Zea mays, L.) cultivation has expanded greatly from tropical to temperate zones; however, its sensitivity to chilling often results in decreased germination rates, weak seedlings with reduced survival rates, and eventually lower yields. We conducted germination tests on the maize-282-diverse-panel (282 inbred lines) under normal (25 °C) and chilling (8 °C) conditions. Three raw measurements of germination were recorded under each condition: 1) germination rate, 2) days to 50% germination, and 3) germination index. Three relative traits were derived as indicators of cold-tolerance. By using the 2,271,584 single nucleotide polymorphisms (SNPs) on the panel from previous studies, and genome-wide association studies by using FarmCPU R package to identify 17 genetic loci associated with cold tolerance. Seven associated SNPs hit directly on candidate genes; four SNPs were in high linkage disequilibrium with candidate genes within 366 kb. In total, 18 candidate genes were identified, including 10 candidate genes supported by previous QTL studies and five genes supported by previous gene cloning studies in maize, rice, and Arabidopsis. Three new candidate genes revealed by two associated SNPs were supported by both QTL analyses and gene cloning studies. These candidate genes and associated SNPs provide valuable resources for future studies to develop cold-tolerant maize varieties.Maize ( Zea mays, L.) is produced (tons) than any other cereal crop, providing a critically important source of food for people, feed for livestock, and biofuel for energy. As world population continues to increase, maintaining the current supply and finding ways to increase the future production of maize are imperative. One strategy has been to simply expand the geographic area planted to maize. Indeed, during the last century, cultivation of maize has expanded into mountainous regions and higher latitudes, even as far as 57°N1. However, because maize originated from tropical regions, it is inherently sensitive to low temperatures. When cultivated under lower temperatures, maize experiences shorter growing seasons, slower growth rates, and higher seedling vitality, all of which result in variable or reduced yields2.Worldwide, more maize 5, glycinebetain (GB)6, antioxidant defense7, Ca2+ influx, membrane fluidity8, metabolism9, and the photosynthetic apparatus10. The evidence links Cold-Regulated (COR) gene expression with changes in many of these morphological and physiological traits, indicating that genetic breeding may be a successful strategy for enhancing cold tolerance in maize2.Chilling temperatures, ranging from 0 to 15 °C, cause changes in maize morphology and physiology during early growth stages. Morphologically, chilling prolongs germination time and growth duration, reduces germination rate, and weakens young seedlings15. For instance, at suboptimal temperatures, QTLs significantly associated with Malic Enzyme (ME) content were detected on chromosomes 1 and 316. Other QTLs, detected on chromosomes 2 and 3, were significantly associated with ascorbate (Vitamin C) and chlorophyll “a” content, respectively. Three QTLs, significantly associated with photosynthetic efficiency (Fv/Fm), were mapped on chromosomes 3, 4, and 8 under chilling-dependent and photo inhibition conditions15. A major QTL on chromosome 6 was linked to the cold-tolerance of photosynthesis under chilling conditions (15/13 °C) (day/night). And, in two different maize populations, a QTL on chromosome 2 was significantly associated with the cold-tolerance of photosynthesis17. However, these findings are less sensitive due to the genetic limitations imposed by using bi-parental mapping populations. This limitation produces low resolution on QTL mapping because of the long linkage disequilibrium extent18.Quantitative trait loci (QTL), COR gene expression and protein activities associated with cold-related traits in maize, have been investigated and partially identified by QTL linkage mapping19. This approach has been successfully used to map QTLs at a better resolution and to detect candidate genes associated with diseases in humans, animals, and plants20. GWAS has also been used to detect a few significant SNPs associated with cold-tolerant traits at the seed germination and seedling stages in maize23. For example, the SNPs associated with relative germination rate (based on bud emergence) under chilling conditions have been detected on chromosomes 1 (position: 146384303), 2 (position: 79181517), 4 (position: 148401277), and 7 (position: 51743082)23. Also, a SNP associated with relative root number at germination was found on chromosome 2 (position:89269445)23 and a SNP associated with Fv/Fm was located on chromosome 1 (position:248397011)22.The development of high-throughput DNA sequencing technologies, including single nucleotide polymorphism (SNP) genotyping and genotyping-by-sequencing (GBS), allows rapid and economical genotyping for many individuals. Genome-Wide Association Study (GWAS) mapping became a powerful alternative, based on linkage disequilibrium (LD) and sufficient genetic background information provided by diverse maize panels24. Our study was designed to accomplish the following objectives: 1) perform GWAS to identify potential SNPs responsible for chilling tolerance during development of root radicles, 2) compare our GWAS results with previous QTL mapping results, and 3) identify and evaluate candidate genes for future studies.Although some potential cold-tolerant genes and QTLs have been identified in maize, the genetic studies of chilling stress on germination is limited. In this study, we selected a diverse maize population panel, composed of 282 inbred lines, that has been successfully used to investigate quantitative traits via GWASst day under chilling (Table The diverse maize association panel demonstrated much greater variation in germination rates (GRs) on day 21 under chilling conditions (8 °C) compared to the panel’s GRs on day 7 under normal conditions (25 °C). Overall, seed GR was substantially suppressed under chilling conditions (median = 30.0%) in contrast to normal conditions (median = 90.00%) — even when GR was measured as far out as the 21st day; however, we did observe germinations in these lines after the 21st day.We also found extreme differences in GR among the 241 inbred lines of the diverse maize association panel under chilling conditions . To obtain robust measurements of DT50, we continued observing germination for a total of 31 days. The accumulative measurements indicated that the initial and final values of DT50 were 10.3 and 29.6 days, respectively, using the Boltzmann function , which was detailed in the Materials and Methods section. For GR, DT50 and GI, we derived corresponding relative traits by dividing each trait’s chilling condition value by its normal condition value, allowing us to make valid comparisons between the two conditions. The relative GR (RGR) had a median of 0.40 Table . The rel26.In total, we measured nine traits, including the six combinations of three traits  and two treatments  plus the three relative traits derived from GR, DT50, and GI values. Seven of the nine traits had mono-modal distributions; both DT50_C and DT50_N displayed weak indications of bi-modal distributions Fig. . Neverth27.Correlations were weak between each trait under chilling and normal conditions (Supplementary Table 28 and software package (http://zzlab.net/FarmCPU). As the measurements were strongly associated with population structure  for 241 maize inbred lines genotyped by 2,271,584 SNPs, using recently developed FarmCPU methodAmong the 17 associated SNPs, only one SNP (ss196436428) was associated with two traits (RDT50 and RGI). Five SNPs were associated with RDT50; two SNPs were located on chromosome 7 and three were distributed on chromosome 2. Eight SNPs were associated with RGI and distributed on chromosomes 1, 2, 4, and 6. The remaining five SNPs were associated with RGR, with two located on chromosome 2, and one each distributed on chromosomes 1, 6, and 9.14; trapping efficiency of PSII (F′v/F′m), carbon exchange rate (CER) at 15 °C, and leaf greenness (SPAD) across temperatures17; and Fv/Fm across different sowing stages in a field experiment29.Among the 17 associated SNPs, five were located directly within QTL regions identified in previous studies and reported to be responsible for cold traits in maize  at suboptimal temperature12, operating quantum efficiency of PSII photochemistry (ɸPSII) at 15 °C, and minimal fluorescence (Fo) across temperatures17. SNP S2_154533439  was detected in a QTL region associated with CO2 fixation and ɸPSII in the third-leaf stage at 15 °C11. SNP S6_156520680  was located in a QTL region associated with shoot nitrogen content (N%) under 15 °C growing conditions17. These findings suggested most of the detected SNPs (10 out of 18) are located within previously identified QTL regions associated with maize cold-related traits.One SNP associated with RGI and two SNPs associated with RGR corresponded to QTLs on chromosomes 1, 2, and 6. SNP S1_258878734 on chromosome 1 at 258,878,734 bp was detected in QTL regions associated with malic enzyme content at suboptimal temperature30 (http://www.maizegdb.org/) to identify candidate genes hit directly by associated SNPs or nearby genes in high LD with the associated SNPs. Among the 17 associated SNPs, seven had hits directly on candidate genes. An additional 11 candidate genes were identified with extended screening on genes in high LD (r2 ≥ 0.8) and within distance of 200 kb of four associated SNPs. The candidate genes of direct and indirect hits are summarized with table and figure  with unknown encoding functions; thus, gene ontology annotations were unavailable. From those genes with known functions, we observed that some candidate genes can be involved in multiple functions. For example, we found four genes  that are involved in all three functional types. Twelve genes are involved in two types, molecular function and biological process , not one line in the maize 282-diverse-panel exhibited 50% germination within 7 days.Measuring GR at the optimal temperature, 25 °C, is a standard practice. Root emergence usually peaks at 2~3 days and germination is completed within 7 days38. However, only about one-half of the lines in the maize diversity panel experienced a GR over 50% before the 21st day. For the majority of the remaining lines, germination continued for almost 31 days.Historically, GR on the 21st day has been used as the standard measurement under chilling conditions (8 °C), which corresponds to GR on the 7th day under normal conditionsUsing the GR at 21 days has two advantages. One advantage is that the methodology and analyses will be consistent with the existing literature. The other advantage is that the length of the experiment is reduced from 31 days to 21 days when GR on the 21st day is of interest. However, because a large portion of our lines were slow to germinate under chilling conditions, we extended our observations and used two complementary metrics—GR on the 21st day and number of days to reach 50% germination. The Pearson correlation coefficient was 85% in the maize 282-diverse-panel. We identified one SNP that was associated with both metrics. And, as expected, we also identified associated SNPs specific to each metric.We found strong correlations among the three relative traits derived from the three raw germination traits. The absolute values of Pearson correlations coefficients were all above 70% and 80% for phenotypic and genetic correlations, respectively. The absolute values of Pearson correlations coefficients between RGI and RGR were 90% and 97% for phenotypic and genetic correlations, respectively. More pleiotropic SNPs were found for the two traits (RGI and RGR) that are closely correlated.This study did not include experimental data to evaluate candidate gene association with maize yield. We did not validate the expression and biological function for candidate genes either. Therefore, implications for future studies can only be prioritized by the confidence of our study results. We investigated multiple dimensions of confidence, including distance, pleiotropy, signal strength, linkage disequilibrium, cloning support, QTL analyses, and genes with GO terms shared with other candidate genes.The null results include the six associated SNPs without candidate genes identified, eight candidate genes without support of QTLs, and three genes without GO terms identified. The possible causes include false positives, lack of recognition, and incomplete or incorrect gene annotation. These SNPs and genes have the lowest priority.The Highest priority goes to five of the candidate genes  that involve functions related to cold resistance, such as freezing tolerance response, ascorbate biosynthesis, and ABA, in maize or analogous species. Two of these five genes (GRMZM2G389768 and GRMZM2G057186) were hit directly, in the exonic region, by the associated SNPs identified in this study. The other three candidate genes  were indirectly hit by two SNPs highly correlated and nearby associated SNPs (ss196436428 and s2_117871531) and were supported by previous QTL analyses  in Beijing, China. CAAS increased the size of this maize seed collection in 2011 and 2012.To cover the environment requirements for seed production, we grew the CAAS-produced, 282 association panel seeds in both tropical and temperate regions to ensure seed quality in the extreme lines. Seeds were planted in China’s tropical Hainan province in winter 2013 and in temperate Heilongjiang province in spring 2014. Diseases, insects, and weeds were adequately controlled during the growing seasons. Physiologically mature ears were harvested from each environment, tropical and temperate. The seeds were dried under 25~30 °C conditions to achieve 14% water content and then stored at −4 °C. A total of 266 lines qualified for germination tests based on the quantity and physiological maturity status of seeds from both regions. Prior to seed germination experiments, all seeds were surface-disinfested with 1% NaOCl (sodium hypochlorite) for 5 minutes and then triple rinsed with sterile distilled water.37, we conducted the experiments in two growth chambers . The temperature was set at 8.0 °C38 for chilling conditions (treatment) and at 25.0 °C for normal conditions (control). Seeds were germinated in Petri dishes with sterile wetted filter paper. The Petri dishes were completely randomized. For each condition (treatment and control), we used three replicates per line and per batch of region where the seeds produced. Each replicate contained 30 seeds. Measurements were conducted daily, starting from day 1. We defined seed germination as observed root emergence. Following the advanced protocol of germination developed by Noli, E. et al.39 and Wen, W. et al.38, daily measurements spanned 7 days under normal conditions and 31 days under chilling conditions. The batches of seeds produced in two regions  were averaged for mapping genes underlying cold tolerance.Following the ISTA protocol40. The GI under chilling conditions (GI_C) was calculated daily, from 0 to 31 days; the GI under control conditions (GI_N) was calculated daily, from 0 to 7 days (Mhatre and Chaphekar41). The relative GI (RGI) was used to compare chilling to control conditions (GI_C/GI_N).We measured a total of nine phenotypic traits. The first six traits included the following: 1) germination (defined as root emergence) rate at 21 days under 8 °C (chilling) (GR21_C); 2) germination at 7 days under 25 °C (control) (GR_N); 3) relative germination rate (RGR), comparing chilling to control conditions (GR21_C/GR_N); 4) days to 50% germination under chilling (DT50_C); 5) days to 50% germination under control (DT50_N); and 6) relative days to 50% germination (RDT50), compared chilling to control conditions (DT50_C/DT50_N). The last three traits were based on a germination index (GI), calculated as GI = ∑(Gt/Tt), where Gt was the number of seeds newly germinated on day t and Tt was the number of days elapsed43.For each trait above, the estimate breeding value (EBV) was calculated by the Best Linear Unbiased Prediction (BLUP) method. The genetic relationship matrix was estimated using about 51,742 SNPs. Together, the phenotypes of each trait and the genetic relationship matrix of individuals were used in rrBLUP to calculate each trait’s EBV44.The standard deviation, range, mean, and median were calculated for each of the nine traits. EBV was used to assess genetic correlations between the traits. R language 3.12 and R package ‘psych’ was used to calculate correlations and to produce the correlation diagram45 and a GBS (genotyping by sequencing) dataset containing 2,219,845 SNP markers downloaded from http://mirrors.iplantcollaborative.org/browse/iplant/home/shared/panzea/genotypes/GBS/v23/Maize282_imputed_AllZea_GBS_Build_July_2012_FINAL.zip . The two datasets were combined and filtered for duplicate SNPs. A total of 2,271,584 SNPs, distributed over the entire maize genome, were used in this study.For genotypic data, we used a set of 51,742 SNP markers derived from an Illumina maize 50 K array28. The first three principal components were fitted as the covariates to control population structure. The plots of the first three principal components reflected the subpopulation structure, including stiff stalk, non-stiff stalk and tropical/subtropical  to calculate LD as the squared correlation coefficient (r2) between each marker within an intra-chromosomal distance of 1000 kb. A histogram was then plotted to analyze the LD decay according genetic distances and pairwise LD across the entire genome. SNPs with a paired r2 ≥ 0.8 were identified within a 400 kb region up- and downstream of the significant cold-trait-associated SNPs. Then, we examined all candidate genes either containing the associated SNPs or in high linkage disequilibrium (r2 ≥ 0.8) with the associated SNPs and within a distance of 400 kb. The B73 RefGen_V2 gene model from the maizeGDB website (http://www.maizegdb.org/) was used to map the loci and genetic information.Genes hit directly by the associated SNPs and genes in high linkage disequilibrium (LD) with the associated SNPs and within 400 kb were selected as candidate genes. We used the PLINK analysis toolset (http://www.ncbi.nlm.nih.gov/) to search for gene annotations and functions in related cereals and ontology plants. The gene ontology (GO) for candidate genes was performed by AgriGO 47 and Blast2GO 3.0 software48.Based on the complementary DNA (cDNA) sequences of the candidate genes, we used previously published evidence and the National Center for Biotechnology Information (NCBI) database (Supplementary Candidate Genes, Tables, Figures


FST ≤ 0.15) or high genetic differentiation (FST > 0.15). A total of 21 outlier loci under positive selection were identified by using four different FST-outlier testing algorithms. The markers and genome locations under positive selection are consistent with the known patterns of selection. These results suggested that genetic differentiation between sun and shade habitats is substantial, radiation-associated, and therefore ecologically determined. Hence, the results of this study reflected effects of natural selection through solar radiation on EST-related SNP genetic diversity, resulting presumably in different adaptive complexes at a micro-scale divergence. The present work highlights the evolutionary theory and application significance of solar radiation-driven natural selection in wheat improvement.Whole-genome scans with large number of genetic markers provide the opportunity to investigate local adaptation in natural populations and identify candidate genes under positive selection. In the present study, adaptation genetic differentiation associated with solar radiation was investigated using 695 polymorphic SNP markers in wild emmer wheat originated in a micro-site at Yehudiyya, Israel. The test involved two solar radiation niches: (1) sun, in-between trees; and (2) shade, under tree canopy, separated apart by a distance of 2–4 m. Analysis of molecular variance showed a small (0.53%) but significant portion of overall variation between the sun and shade micro-niches, indicating a non-ignorable genetic differentiation between sun and shade habitats. Fifty SNP markers showed a medium (0.05 ≤  Triticum dicoccoides, the progenitor of modern tetraploid and hexaploid cultivated wheats, is distributed over the Fertile Crescent and can be found in ecologically highly diverse environments , with a close relative of the goat grass Aegilops speltoides  . Sampling was conducted in pairs in two solar radiation niches: (1) in shade, under the canopies of the oak trees ; (2) in sun, in the circumference around each tree and between trees. The sun-shade niches are abutting and the difference of the samples tested is 2–4 m apart. While (1) is largely shaded during the day, (2) is exposed in daytime to continuous sun radiation and drying. Hence, the soil temperature in the sun niche was almost 10°C higher than in the shade niche. During the growing season (October–May) of wild emmer wheat, the shade niche is under stresses of lower temperature and lower intensity of radiation in contrast to the sun niche. Besides, the two microniches vary significantly in their plant formation . Detailed information about SNP discovery, which was carried out on a panel of 32 lines of tetraploids and hexaploid wheat, has been described in a previous study  using the Illumina Bead Array platform and Golden Gate Assay following the manufacturer's protocol.The 92 wild emmer wheat accessions were genotyped with 1,536 SNP markers. A detailed list of the 1,536 SNPs can be downloaded from the Wheat SNP Database  V. 2010.3. In brief, the default clustering algorithm implemented in GS was first used to identify assays that produced three distinct clusters corresponding to the AA, AB, and BB genotypes expected for biallelic SNPs. Each SNP clusters was manually examined to correct imperfect calling of automated clustering. The accuracy for SNP clustering was validated visually  and heterozygosity were calculated by POWERMARKER Ver. 3.25  using the ARLEQUIN version 3.11 software with 16,000 permutations .Forward stepwise discriminant analysis was performed to distinguish individuals from the two microclimatic niches according to the allele's frequencies, also implemented in the SPSS ver. 13.0 software ] was obtained according to Excoffier and Lischer  using the Bayesian likelihood method via reversible-jump Monte Carlo Markov chain (MCMC). This method is based on a logistic regression model that separates locus-specific effects of selection from population-specific effects of demography. BAYESCAN runs were implemented using default values for all parameters, including a total of 100,000 iterations after an initial burn-in of 50,000 steps. A threshold for posterior odds (PO) > 10 (strong selection) was used as a marker to be considered under selection. This corresponds to a posterior probability >0.91 for the model accounting for selection.The third algorithm for detecting outlier SNPs was implemented in BayeScan 2.1 (https://en.wikipedia.org/wiki/Resampling_(statistics)#Permutation_tests). The method is described as the following. The FST value was calculated for each marker in given subdivision of plants in sunny and shady niches using observed allele frequencies and numbers of plants with non-missed genotype . FST value for the given subdivision of plants was compared with ones obtained for plants randomly subdivided into two groups. To take this multiple comparison problem into account, FDR correction for p-values of individual markers was applied  for gene diversity and PIC  and 659 (48.5%) polymorphic markers were detected in shady and sunny niches, respectively. And also for all the seven homoeologous groups and the two genomes, the polymorphism level in the sunny niche is obviously higher than that in the shady niches  or high genetic differentiation (FST > 0.15).A total of 695 markers were found to be polymorphic in the panel of 92 wild emmer accessions. Among these SNP markers, 115 were polymorphic only in the sun, while 36 were polymorphic only in the shade, and the remaining 544 markers were polymorphic in both shade and sun. The locus-by-locus F = 11.246, P < 0.0001]. Even using three  of the 50 SNP markers, 72.8% of 92 individuals could be correctly assigned to their original micro-niches, and only 13 plants (27.7%) from the shade and 12 plants (26.7%) from the sun were incorrectly classified into the alternative niche. Here too, the difference between the centroids of the two micro-niches was also highly significant . These results clearly suggested obvious genetic differentiation at some loci between the two microclimatic niches and that the observed genetic differentiation at SNP loci is substantial, niche-associated, and therefore, ecologically determined.Forward stepwise discriminant analysis was performed to distinguish individuals from the two microclimatic niches according to 50 divergent markers between the shady and sunny micro-niches. The result indicated that six  of the 50 SNP markers were sufficient to correctly classify 81.5% of 92 individuals into their original microclimatic sunny or shady niches, whereas only ten (21.3%) plants from the shade were incorrectly assigned to the sun, and seven (15.6%) individuals from the sun were incorrectly assigned to the shade. The difference between the distribution center of the two subpopulations was highly significant [FST outlier detection algorithms were used, i.e., ARLEQUIN, LOSITAN, BayeScan, and permutation test, in this study. In ARLEQUIN, a total of 18 outlier loci were revealed as the candidates subjected to positive selection  and permutation test , was highly significant (P = 0.00831) as indicated by permutation test , suggesting divergent adaptive complexes in sun and shade.AMOVA based on 695 polymorphic SNP markers showed that genetic variation within microclimatic niches of wild emmer wheat is obviously larger than that among microclimatic niches, 99.47 vs. 0.53%  Grant No. 31030055, 30870233 (to JP) and 31672482, the Natural Science Foundation of Hubei Province, China Grant No. ZRY1326, and the Taishan Scholars Program of Shandong Province in China.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Obesity is a highly prevalent risk factor for cardiometabolic diseases. Observationalstudies suggest that obesity is associated with psychiatric traits, but causalinference from such studies has several limitations. We used two-sample Mendelianrandomization methods  to evaluate the association of body mass index (BMI) with threepsychiatric traits using data from the Genetic Investigation of AnthropometricTraits and Psychiatric Genomics consortia. Causal odds ratio estimates per1-standard deviation increment in BMI ranged from 0.88  to 1.23 for bipolar disorder; 0.93  to 1.41 for schizophrenia; and 1.15  to 1.40  formajor depressive disorder. Analyses removing potentially influential SNPs suggestedthat the effect estimates for depression might be underestimated. Our findings donot support the notion that higher BMI increases risk of bipolar disorder andschizophrenia. Although the point estimates for depression were consistent in allsensitivity analyses, the overall statistical evidence was weak. However, the factthat SNP-depression associations were estimated in relatively small samples reducedpower to detect causal effects. This should be re-addressed when SNP-depressionassociations from larger studies become available. Obesity is a major public health concern with well-established risk-increasing effects oncardiometabolic diseasesPsychiatric disorders are one of the main causes of years lived with disabilityglobally357610Cohort studies provide support that obesity both predicts and can be predicted bydepression3121517Most of the evidence regarding the association of obesity with psychiatric disorderscomes from observational studies, which present several limitations for causalinference, including residual confounding, measurement error and reverse causation202122Mendelian randomization relies on assuming that any association between the geneticinstrument(s) and the health outcome is entirely mediated by the exposure 2122FTO and MC4R variantsas genetic instruments252725292526293133Previous Mendelian studies on this topic yielded inconsistent findings. However, suchstudies have some limitations, including using only Currently, many GWAS consortia make summary-level results freely available. Such data canbe used to obtain causal effect estimates based on multiple single nucleotidepolymorphisms (SNPs) using the inverse-variance weighting (IVW) methodA flowchart depicting the selection process of the genetic variants used inMendelian randomization analysis is shown in Mendelian randomization results for each psychiatric disorder are shown in TRIM66), rs12401738 (FUBP1), rs9925964(KAT8), rs11191560 (NT5C2), rs11057405 (CLIP1) inbipolar disorder; rs13107325 (SLC39A8), rs11191560 (NT5C2),rs9400239 (FOXO3) and rs4787491 (INO80E) in schizophrenia; andrs571312 (MC4R), rs1462433 (HNF4G), rs6785875 (FHIT) andrs11191560 (NT5C2) in major depressive disorder .The following SNPs – rs number (gene locus) – were classified asinfluential: rs4256980  for bipolar disorder, 0.49 and68.8%(P = 9.9 × 10−24)for schizophrenia, and 0.20 and 18.4% (P = 0.073) formajor depressive disorder. In a random effects meta-regression model, includingan indicator variable of influentiality status reducedτ2 and 2Ivalues of major depressive disorder ratio estimates to 0.14 and 13.4%,respectively, with an adjusted-R2 value (which indicates theamount of heterogeneity explained by the moderators) of 29.3%(P = 0.028), and the test of residual between-instrumentsheterogeneity yielded P = 0.152. Regarding bipolardisorder and schizophrenia, the same procedure had a substantially smallerinfluence, with adjusted-R2 values of 1.1%and 2.4% , respectively. When an indicatorof belonging to a neuronal-related biological category was used instead ofinfluentiality status, all adjusted-R2 values were 0%.Values of the meta-analytical measures of heterogeneity2 and residual2I values for the selected moderators togetherwere 32.9% and 21.5% for bipolar disorder, 8.3% and 66.7% for schizophrenia and68.8% and 6.5% for major depressive disorder. Mendelian randomization odds ratioestimates excluding each and both of the selected biological categories areshown in The results of the forward selection process of biological moderators ofindividual-SNP ratio estimates using random effects meta-regression are shown inIn the present study, we evaluated the association between BMI-associated SNPs andthree psychiatric disorders by Mendelian randomization using summary-level data.Only major depressive disorder presented consistent causal effect estimates usingthe three Mendelian randomization methods and in all sensitivity analyses. The factthat removing influential variants increased, rather than attenuated, the odds ratioestimates is also reassuring because it suggests that the true causal effect mightbe greater than that estimated using all variants simultaneously. However, theoverall statistical evidence for any meaningful associations was weak.Our findings suggest that the commonly positive association between obesity andpsychiatric disorders reported in observational studies3456731213FTO variant rs1421085and common mental disorders was detected in the British Whitehall II studyFTO andMC4R variants were inversely associated with psychological distress in amuch larger Danish cohortFTOMC4RFTO might not be a valid instrument for BMI when mental disorders arethe outcome25MC4R variant rs571312 was identified as an influential  instrument in major depressive disorder analysis, but not in the remainingoutcomes. In the Young Finns cohort, BMI instrumented by a 31-SNP allele score waspositively associated with depressive symptoms29Mendelian randomization studies on the association between obesity and psychiatricdisorders are scarce, with no studies for bipolar disorders and schizophrenia. Apositive association between BMI instrumented using the Obesity and psychiatric disorders may share several dysregulated physiologicalpathways, including inflammation13European ancestry was predominant in all datasets, which increases the plausibilityof the assumption that the two datasets are samples from the same or comparablepopulations. Regarding power, two-sample Mendelian randomization power depends moreon the precision of SNP-outcome than SNP-exposure associationsI2 statistic, and that excluding four influential SNPsincreased the magnitude of the causal effect estimates and further attenuated theI2 statistic.It is impossible to prove empirically whether Mendelian randomization results mostlyreflect causal effects of the exposure or violations of instrumental variableassumptions. In the present study, three Mendelian randomization methods –each with different assumptions regarding horizontal pleiotropy – were used,and all of them were consistent regarding major depressive disorder, but not whenanalyzing bipolar disorder and shcizophrenia. Moreover, both MR-Egger regression andthe weighted median approach  point estimates were stronger than the IVW one. It was alsoreassuring that major depressive disorder presented the smallest heterogeneity inindividual-SNP ratio estimates measured using the conventionalIn general, our findings do not corroborate the notion that BMI has a causal effecton bipolar disorder or schizophrenia. Regarding major depressive disorder, althoughthe point estimates were consistent across a range of analyses, the overallstatistical evidence was weak. Re-addressing this research question onceSNP-depression associations from larger GWAS become available would be warranted toobtain more precise Mendelian randomization estimates . Given the high prevalence of both obesity and depressionworldwide, understanding the mechanisms underlying associations between BMI anddepression, with identification and quantification of causal effects, is of publichealth relevance. Analyses involving schizophrenia were less prone to power issuesbecause SNP-schizophrenia associations were estimated in a relatively large sample.Bipolar disorder, similarly to major depressive disorder, require furtherinvestigation once more precise SNP-outcome associations are available.The final datasets were provided in Supplementary Tables S3–S6.https://www.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files).Locke and colleagues’, under the GIANT consortium, identified 97BMI-associated single nucleotide polymorphisms (SNPs)2 . In studies of unrelated individuals, residuals were calculatedwithin sex and (when relevant) case/control strata. In family studies,residuals were sex-adjusted rather than sex-specific.The outcome was obtained by applying an inverse normal transformation to BMIresiduals on age and ager2 > 0.2. For variants withoutgenes mapping to this interval, the nearest gene was used. This resulted in405 genes, which were annotated based on a manual literature review usingseveral venues. Through this process, those 405 genes were manually curatedinto 25 biological categories containing at least three genes, to which eachof the 97 BMI-associated variants were assigned , which performedlogistic regression adjusting for ancestry-informative principal componentsand assuming an additive effect.Log odds ratio and standard error estimates of SNPs-psychiatric disordersassociations were obtained from the Psychiatric Genomics Consortium (PGC). Aproxy was defined as a genetic variant within 500 kb of the index SNPand in high linkage disequilibrium(r2 > 0.8) with it. If therewas more than one proxy available for the same index SNP, the variantwith the higher r2 was selected. Using 1000Genomes Pilot 1 (CEU population) as the reference panel, 26 proxiesavailable in both SNP-BMI and SNP-major depressive disorder datasetswere identified. An additional search using HapMap release 22 (CEUpopulation) as the reference panel yielded two additional proxies, thustotalizing 28 proxy variants and 90 BMI-associated SNPs. Afterharmonization, the correlation between effect allele frequencies was0.989.Only 62 BMI-associated SNPs were available. Proxies for missing variantswere identified using the SNP Annotation and Proxy Search tool , and threefamily-based studies comprising 1,235 parent affected-offspring Europeanancestry triosIVW method, consisting of a linear regression of SNP-outcome (dependentvariable) on SNP-exposure coefficients (independent variable), weightingby the inverse of the squared SNP-outcome standard errors. The interceptis constrained at zero, which follows from the assumption thatSNP-outcome associations are entirely mediated by the exposure2I statistic – MR-Egger regression, which differs from the IVW method because theintercept is not constrained. This yields a causal effect estimaterobust against horizontal pleiotropy under the InSIDE (InstrumentStrength Independent on Direct Effect) assumption, which requires thatthe SNP-exposure and direct SNP-outcome associations are independent.The intercept provides a test for directional horizontal pleiotropyWeighted median method, which provides a valid causal estimate if atleast 50% of the weights  come from valid instruments,regardless of whether or not horizontal pleiotropic effects of theremaining variants respect the InSIDE assumptionIn Mendelian randomization analyses, all BMI-associated SNPs available for eachpsychiatric disorder were used .The folet al.36Point estimates and standard errors were calculated for the IVW, MR-Egger andweighted median methods using the code provided by Bowden Sample overlap between GIANT and PGC datasets can bias causal effect estimatesfrom Mendelian randomization towards the observational (and possibly confounded)estimate33Mendelian randomization analyses were also performed within SNP subgroups ofbiological function: neuronal-related ; and non-neuronal (comprisingthe remaining categories). Consistency among different biological subgroupswould argue against the role of horizontal pleiotropy in the results.To evaluate if the results were substantially driven by a few instruments, theanalyses were repeated excluding influential SNPs. A SNP was classified asinfluential if at least one of two tests of influence  yielded aP-value < 0.05. These were calculated separately for IVW orMR-Egger regression, but the same SNPs were classified as influential usingeither Mendelian randomization method. The null distributions of these testswere: Student’s t-distribution with degrees of freedom equal to thenumber of SNPs minus 2, for studentized residuals; or the F-statistic with jointdegrees of freedom equal to  (for IVW) or  (for MR-Egger regression), for Cook’s distance2τ, until no additional covariates reachedP < 5%.In exploratory analysis aimed at identifying factors associated with horizontalpleiotropy, individual-SNP ratio estimates  were used to calculatebetween-instrument heterogeneity, which corresponds to horizontal pleiotropy inthe Mendelian randomization contextTwo additional sensitivity analyses were performed: (i) IVW estimates withinsubgroups of SNPs excluding one biological category at a time; (ii) estimationof causal effects using the three Mendelian randomization methods after removingSNPs belonging to the biological categories identified in the forward selectionprocess described above.www.r-project.org).Analyses were performed using R 3.2.4 .


It uses a new 16-genotype probabilistic model to unify single nucleotide polymorphism and insertion and deletion calling in a single variant calling algorithm. In benchmark comparisons with 5 other widely used variant callers on a modern 36-core server, 16GT demonstrated improved sensitivity in calling single nucleotide polymorphisms, and it provided comparable sensitivity and accuracy for calling insertions and deletions as compared to the GATK HaplotypeCaller. 16GT is available at  Single nucleotide polymorphisms (SNPs) and insertions and deletions (indels) that occur at a specific genome position are interdependent; i.e., evidence that elevates the probability of 1 variant type should decrease the probability of other possible variant types, and the probability of all possible alleles should sum to 1. However, widely used tools such as GATK’s UnifiedGenotyper  use sepaIn order to detect SNPs and indels with a unified approach, we developed a new 16-genotype probabilistic model and its implementation, named 16GT. Building on an idea first introduced in Luo et al. , 16GT usP(L|F)∝P(F|L)P(L), where L is an assumed genotype. F refers to the observation of the 6 alleles  at a given genome position. P(L) is the prior probability of the genotype, P(F|L) is the likelihood of the observed genotype, and P(L|F) is the posterior probability of the genotype. The resulting genotype maxL is assigned to the genotype with the highest posterior probability. The distance between the highest posterior probability and the second highest posterior probability is used as a quality metric in 16GT, along with some other metrics introduced by GATK  [Posterior probabilities of these 16 genotypes are calculated using a Bayesian model P and use the resulting P-value as the goodness of fit. When calculating the likelihood of a homozygous genotype, ideally we expect 100% single allele support from the observation. For example, consider genotype “AA”:eP is the probability of an erroneous base call.To test how well an observation fits the expectation of different genotypes, we use a 2-tailed Fisher exact test s is the allele type, n is the number of reads supporting allele is, Q is the base quality, and iM is the mapping quality. f is a function describing how is, Q, and iM change the observation:sP for SNP error and dP for indel error. We define errP as s+PdP, where sP and dP are set to 0.01 and 0.005, respectively. These 2 values were set empirically based on the observation that SNP errors are more common than indel errors in library construction and in the reference genome.For a heterozygous genotype, 50% support is expected for each allele in the genotype; e.g., consider “CG”:In addition, most short read aligners use a dynamic programming algorithm to enable gapped alignment, using a scoring scheme that usually penalizes gap opening and extension more than mismatch. Consequently, authentic gaps that occur at an end of a read are more likely to be substituted by a set of false SNPs or alternatively to get trimmed or clipped. Thus, we applied a coefficient γ to weight indel observations more than SNPs in order to increase the sensitivity on indels.Given (i) a known rate of single nucleotide differences between 2 unrelated haplotypes; (ii) a known rate of single indel differences between 2 unrelated haplotypes; and (iii) a known Transitions to Transversions ratio (Ti/Tv), the 16GT model's prior probabilities are calculated as shown in Table ε is set to the value for human and needs to be changed for other species.Given (i) a known rate θ of single nucleotide differences between 2 unrelated haplotypes; (ii) a known rate ω of single indel differences between 2 unrelated haplotypes; and (iii) a known Ti/Tv ε, transition is expected to occur more frequently than transversion under selective pressure. The default known rates for human genome are θ = 0.001, ω = 0.0001, ε = 2.1, where We benchmarked 16GT with GATK UnifiedGenotyper, GATK HaplotypeCaller   using a For SNPs, 16GT produced the most true positive calls and the fewest false negative calls; i.e., it has the highest sensitivity and specificity among all tools. dbSNP version 138 also reported 79% of 16GT’s false positive calls, which is the highest among other callers. However, we should point out that the GIAB variant set is biased toward GATK because it was primarily derived from GATK-based analyses, as reported previously . As an oFor indels, 16GT produced slightly fewer true positive calls and slightly more false negative calls than HaplotypeCaller, but less than half as many false negative calls as UnifiedGenotyper. dbSNP version 138 covered 65% of 16GT’s false positive indels. Further investigation into the 1462 false positive indels shows that 981 (67%) of them meet all 3 of the following criteria: (i) at least 3 reads supporting the variant; (ii) at least 1 read supporting both the positive and negative strands; and (iii) in over 80% of the reads that support the variant, there exists no other variant in its flanking 10 bp. This suggests that some of these “false positives” might be correct, although further experimental validation would be required to confirm this suggestion. Supplementary Fig. S2 shows 3 examples where the putative false positive from 16GT is likely to be correct.16GT is the firstly publicly available implementation using a 16-genotype probabilistic model for variant calling. Compared with local assembly based variant callers, 16GT provides better sensitivity in SNP calling and comparable sensitivity in indel calling. In the current implementation, 16GT can only be applied to germline variant detection. In the future, we will enhance 16GT to support multi-sample variant calling and GVCF output and to support somatic variant detection and extend the model to support variant calling in species with more than 2 haplotypes.Additional File 1.docxindel: insertions and deletions; SNP: single nucleotide polymorphism; Ti/Tv: Transitions to Transversions.GIGA-D-17-00091_Original-Submission.pdfClick here for additional data file.GIGA-D-17-00091_Revision-1.pdfClick here for additional data file.Response-to-Reviewer-Comments_Original-Submission.pdfClick here for additional data file.Reviewer-1-Report-.pdfClick here for additional data file.Reviewer-2-Report-.pdfClick here for additional data file.Additional File 1.docxClick here for additional data file.


Mangifera indica L. cultivations have been widely planted in tropical areas of India, Africa, Asia, and Central America. However, at least 20 years ago its spreading allowed the development of some cultivars in Sicily, an island to the south of Italy, where the favourable subtropical climate and adapted soils represent the perfect field to create new sources of production for the Sicilian agricultural supply chain. Currently, cultivations of Kensington Pride, Keitt, Glenn, Maya, and Tommy Atkins varieties are active in Sicily and their products meet the requirements of local and European markets. Mango plants produce fleshy stone fruits rich in phytochemicals with an undisputed nutritional value for its high content of polyphenolics and vitamins. This review provides an overview of the antioxidant, anti-inflammatory, and anticancer properties of mango, a fruit that should be included in everyone’s diet for its multifaceted biochemical actions and health-enhancing properties.Historically,  Nowadays, the use of medicinal plants and bioactive phytocompounds has seen more growing interest. The importance of a diet rich in polyphenols has long been sponsored and underlined because of their radical scavenging action, as well as anti-carcinogenetic properties . Fruit aMangifera indica L. (mango) is known as “the king of fruits” because it is the most popular fruit in tropical regions. It is the national fruit of India and the Philippines, and the national tree of Bangladesh [Mangifera, which consists of numerous species of tropical fruits in the family of Anacardiaceae [Mangifera indica L. is native to India and Southeast Asia [Fruits of tropical and subtropical regions are appreciated for their nutritional value, as well as for the presence of health-enhancing compounds. ngladesh . The manrdiaceae . Mangifeast Asia  where itast Asia , Africa ast Asia , Australast Asia , and forast Asia . Over onast Asia ,13. Albeast Asia , this neOur purpose in this review was to provide an exhaustive overview of the main chemical bioactive components and the relevant pharmacological activities  of the different mango fruit fractions. Throughout the manuscript we also address all pieces of information collected so far on Sicilian mango, underpinning its potential benefits.The mango tree is an evergreen, fast-growing, and long-lived orchard. It is very vigorous with a large canopy and an almost circular projection. The leaves are perennial, deep green, pointed, and shiny, while the inflorescence occurs in panicles consisting of about 3000 whitish-red or yellowish–green flowers. In tropical regions the trees can reach up to 30–40 m in height, while in subtropical areas the growth rate is consistently reduced. The mango fruit has hundreds of varieties, each having its own characteristic taste, shape, and size. Each fruit is 5–15 cm long and 4–10 cm in diameter. Usually its weight ranges from 150 g to around 750 g, reaching approximately 390 g in Sicilian mango fruit . The outMangifera indica L., which has been the focus of attention of many researchers around the tropical and subtropical areas, has to be searched for its phytochemical content that qualified mango fruit as a superfruit model [The growing interest for it model .The chemical analysis of mango pulp provided evidence that it has a relatively high content in calories  and is an important source of potassium, fibre, and vitamins. The nutritive value of mango is listed in Mango is also a particularly rich source of polyphenols, a diverse group of organic micronutrients found in plants which exert specific health benefits . PolypheDuring the processing of mango, exocarp and seed are discarded. However, several studies report that these mango by-products also contain high levels of health-enhancing compounds. Mango exocarp has been found to be a good source of polyphenols, carotenoids, dietary fibre, and vitamin E . PolypheMangifera indica L. is used in Cuba as a remedy for diarrhoea, fever, gastritis, and ulcers [In traditional medicine the different parts of the mango tree  are used for their health properties . Decoctid ulcers .Preliminary studies performed on Sicilian mango cultivars indicate that the composition of fruit phytocompounds reflects that found in the fruit cultivated in tropical areas. However, a better analysis must be performed to correlate the total polyphenols  and total flavonoid index  found in Sicilian Kensington Pride to the other tropical cultivars , also in2+ to Fe3+. These derivatives of oxygen, highly unstable and particularly reactive, oxidize atoms or organic molecules, especially cell components such as proteins, lipids, and nucleic acids.Nowadays, particular attention is paid to nutrients capable of counteracting oxidative stress. A certain number of reactive oxygen species, or ROS, including superoxide anions, hydroxyls, and hydrogen peroxide, are produced in the human body. Some of them, such as superoxide anions and hydrogen peroxide, are physiologically generated during the electron transfer in the mitochondrial respiratory chain. Other species, as the hydroxyl radical, one of the more dangerous ROS, is produced by the Fenton reaction, causing the oxidation of FeCells have developed a specific group of enzymatic systems  to remove ROS and many of them are transcriptionally regulated by Nrf2 (nuclear factor erythroid 2-related factor 2)/Keap-1 (Kelch-like ECH-associated protein 1) axis, the master regulatory pathway of the antioxidant response . Under sIt is well known that phytochemical compounds of a phenolic nature commonly found in fruits display free radical scavenging activities, due to the reactivity of the phenol moiety and via hydrogen or electron donation. The large variety of antioxidants, pigments, and vitamins that are present in any part of the mango plant are responsible for the antioxidant and free radical scavenging activities.The analysis of different commercially-ripe mango varieties from Bangladesh, such as Fazli, Langra, Ashwina, Himsagor, and Amrupali, demonstrated, for example, the existence of differences in functional factors and antioxidant constituents  present in the mesocarp which change from one cultivar to another . Among aMangifera indica, containing a mixture of polyphenols and flavonoids, attenuated the colitis symptoms, like body weight loss, colon shortening, and diarrhoea [Several studies showed that phytochemicals contained in mango play an anti-inflammatory role in several chronic pathological disorders associated with inflammatory responses ,39. Infliarrhoea . Moreoveiarrhoea . These eiarrhoea . Furtheriarrhoea  reportediarrhoea . In anotiarrhoea . A largeiarrhoea  showed tiarrhoea  reportedBioactive compounds of mango have been also reported to exert anti-diabetic effects. Diabetes mellitus is a group of metabolic disorders associated with hyperglycaemia caused by defects in insulin secretion and/or action. Hyperglycaemia-induced advanced glycation end products (AGEs) activate their receptors (RAGEs) resulting in NF-κB-mediated release of pro-inflammatory cytokines. Activation of AGE-RAGE axis is associated with diabetic compliance, as cardiomyopathy and nephropathy. Mango mesocarp and leaf extracts produce a significant hypoglycaemic effect in streptozotocin (STZ)-induced diabetic rats ,51,52. FMangifera indica L. exerts cytotoxic effects in pancreatic cancer cells that correlated, among the isolated bioactive compounds, with mangiferolate and isoambolic acid. Ethanolic extract of mango exocarp induced apoptosis in human cervix adenocarcinoma HeLa cells by downregulating the anti-apoptotic factor Bcl-2 and activating caspase proteases [O-galactoside, mangiferin gallate, isomangiferin gallate, quercetin-3-O-arabinopyranoside, and mangiferin. Furthemore, an aqueous extract of mango mesocarp has been reported to exert antitumor activity in a human colon adenocarcinoma cell line, as well as in a rodent model of colorectal cancer [Bioactive components contained in the different parts of mango have also shown anticancer activity in different tumour cell lines. Nguyen et al.  showed troteases . This efl cancer . Abdullal cancer  reportedl cancer . In addiAlthough only mesocarp is the edible fraction of the mango fruit, some cultures also used to eat the mango exocarp, probably knowing that the fruit peel contains a significant amount of healthful compounds that are present only in small amounts in the mesocarp. However, exocarp consumption can also promote an allergic reaction in some people because of either the presence of the oily organic allergen urushiol in the fruit peel , or the Therefore, these experimental observations open up the opportunity to isolate molecules active against cancer and opt for their use as nutritional supplements.Mangifera indica fruit components, provided evidence that many of their anti-scavenging properties can be ascribed to mangiferin. Mangiferin is a plant natural polyphenol of xanthone structure with C-glucosyl linkage and four aromatic hydroxyl groups that have been considered crucial for its antiradical and antioxidant effect as well as for its pharmacological activity [Anacardiaceae and Gentianaceae plant families [Cyclopia sp.), a popular herbal tea widely spread in South African areas. Mangiferin is highly soluble in water and can be easily extracted by infusion or in decoction preparations.Many studies, performed in order to analyse in details the chemical profiles and the mechanistic action of activity . This pofamilies . This moMangiferin is differently distributed in many parts of mango plant and fruit. It has been found in the bark of plant (18.33 g/kg dry weight), in leaves and roots , in mesoThe anti-free-radical action of mangiferin relies on its ability to directly neutralize ROS, such as hydroxyl radicals , superoxCompelling evidence demonstrated that mangiferin displays an efficient iron chelating potential , counterAnother study showed that chronic treatment with mangiferin significantly ameliorated renal dysfunction and reduced levels of AGEs and RAGEs in the renal cortex of diabetic rats. This last effect seems to be related to the ability of mangiferin to induce glyoxalase 1 (Glo-1), a detoxifying enzyme of methylglyoxal .Mangifera indica L. that is used as nutritional supplement in Cuba [®. When administered in animals, Vimang® exhibited an anti-nociceptive and anti-inflammatory action against acetic acid exposure in mice [Beyond the nutraceutical activities observed with mango fruit extracts, a significant antiradical action has also been observed by other parts of the plant. An example of the nutraceutical potential of this plant is provided by an aqueous stem bark extract obtained from selected species of  in Cuba  and comm in mice . Such an in mice ,78. This in mice .® exerts a neuroprotective and cognitive enhancing action for mild cognitive impairment, a prodromal phase of dementia, by increasing the activity of some scavenger enzymes, such as superoxide dismutase, glutathione peroxidase, catalase, and lowering malondialdehyde levels [In some studies it has also been demonstrated that Vimange levels .Mangifera indica are the coastal regions of Portugal, Spain, Greece, Israel, and Italy. In particular, in Sicily, a Southern Italian island, many different provinces have been identified as areas particularly prone to the cultivation of subtropical fruits of mango (Mangifera indica L.), avocado (Persea americana Mill.), papaya (Carica papaya L.), and lychees (Litchi chinensis S.).In the warm Mediterranean climate the main areas dedicated to the cultivation of Glenn, Maya, Tommy Atkins, Kensington Pride, and Keitt mango varieties spread out preferentially in Balestrate , regions between Caronia and Milazzo (Messina), Acireale and Fiumefreddo (Catania) , where tThe evaluation of organoleptic and sensory properties provided evidence that Sicilian mango fruit results of good quality. Analysing six different Sicilian cultivars, Farina et al. defined a sensory profile  based on the evaluation of 10 judges who randomly tested some sensory descriptors  in 20 fruit samples for each mango variety .We summarized the analysis in Taken together, the organoleptic quality and sensory properties of local fresh and ripe mango fruit were considered better compared to those imported by extra-EU countries, such as China and Madagascar, since these fruits often arrive unripe to the consumers who do not appreciate them . HoweverWe believe that a better understanding of the different healthy properties of this fruit could promote its consumption and favour Sicilian mango production. This could also have a strong impact on the Sicilian agricultural system enhancing marketplace requirements and creating new employment opportunities for people directly involved in the agricultural supply chain.In modern society there is a growing interest in finding new bioactive molecules contained in the plants and fruits to be used both in the food and pharmaceutical industries. Fruits and vegetables are excellent sources of essential nutrients because of their high content in phytochemicals, such as phenolic compounds and flavonoids, which help to keep the consumer in good health. In particular, the present study summarised the most accurate evidence of the multifaceted actions of mango and its phytochemicals that have received a great deal of attention because of their beneficial potential in counteracting either the pro-inflammatory molecules or ROS production associated to human pathologies, such as cancer, cardiovascular diseases, aging, and neurodegenerative disorders.The reported investigations were also compared to those known for Sicilian mango fruit. A better characterisation of phytocompounds found in Sicilian mango in comparison to those of tropical areas, as well as a broader analysis of their properties, could improve our knowledge of their biochemical activity and achieve the production of phytopharmaceuticals to associate with the most common therapies for some human disease treatments.With regard to the bio-agronomic aspect, the acquisition of more information about mango’s potential, as well as the development of new supply chain strategies, could be of relevance for the Sicilian agricultural system.


Alces alces), an economically and ecologically important top-herbivore in boreal regions. Reduced representation libraries constructed from 34 moose were high-throughput de novo sequenced, generating nearly 50 million read pairs. About 50 000 stacks of aligned reads containing one or more SNPs were discovered with the Stacks pipeline. Several quality criteria were applied on the candidate SNPs to find markers informative on the individual level and well representative for the population. An empirical validation by genotyping of sequenced individuals and additional moose, resulted in the selection of a final panel of 86 high quality autosomal SNPs. Additionally, five sex-specific SNPs and five SNPs for sympatric species diagnostics are included in the panel. The genotyping error rate was 0.002 for the total panel and probability of identities were low enough to separate individuals with high confidence. Moreover, the autosomal SNPs were highly informative also for population level analyses. The potential applications of this SNP panel are thus many including investigations of population size, sex ratios, relatedness, reproductive success and population structure. Ideally, SNP-based studies could improve today’s population monitoring and increase our knowledge about moose population dynamics.Monitoring of wild animal populations is challenging, yet reliable information about population processes is important for both management and conservation efforts. Access to molecular markers, such as SNPs, enables population monitoring through genotyping of various DNA sources. We have developed 96 high quality SNP markers for individual identification of moose ( Today species –6. A wel species , 8. The  species . Reduced species , 10.With low mutation- and error rates, SNPs are ideal for applications that require high confidence in individual genotypes, e.g., individual recaptures and pedigree analyses –13. Addi, the largest member of the deer (Cervidae) family, is a boreal species with a circumpolar distribution. Through its browsing it influences a wide range of ecosystem processes, such as the composition of woody species and the structure of the tree and shrub layers [th-19th centuries, the current Swedish population of about 300,000 individuals (pre-harvest) is thus descendent from a much smaller population. The most recent population genetic studies of the Swedish moose suggested restricted gene flow and strong substructuring, with a northern and a southern cluster and a central contact zone [Moose b layers , 17. Sucb layers , 19. In b layers . The moob layers . The comb layers –24. Moosact zone , 26. Expact zone . These rde novo genomic sequencing information for autosomal SNP discovery. By specializing the panel for separation of individuals, we target SNPs with relatively high minor allele frequencies (MAF) which risk excluding SNPs informative for population level structure. However, our final panel proved informative at both the individual and population level.The aim of this project was to develop a panel of 96 high quality SNPs for individual identification and sex-determination of moose. We chose a reduced representation sequencing approach to obtain Thirty-four samples, 16 female and 18 male, were included for reduced representation sequencing and consecutive SNP discovery. The selected individuals were sampled at 16 locations from Abisko (68°N) in the north of Sweden to the island of Öland (56°N) in the south of Sweden . One femFrozen whole blood samples were thawed in 37°C water bath for three minutes during moderate shaking. The sample tubes were kept in motion during the subsequent cooling to room temperature to prevent clotting. Samples were allowed to separate before serum was collected through pipetting of the liquid phase. Extraction of genomic DNA was automatized using the QIAsymphony SP instrument together with the QIAsymphony DNA Mini Kit  following the protocol of the manufacturer. Input sample volume of 200 μl and elution volume 100 μl were found to produce the highest DNA yield. DNA quantity and quality was controlled with spectrophotometry  and gel electrophoresis respectively. We aimed for 5 μg DNA/sample with a DNA concentration of > 50 ng/μl to meet the requirements of our library construction. DNA extracts were stored at -20°C.TM 2500 platform were performed by the National Genomics Infrastructure (NGI), SciLifeLab, Stockholm.The DNA sequencing followed a reduced representation library approach. The DNA samples were digested with the restriction enzyme Eae I  in 37°C for 18–24 hours. Eae I was chosen based on simulated digestion results from the “RADtag counter”, a resource provided by SciLifeLab Stockholm and originally developed by GenePool, University of Edinburgh. Using the RADtag counter, the number of genomic cut sites and expected average fragment size were calculated using information about GC content (0.45), genome size, and restriction enzyme cut site. The genome size of moose was assumed to be roughly 3 Gb based on known genome sizes of other ungulates i.e. bovine and sheep. Sequencing parameters were based on the algorithm by Altshuler, Pollara . The aveThe sequence data were delivered demultiplexed. The quality of the reads was evaluated using FastQC 0.10.1  and adapde novo. DNA from six moose individuals were included in the development. Fragments of the Sry region were PCR amplified with the primers CerSRYf (5'-TGAACGAAGACGAAAGGTGGCTCT-3') and CerSRYr (5'-TACCCTATTGTGGCCCAGGTTTGT-3') following the method described in Lindsay and Belant [For sex-determination of moose, five sex-specific SNPs are included in the SNP panel; three of these markers were developed d Belant . The PCRd Belant . The sexCapreolus capreolus), fallow deer (Dama dama), red deer (Cervus elaphus) and reindeer (Rangifer tarandus) occurring in the Swedish landscape. This is useful primarily for genotyping of samples of unknown origin including eDNA. DNA from six individuals of each species were included for SNP discovery. The SNPs “Aa_mt_1”, “Aa_mt_4” and “Aa_mt_5” were developed by Sanger sequencing (see details above) of the 12S region with primers developed by Yang, Tan et al. [Five SNP markers, derived from mitochondrial DNA, are included in the SNP panel for identification and separation of the five different deer species . Three “no template controls” (NTCs), i.e. samples containing water instead of DNA, were included. An evaluation was made of the call rate (number of DNA samples successfully genotyped) and performance of the SNP assays regarding assigning samples to a genotype. The later was done visually for each SNP by assessing the clustering of the DNA samples in the Biomark scatter plot plane, aiming for as distinct allele clusters  as possible. SNPs and DNA samples with less than 75% call rate were removed from further analysis. In the assessment of each SNP, DNA samples that were not clearly assigned to a specific genotype were invalidated .st were performed with Genepop 4.6 [K) from 1 to 10 with 20 repetitions for each K. The most likely number of clusters was selected based on the ΔK method, described by Evanno, Regnaut [The best performing SNPs  were selected for a final genotyping run of sequenced individuals and additional Swedish moose samples  and three NTCs. The sampled individuals were of known sex . Technical replicates were included  for calculation of error rates. The number of replicates was chosen based on previous experience from SNP genotyping of high quality DNA in our lab. The design of the replication, including genotyping 2 samples x 10, allowed for discrimination between drop-out and drop-in misprinting errors. The error rate was calculated for each SNP locus as the ratio between the number of mismatching genotypes (among replicates) and the total number of amplified genotypes including both homozygous and heterozygous loci. The presented error rate is the mean value for the SNPs including both groups of replicates (2 x 10 and 12 x 2). Tests of HWE, LD and estimations of Fepop 4.6 , 37. Theepop 4.6 , 39 withepop 4.6  using thepop 4.6  includedepop 4.6 . To testepop 4.6  and a mu Regnaut . The PCAr > 0.35) were excluded (resulting in n = 50) to avoid biased allele frequencies. Moreover, the genotyping results of the sequenced individuals were controlled against the alleles assigned by the Stacks program to verify that the SNPs were behaving as expected.The tests for HWE, LD, expected and observed heterozygosity were performed on a dataset in which individuals from pairs of relatives  of genomic data. On average, 1.35 million read pairs were produced per sample, varying between 0.61–2.16 million among the samples. The sequences were of satisfying quality with a mean quality score (Illumina 1.9 encoding) exceeding 30 throughout the sequences. More than 240 000 unique stacks of matching sequences were detected using the Stacks “denovo_map” pipeline. Out of these, close to 50 000 stacks contained one or more SNPs. The genomic breadth of coverage after sequencing was estimated to 1.2%.r2 score (highest r2 in remaining SNPs: 0.18), were selected for assay design of which 138 passed in silico assay design.The aim was to develop a panel of 96 SNPs . Consequently the number of SNPs had to be narrowed down considerably . After aSD = 0.02) and 0.45 (SD = 0.08) respectively. Seven SNPs  deviated from HWE after removing nine individuals from pairs of close relatives (r > 0.35). Four SNPs  fell out of HWE including sequenced individuals only. MAF of the SNPs varied from 0.25–0.50 with a mean of 0.43 (r)): mother-daughter: 0.47, mother-son: 0.35 and brother-sister (assumed twins) 0.51. For validation purposes, Ritland’s relatedness estimator [r: 0.46 (mother-daughter), 0.31 (mother-son) and 0.46 (brother-sister). The STRUCTURE analysis resulted in Evanno’s ΔK = 2, hence suggesting two genetically differentiated clusters with a north-south separation in accordance with the sampling locations  was estimated to 0.001 for the two samples replicated 10 times and 0.003 for the 12 duplicated samples. The mean overall error rate per locus was 0.002. The mean expected and observed heterozygosity of the autosomal SNPs were estimated to 0.48 . The male haplotype for the sex-specific SNPs are given in Five SNPs for separation of sympatric deer species are included in the final 96 marker SNP panel . These Shttps://doi.org/10.5281/zenodo.1237474.The sequence information of the SNP-assays is available from the Zenodo repository: de novo reduced representation sequencing, including markers for both sex-determination and sympatric deer species diagnostics, is a suitable tool for moose individual identification and population monitoring which can ensure important information for wildlife monitoring and management.Our final panel of 96 SNPs based on Out of 50 million read pairs of data, more than 50 000 putative SNPs were detected. Hence, sequencing produced a sufficient number of high quality reads for successful SNP discovery. Sequencing was performed in multiple runs and since enough SNPs were detected in the first data delivery, we did not include data from subsequent sequencing efforts. As an effect, the lower than expected amount of data per sample resulted in a lower than expected read depth per sample. This could have made detection of high quality SNPs difficult with an increased risk of including spurious alleles arising from sequencing errors. Due to variations in the number of reads produced from each sample and to avoid excluding informative SNPs sequenced at low depth at the individual level, the minimum stack depth per individual was set at a modest value of three. However, the stack depth per individual should be multiplied with the total number of individuals that the SNP was detected in (i.e. 24–34), leading to stack depth of 72–102 X. Hence, the SNPs passing the quality criteria are true genomic variations and since nearly all of the SNPs have a MAF above 0.3, the risk of erroneous alleles at the population level is practically absent. Moreover, the validation by assay-based genotyping confirmed the selected SNPs to be polymorphic.The genotyping error rate for the final SNP panel was low which is in line with our expectations. Three of the final autosomal SNPs did not meet the quality requirements in the validation genotyping run. Our experience is that out of 96 SNPs, a few can fail due to different reasons . Consequently, these three SNPs are likely to perform well in coming genotyping efforts.In the validation of the autosomal SNPs, seven markers were found to deviate from HWE. Deviations from HWE are expected to some degree when studying natural animal populations since the assumptions of the Hardy-Weinberg model are rarely met . To exemr value between the siblings was close to 0.5, as was the value between the mother and the female calf. But the r value between mother and the male calf was surprisingly low (0.35). Stochastical variation in the r value estimate is unlikely to generate such a low value and the low error rates of the SNP panel suggest that this deviation might have its explanation in factors other than the SNP panel's performance. As with the two female samples unexcitingly displaying as males, mislabeling of samples somewhere in the handling process could be a possible explanation also in this case. To have assumed kinship patterns upset or detect errors are not uncommon when adding genetic information to existing data sets.The statistical power of these SNPs to resolve individuals is high , their est analyses, is in line with previous studies from the same area [The north-south genetic differentiation detected by the SNP panel, as shown by results from STRUCTURE-, PCA- and Fame area , 26, 48.ame area . The geoame area , hence tThe possibility of sex-determination facilitates pedigree construction and enables estimations of sex ratios, which is important information for e.g., wildlife management. One of the five sex-specific SNPs failed to amplify during the validation but worked in later genotyping runs. That amplification fails is always a risk and emphasizes the importance of running enough markers and/or to include replicates. Though the sex of all individuals in the SNP validation was correctly determined, it should be mentioned that assigning females based on the absence of a reaction, as we do here, comes with less certainty than having a positive confirmation. This is since a male sample that fails in the genotyping will appear as a female. The solution to this potential caveat is to use a sample quality threshold and enough markers to reliably determine the sex.Our primary goal was to select SNPs with high MAF and wide geographical distribution of alleles in order to optimize the panel for individual identification of moose. This goal was achieved by the development of a panel of SNPs with high confidence in assigning individuals. Our requirement that alleles show a good geographical distribution comes at the risk of losing power to detect population structure. Despite this, the autosomal SNPs also do well in detecting population substructuring based on allele frequency differences. The final SNP panel is thus promising for studies of moose both at the individual and population level.S1 Figde novo sequencing were sampled in the area around 16 locations throughout Sweden.Moose (n = 34) included for (PDF)Click here for additional data file.S2 FigPrincipal component 1 (PC 1), explaining 10.43% of the variation , separates the 59 samples into a northern- and a southern cluster in accordance with the sampling locations (South/North). PC 2 explains 5.28% of the variation .(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.


Polypoid species play significant roles in agriculture and food production. Many crop species are polyploid, such as potato, wheat, strawberry, and sugarcane. Genotyping has been a daunting task for genetic studies of polyploid crops, which lags far behind the diploid crop species. Single nucleotide polymorphism (SNP) array is considered to be one of, high-throughput, relatively cost-efficient and automated genotyping approaches. However, there are significant challenges for SNP identification in complex, polyploid genomes, which has seriously slowed SNP discovery and array development in polyploid species. Ploidy is a significant factor impacting SNP qualities and validation rates of SNP markers in SNP arrays, which has been proven to be a very important tool for genetic studies and molecular breeding. In this review, we (1) discussed the pros and cons of SNP array in general for high throughput genotyping, (2) presented the challenges of and solutions to SNP calling in polyploid species, (3) summarized the SNP selection criteria and considerations of SNP array design for polyploid species, (4) illustrated SNP array applications in several different polyploid crop species, then (5) discussed challenges, available software, and their accuracy comparisons for genotype calling based on SNP array data in polyploids, and finally (6) provided a series of SNP array design and genotype calling recommendations. This review presents a complete overview of SNP array development and applications in polypoid crops, which will benefit the research in molecular breeding and genetics of crops with complex genomes. Triticum turgidum) and rye  . Based on time of origin, polyploids can be classified as either neopolyploids or paleopolyploids. Neopolyploids are any newly formed polyploid without diploidized genomes, such as potato, cotton, canola, wheat, and sugarcane. On the other hand, paleopolyploids are formed at least several million years ago with ancient and diploidized genomes, such as maize and soybean   marker approach , restriction site associated DNA sequencing (RAD-seq), and the double-digest RAD-seq (ddRAD-seq)  also are valuable sources for SNP discovery in polyploids. For example, 8,327 SNP were identified from the Sanger EST datasets of three potato cultivars and finally filtered to 2,358 high quality SNPs by Hamilton et al. . SimilarSNP array is a high-throughput, relatively cost-efficient, and automatically genotyping assay. It has been widely used in genetic studies of crops, including genome-wide association studies (GWAS)   and population size used for validation will have a great impact on the validation rate. For example, when genotyping a total of 108 hexaploid wheat varieties, a 900K SNP array in wheat showed 99.8K polymorphic SNP probes. However, when genotyping a total of 475 accessions including the relatives of those varieties and progenitors, the number of polymorphic probes increased to 453.1K . SNPs are kept if the Affymetrix P-convert value or Illumina ADT value is higher than 0.6 for both forward and reverse probes. Sometimes, SNPs distribution, such as spreading over all chromosomes evenly or in exonic region  sequence length on either side of the SNP (50 bp for Illumina vs. 20 bp for Affymetrix), and (2) evaluation parameters (Illumina Assay Design Tool (ADT) value vs. Affymetrix P-convert value). The general considerations for array SNP selection include SNP depth, SNP types, SNP frequency, additional variations within probe sequence of target SNPs, and probe sequence parameters. Specifically, (1) The average SNP read depth, or single genotype SNP depth is considered as it is related to the accuracy of SNPs called. If the depth is too low, the SNPs could be called due to sequence errors. If the depth is too high, the SNPs may be called from repetitive sequences. Thus, a range of depths for different crop species is recommended   from 12 G. hirsutum lines (including the five lines from RNA-seq). Similar validation rates were obtained using RNA-seq in Eucalyptus grandis (83%)    is a highly heterozygous autotetraploid, and is the most important non-grain food crop in the world  is widely cultivated, providing over 95% of cotton production in the world  is the main forage legume crop in the world  is an essential economical oilseed crop, which can be used for extracting oil from seed and edible vegetable  mapped markers in one of DH population was constructed  is one of the world's most important cereal crops and has the most progresses in SNP array development among hexaploid crop species  is one of the important cereal crops in the world ; AABBCCDD genome; 2n = 8x = 56, ~698 Mb genome size] is an allo-octoploid crop species  is a diploid, called woodland strawberry  SNPs] , is autopolyploid mostly derived from the interspecific cross between auto-octoploid S. officinarum (2n = 8x = 80) and autoploid S. spontaneum (2n = 4x–16x = 32~128)  at a SNP locus in diploid species, polyploid species usually have more than three possible genotypes. Theoretically, the number of genotypes can be up to five in tetraploids, seven in hexaploids, nine in octoploids, and 13 in dodecaploid. So far, there are two software, fitTetra and ClusterCall, written for tetraploids, which can call five genotypes. Another software, SuperMASSA, was written for all ploidies (so far only successfully reported in sugarcane). Most other polyploid crops were using genotype calling software accompanying Affymetrix or Illumina platform. However, the disadvantage of the software is their inability to identify >3 clusters for Affymetrix or >5 clusters for Illumina platform. The GenomeStudio software from Illumina is able to provide five clusters. As an example, the markers from the 20K Infinium SNP Array . From the report of Affymetrix, SNPs are sorted into six quality classes based on their performance of clustering, three of which report accurate genotypes and are recommended for further validation . The remaining three classes are not considered for further processing, because MHR (mono high resolution) cluster is monomorphic, and a simple three-cluster genotype model are not able to generate complex intensity over three clusters correctly   from Affymetrix Axiom SNP array, there are several main steps for genotype calling Figure  by Best https://support.illumina.com/content/dam/illumina-support/documents/documentation/software_documentation/genomestudio/genomestudio-2-0/genomestudio-polyploid-genotyping-module-user-guide-1000000012407-00.pdf) and Infinium Genotyping Data Analysis . The steps are generally similar as that of Affymetrix except for some platform-specific parameters. The SNP genotypes are called by using GenomeStudio software based on intensity data file and SNP information file. Take allotetraploid cotton as an example , and ClusterCall  and ClusterCall (requirement of F1 population data sets), comparison of these three software programs was impractical. Therefore, to evaluate the impact of different genotype calling software on genotype calling in tetraploid as an example, the results comparing the software between fitTetra and ClusterCall were completed by Carley et al.  scored in at least one F1 population was higher when using ClusterCall  than using fitTetra . By increasing the threshold to one (1) concordance (perfect concordance), the number of concordant markers with ClusterCall decreased to 4,217 compared to 3,478 with fitTetra. Therefore, ClusterCall called genotypes with much higher concordance rate, thus is more accurate than fitTetra. ClusterCall used F1 populations with a large number of progeny as training data sets, which could increase the reliability of inferring genotypes based on chi-squared segregation test. In addition, ClusterCall showed higher accuracy (94.6 vs. 82.5%) and less computation time (9.5 min vs. 7.3 h) than fitTetra. However, fitTetra is dependent on fitting eight settled models and selecting the best fit by using constraints on parameters, such as the means, mixing ratios of the distributions, and Bayesian Information Criterion (BIC). Therefore, ClusterCall can improve the quality of genotype calling by using large F1 training populations, while this has no advantage for fitTetra  common data points (a sample assigned into same cluster with same marker by both tools) were obtained. When comparing the proportion of common genotypes with respect to the total genotypes of each individual, it was noteworthy that both software programs performed better on calling the two homozygous genotypes than calling remaining heterozygous genotypes, according to the concordance rate . To be specific, the average concordance rates in calling two homozygous genotypes were 93.9% in fitTetra and 90.5% in superMASSA. However, the software became more diverged on calling three remaining heterozygous genotypes (average concordance rate 74.1% in fitTetra vs. 76.1% in superMASSA).The Hardy-Weinberg equilibrium (HWE) model was used to assign the genotypes in both fitTetra and superMASSA, which revealed high concordance rate of genotype calls . The difference of genotype calls could be due to the usage of two different parameters to evaluate the models: smallest BIC value for fitTetra, and MAP for superMASSA. Furthermore, the common situations were described in these two software that assuming equal distance for clusters at fixed positions, which may result in assignment of samples to improper clusters (misclassification)  based on the SNP array results in polyploids, three additional open access tools are available beside the native callers from the platforms. In the only comparison of ClusterCall and fitTetra to date, ClusterCall showed higher accuracy and less computation time than fitTetra for genotype calling in an auto-tetraploid species (Carley et al., JW guided the intention and outline of the review. QY prepared the manuscript draft. XY, ZP, LX, and JW critically revised and re-wrote parts of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.