The Vavilov Institute of Plant Genetic Resources (VIR), in St. Petersburg, Russia, houses a unique genebank, with historical collections of landraces. When they were collected, the geographical distribution and genetic diversity of most crops closely reflected their historical patterns of cultivation established over the preceding millennia. We employed a combination of genomics, computational biology and phenotyping to characterize VIR’s 147 chickpea accessions from Turkey and Ethiopia, representing chickpea’s center of origin and a major location of secondary diversity. Genotyping by sequencing identified 14,059 segregating polymorphisms and genome-wide association studies revealed 28 GWAS hits in potential candidate genes likely to affect traits of agricultural importance. The proportion of polymorphisms shared among accessions is a strong predictor of phenotypic resemblance, and of environmental similarity between historical sampling sites. We found that 20 out of 28 polymorphisms, associated with multiple traits, including days to maturity, plant phenology, and yield-related traits such as pod number, localized to chromosome 4. We hypothesize that selection and introgression via inadvertent hybridization between more and less advanced morphotypes might have resulted in agricultural improvement genes being aggregated to genomic ‘agro islands’, and in genotype-to-phenotype relationships resembling widespread pleiotropy. Agriculture must simultaneously intensify, become more sustainable, and achieve greater resilience to pests and climate. New paradigms are needed to increase sustainability in agricultural systems, including methods to explore the genetic potential of the vast but woefully underutilized germplasm resources available for most crop species. Crucial to this effort are the recent advent of low cost, high throughput DNA sequencing technologies and corresponding advances in computational genomics2.A defining challenge of the 21st century is meeting the nutritional demands of a growing human population, using increasingly limited land and water resources and under the spectre of climate change3 provide access to millennia of human-selected adaptations present in landraces. The majority of such genetic diversity has been removed from modern agricultural systems through Green Revolution practices4. Here we combine genomics, computation and phenotyping to characterize molecular and phenotypic variation in a sample of the collection of chickpea (Cicer arietinum L.) amassed by Nikolay Vavilov and his colleagues, linking valuable adaptations to genome intervals and candidate genes and resurrecting the collection’s currently latent power to meet the enormous challenges of 21st century agriculture.However, the above tools are only helpful when they are applied to appropriate germplasm. The Vavilov Institute of Plant Genetic Resources (VIR) in St. Petersburg is a uniquely valuable collection of crop germplasm because it captures the genetic and functional diversity of regionally stratified agriculture typical of one century ago. The VIR’s focus on locations of historical chickpea domestication in the Middle East and of long-standing secondary centres of diversity, including in Ethiopia6. Vavilov contributed importantly to the paradigm that domestication of crops occurred at the species’ ‘centre of origin’, where recurrent selection of the most valuable plants, from generation to generation, resulted in genetic divergence and isolation from wild progenitors, ultimately yielding domesticated species. As cultivation spread regionally, hundreds to thousands of locally distinct forms arose through further selection, drift and gene flow, generating landraces. Landraces dominated agriculture from ~7 KYA until the advent of intensive modern breeding in the mid 20th century – when a few elite cultivated varieties largely displaced landraces.At the beginning of the 20th century, leading agronomists and geneticists recognized the need to preserve and characterize the genetic diversity of cultivated plants and their wild relatives. D. Fairchild and his staff in the USA and many others organized expeditions to sample biodiversity, but it was Nikolay Vavilov and the considerable resources committed by the growth-hungry post-revolution Soviet government that made the largest contribution7. However, legumes were not equal beneficiaries of the Green Revolution. Policy and investment since the 1960’s favoured Green Revolution cereal crops, which were planted on the best agricultural land and received the lion’s share of inputs. Legumes, on the other hand, were often relegated to marginal lands where elevated temperatures, rainfed cropping systems, short growing seasons and poor soils conspire to limit yield potential8. Current grain legume production  in impoverished, food-insecure countries is often significantly short of demand. Simultaneously, modern breeding has collapsed the historical diversity of crops like chickpea. The massive reduction in genetic variation9 constrains crop improvement and genetic gain.Grain legumes, including chickpea, are the primary source of nutritional nitrogen for approximately 30% of the world’s human population, and their consumption contributes to healthy lifestyles10. Knowledge of the genomic basis of phenotypic variation in relatively diverse landrace collections, such as those of the VIR11, will further enable crop scientists to devise solutions to agricultural constraints. The VIR houses 928 accessions of chickpea sampled by Vavilov and his colleagues between 1911–1940, with corresponding phenotypic data collected at semi-regular (2–5 year) intervals during recent decades. Our goal was to test the feasibility of combining genomics and computation with historical records to mine the VIR collection for “genomic gems” that might offer solutions to the tremendous challenges of modern agriculture, including increased productivity with decreased environmental impact. Towards this aim, we present analysis of a subset of 147 landrace accessions for which complete phenotypic data replicated over time are available (see Methods).The VIR collection reflects local crop diversity before the intensification and global homogenization of modern breeding efforts, which have tended to focus on a narrower and narrower set of improved lines11. We have obtained reduced representation sequencing data for these 147 landraces and combined these genomic data with ecogeographic and phenotypic data to deduce patterns of genomic variation and their association with trait values and the sampling environmental data. We first characterize genetic variation among these accessions, then show phenotypic variation, and finally connect genetic, phenotypic, and environmental data via the analysis of covariance between genetic and phenotypic resemblance, and via GWAS.To start analysing the wealth of the VIR germplasm coupled with available phenotypic data, we have limited our scope to the oldest chickpea accessions collected nearly a hundred years ago from one centre of primary chickpea domestication (Turkey) and one centre of secondary diversification (Ethiopia)Genotyping-by-sequencing identified 14,059 segregating SNPs among 147 accessions originating from Turkey and Ethiopia. Principal component analysis (PCA) revealed two eigenvectors separating landraces by country of origin see Fig. . On the 16. Desi genotypes generally have smaller, dark seeds and coloured flowers, while Kabuli genotypes are characterized by larger, light coloured seed and white flowers. Based on the analysis of molecular markers, patterns of geographic distribution, and resemblance to the wild progenitor species, Desi is accepted as the ancestral state15. With this knowledge, and observing patterns shown in Fig. th chromosome. Only the blue clade migrated to Ethiopia , where it gave birth to the low diversity red cluster apparently involving a secondary genetic bottleneck.The patterns above originate from a combination of geographic subdivision, genetic bottlenecks and/or interbreeding, and also potentially reflect different types of biotic or abiotic selection. The history of chickpea domestication and breeding involved differentiation of the crop into two market classes based on a suite of traits, typified by differences in seed size, seed coat tannins and flower colourC. reticulatum, as an outgroup  and Astrakhan, Russia (109 accessions). We felt that an attempt to analyse this data would be highly informative as a ‘proof of concept’, illustrating whether useful inferences might be derived from the VIR historic data. Unfortunately, the lists of phenotypes recorded were overlapping but not completely consistent between these phenotyping experiments. Accordingly, we characterized phenotypic variation separately at each phenotyping site using factor analysis (FA).While VIR germplasm accessions were regrown at consistent intervals of 2–5 years, the corresponding phenotypic records are sometimes shallow and fragmented. Luckily, these accessions of chickpea have been a subject of two detailed analyses, as described in more detail by Vishnyakova FA on the Syrian data identified 3 axes that account for 57% of phenotypic variation Fig. . Factor Ascochyta blight, a devastating foliar disease of chickpea, was the primary phenotype loading onto Factor 2. As was the case for the Syrian analyses, Factor 3 was most associated with plant fecundity  of the observed variation for the Russian field data Fig. . Seed chity Fig. . Interesity Fig. .The degree to which genotype differences explain phenotypic variation depends both on the density of segregating sites and the extent of linkage disequilibrium (LD). If LD extends beyond several GBS loci, then most genome-wide associations should be captured. Our GBS protocol samples ~3% of the genome, which after QC and filtering of mapped reads yielded 14,059 SNPs with known genome locations. Does it yield a map dense enough to analyse genotype-phentoype associations?The analysis of LD decay with the distance between SNPs establishes that significant LD remains up to approximately 1.5 Mb Fig. , a patte18, however our inferences must be treated with substantial caution due to a limited sample size. Both Syrian and Astrakhan phenotyping data reveal strong association between genomic and phenotypic resemblance for most phenotypes  for which population subdivision is strong, is likely to inflate the apparent statistical significance of our inferences. To control for this possibility, we generated q-q plots and observed little evidence for such an inflation , annotated as an ATP-dependent DNA helicase RecG that plays a critical role in recombination and DNA repair in bacteria22. Plant DNA helicases are likely to have a variety of roles, but a phenotype they have been linked to of relevance here is plant growth and development23 and response to abiotic stress. The chloroplastic RecG of Arabidopsis thaliana (At2g01440) is 70.7% identical to the Ca_15093 protein and is differentially expressed in response to stress, including anoxia, cold, drought, genotoxic, hypoxia, heat, osmotic, oxidative, salt and wounding24. Ca4:30295389–30312868 (Ca_14192) encodes a putative chromatin remodelling protein from the sucrose non-fermenting 2 (Snf2) family of DNA helicases/ATPases and is 63% identical to A. thaliana PIE1 (Photoperiod-Independent Early flowering 1). PIE1 belongs to the Swr1 subfamily of Snf2 proteins and is believed to maintain negative control of the salicylic acid-dependent defence pathway27. Another gene, Ca1: 46968371–46976822 (Ca_12942), encodes trehalose 6-phosphate synthetase. Alteration of the amounts of trehalose 6-phosphate and/or trehalose can modulate abiotic stress tolerance. Each of these gene variants could plausibly underlie adaptation to environmental factors.The associations we have uncovered mapped to broad genomic regions because of extended LD. They cannot identify causal relationships between SNPs and phenotypes. Nevertheless, it is of interest to explore potential nature of the associated genes. We report that among 28 significant SNPs, 12 are located in gene sequences and 12 near gene sequences with assignable protein functions. Two of these genes encode protein domains reported to contribute to DNA repair, recombination and/or replication; two have domains involved in plant growth and development; nine contain domains with roles in signal transduction ; and four encode proteins participating in plant defence responses, namely cell wall modification, cell death, redox reactions, immune response and detoxification  putative candidate genes known to play a direct or indirect role in osmoregulation that protects the plants not only from salinity stress but also from other abiotic stresses36. Kale et al.37 localized QTLs between two accessions – one Kabuli and another Desi – and mapped a variety of genes for drought resistance, mostly to the 4th chromosome. Interestingly, the 4th chromosome was found to have the maximum polymorphism (SNPs and SV) rate and maximum density of exonic variants when 35 chickpea genotypes representing parental lines of 16 mapping populations segregating for abiotic, biotic and nutritionally important (protein content) traits were re-sequenced38. Such studies lend support to our broader findings of numerous genes for multiple phenotypes mostly co-localized to a single genomic region. Interestingly, while the evolution of this genomic region has taken place in the geographic region of primary domestication, the evolved genotypes have not gained widespread representation in Ethiopia, which represents an important location of chickpea’s secondary diversification40.We posit that as increasingly advanced varieties accrue through selection on multiple genes, the cost of losing desirable trait complexes through outcrossing with a less advanced form also increases. Co-localization of co-adapted gene complexes in these more derived varieties theoretically mitigates such risk, enabling agricultural advance by reticulate processes, rather than simple fixation and linear descent. We nickname such gene complexes genomic ‘agro islands’. The existence of ‘agro islands’ is also strongly supported by prior data in chickpea. A comprehensive analysis of QTL region contributing to Ascochyta blight resistance that span 3 Mb of 247 48. The proposed explanations included that these genomic islands of divergence could likely form through either genomic rearrangements that bring co-adapted loci close together or because the probability of a new mutation establishing is higher when occurring near another locally adapted mutation50. However, most of the conclusions were derived through the genome scans, i.e. identification of outliers, that is subject to some statistical concerns.Is clustering of alleles contributing to local adaptation and domestication common or an exception? It was in fact demonstrated and discussed in numerous studiesViola cazorlensis, the divergence in floral traits that is potentially under pollinator-mediated selection was significantly associated with large number of loci51. There are also numerous small genomic regions underlying differentiation of sunflower species, and they are typically associated with low recombination rate52. One notable exception is association mapping of shoоt branching that plays an important role in sunflower adaptation to environment53. А large number of SNPs associated with branching map to single wide region of chromosome 10, where B locus responsible for branching is present as a large haplotypic block. This locus was reintroduced into sunflower gene pool to extend the flowering time in R lines. In maize, the loci implicated in domestication are spread around the genome; however there are also a few gene clusters55 controlling a large portions of the phenotypic differences56. An idiosyncratic pattern of gene clustering was found on the fifth chromosome, it is associated with a number of domestication traits. Lemmon and Doebley57 demonstrated that this region may be split into multiple QTLs, none with singularly large effects. In rice domestication loci are also spread across the genome58, although the complexity of this domestication and the potential for ongoing gene flow with the wild relatives means fewer “islands” might have been involved initially. Indeed, many QTLs associated with rice domestication traits map to third chromosome. Significantly reduced nucleotide variation in genomic regions corresponding to these QTLs was demonstrated for one rice domesticated varietal type, tropical japonica. Coalescent simulations based on a complex demographic model inferred from genome-wide patterns of nucleotide variation suggested the third chromosome QTL regions might have been selected in this varietal type59. Interestingly, intron 1 splice donor site mutation in the Waxy gene that leads to the absence of amylose played a critical role in the origin of low amylose non-glutinous temperate japonica varieties. A large gene cluster that spans more than 250 kb and contains 39 genes including Waxy is due to selective sweep in this variety associated with the Waxy mutation55. Overall, more efforts will be needed to understand how common genomic islands are in plants. We require information from a greater number of domesticated crops, varying in center of origin, mating system, and agroecology to better understand the frequency with which genomic islands are involved in domestication.In more detailed analyses, for example in populations of the violet species Since we found that phenotypic variation is shaped into several ‘composite traits’ captured by our factor analyses, we studied whether such composite traits assort among environments. One might hypothesize that a certain value of a composite trait will fit one environment, while not matching another. For example, longer vegetative growth resulting in larger plants with higher yields might be maladaptive in localities with more pronounced seasonality or reduced rainfall. Farmers would likely avoid such maladaptive trait complexes, which would be evidenced as phenotype-environment co-variance. Indeed, such covariance appears frequently in our data set. We observed strong correlations of several environmental parameters with genomic resemblance. This hints that the preponderance of co-localized gene complexes might be co-adapted for a subset of cultivated environments. Whether this hypothesis holds for most domesticated crops (or even for the remainder of VIR’s extensive chickpea landrace collection) can now be tested using the hundreds of thousands accessions hosted at VIR, and for similar collections housed elsewhere, representing a remarkable opportunity to mine extant, but underutilized genomic gems. The resulting discoveries will contribute to meeting current and future agricultural challenges, including feeding a growing world, with nutritious outputs, in the face of increasing climatic variability and stress.29. Reanalysis of the data with these accessions dropped results in general decrease of power but qualitatively similar conclusions.The 147 accessions under study were collected from two countries of origin. Eighty-four plants were from Turkey and 63 were from Ethiopia  were compiled for different traits, translated from VIR’sTwo different subsets of traits were collected from two sites (Syrian and Astrakhan data), so we analysed these two sites separately. One subset of traits was assessed during the period 1996–2004 at VIR’s field experimental station in the Astrakhan region, Russia. At Astrakhan measurements of seed size, seed shape, seed colour, days from sowing to maturing, days from sowing to flowering, bush shape, height of lower pod attachment, Ascochyta tolerance, number of seeds per plant were performed. The second subset of traits of these accessions was assessed during the period 2000–2005 in the water-limited environment of Syria, at ICARDA’s Tel Hadya Research station near Aleppo. In Aleppo days of flowering, days from sawing to flowering beginning, yield index , number of pods per plant, number of seeds per plant, dry weight of plant without seeds, seed weight per plant, dry weight of plant with seeds and roots, weight of 1000 seeds, days from sowing till maturity, plant canopy width, height of lower pod attachment, stem height, branches angle, flower colour were measured.Cicer arietinum L.)60 according to methods in routine use at VIR11 and ICARDA61.In both sites the accessions were planted in a randomized block experiment design, with two replications. Six plants of each replication were analysed. Soil cultivation and agricultural machinery matched the requirements of chickpea. In Astrakhan soils were heavy alluvial-meadow, loams. Sowing was carried out in late April and harvesting in late July to early August. With row spacing of 40 cm, distance between seeds was 6–7 cm. During the growing season six irrigations with sprinkling machines had been conducted and two mechanized processing of row spacing. At Tel Hdaya field station red soils were used. Plants were sown in February and harvested in August. With row spacing of 60–70 cm, distance between seeds was 10 cm. Soils were red soils. Artificial irrigation was absent. Field assessment was carried out with descriptors for chickpea  > 37 and Quality by Depth (QD) > 24. Both metrics take into consideration the quality of the mapping and genotype calls to ensure that only those with highest confidence were used. The SNPs were also filtered to retain those with MQRankSum < |2.0|, which ensure that there is no difference in the Mapping Quality scores for both alleles. This filtering removed nearly 60% of variant sites reported by GATK and only retained those that pass all three criteria. For this SNP calls we then used VCFtools67 to implement the following inclusion criteria: minor allele frequency (MAF) more than 3%, genotype call-rate more than 90%, and Hardy-Weinberg Equilibrium (HWE) exact P-value more than 10−5. Overall, 14059 SNPs remain to further analysis.Genomic DNA was digested with two restriction enzymes, Factor analysis (“varimax” method) was performed using the “psych” R package (R version 3.3.1 was used). The significance of correlations between factors, geographic distribution and bioclimatic variables was tested using the “corrgram” R package.67 was used to calculate the squared correlation coefficient between genotypes to construct LD plots, with LD decay computed according to Hill and Weir68. With this tool we also constructed Depth plot  in population structure we run STRUCTURE70 program with 10 replicates for each K from 1 to 10, using 100,000 burnin period and 100,000 MCMC repeats after burnin. Then Evanno’s test71 implemented in Harvester72 was used to evaluate the best number of clusters. The results of Evanno’s test showed that the best estimation of K parameter is 2. Although separation in these clusters is reflected in separation of first principal component on the principal component plot, we haven’t found any biological meaning of the clusters. So we choose K = 3 which is suboptimal, but the separation coincides with well-resolved clusters of Fig. 73 and GCTA74 formats using the PLINK toolset75. GWAS analyses were performed using the FASTLMM toolset . Q-values  were calculated and a q-value threshold of <0.05  was used to determine significant SNPs. In order to assess LD blocks , covering significant SNPs, we used the Haploview77. The GCTA program was used to estimate explained variance of polymorphisms 80. The ‘land suitability for cultivation’ dataset was downloaded from the Nelson Institute, Centre for Sustainability and the Global Environment, University of Wisconsin-Madison, (SAGE)82. Koppen-Geiger climate zones were acquired from the Center for International Development at Harvard University83. Data layers that came in vector format were rasterized to match a spatial resolution of 30 sec, which corresponds to approximately 1 sq km at the equator. The data were interpolated from average monthly recordings from weather stations84. The data layers selected for the current study are given in Supplementary Table 85.Environmental data were downloaded in the form of GIS layers from the WorldClim – Global Climate DataSupplementary informationSupplementary Table S3Supplementary Table S4Phenotypes+enviroment


Manihot esculenta Crantz) is a major tuberous crop produced worldwide. In this study, we sequenced 158 diverse cassava varieties and identified 349,827 single-nucleotide polymorphisms (SNPs) and indels. In each chromosome, the number of SNPs and the physical length of the respective chromosome were in agreement. Population structure analysis indicated that this panel can be divided into three subgroups. Genetic diversity analysis indicated that the average nucleotide diversity of the panel was 1.21 × 10-4 for all sampled landraces. This average nucleotide diversity was 1.97 × 10-4, 1.01 × 10-4, and 1.89 × 10-4 for subgroups 1, 2, and 3, respectively. Genome-wide linkage disequilibrium (LD) analysis demonstrated that the average LD was about ∼8 kb. We evaluated 158 cassava varieties under 11 different environments. Finally, we identified 36 loci that were related to 11 agronomic traits by genome-wide association analyses. Four loci were associated with two traits, and 62 candidate genes were identified in the peak SNP sites. We found that 40 of these genes showed different expression profiles in different tissues. Of the candidate genes related to storage roots, Manes.13G023300, Manes.16G000800, Manes.02G154700, Manes.02G192500, and Manes.09G099100 had higher expression levels in storage roots than in leaf and stem; on the other hand, of the candidate genes related to leaves, Manes.05G164500, Manes.05G164600, Manes.04G057300, Manes.01G202000, and Manes.03G186500 had higher expression levels in leaves than in storage roots and stem. This study provides basis for research on genetics and the genetic improvement of cassava.Cassava ( Manihot esculenta Crantz, 2n = 36) is one of the three major tuberous crops produced worldwide. Cassava, a crop introduced in China in the 1820s, is the main food consumed by approximately 600 million people in tropical and subtropical regions; this crop is also the main material for starch and alcohol production  locates all the quantitative trait loci affecting phenotype using a sufficient amount of markers and linkage disequilibrium (LD) between alleles . With ththaliana , and milthaliana ; consequthaliana , carotenthaliana , and disthaliana , have alIn the present study, a total of 158 germplasms were collected from the Chinese Cassava Germplasm Resources; these germplasms were genotyped using of AFSM technology . On the Figure 1A and Supplementary Table S1).A total of 158 cassava accessions were collected from ITBB CATAS and utilized for association analysis. Among them, 58 accessions originated from South America , 24 from Southeast Asia , 61 from China, 4 from Africa, and 11 from unknown regions . Each accession was grown in a plot with one row, and each row consisted of five or six plants. The distance between plants was 0.8 m. A total of 11 agronomic traits were finally selected. Of which, lobular length is the length of the maximum length of the intermediate lobe; the width of the lobular is the widest part of the middle lobe; the petiole length is the length of the petiole; the Leaf aspect ratio is the ratio of lobular length to the width of the leaflet; the first branch height is the height from ground to the first branch of the main stem; Stem diameter is the diameter of the main stem measuring 5 cm from the ground; the number of the storage roots refers to all the roots of a single plant; the storage roots weight means the weight of all the roots of a single plant; the dry matter content means the ratio of the weight of sliced and dried 200 g of fresh root to the weight of fresh root; dry matter weight means the product of fresh root and dry mater content; the starch content was determined by the method of optical rotation.A randomized complete block design was used to grow the 158 accessions. Each accession was grown with three replications in an experimental farm at Hepu, Guangxi Province ; Wuming, Guangxi Province ; Danzhou, Hainan Province ; and Chengmai, Hainan Province  in the 2013/2014/2015 growing seasons (1 was utilized to achieve the best linear unbiased prediction (BLUP) of each trait in each line. This script is based on a linear model described by previous study  through snpEff . SNPs we4 were used to construct a phylogenetic tree, which is displayed in FigTree5. The genetic relatedness between individuals was assessed by using all the SNPs; assessment was carried out using principal component analysis (PCA) with GCTA tool  and population pairwise F-statistics (FST). According to the standard description , software .n, where n is the total number of SNPs and indels. SAMtools was used for manual verification of regions with significant association from the aligned resequencing reads against the AM560 genome (obtained from JGI).In this study, we used 25,989 high-quality SNPs and indels (MAF > 0.05 and HWE > 0.001)  to perfo10-based FPKM-changed fold values were used to draw a heatmap in R using pheatmap package6.Based on the SNP annotation and LD decay of cassava, we take the gene of the association site itself as the candidate gene; if this site is located at the upstream and downstream of other genes, the upstream and downstream genes are also taken as candidate genes; if this site is located in the interval, the nearest upstream and downstream genes of the site are selected as candidate genes. The gene codes in the cassava genome were used to search the transcriptomics obtained from a gene bank; these gene codes were fresh leaf (FL), stem, early storage root (ETR), medium storage root (MTR), and later storage root (LTR) data with the following genotypes: KU50, Arg7, and W14. The RNA-seq data are available in a previous article . The NCBSupplementary Table S2). The mapping results of the 158 accessions were utilized to identify the SNPs of each accession using SAMtools .We generated 120 Gbp of sequence from the 158 cassava accessions. When the sequencing reads from each sample were mapped to the reference genome of AM560 (obtained from JGI), more than 0.6 billion of 150 bp paired-end reads was generated  and lowest (only 0.155) mean PIC values, respectively.All SNPs and indels were filtered using cut-off values of MAF > 0.05 and HWE > 0.001 ; finallyTable 1). A total of 2,183 SNPs in the CDS were synonymous, and 2,238 were nonsynonymous. Consequently, the amino acid was altered. Thus, in the genome, the ratio of the number of nonsynonymous to synonymous SNPs was 1.03, which is higher than that of Arabidopsis (0.83)  were found in the gene region, and 4,421 SNPs (22.2%) were observed in CDS (s (0.83)  and lowes (0.83)  and rices (0.83) . Among tK) of 1–12, the most significant change was observed when K = 3 . These parameters indicated that the 158 genotypes can be classified into three groups. We used a membership threshold probability with maximum value. Additionally, 19, 126, and 13 lines were assigned into subgroups 1, 2, and 3, respectively . Most subgroup 1 accessions came from South America (10), and nine accessions came from China (4), Southeast Asia (4), and Africa (1). Subgroup 2 accessions were mainly collected from China (51) and South America (41), and others came from Africa (3), Southeast Asia (20), and unknown regions (11). Subgroup 3 accessions were collected from China (6) and South America (7).All SNPs were utilized to calculate the population structure of the association panel using ADMIXTURE. According to clustering inference with possible cluster range . The neighbor-joining phylogenetic tree was constructed to determine the genetic relationships among cassava accessions in the panel. No significant connection existed between the genetic relationships and geographical origins . This condition may be due to that the cassava promotion is relatively short, and no geographical differences exist.The PCA based on all the genome-wide SNPs demonstrated that 6 and 2.9% of the genetic variance was ascribed to the first two principal components, respectively. The three subgroups in the cassava panel along the PC1 axis were significantly correlated, as inferred by using ADMIXTURE  between the three subgroups presented different population differentiation levels. The corresponding FST values between the subgroups ranged from moderate for subgroup 2 versus subgroup 1 (0.07) and subgroup 2 versus subgroup 3 (0.063) to little (0.034) for subgroup 1 versus subgroup 3 .According to the SNP data, the genetic diversity (π) among all sampled landraces was 1.21 × 10The highly heterozygous genotypes (heterozygosity = 0.14) agreed well with the asexual propagation of cassava. Subgroup 2 showed lower average heterozygosity (0.11) than those in subgroups 1 (0.26) and 3 (0.25). The genetic distance among the 158 cassava accessions was in the range of 0.03–0.32, with an average of 0.15. Nevertheless, subgroup 2 showed the lowest average genetic distance (0.13) and the smallest range (0.03–0.23). By contrast, subgroups 1 and 3 presented similar average genetic distance , which ranged from 0.12 to 0.31 and from 0.11 to 0.28, respectively.r2) decreased with physical distance between SNPs in all cassava groups . The LD level measured in each group is the chromosomal distance when LD decreased to 0.2. In agreement with results for other crops, the LD level in cassava in subgroup 2 was lower (6 kb) than those in subgroups 1 (8.5 kb) and 3 (9 kb). The observed LD level in cultivated cassava groups was 8 kb, which was lower than that in cultivated maize (30 kb) (Oryza indica) (The LD (indicated by  (30 kb) , cultiva indica) , or cult indica) , but hig indica)  and pota indica) .Supplementary Table S5. The leaf aspect ratio, which varied from 3.01 to 11.55 , showed the minimum coefficient of variation of 30% in Hepu (2015); in Chengmai (2015), the leaf aspect ratio, which varied from 2.87 to 14.67 , displayed the maximum coefficient of variation of 40%; the average variation coefficient was 4.65 (35%), 4.18 (38%), 4.23 (35%), and 4.44 (31%) in Hepu (2013), Chengmai (2014), and Wuming , respectively. This trait was observed stable in different environments. Dry mass weight varied from 0.15 to 0.99 , and its minimum variation coefficient was 34% in Chengmai (2013). In Wuming (2013), the dry mass weight varied from 0.12 to 2.69  and showed the maximum variation coefficient of 53%; the average variation coefficient was 0.78 (47%), 1.09 (39%), 0.35 (54%), 1.26 (42%), 3.12 (42%), 0.61 (35%), 0.76 (52%), 1.26 (45%), and 1.15 (49%) in Hepu , Chengmai , Danzhou , and Wuming , respectively. Dry mass weight was highly diverse in different environments.All the 11 traits in the cassava association panel, which comprised 158 accessions, were evaluated with three replications in four locations for 3 years. We detected extensive phenotypic variations, as displayed in the descriptive statistics in Figure 4).We also calculated the broad-sense heritability of each trait to determine the relationship between genetic and environmental factors during trait variation. Among these 11 traits, leaf length and width ratio, leaflet width, and first branch height showed the largest broad-sense heritability of 0.92, 0.86, and 0.79, respectively. The phenotype variation values of these traits were low in different years and locations, which indicated that genetic factors were the main reasons for these traits rather than environmental factors. Dry mass weight, storage roots weight, and petiole length presented the smallest broad-sense heritability of 0.38, 0.49, and 0.54, respectively; the phenotype variation values of these traits varied among different years and locations, which suggested that these traits were influenced with environmental factors. The phenotype variation values of other traits ranged between 0.47 and 0.73, and these traits varied under different environments. These results demonstrated that environmental factors affected the phenotypes of cassava. Thus, we calculated the BLUP value of each phenotype as the phenotype value for GWAS analysis to reduce the effect of environmental factors on phenotype variation . This approach considered the genome-wide patterns of genetic relatedness. A total of 17 and 34 association signals were identified with P < 3.85 × 10-5 using the compressed MLM (Table 2) and simple model (Supplementary Table S6). A total of 36 associations were detected for the 11 agronomic traits. Figure 5 and Supplementary Figures S12 show the Manhattan plots for both models of all the traits. Detailed information about all significant associations is summarized in Supplementary Table S7. Storage root quality showed four association signals. Approximately 12% of phenotypic variance on average was attributed to the peak SNPs at identified loci. The seven peak SNPs, which were identified from yield component, explained ∼14.95% of phenotypic variance on average (12.9–15.28% for different loci). Morphological characteristics exhibited 11 association signals and explained 12.23–20.86% of the phenotypic variance. A total of 14 association signals were identified from leaf characteristics, which explained 11.86–22.56% of the phenotypic variance with an average 16%.The 11 traits were classified into four categories, namely, leaf characteristics , morphological characteristics (stem diameter and first branch height), yield components , and storage root quality (dry matter content and starch content). These traits were considered for GWAS with whole-genome diverse SNP markers. Association signals were identified using the simple model and the compressed MLM. The compressed MLM approach significantly decreased the false positives, as shown in quantile–quantile plots (Supplementary Table S7). A total of 62 candidate genes were identified in the peak SNP sites (or adjacent to these sites) of 36 associated loci (Table 3). Six candidate genes were related to storage root quality; these genes included Manes.13G023300 , which participates in polysaccharide synthesis; Manes.16G000700 (AAA-type ATPase family protein), which is a generator of mechanical force and a key player in protein degradation throughout evolution . Two candidate genes, including an unknown functional protein  and a functional protein , were annotated; a transmembrane receptor protein, which may be related to the cell wall formation and maintenance of cell integrity, is also obtained by annotations . One SNP locus was mapped to lobular length and leaf aspect ratios, and one gene called Manes.06G034000  was obtained through annotation .Among the yield component-related candidate genes, one SNP locus, which was located in chromosome 2, was associated with fresh weight and dry mass weight , with 3.0 and -3.0 as the upper and lower limits, respectively. Among the candidate genes identified by yield components and storage root quality, the following genes showed high expression levels in storage root tissues: Manes.13G023300, Manes.16G000800, Manes.02G154700, Manes.02G192500, and Manes.09G099100. The expression levels of 11 genes related to morphological characteristics showed no variation in different tissues. The following genes, which are associated with leaf characteristics, exhibited high expression levels in leaves: Manes.05G164500, Manes.05G164600, Manes.04G057300, Manes.01G202000, and Manes.03G186500. The new genes identified in the present study are promising candidates for follow-up studies on the genetic architecture of these traits.Among the 62 candidate genes, 40 genes presented expression profiles in different cassava tissues according to previous RNA-seq data. We used logM. esculenta Crantz . Thus, this panel showed low genetic differentiation. This study obtained results similar to those of previous results; the geographic subpopulations of cultivated cassava show low FST values among themselves (0.01–0.05) (Hence, detailed knowledge about population structure in an association panel is crucial to prevent any sham association . Differer2 decreases to 0.1 (The LD decay over a known genetic distance is crucial in the determination of the number and density of a molecular marker for GWAS and selection strategies . Cassavas to 0.1 .Phenotypic identification of cassava grown in suitable environment is very difficult because they are sensitive to typoon and rains; to reduce phenotypic error, we evaluated all the traits of the cassava panel in four locations for three years; the BLUP, which can be integrated on numerous environmental data, was used; the environmental effect was removed, and a stable individual genetic phenotype was obtained . Many caIn this study, the GWAS potential is limited with the small population size and low genetic diversity from cassava samples. Cassava accessions for AFSM resequencing should be collected worldwide. Comprehensive phenotyping is currently in progress, and associations in this broad sampling can be studied in the future. Given that it allows the addition of new SNPs and the improvement of imputation efficiency at a low sequence coverage, genome sequencing is effective for GWAS in studies that aim to improve map resolution and new-allele identification through continuous population expansion. This study provides basis for a long-term collective exploration to determine valuable genes and alleles from the world germplasm collection and consequently improve cultivars.WW and ZX conceived and designed the studies. SZ and ZX performed the data analysis. SZ wrote the manuscript. SZ, JY, CL, KL, MZ, SF, XC, JP, PM, CHL, XZ, ZL, CJL, QL, and ZX identified the phenotypes. WW and ZX revised the paper. All authors read and approved the final 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.


Weak geographical patterns within either fiber or linseed accessions were confirmed, but specific trait performance was identified in East Asia for fiber type, and South Asia and North America for linseed type. Relatively high broad-sense heritability was obtained for seed quality traits, followed by agronomic traits and resistance to powdery mildew and fusarium wilt. Diverse phenotypic and genetic variability in the flax core collection constitutes a useful resource for breeding.Assessment of genetic variability of plant core germplasm is needed for efficient germplasm utilization in breeding improvement. A total of 391 accessions of a flax core collection, which preserves the variation present in the world collection of 3,378 accessions maintained by Plant Gene Resources of Canada (PGRC) and represents a broad range of geographical origins, different improvement statuses and two morphotypes, was evaluated in field trials in up to 8 year-location environments for 10 agronomic, eight seed quality, six fiber and three disease resistance traits. The large phenotypic variation in this subset was explained by morphotypes (22%), geographical origins (11%), and other variance components (67%). Both divergence and similarity between two basic morphotypes, namely oil or linseed and fiber types, were observed, whereby linseed accessions had greater thousand seed weight, seeds m Linum usitatissimum L.) is a multipurpose crop grown for production of stem fiber and seed oil , South America (SA), Eastern Asia (EA), Western Asia (WA), Southern Asia (SA), Central and Eastern Europe (CEE), Western Europe (WE), Southern Europe (SE), Northern Europe (NE), Oceania (OC), and Africa (AF). Detailed information of the accessions is provided in Tables The 391 accessions were evaluated for agronomic, seed quality and fiber traits in field trials from 2009 to 2012 at two Canadian locations: Morden, Manitoba and Kernen Crop Research Farm near Saskatoon, Saskatchewan. Evaluation of resistance to diseases was conducted from 2010 to 2015 at Morden, Manitoba. A type-2 modified augmented design (MAD2) , bolls m−2 (BM2), and seeds m−2 (SM2), were determined as previously described , oil content (OIL), and fatty acid composition (FAC). FAC includes palmitic acid (PAL), stearic acid (STE), oleic acid (OLE), linoleic acid (LIO), and linolenic acid (LIN). FAC for all test accessions was obtained by gas chromatography  of fatty acids methyl esters extracted from seeds according to AOAC method 996.06 , cell walls (CEW), cellulose (CEL), shive (SHI) and lignin (LIG), were determined by NIR spectroscopy and a calibration curve developed by Light Solutions  and Schweitzer Mauduit  was provided to us by the Composite Innovation Center . Straw weight (STR) was measured based on the fresh weight of the straw of 2 × 0.5 m rows after boll stripping.Fusarium oxysporum f. sp. lini (Bolley) Snyd. & Hans, pasmo (PAS) cause by the fungus Septoria linicola (Speg) Garassini  and powdery mildew (MIL) caused by Oidium lini Skoric, were independently evaluated in separate disease nurseries at Morden, MB from 2010 to 2015.Disease reactions to fusarium wilt (WIL) caused by the fungus For fusarium wilt evaluation, the trials relied on natural infection in the wilt nursery where susceptible cultivars have been continually seeded since 1950. The flax cultivars Bison and Novelty served as resistant and susceptible checks, respectively, and were seeded after every 10 flax entries. The same experimental design described above was adopted. Disease assessment was conducted at seedling, early flowering and late flowering/green boll stages using a 0–9 scale where 0 represents vigorous plants devoid of any signs of wilt and 9 corresponds to plots where all plants were severely wilted or dead , 3–4, moderately resistant (MR), 5–6, moderately susceptible (MS) and 7–9, susceptible (S) phenotypes.All phenotypic data from the field trials and laboratory measurements were adjusted as previously described using the MAD pipeline ,ij ~ Nik ~ Njk ~ Nijk ~ N, environments (E), and their interactions were estimated using the MAD pipeline  of a trait on a plot basis across environments was used because the entry mean based H2 was overestimated in the MAD2 design  method  based on phenotype was calculated as ΔG = kkk is the intensity of selection which would equal 2.06 if 5% of the individuals were selected from the normally distributed population and, where Broad-sense heritability  assuming unequal variances (POOL = NO) in two morphotypes was used to construct linear discrimination function, and the CROSSVALIDATE option to display cross validation error-rate estimates. The linear discrimination function for morphotype contains coefficients for the constant term and 27 traits (or variables) for fiber and linseed type, respectively. Cross-validation was performed to assess the classification accuracy. Then the discrimination function was applied to each of the 29 accessions of unknown morphotype to calculate posterior probability of membership in the fiber or linseed morphotype groups. According to the posterior probability of an accession in fiber and linseed, the morphotype with a higher probability was assigned to the accession.http://cran.r-project.org/) package “prcomp” was used for PCA. The biplot of the first two PCs was drawn using ggplot function with a function of state_ellipse (level = 0.95) to draw 95% normal confidence ellipses. The Euclidean distance matrix of accessions was calculated using the “dist” function with the “euclidean” method. The Ward algorithm in the function “hclust” of the R package “stats” was used for hierarchical cluster analysis. The means and standard deviations of traits for clusters were obtained from cluster analysis. A one-way ANOVA with multiple comparisons (Tukey's range test) was performed to test significance among different clusters.Principal component analysis (PCA) and cluster analysis were performed to analyze trait variations. The first several principal components (PCs), accounting for more than 85% of the cumulative variance, were used to calculate Euclidean distances among accessions for fiber and linseed accessions, respectively. The R  and between-region distances. Then the matrix of between-region distances was further analyzed for cluster analysis. The R package “ggplot2” was used to draw figures.To explore the relationship of trait performance with geographical origin, the means of traits for different geographic regions were calculated and compared using one-way ANOVA with multiple comparisons (Tukey's range test) to test significance among different geographical regions. In addition, the Euclidean distances among accessions were averaged with respect to geographical regions using the function “G) showed that high potential selection gains of more than 10% were expected in 18 traits if 5% of the accessions were selected; this was particularly high for STR (65.7%), PLH (39.00%), LIO (57.6%), MIL (43.8%), SM2 (28.6%), and TSW (25.3%).Significant differences among accessions were observed for all 27 traits in both years and locations Table . As expePhenotypic variations of all accessions were partitioned into components according to their morphotype, geographical origin and other factors for all 27 traits Table . On averH2 for 26 of the 27 traits under study  and linseed (299) subgroups for the 27 traits. A total of 22 traits, the exceptions being YLD, SEB, STE, OLE, and LIN, showed significant differences between the linseed and fiber flax accessions at the 5% probability level Figure . On averIL Table . SEB, TSThe 92 fiber accessions were sampled from eight geographical regions, including CEE (39), WE (22), NA (13), EA (8), NE 4), WA (3), SAS (2), and AF (1), while the 299 linseed accessions were selected from 11 geographical regions, namely NA (119), SAS 52), CEE (46), WE (25), AF (8), NE (7), SE (5), OC (3), and EA (2) Table . The per, WA (3),2, CEE 4. AccessiFor linseed accessions, significant differences between at least two of the nine geographical regions were observed in 20 of the 27 traits, the exception being TSW, FIB, LIG, SHI, PAL, LIN, and MIL Figure . HoweverHierarchical cluster analysis was performed separately for fiber and linseed accessions. According to the means and distances between and within clusters, the 92 fiber accessions grouped into three clusters Figure . CharactThe 299 linseed accessions grouped into eight clusters Figure . The cluA core collection consists of a limited number of accessions that represent the breadth of the genetic diversity of a large whole germplasm collection of a given crop . This study represents the most comprehensive assessment of phenotypic performance of this flax core collection to date. The observations in multiple environments will be useful for breeding selection, genetic diversity evaluation, association mapping studies and genomic selection. The study revealed the large genetic variability and the selection potential for most traits, especially seed yield, straw weight (an indicator of fiber yield), disease resistance and other agronomic traits through Phylogenetic analyses supported the hypothesis of a single domestication origin of pale flax as the wild progenitor, first domesticated for its oil rather than fiber use (Allaby et al., Despite the divergence between fiber and linseed types, we noticed that only 17 out of the 92 fiber accessions (Cluster 2 in Table The 391 accessions of the core collection from 38 countries were grouped into 11 geographical regions Tables  and S2. Several studies were performed at the molecular level. Fu  used 67 We assessed the genetic variability of 27 traits of a flax core collection evaluated in up to 8 year-location environments. Large variability for most traits was quantified in both fiber and linseed accessions. Both divergence and similarity between fiber and oil morphotypes should help breeder's decision toward the development of fiber, linseed or dual purpose varieties. Weak patterns among geographical regions were observed but, more importantly, germplasm with specific characteristics was identified and clustered. This data will guide breeders toward better educated decision of germplasm utilization in flax genetic improvement. The phenotypic evaluation of 27 traits over multiple environments constitutes a valuable resource for breeding selection, genetic diversity evaluation, association studies and genomic selection.SC, SD, KR, HB, and FY conceived and designed the study. SD, KR, and HB implemented field trials and performed the phenotyping. FY, GJ, and JX performed data analysis and prepared tables and figures. FY and JX drafted the manuscript. All authors reviewed and edited 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.


In the dawning era of large-scale biomedical data, multidimensional phenotype vectors will play an increasing role in examining the genetic underpinnings of brain features, behaviour and disease. For example, shape measurements derived from brain MRI scans are multidimensional geometric descriptions of brain structure and provide an alternate class of phenotypes that remains largely unexplored in genetic studies. Here we extend the concept of heritability to multidimensional traits, and present the first comprehensive analysis of the heritability of neuroanatomical shape measurements across an ensemble of brain structures based on genome-wide SNP and MRI data from 1,320 unrelated, young and healthy individuals. We replicate our findings in an extended twin sample from the Human Connectome Project (HCP). Our results demonstrate that neuroanatomical shape can be significantly heritable, above and beyond volume, and can serve as a complementary phenotype to study the genetic determinants and clinical relevance of brain structure. Neuroanatomical shape measurements are multidimensional geometric descriptions of brain structure. This study develops multivariate heritability analysis methods and examines structural brain MRI scans and genetic data to estimate the heritability of neuroanatomical shape. Such phenotypically rich studies open up the opportunity to analyse collections of multidimensional phenotypes, which can be more informative than scalar traits.The exponential progress in genomic technologies has accelerated the examination of the genetic underpinnings of complex phenotypes, such as psychiatric and neurological disorders, many of which are highly heritable1345Brain imaging is playing an increasing role in the study of the relationship between genetic variants, neuroanatomy, behaviour and disease susceptibility789Neuroanatomical shape measurements—multidimensional geometric descriptions of brain structure—have attracted increasing attention in medical image analysis. Shape measurements characterize isometry-invariant  geometric attributes of an object, which provide a rich description of an anatomical structure and can encompass volumetric variation. Such measurements may thus offer increased sensitivity and specificity in examining the clinical relevance and genetic underpinnings of brain structure. Recent studies have shown that the shape of subcortical brain regions and cortical folding patterns provide information that is not available in volumetric measurements and is predictive of disease status, onset and progression in schizophrenia151618202223242628293031This paper makes two major contributions to the investigation of the genetic basis of neuroanatomical shape. First, we extend the theoretical concept of heritability to multidimensional traits, such as the shape descriptor of an object, and propose a novel method to estimate the heritability of multidimensional traits based on genome-wide single-nucleotide polymorphism (SNP) data from unrelated individuals (known as SNP heritability). Our estimation method builds on genome-wide complex trait analysis33363839BrainPrint, can provide an accurate and holistic representation of brain morphology, and has been successfully applied to subject identification, sex and age prediction, brain asymmetry analysis, twin studies, and computer-aided diagnosis of dementia40The truncated LBS is a multidimensional shape descriptor, which can be obtained by solving an eigenvalue problem on the two-dimensional (2D) boundary surface representation of an object. It is invariant to the representation of the object including parameterization, location and orientation, and thus does not require spatial alignment with a population template, making it computationally efficient and robust to registration errors. LBS also depends continuously on topology-preserving deformations, and is thus suitable to quantify differences between shapes. Recent empirical evidence suggests that the LBS-based shape descriptor provides a discriminative characterization of brain anatomy and offers state-of-the-art performance for a range of shape retrieval and segmentation applications42P values for the statistical significance of the heritability estimates. The parametric  and nonparametric (permutation-based) P values were virtually identical, confirming the accuracy of the s.e. estimates we computed (see Methods). We observe that none of the volume heritability estimates were statistically significant after correcting for multiple comparisons  at q=0.05), possibly due to sample size limitations. Only the volumes of the caudate, corpus callosum and third ventricle achieved a heritability that was nominally significant in our sample (uncorrected P<0.05). P=0.828).To benchmark our shape results, we first computed SNP heritability estimates for the volumetric measurements of an array of brain regions using the GSP sample. q=0.05. We observe that this is in contrast with the case of volume, where despite a similar heritability range, no estimate reached FDR-corrected significance. The main reason for this discrepancy is the theoretically guaranteed reduced s.e.'s in SNP heritability estimates of multidimensional traits . The shape of the accumbens area was also marginally significantly heritable with an uncorrected P value <0.05. As in the case of volume, the parametric  P values were virtually identical to the permutation P values, suggesting that our s.e. estimates are accurate (see Methods).Neuroanatomical shape measurements provide a geometric characterization and a rich description of brain structure. We therefore hypothesize that analysing the shape variation of neuroanatomical structures can identify genetic influences beyond captured by volumetric measurements. r=0.562 and P=0.057). We conclude that close to 30% of the variation in shape heritability across structures can be attributed to the reliability of the shape descriptor. This suggests that for structures that exhibited low shape heritability , a more accurate image segmentation and shape analysis pipeline might yield an increased estimate of heritability. We further conducted a sensitivity analysis of shape heritability estimates, with respect to the two free parameters of the LBS-based shape descriptor: number of eigenvalues incorporated and amount of smoothing applied to the surface mesh representing the geometry of the object. We sought to replicate these findings in the HCP sample, in which subjects are healthy and have a similar age range as the GSP sample. We selected 590 non-Hispanic/Latino Europeans aged between 22 and 35 years, comprising 72 MZ twin pairs, 69 DZ twin pairs, 253 full siblings of twins and 55 singletons . We estimated the shape heritability for brain structures that had significantly heritable shapes in the GSP sample using an ACE model , where the additive genetic similarity was derived from pedigree information and the common environment term reflected household sharing between subjects. We also obtained the s.e. of the shape heritability estimates using a block bootstrapping procedure (see Methods). All the shapes we analysed were significantly heritable in the HCP sample: accumbens area 0.309±0.081; caudate 0.583±0.062; cerebellum 0.653±0.060; corpus callosum 0.558±0.068; hippocampus 0.363±0.095; third ventricle 0.536±0.067; and putamen 0.483±0.106. We also observe that the HCP shape heritability estimates were consistently larger than the GSP estimates, which is theoretically expected because the SNP heritability estimated from unrelated GSP subjects only captured the genetic variation tagged by common SNPs in the data set, and is thus a lower bound for the classical narrow-sense heritability estimated from familial data such as the HCP sample.The LBS-based shape descriptor is suitable to efficiently and accurately extract intrinsic properties of the shape of brain structures from a large number of individuals, but is not designed to visually inspect shape differences. Here we propose a strategy to visualize the principal mode of shape variation. Specifically, it can be shown that the first principal component (PC) of the multidimensional LBS-based shape descriptor captures the greatest shape variation and has the largest impact on the overall heritability estimate of the shape (see Methods). We thus visualized shape variation along the first PC of the shape descriptor for brain structures with significantly heritable shapes in the GSP sample: right caudate, cerebellum, corpus callosum, right hippocampus, third ventricle and left putamen. The illustrations of contralateral structures , which showed similar shape variation, are provided in The first PC of the right caudate captured 77% of the shape variation and had a SNP heritability estimate of 0.88. Moving along the principal mode of shape variation, the right caudate had a larger  head with a corresponding shorter (longer) tail. For the cerebellum, the first PC explained 69% of the shape variation and had a SNP heritability of 0.61. A clear expansion (contraction) of the anterior lobe and a corresponding contraction (expansion) of the posterior lobe can be observed along the principal axis. The first PC of the corpus callosum captured 41% of the shape variation and had a SNP heritability of 0.41. The principal mode captured an expansion (contraction) of the middle corpus callosum along the dorsoventral axis and a corresponding shrinking (enlargement) of the anterior and posterior part of the structure. For the right hippocampus, the first PC explained 69% of the shape variation, had a SNP heritability of 0.47 and exhibited dorsoventral widening (narrowing) of the body and corresponding lateral and anterior–posterior contraction (expansion). The first PC of the third ventricle captured 69% of the shape variation and had a SNP heritability estimate of 0.80. The principal mode captured an enlargement (shrinking) of the posterior protrusions and an expansion (contraction) of the lateral walls, coupled with a corresponding contraction (expansion) of the roof of the cleft. Finally, first PC of the left putamen explained 61% of the shape variation, had a SNP heritability of 0.70, and captured lateral widening (narrowing) and a corresponding contraction (expansion) along the dorsoventral and anterior–posterior axes.This work makes two contributions to neuroscience and genetic research. First, we extend the concept of heritability to multidimensional traits and present an analytic strategy that generalizes SNP heritability analysis. The heritability estimator we propose for multidimensional traits has reduced uncertainty in its point estimate relative to univariate estimates, and thus offers more statistical power. Our empirical analyses confirmed this theoretical expectation. Moreover, and in the same line, we provide methods that can easily adjust for covariates in multivariate models, and also both parametric and nonparametric inferential tools that can assess the significance of a heritability estimate. Our approach opens the door to the genetic characterization of shape measurements and other multidimensional traits.q=0.05. This is in contrast to the volume heritability estimates of the same set of brain structures on the same sample, none of which reached FDR-corrected significance. Although our heritability analysis of volume, which was used to benchmark the shape analysis, may be less informative compared with more powerful twin studies and large-scale meta-analyses in the literature, the increased statistical power and the additional information shape analysis can provide relative to volumetric analysis demonstrate the usefulness of our methods and underscore the potential of leveraging multidimensional traits when analysing data sets with moderate sample sizes.Second, we use the proposed approach to quantify the SNP heritability of the shape of an ensemble of anatomical structures spanning the human brain in a group of young and healthy subjects. The shape of caudate, cerebellum, corpus callosum, hippocampus, third ventricle and putamen exhibited moderate-to-high heritability , after controlling for volume. All of these estimates achieved FDR-corrected significance at Using the extended twin data from the HCP, we also replicated significant shape heritability estimates observed in the GSP sample. Our HCP estimates were consistently larger than the GSP estimates, which is theoretically expected because SNP heritability estimated from the unrelated sample in GSP does not capture genetic contributions  that are not tagged by genotyped SNPs, and thus explains a smaller proportion of the phenotypic variation. However, additional factors may contribute to the difference between SNP and familial heritability estimates, which include improper modelling of shared environment, assortative mating, genetic interaction (epistasis), suboptimal statistical methods for heritability estimation and differences in sample characteristics such as age range, ethnic background and environmental exposures354647484950et al.et al.ɛ4 allele on hippocampal morphology in depressive and Alzheimer's disease patients. Variants involved in the regulation of the FKBP5 gene were recently associated with hippocampal shapeet al.et al.A handful of prior neuroimaging studies have explored the shape of certain brain structures as potential phenotypes in examining genetic associations. For example, Qiu However, to date, outside of these notable exceptions, most structural imaging genetics studies have utilized scalar measurements  as phenotypes. In the present study, we accounted for potential volume effects in our shape analyses by normalizing the LBS-based shape descriptor for size and additionally including the volumetric measurement of the corresponding structure as a covariate when estimating heritability. Our results show that shape measurements provide a rich and novel set of phenotypes for exploring the genetic basis of brain structure, and may identify novel genetic influences on the brain that are not detectable with conventional analyses based on the volume of structures.There are several biological mechanisms that might lead to shape differences with minimal effect on the overall size of the structure. These include localized volumetric effects that are confined to subfields, sub-nuclei or other sub-regions that make up the structure. Shape analysis may provide significant information about neurodevelopmental abnormalities, such as those associated with neuronal migration, synaptogenesis, synaptic pruning and myelination. Shape measurements might for example shed light on morphogenetic mechanisms that involve mechanical tensions along axons, dendrites and glial processesThe shape analysis literature offers an expanding list of methods to quantify and characterize shapeIn the present study, in light of the similar shape variation of bilateral brain structures as observed in et alThe heritability analysis of multidimensional traits developed here can be applied to phenotypes other than shape that are intrinsically multivariate. Another application might involve heritability or genetic association analyses combining related traits to obtain more stable effect estimates. For example, it can be used as an alternative to PC analysis and factor analysis when investigating the genetic basis of various psychometric or behavioural traits. Also, voxel- or vertex-level neuroimaging measurements are often noisy, and analysing these measurements in homogeneous brain regions in a multivariate manner may increase the reliability and reproducibility of the results. Finally, the empirical genetic similarity matrix can be computed with other SNP grouping strategies  to model the genetic influences from a specific genomic region or partition the heritability of multidimensional traits, as in Yang g, common (or shared) environment c and unique (subject-specific) environment e, the variance component model takes the following form:We start with a brief review of variance component models  for the heritability analysis of univariate  traits, which provide a general statistical framework that can handle both familial designs and unrelated individuals randomly sampled from the population. Assuming, for the moment, no covariate needs to be adjusted, and a trait can be partitioned into the sum of additive genetic effect y=⊤ is a vector comprising quantitative traits from N individuals, K is a genetic similarity matrix, Λ quantifies shared environment between pairs of individuals and I is an identity matrix.where K is twice the kinship matrix, K=2Φ, and indicates expected additive genetic covariance among individuals. The ijth entry of the kinship matrix, ϕij, known as the kinship coefficient, defines genetic relatedness for subjects i and j, and in general can be derived from pedigree information57ϕij=1/2 for MZ twins and ϕij=1/4 for DZ twins and full siblings, and we assume that twin pairs and their non-twin siblings share the same environment and the corresponding elements in Λ are 1.In familial studies, K is the empirical genetic similarity matrix for each pair of individuals estimated from genome-wide SNP data, and the corresponding variance component parameter When modelling unrelated individuals randomly sampled from the population, The heritability of a univariate  trait is defined ash2 measures the narrow-sense heritability of a trait, while in unrelated subject studies, h2 measures additive heritability attributable to common genetic variants  is the matrix vectorization operator that converts a matrix into a vector by stacking its columns, We define the heritability of a multidimensional trait asP] that can be explained by the total additive genetic variance tr[ΣA], and yields a heritability measure that is bounded between 0 and 1. When the trait is univariate, ΣA, ΣC and ΣE become scalars, and h2 in unrelated subject studies to emphasize that it only captures genetic influences due to common genetic variants and is a lower bound for the narrow-sense heritability estimated using familial designs.where T applied to the trait dimensions in model (3), that is,Our definition of heritability is invariant to rotations of the data. For a linear transformation YT can be computed as follows:using the properties of vectorization and the Kronecker product, the covariance structure of the transformed trait Therefore, the transformed heritability isT is an orthogonal matrix satisfying TT⊤=T⊤T=I, we have h2.When The definition of heritability in mγm=1, and mth component of the trait. Therefore, our definition of the heritability of a multidimensional trait is essentially a weighted average of the heritability of its individual components.where Y=G+E and its distributional assumptions The model (3) can in principle be fitted using likelihood-based methods. However, this can be computationally expensive when the dimension of the trait is moderate. Here we derive an alternative moment-matching estimator for unrelated subject studies where the common environmental matrix Λ vanishes. Specifically, the multivariate model yryr, ys], onto the empirical genetic similarity matrix K and identity matrix I. In particular, we consider the following multiple regression problem:Therefore, an unbiased estimator of where and can be explicitly written asK]/N, κ=tr[K2]/N and vK=tr[K2]−tr2[K]/N=N(κ−2). Therefore, it can be seen that unbiased estimates of the genetic and environmental covariance matrices are as follows:where we have defined Let For scalar traits, QA=(K−I)/vK, QE=(κI−K)/vK, and also tP=tA+tE. Let t=⊤ and define f(t)=tA/(tA+tE)=tA/tP. Using a Taylor expansion, we can approximate the variance of the function f as follows:We now derive the variance of wheret], we define Vrs=cov=K+I, and notice that for any symmetric matrices Qα and Qβ,To compute cov[Therefore,M. We approximate K are small, such that K≈I and Vrs=K+I≈I+I=I; and (2) the phenotypic covariance ΣP is known or can be estimated with very high precision. Using assumption (1), the covariance of t can be simplified as follows:Therefore,P with its empirical estimate K, the estimator (21) only depends on the sample size and the phenotypic covariance structure.where in the last approximation we have used assumption (2) and replaced ΣFor scalar traits, M traits are perfectly correlated. Therefore, combining multiple traits reduces the variability of heritability estimates relative to analysing each trait individually.This inequality becomes an equality if and only if P value can be computed by conducting a Wald test. Since the null hypothesis, To measure the significance of a heritability estimate, a χ2 distribution with all probability mass at zero, and χ2 distribution with 1 degrees of freedoma half–half mixture of K. For each permutation r=1, 2, ⋯, Nperm, we record the heritability estimate P value can be computed as63Alternatively, permutation inference can be used by shuffling the rows and columns of the empirical genetic similarity matrix K and the common environmental matrix Λ. Therefore, when analysing the HCP data, we estimate the heritability of each individual component of a multidimensional trait using the restricted maximum likelihood algorithm and combine these estimates using a variance-weighted average as derived in A moment-matching estimator can be analogously derived for familial data analysis, but has low statistical efficiency due to the strong correlation between the genetic similarity matrix Nboots times  to yield bootstrap heritability estimates b=1, 2,⋯, Nboots. The variance of the heritability estimate is then estimated as64To estimate the variance of the heritability estimate of a multidimensional trait, we employ a block bootstrapping procedure whereby families are randomly resampled with replacement to produce a bootstrap sample and the heritability is re-estimated. This procedure is repeated for where When covariates or nuisance variables need to be adjusted, model (3) becomes a multivariate linear mixed effects model:X is an N × q matrix of covariates, and B is a q × M matrix of fixed effects. We employ a strategy described in ref. N × (N−q) matrix U, satisfying U⊤U=IN−q) × (N−q)−1X⊤. The matrix U⊤ projects the data from the N dimensional space onto an N−q dimensional subspace:with the distributional assumptions where et al.36The Harvard/MGH Brain GSP is a neuroimaging and genetics study of brain and behavioural phenotypes. More than 3,500 native English-speaking adults with normal or corrected-to-normal vision were recruited from Harvard University, MGH and the surrounding Boston communities. To avoid spurious effects resulting from population stratification, we restricted our analyses to 1,320 young adults (18–35 years) of non-Hispanic European ancestry with no history of psychiatric illnesses or major health problems . All participants provided written informed consent in accordance with guidelines set by the Partners Health Care Institutional Review Board or the Harvard University Committee on the Use of Human Subjects in Research. For further details about the recruitment process, participants and imaging data acquisition, we refer the reader to Holmes The HCP collects imaging, behavioural and demographic data from a large population of healthy adults and aims to shed light on anatomical and functional connectivity within the healthy human brain. We used preprocessed structural MRI data from the WU-Minn HCP consortium and selected subjects that have a similar age range (22–35 years) and ancestry (non-Hispanic/Latino European) as the GSP sample. The 590 subjects we analysed  come from 249 families and comprise 72 MZ twin pairs, 69 DZ twin pairs, 253 full siblings of twins and 55 singletons . Further details about the recruitment process, imaging data acquisition and MRI data preprocessing can be found in41https://www.cog-genomics.org/plink2)We used PLINK 1.90 . The first M eigenvalues of the LBS can be used to define a description of the object, which provides a numerical fingerprint or signature of the shape, and is thus known as (length-M) ‘Shape-DNA'.where Δ is the Laplace–Beltrami operator, a generalization of the Laplacian in the Euclidean space to manifolds, http://freesurfer.net)41http://reuter.mit.edu/software/brainprint).We used FreeSurfer  of the principal axis. These average probability images were offset to achieve identical volumes when thresholded at 0.5. The difference of the two extreme shapes was depicted on the sample-specific population average, by visualizing the difference in the probability values. Blue indicated that the average shape at −2 s.d. achieved a higher probability value and thus was larger in those regions than the average shape at the +2 s.d. For red regions, the opposite was true.http://neuroinformatics.harvard.edu/gsp/. The HCP data analysed during the current study are publicly available at http://www.humanconnectome.org. Other data are available from the corresponding author on reasonable request.The Brain GSP data analysed during the current study are publicly available at How to cite this article: Ge, T. et al. Multidimensional heritability analysis of neuroanatomical shape. Nat. Commun.7, 13291 doi: 10.1038/ncomms13291 (2016).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary Figures 1-3 and Supplementary Table 1


An inverse relationship between allergies with glioma risk has been reported in several but not all epidemiological observational studies. We performed an analysis of genetic variants associated with atopy to assess the relationship with glioma risk using Mendelian randomisation (MR), an approach unaffected by biases from temporal variability and reverse causation that might have affected earlier investigations.Two-sample MR was undertaken using genome-wide association study data. We used single nucleotide polymorphisms (SNPs) associated with atopic dermatitis, asthma and hay fever, IgE levels, and self-reported allergy as instrumental variables. We calculated MR estimates for the odds ratio (OR) for each risk factor with glioma using SNP-glioma estimates from 12,488 cases and 18,169 controls, using inverse-variance weighting (IVW), maximum likelihood estimation (MLE), weighted median estimate (WME) and mode-based estimate (MBE) methods. Violation of MR assumptions due to directional pleiotropy were sought using MR-Egger regression and HEIDI-outlier analysis.P = 0.041) and MLE , but not by WME  or MBE .Under IVW, MLE, WME and MBE methods, associations between glioma risk with asthma and hay fever, self-reported allergy and IgE levels were non-significant. An inverse relationship between atopic dermatitis and glioma risk was found by IVW  0.93–1.00, Our investigation does not provide strong evidence for relationship between atopy and the risk of developing glioma, but findings do not preclude a small effect in relation to atopic dermatitis. Our analysis also serves to illustrate the value of using several MR methods to derive robust conclusions.The online version of this article (10.1186/s12916-018-1027-5) contains supplementary material, which is available to authorized users. Although glioma accounts for approximately 80% of malignant primary brain tumours , to dateSeveral case-control studies have shown that self-reported allergic conditions may protect against glioma . For exaAssaying IgE potentially reduces bias stemming from self-reporting despite levels not necessarily corresponding to specific allergies or equating to a single allergic response. Nevertheless, measurement of IgE has been explored by a number of researchers seeking to identify risk factors for glioma –10. In aSeveral mechanisms have been proposed to explain a possible association between atopic disease and glioma . The finMendelian randomisation (MR) analysis can be used to minimise potential biases in conventional observational studies and to determine the causal association of an exposure with an outcome such as disease risk . The cauTo examine the nature of the association between atopy and glioma, we implemented two-sample MR  to estimTwo-sample MR was undertaken using GWAS data. Ethical approval was not sought for this specific project because all data came from the summary statistics of published GWAS, and no individual-level data were used.Glioma genotyping data were derived from the most recent meta-analysis of GWAS in glioma, which related > 10 million genetic variants (after imputation) to glioma, in 12,488 glioma patients and 18,169 controls from eight independent GWAS datasets of individuals of European descent [i.e. P ≤ 5.0 × 10− 8) in individuals with European ancestry were used as IVs. To avoid co-linearity between SNPs for each trait, we excluded SNPs that were correlated  within each trait, and only considered the SNPs with the strongest effect on the trait for use as IVs  and standard errors , asthma and hay fever, IgE level, and self-reported allergy, by the NHGRI-EBI GWAS Catalog –26 at geper Burgess et al. [The association between each atopy-related trait and glioma was examined using MR on summary statistics using the inverse-variance weighting (IVW) method and maximum likelihood estimation (MLE) as s et al. . The IVWXk corresponds to the association of SNP k  with the atopy-related trait Yk is the association between SNP k and glioma risk (as log OR) with standard error Where optim was used to estimate β. Yk and Xk was taken to be 0 as they were derived from independent studies.For the MLE, a bivariate normal distribution for the genetic associations was assumed, and the R function i.e. a gene influencing multiple traits) between the SNPs influencing the exposure and outcome disease risk [et al. [P ≤ 0.01. Additionally, we derived weighted median estimates (WME) [A central tenet in MR is the absence of pleiotropy   and modees (WME)  to estabAtopic dermatitis, asthma and hay fever, and self-reported allergy as well as all of the disease outcomes  are binary. The causal effect estimates therefore represent the odds for outcome disease risk per unit increase in the log OR of the exposure disease . These OP < 0.05 as being satisfactory to derive conclusions. To assess the robustness of our conclusions, we initially imposed a conservative Bonferroni-corrected significance threshold of 0.0125 (i.e. 0.05/4 atopy-related traits). We considered a P value ≥ 0.05 as non-significant (i.e. no association), a P < 0.05 as evidence for a potential causal association, and a P < 0.0125 as significant evidence for an association. All statistical analyses were undertaken using R software (Version 3.1.2). The meta and gsmr packages were used to generate forest plots and perform HEIDI-outlier analysis [For each statistical test we considered a global significance level of analysis .a priori using the methodology of Burgess et al. [The power of a MR investigation depends greatly on the proportion of variance in the risk factor that is explained by the IV. We estimated study power s et al. , making s et al. –36, as ws et al. . Additioi index genetic variants, N be the total number of genetic variants, and j index individuals. Genetic variants gij were generated independently by sampling from a Binomial distribution with probability pj drawn from a Uniform distribution, to mimic bi-allelic SNPs in Hardy–Weinberg equilibrium. Let wj correspond to the per-allele OR for the exposure disease, sampled from ORs reported for genome-wide significant SNPs reported in the GWAS Catalog [v be the OR for the outcome disease per doubling in odds of the exposure disease. For each individual, exposure disease odds xj, outcome disease odds yj, exposure disease status aj, and outcome disease status bj were determined as follows:Through simulation we evaluated the suitability of using each employed MR method in a two-sample setting with binary-exposure and binary-outcome data. Let  Catalog , and v bx0 = 0.0005 and y0 = 0.01 were chosen to ensure the prevalence of the simulated exposure and outcome diseases were similar to that of the atopy-related traits and glioma, respectively  and (2) a causal relationship between exposure and outcome (v = 1.33). We performed 100 simulations for each scenario for each binary atopy-related trait.Data for 1,000,000 individuals were simulated and partitioned at random to reflect the two-sample setting. Cases and controls for the exposure and outcome GWAS were sampled from each half of the dataset using the exposure and outcome disease statuses of each individual, and association statistics computed under an additive logistic regression model. To ensure the simulated data closely resembled the atopy-related trait and glioma data, the simulation analysis was repeated for each binary atopy-related trait using the same number of genetic variants as IVs and the same numbers of case and control individuals as used to estimate the atopy-related trait and glioma association statistics , was strongly associated with risk of glioma (P = 2.10 × 10−34). Testing for pleiotropy using HEIDI-outlier analysis formally identified rs909341 as violating the assumption of the instrument on the outcome. Henceforth, we confined our analysis of the relationship between atopic dermatitis and glioma to a dataset excluding this SNP.The atopic dermatitis risk SNP rs909341, which is highly correlated with the chromosome 20q13.33 glioma risk SNP rs2297440 . Including rs909341 in the analysis for atopic dermatitis, the I2 value was 90% and Phet < 10− 4  were identified between genetically conferred risk of raised IgE level , asthma and hay fever , or self-reported allergy  with risk of all glioma. There was some support for an inverse relationship between atopic dermatitis and glioma risk , albeit not significant after adjustment for multiple testing.The results of the IVW, MLE, WME, MBE and MR-Egger methods are summarised in Table P = 0.066), IgE levels  or self-reported allergy  with risk of all glioma. For atopic dermatitis, an OR of 0.96  was shown, which remained significant after adjusting for multiple testing. Figure Xk and Yk is zero for each of the atopy-related traits demonstrating the consistency of findings. Specifically, for a correlation in the range −0.15 to 0.15, the association between atopic dermatitis and glioma risk remained significant.Using MLE, no associations were identified between asthma and hay fever , 0.63 for IgE levels , 0.99 for asthma and hay fever  and 0.92 for self-reported allergy , with intercepts of −0.004 , 0.027 , −0.007  and 0.017 . Collectively, these findings provide possible evidence of systematic bias in the IVW estimate for IgE level and self-reported allergy, which might have arisen through overall unbalanced horizontal pleiotropy. There was no such evidence for such pleiotropy in respect of atopic dermatitis.The respective effect estimated from MR-Egger regression Fig.  were 0.9P = 0.00625 , no histology-specific associations were shown by the IVW method between asthma and hay fever, IgE levels and self-reported allergy and glioma risk, with the respective ORs for the IVW method being 0.97, 0.92 and 1.04 for GBM tumours, and 0.96, 0.97 and 1.04 for non-GBM tumours  was shown for GBM but not for non-GBM . The association between atopic dermatitis and risk of GBM was also apparent in the MLE analysis, which provided an OR of 0.94 . MR-Egger regression provided for an intercept of −0.007 . As with the analysis of all glioma, the association between atopic dermatitis and GBM was weaker under the WME  and MBE  frameworks.We explored the possibility that a relationship between atopy and glioma might be subtype specific, considering GBM and non-GBM separately. Imposing a stronger significance threshold of Although previously implemented in other studies , 38, ratTo our knowledge, this is the first MR study evaluating a range of atopy-related traits with glioma risk. Overall, our results provide evidence for a causal protective effect of atopic dermatitis with GBM tumours, but do not provide evidence that asthma and hay fever, raised IgE levels, or self-reported allergy is protective against the risk of developing glioma.Possible mechanisms explaining an observed inverse relation between the risk of atopic dermatitis and the risk of glioma have been suggested in previous papers , postulai.e. atopic traits), and does not have a direct effect on glioma risk. We tested this assumption using MR-Egger regression and HEIDI-outlier analysis and found possible evidence of violation of this assumption for IgE and self-reported allergy. It is notable that self-reported allergy does not show an approximately quadratic response to correlation, in contrast to asthma and hay fever, atopic dermatitis and IgE level. This is likely to be a consequence of imprecise estimates of the association between SNPs and allergy, illustrating the inherent issue in attempting to make use of self-reported allergy data as an atopy-related trait.A key assumption in MR is that the instrument affects glioma risk through its effect on a specific phenotype/exposure . Further, allergies may develop later in life, and patients may not necessarily exhibit symptoms. This introduces the possibility of bias and error due to the time varying association of SNPs with the exposure. However, it has been suggested that seasonality does not have a significant effect .An additional possible explanation for the lack of causal association between IgE levels and glioma risk seen in this study is that the causality is in fact reversed, which could result in epidemiological observational studies reporting inverse relationships , 9, but Using data from large genetic consortia for multiple atopy-related traits and glioma risk has enabled us to more precisely test our study hypotheses than if we had used individual-level data from a smaller study. Through simulation scenarios, the IVW, MLE, WME, MBE and MR-Egger methods have been demonstrated to accurately estimate causal effects using summary-level data , 31, 45.Epidemiological observational studies have reported inverse relationships between atopy-related traits and glioma risk, with ORs in the range 0.43–0.96 for asthma , 0.42–0.Our MR analysis has several strengths. Firstly, by utilising the random allocation of genetic variants, we were able to overcome potential confounding and reverse causation that may bias estimates from observational studies. Secondly, given that a poor outcome from glioma is almost universal, it is unlikely that survival bias will have influenced study findings. Lastly, the findings from this study represent the association of a lifelong atopy with glioma in the general European population.Nevertheless, our study does have limitations. Firstly, while it is entirely appropriate to implement different MR methods to assess the robustness of findings, they have a differing power to demonstrate associations, with the WME, MBE and MR-Egger methods having less power than IVW and MLE. Irrespective of such factors, our study only had 80% power to detect ORs of 1.16, 1.09, 1.16 and 1.22 for asthma and hay fever, atopic dermatitis, IgE level and self-reported allergy, respectively  and 95% confidence intervals generated from single nucleotide polymorphisms (SNPs) associated with atopic dermatitis, including rs909341. ORs for individual SNPs are listed according to magnitude of effect in the instrumental variable analysis and are presented with pooled effects using the inverse-variance weighting method. Squares represent the point estimate, and the bars are the 95% confidence intervals. (DOCX 89 kb)Additional file 2:Table S1. Summary of the eight glioma genome-wide association studies. (XLSX 29 kb)Additional file 3:Table S2. Table of single nucleotide polymorphisms (SNPs) reported in the NHGRI-EBI Genome-wide Association Studies Catalog for each trait, with correlations between SNPs. (XLSX 48 kb)Additional file 4:Table S3. Percentage of variance explained by the combined sets of single nucleotide polymorphisms used as instrumental variables. (XLSX 33 kb)Additional file 5:Table S4. Range of odds ratios for which study had < 80% power, for each atopy-related trait . (XLSX 9 kb)Additional file 6:Table S5. Simulation analyses. (XLSX 28 kb)Additional file 7:Table S6. Inverse-variance weighting, maximum likelihood estimation, weighted median estimate, mode-based estimate and Mendelian randomisation-Egger test results for combined atopy-related instrumental variables and glioma subtypes. (XLSX 39 kb)