . This gene encodes a formin-type actin nucleation factor and affects cell expansion and plant morphogenesis in rice, including bent uppermost internode, dwarfism, wavy panicle rachis, and increased gravitropic response [qPHT-3-1, was only 60.6 kb away from OsBLE2 (27.858 Mb). This gene may be involved in brassinolide (BL)-regulated growth and development processes in rice. Transgenic rice expressiong antisense of this gene demonstrates repressed growth [qPHT10-2, was detected only 13.1 kb away from the EBL1 gene (13.348 Mb), and silencing of this gene leads to plants with reduced plant height [qPHT-10-3, was in the proximity of the plastochron (PLA)1 gene (14.018 Mb). PLA1 was isolated by map-based cloning and encodes a cytochrome P450, CYP78A11, which potentially catalyzes substances controlling plant development, including leaf primordia, bracts of the panicle, and elongating internodes [A QTL was detected on chromosome 1 at position 35.367 Mb  SNP assay (https://gsl.irri.org/services/genotyping/rica) used for genomic selection [The C7AIR is available from Illumina for rice genetics research and breeding applications around the world. Since its release it has been used to develop inter-specific chromosome segment substitution lines (CSSLs) involving crosses between election .indica, aus, and tropical japonica subpopulations of O. sativa, between pairwise combinations of indica, aus, tropical japonica, temperate japonica, or aromatic (Group V) subpopulations, and between O. sativa and O. rufipogon. The array is less informative for detecting polymorphism within temperate japonica or the aromatic (Group V) subpopulation, and was not designed to target SNPs specific to O. glaberrima Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.