The mean r2 value was 0.43, if pairwise distance of marker was less than10 kb and it decreased further to 0.21 for 25–50 kb markers distance. Further, the effect of minor allele frequency and sample size on LD estimate was investigated. The LD value decreased with the increase in inter-marker distance, and increased with the increase of minor allelic frequency. The estimated inbreeding coefficient and effective population size were 0.04, and 46 for present generation, which indicated small and unstable population of Vrindavani cattle. These findings suggested that a denser or breed specific SNP panel would be required to cover all genome of Vrindavani cattle for genome wide association studies (GWAS).The objective of this study was to calculate the extent and decay of linkage disequilibrium (LD) in 96 crossbred Vrindavani cattle genotyped with Bovine SNP50K Bead Chip. After filtering, 43,821 SNPs were retained for final analysis, across 2500.3 Mb of autosome. A significant percentage of SNPs was having minor allele frequency of less than 0.20. The extent of LD between autosomal SNPs up to 10 Mb apart across the genome was measured using r Linkage disequilibrium (LD), defined as the degree of non-random association of allele between loci or correlation between genotypes of markers, is an important concept in the understanding of gene mapping and its application in genetic studies. The LD between markers provides insight in exploring the level of diversity between different breeds, inferring the frequency of recombination events, investigating the change in effective population size across generations, and identifying genomic regions to improve economically important traits –3. The pr2 = 0.20) were extended to <100 kb, which indicated that 50K SNPs captures most of the LD information necessary for GWAS in taurine breeds [r2 = 0.20 to 0.34) was observed lower (<30kb) within indicine cattle, which indicated use of High density chip for genomic studies in these cattle [The declining cost of high throughput genotyping provides a new opportunity to explore genome-scale studies, especially Genome Wide Association Studies (GWAS) and Genomic Selection (GS). The accuracy and precision of the genomic studies rely on the level and pattern of linkage disequilibrium (LD) between markers across genome . The LD e cattle . The redtaurus cattle  and Bos indicus cattle (Hariana) [2) and estimate the effective population size in crossbred Vrindavani cattle using a 50K Bovine chip.Vrindavani is four breed synthetic crossbred cattle strain developed in India by crossing between Bos Hariana) . RecentlHariana) . FurtherHariana) . HoweverHariana) , and givA total of 96 female Vrindavani crossbred cattle were selected from ICAR-Indian Veterinary Research Institute (IVRI), Izatnagar, Bareilly (Uttar Pradesh), India. The experiment was approved by the Institutional Animal Ethics Committee (IAEC) of ICAR-Indian Veterinary Research Institute (IVRI). DNA was extracted from blood samples and genotypic data were generated using the Bovine 50K SNP Bead Chip v2 .−05 were removed. Only SNPs that were uniquely mapped to autosomes were included in the analysis. A total of 43,821 markers were retained for further analysis. The length of chromosome (Mb), number of markers on individual autosome and the mean distance between markers were estimated using R software [The HiScan images and genotypes were analysed using Genome Studio software (Illumina). The genotyping rate was 0.99, and a total of 53,218 SNPs were identified across the genome at a mean distance of 37.4 kb between markers. Quality control (QC) was performed using the PLINK v1.9 software . The SNPsoftware .The MAF for all autosomal SNPs was calculated using PLINK v1.07 as default settings using command—file data—freq . The disFi is the estimated inbreeding coefficient of the iih animal; Oi is the number of observed homozygous loci in the iih animal, Ei is the number of expected homozygous loci and Li is the number of genotyped autosomal loci.Inbreeding coefficient (F) was calculated as a function of the expected and observed homozygote difference using PLINK v1.07 . The equNe, linkage disequilibrium represented by r2, and recombination rate (c). This is given by:T is the effective population size t generations ago, ct is the recombination rate, r2adj is the LD value adjusted for sample size, and α is he correction for occurrence of mutation.The Effective population size (Ne) was estimated using the SNeP tool , based o2) was used to evaluate the extent of LD measure, as r2 was considered is considered as more robust and not influenced by change in allele frequency and population size [2 estimate is an important estimator to measure loci required for genome wide association study and QTL mapping [2) estimate is represented as follows:A1, PA2, PB1 and PB2 are frequencies of each allele at loci A and B, and P11, P12, P21 and P22 are the frequencies of haplotype A1B1, A1B2, A2B1, A2B2, respectively.The square of the correlation coefficient between two loci , five different subset of population  were randomly selected from the total population and the extent of LD was explored for each sub-population. To visualize the effects of MAF and sample size on the genome-wide LD (r2) levels was also plotted accordingly.Further the effect of minimum allelic frequency (MAF) and sample size on the extent of LD was investigated. The LD was calculated as previously described with four different MAF thresholds  for physical distance of 10 Mb of genome. In order to determine the effect of sample size on LD was found to be longest, while the BTA25 (42.80Mb) was shortest across the genome. The distribution of SNPs was proportional to length of chromosome; the highest number of SNPs were on BTA1 (2798), and the lowest on BTA25 (792). The average distance between SNPs was 57.24 kb, where the longest distance between SNPs was 3.26 Mb on BTA10 and shortest distance was 0.01 kb on BTA15. The descriptive statistical results of the SNP marker for each autosome are shown in The mean minor allele frequency (MAF) across all autosomes was 0.26 . After f2 (e for Vrindavani. The estimate of mean inbreeding coefficient (F) was 0.04.The effective population size was estimated over the past 50 generations from the average r2 . The est2 method. The free recombination generally occurs at a physical distance of more than 10 Mb [2 of markers for distance of 10 Mb was 0.07. The mean LD (r2) for physical distance intervals is presented in 2. For the physical distance of less than 10 kb, the average r2 was 0.43 and it decreased to 0.21 for the distances of 25 to 50 kb. Results revealed the decline in mean r2 value with the increase in physical distance between markers.The level of LD among SNPs was estimated by ran 10 Mb  thus, th2) was found different across each autosomes according to the physical distance. The mean r2 was estimated for markers physical distance separated by intervals of 0–10 kb, 10–25 kb, 50-100kb, 100-500kb, 0.5-1Mb, 1-5Mb, and 5–10 Mb per autosome , and increases significantly at higher MAF threshold. The value of mean r2 ranged from 0.45 to 0.06 for MAF > 0.05, 0.49 to 0.07 for MAF> 0.10, 0.53 to 0.07 for MAF> 0.15, and 0.57 to 0.08 for MAF> 0.20, respectively.The effect of MAF on LD extent was estimated using four different threshold level of MAF: >0.05, >0.10, >0.15 and 0.20 for physical distances up to 100kb between the SNP pairs . Results2) extent . These findings indicated that the size of samples should be at least 50 animals for precise estimation of r2.In this investigation, sample size (n) of 10, 25, 50, 75, and 90 were selected at random from the total population to assess the effect of sample size on LD (r) extent . The ave2) between SNPs was high, when the threshold of MAF was high (>0.20), particularly at smaller distance. Similar effect of allele frequency on r2 estimate was also observed in other cattle breeds [After quality control, a total of 43,821 autosomal SNPs retained, which were distributed across 2500.30 Mb region of Vrindavani genome. The average MAF value was 0.26, which is comparable with the value reported for different cattle breeds . Therefoe breeds , 18, sug2 method as it is less influenced by low allele frequency [2 value was 0.43 which was lower as compared to the LD estimates previously reported for taurine breed, Angus (0.46) and Hereford (0.49), but higher than the value reported for indicine breeds of Brahman (0.25), Nellore (0.27) cattle [2 for was highest for BTA16 and BTA24 and lowest BTA18 and BTA15. However, no relationship was observed between chromosomal size and r2 estimate [2), different sample size was used to calculate r2. Khatkar et al. [2, which is consistent with the previous study [The LD between markers was estimated using rrequency  and smalrequency . At inteestimate . To explus study .2 at distance more than 100kb for all autosomes. Moreover, the LD estimate decreased from 0.43 to 0.21 at 10kb and 50kb marker distance, respectively. Similar level of LD estimates at 40-60kb have been reported for taurine cattle breeds [To analyse the LD decay, the physical distance between markers were classified in different interval. The results showed rapid decay of re breeds , 20. How2 = 0.2, where 2.87GB is the bovine genome size) SNPs were required to capture LD information for genomic studies. In our study, the LD (r2) estimate was 0.21 at distance of 50 kb between SNPs. This indicates that a minimum of 57,400 (2.87 GB/ 50kb at r2 = 0.2) SNPs is required for genomic association studies in Vrindavani cattle. Previous studies have also indicated the requirement of large number of SNPs to cover the genome for genomic studies when analysing data from indicine, crossbred and multi-breed populations [The reliability and accuracy of genomic association studies and genomic prediction is dependent on the amount of LD between marker. Mckay et al.  reportedHOM analysis [e was 40 in Vrindavani, and a decreasing trend of Ne was reported for over five generations. The FAO has recommended Ne should be at least 50 for maintaining proper breeding plan. In the current study, 5 generations ago the estimated Ne (46), was below the recommended number (50) of FAO. The low Ne estimate may be attributed to the small well protected population size and fluctuating breed ratio in the admixed Vrindavani population [For better understanding of the population structure, the inbreeding coefficient and the effective population size were estimated. The inbreeding coefficient was 0.04, which was comparable to previously reported 3% and 6% inbreeding coefficient in Vrindavani population using ROH and Fanalysis . The curpulation .2> 0.2 extended up to 50 kb indicates requirement of high density SNP panel for precise and accurate estimation of whole genome association studies in Vrindavani cattle. Furthermore, declining trend of Ne estimates was observed in Vrindavani population indicates the requirement of breeding plan that could maintain the sufficiently large Ne. However, further confirmatory investigating for the extent of LD and effective population is required in larger population using high density array of SNPs.This study reported the magnitude of LD between markers crossbred Vrindavani cattle using 50k Bovine Chip. The estimated r


Oncorhynchus kisutch). In both natural-origin and hatchery mate pairs, we observed more SNPs with negative assortment than positive assortment. However, only 3% of the negative assortment SNPs were shared between the two mating groups, and 1% of the positive assortment SNPs were shared between the two mating groups, indicating divergence in mating cues between wild and hatchery-raised salmon. These findings shed light on mate choice in general and may have important implications in the conservation management of species as well as for improving other captive breeding scenarios. There remains much to discover about mate choice in salmon and research described here reflects our intent to test the potential of ongoing advances in population genomics to develop new hatchery practices that may improve the performance of hatchery offspring, lessening the differences and thus potential impacts upon wild stocks.Captive breeding can affect how sexual selection acts on subsequent generations. One context where this is important is in fish hatcheries. In many salmon hatcheries, spawning is controlled artificially and offspring are reared in captivity before release into the wild. While previous studies have suggested that hatchery- and natural-origin fish may make different mate choice decisions, it remains to be determined how hatchery fish may be making different mate choice decisions compared with natural-origin fish at a genetic level. Using genotyping-by-sequencing, we identify single-nucleotide polymorphisms (SNPs) associated with variation in mate pairings from a natural context involving hatchery- and natural-origin coho salmon ( Drosophila—Recent evidence suggests that captive breeding can affect mating preferences among offspring  and thatOncorhynchus). Pacific salmon are a culturally and economically important species in many places in the northern hemisphere. Unfortunately, salmon stocks have been declining as the result of human activity that increases coastal pollution and habitat destruction in traditional salmon production regions. In an attempt to supplement declining salmon runs, stocks have been bred in fish hatcheries on the western coast of North America since the 19th century. While there are many positive aspects to improvements in hatchery practice that have occurred over the last two centuries, there remain some concerns about these salmon hatcheries including, but not limited to, the observed reduction in reproductive success of hatchery fish  is a culturally, ecologically and economically important species with strong commercial and recreational fisheries on the west coast of North America, Alaska, and Russia. Coho are anadromous and semelparous. Adult males and females as well as precocious jacks, which are males who sexually mature a year earlier than other males and females, return to their natal sites to reproduce. The semelparous nature of their mating system and their fidelity to spawning grounds facilitate measurement of overall lifetime reproductive success. Both males (Oncorhynchus tshawytscha)    and Atlao salar) .Here, we use a previously established pedigree of both hatchery- and natural-origin coho salmon from the Umpqua River in southern Oregon  and genoIn this study, we test for different patterns of nonrandom mating in the offspring (F1) of controlled crosses that were released into the wild and of natural-origin fish. The controlled crosses involved both wild- and hatchery-derived parents. We used reduced-representation SNP data to test hypotheses of positive vs. negative assortment of F1 mate pairs that were the product of wild parents or hatchery parents. Lastly, we used a reference genome to annotate subsets of positively and negatively assorting SNPs.n = 100 in 2002 and 2003) or natural-origin × natural-origin fish (n = 100 in 2002 and 102 in 2003). All crosses had unique parents. The resultant offspring were reared in the hatchery and then released into a tributary of the Umpqua  either as fry or as 1-year smolts. After a year in freshwater, coho salmon migrate to sea for 2 years before returning as 3-year-old adults. Some males sexually mature after only a year at sea and return to their natal streams as 2-year-old jacks.Our approach takes advantage of a previous set of controlled crosses with hatchery-raised and wild fish, natal spawning behavior, and sampling and genotyping over two generations . CrossesThe F1 of these crosses were sampled in 2005. Fish returning to Calapooya Creek  were caught in a fish trap at the base of Nonpareil Dam on Calapooya Creek. Returning fish included offspring (F1) from the mate crosses performed in 2002 (adults) and 2003 (jacks) as well as the offspring of any wild matings in those years (The parentage of the F1 (2005) and F2 (2007 and 2008) generation  was deten = 36). Minimum concentration of starting DNA was 4.00 ng/μL. The amount of DNA across samples was normalized to 100 ng. For the remaining samples, DNA was digested using an enzyme combination of PSTI (6-cutter) and MSPI (4-cutter). After fragmentation, a unique barcode for identification of individual samples and a common adapter were ligated to the DNA fragments before pooling. Amplification of fragments and sequencing (150 base pairs [bp] paired-end reads) was performed using Illumina HiSeq 3000 . Read quality was assessed using FastQC.DNA was extracted from samples of the F1 generation using a Qiagen  kit as per the manufacturer's instructions. Subsequent GBS was performed at the Center for Genome Research and Biocomputing at Oregon State University. Extracted DNA was quantified using dsDNA fluorophore and tested to be of suitable molecular weight for analysis and any samples that did not contain enough DNA for GBS were discarded  using BWA v. 0.7.12 Next, raw reads were processed through the STACKS v. 2.41 pipeline  a locus must be present in to process a locus was 1 and the minimum percentage of individuals in a population (r) required to process the locus was 0.8. Data analysis was restricted to the first SNP per locus. We did not include in the analysis individuals for which the number of reads in the bam file was less than 1,000,000 reads . This left 814 individuals for further analyses.Alignments were made to the indexed reference genome using the After SNPs were called, we used a modified version of the program of vcftools to further filter data. First, we removed 36 individuals that had more than 20% missing data. We excluded 10,204 sites where the mean depth was less than 5 or exceeded 30 over all individuals. Finally, three sites where the proportion of missing data was greater than 80% were excluded. After filtering, 15,451 sites remained. No sites were removed for being out of Hardy–Weinberg equilibrium . To ascertain whether individuals were mating nonrandomly at each given SNP, and whether an individual's recent ancestry  affected mate choice pairings relative to each SNP, we first converted genotype information of each individual to minor allele dosage, which is the probability that any allele selected at random will be the minor allele. For any given locus, this probability was assigned 0 if an individual did not possess a minor allele, 1 if they had one minor allele, and 2 if they had two minor alleles. Next, we compiled all unique mating combinations of the F1 generation  to identify any genes associated with SNPs related to different mating patterns between hatchery- and natural-origin fish. We only annotated genes that had a SNP within the gene itself. We did not identify upstream or downstream genes.Finally, we used the coho reference genome at the National Center for Biotechnology Information (NCBI) , there were more SNPs that showed negative assortment than positive assortment, and there were relatively few SNPs shared between categories of mate pairs . PositivFor mate pairs of natural origin, there were 838 SNPs where the average number of shared alleles among mate pairs was less than the 2.5th percentile of the null model, consistent with a greater degree of disassortative mating than would be expected by random mating . In contFor mate pairs of hatchery origin, there were 728 SNPs where the average number of shared alleles among mate pairs was less than the 2.5th percentile of the null model, consistent with a greater degree of disassortative mating than would be expected by random mating . In contOverall, our results suggest support for the theory of mate choice for heterozygosity in both natural-origin and hatchery-origin mate pairs, at least among SNPs we identified. While positive assortment based on phenotypic characters is relatively common in animals see , there iAlthough the majority of natural-origin mate pairings showed negative assortment at our identified SNPs, there were SNPs where natural-origin mate pairs demonstrated positive assortment. Notably, there was at least one immune-relevant marker, class I histocompatibility antigen, F10 alpha chain-like, where we did not observe any evidence of mate choice for heterozygosity, but saw assortative mating in natural-origin, but not hatchery-origin mate pairs ; howeveGasterosteus aculeatus), male mating success is affected by both the temperature of the parental (F0) and offspring's (F1) developmental environment. Specifically, a mismatch in those environments resulted in lower reproductive success of the F1 generation (Ours is not the first study to identify a potential role of captive breeding in subsequent mate choice. neration .Coho salmon use phenotypic traits that may be physical or behavioral when choosing their mates (see Understanding mate choice in captive-bred species has been identified as important for establishing and maintaining successful conservation and captive breeding programs . Becauseobab020_Supplemental_FilesClick here for additional data file.


T. aestivum L.) as the most grown crop worldwide is of utmost importance for global food security. Thousand kernel weight (TKW) in wheat is closely associated with grain architecture-related traits, e.g. kernel length (KL), kernel width (KW), kernel area (KA), kernel diameter ratio (KDR), and factor form density (FFD). Discovering the genetic architecture of natural variation in these traits, identifying QTL and candidate genes are the main aims of this study. Therefore, grain architecture-related traits in 261 worldwide winter accessions over three field-year experiments were evaluated.The future productivity of wheat . Further analysis reveals the four novel candidate genes potentially involved in more than one grain architecture-related traits with a pleiotropic effects e.g. TraesCS6A01G383800 gene on 6A encoding oxidoreductase activity was associated with TKW and KA. The allelic variation at the associated SNPs showed significant differences betweeen the accessions carying the wild and mutated alleles e.g. accessions carying C allele of BobWhite_c5872_589, TraesCS6A01G383800 had significantly higher TKW than the accessions carying T allele. Interestingly, these genes were highly expressed in the grain-tissues, demonstrating their pivotal role in controlling the grain architecture.Genome-wide association analysis using 90K SNP array in FarmCPU model revealed several interesting genomic regions including 17 significant SNPs passing false discovery rate threshold and strongly associated with the studied traits. Four of associated SNPs were physically located inside candidate genes within LD interval e.g. BobWhite_c5872_589  found to be inside These results are valuable for identifying regions associated with kernel weight and dimensions and potentially help breeders in improving kernel weight and architecture-related traits in order to increase wheat yield potential and end-use quality.The online version contains supplementary material available at 10.1186/s12870-021-03183-3. T. aestivum L.), the main source of protein and calories in human food is a major aim to ensure global food security. By 2050, the world population will reach 9800 billion and the annual rate of food demand will reach 1.6% surpassing the current annual genetic gains of this crop [Meeting the globally increasing demand for wheat  but new efforts are required exploring different environments and germplasm from different origins in order to enhance grain yield and quality.Exploration of crop genetic resources is useful to recognize sources of variation for agronomic and physiological traits and discovery of new alleles for improving yield potential, their components as well as grain quality traits , 29, 30.ectively , 34, 35,ectively , 36–38 aectively . Genome-ectively –43. Up tPpd-A1) at 2A (TRITD2Av1G019250), TRITD4Bv1G171270 at 4B encoding a Big Grain 1 protein, and two other candidate genes at 6B (TRITD6Bv1G005370 and TRITD6Bv1G005450) encoding an acid β-fructofuranosidase have been detected within QTL for KL and TKW in recombinant inbred lines (RILs) population [TRITD1Bv1G118820, TRITD2Av1G189400 and TRITD7Bv1G173200 encoding auxin response while TRITD4Bv1G175480 involved in auxin signaling and TRITD3Av1G012070, encoding for a Flavin-containing monooxygenase as an auxin biosynthesis. Moreover, candidate genes encoding for cytochrome P450 were found in most of the TKW QTL [In addition, most of what is known about genetic control of TKW had been carried out using bi-parental populations which present some limitations , 38, 44.TraesCS4A02G229100 which is an auxin-regulated gene involved in organ size and TraesCS4A02G2 corresponding to a polygalacturonase involved in carbohydrate metabolic process increasing kernel size and TKW [Other candidate genes as  and TKW  were fouTherefore, to gain deeper insights into the genetic basis of grain architecture-related-traits that could be of interest for the future improvement of grain yield and quality, a GWAS was undertaken in a diverse winter wheat panel of 261 genotypes tested during 3 years. For this purpose, 17,093 SNPs markers with recently known physical position were used to detect the most significant and effective SNP and for the identification of candidate genes underlying the studied traits. Our results showed 17 highly associated SNPs across 9 chromosomes of which four multi-traits associated SNPs were reported on chromosomes 1B, 2A, 5B, and 6A. These SNPs were located within the physical positions of candidate genes which are potentially associated with grain architecture-related traits and potentially involved in improving grain weight and size.The markers were distributed within the whole genome. The highest coverage of markers (51.5%) was on genome B with 8809 SNPs, genome A was covered by 38.6% of the whole markers with 6595 SNPs, while D genome presented the lowest coverage, 9.9% (1689 SNPs). The homoeologous group 1 chromosome had the highest number of SNPs (17.96%), while the chromosomes of group 4 presented only 7.02%. Chromosome 5B harboured the highest number of SNPs with 1784 markers, while chromosome 4D held only 46 SNPs , 146 genotypes from Eastern Europe-Western Asia (55.6%), and 42 North-American accessions (16%) Table . HeatmapG × Y (environment) interactions (p < 0.001) , 13.10–21.92 mm2 (2017), 12.81–21.75 mm2 (2018) and 13.26–21.52 mm2 (BLUEs). KDR ranged from 1.58 to 2.15 (2016); 1.60 to 2.15 (2017); 1.58 to 2.07 (2018) and 1.59 to 2.11 (BLUEs). BLUEs values for FFD ranged from 1.66 to 2.13. FFD varied between 1.58–2.12 (2016), 1.63–2.12 (2017) and 1.66–2.13 (2018)  varied from 31.06 to 60.16 g, showing ranges of 29.08 to 61.11 g (2016), 31.15 to 60.37 g (2017), and 31.04 to 63.05 g (2018) as shown in Table 8) Table . Boxplot8) Table .Fig. 1BP < 0.001) and positive correlation with KA (0.91), KW (0.82), KL (0.78), FFD (0.68) and a weaker association with KDR (0.13). The KA revealed a strong correlation with KL (0.90), KW (0.84) while weaker associations were detected for KDR (0.24) and FFD (0.33). The KL and KW presented a positive correlation (0.55), while a negative association was reported for KW and KDR (− 0.31) and KDR with FFD (− 0.14)  compared to those from Central and Northern Europe   and North America which presented the lowest values   presented higher TKW (48.18 g) and KA   was annotated as a protein of unknown function DUF1677 in rice (O. sativa). The candidate gene TraesCS2A01G136800 is located on chromosome 2A  and has a role in Heat shock protein DnaJ, cysteine-rich domain. The alleles of SNP Excalibur_c12169_1088  which is located within the interval of the previous candidate gene did not show a significant effect on the associated traits as shown in Fig. TraesCS5B01G274000 ; the carrying alleles are C and T where allele C affect significantly KA and KL (p < 0.05)  and encodes an oxidoreductase activity, acting on a sulfur group of donors, NAD(P) as acceptor. The SNP BobWhite_c5872_589 located within the gene  held C and T alleles, where allele C affected TKW significantly (p < 0.01) but had no significant effect on KA.The candidate gene TraesCS6A01G383800 shows the highest expression in most of the grain tissues  while genes TraesCS1B01G303200 and TraesCS2A01G136800 also showed high values. For their part, TraesCS5B01G274000 gene had very low expression compared with the other three but showed high expression values for embryo proper  which annotates Gliadin/LMW glutenin/Bifunctional inhibitor/plant lipid transfer protein/seed storage helical domain (Table Sp < 0.01) but had no effect on KA compared to allele T (Data not shown).Interestingly in Chromosome 1A, the SNP TA001286–0611-w showed a significant association with TKW and a weaker association with KA LOD = 3.18).18. This10 > FDR). FDR threshold was above than -log10 > 4.2 for the studied traits. Therefore, we reduced the number of spurious associations by only considering those SNPs, which exceeded the FDR. Markers were identified on chromosomes 1A (1), 1B (2), 2A (2), 2B (3), 3A (1), 4B (1), 5B (4), 6A (2), 6B (1)  threshold (−log (2), 2B , 3A (1),(1) Fig. .Fig. 3M10 > 5). The most significant marker (−log10 = 6.91) was Excalibur_c12169_1088 located in chromosome 2A and affected this trait by + 2.176 g (R2 = 5.2%). The phenotypic variation explained by markers (R2) ranged between 3.2 to 5.2% , 2A (1), 2B (2), 6A (1) and 6B (1). Four of these markers located on chromosomes 2A, 2B, 6A, and 6B showed highly significant associations , Excalibur_c12169_1088 on chromosome 2A  and wsnp_Ex_c1600_3051075 on chromosome 1B  were the most significant ones detected. Marker effects oscillated between 1.9 to 2.6% , 2A (1), 2B (1), 3A (1), 4B (1), 5B (1) and 6A (1) were reported. Six of these MTAs located on 1B, 2A, 2B, 3A, 5B and 6A showed -log2 reached values of 2.0 to 2.2%.Three MTAs related to kernel length were detected on chromosome5B  that showed high effects in these traits. R10, MAF, effect, related candidate genes, and their functions are shown in Supplementary Table No MTAs were detected for FFD, KDR and KL when the -log10 > FDR threshold was used. Conversely, several MTAs were reported between the -log10 > 3 and -log10 < FDR range: 5 (KA), 2 (FFD), 19 (KDR), 3 (KA), 1 (TKW), 23 (KL). Information about SNP detected for each trait, their physical position, −logFor all of these markers associated with the studied traits, we detected the closest candidate genes based on the physical position within local blocks of LD. Our results showed that four MTAs across four chromosomes are associated with more than one trait. Those MTAs were detected on 1B (TKW-KA), 2A (TKW-KA), 5B (KA-KL) and 6A (TKW-KA)  which can be used in further analysis.Our analysis detected 17 marker-trait associations related to grain architecture traits across 9 chromosomes, indicates that these are quantitative traits under polygenic control as was reported . These SIn this study, TKW among 261 winter wheat genotypes showed variability from 31 to 60 g and 7 MTAs related to this trait were detected on chromosomes 1A, 2A, 2B, 5B, 6A, and 6B. Previous studies identified QTL on chromosomes 1A , 2A 9, , 51, 2B In the same way, extensive genotypic variation and significant G × E interactions were reported for KA, KL, KW, FFD, and KDR. Although previous studies reported QTL associated with KA , 51, 56,The identification of underlying genes with annotations related to the grain architecture traits provides further reliability for the MTAs identified in the current study. Although high number of candidate genes have been detected, of which four HC candidate genes reported in our work have shown effects in other agronomical traits, none of them have been documented as effective on the size, shape, and weight of the grain and can be considered novel.TraesCS1B01G303200 (chromosome 1B) that annotated as Protein of unknown function DUF1677 (O. sativa) showed its influence on TKW and KA. Even though Gerard et al. [In this work, we found that the allelic variation at the locus of the gene d et al.  documentTraesCS2A01G136800 on chromosome 2A has a role in Heat shock protein (HSP) DnaJ, a cysteine-rich domain, which is a major protein in response to stresses  which is the most common domain of many nucleotide-binding protein folds. The energy from NTP hydrolysis is typically utilized to induce conformational changes in other molecules, which constitutes the basis of the biological functions of most P-loop NTPases. P-loop NTPases show substantial substrate preference for either ATP or GTP [The candidate gene P or GTP . AlthougTraesCS6A01G383800 (chromosome 6A) encodes oxidoreductase activity, acting on a sulfur group of donors, NAD(P) as acceptor. It has been reported that GSH-dependent protein-disulphide oxidoreductase (TPDO) increases the activity in a period of maximum synthesis of storage proteins in wheat grains, which is the third week after anthesis. There is a correlation between TPDO activity in maturing grains and dough extensibility as the enzyme reduces SS bonds in high-molecular-weight polymers [The last identified candidate gene polymers . This hipolymers  but no aTraesCS1A01G007200 gene located on chromosome 1A associated with TKW and with a weaker association with KA has a nutrient reservoir activity molecular function and annotates Gliadin/LMW glutenin//Bifunctional inhibitor/plant lipid transfer protein/seed storage helical domain. It is known that gliadin and glutenin proteins have a major role in grain quality determination [As was previously described, we found that the mination . Its rolmination , 74, itsTraesCS6A01G383800, respectively, showed the importance of allele C in increasing TKW, where most of the accessions carried allele C originated mainly from Central-Northern European countries.The highly significant SNPs within the candidate genes were analyzed using statistical comparison of alleles at each SNP with the associated traits. This approach have been used recently to explore the alleles for enhancing agronomic traits and abiotic tolerance , 75, 76.To our knowledge, our study involves the first GWAS analysis performed by FARM-CPU algorithm to identify several key genomic regions associated with TKW and five other grain architecture traits in a new and wide winter wheat panel consisting of 261 genotypes from 30 countries during three years. Genome-wide association analysis using 90 K SNP array revealed many MTAs including 17 highly associated SNPs across 9 chromosomes of which four multi-traits associated SNPs were found on chromosomes 1B, 2A, 5B, and 6A. We reported four novel candidate genes related to these traits showing high expression values in different grain tissues.Future studies should deepen the relationships between the function of these genes and their effect on grain architecture. These results will be valuable for identifying regions associated with kernel weight and its dimensions and could be useful for providing further insights for increasing grain quality, milling performance, and grain yield.Institute of Field and Vegetable Crops (IFVCNS), Novi Sad, Serbia and parental lines of Western European hybrid breeding programs were also included in this set  over three consecutive years  with three replicated blocks in 5 m2) and TKW (g) using the MARVIN Digital Seed Analyser . In addition, to evaluate the differences in grain density and the deviation of a shape from a cylindrical form, the factor form density (FFD) and kernel diameter ratio (KDR) were calculated according to Giura and Saulescu [After harvesting, 200 random kernels of each plot (per accession and year) were used to assess kernel size traits including, kernel length , kernel width , kernel area  for each trait over years was calculated using the following equation, all these calculations were accomplished using Genstat 19:σg2, σgy2, and σe2 are mean squares for genotype, genotype × year (environment) interaction and residual error, respectively, y is the number of years and r represent the replicates. Summary statistics across different years were corroborated by mean, minimum, maximum, standard deviation and coefficient of variation using Genstat 19 software. A correlation matrix between traits and boxplots of each trait and differences among geographical regions were performed using MVApp v2.0.Analysis of variance (ANOVA) for all measured traits was performed and broad-sense heritability (The restricted maximum likelihood (REML) algorithm was applied to calculate the Best Linear Unbiased Estimators (BLUEs) for each trait in each accession across the years by considering the accession as a fixed effect and the environment as a random using Linear and Nonlinear Mixed Effects Models (nlme) package in R .http://www.traitgenetics.com) the 90 K iSELECT chip [http://www.wheatgenome.org/, IWGSC RefSeq v1.1) and then used to determine the population structure, linkage disequilibrium (LD) and for GWAS calculation. The genome-wide pairwise estimates of LD were calculated as a squared correlation between pairs of polymorphic SNPs (r2) for the whole-genome using GenStat 18 [2) vs. the distance between pairs of polymorphic SNPs (Mbp) using R-package GGPLOT2 [2 = 0.2 is approximately 2.0 Mbp and we used this window to discover the candidate genes with a reasonable distance as suggested by 3. The genetic relationships among genotypes as the population structure were revealed by principal component analysis (PCA), using Genomic Association and Prediction Integrated Tool (GAPIT 3) package in R [The panel was genotyped by TraitGenetics GmbH  were considered as significant marker-trait associations (MTAs). FDR was calculated for each trait separately at the significance level of 0.01 and used in further analyses as recommended by Alqudah et al. [GWAS was performed using BLUEs values in each accession for each trait applying different statistical models through GAPIT in R. FARM-CPU model was applied by considering the random effect model (REM) and the fixed effect model (FEM) iteratively and associated markers as a cofactor that empowered us to avoid any false-negative and control the false positive associations by preventing model overfitting , 84–86. h et al. .http://www.wheatgenome.org/, IWGSC RefSeq v1.1). Because, each block of LD contains high number of candidate genes, we have selected the candidate genes which have SNPs within their physical positions. WheatMine platform (https://urgi.versailles.inra.fr/WheatMine/begin.do) was used to search for the gene ontologies (GO) and InterPro number and description. The underlying genes were further examined for their association with grain architecture traits using previously published literature.Those highly significant MTAs based on their physical positions were further used to identify the high-confidence (HC) putative candidate genes, based on their physical positions within the LD ± 2 Mbp window, using the reference genome sequence of Chinese Spring by blasting against IWGSC RefSeq annotation v1.1 (http://www.wheat-expression.com/).In-silico gene expression analysis in different grain tissues of the multi-traits candidate genes was analyzed using RNA-Seq expression data from Wheat Expression Browser , Kernel length (KL), Kernel width (KW), Kernel area (KA), Kernel diameter ratio (KDR) and Factor form density (FFD) in an experiment with 261 wheat genotypes evaluated during 3 years. Table S3. Significant marker-trait associations and candidate genes for grain architecture traits in 261 winter wheat genotypes.Additional file 2: Table S4. List of candidate genes within the most important four LD blocks.Additional file 3: Figure S1. Origin of the genotypes of the panel. Figure S2. Single nucleotide polymorphism (SNP) density on 21 wheat chromosomes. The x-axis shows the interval distance in Mb. Figure S3. Screen plot for the first ten principal components (PCs) explains the variation using 90K chip and Population structure based on their origin. Figure S4. Kinship heat map of the panel. Figure S5. The linkage disequilibrium decay in the wheat population. Figure S6. Phenotypic variation and Heritability (H2) for Thousand-kernel weight (TKW), Kernel length (KL), Kernel width (KW), Kernel area (KA), Kernel diameter ratio (KDR) and Factor form density (FFD) using BLUEs values. Figure S7. Variation on Thousand-kernel weight (TKW), Kernel length (KL), Kernel width (KW), Kernel area (KA), Kernel diameter ratio (KDR) and Factor form density (FFD) based on the origin of genotypes. C-N Europe ; E Europe-W Asia (Eastern Europe-Western Asia); N America (North America). Figure S8. Overview of significant markers trait associations identified for Thousand-kernel weight (TKW), Kernel length (KL), Kernel width (KW), Kernel area (KA), Kernel diameter ratio (KDR) and Factor form density (FFD) using BLUEs values. Multitraits MTAs are indicated with gray rectangles.


ST and an integrated haplotype homozygosity score (iHS). Our results find that seven significant SNPs  using the FST analysis and 55 significant SNPs  using the iHS analysis. Of the candidate genes that contain significant markers, or are in proximity to them, we identify several belongings to the keratin genes, RXFP2, ADCY1, ENOX1, USF2, COX7A1, ARHGAP28, CRYBB2, CAPNS1, FMO3, and GREB1. These genes are involved in wool quality, polled and horned phenotypes, fertility, twining rate, meat quality, and growth traits. In summary, our results provide shared founders of Gotland sheep and insight into genomic regions maintained under selection after the breed was formed. These results contribute to the detection of candidate genes and QTLs underlying economic traits in sheep.Gotland sheep, a breed native to Gotland, Sweden , split from the Gute sheep breed approximately 100 years ago, and since, has probably been crossed with other breeds. This breed has recently gained popularity, due to its pelt quality. This study estimates the shared ancestors and identifies recent selection signatures in Gotland sheep using 600 K single nucleotide polymorphism (SNP) genotype data. Admixture analysis shows that the Gotland sheep is a distinct breed, but also has shared ancestral genomic components with Gute (~50%), Karakul (~30%), Romanov (~20%), and Fjällnäs (~10%) sheep breeds. Two complementary methods were applied to detect selection signatures: A Bayesian population differentiation F Gotland sheep are a Swedish breed prized for their pelts that are uniformly grey in color with curly fleece. Gotland sheep are native to Gotland, a Swedish island in the Baltic Sea ,2. They Breeding efforts in Gute sheep have been focused on conservation rather than genetic improvement of production and marketable traits . HoweverWith cost-effective genotyping technologies, it is possible to genotype several individuals from different breeds to scan their genome to uncover regions that are under putative selection . The ideThe purpose of our study was to estimate population structure and identify candidate genes underlying recent artificial selection in Gotland sheep. This study builds on other studies of population structure in Swedish North European short-tailed sheep breeds  by incluhttps://doi.org/10.5061/dryad.34tmpg4gj (accessed on 11 March 2021). This dataset included SNP genotypes from five Swedish sheep breeds, of which only Gotland (n = 19), Gute (n = 22), and Fjällnäs (n = 10) sheep were retained for the subsequent analyses (n = 24) and Karakul (n = 20) sheep from Russia . In this equation Φ(iHS) denotes the Gaussian cumulative distribution function, which was calculated using the pnorm R [piHS was the two-sided p-value for testing the null hypothesis . A piHS value less than 0.0001 was significant.To calculate iHS , un-phas pnorm R  functionftp://ftp.ensembl.org/pub/release-100/gtf/ovis_aries/ (accessed 12 March 2021) [ST  or iHS (piHS < 0.0001). Finally, we performed a literature review to annotate the functions of the identified genes.To identify candidate genes located in genomic regions under selection, we used ovine gene annotation data (Ovis-aries.OAR_v3.1.100) downloaded from the Ensembl genome browser website: ch 2021) ,34. GenoA total number of 464,612 autosomal SNP genotypes from 105 animals  with a mean genotyping call rate of 99.39% remained after the quality control process. The average distance (±SD) between adjacent SNPs was 5.25 (± 5.74) Kb. As presented in The population structure of Swedish indigenous sheep breeds, including Gotland and Gute sheep, has already been studied with high-density SNP genotype data . In the ST [ST > 0.4659) were 0.0016% of all polymorphic SNPs tested. The most significant SNP was located on OAR10 at 29,455,959 bp  on OAR25 at 1,759,489 Kb, which was located inside the PGBD5 gene. No association between PGBD5 and a specific trait in livestock has been reported so far. However, in the vicinity of this SNP , we identified another candidate gene, GALNT2. An association between a polymorphism in GALNT2 and serum lipid level was previously reported in humans [q-value = 0.011), which was located inside EDEM3. In sheep, EDEM3 has been identified as a candidate gene for resistance to gastrointestinal nematodes [EDEM3 has been associated with three different phenotypes in other livestock species, and it is difficult to make an educated guess as to why this region has been under selection in Gotland sheep.We also found a significant SNP . In the surrounding genomic region of this SNP (200 Kb upstream and downstream), we identified the following genes: KRT4, KRT3, KRT77, KRT1, KRT73, KRT72, KRT74, KRT75, KRT82, KRT84, and KRT85. The last three genes are known as ty including KRT85 in secondary follicles, were reported to be in association with bulb deflection and follicle curvature [KRT85 in the determination of follicle and fiber morphology. Of the type II keratin genes, KRT83 was already reported to be associated with wool traits [The last significant SNP identified by BayeScan was located on OAR3 at 133,648,712 . Therefore, the wool/fiber Keratin genes may provide insights into the differences between Gute and Gotland pelts, as well as the variation within Gotland individuals, which deserves further investigation with more samples, recorded phenotypes, and whole genome sequencing that can help identify causal variants with greater power and precision.10) p-values for iHS statistics. In cattle, more than 50,000 SNPs are recommended to accurately detect selection signatures using an EHH-based statistic [piHS < 0.0001), which was 0.014% of all SNPs  tested for the null hypothesis. One hundred and twenty-three protein-coding genes were identified in the 200 Kb upstream and downstream of the significant SNPs , we found USF2, among other genes  on OAR4. Through the production of cAMP, this gene is involved in oocyte meiotic arrest and resumption [ADCY1 was also reported as a candidate gene under selection in a high fecundity goat breed compared with a low fecundity dairy breed in a study detecting selection signatures [tatistic  such as tatistic , and in tatistic ,43. Thisant SNPs ; of theser genes . In a stARHGAP28 . ADCY1 isumption . ADCY1 wgnatures .COX7A1 which is 82.8 Kb upstream of a significant SNP (−log10piHS = 4.95) on OAR14. This gene is involved in the oxidative phosphorylation pathway, and was significantly enriched as observed by Tang et al. [COX7A) in a highly prolific ewe, may, therefore, be explained by providing more energy supply to follicular development that would result in high prolificacy [ARHGAP28 is 13.6 Kb downstream of the next most significant SNP (−log10piHS = 4.5) on OAR 23. In a GWA study regarding twinning rate in cattle, the most significant SNP was identified in the proximity of two genes, one of which was ARHGAP28 [ARHGAP28 may directly be involved in the ovulation process [ARHGAP28 was also identified as among the progesterone-regulated genes in humans and mice as it demonstrated a −3.1 fold change in gene expression in a case group  in comparison with a control group [Another candidate gene in our study is g et al.  in a higg et al. . The obslificacy . ARHGAP2ARHGAP28 . These a process . ARHGAP2ol group . Indeed,ST and iHS were applied. Conceptualized by Weir and Cockerham [ST has been broadly used to study population differentiation and selection signatures in different species. Indeed, FST measures differences in allele frequency between two populations in each locus, and those loci showing the highest differences are deemed as selection signatures. In the current study, we used a Bayesian approach implemented in BayeScan to detect FST outliers. The sample size in our study was relatively small ; however, the BayeScan algorithm accounts for the uncertainty in allele frequencies of small sample sizes. This method can handle a very small sample size without introducing bias, but has reduced statistical power to detect selection signals.In our study, we wanted to detect selection signatures maintained in the genome of Gotland sheep, for which two different, but complementary methods including Fockerham , FST hasST values between Gute and Gotland sheep obtained with BayeScan was 0.158, suggesting a relatively high genetic differentiation. This can be attributed to the fact that the additional breeds used in synthesizing Gotland sheep were relatively highly differentiated, as shown in the PC analyses. In the case of comparing two relatively distinct populations, a locus with a relatively high FST value may not provide sufficient evidence for divergent selection than the same value, while comparing two closely related populations. Moreover, previous studies reported that the FST approach, in most cases, suffers from type I error  and bias [ST, i.e., the BayeScan algorithm, is that false discovery rate (FDR) is used to account for multiple testing, and posterior probabilities are calculated for each locus. In a study on the performance of FST outliers using simulation, Lotterhos and Whitlock [p-values, and therefore, improve the performance of BayeScan [The average Fand bias ,66. HoweWhitlock  showed tBayeScan . PreviouBayeScan . In our ST method . No SNPs were found to be significant for both methods, which could be explained because iHS is powerful in detecting ongoing selection signatures with an intermediate allele frequency; while FST is useful in detecting selection where the target allele frequency is approaching fixation. Although none of our selective sweeps were found with both methods, we identified candidate genes associated with economically important traits. Despite the conservative nature of EHH-based methods, our iHS  analysisThis research investigated the population structure of Gotland sheep. We showed that while Gotland sheep is a distinct breed, it has a shared ancestry with Gute, Karakul, Romanov, and Fjällnäs breeds. Analysis of population differentiation showed a high level of genetic differentiation between Gute and Gotland sheep breeds. Although the Gotland breed originated from the same population of Gute, this research showed important genes, e.g., pelt quality and horned/polled phenotype that are involved in their differentiation. We also identified genes in significant genomic regions underlying fertility traits. We expected to identify some genes related to reproduction and fertility because prolificacy is economically beneficial for commercial producers and was likely selected after crossing with Romanov sheep. Given that interpreting the results inferred from signatures of selection is not straightforward, a supplementary genome-wide association study with a larger sample size, and the use of whole-genome sequence data, may be beneficial to validate the results and determine biological functions of selective sweeps.


The major bottleneck of vg is its high cost of reads mapping to a variation graph. In this paper we study the problem of SNP calling on a variation graph and we present a fast reads alignment tool, named VG SNP-Aware. VG SNP-Aware is able align reads exactly to a variation graph and detect SNPs based on these aligned reads. The results show that VG SNP-Aware can efficiently map reads to a variation graph with a speedup of 40× with respect to vg and similar accuracy on SNPs detection.Sequencing technologies has provided the basis of most modern genome sequencing studies due to its high base-level accuracy and relatively low cost. One of the most demanding step is mapping reads to the human reference genome. The reliance on a single reference human genome could introduce substantial biases in downstream analyses. Pangenomic graph reference representations offer an attractive approach for storing genetic variations. Moreover, it is possible to include known variants in the reference in order to make read mapping, variant calling, and genotyping variant-aware. Only recently a framework for variation graphs,  Although massive sequencing projects over the past decade such as the 1000 Genomes Project , the abiThe reliance on a single reference human genome could introduce substantial biases in downstream analyses and result in an inability to detect disease related genetic variants , 5. In fAccurate genotype calls and allele frequency estimations are crucial for population genomics analyses. In  the authA series of large-scale projects have characterized more than 660 million single-nucleotide polymorphisms . Althouvg is the first openly available variation graph tool to scale to multi-gigabase genomes. It provides read mapping, variant calling, and visualization tools [Pangenomic graph reference representations offer an attractive approach for storing genetic variation of all types . These gon tools .vg toolkit [Aligning a sequence to a sequence is a well-studied problem with many highly optimized tools , 13. In  toolkit  providesvg toolkit [vg is able to map reads into arbitrary variation graphs using generalised compressed suffix arrays [vg performs the alignment using graphs but, unfortunately, it uses a lot of time and memory, especially for the construction of graphs. toolkit  improvesx arrays . vg perfvg, named VG SNP-Aware, with the aim of boosting the most demanding phase of reads to graph alignment. It is possible to reduce the mapping time used by vg by considering only exact matches and using a depth-limited like search on the graph. VG SNP-Aware is able to significantly reduce the time of the alignment phase, while ensuring high precision values on SNPs detection.In this paper we study the problem of SNPs detection using a variation graph. We present an improvement of 22.1A variation graph is used to represent multiple sequences, coming from different reference genomes, to cover-up all possible known variants. The goal is to have a sequence graph together with a set of paths, representing all possible sequences from a population.nodes, containing the sequence;edges, connecting two nodes via either of their respective ends;paths, representing the possible sequences from a population.The structure is defined byG = , it consists of a finite set of nodes V and a set of edges E ⊆ V × V. Each node vi represents a sequence from the alphabet u, v) ∈ E is an edge from node u to node v. The in-degree of a node is the number of incoming edges whereas the out-degree of a node is the number of outgoing ones. Let P = vo…vP−1 be a string over a set of nodes V, we denote by P a path in the graph G = , if all the edges of the path are in E. Graphs are bidirectional and each node has two orientations, each one representing a DNA strand. Nodes can be traversed in either forward or reverse direction.Given a graph A position in the graph is defined by the node ID, the direction and an offset with respect to the sequence represented by the node. Nodes are stored by ID, and the offset indicates the position of a single nucleotide on the sequence represented by the node. The offset counts from the start of the node in the specified orientation.vg uses a succinct representation of the variation graph (XG) and a Generalized Compressed Suffix Array (GCSA) index [k = 32 characters as nodes, if the shorter strings identify the start nodes of the corresponding paths uniquely. The pruned graph is encoded with a generalization of the FM-index. The index includes also extensions based on suffix trees.To enable read mapping and other random access operations on large graphs, in addition to the variation graph, A) index . With thA) index . GCSA2 arc, return true if a node is into the reverse complement strand, false otherwise;offset, return the offset of the node of the original graph;id, return the identifier of the node of the original graph.The GCSA2 index supports the followings queries:Thanks to the GCSA2 queries it is possible to obtain the location of sequences on the variation graph, in linear time.vg uses the “seeding and extending” technique to align reads to the graph. The alignment process is based on maximal exact match sequences between the query read and the reference graph. The vg mapping starts by finding seed matches using the GCSA2 indexes, then it clusters them (in case of multiple seeds close together) and, finally, it performs a local constrained dynamic programming alignment of the read on a region of the graph around each cluster. More details can be found in [vg is the amount of computing time and memory required for reads mapping. In the following we will see how SNPs can simplify the variation graph and the alignment of reads to it.found in . The maj2.2vg represents the two nucleotides of a biallelic SNP as two possible nodes on graph and for each of them provides an edge from and to it. This approach allows to have multiple paths which admits all possible sequences on graph, including the reference base or the alternative base of SNPs. The naming convention applied to the path requires alternative path having a name starting with “alt_”, this allows to distinguish it from the reference paths.In vg creates a path for each chromosome and for each variant. Thus, node with ID 1, is part of chr1 path. Node “2”, since it contains the reference nucleotide, contains both the chr1 path and the variant path , whereas node “3” belongs only to the alt_path1, since it contains the alternative nucleotide. The construction of a variation graph starts from a reference genome and a set of set of SNPs. In this specific case, where only SNPs are considered, it is possible to study the properties of node IDs in the graph in the following way. If we consider the forward direction, the resulting node IDs are increasing, otherwise they are decreasing. This is due to the fact that nodes creation is sequential into the forward direction. In vg, this graph does not contains cycles or arcs between distant nodes that aim to reuse one or more nodes in the graph. These two properties, are due to the fact that we exploit the graph only in case of SNPs. This allows VG SNP-Aware to follow up the consecutive nature of node IDs for the alignment of reads.It is important to note that each node belongs to one or more paths. 2.3VG SNP-Aware introduces an optimised alignment process for SNPs, with the idea to exploit only exact alignments, using a depth-limited search like. The process is composed by two distinct phases: searching for initial node and mapping from it. Both phases are based on the characteristics of the variation graph in case of SNPs. In the following we describe these two steps.k = 32 characters, also the initial alignment follow the same constraint. The research is carried out on the GCSA2 index thanks to a backwards searching algorithm, starting from the last character of the read. If and only if the algorithms finds one or more exact-matches on the index, it tries to map the entire read to the graph in the next phase, otherwise the read is rejected. The number of possible initial nodes can be quite large. For this reason we decided an heuristic in order to select the most promising starting nodes. Starting from the first seed node, the mapping phase tries to align the read on the graph exactly, e.g. without gaps. If the alignment includes a nucleotide belonging to an SNP, this alignment represents the best choice, and the alignment of the read is recorded as the best alignment and the process stops. Otherwise, the algorithm tries to map the read from an other initial node, if it exists. Only reads with one or more reference or alternative base of SNPs are included in the output file, and then used in the genotyping process.Given a read, in the first phase, the algorithm searches for one or more initial nodes to be used as seed for the alignment. The initial node is a node in the graph that shares at least 2 characters with the reads. Since by construction the string in a node is limited by –N, where N is the length of the read. This fairly wide choice allows to achieve alignment on graph ensuring finite loop in case of false positives on the initial nodes.The maximum number of explored nodes are limited by 2 × –For each node, only 4 neighboring nodes can be visited.The mapping algorithm tries to find a path in the graph, starting from an initial match, that represent the exact alignment of the read. Nodes founds on GCSA2 index must be firstly translated with respect to the IDs present in the variation graph, thanks to the GCSA2 id query seen previously. The mapping algorithm uses the rc query on the initial node to decide the direction of the alignment: if rc returns true it looks for a node with an ID lower than the previous one, if rc return false it looks for a node with an ID greater than the previous one. Obviously, this choice is optimal for the biallelic or multiallelic SNPs, but it will not be correct if there are other types of variants which tend to reuse graph nodes. Then, the algorithm tries to align the read exactly, with no gaps, at each node crossed by the search, until the read is completely aligned or a stop condition has been reached. The alignment strategy implements a depth-limited search, where the search space of neighbouring nodes is limited. The graph is explored with a depth first search, that is going as deep as possible following the direction obtained thanks to the initial node. Also, we use two stop conditions on the number of nodes visited. The stop conditions used by the alignment algorithm are the following:These values allow to make a safe and faster alignment. Tests with different values were conducted without leading to significant improvements. In general larger values of these parameters will only increase the alignment time, without increasing the number of aligned reads.In 3vg and VG SNP-Aware. A summary of the complete vg pipeline is reported in vg constructs a graph from the reference genome and a set of genomic variations. Then, the graph is indexed and a series of intermediate index files are produced. These indexes are used for reads to graph alignment with the vg map command. Finally, the variants are detected using the aligned reads and the variation graph.In this section we describe the main differences between the original implementation of vg map command. The new algorithms for initial node search and for mapping have been included in the vg map command. In particular it is possible to use the original vg mapping or our new version presented here.The VG SNP-Aware implementation follows the same pipeline. The main differences are in the implementation of the –snp-aware: performs the alignment on the graph with the VG SNP-Aware algorithm described in –printsnp: allows to reduce the output size to only reads mapping to reference or alternative base of some SNPs.The original map command works with few parameters that allow you to set the graph, the index and the dataset of reads. The new map command of VG SNP-Aware includes also the following parameters:vg view command to switch from JSON to GAM files and vice versa. Here you can find an example of the command options:–vg map -f reads.fq -x graph.xg -g graph.gcsa –printsnp –snp-aware -j–vg map -f reads.fq -x graph.xg -g graph.gcsa –snp-aware -jTo use VG SNP-Aware the snp-aware parameter is required while printsnp is an optional parameter but it is recommended. The -j parameter is mandatory to obtain as output the JSON mapping file. It is possible to use the The pseudocode of VG SNP-Aware alignment of reads to the graph is presented below.VG SNP-Aware mapping .The algorithm starts by searching for an initial match between the read and the nodes in the graph. This search is implemented using the GCSA2 index. Thanks to the GCSA2 functionalities, the algorithm obtains the initial node ids on the original graph. Once the list of initial nodes is populated it is possible to align the read on graph thanks to a depth-limited like search that starts from these nodes. The main idea behind the algorithm proposed here, is to try to align the read at each node crossed by the search, until the read is totally consumed or a stop condition has been reached. The search strategy explores the graph going as deep as possible. The edges of the graph are explored starting from the last node discovered, searching for unexplored edges coming out of it. These candidate nodes are queried for the string_to_map, and if a match is found the search continues along this path. In this case the string_to_map is updated, removing the matching string, and the new node is stored in the alignment path. The search stops if the read is completely aligned or if one of the two stopping conditions is meet. Then, the final alignment path is stored, and if it contains an SNP the search stops and the read is mapped.4vg and VG SNP-Aware. For vg two possible alignment modes are considered, the first one indicated with a generic vg calculates the alignment with possible mismatches, and it is the default option for vg. The second one, indicated with vg Exact, uses the parameter K to set the length of the reads and to search only for exact alignments. VG SNP-Aware proposed in this work, searches for exact alignments as vg Exact, but with a more efficient searching strategy. All tests are based on vg version 1.29.0-Sospiro.In this section we report the results obtained by the tools 4.1The analysis focuses on two fundamental points, the time taken to complete the alignment and the genotyping process, and the accuracy of the results obtained by the respective tools.The datasets used in this study are two sets of real reads from the same individual NA12878) from the 1000 Genomes Project 2878 from–20. For To measure the accuracy, we use RTG tools and the SNP list which are also genotyped in the GIAB gold standard. Each evaluation comprises the standard metrics: True Positive, False Positive, False Negative, Precision, Recall and F-Measure. All tests were performed on a 14 lame blade cluster DELL PowerEdge M600 where each lame is equipped with two Intel Xeon E5450 at 3.00 GHz, 16 GB RAM and two 72 GB hard disk in RAID-1 (mirroring).4.2In this section we test the requirement of computational resources for all tools. First of all the memory required by all tools is very similar. The memory is mainly dominated by the size of the variation graph, and the corresponding data structures, and it varies only slightly with the size of the input reads. As for the running time the major variations are in the reads mapping phase, where the three tools follow different alignment strategies.vg requires 8685 min to align 179 million reads, that are about 6 days of computation. In the case of vg Exact the mapping phase requires 1003 min (16.7 h), whereas VG SNP-Aware only 225 min (3.75 h). Thus, on the low coverage dataset, VG SNP-Aware achieved a speedup of 4.45× with respect to vg Exact, and 38.6× with vg. If we consider the total running time for mapping and genotyping the speed up of VG SNP-Aware remain 3.1× and 28.7×, w.r.t. to vg Exact and vg respectively.On the low coverage dataset vg and vg Exact the running time of the genotyping phase is a small fraction of the cost of mapping, whereas in the case of VG SNP-Aware the time for mapping and genotyping are similar. This affects the total speedup. We believe that the current implementation of the genopyting phase can be improved in the case of VG SNP-Aware, considering only SNPs calls like in [It is worth noting that the genotyping phase is the same for all tools. However, for  like in .vg requires about 8 days of computation for reads mapping, vg Exact 1 day, whereas VG SNP-Aware 5 h. As the size of the input dataset grows also the speedup for reads alignment increases, and for the high coverage dataset VG SNP-Aware is 40.6× times faster than vg, and 5.1× than vg Exact. As for the total running time of mapping and genotyping the speedups on the high coverage dataset are in line with those of the low coverage dataset.If we consider the high coverage dataset, that contains 287 million reads vg Exact of 70 and 66% respectively for the GAM and JSON file. However, it should be noted that a similar strategy can be applied also in the other tools.In In summary, the most demanding phase is reads alignment and VG SNP-aware is considerably faster, up to 40×, than its competitors.4.3vg, vg Exact, VG SNP-Aware are compared with the gold standard [In this section we test the genotyping accuracy of all tools on both datasets. The VCF file produced by standard . In Tablvg Exact and VG SNP-Aware are similar, where VG SNP-Aware has an increment of true positive and false positive. Since both find exact alignment, these results are in line with what expected. Note that the number of true positive and false positive depends on the SNPs contained in dbSNPs and how many of them are contained also in the gold standard. This means that VG SNP-Aware identified a greater number of SNP variants present in the dbSNP that are not present in the gold standard. This observation has been reported also in other mapping-free tools like LAVA [vg, thanks to the fact that it includes mismatches in the reads alignment, it detects the highest number of true positives.For the low coverage dataset the results of ike LAVA , that usvg Exact and VG SNP-Aware have similar results in terms of true positive and false negative. As in the previous case, the number of false positive is greater in VG SNP-Aware and this means that the precision values are slightly lower than vg and vg Exact. However, in terms of F-measure vg Exact and VG SNP-Aware have very similar values on both datasets.On the high coverage dataset, thanks to the 10X coverage, the number of detected SNPs is much higher for all tools. Again, vg achieves the best overall performance in terms of the number of detected SNPs, because it allows for inexact reads alignment, however it is extremely slow and it requires days of computation on a cluster. VG SNP-Aware is able to detect SNPs with a slightly lower precision, but with a 40× speedup on reads mapping.In summary, 5vg. If we consider vg Exact the speedup is still high, 5.1×. The precision of VG SNP-Aware is only slightly lower to that of vg and in line with vg Exact. As the coverage of the dataset grows, the number of aligned reads by VG SNP-Aware increases, thus improving the number of detected SNPs. VG SNP-Aware can also reduce the size of the output alignment files, by reporting only reads mapped near the SNPs. As future direction of investigation it would be interesting to increase the precision by using the information of quality values. Also, VG SNP-Aware could be extended for the detection of other genetic variations such as insertions and deletions. Furthermore, it would be interesting to speedup the genotyping phase of vg.In this paper we presented VG SNP-Aware an algorithm to speed up the alignment of reads to a variation graph with application to SNP detection. In the case of SNP detection the structure of a variation graph can be exploited for efficiently mapping of reads. VG SNP-Aware is able to reduce the number of accesses to the graph by exploiting the sequential nature of nodes’ IDs. We tested different tools on popular benchmarking datasets for reads genotyping using a variation graph. The results show that VG SNP-Aware is able to align reads to the graph with a speedup of 40× with respect to the most popular tool


Since the last few years, the growing interest in the use of natural and synthetic antioxidants as functional food ingredients and dietary supplements, is observed. The imbalance between the number of antioxidants and free radicals is the cause of oxidative damages of proteins, lipids, and DNA. The aim of the study was the review of recent developments in antioxidants. One of the crucial issues in food technology, medicine, and biotechnology is the excess free radicals reduction to obtain healthy food. The major problem is receiving more effective antioxidants. The study aimed to analyze the properties of efficient antioxidants and a better understanding of the molecular mechanism of antioxidant processes. Our researches and sparing literature data prove that the ligand antioxidant properties complexed by selected metals may significantly affect the free radical neutralization. According to our preliminary observation, this efficiency is improved mainly by the metals of high ion potential, e.g., Fe(III), Cr(III), Ln(III), Y(III). The complexes of delocalized electronic charge are better antioxidants. Experimental literature results of antioxidant assays, such as diphenylpicrylhydrazyl (DPPH) and ferric reducing activity power assay (FRAP), were compared to thermodynamic parameters obtained with computational methods. The mechanisms of free radicals creation were described based on the experimental literature data. Changes in HOMO energy distribution in phenolic acids with an increasing number of hydroxyl groups were observed. The antioxidant properties of flavonoids are strongly dependent on the hydroxyl group position and the catechol moiety. The number of methoxy groups in the phenolic acid molecules influences antioxidant activity. The use of synchrotron techniques in the antioxidants electronic structure analysis was proposed. Reactive species are byproducts of crucial cellular processes and include reactive oxygen species (ROS) and reactive nitrogen species (RNS). They are formed in the mitochondrial respiratory chain, in the metabolism of purine nucleotides and arachidonic acid, prostaglandin synthesis, and enzymatic reactions catalyzed by, among others, xanthine oxidase and tryptophan dioxygenase . ReactivAntioxidant properties are provided by polyphenols . The occThe most common antioxidant assays used in an analysis of food, nutrition, and supplements are free radical diphenylpicrylhydrazyl (DPPH), ferric reducing activity power assay (FRAP), and 2,2′-azinobis(3-ethylbenzothiazoline-6-sulfonate) (ABTS). DPPH antioxidant assay can be applied for the estimation of the antiradical activity of functional foods such as herbal extracts and natural or synthetic pure compounds due to high stability, experimental feasibility, and low cost of DPPH radical . The DPP4 x 7H2O at the known scope of concentration.The second crucial antioxidant assay is FRAP, which evaluates the total antioxidant activity in the reaction of reduction of ferric tripyridyl triazine (Fe III TPTZ) complex to blue ferrous tripyridyl triazine (Fe(II)-TPTZ) form. The reaction has a place at low pH and in the presence of an antioxidant, which can be monitored by measuring the change in absorption at 593 nm . The abs•+ for the evaluation of antioxidant properties of natural compounds can be applied. Apart from good solubility in organic solvents, ABTS assay can be implemented over a wide range of pH values. The total antioxidant activity of the compound in the ABTS assay is designated using the spectrophotometric method BPh4, [LMnIII(B2O3Ph2)(ClO4)], and [LMnV(acac)N] BPh4) that permitted to analyze ligand substitution and oxidation state change of an Mn ion with similar metal ligands  exist or whether there is an oxo radical or water or a hydroxy group. The extended X-ray absorption fine structure (EXAFS) can supply valuable data on bond lengths however is usually insensitive to H atoms. The authors of the study  selected ligands .The electron orbitals of ligands in 3d transition metal complexes can be analyzed by means of X-ray emission spectroscopy (XES). The authors carried out a comprehensive experimental and theoretical study of the X-ray emission from the valence band of Mn coordination complexes. The spectra show a signature of the ligand type and even allow a calculation of the ligand protonation degree. The Kohn–Sham orbitals of the density functional calculations help to understand the derivation of changes in spectra based on analysis of molecular orbitals that contribute to the spectral features .In the paper , the autThe work  describeThe electronic structure of transition metal elements, especially orbital contribution and the relative energy position of the highest occupied and the lowest unoccupied states, is of significant importance for the design of efficient antioxidants. According to , by tuni−1/2960 cm−1, 2920 cm−1/2960 cm−1, 3012 cm−1/2960 cm−1, and 1630 cm−1/1650 cm−1 for lipid oxidation, lipid saturation, lipid desaturation, and proteins aggregation have been analyzed, respectively [The synchrotron radiation-based Fourier Transform Infrared Microspectroscopy (FTIRM) has been recently used to determine oxidative stress, e.g., photo-oxidative damage, results of radiation in various types of cells such as fibroblast, carcinogenic, and plasmatic cells, being able to correlate the infrared signals shifts with lipid and protein peroxidation . Enterocectively . The res3–1.5 × 104.The DEMETER dual electron microscopy and spectroscopy beamline with the X-PEEM method of the Solaris National Synchrotron Radiation Centre in Poland, designed for complete electronic and magnetic structure determination using a photoemission microscope with soft X-rays as an excitation source, XAS, XMCD, and XPS spectroscopies, can provide detailed information on ligands complexed with metals to understand the molecular mechanisms of correlation between their structure and antioxidant properties. The main advantage of the method is the ability to select the energy of excitations and the possibility of tuning to the characteristic edge of the absorption. The spectral resolution of the beamline is E/dE = 3 × 10The serious question is how the electronic charge distribution in ligands changes as the result of complexing with metals and how it influences the antioxidant properties of ligands. The study of the correlation between the molecular structure and antioxidant properties with the use of synchrotron radiation may provide great information unavailable using conventional research methods. The design of biologically effective ligands of antioxidant properties is crucial in the food industry, medicine, and many other areas; therefore, it requires further investigations using synchrotron radiation techniques.3 groups in a ring, the better we observe antioxidant properties. However, we point out and present preliminary evidence that: The distribution of the electronic charge and the delocalization of electronic charge improve the antioxidant properties of ligands. Complexation of ligands with metals of high ionic potential (Fe (III), Ln (III) Y (III)) increases antioxidant properties. So the improvement of the antioxidant properties does not depend only on one factor (the number of OH and OCH3 groups), but also other very important effects. These properties were summarized in The review of recent developments in effective antioxidants, in particular, the structure and antioxidant properties, confirms that the antioxidant properties of flavonoids increase due to the presence of the hydroxyl group in the C3 position and the catechol moiety. The presence of an intramolecular hydrogen bond between the C3 group and the 3’4’ catechol system is responsible for the strong antioxidant properties of flavon-3-ol and flavan-3-ol. Antioxidant activity elevates with the increase of the methoxy group number in the phenolic acid molecules. It is caused by decreasing enthalpy of -OH bond dissociation, which corresponds with the increase in antioxidant properties. To predict which of the hydroxyl groups in the molecule participates in the creation of the radical structure, the heat of formation values of individual radicals can be analyzed. In the case of kaempferol and quercetin, the 3-OH hydroxyl group is the most involved in the formation of radical structure. Among the major findings is that with the agreement on the literature research, the more -OH or -OCH


During TSD, carriers of the A allele for ADORA2A (rs5751862) and TNF-α were significantly more impaired for cognitive responses than their respective ancestral G/G genotypes. Carriers of the ancestral G/G genotype of ADORA2A rs5751862 were found to be very similar to the most resilient subjects for PVT lapses and Go/noGo commission errors. Carriers of the ancestral G/G genotype of COMT were close to the most vulnerable subjects. ADORA2A (rs5751862) was significantly associated with COMT (rs4680) (p = 0.001). In conclusion, we show that genetic polymorphisms in ADORA2A (rs5751862), TNF-α (rs1800629), and COMT (rs4680) are involved in creating profiles of high vulnerability or high resilience to sleep deprivation. (NCT03859882).Several genetic polymorphisms differentiate between healthy individuals who are more cognitively vulnerable or resistant during total sleep deprivation (TSD). Common metrics of cognitive functioning for classifying vulnerable and resilient individuals include the Psychomotor Vigilance Test (PVT), Go/noGo executive inhibition task, and subjective daytime sleepiness. We evaluated the influence of 14 single-nucleotide polymorphisms (SNPs) on cognitive responses during total sleep deprivation (continuous wakefulness for 38 h) in 47 healthy subjects (age 37.0 ± 1.1 years). SNPs selected after a literature review included SNPs of the adenosine-A2A receptor gene (including the most studied rs5751876), pro-inflammatory cytokines , catechol-O-methyl-transferase (COMT), and PER3. Subjects performed a psychomotor vigilance test (PVT) and a Go/noGo-inhibition task, and completed the Karolinska Sleepiness Scale (KSS) every 6 h during TSD. For PVT lapses (reaction time >500 ms), an interaction between SNP and SDT ( Many occupations such as military personnel, firefighters, police officers, and hospital staff are often exposed to sleep loss, which increases the risk of accidents . It has Adenosinergic mechanisms that are implicated in sleep/wake homeostasis were previously evidenced to contribute to individual differences in waking-induced impairment of neurobehavioral performance and functional aspects of EEG topography associated with sleep deprivation . In a reThus, the deleterious effects of TSD on neurobehavioral performances would not be based on a single genetic polymorphism, and few studies have evaluated the influence of multiple genetic polymorphisms when compared with vulnerability categories based on tertiles of change in cognitive performances during TSD.n = 5) [Among available techniques to assess genetic polymorphism, the sequencing and the conventional TaqMan PCR methods are highly reliable . There in = 5) . AnotherThe aim of our study was to compare influence of 14 SNPs on the performance degradation of sustained attention using PVT, executive function using the inhibition Go/noGo task, and subjective sleepiness in 47 healthy subjects during TSD. The SNPs selection was based on the studies cited above. The second objective was to evaluate influences of genetic polymorphisms that are immediately involved in vulnerability to sleep loss, as well as to distinguish between high vulnerability and high resistance profiles.Forty-seven healthy subjects (37.0 ± 1.1 years) were included in this study after providing their informed written consent. The study received the agreement of the Cochin–CPP Ile de France IV (Paris) Ethics Committee and of the French National Agency for Medicines and Health Products Safety (ANSM) (Ile de France IV) and was conducted according to the principles expressed in the Declaration of Helsinki of 1975, as revised in 2001. The study has been recorded in the clinical trial base .Subjects were free from medical, psychiatric, and sleep disorders. Subjects had not travelled between time zones within 7 days prior the study. Exclusion criteria included physical or mental health troubles according to (I) Hospital Anxiety and Depression scale, HAD ≥16 ; (II) siThe 3-day in-laboratory experimental protocol included (I) a habituation/training day (D0), (II) a baseline day (D1), (III) a total sleep deprivation (TSD) day beginning on D1 at 7:00 until D2 at 21:00 (meaning 38 h of continuous wakefulness), and (IV) a recovery night until the end of the study (09:00 on D3).Subjects were in individual temperature-controlled (22 ± 1 °C), 3 × 4 m rooms that included a bed, restroom facilities, and a computer workstation. Laboratory illumination was maintained at 150–200 lux during the entire period of sleep deprivation (with lights off during sleep periods). Subjects were asked to maintain regular caffeine consumption for the week prior to the experiment, and exercise, tobacco, and alcohol were prohibited 48 h before and during the study. Meals and caloric intake were standardized for all subjects . When not engaged in any specific testing or meals, subjects followed a standardized activity program . At least 2 investigators were systematically present in the laboratory. Two teams of 12 h shifts were organized to maintain a good level of investigator alertness. When the subjects were about to fall asleep , they were gently and immediately woken up .The sleepiness scale (KSS), psychomotor vigilance test (PVT), and executive Go/noGo task were performed in this order at six times between 07:00 on baseline (D0) to 21:00 on D2 . All subjects had a systematic habituation period for tests at D0 (habituation/training day) in order to reduce a learning bias. During all the periods in the sleep laboratory, including the tests, actimetry activity was recorded to confirm that all subject stayed awake during the 38-h continuous wakefulness period.The KSS is a subjective scale used to grade the subject awakening from 1 to 9; 1 represents “extremely alert” and 9 represents “extremely sleepy” . The comWe utilized a computer-based version of the 10 min PVT. This test is easily reproducible, the number of trials per session reduces the hazard bias, and the data are simple enough to be processed efficiently. Subjects were asked to respond, by clicking the left mouse button, to the appearance of a visual stimulus (a millisecond counter) as quickly as possible without making false starts. The inter-stimulus interval, defined as the period between the last response and the appearance of the next stimulus, varied randomly from 2 to 10 s. The number of PVT lapses of attention is defined as reaction time >500 ms. The reaction time (RT) in milliseconds for a 1 s period and PVT response was regarded valid if RT was ≥100 ms. Results are expressed as the number of lapses and speed (1/reaction time × 1000) .In this test, subjects were required to respond or not to a stimulus on a screen. After the appearance of a fixation cross in the center of the screen for 500 ms, an arrow appeared in the center of the screen for 1 s. Depending on the test instruction, subjects have 2 s to respond when the arrow pointed out on the right (“Go” response) and not to respond when it pointed on the left (“noGo“ response). The proportion is always as followed: 67% of “Go” trials and 33% of “noGo” trials. Subjects have 2 s to respond, and their response was directly followed by a new trial in order to determine the capacity to consciously inhibit non-relevant automated responses (inhibition process). The total duration of the task is around 7 min 30 s . Performn = 47) after rinsing the mouth with water and at least 30 min after eating or drinking. DNA from saliva collected in Oragene containers should be stable for at least 5 years at ambient temperature. After manual cell lysate preparation, the genomic DNA purification was performed by the Autopure LS instrument . DNA quantity, integrity, and ability to PCR were evaluated by Quality Controls. Participants were genotyped by predesigned or customized probes, and TaqMan SNP genotyping assays were provided by Thermo Fisher Scientific . PCR was performed on GeneAmp PCR System 9700, and a 7900HT system with SDS software version 2.4  was used for fluorescence detection and allelic discrimination. A check by Sanger sequencing of PCR products was carried out for rs5751876 because ADORA2A gene includes many SNPs in this region.Oragene DNA kits OG-500  were used to collect whole saliva samples from healthy adult volunteers . Values were expressed as mean ± 95% confidence interval (95% CI).Effects of sleep deprivation on PVT, Go/noGo, and KSS score were assessed using a one-way repeated measure analyses of variance (ANOVA) followed by a post hoc comparison with baseline values at 2 h of wakefulness. The associations between SNPs were checked using a chi-squared test.The primary objective was to demonstrate the differences between genetic polymorphisms on the cognitive parameters and KSS score at the nadir time point (at 8:30 on D2). Results were analyzed using an ANOVA, including fixed effects for genetic polymorphism (non-repeated measure) and time awake (repeated measure). If a genotype group of homozygous mutation counts less than 6 members, heterozygous and homozygous alleles were pooled for graphic and statistical analysis.A one-way analysis of variance (ANOVA) was used to determine the percentage of variance that was explained by genotype, as well as to estimate Cohen’s local effect size f2, at the nadir of performance (at 8:30 on D2). Effect sizes of 0.0099, 0.0588, and 0.1379 were considered small, moderate, and large, respectively ,42.The subject-specific averages for vulnerability to sleep loss were analyzed using nonparametric one-way analysis of rank scores with genotype as independent variable. For reference, the sample was also divided into tertiles on the basis of subjects’ rank order of vulnerability  over the 24 h period of sleep deprivation). The temporal profiles of the genotypes as revealed by the ANOVA were visually compared to the group-average temporal profiles of each of the tertiles .p-value of the comparison with the most frequent combination were calculated using a Bonferroni test.For the 4 SNPs identified as having the largest effect on both performance tasks and KSS score during prolonged wakefulness, we conducted a combination analysis using SNPstats R software  to assesSubjects’ characteristics are presented in http://www.internationalgenome.org, last access the 10 October 2021) . The pairwise linkage disequilibrium analysis showed moderate to strong LD between ADORA2A variants [The genotype prevalence of the 14 SNPs was most of the time similar to the 1000 Genomes Project data on the GRCh38 reference assembly (er 2021) , althougvariants .The IL-1β genotype , ADA, DRD2, and BDNF) of one individual were missing because undetected at the probe level, although the DNA was of good quality. This is inherent to the Taqman reference method, with 1 to 4%, depending on the polymorphisms, not detected.p = 0.004 and p = 0.030), one SNP (rs5751862) with IL1-β (p = 0.044), and four SNPs  with COMT (p < 0.001 for the first three and p < 0.01 for the last). In addition, the IL1-β SNP was associated with ADA and COMT (p < 0.001 and p < 0.01) and IL-6 with BDNF (p < 0.01).Two SNPs of ADORA2A  were significantly associated with TNF-α . PVT lapses and PVT speed were correlated with KSS scores  and Go/noGo commission errors . No significant correlation was observed between Go/noGo commission errors and KSS score .Pearson correlations analyses showed significant correlations with an R46,6 = 72.2, p < 0.001) and number of lapses , the decrease of inhibitory capacity in the Go/noGo task illustrated by commission errors , and the decrease of daytime sleepiness illustrated by the KSS score   .p < 0.001), PVT speed , and Go/noGo commission errors . For KSS, the greatest difference was observed after 20 h of prolonged wakefulness . An awakening effect after 26 h of prolonged wakefulness was significant (p ≤ 0.001) for all 14 studied polymorphisms.The largest effect of prolonged wakefulness was observed after 26 h of prolonged wakefulness in comparison to 2 h of wakefulness for PVT lapses  were, respectively, F and “F or F”. For the interaction (SNP × TSD), the degrees of freedom were F or F.PVT performance: On the PVT lapse number at the nadir time point, there was a significant SNP effect for 7 SNPs out of 14, including the three proinflammatory cytokines  and four SNPs of ADORA2A  (2298383) . The SNP2298383) . Figure 2298383) .Go/noGo performance: At the nadir time point, there is a significant SNP effect for six SNPs , ADA, and PER3) without significant interaction with TSD. A significant interaction without SNP effect was present for COMT and there was an SNP effect and interaction for PER3 . This latter combination included A allele carriers of ADORA2A rs5751862 (representing ≈75% of our population) and IL1-β (≈83% of our population).For Go/noGo commission errors, there were four allele combinations  that were significantly less degraded than the most frequent allele combination AAGG . This lap = 0.03)  that was significantly less degraded than the most frequent allele combination GACC ( = 0.03) . This laOur results indicate the neurobehavioral vulnerability/resilience of three genetic polymorphisms at the nadir of impairments corresponding to 26 h of continuous wakefulness, the rs5751862 of ADORA2A, TNF-α (rs1800629), and COMT (rs4680), in a group of 47 healthy subjects. Cognitive performance impairments and subjective sleepiness have been largely described during continuous wakefulness both in non-executive tasks such as psychomotor vigilance test (PVT) that specifically measures sustained attention processes and in the executive inhibition Go/noGo task . We firsOf the potential biological mechanisms underlying interindividual neurobehavioral vulnerability to sleep loss, the 14 SNPs selected here represented suitable candidates (see review by ), includThe ADORA2A locus is located on human chromosome 22 and contains a set of SNPs, the most studied being rs5751876  and other polymorphisms such as rs5751862 and rs2236624, which are in high linkage disequilibrium with the rs5751876 . These Sp = 0.004) and IL1-β (p = 0.044). The pro-inflammatory cytokine TNF-α is involved in sleep/wake regulation [In our study, there were significant associations between the ADORA2A rs5751862 and the two pro-inflammatory cytokines, TNF-α , which is illustrated at the 26 h nadir of the executive Go/noGo inhibition task vulnerability/resilience temporal profiles, with homozygous mutated A/A of ADORA2A rs5751862 being highly sensitive to TSD as is the ancestral G/G of COMT (rs4680). The catechol-O-methyltransferase (COMT) gene, the major enzyme degrading catecholamines, particularly dopamine, has also been implicated in vulnerability to sleep loss . The SNeview in ,56. In oeview in . In compeview in  previouseview in  and 36.2eview in . With reIn this study, the effect size and ANOVA analyses on the influence of the 14 SNPs on the neurobehavioral vulnerability to TSD evidenced the possible influence of the two other pro-inflammatory cytokines, IL1-β and IL-6, notably, the IL-6 SNP significant effect on PVT lapses and speed, Go/noGo commission errors, and KSS score. We showed that subjects carrying T allele of the IL-6 SNP (rs4719714) were more degraded than those carrying the ancestral A/A genotype at the nadir of Go/noGo inhibition task, at which time mutated ADORA2A rs5751862 subjects and the low activity dopamine-related G/G COMT ones were the most degraded. The IL-6 and IL1-β cytokines have some sleep regulation properties, and sleep deprivation increases daytime IL-6 and causes somnolence and fatigue during the next day ,57. The n = 14 carriers of the homozygous long allele (PER35/5) and n = 15 carriers of the homozygous short allele (PER34/4) [Among the 14 SNPs, the effect size are medium between PER3 and cognitive responses on psychomotor vigilance and executive inhibition tasks. However, the ANOVA analysis showed a PER3 SNP significant effect and interaction with TSD for the Go/noGo inhibition task, and a significant interaction with TSD without SNP effect on PVT lapses. PER3 is a circadian gene linked to diurnal preference. We confirmed a previous study showing that PER3 influences PVT lapses performance and sleepiness during TSD, comparing PER34/4) . In our PER34/4) . As the p < 0.01), which would merit further study in a larger number of subjects, especially since both polymorphisms are associated with the fatigue symptom [Our results evidenced low effect size for the BDNF polymorphism with cognitive responses and sleepiness, with only one significant ANOVA interaction regarding PVT speed. The association analysis highlights its positive relationship with the IL-6 polymorphism , TNF-α (rs1800629), and COMT are involved in creating profiles of high vulnerability or high resilience to sleep deprivation. These results could be used for the benefit of the Armed Forces to individualize and optimize countermeasure strategies to limit performance impairments induced by sleep deprivation. In a previous study, Maire et al.  evidence2, TNF-α