Ophiophagus hannah). Our results provide the first evidence of FP in this species, and provide novel evidence that rejects gametic duplication and supports terminal fusion as a mechanism underlying parthenogenesis in snakes. Moreover, we precisely estimated heterozygosity in parthenogenetic offspring and found appreciable retained genetic diversity that suggests that FP in vertebrates has underappreciated evolutionary significance.Facultative parthenogenesis (FP) is widespread in the animal kingdom. In vertebrates it was first described in poultry nearly 70 years ago, and since then reports involving other taxa have increased considerably. In the last two decades, numerous reports of FP have emerged in elasmobranch fishes and squamate reptiles (lizards and snakes), including documentation in wild populations of both clades. When considered in concert with recent evidence of reproductive competence, the accumulating data suggest that the significance of FP in vertebrate evolution has been largely underestimated. Several fundamental questions regarding developmental mechanisms, nonetheless, remain unanswered. Specifically, what is the type of automixis that underlies the production of progeny and how does this impact the genomic diversity of the resulting parthenogens? Here, we addressed these questions through the application of next-generation sequencing to investigate a suspected case of parthenogenesis in a king cobra ( Originally limited to a few instances in captivity, FP has been described as an outcome of reproductive error9. However, reports of vertebrate FP continue to increase both in number and taxonomic diversity, including cases from the wild10. With this accumulating information, our understanding of this reproductive mode has expanded greatly8, and commonalities have been detected across all vertebrate lineages shown capable of FP. Important commonalities include: (i) constraints that the sex chromosome system itself places on the sex of parthenogens11, (ii) the capacity to switch between sexual and asexual modes12, and (iii) the ability to produce consecutive  FP births18. Recently, second generation FP  has been documented in the white-spotted bamboo shark, Chiloscyllium plagiosum19, and observed in the royal python, Python regius . Furthermore, a parthenogenetic boa constrictor, Boa imperator, was found to be capable of sexual reproduction [W. Booth. unpubl. data]. Combined, these diverse data strengthen the view that FP is not an uncommon mode of reproduction in certain vertebrate taxa, and suggest that its significance in vertebrate evolution has been underestimated8.Asexual reproduction in otherwise sexually reproducing species—a phenomenon termed facultative parthenogenesis (FP)8. Under this mode, the egg nucleus fuses with its second polar body, resulting in the retention of reduced levels of heterozygosity limited to the telomeric regions of chromosomes8 , in contrast, is predicted to result in genome-wide homozygosity8  is the most commonly cited mechanismes8 Fig. a. Few stty8 Fig. a. Accura7. These concerns are further amplified by the findings that FP may be heritable6, which could lead to greater incidence of this phenomenon in small populations as a result of inbreeding3. Despite advances that recent studies have made towards understanding FP in vertebrates9, the fundamental questions of: (i) resolving which automictic mechanism(s) underlie FP, and (ii) how FP impacts individual-level genetic diversity on a genome-scale, remain unresolved.Arguably, with such indeterminate outcomes, understanding which mechanism of automixis is operating has important implications on the levels of genetic diversity retained in parthenogenetic offspring, which in turn has major ramifications for understanding the impact that these parthenogens might have on the genetic diversity of natural populations. This consideration is particularly significant when the status of a species, or population, is threatened or endangered26; thus, snakes represent an ideal model system with which to address outstanding questions. Following the publication of its genome27, the king cobra, Ophiophagus hannah  data provide an unprecedented, genome-scale view of the impact of FP on genetic diversity and inheritance, confidently providing support for a terminal fusion-based mechanism of development that results in the maintenance of appreciable levels of heterozygosity in parthenogenetic offspring.Compared to other vertebrate lineages, recent studies in snakes have generated novel and field-advancing results that have significantly contributed to our understanding of FPnah Fig. b, has em29. In cases of facultative parthenogenesis (FP) involving several lineages of colubroid snakes, particularly natricines  developmental abnormalities to both the head region and or body are not uncommon30. These deformities include, but are not limited to, incomplete development of the brain, impartial fusion of the cranium, uni- or bilateral absence of one of both eyes , loss or the addition of body scales, scoliosis of the vertebral column. The craniofacial abnormalities observed in the neonate king cobras are similar to those described for the checkered garter snake . Allen et al.20 reported similar post-cranial developmental abnormalities  associated with FP in an elapid species, the death adder (Acanthophis antarcticus). DNA was extracted from shed skin from the mother and muscle tissue from the offspring using standard phenol–chloroform methods. All extractions were quantified using Qubit broad-range DNA assays (Thermo Fisher Scientific) following the manufacturer’s instructions. Ophiophagus hannah is a widespread species with high morphological and genetic variability32, and likely constitutes a species complex. A taxonomic revision is in progress . To future-proof the identifiability of our specimen in relation to future taxonomic changes, we Sanger-sequenced parts of the mitochondrial genes for NADH dehydrogenase subunit 4 (ND4) and 16S ribosomal RNA of the mother  as barcode vouchers. For primers and PCR conditions see Maddock et al.33.The mother (Cobra UK) was an adult, originally imported to Germany from Sumatra as a 70 cm juvenile in September 2012. Kept in isolation, on 3 August 2015, at ~ 200 cm, she laid 24 eggs. Two eggs were viable, determined through the detection of blood vessels following egg candling, and were artificially incubated in a Jaeger incubator at 27–28 °C in a substrate consisting of moist vermiculite. One egg was opened on 26 October 2015, revealing a male offspring (offspring #1—Cobra211), which died two days later. The second egg, opened on 12 November 2015, also contained a male offspring (offspring #2—CobraFV807), which died within three days. Both fetuses were sexed based on morphology and were undersized  and common Sau3AI (4 bp) restriction enzymes, estimated to target approximately 200,000 genomic loci per individual. To allow for hierarchical pooling and multiplexing of samples, barcoded Illumina adapter oligonucleotides were ligated to the ends of digested DNA. These adapters also included 8 bp unique molecular identifiers . Following adapter ligation, samples were pooled, and these pools were size selected for a range of 430–600 bp using a Blue Pippin (Sage Science). After size selection, samples were PCR-amplified with pool-specific indexed primers, and amplification products were further pooled into a single sample based on molarity calculations from analysis on a Bioanalyzer (Agilent Technologies) using a DNA 7500 chip. The final pooled libraries were sequenced on an Illumina HiSeq 2000 lane, resulting in at least 2 million reads per sample  using the mem algorithm in BWA v. 0.7.12-r103938. SAMtools v. 1.3.140 and Picard v. 1.106 were used to process mapping files for each sample and merge mappings for downstream analyses. GATK v. 3.8-0-ge9d80683643 was used to perform realignment around indels with default settings. The mapping data was then used to isolate the coordinates of RAD loci present in all individuals by identifying high quality (Phred ≥ 30) sites where read depth was at least 5 per individual, merging adjacent sites together to form loci , and filtering away loci shorter than 100 bp. Using these RAD loci footprint coordinates, a new reference sequence from the O. hannah reference genome was exported using BEDtools v. 2.29.044 that only contained the RAD locus regions.Raw Illumina sequence data were filtered to remove PCR clones using the adapter UMIs and the clone_filter tool from the Stacks v. 1.35 analysis pipeline43. BWA was used to map quality-trimmed reads to the RAD locus reference and SAMtools, Picard, and GATK were used to quality control and realign around indels, as above. Variants were called using GATK HaplotypeCaller using default settings by first producing GVCF variant files for individuals before performing joint genotyping across all samples. Variants were filtered using BCFtools and VCFtools v. 0.1.1545 as follows: (1) indels were excluded; (2) genotype calls for individuals with a read depth of less than 5 were set as missing data; (3) SNPs within 3 bp of indels and clusters of indels separated by 10 bp or less were excluded; (4) variants with a Phred quality score below 30 were excluded; (5) SNPs with significant statistical biases were removed using the hard filter ‘QD < 2 || FS > 60.0 || MQ < 40.0 || MQRankSum < − 12.5 || ReadPosRankSum < − 8’; (6) SNPs with a total depth greater than 2 × average depth (= 100) and less than 0.5 × average depth (= 25) were excluded; and (7) non-biallelic SNPs were excluded. We further thinned SNPs to avoid the potential effects of linkage by randomly selecting one variant per 50 kb region of the king cobra genome.For genotyping, a second round of mapping was conducted and variant calling performed using the GATK best-practices guidelines46 package in R v. 3.4.347 was used to calculate two measures of genome-wide heterozygosity for each individual: proportion observed heterozygosity  and homozygosity by loci . Two measures of pairwise relatedness were calculated between all samples using the R package Demerelate49: the shared alleles index (Bxy50) and genotype sharing (Mxy51)—both measures vary between 0 (no relatedness) and 1 . A custom R function available from https://github.com/darencard/parthenogenesis was used to sample 100 bootstrap datasets to measure variance in each measure. The proportions of retained and lost maternal heterozygosity in each offspring was quantified and we used a binomial test to determine whether there were equal proportions of loci that are homozygous for the reference (0/0) or alternative (1/1) allele at loci where heterozygosity was lost in offspring, as is expected under a model of independent inheritance of unlinked loci. We estimated the Jaccard index to quantify patterns of retained heterozygosity shared in both offspring in the empirical dataset compared to randomly permutated datasets based on the formula A, and B represent sets of loci in the two offspring.Filtering of loci  resulted in a dataset of 20,562 independent  RAD loci with genotypes called in the mother and both offspring. Given a genome assembly size of approximately 1.6 Gb, we expect a RAD locus density of one marker per 80 kb. The Rhh11), strongly support the conclusion that these offspring are the product of FP, and provide the first evidence of this mode of reproduction in the king cobra.Here we provide the first in-depth, genome-wide assessment of genetic diversity for a squamate reptile reproducing via FP, through the comparison of two parthenogenetic male king cobra siblings with their mother. The analysis of thousands of loci from throughout the genome indicates that both offspring had significantly reduced levels of heterozygosity relative to the mother, though offspring #2 retained slightly higher amounts than offspring #1 . This discrepancy may stem from technical differences between SNP and microsatellite markers but given that non-trivial retained heterozygosity is also observed in a far more stringently filtered set of SNPs , we speculate that previous studies based on small numbers of microsatellite loci had reduced power to detect retained heterozygosity. Further exploration of FP using genomic datasets is necessary to better understand how much maternal heterozygosity is retained in parthenogenetic offspring. Moreover, affirming a pattern of significant retention of heterozygosity will require reconciling our current understanding of the impact of FP on genetic diversity and the evolutionary trajectories of natural populations.Using RADseq, we conclusively show, through the presence of retained heterozygosity in FP offspring, that the developmental mechanism of automixis is TF over GD. Here, the detection of low, but clearly identifiable levels of heterozygosity in each offspring Fig. a, is in 27. However, more indirect measures of genomic composition may provide information on the locations of regions of retained heterozygosity, providing further support for a model of terminal fusion (TF) automixis in this species. Under the TF automixis model, GC content is expected to be inflated around areas of retained heterozygosity due to higher rates of recombination and GC-biased gene conversion in telomeric regions , which is greater than expected by chance  in both offspring is not expected under GC-biased gene conversion and is logical in a situation where there is reduced power to call alternative SNPs  due to low sequencing coverage or sample numbers. This pattern may therefore be a result of a statistical artifact, but additional studies that leverage high-coverage, genome-wide data in larger numbers of samples should provide a better opportunity to further explore the biased patterns of inheritance observed in this study.Terminal fusion automixis does result in the retention of maternal heterozygosity in offspring, but the majority of heterozygosity found in centromeric regions ends up purged in the genomes of offspring Fig. a. Under ed Table . While ta genome , this ma20, and microsatellite data presented in a North American garter snake17, this likely represents the underlying developmental mechanism in all species of snakes presently reported to have reproduced through facultative parthenogenesis8. Our genomic dataset also illuminates patterns of inheritance during TF, including the first precise estimates of appreciable retained heterozygosity in parthenogenetic offspring that have important biological and evolutionary implications. These results advance our understanding of FP in vertebrates and identify new areas of research for which snakes, and likely other squamate reptiles, represent a robust model system.In summary, here we used a genome-scale dataset to study parthenogenesis in a non-traditional model vertebrate and find conclusive support for terminal fusion automixis-mediated facultative parthenogenesis in the king cobra. When considered alongside genomic data recently reported in two species of Australian elapidSpecimens were maintained in a private collection (see Acknowledgments), with tissues collected non-invasively  or post-mortem (offspring).Supplementary Figure 1.Supplementary Figure 2.Supplementary Figure 3.Supplementary Information.


In June 1998 their mothers responded to questionnaires about their objective, cognitive, and subjective levels of stress from the ice storm. When children were 11 1/2 years old, T1-weighted structural magnetic resonance imaging (MRI) scans were obtained using a 3T scanner and analyzed to determine hippocampal volumes. We collected and genotyped the children’s saliva DNA. Moderation analyses were conducted to determine whether either or both of the SNPs moderate the effect of PNMS on hippocampal volumes. We found that objective hardship was associated with right hippocampal volume in girls, and that the BDNF and COMT genotypes were associated with left hippocampal volume in boys and girls. In addition, SNPs located on COMT moderated the effect of maternal objective distress in boys, and subjective distress in girls, on both right hippocampal volume. Thus, we conclude that an individual’s genotype alters their susceptibility to the effects of PNMS.Gene-by-environment interactions influence brain development from conception to adulthood. In particular, the prenatal period is a window of vulnerability for the interplay between environmental and genetic factors to influence brain development. Rodent and human research demonstrates that prenatal maternal stress (PNMS) alters hippocampal volumes. Although PNMS affects hippocampal size on average, similar degrees of PNMS lead to different effects in different individuals. This differential susceptibility to the effects of PNMS may be due to genetic variants. Hence, we investigated the role of genetic variants of two SNPs that are candidates to moderate the effects of PNMS on hippocampal volume: COMT (rs4680) and BDNF (rs6265). To investigate this, we assessed 53 children who were  The hippocampus plays a key role in memory formation and learning  and has Prenatal maternal stress (PNMS) is one such factor that has been thoroughly researched for its effects on hippocampal development in animals. These studies have provided substantial evidence that PNMS affects hippocampal development . MagnetiIn humans, O-methyltransferase (COMT) Val158Met and BDNF Val66Met moderated the association between adverse life events and hippocampal volume in a large sample of healthy humans. The rs4680 COMT gene variant induces a Val to Met amino acid transition at codon 158 , resulting in a 4-fold decrease in enzyme activity in Met carriers. in utero neurodevelopment, further research is required to elucidate this relationship. The research to date has investigated only short-term effects of maternal anxiety on brain structure, demonstrating effects on children up to 6 months old.Indeed, single-nucleotide polymorphisms (SNPs) that affect brain development were observed to moderate the effect of maternal anxiety during pregnancy on the child’s brain structure. For example, a SNP rs6265 converting a valine  to methionine (Met) on the brain-derived neurotrophic factor (BDNF) gene, which promotes the growth, maturation and survival of nerve cells, influences the degree to which maternal anxiety induces DNA methylation in the offspring, and influences the relationship between the offspring’s methylation and brain volume . SpecifiUnlike animal studies, human research often lacks a randomly assigned stressor. The limitation of stressors from human studies is that they may be associated with an individual’s traits, such as impulsivity or neuroticism, which may be transmitted to the offspring genetically. If a pregnant woman experiences stress that she or the father may have induced, in part, by their own temperament , and their child grows up to develop a similarly difficult temperament, it becomes almost impossible to determine the extent to which the association between the stress in pregnancy and the child’s difficulties are due to genetic transmission, the intrauterine environment, and the postnatal rearing environment. Natural disasters randomly affect large populations and have a sudden onset that affects pregnant women in different stages of pregnancy. Project Ice Storm provides a unique opportunity to determine the effects of the mothers’ objective hardship, subjective distress and cognitive appraisal of a stressor through a quasi-randomly assigned event. Consequently, we use natural disasters to study the effects of prenatal maternal stress on the development of the offspring.In the current project, studying PNMS derived a natural disaster, our goal was to (1) determine the effect of three measures of PNMS  on left and right hippocampal volume in young adolescents; (2) determine the effect of genetic variants of COMT Val158Met and BDNF Val66Met on left and right hippocampal volume; and (3) determine the extent to which selected SNPs moderate the effect of PNMS on hippocampal volume. Since sex is an important determinant for the effects of stress on brain development, we expected that SNPs would differentially affect males and females.Following the ice storm in January 1998, our research group contacted obstetricians in the Montérégie, a region southeast of Montreal, Canada that was highly affected by the crisis. Physicians from four hospitals in the region identified women who met the following criteria: (1) were pregnant during, or within 3 months of the ice storm; (2) French Canadian; and (3) 18 years old or older. The families that responded were significantly better educated and had higher incomes than the regional averages. Families who gave consent have been assessed periodically. The protocols were approved by the Research Ethics Board of the Douglas Hospital Research Centre.in utero during the ice storm, or were conceived within 3 months of the ice storm, were assessed at the age of 11 1/2 years. Among the 53 children, 15 (28.3%) had been in their first, 14 (26.4%) in their second, and 10 (18.9%) in their third trimester on January 9, 1998 (the peak of the ice storm). The remaining 14 (26.4%) children were conceived within 3 months of the storm; they were also considered as “exposed” because maternal stress hormones could still be elevated within 3 months of a major stressor. The participants included 27 boys and 26 girls for whom both brain and genotype data were available. We included children who were conceived within 3 months following the ice storm (preconception) because of the potential for long-term effects of the ice storm which continued to affect the population after the reference date of January 9, 1998. All participants were right-handed.In this study, 53 children who were Objective hardship, subjective distress, and cognitive appraisal measures were collected through maternal questionnaires mailed to the families on June 1, 1998, 5 months after the beginning of the ice storm.To estimate objective hardship of the mother, our group developed a questionnaire with items to evaluate four categories of exposure objectively . Questions in each category quantified experiences such as “number of days without electricity” and “number of displacements from home”. Each category has a maximum score of eight points and is summed to create the Storm32 score . In our r = 0.76)  , which i = 0.76)  and was The mother’s cognitive appraisal of the storm was assessed by asking “If you think about all of the consequences of the ice storm on your household members, would you say they were” and providing five response options on a Likert scale . As our interest is the effect of negative cognitive appraisal about the ice storm on child outcomes, we compared the “Negative cognitive appraisal group” (recoded as 0), which included participants who had rated the consequences as very negative and negative, with a “Neutral/Positive cognitive appraisal group” (recoded as 1), which included participants who had rated the consequences as none, positive, or very positive.Unité de Neuroimagerie Fonctionnelle (UNF) du Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal (CRIUGM) on a 3.0T Siemens MAGNETOM Trio TIM Syngo , with a 12-channel head coil. A total of 65 children underwent a three-dimensional, high-resolution, whole brain, structural T1-weighted magnetization-prepared gradient-echo image (MP-RAGE) sequence; TR = 2,300 ms, TE = 2.98 ms, TI = 900 ms; 256 mm field of view, 1 mm slice thickness, 176 slices, sagittal acquisition, time = 9 min. For this study, only 53 children’s data were analyzed, as rest of 12 children had no genetic data.Anatomical magnetic resonance imaging (MRI) was performed at the All MR images were converted from their standard Digital Imaging and Communications in Medicine (DICOM) format to MINC2 . Images were then corrected for intensity non-uniformity and underwent normalization for signal intensity .For each subject, total intracranial volume (TIV) was automatically obtained using the Brain Extraction based on non-local Segmentation Techniques (BEaST) method . The res1 and when used with the MAGeT-Brain2 segmentation technique produce reliable delineations of the hippocampus, including subfields.Bilateral hippocampal volumes, including subfields  were automatically segmented using the Multiple Automatically Generated Templates brain segmentation (MAGeT-Brain) algorithm, which includes input from digital atlases by Whole left- and right-hippocampal volumes were delineated by merging the automated segmentation outputs of the hippocampal subfields to produce a single label for the whole hippocampus in each hemisphere. To increase the precision and validity of volumetric results, all hippocampal labels underwent manual corrections by an expert rater following the Pruessner segmentation protocol . FinallyWhen Project Ice Storm children were 8 1/2 years old, saliva samples were collected during a laboratory assessment using Oragene DNA self-collection kit (OG-500) (DNA Genotek) and stored at room temperature until further analysis. DNA extraction was performed using PrepIT-L2P kit (DNA Genotek) according to the manufacturer’s instructions. DNA yield was measured using NanoDrop 8000 Spectrophotometer V2.1 (Thermo Fisher Scientific). DNA was stored at −80°C until analysis. rs6265 (BDNF) and rs4680 (COMT) were genotyped using Sequenom iPLEX Gold Technology  at McGilThe major/major homozygote, heterozygote, and minor/minor homozygote genotype frequencies of each SNP are presented in Many factors are known to alter fetal development, and thus, induce long-term effects on hippocampal volume. To control for this, we tested the effect of some of these factors  on HCV/TIV ratio in boys and girls. We controlled for factors that were correlated with HCV/TIV. The correlations between these potential control variables and HCV/TIV are presented in Correlations and ANOVAs were conducted to determine the associations between PNMS and SNPs on HCV/TIV ratio. Moderation hierarchical regression analyses were conducted to determine whether either of the two SNPs moderate the effect of PNMS on HCV/TIV ratio. More specifically, analyses tested whether the two SNPs of interest moderate the effect of objective hardship, subjective distress, and/or cognitive appraisal on left and right HCV/TIV ratios. First, as mentioned above , potentir = 0.324, p = 0.099). In girls, objective hardship was positively correlated with right HCV/TIV ratio . Neither subjective distress nor cognitive appraisal was significantly associated with HCV/TIV ratio in girls or boys.As shown in r = 0.561, p = 0.002) with a similar trend on the right HCV/TIV ratio   and Tukey post hoc test demonstrated that in boys the major homozygote genotype (GG) was significantly associated with larger left HCV/TIV ratio compared to the heterozygote (GA) (p = 0.037) and that of the minor homozygote (AA)  in boys  . An ANOVote (AA) . For BDN in boys , howeverr = −0.457, p = 0.019), with a similar trend with right HCV/TIV   associated with larger left HCV/TIV ratio (= 0.082) .Moderation hierarchical regression analyses were conducted to test whether the two SNPs of interest moderate the effect of objective hardship, subjective distress and cognitive appraisal on the ratio of left and right HCV/TIV ratio.p = 0.045) of variance in the left HCV/TIV. Next, a marginally significant 9.3% of additional variance was explained by objective hardship levels (p = 0.099): greater hardship was associated with larger left HCV/TIV (p = 0.020) with significant effects from COMT genotypes [major homozygotes (GG) vs. heterozygotes (GA) vs. minor homozygotes (AA)], or 14.9% (p = 0.026) additional variance explained when comparing effects from major homozygotes (GG) vs. minor allele carriers (AA + AG). The full models explained 40.6 and 39.2% of variance, respectively. The main effect of minor homozygotes (AA) vs. major allele carriers (AG + GG) and the interactions were not significant.In the objective hardship model, we found that the covariate, maternal alcohol consumption during pregnancy, explained 15.1% ( HCV/TIV . The addp = 0.035). Subjective distress explained a non-significant additional 1.8% of variance. The main effects of the COMT genotypes [major homozygotes (GG) vs. heterozygotes (GA) vs. minor homozygotes (AA)] significantly increased variance explained by 15.3% (p = 0.025) while a significant additional 13.8% of the variance (p = 0.035) was explained by the major homozygotes (GG) vs. minor-allele carriers (AA + AG) genotypes. The full models explained 41.4 and 40.0% of variance, respectively. The main effect of minor homozygotes (AA) vs. major allele carriers (AG + GG) as well as the interactions were not significant.In the subjective distress model, maternal alcohol consumption and objective hardship explained a significant 24.4% of variance in the outcome (p = 0.035). Cognitive appraisal explained a non-significant additional 0.7% of variance. The COMT genotype main effect (major homozygote vs. heterozygote vs. minor homozygote) significantly increased variance explained by 17.3% (p = 0.018), while a significant additional 14.5% of the variance was explained by the comparison of the minor homozygotes vs. major-allele carriers (p = 0.031). The full models explained 42.3 and 39.6% of variance, respectively. The main effect of minor homozygotes (AA) vs. major allele carriers (AG + GG) as well as the interactions were not significant.In the cognitive appraisal model, maternal alcohol consumption during pregnancy and objective hardship explained a significant 24.4% of variance in the outcome (p = 0.03) of the variance. Neither the main effect of objective hardship (6.7%) nor of COMT genotypes (1.3–2.5%) were significantly associated with right HCV/TIV. However, COMT genotypes [major homozygotes (GG) vs. minor-allele carriers (AA + AG)] were found to significantly moderate the effect of objective hardship on right HCV/TIV ratio , with the full model explaining 37.8% of the variance in right HCV/TIV volume (p = 0.084) with greater objective hardship associated with smaller volumes; however, for minor-allele carriers (GA and AA), there was no association between objective hardship and right HCV/TIV volume (p = 0.106). Below objective hardship levels of 10.86 (slightly below the group average) there was a significant difference in right HCV/TIV volume between genotypes, with major homozygotes having larger volumes than the minor-allele carriers. At low levels of maternal objective hardship, the mean right HCV of GG boys is approximately 4 standard deviations (SD) above the group mean while at moderate levels of hardship the GG and A-allele carriers all averaged right HCV at the group mean. The other interactions with objective hardship were not significant.In the objective hardship model, we found that the covariate, maternal alcohol consumption during pregnancy, explained 17.5% , and the covariate maternal alcohol consumption during pregnancy, explained 25.5–42.3% of the variance.There were no significant interaction effects between PNMS and BDNF genotypes on hippocampus volumes in boys.p = 0.021) of variance. There were no significant main effects of objective maternal stress (0.8%) nor of COMT genotypes (<0.1–0.7%), and no significant interaction effects.In the objective hardship model the covariate, SES, explained 20.1% (p = 0.067). Controlling for these covariates, subjective distress explained a non-significant additional 0.1% of variance, and COMT genotypes main effect were also non-significant (<0.1–0.7%). However, the COMT genotypes [major homozygotes (GG) vs. heterozygotes (GA) vs. minor homozygotes (AA)] were found to significantly moderate the effect of subjective distress on left HCV/TIV ratio , with the full model explaining 35.8% of the variance in the outcome (p = 0.092) with greater subjective distress related to smaller volumes. Left HCV/TIV ratio is approximately 1 SD above the mean at low levels of maternal subjective distress but at high levels of distress  they are about 1 SD below the mean. For minor homozygotes or heterozygotes, however, there was no association between subjective distress and left HCV/TIV ratios. There is no statistical region of significance within the observed range. The COMT comparison of major homozygotes (GG) vs. minor-allele carriers (AA + AG) , with the full model explaining 54.1% of the variance in left HCV/TIV volume (p = 0.0026) with greater subjective distress related to smaller volumes; at low maternal subjective distress, the left HCV/TIV ratio for GG homozygotes was about 3 SD above the mean, while at high levels it was about 1.3 SD below the group mean. However, for minor-allele carriers, there was no association between subjective distress and left HCV/TIV ratios which remained at about the group mean level. Below log-transformed subjective distress level of 1.480  there was a significant difference in left HCV/TIV volume between genotypes, with major homozygotes having larger volumes than the minor-allele carriers. However, above log-transformed subjective distress level of 2.771  major homozygotes had significantly smaller volumes than the minor-allele carriers. The interaction with minor homozygotes vs. major allele carrier was not significant.In the subjective hardship model, SES and objective hardship explained together a marginally significant 21.0% of variance in the outcome ( outcome . As showAA + AG)  was alsoV volume . As showControlling for SES and objective hardship, the main effect of cognitive appraisal and its interactions with COMT genotypes were not significant.p = 0.048). However, there was no significant main effect of any genotypes on right HCV/TIV volumes (R2-change = 1.5–6.6%), or of any interactions involving objective hardship.In the objective hardship model, objective hardship had a significant main effect on right HCV/TIV ratio, such that higher PNMS was associated with a larger ratio, explaining an additional 15.4% of the variance (R2-change = 0.9%) and COMT genotypes (R2-change = 1.1–6.6%) on right HCV/TIV volumes were not significant. However, COMT genotypes [major homozygotes (GG) vs. minor-allele carriers (AA + AG)] moderated the effect of subjective distress on right HCV/TIV ratios  with the full model explaining 41.8% of the variance in the outcome (p = 0.008) with greater subjective distress related to smaller volumes; at low subjective distress GG homozygotes had right HCV/TIV ratios about 1.25 SD above the group mean while at high levels their HCV/TIV ratio was about 1.6 SD below the mean. For minor-allele carriers (AA + AG), however, there was no association between subjective distress and right HCV/TIV ratios. Below subjective distress level of 0.717  major homozygotes (GG) had significantly larger volumes than the minor-allele carriers (AA + AG), and above subjective distress level of 2.705  major homozygotes (GG) had significantly smaller volumes than the minor-allele carriers (AA + AG). The other moderations involving subjective distress were not significant.Controlling for objective hardship, the effect of subjective distress , and SES for the HCV/TIC ratio, explained 16.8–54.1% of the variance.Additionally, there were no significant interaction effects between PNMS and BDNF genotypes on hippocampus volume in girls.The main objective of this study was to test whether children’s genotype would moderate the effects of maternal stress derived from 1998 Quebec ice storm during pregnancy on their hippocampal volume, thereby resulting in differential effects of PNMS on brain structure.First, we examined the main effect of each component of PNMS on hippocampal volume. The previous literature reported associations between increased prenatal maternal anxiety and reduced hippocampal growth , and theSecond, zero-order correlations revealed associations between genotypes and hippocampal volume with COMT (rs4680) and BDNF (rs6265). Major homozygotes of COMT  are associated with larger hippocampal volume in boys, while major homozygotes of BDNF  are associated with larger hippocampal volume in girls. Although COMT Val carriers were reported in previous studies to have smaller hippocampal volumes than Met allele carriers in healthy individuals , our obsThe only significant gene-by-environment interactions involved the COMT genotype. Under maternal objective hardship conditions that are below the group mean , boys wiRemarkably, in our study adolescents in the major homozygote group have a significantly larger hippocampal volume in low objective and low subjective PNMS conditions compared to high PNMS conditions, while heterozygotes and minor homozygotes do not seem to be affected by the levels of PNMS. Therefore, our results suggest that as maternal stress increases in severity children who are carriers of the Val/Val genotype are more likely to show reductions in hippocampal volume than Met carriers, indicating that the major homozygotes may be more susceptible to the effects of PNMS.both male and female adults (In contrast to the sex differences in the effects of the COMT and BDNF genotypes on hippocampal volume at early adolescence, it has previously been reported that the minor homozygote BDNF genotype is associated with reduced hippocampal volume in e adults . Given te adults . Sex hore adults . These sAlthough the results described here further our understanding of the interaction between genetic variants and PNMS on hippocampal structure in children, as our sample included only 53 children, further research with larger cohort is required to validate these findings. Given the importance of the BDNF gene, it is unfortunate that our sample was missing subjects with the minor homozygote genotype for a full investigation of this gene’s effects. On the other hand, natural disasters provide unique opportunities to study the effect of an objective, randomly assigned stressor in a human population, thereby overcoming many limitations of previous human gene-by-environment research. The 1998 Quebec ice storm was a powerful enough stressor that high scores on the maternal objective hardship scale predicted a full standard deviation lower IQ in the children at age 2  and agaiOur findings support the hypothesis that individuals exhibit differential susceptibility to the effects of PNMS, and that genetic variants that alter the function or expression of proteins, underlie these differences. We showed that, in some cases, genetic variants, PNMS, and the gene-by-environment interaction combined explained up to half the variance in hippocampal volume. This is significant as it increases our understanding of how genetic and environmental factors work in combination to affect hippocampal development.This work has increased our understanding of gene-by-environment interactions during prenatal brain development. Specifically, we have found that a SNP located on COMT significantly moderates the effects of PNMS on hippocampal volume, resulting in differential susceptibility between COMT genotypes to the effects of PNMS. In addition, the effect of different aspects of PNMS – objective hardship and subjective distress – on hippocampal volume was differentially moderated by the SNPs of interest. When moderated by SNPs located on COMT, subjective distress exhibited greater effects on hippocampal volume than objective hardship and cognitive appraisal in girls, while objective hardship had the most effect in boys. Overall, in accordance with our hypothesis, these results suggest that a child’s genotype can alter their vulnerability to the effects of PNMS; however, these effects are often specific to a particular sex and/or aspect of stress.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Research Ethics Board of the Douglas Hospital Research Centre. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.SK designed and implemented Project Ice Storm. LC-L, SY, and SK conceived of the current experiment. RD ran the automated segmentation pipelines, did the manual corrections of the amygdala, and total brain volume segmentations derived from the automated segmentations. GE, LC-L, and SY ran the statistical analyses. SY interpreted the data and drafted an early version of the manuscript. LC-L, SK, and DL provided intellectual contributions for the rationale, interpretation of the data, and prepared the final manuscript for submission. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Drought threatens the food supply of the world population. Dissecting the dynamic responses of plants to drought will be beneficial for breeding drought-tolerant crops, as the genetic controls of these responses remain largely unknown.ZmcPGM2 and ZmFAB1A, which regulate i-traits and drought tolerance. Moreover, the value of the candidate genes as drought-tolerant genetic markers is revealed by genome selection analysis, and 15 i-traits are identified as potential markers for maize drought tolerance breeding.Here we develop a high-throughput multiple optical phenotyping system to noninvasively phenotype 368 maize genotypes with or without drought stress over a course of 98 days, and collected multiple optical images, including color camera scanning, hyperspectral imaging, and X-ray computed tomography images. We develop high-throughput analysis pipelines to extract image-based traits (i-traits). Of these i-traits, 10,080 were effective and heritable indicators of maize external and internal drought responses. An i-trait-based genome-wide association study reveals 4322 significant locus-trait associations, representing 1529 quantitative trait loci (QTLs) and 2318 candidate genes, many that co-localize with previously reported maize drought responsive QTLs. Expression QTL (eQTL) analysis uncovers many local and distant regulatory variants that control the expression of the candidate genes. We use genetic mutation analysis to validate two new genes, Our study demonstrates that combining high-throughput multiple optical phenotyping and GWAS is a novel and effective approach to dissect the genetic architecture of complex traits and clone drought-tolerance associated genes.The online version contains supplementary material available at 10.1186/s13059-021-02377-0. Zea mays), with more than one billion tons annual production (http://www.fao.org/wsfs/world-summit/en) [Maize (mmit/en) , is a wommit/en) –5. TherePlant drought tolerance is a complex trait that is controlled by multiple quantitative trait loci (QTLs) with small effects , 7. So fBrassica napus [The major challenge for dynamic drought study is the “phenotyping bottleneck,” owing to the lower-throughput, costly, and labor-intensive process of conventional crop phenotyping , 15. In ca napus , droughtca napus , salinitca napus , and droca napus . Most ofca napus –24. So fIn this study, we applied drought stresses to a maize association panel consisting of 368 genotypes over a course of 98 days. Many dynamic multiple optical image-based traits (hereafter referred as i-traits) were detected via a high-throughput multiple optical phenotyping system and the high-throughput image analysis pipelines we developed. These i-traits were collected non-destructively by multiple optical imaging, including RGB imaging, hyperspectral imaging (HSI), and X-ray computed tomography (CT), thus reflected the broad external (RGB i-traits) and internal (HSI and CT i-traits) responses of plant to drought. I-trait-based GWAS resulted in the identification of thousands of candidate genes. Gene-trait network indicated that there were distinct genetic controls of different types of i-traits. Many previously identified drought-tolerant genes were included in the candidate genes, and dozens of hotspot genes associated with multiple i-traits were identified. We further revealed the regulatory variants that control the candidate gene expression via eQTL analysis. In addition, we validated the roles of two new genes in the regulation of i-traits and maize drought tolerance through genetic mutation analyses. Furthermore, several i-traits associated well with survival rate and known drought-related spectral index were selected as potential markers for drought tolerance maize cultivar screening and breeding. The huge amounts of “genetic treasures” detected in our study indicate that combining high-throughput multiple optical phenotyping and GWAS is a powerful and promising approach to dissect the genetic architecture of complex crop traits and clone causal genes.To gain insights into how maize plants respond to drought, we cultivated a maize association mapping population (AMP), which consists of 368 inbred lines and has 1 M SNPs among the population , in a grAdditional file 6: Video S5. Operation procedure of filtering outlier.Additional file 7: Video S6. Operation procedure of T-test.Additional file 8: Video S7. Operation procedure of Multi-layer perception (MLP).Many i-traits changed dynamically during the drought treatments and growth stages  of each individual i-trait over the growth period with or without drought stress, and the middle number of H2 of these i-traits was 0.4  was performed to capture the phenotypic variations in the entire maize population. For the RGB- and HSI-derived i-traits, at D52 with more severe stress, PC1 alone explained more than 50% of the phenotypic variation, which clearly separated the WW plants from those undergoing DS . We identified a total of 4322 distinct significant SNPs associated with 2989 i-traits  to detect significant SNP-trait associations. More than 2989  i-traits had at least one significant associated SNP , Zmhdz10 (GRMZM2G041127), ZmDREB2A (GRMZM2G006745), and ZmDREB2.7 (GRMZM2G028386) [ZmNAC111 positively regulates maize drought tolerance [ZmNAC111 was significantly associated with the HSI-derived ratio i-trait ddT136_D46_R    but much higher  ZmNAC111 expression under DS  play key roles in plant drought tolerance . In our G028386) , 34 wereZmDREB2.7) was associated with 13 HSI-derived i-traits and encoded AP2/EREBP ERF TFs. ZmDREB2.7 belongs to the AP2 DREB subfamily and positively regulates maize drought tolerance [ZmDREB2.7 was significantly associated with HSI-derived i-trait lgA15_D34_WW (the logarithm of the 434 nm average reflectance under well-water condition at 34 days after sowing)  and 12 other i-traits  with two other SNPs in the coding region and the reported drought-tolerant causal allele, five polymorphic sites in the promoter region (R2 = 1) [P = 4.27 × 10−8, t-test) and higher survival rates  after drought stress  was associated with 241 HSI-derived ratio i-traits, and both genes shared 34 associated i-traits  are important signaling molecules in stress responses . The memP ≤ 4.2 × 10−8, MLM, Additional file The difference in gene expression could originate from changes in local and/or distant regulation . We nextmyb15), GRMZM2G163848 (iap3), GRMZM2G045404 (ibr5), sugar metabolism: GRMZM2G111324 (ogh17), GRMZM2G318780 (scs3), GRMZM2G171373 (hk1) and peroxide metabolism: GRMZM2G162688 (sip), GRMZM5G872256 (gs1). In addition, many significant peaks were repeatedly detected for genes encoding TFs that regulate multiple biological processes or stress responses , GRMZM2G012391 (p450), protein phosphorylation: GRMZM2G002100 (mapk6), GRMZM2G146553 (cipk3), heat stress response: GRMZM2G428391 (hsp70). Some significant peaks or enhanced significance of the peaks were detected for TF genes under DS conditions  were dynamic (detected under WW or DS condition), only 31% of the eQTLs were static (detected under both WW and DS conditions). Similar ratios of dynamic (74%) and static (26%) eQTLs were observed in distant  eQTLs  and ZmFAB1A (1-phosphatidylinositol-4-phosphate 5-kinase or forms aploid and binucleate cells 1) which are annotated in sugar metabolic pathway and inositol phosphate metabolic pathway, respectively  showed significant  association with i-trait ddT200_D40_R   with four other less significant SNPs (P < 10−4)  of ZmcPGM2  plants when there was no stress, but the levels of this i-trait were higher in Zmcpgm2 than those in WT plants when the stress was more severe (SM ≤ 15%)  were much higher in Zmcpgm2 than in WT plants , and the most significant SNPs were chr5.S_10857363 and chr5.S_10858751 , which were completely linked to each other (R2 = 1) and highly linked with Chr5.S_10856121 (R2 = 0.81) , the levels of this i-trait in Zmcpgm2 mutants were higher than those in WT plants  maize stem thickness.ZmcPGM2 expression was inhibited by severe drought stress [ZmcPGM2 was significant associated with maize SR , and plants with the A allele showed higher survival rates than those with the C allele [ZmcPGM2 in regulation of maize drought tolerance. SNP Chr5.S_10856121 had strong LD (R2 > 0.8) with four other less significant SNPs . Re-sequencing to the genomic DNA of ZmcPGM2 in the maize populations did not detect more significant genomic variations. Further analyses showed that SNPs Chr5.S_10856121, chr5.S_10857363 and chr5.S_10858751 are synonymous variations, while chr5.S_10855874 and chr5.S_10855875 are located in ZmcPGM2 3′-untranslated region, and showed significant associations with i-trait ddT200_D40_R and SR  showed slightly higher levels in WT plants under WW conditions, these indices were significantly higher in Zmcpgm2 mutants after severe drought stress (SM < 15%)  is an important maize flowering trait, the shorter the ASI, the better for pollen and silk to meet with each other to produce seeds. We observed that the ASIs of Zmcpgm2 mutants were significantly shorter than those in WT plants under both WW and DS conditions in the field, indicating that ZmcPGM2 could also play important roles in flowering regulation.t stress   that showed significant association with i-trait dT233_D40_R   explained 7.2% of the phenotypic variance and had high linkage with 10 other significant SNPs (R2 = 0.9)   showed that 4 were synonymous variations and 7 were missense variations, including chr6.S_117795068 (46Asp/Glu), chr6.S_117795706 (231Asp/Asn), chr6.S_117795706 , chr6.S_117795706 , chr6.S_117795706 (1020Pro/Arg), chr6.S_117795706 (1072Met/Thr), chr6.S_117795706 (1112Gln/Pro), which could be potential causative variations. We further verified the function of ZmFAB1A in drought tolerance and the results showed that Zmfab1a mutants had higher survival rates than those of WT plants after drought stress  is helpful in rapid selection of the superior genetic components that associated with given phenotypes. Because GS utilizes all genetic makers to predict the performance of certain candidates in selection, it is therefore a very useful and effective approach to predict the values of certain genetic makers in breeding . Based oZmcPGM2, had wavelengths of 780–1000 nm  in explaining the phenotypic variance of survival rates using a linear stepwise regression model. The results showed that up to 60% of the phenotypic variance in survival rates could be explained by combining 15 i-traits across the 4 time points  with small effects, and the traditional phenotyping of drought tolerance is still a bottleneck of genetic studies , 52. In ZmcPGM2 and ZmFAB1A  were also important for the fitness of maize to drought  its relative dwarfism but stronger stems under drought stress . The entire experimental design, inspection dates, weather conditions, and SM are provided in Additional file After a binary data stream of HSI imaging was acquired for one plant, the binary data stream was reorganized to 250 hyperspectral images. After image segmentation and trait calculation, 2000 hyperspectral i-traits, which included total reflectance related traits, average reflectance related traits, and logarithm related traits, were calculated  was collected by the high-throughput micro-CT-RGB system (HCR) . The pow± 3σ. The basic concept of 3σ or PauTa criterion is to take the given confidence probability 99.7% as the standard and the triple standard deviation limit as the basis. Any error exceeding this limit is considered not belonging to the category of random error, but to the gross error. The measurement value with gross error is called abnormal value, which is eliminated from the measurement data. (2) After filtering outliers, an independent-samples t-test was used to select i-traits with significant differences between the DS group and the WW group, using a 95% confidence interval. (3) Multilayer perceptron (MLP) was used to sort the i-traits depending on their importance for classification of the DS group and the WW group. In order to reduce the error due to random results, the multilayer perceptron operation was repeated five times. If the average value of importance of an i-trait was less than 50%, it would be deleted. (4) Finally, we also checked the heritability (H2) of the i-traits, which was calculated for each i-trait as follows:After a total of 26910 i-traits were obtained, the drought-related i-traits were selected using the following steps: (1) First, a 3σ criterion was used to eliminate abnormal data, which were defined as values greater than the mean valueσ2G is the genotypic variance, σ2e is the error variance, and r is the number of replications. The i-traits with higher heritability (H2 ≥ 0.2) were retained for further analysis. The outlier filtering was performed using LabVIEW 2015 . The multilayer perceptron and independent-samples t-tests were performed with SPSS software version 25.0 . The heritability was calculated using the lmer function in the lme4 package in the R environment [http://www.r-project.org/) [where ironment  (http://ct.org/) , and her−6  [In this study, a genome-wide association study (GWAS) for i-traits was conducted to test the statistical associations between genotype and phenotype (i-traits) using a mixed linear model , 73, 74 rs used) . For eac−7 . Then, we extended the 10-kb interval between significant SNPs upstream and downstream as an eQTL interval. If the candidate gene was within this interval, it was considered to be cis-regulated; otherwise, it was trans-regulated.In order to determine whether the candidate genes were regulated at the transcriptional level, GWAS was used to analyze the relationship between the whole genome SNPs and the expression levels of i-trait-associated candidate genes. In this study, we used the expression data from 197 diverse inbred lines (from a previous published 540 inbred lines for association mapping) under DS and WW conditions, combined with 1.25 million SNPs  for GWAShttp://bioinfo.cau.edu.cn/agriGO/index.php [GO enrichment analysis was conducted using agriGO   [The KEGG pathway enrichment analysis was completed in two steps. First, the maize candidate gene IDs were converted and filtered into Entrez Gene IDs by customized scripts using gene information (Zea_mays.gene info) from GenBank. Then, the Entrez IDs were called by the Gene-list Enrichment tool in KOBAS3.0 (/kobas3)  to do KE/kobas3) . The cut/kobas3)  (versionhttp://www.r-project.org/) [The genome selection analysis was divided into three steps: First, the whole maize genome genes were divided into two gene pools: the candidate gene pool and the random gene pool (excluded candidate genes), and the SNP with the most significant association with survival rate was selected to represent this gene. Second, different number of the most significant SNPs  were selected for candidate genes. For random genes, SNPs corresponding to the number of candidate genes were randomly selected each time, and the process was repeated 50 times. Finally, after obtaining all SNPs of the selected genes (MAF ≥ 0.05), the R package RR-BLUP (ct.org/)  and BGLRct.org/)  was usedZmcpgm2 mutant plants were used for the metabolomic experiments with three biological replicates of each. Leaf samples frozen in liquid nitrogen were ground in a Mixer/mill  with a steel ball for 30 s at 30 HZ. Fifty milligrams of each sample was extracted with 3:1 methyl tert-butyl: ether: methanol, v:v, in which 10 μL of 1 mg/mL 13C ribitol was added as an internal standard [Leaves of WW and DS B73 wild type and standard . In totaOne microliter was taken from each sample and injected into the GC-MS at 270 °C in a split mode (50: 1) with helium carrier gas (> 99.999% purity) flow set to 1 mL/min and separated by a DB-35MS UI  capillary column. The temperature was isothermal for 4 min at 90 °C, followed by an 8 °C per min ramp up to 205 °C, then held for 2 min, and finally ramped up at a rate of 15 °C per min to 310 °C, held for 2 min. The transfer line temperature was set to 300 °C, and the ion source temperature was set to 230 °C. The mass range analyzed was from m/z 85 to 700. Agilent MassHunter Qualitative Analysis (version B06.00) software and Agilent MassHunter Quantitative Analysis (version B.07.01) were jointly used for GC-MS data analyses. NIST library and in-house database established using authentic standards were used together for metabolite identification.Zmcpgm2 and Zmfab1a and identified their genotypes by the KASP method . The putative target EMS sites of the genes were then sequenced to confirm the mutation  for i-trait  detection. Each genotype was divided into two treatments: DS and WW, with at least 10 pots per treatment. The maize plants were transferred into the RAP at the 2-leaf stage. Drought treatments and planting methods were as described above. All the maize seedlings were subjected to RGB and HSI imaging at 4 time points . At the flowering stage, all maize plants were screened by CT imaging at 4 time points with at least 5 pots per treatment . The inspection dates, weather conditions, and SM data are provided in Additional file From late March to early July 2019, we planted Zmcpgm2, Zmfab1a, and B73 in autumn 2019. Each genotype was divided into two treatments: DS and WW, with at least 10 pots per treatment in 3 replicates. Irrigation was stopped at the 4-leaf stage, and the drought treatment was the same as described previously. When the SM reached 10%, the plants were re-watered, and the survival rate was determined 3 days after re-watering.For the survival rate experiment, we planted Zmcpgm2, Zmfab1a, and B73 seeds germinated on a petri dish, they were transplanted into plastic pots (length × width × height = 42 cm × 32 cm × 15 cm). Each pot was filled with 12.5 kg soil. Fertilizing was performed before transplanting and the 3-leaf stage. The mutants and B73 were grown in the pots side-by-side, with a total of 18 plants for each genotype, and there were 3 replicates of each experiment. Before the maize 4-leaf stage, the pots were planted outdoors. The temperature of the growth chamber was 28 °C and the light cycle was 12 h light/12 h dark. Drought stress was initiated by ceasing irrigation at the 4-leaf stage. During the drought stress, we used a LI-COR6800  to measure the photosynthesis parameters. Every other day, 6 leaves of the mutant and B73 in each pot were measured starting at 8:30 a.m. After the daily measurement, we used a DELTA-T Soil moisture Kit  to measure SM.The photosynthesis experiment was conducted in autumn of 2019. After Zmcpgm2, Zmfab1a, and B73 at the 6-leaf stage and were exposed to air at room temperature. These leaves were weighed at various time intervals, and the loss of fresh weight (percentage) was used to calculate water loss rate.For water loss measurements, leaves were detached from https://github.com/fenghuifh2006/Maize-RGB-CT-HSI-program and 10.5281/zenodo.4690730 [The selected RGB, HSI, and CT i-traits are listed in Additional file .4690730 , 85. All.4690730 . All othAdditional file 1: A master file for Table S1-22.Additional file 2: Video S1. High-throughput phenotyping to extract i-traits.Additional file 3: Video S2. High-throughput hyperspectral images acquisition and analysis.Additional file 4: Video S3. High-throughput RGB images acquisition and analysis.Additional file 5: Video S4. High-throughput CT images acquisition and analysis.Additional file 9: Note S1-S2. Information about the i-traits and the operation guide for the HSI, CT, and RGB data processing programs.Additional file 10: Figures S1-S11.Additional file 11. Review history.


Zymoseptoria tritici from four continents. We measured 50 life-history traits, including virulence and reproduction on 12 different wheat hosts and growth responses to several abiotic stressors. To elucidate the genetic basis of adaptation, we used genome-wide association mapping coupled with genetic correlation analyses. We show that most traits are governed by polygenic architectures and are highly heritable suggesting that adaptation proceeds mainly through allele frequency shifts at many loci. We identified negative genetic correlations among traits related to host colonization and survival in stressful environments. Such genetic constraints indicate that pleiotropic effects could limit the pathogen’s ability to cause host damage. In contrast, adaptation to abiotic stress factors was likely facilitated by synergistic pleiotropy. Our study illustrates how comprehensive mapping of life-history trait architectures across diverse environments allows to predict evolutionary trajectories of pathogens confronted with environmental perturbations.The adaptive potential of pathogens in novel or heterogeneous environments underpins the risk of disease epidemics. Antagonistic pleiotropy or differential resource allocation among life-history traits can constrain pathogen adaptation. However, we lack understanding of how the genetic architecture of individual traits can generate trade-offs. Here, we report a large-scale study based on 145 global strains of the fungal wheat pathogen  Escherichia coli [Candida albicans [Aspergillus fumigatus [Adaptation to heterogeneous environments (both biotic and abiotic) is critically important for pathogens to successfully infect and colonize their hosts, disseminate to new hosts and cause epidemics, and survive in the absence of their host. Adaptation to new environments is contingent on genetic variation in life-history traits. As a consequence, adaptation can be severely constrained in pathogen populations of low genetic diversity. Furthermore, trade-offs between advantageous traits can impose limits on adaptive evolution , 2. Tradhia coli , fitnessalbicans , and a sumigatus . The mutTrade-offs are defined as phenotypic correlations that prevent organisms from reaching maximum fitness in specific environments and are often influenced by environmental conditions. Trade-offs impose evolutionary constraints if the expression of each trait is governed by the same mutations . PleiotrZymoseptoria tritici is a major fungal pathogen that poses a significant threat to global wheat production by causing septoria tritici blotch (STB) disease [Z. tritici virulence  and reproduction  may be constrained at the genetic level. Later in the infection cycle, the pathogen forms sexual ascospores that are dispersed over long distances by wind. Across the global distribution range, populations vary in their degree of thermal adaptation and fungicide resistance as a result of differences in temperature and fungicide exposure [Z. tritici model offers powerful experimental approaches to map phenotypic trait architectures using genome-wide association mapping , shows g , Israel (n = 30), two nearby field sites in Switzerland (n = 2 and n = 30). The field population in the USA was sampled from two distinct cultivars growing in a randomized block design: Oregon.R, n = 26; Oregon.S, n = 30 All samplings were made between 1990 and 2001 . We used Genome Analysis Toolkit (GATK) v.4.0.1.2 [Z. tritici is haploid). Joint variant calls were performed using GenotypeGVCFs on a merged gvcf variant file with the option -maxAltAlleles 2. We used VariantFiltration and SNPs were removed if any of the following filter conditions applied: QUAL < 250 ; QD < 20.0 ; MQ < 30.0 ; −2 > BaseQRankSum > 2; −2 > MQRankSum > 2; −2 > ReadPosRankSum > 2; FS > 0.1. The genotyping accuracy was previously investigated and found to be highly congruent with alternative SNP callers [In addition to the already deposited Illumina whole genome sequencing data for 130 isolates  to trim e IPO323  and dupl.4.0.1.2  for sing callers . The finLr34). ArinaLr34 carries the wheat leaf rust resistance gene Lr34, which provides resistance to wheat stripe rust [Z. tritici. The 1011, 1204, 4391, and 5254 landraces were selected from the Swiss National Gene Bank (www.bdn.ch). Six pots with three seeds of each cultivar were sown per pot and placed on a tray in a 2 × 3 array. The experiment was divided into two phases (6 cultivars at a time) due to space limitations. The trays were kept at 22 °C (day) and 18 °C (night) with 70% relative humidity (RH) and a 16-h photoperiod in a greenhouse chamber. Each tray containing two-week-old seedlings of six cultivars was inoculated uniformly with each isolate using an airbrush spray gun until run-off. Three independent inoculations were performed to generate three biological replications in different greenhouse chambers in both phases. All leaves from each host-by-isolate combination were collected on the same day between 19–26 days post inoculation (dpi) and analyzed using automated image analysis . The AIis  for four to five days at 18 °C. Blastospore suspensions were collected from each plate by adding 0.6 ml of sterile water and diluted to a final concentration of 200 spores/ml using KOVA counting slides . For each isolate, a 500 µl spore suspension was spread on Petri plates containing potato dextrose agar  using a sterile glass rod. Plates were kept at 15 and 22 °C at 70% RH. Each plate was photographed at 8, 11 and 14 dpi with a digital camera.Fungal colony growth rate (mm per day), temperature sensitivity, mean colony area, fungicide resistance, and melanization in the presence or absence of fungicide were measured in vitro (Supplementary Table √(mean colony area/π)) were estimated and fitted over the three time points  following a general linear model to obtain growth rates (mm/day) at 15 and 22 °C. Temperature and fungicide sensitivity of each isolate was calculated using the growth rate ratio between 15 and 22 °C, between 22 °C and growth rate at 22 °C on PDA amended with propiconazole , respectively. In addition, we used mean colony area measured for each isolate at 14 dpi on PDA at 15 °C (MCA_15 °C_14_dpi), 22 °C (MCA_22 °C_14_dpi), and 22 °C amended with 0.05 ppm propiconazole (MCA_14_dpi_azole), to estimate the ratio of colony area in cold (RCA_14_dpi) and fungicide environments (RCA_14_dpi_azole) compared to the control environment.The digital images of each plate from five technical replications were used for AIA in ImageJ following the scripts by Lendenmann et al.  to obtai4 spores/ml) in Sabouraud-dextrose liquid medium with the following concentrations of propiconazole: 0.00006, 0.00017, 0.0051, 0.0086, 0.015, 0.025, 0.042, 0.072, 0.20, 0.55, 1.5 mg/L and a control without fungicide. Each microtiter well was filled with 100 μl of medium at a given concentration of fungicide and 100 μl of spore suspension using the above spore concentration. Three technical replicates were used for each isolate. The plates were sealed and incubated in the dark for four days at 22 °C with 80% relative humidity. Fungal growth was measured with an Elisa plate reader  by examining the optical density (OD) at 605 nm wave length. The growth inhibition at different fungicide concentrations was used to estimate the dose-response curves. The dose-response curves were used to estimate the EC50 value for each isolate using the drc v.3.0-1package [Fungicide resistance assays were carried out using propiconazole on microtiter plates. Growth inhibition was tested by growing spores adjusted to a spore concentration of 2.5 × 101package  in the R1package .We estimated melanization of each isolate at 8, 11, 14, and 18 dpi grown in three different conditions, (i) 15 °C, (ii) 22 °C, (iii) 22 °C in the presence of fungicide (0.05 ppm propiconazole). The same protocol described for growth assays above was also used to assess fungicide resistance. At each time point, digital images of each plate were captured and analyzed using the AIA protocol described in Lendenmann et al. . We recoin planta trait obtained by Dutta et al. [z-scores (mean = 0 and sd = 1) of LSmeans for each isolate. The z-scores were used to reduce variability among different traits as they were expressed in different units. We excluded the morphological stress response data from the heatmap as clustering did not allow for binary data. The hierarchical clustering was performed following the agglomerative algorithm “complete” based on Euclidean distances.We used log-transformed least-square means (LSmeans) for each host×isolate combination and for each a et al. . The hosa et al. , 48. Thea et al.  using thn = 145), we used an MLM instead of a compressed MLM, which groups individuals according to phenotypes following the implementation in GAPIT [y = Xb +Zu + e, where y is the vector of phenotypes, X and Z are the known design matrices, b is the unknown vector of fixed effects (SNPs), u is the vector of random effects (based on the GRM) and e is the vector of residual effects.We performed a genome wide association study (GWAS) for all 50 traits and 145 isolates based on 716'619 SNPs. Prior to GWAS, we conducted a principal component analysis (PCA) on the SNP dataset to investigate the population structure using TASSEL v.20200220  implemenin GAPIT . The folWe used the stringent Bonferroni threshold (α = 0.05), false discovery rate (FDR) at 5 and 10% to retrieve SNPs at different levels of statistical confidence using the R package q-value . For theh2snp; equivalent to narrow-sense heritability) for each trait was estimated using the genome-wide complex trait analysis (GCTA) tool v.1.93.0 [h2snp was estimated using a genome-based restricted maximum likelihood (GREML) approach using the phenotypic data of each trait and considering the additive effect of all the SNPs represented by the GRM. The tool uses the GRM as a random factor and estimates the proportion of phenotypic variance attributed to the random factor. GCTA estimates the genetic relatedness between two individuals  from all the genome-wide SNPs following the formula from Yang et al. [xij is the number of copies of the reference allele for the ith SNP of the jth individual and pi is the frequency of the reference allele and N is the number of SNPs. As Z. tritici is haploid, the average relatedness among all pairs of unrelated individuals  was close to two as opposed to the expected value of one for humans [The SNP-based heritability . The bivariate GREML approach estimates the genetic covariance (σg12) by quantifying whether two individuals tend to show phenotypic similarity considering their genetic similarity (based on the GRM). Here, the variance-covariance structure of the genetic values is described by the GRM and the phenotype is modeled as a function of individual genetic values. Genetic correlation coefficients are estimated as the ratio of the genetic covariance (σg12) and genetic variance of two traits (σg1 and σg2). We visualized genetic correlation coefficients as a network using the R package “qgraph” [z-scores of the phenotypes based on Pearson’s correlation coefficients. Multiple testing adjustments of p values were performed by following the Benjamini and Hochberg [α = 0.05. As the morphological stress response was recorded as a binary trait, we used a point biserial correlation [To investigate whether traits are under shared genetic control, we estimated genetic correlations between all possible trait pairs following a bivariate GREML approach in GCTA , 67 incl“qgraph” . PhenotyHochberg  method arelation  to correrelation .Z. tritici isolates collected across the distribution range of the pathogen including the Middle East (the center of origin), Europe, North America and Australia  for each of the traits using a genome-based restricted maximum likelihood (GREML) approach. To account for genetic relatedness among isolates, we used genome-wide SNP data as a random effect. The estimated h2snp for virulence ranged from 0 to 0.59  and for reproduction from 0.43 (SE = 0.15) to 0.91  compared to the mean h2snp for virulence . The higher heritability suggests that the short-term response to selection may be faster for reproduction. The heritability for growth traits, fungicide resistance, morphological stress response and thermal tolerance traits ranged widely from 0 to 0.99 with fungicide resistance traits showing the highest heritability . Howeve. CYP51; .Fig. 2Php value inflation (see Methods for more details on the procedure). For the majority of the traits, including virulence, reproduction and fungicide resistance, we detected between 18 and 1422 significantly associated SNPs at a 10% false discovery rate (FDR) threshold. We identified no phenotype-genotype association at the very stringent Bonferroni threshold (α = 0.05). This suggests that most traits have a polygenic architecture with many loci of small effect. The large set of GWAS on individual traits allows to search for SNPs significantly associated with more than one trait. Such shared SNPs can point to a shared genetic control among traits or pleiotropic effects. We detected 249 SNPs shared among fungicide resistance traits including colony growth  substantial linkage disequilibrium at this distance and (2) the average distance between genes in the genome is ~1 kb [To directly identify loci associated with phenotypic trait variation, we performed GWAS for each trait individually using a mixed linear model. Because the mapping population showed genetic heterogeneity due to population structure, we included the GRM as a random factor to control for is ~1 kb , 32. Bas7_1897774) significantly associated both with fungicide resistance  and colony growth , which overlaps with the coding sequence of a protein controlling virulence on a specific wheat cultivar  overlapped with a gene encoding a papain-like cysteine peptidase superfamily (Zt09_8_00399). The most significant SNPs shared for reproduction on the elite cultivars Greina, Titlis and Toronit overlapped with a gene encoding an autophagy-related protein (Zt09_1_00319). For fungicide related traits, the most significant shared SNPs overlapped with genes encoding an effector candidate (Zt09_2_00572) and a prenyltransferase . How such gene functions could facilitate trait expression across different environments remains to be elucidated. We performed gene ontology enrichment analysis of all gene sets associated with individual traits or gene sets close to SNPs shared among different traits. We found one significantly enriched gene ontology for a single trait. The enrichment was for fungicide resistance  and showed an over-representation of phosphorus-containing transfer functions  and phenotypic correlation  were highly variable . Negative correlations may underlie conflicts in the expression of different pathogenicity traits possibly related to polymorphism at the level of the host immune system. The network representation of the genetic correlations reveals particularly strong negative correlations for ratio of colony area in presence of azole with colony growth and reproduction on the wheat cultivar Drifter  to strong  genetic correlation coefficients between morphological stress response  and colony melanization in multiple environments suggests a shared genetic control for these traits. Furthermore, fungicide resistance showed a negative genetic correlation with virulence on the cultivar ArinaLr34  and Toronit , reproduction on the landrace Chinese Spring  and the degree of reproduction specialization . Overall colony growth rates at 22 °C had mostly very low genetic (often negative) and phenotypic correlation coefficients with pathogenicity traits. This suggests that mutations favoring colony growth may not favor overall plant colonization. Overall, the observed genetic correlations suggest that pleiotropic effects across multiple genes could govern major phenotypic traits of the pathogen.To expand our understanding how loci across the genome impact phenotypic trait variation, we analyzed correlation among traits both at the phenotypic and genetic level. Overall, both genetic correlation between traits . We also found an excess of 1.1 in the genetic correlation for the morphological stress response and melanization at 22 °C at 8 dpi in presence of fungicide . Such disparity can occur when environmental effects  act in the opposite direction of genetic effects. Overall, we detected 20 pairs of traits with genetic correlation coefficients showing sign changes  compared to the corresponding phenotypic correlation coefficients. Given the discordance between phenotype and genetic correlations, we therefore used only genetic correlation coefficients for the subsequent analyses. The average genetic correlation for trait categories can be informative for the degree of shared genetic control of the traits  were particularly low compared to the other cultivars. This indicates that the genetic control of pathogenicity traits on these hosts is the most differentiated compared to the other hosts. The negative average genetic correlation for reproduction specialization  is in contrast to the positive overall average genetic correlation for virulence  and reproduction  . Hence, we analyzed how strongly pathogenicity traits correlate between hosts Fig. . We foun04) Fig. . This suA mechanistic view of the adaptive landscape across the life cycle is of fundamental importance to understand pathogen evolution . Yet, weZ. tritici retains such high levels of adaptive genetic variation along environmental gradients remains unclear. A key factor maintaining variation may be genetic trade-offs among life history traits constraining directional selection [We show that a substantial proportion of the overall phenotypic variation is explained by common variants , consistent with polygenic trait architectures with many loci of small individual effect , 77–79. election , 86.Z. tritici is both constrained and facilitated by genetic correlations that vary according to the life-history traits and the environment. Unlike phenotypic correlations, genetic correlations estimated using mixed models are robust to confounding effects of population structure and directly reflect the genetic contributions to adaptive trait co-variation [Z. tritici [Adaptation to complex environments can be severely constrained by genetic trade-offs, but populations can also experience rapid adaptation through correlated responses to selection if genetic correlations are positive , 87, 88.ariation , 90. Giv tritici , 91, 92.Lr34 or the landrace 1011 very likely imposed selection pressure on pathogen populations to evolve a distinct, host-specific arsenal of virulence factors. Consistent with theoretical predictions [Z. tritici growth outside of the host was largely independent and distinct from traits promoting host infection indicating that very few loci show pleiotropic effects. Nevertheless, on some host backgrounds, the ability to cause disease could be constrained by the pleiotropic effects of stress-related traits such as fungicide resistance or melanization. Genetic constraints governing growth in the absence of the host and pathogenicity could be a major impediment for pathogen populations to maximize fitness on hosts. This would be due to the fact that fitness outside the host is affected by correlated responses to selection on traits expressed on the host. The finding that Z. tritici populations carry variants with negative pleiotropic effects for infection despite apparent costs is intriguing. Variants underlying strong stress-related trait expression variation should be highly selectable and are likely underpinning survival under harsh climate as shown by strong synergistic pleiotropic effects. Therefore, maladaptive trait expression on the host could well be maintained in pathogen populations if the underlying variants are favored under harsh climatic conditions [A major finding on the evolution of pathogenicity is the positive correlation between virulence and reproduction. Such correlation could stem from convergent evolution selecting for similar synergistic mutations in shared resistant backgrounds of cultivated wheat worldwide , 93. Mordictions , 95, ourdictions . In contdictions , 97), genditions –100.Our study reveals remarkable complexity in pleiotropic effects across the life cycle in a major eukaryotic plant pathogen. Furthermore, adaptation to host and non-host environments has a largely polygenic basis. Our study demonstrates the power of conducting a large-scale GWAS across a wide range of environmental conditions. We show that trade-offs are not ubiquitous, but rather facilitation  plays an important role in determining the evolutionary potential of pathogen populations across environments. By leveraging information on facilitation and trade-offs, the response of pathogens to changing environments becomes more predictable. Furthermore, knowledge on trade-offs can be exploited in innovative disease management models that exploit evolutionary weaknesses of pathogens. For example, trade-offs between pathogen virulence on specific cultivars and fungicide resistance could be exploited by planting specific wheat genotypes in areas where fungicide resistance emergence becomes a risk. A clear understanding of facilitation and correlated responses to selection can also be applied to prevent catastrophic breakdowns in resistance. The antagonisms among life-history traits on different hosts can inform crop resistance management by spatially or temporally diversifying agricultural fields with different hosts according to the prevalent environmental conditions. By creating complex but specifically designed host environments, pathogen populations could be constrained in their ability to cause serious host damage. Retracing specific biochemical and developmental pathways underpinning pleiotropic effects is greatly facilitated by the multi-trait genome-wide mapping approach and will provide a deeper insight into principles governing pathogen adaptation in heterogeneous environments.Supplementary InformationSupplementary Tables