The majority of the Kazakhs from South Kazakhstan belongs to the 12 clans of the Senior Zhuz. According to traditional genealogy, nine of these clans have a common ancestor and constitute the Uissun tribe. There are three main hypotheses of the clans’ origin, namely, origin from early Wusuns, from Niru’un Mongols, or from Darligin Mongols. We genotyped 490 samples of South Kazakhs by 35 Y-chromosomal SNPs (single nucleotide polymorphism) and 17 STRs (short tandem repeat). Additionally, 133 samples from citizen science projects were included into the study.We found that three Uissun clans have unique Y-chromosomal profiles, but the remaining six Uissun clans and one non-Uissun clan share a common paternal gene pool. They share a high frequency (> 40%) of the C2*-ST haplogroup (marked by the SNP F3796), which is associated with the early Niru’un Mongols. Phylogenetic analysis of this haplogroup carried out on 743 individuals from 25 populations of Eurasia has revealed a set of haplotype clusters, three of which contain the Uissun haplotypes. The demographic expansion of these clusters dates back to the 13-fourteenth century, coinciding with the time of the Uissun’s ancestor Maiky-biy known from historical sources. In addition, it coincides with the expansion period of the Mongol Empire in the Late Middle Ages. A comparison of the results with published aDNA (ancient deoxyribonucleic acid) data and modern Y haplogroups frequencies suggest an origin of Uissuns from Niru’un Mongols rather than from Wusuns or Darligin Mongols.The Y-chromosomal variation in South Kazakh clans indicates their common origin in 13th–14th centuries AD, in agreement with the traditional genealogy. Though genetically there were at least three ancestral lineages instead of the traditional single ancestor. The majority of the Y-chromosomal lineages of South Kazakhstan was brought by the migration of the population related to the medieval Niru’un Mongols. Patrilineal populations tend to have deep and extensive paternal genealogies. Such populations from the Eurasian steppe are traditionally divided into many descent groups . Since clan affiliation is paternally inherited along with the Y chromosome, a joint comprehensive study of the clan structure and the Y chromosome variation increases the depth and accuracy of a reconstruction of the demographic history.Zhuzes  in South Kazakhs (Senior Zhuz) is characterized by three clades of haplogroup C2  is a major one. Among the eight clans of the Uissun tribe, C2-M217 is the most frequent for six of them . It is also the most frequent for the non-Uissun clan of Jalair. For the two remaining Uissun clans, the major haplogroups are N1a1a-M178 (80% of the Syrgeli clan) and J1*-M267(xP58) (74% of the Yssty clan). For the two remaining non-Uissun clans, the major haplogroups are Q-M242 (53% of the Kanly clan) and G2-P15 (35% of the Shanyshkly clan).N < 20) the samples for the remaining clans are representative (N = 63 on average); therefore, there is strong evidence for the prevalence of haplogroup C2-M217 among Kazakhs of the Senior Zhuz. It was reported, that C2* is the major haplogroup (88%) in the Sary-Uissun clan [Though sample sizes for the four clans are small  than without substructuring into groups (FST = 0.24)  are very specific Fig.  in contrFST = 0.2 . This haplogroup, also known as Star Cluster (ST), is clearly distinguished within M217 by STR haplotypes. It corresponds to the subclade marked by the SNP F3796 . This liN = 194, Additional file N = 549, Additional file N = 44) and γ-cluster (N = 122), and only a few samples (N = 9) entered the β-cluster (dating 701 CI 95% 909–493 years), previously identified for the Kerey clan of the Middle Zhuz. The α-cluster (dated 746 CI 95% 1104–388 years) was mainly composed of the the (non-Uissun) Jalair clan members. The γ-cluster (dating 742 CI 95% 916–568 years), which we called “Uissun” cluster, mainly included representatives of the four Uissun clans, namely, Dulat, Alban, Suan, and Sary-Uissun  was constructed using 15 STR loci of the Y chromosome according to data on 743 individuals from 25 populations of Eurasia ). What is the reason for a significant discrepancy between 1500 years estimated by STRs and 2500 years resulted from SNP data? It may be explained by the incomplete mapping of STR clusters and the topology of the phylogenetic tree, since different SNP subclades may have the same STR haplotypes, as shown in Additional file www.yfull.com), which is very close to STR dating (746 CI 95% 1104–388 years). The TMRCA of the β-cluster based on SNPs from three sequenced Y chromosomes (~ 650 years ago (TMRCA CI 95% 346–982 ybp), C2-F8949 subclade, previously identified as the only Kazakh subclade on the C2*-ST network [The TMRCA (time to the most recent common ancestor) of the C2*-ST cluster Fig.  based onThe nearby coincidence of ages of all three clusters suggests the rapid population growth of Kazakh clans in 13-14th centuries. It nicely coincides with the expansion period of the Mongol Empire. It is important to note that the TMRCA of the “Uissun” γ-cluster coincides with the lifetime of the proposed Uissun’s ancestor and Genghis Khan’s ally Maiky-biy (thirteenth century).We found that C2-F3796 subclade of haplogroup C2*-ST is the most common in the population of South Kazakhstan. In this sense C2*-ST is a key to decipher direct paternal ancestor of the Senior Zhuz clans. Moreover, according to historical studies [The oldest known specimen of this lineage (subclade C2-Y4580*) originated from the Mongolian-Buddhist burial of Ulus Dzhuchi (700 years ago) in Central Kazakhstan . It is The only sample of the Wusun culture studied to date  belongs to the haplogroup R1a1a-Z93(xZ94) (subclade R1a1a-Y41571) . Other aAccording to The Secret History of the Mongols, the early Mongols were divided into Niru’un and Darligin Mongols . Which oN = 71) has been genotyped by the high resolution SNPs within the haplogroup C2*-ST. First, we genotyped F3796 and F8951, defining two parallel clades, in all 71 samples . The results indicated the genetic similarity of the six Uissun and one non-Uissun clans to each other, while the other four clans (two Uissun and two non-Uissun clans) have the specific paternal pools. Thus, the genetic data have not reproduced the traditional genealogy in all details; however, the genetic evidences were consistent with the common origin of the most clans from South Kazakhstan. Moreover, the significant part of the population originated from three founders which all lived about 700–800 years ago, in contrast to one founder, according to traditional genealogy. These three clusters were identified within the C2*-ST (C-F3796) haplogroup. The first cluster is typical for most Uissun clans, the second one is common for the non-Uissun clan Jalair of South Kazakhstan, and the third one is typical for the North Kazakhstan clan of Kerey, but also includes individual samples from South Kazakhstan. The predominance of the haplogroup C2*-ST in South Kazakhstan suggests the origin of the majority of Y-chromosomal lineages from the Niru’un Mongols.The molecular genealogy of the male descendants of the Niru’un Mongols assumes a detailed study of all branches . Fragment analysis of 17 Y-STR loci was performed with the Y-filer PCR Amplification Kit (Life Technologies) on the ABI 3130xl and 3500xl genetic analyzers. Y-STR haplotypes of C2*-ST were identified based on the Wei paper [N = 70) belonged to C2*-ST and one sample belonged to Daur Clade were genotyped by F3796 and F8951 SNPs. Subclades of C2*-ST were also genotyped by . Phylogenetic networks of Y-STR haplotypes were constructed using the Network 5 and Network Publisher software [− 3 mutations per STR per generation was used [Zhuz were collected from citizen science databases [Genomic DNA extraction, genotyping, statistical analysis and median network analysis were done as described previously . The 35 ei paper . A subsesoftware , 25, excwas used . The genwas used . Additioatabases .Additional file 1: Text S1. Genealogical description of the Senior Zhuz clans.Additional file 2: Table S1. Y chromosome SNP and STR data of the Uissun tribe.Additional file 3: Table S2. Y chromosome SNP and STR data of the Uissun tribe from citizen science.Additional file 4: Table S3. Variation in Y-chromosomal haplogroup frequencies between Uissun clans.Additional file 5: Figure S1. Genetic relationships of Great zhuz’s clans using Y-SNPs: Multidimensional scaling plot (MDS)Additional file 6: Figure S2. Genetic relationships of Great zhuz’s clans using Y-SNPs: Principal component analysis (PCA) of the Uissun tribeAdditional file 7: Table S4. 15 Y-STR haplotypes of all C2*-ST samples from previous studies included in the network.Additional file 8: Figure S3. Y-chromosome’s Network of C2*-ST based on 15 Y-STRs.Additional file 9: Figure S4. Comparison between STR network and SNP phylogenetic tree of the Y chromosome.Additional file 10: Table S5. Y-chromosome SNP and STR data of Konyrat tribe: update up to C2b1a3a-M407Additional file 11: Figure S5. Genealogy of the Niru’un Mongols.


Dwarf bunt-resistant bread wheat accessions and SNP markers associated with DB resistance identified in this study are valuable resources for characterization and deployment of DB resistance in bread wheat.Tilletia controversa J.G. Kühn, can significantly reduce grain yield and quality on autumn-sown wheat in regions with prolonged snow cover. DB can be managed with the use of resistant cultivars. The objectives of the present study were to characterize DB resistance in a large set of bread wheat accessions from the National Small Grains Collection and use a genome-wide association study approach to identify genetic loci associated with DB resistance. A total of 292 accessions were selected using historical DB resistance data recorded across many trials and years in the Germplasm Resources Information Network (GRIN) and re-tested for DB resistance in replicated field nurseries in Logan, UT, in 2017, 2018, and 2019. Ninety-eight accessions were resistant with DB normalized incidence ≤ 10%, and twenty-eight of these were highly resistant with DB normalized incidence ≤ 1% in both GRIN and the field nurseries. Based on the presence of marker haplotypes of the four published dwarf bunt QTL on 6DS, 6DL, 7AL, and 7DS, highly resistant accessions identified in this study may provide novel resistance and should be further evaluated. This study validated one previously identified QTL on 6DS and identified an additional locus on 6DS. These loci explained 9–15% of the observed phenotypic variation. The resistant accessions and molecular markers identified in the present study may provide valuable resources for characterization and deployment of DB resistance in bread wheat.Dwarf bunt (DB), caused by The online version of this article (10.1007/s00122-020-03532-0) contains supplementary material, which is available to authorized users. T. aestivum L.) is an important food staple and 772 million t were harvested globally in 2017 (FAOSTAT Tilletia controversa J.G. Kühn [as ‘contraversa’] in L. Rabenhorst (Kühn Tilletia caries (DC.) Tul. & C. Tul. [syn. T. tritici (Bjerk.) G. Wint.] and Tilletia laevis J. G. Kühn [syn. T. foetida  Liro], are destructive diseases of bread wheat and durum wheat (T. turgidum subsp. durum Desf.)  Goates . While tInitiation of DB and CB begins when dikaryotic infection hyphae penetrate emerging seedlings thereby infecting the developing apical meristem , Bt11 (PI 554119), and Bt12 (PI 119333) were broadly effective against most races of DB and CB.DB and CB resistance is putatively controlled by gene-for-gene interactions, and it is assumed that the same genes confer resistance to both diseases  are classified as resistant based on a DB incidence threshold of ≤ 10% proposed by Goates . The purhttps://npgsweb.ars-grin.gov/gringlobal/search.aspx. Using a resistance threshold of ≤ 10% disease incidence relative to the susceptible check , and two known susceptible winter cultivars ‘Wanser’ (CItr 13844) and ‘Cheyenne’ (CItr 8885), were also included in the GWAS panel. Supplementary File 2 lists the accession number, name, taxon, geographic origin, improvement status, pedigree, and DB incidence for each of the 292 accessions.DB resistant and susceptible accessions were selected for this panel based on data from the US National Germplasm System online database: Germplasm Resources Information Network (GRIN), accessed at k Goates , only 12k Goates , includi6 spores ml−1 water). A composite of teliospores from infected spikes previously collected in the USU DB nursery were used for the inoculations. Disease incidence was assessed on fully mature adult plants, Zadoks stage 94  relative to the susceptible check cultivar ‘Cheyenne’ in each trial was collected from NSGC accessions grown at the Green Canyon USDA-ARS disease screening nursery in Logan, UT . DB field trials conducted in 2017, 2018, and 2019 were evaluated near the Green Canyon site at the Utah State University (USU) Research Farm in Logan, UT . USU field trials were sown with a head row planter on October 10, 2016, September 27, 2017, and September 18, 2018 with one accession per 1-m row and two replications in 2017 and 2018 and one replication in 2019. Each row was inoculated after seedling emergence on November 4, 2016, October 24, 2017, and November 6, 2018, with approximately 100 ml of a concentrated DB teliospore suspension  and macerated in CTAB extraction buffer with a bead grinder. The aqueous layer was separated in chloroform, extracted, and the precipitate was washed with isopropanol and then ethanol. Resulting DNA pellets were suspended in Tris (10 mM) and sent to the USDA-ARS Small Grains Genotyping Laboratory in Fargo, ND, where samples were genotyped using the 90 K iSelect SNP assay as described by the manufacturer . Allele clustering was completed using Genome Studio v.2.0.2 (Illumina), and the resulting set of 41,511 polymorphic SNPs was exported to JMP Genomics v.9.0  for filtering. Markers were excluded if minor allele frequency (MAF) was < 4%, or missing data were > 10%. Heterozygous calls were also removed. Accessions were classified as duplicates and removed if they were ≥ 99.7% identical across all polymorphic SNPs. A final group of 246 bread wheat accessions were selected, and 19,281 SNP markers were aligned with the physical wheat annotation  shift from normality. Similarly, square root and log10 transformations of the trials indicated significant (P < 0.0001) deviations from normality, and the untransformed DB NI data were used in all further analyses. A mixed model with genotype set as a fixed effect and trial as a random effect was used to calculate best linear unbiased estimates (BLUEs) for DB NI across trials and replications (Henderson 2) was calculated using the formula: Unless stated otherwise, all statistical analyses were conducted using JMPr2 values between each marker within chromosome groups (Supplementary File 3). An IBS familial relationship matrix (k matrix) and heat map were generated using the Ward hierarchical clustering method (Ward Jr and Hook Ward and Hook Genome-wide linkage disequilibrium (LD) was calculated as A principal component analysis with ten principal components (PCs) was generated (Q matrix) to explore population stratification, and the resulting scree plot was used to estimate the optimal number of PCs that would explain the most variation in the models . Broad-sense heritability (H2) for DB NI was estimated at 0.93. Best linear unbiased estimates derived from the mixed model of DB NI across trials are listed in Supplementary File 2. By design, the field trials were composed of approximately 50% resistant and 50% susceptible accessions as classified based on GRIN data  found no significant trial effect, but there was a significant genotype and genotype-by-trial effect P < 0.000. Broad-sability H for DB Nr2) between data sets ranged from 0.70 and 0.93, and all estimates were significant at P < 0.0001. GRIN was correlated with the 2017, 2018, and 2019 trials, and BLUEs with correlation coefficients of 0.76, 0.77, 0.70, and 0.85, respectively. The correlation coefficient between the 2017, 2018, and 2019 USU trials was 0.88, 0.76, and 0.78, respectively. In the 2017 USU field trials, the r2 between replications was 0.86, and in 2018, the r2 was 0.87 between the two replications.A Spearman’s rank-sum nonparametric correlation was used to measure the degree of similarity between and among the GRIN, 2017, 2018, and 2019 USU field trial means and BLUEs. Correlation coefficients . Of these, 28 were highly resistant with a DB NI ≤ 1% across all trials Table . These hThere were 44 accessions that were ≥ 99.7% identical across the 19,281 SNPs. These duplicate and near-duplicate accessions originated from similar geographic regions and had similar DB NI across data sets (Supplementary File 2), and were removed for further analyses. Genetic similarity among the 246 non-duplicated accessions ranged from 53% to 99% with a mean similarity of 67%.Based on the STRUCTURE HARVESTER Δk values, there were six distinct subpopulations (k = 6) in the panel, and these groupings were supported by visual assessment of the Ward hierarchical clustering heat map and principal component analysis Fig. a, b. SubP < 0.0001. Subpopulation 6 had the lowest mean BLUE DB NI of 8.5%, and subpopulation 1 had the highest mean BLUE DB NI of 74.0%. Of the 98 accessions that were resistant across trials . A median r2 of 1 was found between SNP markers that were completely linked with an inter-marker physical distance of 0 Mbp. LD median r2 decreased to 0.1 at an inter-marker distance of 0.1 to 1 Mbp indicating an LD decay rate of 90% over the 1 Mbp interval.Genome-wide marker-pair r2 = 0.3 as a threshold for genome-wide QTL confidence intervals in wheat. In the present study, the largest spline curve r2 value was 0.45. When the smoothing spline curve was set to r2 = 0.3, the physical distance was 0.67 Mbp, and when the curve was set to r2 = 0.1, the distance was 6.80 Mbp.A smoothing spline curve with lambda equal to 10,000 was fit to the LD scatter plot to determine a genome-wide QTL confidence interval (Supplementary File 4). Others  associated with DB incidence in at least one trial or BLUE  ranged from 0.09 to 0.15, and average DB NI BLUE values for accessions carrying resistance alleles ranged from 16.1 to 40.8 , PI 119333 (Bt12), PI 173437 (Btp), and PI 173438 (unknown Bt), all either originated in Turkey or had a Turkish landrace in their pedigree. PI 119333 and PI 173437 had similar haplotype profiles (Table Bt12) shares a similar haplotype profile to six other highly resistant accessions, and PI 173438 (with unknown Bt) shares a similar profile with two other highly resistant accessions including PI 476212 , many highly resistant breeding lines are derived from resistant Turkish landraces. PI 178383 and PI 476212 are in the pedigrees of several DB resistant cultivars, such as ‘Weston,’ ‘DW,’ ‘Golden Spike,’ and ‘UI Silver’ . Less stringent significance thresholds have been used in other bread wheat GWAS panels with small population sizes . Based on the physical position, this QTL spanned a relatively narrow section of the chromosome from 6.97 to 7.29 Mbp, which is within the flanking position of the Bt10 gene  and PI 178383 were screened with several Ug99 stem rust races. These two accessions were resistant to many of the same stem rust races as lines containing SrTmp and SrCad (unpublished data). Further studies are needed to determine if the 6DS region contains one or more genes that confer resistance to DB, CB, and wheat stem rust.Additionally, Menzies et al.  hypothesQ.DB.ui-7DS  across the 19,281 SNPs, but the 7DS QTL reported by Chen et al. . All the highly resistant accessions and 12 of the bunt differentials contained the DB-6D1 haplotype group. Conversely, the resistant haplotype for DB-6D2 was strongly associated resistance . For instance, 48% of accessions with the ce Table ; howeverThe present study evaluated the DB responses recorded in the GRIN database for 292 wheat accessions rated in three field trials and identified 98 accessions that were resistant and 28 accessions that were highly resistant across all three years of USU field trials and in GRIN. Additionally, four SNP markers associated with DB resistance were identified, one marker-trait association group on 6D was consistent across several data sets, and one marker-trait association group on chromosome 6D was not previously reported. Of the highly resistant landrace accessions, six have novel resistance haplotype profiles. These resistant accessions and haplotype regions can be used to confirm resistance loci in biparental mapping populations for introgression into advanced wheat breeding lines.Supplementary material 1 (DOCX 18 kb)Supplementary material 2 (XLSX 59 kb)Supplementary material 3 (DOCX 12 kb)Supplementary material 4 (PPTX 113 kb)Supplementary material 5 (PPTX 120 kb)Supplementary material 6 (PPTX 78 kb)Supplementary material 7 (XLSX 3885 kb)Supplementary material 8 (XLSX 11 kb)Supplementary material 9 (XLSX 38 kb)Supplementary material 10 (XLSX 9 kb)Supplementary material 11 (XLSX 9 kb)Below is the link to the electronic supplementary material.


The cases included 37 people representing 11 families and 15 children conceived after the accident. Exposed families incurred radiation absorbed doses in the range of 0.2 to 0.5 Gray. The control group included 15 families and 15 children also conceived after 1987 in Goiânia with no history of radiation exposure. DNA samples from peripheral blood were analyzed with the Affymetrix GeneChip® CytoScanHD™ to estimate point mutations in autosomal SNPs. A set of scripts previously developed was used to detect de novo mutations by comparing parent and offspring genotypes at the level of each SNP marker. Overall numbers of observed Mendelian deviations were statistically significant between the exposed and control groups. Our retrospective transgenerational DNA analysis showed a 44.0% increase in the burden of SNP mutations in the offspring of cases when compared to controls, based on the average of MFMD for the two groups. Parent-of-origin and type of nucleotide substitution were also inferred. This proved useful in a retrospective estimation of the rate of de novo germline mutations in a human population accidentally exposed to low doses of radiation from Cesium-137. Our results suggested that observed burden of germline mutations identified in offspring was a potentially useful biomarker of effect to estimate parental exposure to low doses of IR and could become an important marker suitable for biomonitoring human population exposed to environmental mutagens.We aimed to estimate the rate of germline mutations in the offspring of individuals accidentally exposed to Cesium-137 ionizing radiation. The study included two distinct groups: one of cases, consisting of males and females accidentally exposed to low doses of ionizing radiation of Cs In 1987, a series of unexpected events resulted in a major radiological accident in Goiânia, Goiás, Brazil, causing human, animal, plant and environmental exposure to gamma ray ionizing radiation (IR) of Cesium-137 and contamination by the radionuclide , 3.Following the accident, the exposed population has been extensively monitored using genetic biomarkers, as they have been shown to be efficient biomarkers of exposure to gamma rays . HoweverRadiation-absorbed dose relates to the estimated quantity of energy deposited in the mater per unit of mass. Thus, it can be used as an indirect measurement of the harmful biological effect of the radioactive energy on the cellular system. It is calculated by estimating the concentration of energy from radiation exposure deposited in each organ, using a reference value, the type of radiation and the potential for radiation-related mutagenic changes in each organ or tissue .The exposure of cells to IR delays the normal progression of the cell cycle –17, initThe mutagenic effects of IR on the human germ line cells are of concern, as they lead to the accumulation of mutations in the offspring of irradiated parents, amounting to an increase in the mutational burden . DespiteAdvances in the methodologies of genomic analysis have greatly increased the volume of nucleotide sequence data, enabling the identification of thousands of SNPs (single-nucleotide polymorphisms). Variations in SNPs are important to determine genotypic and phenotypic relationships, within and between species and populations, and also to identify variants related to genetic diseases in humans and animals . In this® CytoScanHD™ , is considered to be a high-density matrix, including about 750,000 polymorphic markers with an average genotyping accuracy of >99% [In recent decades, several genotyping technologies have been developed to characterize SNPs all producing genotype matrices with hundreds of thousands of datapoints. Algorithms based on parametric and nonparametric statistical models have been used to determine the genotype of each SNP from the fluorescence signal intensity of marked probes, which are scanned, captured, and arranged in a matrix format , 29. One of >99% .In the aforementioned context, the general objective of the current study was to quantify Mendelian deviations (MD) in genome-wide autosomal SNP data from a cohort of people conceived after parental exposure to Cesium-137 IR, and a group of non-exposed people from the same geographical area. The rate of MD was applied to evaluate if the observed burden of germline mutations identified in the offspring could be a potentially useful biomarker of parental exposure to low doses of IR.® CytoScanHD™ (Thermo Fisher Scientific).The experiment was designed as a case-control observational study. The group of cases consisted of 11 families, of whom at least one of the parents was accidentally exposed to IR during the Cesiu-137 accident, totaling 37 participants . The radiation absorbed doses for the exposed parents ranged from 0.2 to 0.5 Gy [Cases and controls participated voluntarily in the study, which was approved by the ethics committee on research with humans from the Pontifical Catholic University of Goiás (PUC-Goiás)–CAAE number 49338615.2.0000.0037. At the time of blood collection, participants answered a lifestyle questionnaire and signed an informed consent form. A total of 10 mL of peripheral blood in EDTA was voluntarily donated by all participants. Total genomic DNA was isolated from whole blood using Illustra blood genomicPrep Mini Spin Kit®  and stored at −20°C. The remaining biological material was stored according to CNS Resolution 441/11.® arrays (Thermo Fisher Scientific) in order to collect individual genotypes from polymorphic autosomal markers. SNP genotypes were generated using ChAS© (Thermo Fisher Scientific). Every array met the quality controls recommended in the manufacturer’s guidelines. SNP genotypes were filtered based on individual call confidence levels for each marker, thus calls with confidence levels <5x10-2 and invalid  in one or more samples were removed from the dataset. Therefore, only markers with quality-controlled genotypes in all samples were considered for the analysis. Genotyping was based on the hg19 version of the human genome hosted on the UCSC Genome Browser . We also applied the CpG island track from UCS browser in order to stablish the rule out C>T mutations at CpG sites. As the array genotypes didn’t allow the discrimination from which strand the damage was derived, all substitutions were included in the data sets.Chromosomal microarray analyses were carried out in GeneChip CytoScanHD2 threshold of 0.1. Pruning resulted in a subset of 2.789 SNPs that were used to estimate principal components and to generate plots for each test group.Principle Component Analysis (PCA) methods were used to assess whether participants in the case and control groups came from the same genetic population, the dataset contained about 522K SNPs. This step was also included to assess whether individual sample quality effects may have generated spurious results. SNPs with minor allele frequencies (MAF) below 0.01 were removed from the dataset, including all mendelian errors in the samples. Data pruning of the final dataset was performed using the PLINK (2.0) package  to gener© format. The SIPO pipeline was listed in , which had the parent of origin previously inferred following mendelian transmissions rules. Thus, at the end of the pipeline, the deductions aided to attribute the origin of a mutation to the parent who had both the lowest confidence interval value for that particular mutated SNP and who transmitted that chromosomal segment to the child based on the nearest variant SNP.In some situations, SIPO was not able to identify the parental origin of a SNP based on Mendelian transmissions. To solve this challenge, two deductions were incorporated into the pipeline. The first deduction was coded into SIPO to identify confidence interval values of individual SNPs using ChASMD) in the offspring, using MD = Total MD; b is a biallelic locus (2); nvp is the number of valid SNPs in the array according to the assembly of the human reference sequence (GRCh37/hg19) as indicated in S1 Table.The total count of MD was used to estimate the germline mutation frequency  and were all included in subsequent statistical analyses. The lowest individual numbers of MDs were 972 and 682, and the highest were 2,875 and 1,635 for the case and control groups, respectively (PCA results using a subset of LD-pruned data (522 Kb SNPs) indicated subjects included in both case and control groups belonged to the same population and there were no recognizable additional confounding factors associated with the test groups other than exposure to Cs-137 . Therefoectively . Observede novo base substitutions in an assayed SNP of a child born to a parent exposed to IR. Thus, a total of 18,429 and 26,533 SNPs showed MD for control and cases, respectively. Thus, the overall frequencies of germline mutations observed in the different trios were, on average, 1.3 x10-3 and 0.9 x10-3 mutations per polymorphic marker. The Student’s T test showed the difference in the means was statistically significant assuming equal variances for both groups (p = 0.002). Tables MD for the two groups. The current study pioneered the application of SNP data analysis to identify MD and estimate germline mutations in the offspring of humans accidentally exposed to low absorbed doses of IR. Current findings corroborated our first study reporting the usefulness of small CNVs to estimate de novo human germline mutation rates in a similar cohort [de novo CNV and SNVs as biomarkers of effect for paternal exposure to IR in mice. Moreover, a recent study using whole genome sequencing data from an offspring of radar soldiers potentially exposed to IR found the differences in the frequency of de novo SNVs might be suited for the assessment of DNA damage from IR in humans [In the current study, mutation burden was defined by the number of r cohort . A previr cohort  also desn humans .MD in our cohorts. Our results were statistically significant , suggesting that low absorbed doses of IR could predict an increase of the mendelian deviation in the exposed group, which could be linearly fitted  and for exposed mothers was 1.3 x10-3 (±0.5 x10-3), with no statistical differences (p = 0.195) intragroup.To date, there is extensive evidence supporting sex differences in mutation rates, with older fertile males expected to contribute more to the burden of a mutational health hazard than older females. A greater number of continuous cell divisions in the male germ line has been implicated as one reasonable explanation for such difference on paternal age effect , 39. Howde novo point mutations in the offspring [With respect to the potential parental age effect, our control group revealed older fathers contributed more MDs to their offspring , which cffspring , 42, othffspring . To dateffspring . New genSingle base substitutions have been a common and frequent mutational event subjacent to cell divisions spontaneously that rise as a consequence of DNA replication errors or induced by environmental stressors, such as IR. Some previously published studies on the types of DNA spontaneous base substitutions indicated all possible substitutions are well represented in germline cells . Such stde novo mutations in 6 classes based on parental and derived alleles . The arithmetic mean of the MD in the offspring of case and control groups are shown in In the context described before, MFMD of a small cohort of children conceived after their parents were accidentally exposed to ionizing radiation from Cs-137, we suggest the application of the current study design to larger cohorts. It might be advisable to include a wider range of absorbed doses, resulting from either therapeutic or occupational exposures, to assess the potential of Mendelian deviations as retrospective biomarkers for IR exposure in human populations. In the present study, the case and control groups belonged to the same population and, therefore, were subjected to similar general environmental effects. Thus, it was safe to conclude that the average MFMD was higher in the exposed group as a result of higher germline base substitutions than in the control group, which could be reasonably assumed as a consequence of parental exposure to low doses of IR. In this context, low doses of low-LET radiation induced MD in autosomal SNPs that could be identified, quantified and, therefore, used as a biomarker of effect to study human populations according to their history of exposure to environmental mutagenic insults.To validate the findings of the current study, which analyzed the MFThis study pioneered the analysis of MDs using autosomal SNP data observed in parent-offspring trios as biomarkers of effect to low doses of ionizing radiation. We succeeded estimating retrospectively the germline mutation frequency of SNPs in a human population accidentally exposed to low doses of radiation from Cs-137 and estimated the burden of germline mutations in the offspring.MD of autosomal SNPs, for both case and control groups, mother and fathers contributed equal numbers of de novo MD to their offspring. After accounting for age, our control group revealed older fathers contributed more MD to their offspring, which could be modeled by number of mitotic spermatogonia divisions as a function of age, supporting previous findings of male-mutation bias. However, our study failed to detect the maternal age effect on the frequency of MDs.We found the sex of the progenitors had no effect on the MFMD of the offspring of those accidentally exposed to low doses of IR, from a radiological accident in Goiânia. Low absorbed doses of IR could predict the increase of the mendelian deviation in the exposed group. Therefore, we concluded that MFMD is a potentially useful biomarker to estimate parental exposure to IR and suitable for human population biomonitoring. In this context, future studies involving the behavior of MDs following diverse genomic and mutagenic hazards, caused by exposure to environmental agents, may provide important knowledge of the biological effects, mechanisms, and risks resulting from human exposure to such agents.In summary, there was a 44.0% increase in the MFFinally, we are confident SNP array data can be used to estimate ionizing radiation-induced mutagenesis in human populations, provided the appropriate bioinformatics and statistical tools are used to extract the necessary information for biological inferences and to validate the scientific hypotheses underlying each investigation.


Agropyron cristatum (L.) Gaertn.] provides high quality, highly palatable forage for early season grazing. Genetic improvement of crested wheatgrass has been challenged by its complex genome, outcrossing nature, long breeding cycle, and lack of informative molecular markers. Genomic selection (GS) has potential for improving traits of perennial forage species, and genotyping-by-sequencing (GBS) has enabled the development of genome-wide markers in non-model polyploid plants. An attempt was made to explore the utility of GBS and GS in crested wheatgrass breeding. Sequencing and phenotyping 325 genotypes representing 10 diverse breeding lines were performed. Bioinformatics analysis identified 827, 3,616, 14,090 and 46,136 single nucleotide polymorphism markers at 20%, 30%, 40% and 50% missing marker levels, respectively. Four GS models  were examined for the accuracy of predicting nine agro-morphological and three nutritive value traits. Moderate accuracy (0.20 to 0.32) was obtained for the prediction of heading days, leaf width, plant height, clump diameter, tillers per plant and early spring vigor for genotypes evaluated at Saskatoon, Canada. Similar accuracy (0.29 to 0.35) was obtained for predicting fall regrowth and plant height for genotypes evaluated at Swift Current, Canada. The Bayesian models displayed similar or higher accuracy than rrBLUP. These findings show the feasibility of GS application for a non-model species to advance plant breeding.Crested wheatgrass [ Agropyron cristatum (L.) Gaertn.] is a perennial, outcrossing, cool season grass native to Eurasia. It belongs to the genus Agropyron, and has three ploidy forms: diploids, tetraploids and hexaploids [Triticum aestivum), and barley (Hordeum vulgare) breeding [Crested wheatgrass [xaploids –4. Crestxaploids , and is xaploids , 6. It ixaploids –9. It isxaploids , 8. Its xaploids , 8, 10. breeding –14. HighGenetic variation within and among crested wheatgrass populations is high, offering ample possibility of genetic improvement . BreedinAvailability of molecular markers for non-model polyploid plant species like crested wheatgrass is a recent development. Next generation sequencing technologies have offered genome-wide markers for crops with no prior sequence information. Genotyping-by-sequencing (GBS) is a powerful genomic approach for identification of genome-wide single nucleotide polymorphisms (SNPs) of non-model plants –24. ThisMedicago sativa L.) [Thinopyrum intermedium L.) [Panicum virgatum L.) [Lolium perenne L.) [Early marker assisted selection (MAS) was largely based on limited number of DNA markers and their association with quantitative trait loci (QTL) and could out-perform phenotypic selection alone . Howevertiva L.) –37, intedium L.) , switchgatum L.) –42, and enne L.) –45. Howeenne L.) , 15, 46,enne L.) –49 and ienne L.) .This study was conducted with the objectives: (1) to apply GBS in combination with the Universal Network Enabled Analysis Kit (UNEAK) and the Haplotag pipelines to identify genome-wide SNP markers and; (2) to assess the feasibility of GS for complex traits in diploid and tetraploid crested wheatgrass lines.I confirm that the University of Saskatchewan and Agriculture and Agri-Food Canada ethics committees approved this study. We don't issue field permit. This is a part of our regular research study.The study material comprised ten lines of crested wheatgrass  . AC ParkThe agro-morphological traits evaluated for the 160 genotypes in Saskatoon and 165 genotypes in Swift Current included early spring vigor score (ESV), heading days (DTH), plant height (PH), leaf width (LW), clump diameter (CD), dry matter yield (DMY), regrowth score after harvest (RGAH) and fall regrowth score (FRG). Tillers per plant (TPP) was evaluated at the Saskatoon site. Descriptions of the measurement of these agro-morphological traits are presented in 2000 fiber analyzer  following manufacturer’s instructions.The genotypes were sub-sampled after dry matter determination during the growing seasons of 2015 and 2016 for forage nutritive value determination. The sub-samples were ground through a 1-mm screen Wiley mill . The ground samples were stored in plastic bags prior to determination of crude protein (CP), neutral detergent fiber (NDF) and acid detergent fiber (ADF). Nitrogen concentration was determined by the Dumas combustion method using the Leco CN 628 Dumas analyzer  for all the Saskatoon samples and the 2015 Swift Current samples, while the Kjeldahl method was used for samples from Swift Current in the year 2016. Then, nitrogen content was converted to CP multiplying by a conversion factor of 6.25. NDF and ADF concentrations were determined using an automated Ankom® Plant II Kit  were used to extract DNA from 0.1 g finely ground tissue and was eluted in a 1.5 mL Eppendorf tube with Elution Buffer. DNA quality was measured by comparing the absorptions at 260 and 280 nm using NanoDrop 8000 . Further quantification of the DNA samples was through the Quant-iTTM PicoGreen® dsDNA assay kit  and final dilution to 60 ng/μl with 1× TE buffer was done before sequencing analysis.For each of the 325 genotypes, protocols of NucleoSpinPstI and MspI  digested 200 ng of purified genomic DNA in each library. On to the 5′ and 3′ ends of the restriction fragments, ligation of customized adapters by T4 ligase was carried out. Then, AMPure XP kit  was used for purification of the ligated fragments. Through PCR amplification, Illumina TruSeq HT multiplexing primers were added following the purification. The amplicon fragments were further quantified, concentrated, and pooled to form 4 subgroups of 12 samples each. Using a Pippin Prep instrument , pre-selection of the samples in the subgroups for an insert size range of 100–400 bp were done before pooling the samples into a library. Each pooled library was diluted to 6 pM, and denatured with 5% of sequencing-ready Illumina PhiX Library Control  that can serve for calibration. Sequencing was completed using an Illumina Hiseq2500 Instrument with paired-ends of 125 bp in length. HiSeq runs of 6 libraries generated 672 FASTQ sequence files from 336 genotypes  of 10 lines (one forward and one reverse for each of 336 genotypes). All FASTQ files were deposited to Sequence Read Archive (SRA) database under NCBI with SRA accession PRJNA599212 and submission ID SUB6703766 (https://www.ncbi.nlm.nih.gov/sra/PRJNA599212).A genetic diversity-focused GBS (gd-GBS) protocol was used for the preparation of multiplexed GBS libraries , 23. BriPstI residue (TGCAG) at the beginning, each FASTQ file of 125 bp was split with a custom Perl script fastqHiseqCutandCode-Pst.pl available via Figshare  to get the first 64 bases with the PstI residual restriction site. The script also provided an arbitrary barcode sequence (CATCAT) at the start of each sequence fragment, since the UNEAK pipeline expects to de-convolute barcoded sequence reads which are not already separated by sample. The 70-base-long fragments formed, thereafter, were recognized by the UNEAK-GBS pipeline [Bioinformatics analysis began with sequence (FASTQ) data cleaning, using Trimmomatic version 0.36  to removpipeline , and pas10.6084/m9.figshare.11530677) describes the procedures to run UNEAK. Two types of meta data files, a single mergedAll.txt  provided as S3 Text in Figshare (10.6084/m9.figshare.11530677) and a set of individual tagCount files (one per sample) needed for the Haplotag pipeline were generated from the UNEAK run.The fragment set (70 bases long) was analyzed with UNEAK and the Haplotag pipeline , resulti10.6084/m9.figshare.11530677, S4 Text) and generated a matrix of samples by SNP loci as described in S2 Text, Section B. A set of tag-level haplotypes (“HTgenos”) are first generated by Haplotag, followed by a set of SNP data derived from these haplotypes (“HTSNPgenos”). These two data types are technically redundant, so choosing one of them relies on the implementation and preference of software. In the present study, most (97.5%) haplotypes were found to contain only a single SNP; thus, we decided to analyze the SNP dataset for simplicity and compatibility with downstream analysis software.Haplotag was run with the parameters and filtering threshold settings described in the HTinput.txt file available via Figshare  was used in further analyses. Additionally, S1–S4 Files and S9–S15 Text available via Figshare (10.6084/m9.figshare.11530677) provide descriptions of the genotypes, phenotypes, the batch files, and custom Perl and Shell scripts used. Analyses from FASTQ file separation to SNP generation were conducted using Microsoft Windows 7 64-bit OS with an Intel (R) Xeon (R) CPU E5-2623 v3 @ 3.00 GHz (8 threads) and 32 GB RAM.The character by Taxa (CbyT) program supplied by N. Tinker  was usedThe SNP marker information for the missing markers at each level  were reconstructed using a probabilistic Principal Component Analysis (probabilistic PCA) that integrates expectation maximization approach with probabilistic models using the R package “pcaMethods” , 54. FolOur study materials consisted of diploid and tetraploid crested wheatgrass lines. Thus, it was important to determine the existence of genetic structure in the study material and estimate the effect of SNPs arising from the genetic structure prior to genomic selection analysis. For this, a principal coordinates analysis (PCoA) was conducted using the R routine AveDissR  to assesOutliers for each of the phenotypic traits at each location were examined using studentized deleted residuals  from a mH^) , 61.To assess the applicability of genome-wide markers to predict phenotypes of agro-morphological and nutritive value traits in crested wheatgrass, four additive genomic selection models, including BayesA , BayesB The prediction accuracy of the models was assessed by splitting the data into a training set and a validation set and repeated 500 times. The genotypes were randomly partitioned into two equally sized subgroups. Then, for each repeat, the randomly sampled half of the subgroups was used as the training set and the remaining half as the validation set. The validation set was used to assess the correlation between observed and predicted trait values (GEBVs). For GS model comparison, the same training and validation sets were used for all four GS models. Prediction accuracy of each model for each trait was the average Pearson correlation coefficient across the 500 repeats. A nonparametric ANOVA with two factors was considered to assess the effects of GS model and location, as the estimates in this study were not always normally distributed, and the R “ARTool” package  was applThere was substantial variation in each of the agro-morphological and nutritive value traits Tables  and 4. I10.6084/m9.figshare.11530677). In addition, this analysis also generated many meta-genomic files associated with the SNP discovery, which are described and accessible via Figshare . The data filtering done by removal of SNPs differing with technical replicates and again with SNPs having MAF <0.05 resulted in 286, 1,206, 5,437 and 17,003 SNPs at 20%, 30%, 40%, and 50% levels of missing data in Saskatoon, respectively  of SNP markers . In SwifComparison of four additive GS models, BayesA, BayesB, BayesCπ and rrBLUP at a SNP density corresponding to 50% missing level for traits evaluated at Saskatoon exhibited similar prediction abilities of the Bayesian models for CD, DMY, ESV, TPP, RGAH, and ADF, while prediction accuracy of rrBLUP was the lowest  for thesOur results demonstrated prediction accuracy of the GS models varied for each trait at each level of SNP density (corresponding to missing SNPs levels) at each location. Overall, in Saskatoon, the prediction accuracy improved with increase in SNP densities for DMY and LW. An increase in prediction accuracy with increasing SNP density from 20 to 30% missing levels, then a decrease at the 40% missing level, followed by an increase at the 50% missing level was observed for DTH, PH, CD,TPP and ESV. While, for RGAH, ADF, NDF, and CP, prediction accuracy of the models decreased with increasing SNP densities up to the 40% missing level and then improved with increasing SNP densities at 50% missing level except for rrBLUP. The prediction accuracy of the models for FRG increased up to 30% missing level and later decreased with increasing SNP densities up to 50% missing levels . EvaluatSimilarly, for the traits evaluated at Swift Current, an increase in the prediction accuracy with increasing SNP density was observed for FRG and CP. An increase in prediction accuracy with increasing SNP density from 20 to 30% missing level, then a decrease at 40% missing level, followed by an increase with increasing SNP density at the 50% missing level was observed for LW and PH. Increase in prediction accuracy with increasing SNP density from 20 to 30% missing level followed by a decrease with increasing SNP density up to 50% missing level was observed for DMY, DTH, ESV, ADF and NDF. Decreasing prediction accuracy with increasing SNP density was observed for CD and RGAH . EvaluatThis study, for the first time, investigated the prediction accuracy of genomic selection models in crested wheatgrass breeding utilizing the gd-GBS application for SNP marker generation. This study assessed and compared the prediction accuracy of four GS models at four levels of SNP densities. Overall, the GS models varied in prediction accuracy of the evaluated traits at each level of SNP density at each location. The findings revealed Bayesian models provided higher prediction accuracies than rrBLUP for the agro-morphological and nutritive value traits in crested wheatgrass. The traits with unknown genetic architecture in crested wheatgrass might have been under the influence of certain QTLs rather than a large number of QTLs, which would result for the higher prediction accuracies of Baysesian models compared to rrBLUP as rrBLUP assumes that all markers have common variance with small but non-zero effect . The relZea mays) and 0.3 in winter wheat [Given the long breeding cycles required for varietal development in crested wheatgrass, genomic selection could significantly reduce the time for each breeding cycle by estimation of GEBVs at the seedling stages without phenotyping over multiple site-years. The prediction accuracies for the agro-morphological traits and the three forage nutritive value traits in our study are comparable to findings in other perennial forage crops , 39. Moder wheat .Beta vulgaris L.) [The present study also showed certain negative prediction values ranging from -0.11 to -0.01, for DMY, DTH, LW, CD, ESV, RGAH, FRG, ADF, NDF, and CP, in particular, at Swift Current site. Negative prediction values have also been reported from genomic selection studies in maize , 69, sugaris L.) , perenniaris L.) , 45 and aris L.) –41. Thesaris L.) . In thisaris L.) . Low to aris L.) . In thisOryza sativa L.) reported reduction in prediction accuracies with incorporation of data from uncorrelated environments [Our study showed differences in the prediction accuracy of models for the same trait evaluated in two different environments, and the prediction accuracy was statistically significant for the GS models, location and their interactions for most of the traits. The prediction accuracy was moderate for forage yield related traits in Saskatoon, while it was moderate for PH and FRG in Swift Current. This difference can be attributed partly to the difference in the genotypes being used in the study and the inability of the genomic selection models used to account for G × E effect. SNP variation within-family is significantly higher than among families in crested wheatgrass  owing toronments .Brassica napus) evaluated in two different years were reasoned to be influenced by pleiotropic effects of certain genetic factors in specific environments [In this study, we also investigated the effect of SNP densities in the prediction accuracy of GS models with the use of four different SNP densities corresponding to the SNP missing levels. Our results showed increasing, decreasing and a mixture of increasing and decreasing trends of prediction accuracy with increasing SNP densities for the agro-morphological and nutritive value traits Figs  and 3. Hronments .This study assessed whether SNPs generated using the GBS application in diploid and tetraploid crested wheatgrass plants could predict the GEBVs of agro-morphological and nutritive value traits in crested wheatgrass using four GS models. Overall, the moderate prediction accuracies observed for DTH, LW, PH, CD, ESV and FRG, demonstrate that a genomic selection approach could increase the genetic gain for these traits in a cost-effective way by reducing the length of the selection cycle. This approach could be utilized for the development of high-quality, high-yielding, late maturing crested wheatgrass for extended summer grazing. However, the low prediction accuracies observed for certain traits in our study might be improved by increasing the number of replications for improved phenotyping, refining the training population using a higher number of breeding lines, increasing the population size, using different genotyping approach to increase the genome coverage and using different genomic selection models that account for additive and dominant gene actions –83, and This genomic selection analysis in combination with GBS revealed moderate predication accuracies (0.20 to 0.35) for several agro-morphological traits in crested wheatgrass breeding. This finding is encouraging for the application of GS in perennial forage crop breeding.


In recent years, mechanistic, epidemiologic, and interventional studies have indicated beneficial effects of omega-3 polyunsaturated fatty acids (n-3 PUFA) against brain aging and age-related cognitive decline, with the most consistent effects against Alzheimer’s disease (AD) confined especially in the early or prodromal stages of the pathology.In the present study, we investigated the action of n-3 PUFA supplementation on behavioral performances and hippocampal neurogenesis, volume, and astrogliosis in aged mice subjected to a selective depletion of basal forebrain cholinergic neurons. Such a lesion represents a valuable model to mimic one of the most reliable hallmarks of early AD neuropathology.Aged mice first underwent mu-p75-saporin immunotoxin intraventricular lesions to obtain a massive cholinergic depletion and then were orally supplemented with n-3 PUFA or olive oil  for 8 weeks. Four weeks after the beginning of the dietary supplementation, anxiety levels as well as mnesic, social, and depressive-like behaviors were evaluated. Subsequently, hippocampal morphological and biochemical analyses and n-3 PUFA brain quantification were carried out.The n-3 PUFA treatment regulated the anxiety alterations and reverted the novelty recognition memory impairment induced by the cholinergic depletion in aged mice. Moreover, n-3 PUFA preserved hippocampal volume, enhanced neurogenesis in the dentate gyrus, and reduced astrogliosis in the hippocampus. Brain levels of n-3 PUFA were positively related to mnesic abilities.The demonstration that n-3 PUFA are able to counteract behavioral deficits and hippocampal neurodegeneration in cholinergically depleted aged mice promotes their use as a low-cost, safe nutraceutical tool to improve life quality at old age, even in the presence of first stages of AD. Omega-3 polyunsaturated fatty acids (n-3 PUFA) are essential dietary nutrients that constitute the major components of neuronal membranes and are key modulators of many neural functions throughout life –4. TheirThe ever-increasing number of elderly people translates into increasing demands for social-health and care services, particularly with respect to age-related neurodegenerative diseases, such as Alzheimer’s disease (AD). AD is the most common progressive dementia in people over the age of 65 years , 11. MajThe etiology of AD is still unknown, and no effective therapy has been yet identified to defeat the disease. Currently, there are only symptomatic treatments to attenuate AD-related cognitive deterioration which have no effects on AD progression –24, and . When present, cognitive improvements associated with n-3 PUFA supplementation have been mostly demonstrated in patients with very mild AD  and social behaviors  were found among groups  and no differences among groups were found in the duration of immobility during the test session . These findings indicate that in aged mice the re-exposure to an aversive condition induced a despair response affected by neither the cholinergic depletion nor the n-3 PUFA treatment.As shown in Fig. Considering the fact that in mice adult neurogenesis is predominant in the dorsal hippocampus respect to the ventral hippocampus –104 and 3; sham n-3 PUFA = 3.96 ± 0.32 mm3; sham n-3 PUFA vs. sham oil, p = 0.03; Fig. 3; sap n-3 PUFA = 4.1 ± 0.43 mm3; sap n-3 PUFA vs. sap oil, p = 0.02; Fig. 3; sham n-3 PUFA = 0.48 ± 0.02 mm3; sap oil = 0.32 ± 0.02 mm3; sap n-3 PUFA = 0.47 ± 0.01 mm3; sham n-3 PUFA vs. sham oil, p = 0.02; sap n-3 PUFA vs. sap oil p = 0.04; Fig. 3; sham n-3 PUFA = 0.24 ± 0.01 mm3; sap oil = 0.14 ± 0.04 mm3; sap n-3 PUFA = 0.25 ± 0.03 mm3; sham n-3 PUFA vs. sham oil, p = 0.04; sap n-3 PUFA vs. sap oil, p = 0.04; Fig. In the dorsal region of the hippocampus, n-3 PUFA supplementation induced a significant volume preserving in both sham . Furthermore, n-3 PUFA administration induced significantly increased proliferation in both sham (sham n-3 PUFA = 375.6 ± 40.6 BrdU+ cells) and immunotoxic lesioned groups  with respect to their oil-treated counterparts , which was interestingly counteracted by the n-3 PUFA treatment . In particular, post-hoc comparisons showed that saporin-lesioned mice treated with n-3 PUFA showed reduced levels of GFAP, compared to oil-treated animals, whereas n-3 PUFA had no effect on sham-lesioned mice .To examine whether the n-3 PUFA exert an anti-inflammatory activity in our mouse model, we analyzed the hippocampal GFAP levels Fig. a, b. TheF1,12 = 41.98, p < 0.0001; diet: F1,12 = 0.88, p = 0.37; lesion x diet: F1,12 = 1.56, p = 0.23; Fig. Lesion verification through immunoblot analysis indicate an extensive reduction of VAChT in the hippocampus of both saporin-lesioned mice . No significant correlations of EPA+DHA+DPA levels with anxiety parameters  were evident. Interestingly, a significant positive correlation was found between EPA+DHA+DPA levels and the discrimination index in NORT  in the DG occurred after both sham and saporin lesions in aged mice, but the increase in neuronal differentiation of newborn neurons  persisted only after the immunotoxic lesion, probably to compensate for the cholinergic neuronal loss. Similar neurogenic effects of n-3 PUFA have been reported also in one of our previous studies [In our study, the n-3 PUFA stimulation of neuronal progenitor proliferation  –151, andImportantly, several dietary components  consumed in anti-inflammatory dietary patterns associated with slower rate of cognitive decline (such as Mediterranean diet) are reported to be able to inhibit neuroinflammation associated with AD via several immune pathways within the brain and indirectly from the gut microbiome and systemic circulation . For exaThe first limitation of the present study may be the lack of young groups of mice lesioned and treated as the aged ones to also evaluate the age effect. For example, in the SIT, we found no significant effects of lesion or diet, but we can only hypothesize a floor effect linked to an age-related decrease in social interest based on previous studies in rodents , 76, 119Secondly, we are aware that the use of aged mice exposes to the risk of an obvious reduction of the sample size, as the experiment goes by, due to increased mortality rate, especially following surgery. Therefore, we have a reduced number of measurements in some experimental groups, as for example in the n-3 PUFA brain levels and PT. Differently, the reduced number of subjects for SIT  was due to video recording troubles, while the exclusion of some animals from NORT was due to the failure to meet the contact time criterion, maybe linked to the age-related reduced interest of aged animals also for inanimate stimuli . Anyway,A final potential limitation of the present study could have been that the animals received a prolonged n-3 PUFA supplementation via gavage. However, we feel to exclude any potential detrimental effect of it, since we previously demonstrated that 8 weeks of gavage did not modify the behavioral, morphological, and biochemical measurements in untreated naïve aged mice .The n-3 PUFA treatment represents a form of neuroplasticity promoter even at old age and in the presence of a massive cholinergic loss, thus supporting their use to delay AD pathology progression. Moreover, the discovery of a strict link between preserved hippocampal volume and neurogenesis and reduced astrogliosis as underpinnings of maintained cognitive and behavioral performances during aging shed new light on the n-3 PUFA mechanisms of action.Additional file 1: Suppl. Table 1. NORT object preference, grooming and defecation data. Mean and S.E. of the following NORT parameters in the four experimental groups: preference for one of the two identical objects during the training trial ; preference for the novel object during the test trial ; grooming time (s) and number of defecations in all task trials.Additional file 2: Suppl. Table 2. Brain levels of n-3 PUFA. Mean and S.E. of EPA + DHA + DPA brain levels in the four experimental groups.


CYP2D6 gene locus has been extensively studied over decades, yet a portion of variability in CYP2D6 activity cannot be explained by known sequence variations within the gene, copy number variation, or structural rearrangements. It was proposed that rs5758550, located 116 kb downstream of the CYP2D6 gene locus, increases gene expression and thus contributes to variability in CYP2D6 activity. This finding has, however, not been validated. The purpose of the study was to address a major technological barrier, i.e., experimentally linking rs5758550, also referred to as the “enhancer” single-nucleotide polymorphism (SNP), to CYP2D6 haplotypes >100 kb away. To overcome this challenge is essential to ultimately determine the contribution of the “enhancer” SNP to interindividual variability in CYP2D6 activity.The CYP2D6 haplotypes . To experimentally validate predicted linkages, DropPhase2D6, a digital droplet PCR (ddPCR)-based method was developed. 10X Genomics Linked-Reads were utilized as a proof of concept.A large ethnically mixed population sample  was computationally phased to determine linkage between the “enhancer” SNP and CYP2D6 haplotypes including CYP2D6*1, *2, *5, and *41 and suggested that linkage is incomplete, i.e., a portion of these alleles do not have the “enhancer” SNP. Phasing also revealed differences among the European and African ancestry data sets regarding the proportion of alleles with and without the “enhancer” SNP. DropPhase2D6 was utilized to confirm or refute the predicted “enhancer” SNP location for individual samples, e.g., of n=3 samples genotyped as *1/*41, rs5758550 was on the *41 allele of two samples and on the *1 allele of one sample. Our findings highlight that the location of the “enhancer” SNP must not be assigned by “default.” Furthermore, linkage between the “enhancer” SNP and CYP2D6 star allele haplotypes was confirmed with 10X Genomics technology.Phasing predicted that the “enhancer” SNP can occur on numerous Since the “enhancer” SNP can be present on a portion of normal, decreased, or no function alleles, the phase of the “enhancer” SNP must be considered when investigating the impact of the “enhancer” SNP on CYP2D6 activity. CYP2D6 gene encodes the cytochrome P450 2D6 enzyme, which contributes to the metabolism and bioactivation of numerous clinically used drugs  has been proposed to impact CYP2D6 activity by modulating expression levels, and thus may account for unexplained variability, especially within a given diplotype  transcription levels   . Based ongeably) . There afinition , but alsCYP2D6*1 alleles have “A” at this position.The “enhancer” SNP (rs5758550) is listed by dbSNP as G>A with “G” being the minor allele with a global frequency of 28% per the gnomAD database. Wang et al. ascribed increased activity to the presence of “G” and hence referred to this nucleotide as the SNP of primary interest in their publications. Throughout this report, we refer to “G” (the “enhancer” SNP) as the variant allele and “A” as the reference allele, consistent with the dbSNP database as well as our own observations that the vast majority of CYP2D6 gene locus and rs5758550 poses a challenge to unequivocally determining the phase of CYP2D6 gene locus SNPs and the “enhancer” SNP, unless both alleles carry the rs5758550 SNP. While statistical inference of linkage can be predicted by bioinformatics tools, experimental validation remains the gold standard. Methods often utilized to establish SNP linkage such as allele-specific long-range PCR or single molecule sequencing either do not allow amplification over 20 kb or are costly and labor-intensive.The long physical distance between the In droplet digital PCR (ddPCR), a reaction is divided into up to 20,000 nanoliter-sized droplets, enabling PCR amplification from a single DNA molecule; this technology, combined with allele-specific TaqMan® probes, can rapidly phase two SNP loci that are thousands of bases apart . This meCYP2D6*2 alleles with and without the “enhancer” SNP? Do other haplotypes containing rs16947 also have the “enhancer” SNP—all or just some? Are there population-specific differences? In other words, to further investigate the impact of the “enhancer” SNP on CYP2D6 activity, it is essential to know which haplotypes  can carry this SNP and experimentally establish on which allele the “enhancer” SNP is located in heterozygous samples. To provide this crucial information, we 1) computationally phased a large dataset of >3,000 ethnically diverse samples in order to determine which CYP2D6 star alleles carry the “enhancer” SNP, and 2) established a ddPCR-based assay, referred to as the DropPhase2D6 assay, that can experimentally confirm linkage of computationally predicted haplotypes. While this investigation focuses on CYP2D6, it can easily be adapted to other genetic loci.This investigation aimed to address a number of questions that arose from the work published by Wang and colleagues including: what is the proportion of Genomic- and Ontogeny-Linked Dose Individualization and cLinical Optimization for Kids (GOLDILOKs), CYP2D6 genotype and neurocognitive dysfunction in methamphetamine users with and without HIV and medication use in pregnant women). Studies into which participants were initially enrolled in were approved by the institutions at which the studies were performed. Written informed consent to participate in a study was provided by participants. The use of repository samples and human liver tissue samples was approved by the Children's Mercy Hospital internal review board. DNA was sourced from either whole blood, saliva, or liver tissue. The panel also included samples obtained from the Coriell Institute for Medical Research (www.coriell.org) (n=254), a PGx repository maintained at Children's Mercy (n=115), and liver tissue samples obtained from the Liver Tissue Cell Distribution System (n=150), the National Institute of Child Health and Human Development (NICHD)–supported tissue retrieval program at the University of Maryland Brain and Tissue Bank for Developmental Disorders  (n=103) and Sekisui XenoTech LLC (n=28). Ethnicities were self-reported.Sample and genotype data were compiled from previous studies  as well CYP2D6 RefSeq NG_008376.3 with the ATG start codon being +1 according to PharmVar at https://www.pharmvar.org/gene/CYP2D6.Genomic DNA was prepared from whole blood or liver tissue and genotyped for a minimum of four SNPs  as previously described  and detaCYP2D6*5 gene deletion. The majority of samples had results for all 15 SNPs and the gene deletion. Samples known to contain rare alleles  were excluded from the computational data analysis. Ethnicity was available for n=2,564 samples. Phasing was performed for all samples (n=3162), regardless of ethnicity . Separate population-specific analyses were performed for samples with African (n=603) and European (n=1474) ancestries (dataset_A and dataset_E).Haplotypes were constructed from samples (n=3162) (Table 2) using PHASE (v2.1.1) . Fifteen2PO4, 137 mM NaCl, 1.8 mM anhydrous Na2HPO4, pH 7.4) in 20% of the original blood volume, and immediately used for DNA extraction or frozen at −80°C.Long-distance phasing by ddPCR requires high molecular weight (HMW) DNA samples. To assess the suitability of DNA for DropPhase2D6, different commercially available kits were evaluated. To limit shearing, all pipetting steps (including handling of blood and DNA) were carried out with 200 µl BioClean Ultra™ Wide-O LR Filter Sterilized pipette tips  and 10 µl BioClean Ultra™ Filter tips cut with a scalpel to create a wide bore tip. Ethylene-diaminetetraacetic acid (EDTA) whole blood samples were processed and frozen at −80°C within 24 hrs of collection. White blood cells were isolated from blood, resuspended in 1x phosphate-buffered saline .To serve as a comparison to HMW DNA preparations, DNA was isolated with this silica column-based DNA extraction kit. Blood and tissue samples were prepared as per manufacturer's protocol.DNA was extracted from whole blood or white blood cells with the PrepFiler Forensic DNA Extraction Kit as described by DNA was extracted from whole blood, white blood cell suspensions or 25 mg of frozen liver tissue per manufacturer's protocol with the following modifications: 50 µl of suspended white blood cells or 50 µl of whole blood was used instead of 200 µl whole blood and the tubes were gently flicked instead of shaken at 1,400 rpm.DNA was extracted from whole blood, white blood cell suspension or 25 mg of frozen liver tissue per manufacturer's protocol with the following modifications: 100 µl of frozen whole blood or 15 µl of white blood cell suspension was substituted for fresh whole blood. Cell suspensions were centrifuged at 16,000xg for 10 min, instead of 5 min, to pellet the cells and then washed with 100 µl of the nuclei isolation buffer after removing the supernatant. This additional wash step facilitated the removal of excess lysis debris.CYP2D6*4 core SNP (1847G>A) was validated after the method was established for 2851C>T. DropPhase2D6 assay names, their TaqMan® assay IDs, and the SNP-specific probes labeled with fluorophores are shown in TaqMan® SNP assays were custom ordered from Life Technologies . Each 40x assay consisted of two probes, one with a fluorophore and quencher while the other had only a non-florescent quencher (NFQ), referred to in this report as a “dark probe.” For 2851C>T (rs16947), two 40x assays were designed, i.e., T-FAM+C-dark and C-FAM+T-dark, allowing us to detect signal for either the “C” or “T” allele. For all other assays, VIC labeled probes were used. Another probe set for the 2O was added for a total volume of 22 µl, and 4) the reaction was gently mixed by pipetting 15 times with a Rainin wide-orifice P200 tip; 5) the 96 well plate was gently vortexed at low speed for three pulses and spun for 2 min at 2,000xg. After generating droplets using the Auto Droplet Generator , the plates were heat-sealed with a foil cover (Bio-Rad) and cycled in a C1000 Touch Thermocycler (Bio-Rad). The thermal cycling protocol was as follows: 10 min at 95°C for 1 cycle, 40 cycles of 30 s at 94°C and 1 min at 60°C, and a final cycle of 98°C for 10 min. Droplets were read on the QX-200 Droplet Digital PCR system (Bio-Rad) and linkage analysis was performed with the QuantaSoft™ Analysis Pro software v.1.0.596. Linkage is expressed as a percent of all molecules that are linked as described in For each linkage reaction, two 40x assays were combined: an rs16947-FAM assay with a VIC-containing assay. For example, to evaluate linkage between rs16947 “T” and rs5758550 “G,” the rs16947varT and rs5758550varG assays were used. We will refer to the combination of the two custom TaqMan® SNP assays as a “duplex.” DropPhase2D6 was carried out as follows: 1) the two selected 40x assay mixes were combined with ddPCR Supermix for Probes without dUTP  and added to each well; 2) 10–30 ng HMW DNA was added; 3) HEcoRI-HF restriction enzyme (RE)  at 37°C for 1.5 h followed by RE inactivation at 65°C for 20 min. Digested DNA was subsequently used in place of HMW DNA as a no-linkage control. The second strategy included testing all possible allelic configurations of a compound heterozygote. This is achieved by performing two duplex reactions. One duplex reaction is expected to be in linkage (interrogated SNPs are in cis) while the other duplex reaction is expected not to show linkage (interrogated SNPs are in trans). For example, in a sample genotyped as CYP2D6*1/*2 (heterozygous for rs16947 and rs5758550), linkage between the interrogated SNPs is observed when using the rs16947varT+rs5758550varG duplex reaction, while the rs16947refC+rs5758550varG duplex reaction results in no linkage. If linkage cannot be established with one of the duplex reactions, DNA quality is likely insufficient.Two control strategies were utilized for the DropPhase2D6 experiment. First, 150 ng of HMW DNA was digested with 10 U of To determine linkage, we adopted a conservative cut-off value of 5% linked molecules, i.e., linkage must be greater than 5% for SNPs over 100 kb apart to be called as linked. Additionally, the difference between the positive and negative control duplex reactions must have at least a 5% linked molecules difference. For example, interrogated SNPs are determined to be linked, if the rs16947varT+rs5758550varG duplex reaction yields e.g., 10% linked molecules and the rs16947refC+rs5758550varG duplex reaction yields 0.3% linked molecules and thus differ by >5% between the two reactions. The SNPs are not called linked if the duplex reaction yields e.g., 4.5% linked molecules and the rs16947refC+rs5758550varG duplex reaction yields 0.5% linked molecules.Assay conditions linking the “enhancer” SNP with 1847G>A were identical to those described for 2851C>T.(https://www.10xgenomics.com). Publicly available 10X Genomics Linked-Read data from the Illumina HiSeqX-PGx Cohort were used as proof-of-concept for phasing the “enhancer” SNP with CYP2D6 haplotypes, i.e., selected star allele-identifying core SNPs. Data were obtained at https://github.com/Illumina/Polaris/wiki/HiSeqX-PGx-Cohort and analyzed with Long Ranger v2.2.2 and Loupe software v2.1  against the GRCh37 reference genome.The 10X Genomics Linked-Reads platform allows phasing of sequence variations across >10 Mb haplotype blocks CYP2D6 haplotypes  carry the “enhancer” SNP, a dataset comprising genotype information from 3,162 samples (based on 15 SNPs and a surrogate “SNP” representing the CYP2D6*5 gene deletion) was subjected to computational haplotype phasing. Phasing was performed on all samples  and by ethnicity/race (n=1482), European , and n=609, African . Haplotype predictions and frequencies for all three datasets are summarized in CYP2D6*5, *29, and *41 haplotypes, depending on whether phasing was performed on the entire multiethnic cohort or within their racial/ethnic group. For example, separately phasing the European and African datasets suggested that only one of the alleles phased as CYP2D6*29 has the “enhancer” SNP. In contrast, when phasing was done on the entire cohort, 16 alleles were predicted to have the “enhancer” SNP on a CYP2D6*29 allele. However, as described below, experimental phasing with DropPhase2D6 ultimately demonstrated that the “enhancer” SNP was not located on the CYP2D6*29 allele in this sample.In order to determine which CYP2D6*2 core SNP  including CYP2D6*2, *17, and *35 among others (CYP2D6*2 in the European subset were predicted to carry rs5758550 “G” (95.8%), only 71.3% of samples with African ancestry were predicted to carry it , a finding that needs to be viewed with caution. For example, since CYP2D6*1 is a default assignment  the number of true *1 alleles carrying the “enhancer” SNP may be lower. Similarly, CYP2D6*11, *12, *19–*21, *59, and numerous other haplotypes were defaulted to a CYP2D6*2 assignment which likely contributed to an over-estimation of true *2 alleles carrying the “enhancer” SNP.Phasing revealed that the “enhancer” SNP is most often found on haplotypes carrying the g others . HoweverCYP2D6*2 alleles. As shown in CYP2D6*1 alleles do not have either SNP, while the majority of CYP2D6*2 alleles possess both. This table provides predicted frequencies stratified by allele and the presence of −1584C>G (rs1080985) and/or rs5758550 (“enhancer” SNP). For example, within dataset_E (Europeans) 509 alleles were called CYP2D6*2 by the PHASE algorithm of which four (0.7%) were predicted to not have −1584C>G or the “enhancer” SNP, 17 (3.3%) were predicted to have −1584C>G, 11 (2.1%) were predicted to have the “enhancer” SNP, and 477 (93.7%) were predicted to have both SNPs. It follows that the cumulative predicted frequency of CYP2D6*2 alleles with the “enhancer” SNP is 95.8%.Another SNP of interest was −1584C>G (rs1080985) which in the past has been implicated to play a regulatory role for CYP2D6 function . This SNCYP2D6*2 alleles carry the “enhancer” SNP as well as the SNP at position -1584. Hispanics (n=176 subjects), East Asians (n=98 subjects), and subjects of Indian ancestry (n=210), 69/352, 26/196, and 77/410 alleles were phased as CYP2D6*2 with 62 (90%), 18 (69%), and 74 (96%), respectively and predicted to carry both SNPs. In African Americans, however, only 42% of CYP2D6*2 alleles are computationally predicted to have the “enhancer” SNP as well as −1584C>G. Differences were also observed for other alleles between these populations and our larger European and African-ancestry cohorts (not shown). These predictions are, however, estimates due to the small numbers of alleles observed with or without the “enhancer” SNP and HMW DNA was not available for experimental confirmation.For other ethnic groups it was also observed that the majority of To establish DropPhase2D6, assays were tested using six Coriell DNA samples with known genotypes for rs5758550 (“enhancer” SNP) and rs16947 (When interrogating NA20360 (homozygous variant), using the rs16947varT+rs5758550varG duplex reaction .CYP2D7 gene is unlikely since no other cluster(s) were present in NA19663 (or other samples with the same genotype) , 75 kb (rs5758562 “C”), and the “enhancer” SNP at over 116 kb (rs5758550 “G”)  the FAM-labeled assay for rs16947 “C” (reference) was paired with the VIC-labeled assay for rs5758550 “G” (variant). This duplex reaction served as a negative control, while the rs16947varT+rs5758550varG duplex reaction detected linkage for sample HG00594. Furthermore, no linkage signal is observed for the rs16947varT+rs5758550varG duplex reaction when the DNA was pretreated with EcoRI. Thus, the combined results of these two duplex assays corroborate that rs16947 “T” and rs5758550 “G” are cis-configured, while rs16947 “C” and rs5758550 “G” are trans-configured  , ambiguous phase call and/or was genotyped for a rare allele e.g., CYP2D6 haplotypes or have unique haplotypes regarding the “enhancer” SNP (Coriell IDs were kindly provided by Danxin Wang)   were phased together, 16 of the 129 CYP2D6*29 alleles were predicted to have the “enhancer” SNP. In contrast, when phasing was performed using separate datasets, the European (dataset_E) and African ancestry (dataset_A), PHASE analysis predicted only one CYP2D6*29 allele to carry the “enhancer” SNP. We attribute the observed differences in the phasing results to population-specific haplotype structures. As shown in CYP2D6*29 was experimentally confirmed by DropPhase2D6 in eight samples genotyped as compound heterozygotes for the *29 and “enhancer” SNPs (HMW DNA was not available for the other eight samples). Of particular interest is that DropPhase2D6 linked the “enhancer” SNP to the CYP2D6*4 allele of a *4/*29 sample, while PHASE predicted it to be on the *29 allele. This was unexpected, since only one of the 971 CYP2D6*4 alleles was predicted to have the “enhancer” SNP. The CYP2D6*4 allele was sequenced and found to match the *4.004 suballele definition. It remains to be seen, however, whether this suballele always carries the “enhancer” SNP.Computational phasing can be utilized to infer CYP2D6-“enhancer” haplotypes present in a population and their frequencies . These lCYP2D6*2 allele carries −1584C>G and the “enhancer” SNP. Also, worth highlighting is that the converse appears to be true for CYP2D6*41, i.e., the vast majority are predicted to lack −1584C>G (rs1080985) and the “enhancer” SNP. Phasing also revealed that a significant number of the CYP2D6*5 gene deletion alleles among the European and African ancestry cohorts have the “enhancer” SNP  and that CYP2D6*1 alleles with the “enhancer” SNP are predominately found among samples with African ancestry , for which phasing predicted the “enhancer” SNP to be on their CYP2D6*2 allele  because these alleles are not routinely genotyped . Thus, the number of true *1 alleles carrying the “enhancer” SNP is likely lower. Similarly, CYP2D6*11, *12, *19, *21, *59, and numerous other haplotypes were defaulted to a CYP2D6*2 assignment which likely contributes to an over-estimation of true *2 alleles carrying the “enhancer” SNP. A limited number of samples have been genotyped for additional variants revealing, among others, two samples heterozygous for the CYP2D6*59 allele (CYP2D6*1/*59 and *4/*59). DropPhase2D6 unequivocally linked the “enhancer” SNP with the CYP2D6*59 allele. Another two samples for which HMW DNA was available were genotyped as CYP2D6*1/*45; one had the “enhancer” SNP on the *45 allele while the other had it on the *1 allele , 2851C>T (rs16947), and the “enhancer” SNP (rs5758550) are highly linked in some populations, but not others. Therefore, one cannot necessarily assume that each CYP2D6*3, *4, *6, *9, *10, *29, and *45) or almost always be present on others . However, these are predictions and should be viewed with caution. A case in point is CYP2D6*4. We experimentally confirmed the “enhancer” SNP on the CYP2D6*4 allele in one sample; however, this was not the subject predicted by PHASE. Although CYP2D6*4 alleles with the “enhancer” SNP appear to be rare, their frequency may be under-estimated. Thus, it is important to realize that computational phasing, while being a powerful tool, does not necessarily accurately predict on which allele the “enhancer” SNP is located.Our computational phasing data suggests that the “enhancer” SNP may not, or only in rare cases, be present on certain haplotypes  and freeze-thaw cycles.HMW DNA is a prerequisite for long-distance phasing. To support this study we sought to identify a method that produces DNA of the required integrity from a broad range of biological samples including whole blood, white blood cells and tissue (used fresh or frozen). Of the tested commercially available products, the dialysis-based Since existing and previously handled Coriell DNA samples produced highly variable linkage results or failed altogether, selected DNA samples were re-purchased and handled as described for HMW DNA. Despite these precautions, less than half produced results surpassing the 5% linkage threshold. While all DNAs were of “high quality” (per gel electrophoresis), their integrity was not quite sufficient for long-distance linkage analysis. These challenges could be overcome, however, with a new DNA isolation method the Coriell Institute is planning to offer in the near future.CYP2D6 haplotypes. The typical turnaround time including DNA isolation is approximately 24 h.Experimentally linking SNPs over distances exceeding those that can be amplified by long-range PCR (up to approximately 20 kb) remains a challenge. Although technologies such as 10X Genomics Linked-Reads (discussed in more detail below) are establishing themselves as powerful tools, cost is a major barrier for their use in routine clinical and research applications and the vendor has recently announced that support for the platform will end in July, 2020. ddPCR is increasingly utilized for DNA and RNA quantification, detection of tumor-derived DNA and pathogen detection to name a few , but cani.e., the number of droplets producing both FAM and VIC signals (orange cluster) does not exceed or barely exceeds what would be expected by chance. Since there is no standard procedure, we utilized a conservative cut-off value of 5% difference between the percent linkage observed and respective negative controls to call linkage  and rs16947 . These tCYP2D6*2 alleles and many others) or rs3892097 (present in CYP2D6*4) with the “enhancer” SNP (rs5758550) could also be viewed as a limitation. Indeed, linkage could not be established for samples which are homozygous (reference or variant) for rs16947, e.g., samples with a CYP2D6*17/*41, *17/*29, or *1/*9 genotype. However, concordant DropPhase2D6 assay results lends credibility to the approach. We are currently validating additional probe sets that will allow us to analyze samples which have eluded analysis with the probe sets described in this report.One particular limitation for ddPCR-based long-range linkage analyses such as DropPhase2D6 is the availability of HMW DNA, which may prevent re-analysis of samples for which there are no source materials  available to prepare DNA of sufficient integrity. The fact that the development of DropPhase2D6 was limited to assays linking rs16947 (present in CYP2D6*2 allele and a given star allele can occur with and without the “enhancer” SNP. Additionally, there are substantial differences among populations in the frequency of alleles with and without the “enhancer” SNP. Thus it remains to be seen, whether the inclusion of this SNP will improve phenotype prediction from genotype data. Furthermore, it has also been proposed that testing the “enhancer” SNP in lieu of SNPs that identify particular star alleles is superior over current genotype approaches. DropPhase2D6 will allow us to map the “enhancer” SNP to star alleles, which is essential for future studies assessing the relationship between CYP2D6 genotype and activity.In conclusion, the “enhancer” SNP is not confined to the All datasets generated for this study are included in the article/The studies involving human participants were reviewed and approved by the Children's Mercy Hospital Internal Review Board. Written informed consent to participate in this study was provided by the participants.AG and SL conceived the study. AG, EB, and RG designed the study and assays. EB and WW conducted experimental work. AG, EB, RG, NM, and WW analyzed data. MC and AB contributed materials and data. AG, EB, WW, RG, and NM wrote the manuscript. All other authors provided critical feedback.This work was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development grant U54 HD090258-01. The Liver Tissue Cell Distribution System is funded by the National Institutes of Health [Contract N01-DK-7-0004/HHSN267200700004C]. The project, entitled “Laboratory of Developmental Biology,” was supported by an award from the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health [Award 5R24HD0008836].The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


MXD3 mRNA expression levels in various cattle tissues comprising heart, liver, spleen, lung, kidney, Longissimus dorsi muscle and subcutaneous fat in Chinese Qinchuan and Xianan cattle breeds. The RT‐qPCR data showed that the MXD3 gene was variably expressed between all tissues and at levels that were significantly different between two breeds (p < .05). We used the polymerase chain reaction‐restriction fragment length polymorphism (PCR‐RFLP) method to investigate the possible association between single‐nucleotide polymorphisms (SNP) within the MXD3 gene and five different growth traits in cattle. We found two intronic SNPs (g.2694 C>T and g.3801 T>C) and one SNP in 3′untranslated region (3′UTR) (g.6263 G>A) of MXD3 gene. Association analysis revealed strong associations between pairwise and triple SNP combinations and the growth traits. Based on these results, we suggest that MXD3 polymorphisms could be useful as molecular markers in the Chinese beef cattle breeding program.Max dimerization protein 3 (MXD3) belongs to the MYC superfamily of basic helix‐loop‐helix leucine zipper transcription factors, and MXD3‐MAX heterodimers can bind to promoters of target genes to modulate their expression. The aim of this study was to determine the  MXD3 and its expression level in various tissues of cattle.Three single‐nucleotide polymorphisms in  MXD3 gene was also enriched in the Gene Ontology (GO) terms of DNA binding, protein binding, negative regulation of transcription, DNA‐dependent and protein dimerization activity  and XN cattle  were collected from two farms: the farm of Fufeng country in Shaanxi and the farm of Nanyang city in Henan provinces of China, respectively. All of the individuals were females. Five growth traits for association analysis were recorded at the age of 2 years old, females with no pregnancy both in QC (n = 141) and XN (n = 170) cattle following Gilbert's method , body length , body height , chest circumference  and hip cross height . The genomic DNA from each cattle was purified from 2% heparin‐treated jugular blood samples and diluted to 50 ng/µl and subsequently stored at −20°C for further usage following the standard procedures , three mutations within bovine MXD3 were found. Accordingly, pairs of primers were designed to genotype the mutations based on restriction fragment length polymorphism (PCR‐RFLP) method using restriction enzyme Taq I, Hha I and Pvu II (Thermo Fisher Scientific) (Table Using the bovine genome sequences in GenBank (Accession No. AC_000164), seven pairs of primer were designed by Primer v5.0 software  Table . Genomicc) Table .2.3longissimus dorsi muscle and subcutaneous fat) were collected from three QC and three XN cattle when they were slaughtered at the cattle plant. After washing with phosphate buffered saline (PBS), samples were put immediately into liquid nitrogen and stored at −70°C for the subsequent steps; each tissues had three replicates. Next, Trizol reagent  was used to extract the RNA. The RNA purity and concentration were determined using a NanoDrop 2000 spectrophotometer and we randomly chose three samples to check the purified RNA by 0.8% agarose gel electrophoresis  , ACTB (beta actin), RPL19  and EMD (emerin) were selected as the most appropriate reference genes and the relevant primers used in qRT‐PCR were shown in Table r2) was between 0.9312 and 0.9992, whereas the slope was −1.922 to −2.925 , expected heterozygosis (He) and polymorphism information content (PIC) were analysed based on Nei's methods   in 311 samples including QC (n = 141) and XN (n = 170) cattle at the same age (2 year‐old). The preliminary statistical analyses indicated that birth years, seasons and farms did not have significant associations with growth traits, so we used two‐way analysis of variance (ANOVA) for the five growth traits in 2‐year‐old QC and XN cattle. The models were as follows:Y is the growth trait, μ is the overall mean of each trait, Breed is the cattle breed (i.e. QC and XN cattle), Marker is the SNP  and the pairwise interacted SNP  and e is the residual error. Multiple comparisons were performed for SNPs and their interactions using least significance difference (LSD) method based on R package agricolae (version 1.3‐1).Hardy–Weinberg equilibrium (HWE) was tested using the Predictive Analytics Software (PASW) (version 18) based on the chi‐squared test. Population genetic coefficients including effective allele numbers (33.1MXD3 is located at position ~40.1 Mb on Bos taurus autosome (BTA) 7 and consists of 6 introns and 7 exons. In this study, we identified three SNPs, i.e. g.2694 C>T  in intron 3, g.3801 T>C  in intron 5 and the g.6263 G>A , which was located in the 3′‐untranslated region (3′‐UTR) within the MXD3 . In addition, He and Ne were approximately 0.5 and 2.0, respectively, and the PIC values were close to 0.37, indicating medium genetic diversity in the MXD3 locus in the QC and XN populations , whereas in XN cattle strong LD only occurred between g.2694 C>T and g.3801 T>C loci ((D′ = 0.87 and r2 = 0.44). Haplotypes often provide more reliable information than a single marker  based on an ANOVA analysis, and in general, the XN cattle showed better performance than QC cattle . For example, extremely significant associations were detected between SNP1‐SNP2 and BL, BW and HCH (p < .001). Also, significant associations were observed between SNP1‐SNP2 and the two growth traits, BH and ChC (p < .05). In addition, the CCTC combined genotype of SNP1‐SNP2 demonstrated higher performance than other combinations  A geometric mean of the two most stably expressed genes (RPL19 and EMD) combined as suggested by Vandesompele et al.   Usi4MXD3 gene and evaluated the possible association of sequence variants with growth traits in XN and QC cattle. The MXD3 plays important regulatory roles in physiological processes such as cellular proliferation and differentiation and mutations in MXD3 are associated with tumorigenesis, i.e. leukaemia and medulloblastomas  and excavated the potential relations between three SNPs and cattle growth traits. Our data showed ubiquitous expression of MXD3, yet at highly variable levels, in all the tissues that we investigated. Importantly, the expression data were obtained using two different approaches for accurate normalization. Thus, many commonly used housekeeping (HK) genes employed in qRT‐PCR studies often exhibit tissue‐dependent variation  in intron 1 of the growth hormone‐releasing hormone (GHRH) gene, which was significantly associated with body weight in QC cattle  and one SNP (g.6263 G>A) in the 3′UTR of The detection of gene variants associated with economically important traits has provided insights into the genetic architecture of complex traits and diseases in cattle (Bouwman et al., The authors declare that they have no conflict of interest.DH performed the experiments, analysed the data, and DH and BT drafted the manuscript. JSB and XW improved the manuscript. SJP, XYL and YZH developed and collected the samples. HC and XW conceived and designed the study, reviewed the study and supervised the research.All animal procedures were carried out according to protocols approved by the College of Animal Science and Technology, Northwest A&F University, China. Click here for additional data file.