Synomics Insights platform that is based on novel combinatorial network and machine learning methods to detect key metagenomic and host genetic features for methane emission and residual feed intake (RFI) in dairy cattle. The other objective was to compare the results with publicly available standard tools, such as those found in the microbiome bioinformatics platform QIIME2 and classic GWAS analysis. The data set used was publicly available and comprised 1,016 dairy cows with 16S short read sequencing data from two dairy cow breeds: Holstein and Nordic Reds. Host genomic data consisted of both 50 k and 150 k SNP arrays. Although several traits were analyzed by the original authors, here, we considered only methane emission as key phenotype for associating microbial communities and host genetic factors. The Synomics Insights platform is based on combinatorial methods that can identify taxa that are differentially abundant between animals showing high or low methane emission or RFI. Focusing exclusively on enriched taxa, for methane emission, the study identified 26 order-level taxa that combinatorial networks reported as significantly enriched either in high or low emitters. Additionally, a Z-test on proportions found 21/26 (81%) of these taxa were differentially enriched between high and low emitters . In particular, the phylum of Proteobacteria and the order Desulfovibrionales were found enriched in high emitters while the order Veillonellales was found to be more abundant in low emitters as previously reported for cattle . In comparison, using the publicly available tool ANCOM only the order Methanosarcinales could be identified as differentially abundant between the two groups. We also investigated a link between host genome and rumen microbiome by applying our Synomics Insights platform and comparing it with an industry standard GWAS method. This resulted in the identification of genetic determinants in cows that are associated with changes in heritable components of the rumen microbiome. Only four key SNPs were found by both our platform and GWAS, whereas the Synomics Insights platform identified 1,290 significant SNPs that were not found by GWAS. Gene Ontology (GO) analysis found transcription factor as the dominant biological function. We estimated heritability of a core 73 taxa from the original set of 150 core order-level taxonomies and showed that some species are medium to highly heritable (0.25–0.62), paving the way for selective breeding of animals with desirable core microbiome characteristics. We identified a set of 113 key SNPs associated with >90% of these core heritable taxonomies. Finally, we have characterized a small set (<10) of SNPs strongly associated with key heritable bacterial orders with known role in methanogenesis, such as Desulfobacterales and Methanobacteriales.Cattle production is one of the key contributors to global warming due to methane emission, which is a by-product of converting feed stuff into milk and meat for human consumption. Rumen hosts numerous microbial communities that are involved in the digestive process, leading to notable amounts of methane emission. The key factors underlying differences in methane emission between individual animals are due to, among other factors, both specific enrichments of certain microbial communities and host genetic factors that influence the microbial abundances. The detection of such factors involves various biostatistical and bioinformatics methods. In this study, our main objective was to reanalyze a publicly available data set using our proprietary  Furthermore, they are subject to strong total count constrains due to large variability in DNA library sizes and normalization as well as constraints for the maximum number of sequence reads from the sequencing instrument . AlthougWhether or not the host influences the rumen microbial community and, consequently, methane production needs to be better elucidated. If reduced methane production is a consequence of poor symbiosis of the host with rumen microbes and, thus, fiber digestibility, there is a risk that genetic selection for hosts with reduced methane production will act against the very symbiosis that has laid ruminants and rumen microbes’ coexistence. Evidence of this has probably been observed in sheep because low-methane animals showed lower feed digestibility than high-methane-emitting animals . In a schttps://ccb.jhu.edu/software/kraken/). This tool assigns a kmer to the low common ancestor (LCA) of all the genomes containing it. The collective assignment of all kmers then informs the classification of the sequence to the LCA.Following the systems genomics concept proposed by First, raw Illumina reads were processed to obtain count frequency tables at any given taxonomic level using the platform QIIME2 . Our Synr2 value (--ld-window-r2 0.8). GWAS was performed with PLINK1.9.Raw genotype data were processed similarly for GWAS and Synomics Insights. Briefly, animals with outlier genetic backgrounds were removed by calculating the first five principal component analysis (PCA) components and identifying outliers. Variants were filtered for minor allele frequency threshold (maf = 0.05), genotype frequency (geno = 0.05), and Hardy–Weinberg disequilibrium (hwe = 0.001). Linkage disequilibrium thinning was done by applying a rolling window and calculating the correlation 2) analysis, each microbe is a quantitative “trait” that is encoded by host genetics. For calculating taxon heritability, the taxonomic level was at the order level. The taxa abundance was considered as a quantitative trait and given as individual file input in the Synomics Insights platform. They were first subject to quality control (QC) before heritability analyses as follows. It consists of selecting only those taxa whose frequency is not null in at least 70% or 90% of the 995 animals making up the full set and, in any case, not less than 600 animals. All taxa that do not satisfy this requirement are discarded. Heritability is computed using the software program GCTA   . GCTA ish2 estimate of clonal repeatability. The procedure was required also to set a threshold on heritability (h2 ≥ 0.15) for any taxon to be further analyzed using GWAS for determining host genetic influence on taxon abundance.The GCTA method estimates the proportion of additive genetic variance for a taxon trait and, thus, narrow-sense heritability and so should be lower than the p-value of the statistical test including Fisher’s exact test and the Z-score test. For evaluating the predictive ability of classifying cases vs. controls of Synomics Insights features, we used the Youden’s J statistic: J = sensitivity + specificity -1.The core technology behind Synomics Insights is based on combinatorial analysis aimed at identifying combinations of features that are characteristics of disease vs. healthy individuals or phenotype categories . These features may include genetic variants  or other multi-omics features, such as transcripts; CpG sites; or even epidemiological, environmental, or other nongenetic/nonbiological factors. A foundational component of the platform is the same as that used in Precision Life for human biomedical applications . The algcountry of origin, farm site code, and intake phenotypes as common significant predictors of the three traits with the addition of lactation number for milk yield. We used this smaller subset of covariates in a linear regression model to identify which ones are strong predictors of each target treat. We found country, farm, starch intake, C protein intake, and NDF intake to be the best predictors for milk production (p < 10–16); farm, starch intake, dry matter, and NDF intake to be the best predictors of methane emission (p < 10–16); and farm, dry matter intake and C protein intake to be the best predictors of RFI (p < 10–16).All initial 56 phenotypic variables were used as predictors in a linear Lasso regression model with methane, RFI, and milk as independent variables. The LASSO regression shrunk the original set of 56 phenotypes down to 4–10 variables with a nonzero coefficient, and these were further used in a linear regression model to identify the best predictors. Overall, Lasso identified Our Synomics Insights platform can currently only use a binary classification of samples in cases and controls (0/1). Therefore, for each linear regression, we computed the residuals of the trait values using the model predictions. The residuals were then used to binarize the data set and obtain a set of cases and controls. Specifically, we tested the use of percentiles and Z-normalization and found that the two methodologies provided similar outcome with respect to the ability of our Synomics Insights to identify good classifiers of cases and controls among bacterial taxa . Given tOrder and species relative taxonomy abundance levels were used as input to our Synomics Insights platform after relative proportions were binned . It was possible to observe already in the input data a higher abundance of Desulfovibrionales in high compared with low methane emitters and an opposite trend for other orders, including Veillonellales . Our eng2-consuming group of bacteria, were most often connected with methanogenic archea, such as Methanomicrobiales and Methanosarcinales , allows us to perform comparative network analysis of bacterial taxa between the two groups. In these networks, the frequency at which a given taxon is found to be associated to another in the Synomics Insights networks can be represented by the thickness of connecting edges. The abundance of the taxon can be visualized with different node shapes for either highly abundant (bins 7–9) or very little abundant (bins 1–3) . Focusinrcinales . Veillonrcinales .We used the original nonbinned table of relative abundance of order-level taxonomies to find differentially abundant taxa between high and low methane emitters. Using QIIME2 as a platform for analysis, we used two widely known tools for differential abundance analysis: linear discriminant analysis effect size (LEfSe)  and ANCOp-value < 10–6) and in high milk producers . Veillonella and proteobacteria have been previously found to be associated with the rumen of low-methane-emitting cattle . Also, Clostridiales were more abundant in high methane emitters compared with low emitters  of Synomics Insights, we grouped taxa in highly abundant (bins 7–9) or little abundant (bins 1–3) and statistically quantified differences in the abundance of these groups between low and high emitters either relatively to all the taxon:bin combinations within the same group or across all animals. We found much higher abundance of the phylum Proteobacteria in low vs. high methane samples in our results   .h2 > 0.15)  . An anal > 0.15) . Similar > 0.15) . Relativ > 0.15) . Our resp-value <10–2, whereas no SNP passed a more stringent cutoff for GWAS analysis (<10–8) and were relatively distributed across all chromosomes with some host spots on chromosomes 7, 12, 25, and 26 , which included Annexin A5, MYZAP (Adherin), poly-(ADP-ribose)-polymerase and a potassium channel . We found 307 SNPs with a , and 26 . We focu, and 26  found th channel .To find host SNP:genotype networks linked to changes in the core 11 heritable bacterial orders through Synomics Insights, we normalized and binarized each taxon as for phenotype traits, obtaining a set of cases and controls in which cases had high abundance and controls low abundance for the given taxonomy. Synomics Insights finds networks of SNP:genotype combinations that are significantly enriched in cases vs. controls. We compared the predictive ability of the best SNP:genotype combinations obtained from our platform to the best obtained from GWAS and found that in 6/10 of the taxa tested , the SNP:genotype combinations identified by Synomics Insights were better classifiers of cases and controls compared with GWAS SNPs . This rep-value = .58) for proteins containing a TBC (Tre-2/Bub2/Cdc12) domain (There was not a great amount of overlap 4) between GWAS SNPs and those identified by Synomics Insights using the 11 core heritable microbial orders as trait. A total of 1290 SNP:genotype combinations were identified exclusively by our platform. Among these SNPs, 376 were shared by nine of the 11 heritable orders of bacteria, including Methanomassiliicoccales, and 113 were shared by >90% of all the 73 core taxonomies, including those not very heritable. Of the 376 SNPs present in the core heritable bacteria, 117 mapped in protein coding genes. A functional enrichment analysis (DAVID) performed with these genes did not show significant pathways or functions; however, it did present a significant enrichment in nucleotide binding properties . Using a) domain .Proving a direct relationship between changes in heritable members of the rumen microbial population and methane production or nutrient utilization has not been easy. It is well established that complex traits are normally caused by many genetic determinants often with very little individual effects. Furthermore, the influence of host genetics is entwined with confounding effects such as diet, geographical location, and others . WhereasSeveral ML techniques have been applied mostly to human microbiome data, the most common being random forest, support vector machines, logistic regression, and k-NN. However, underrepresented ML tools, such as DL, spatiotemporal, and dynamic modeling, methods for longitudinal and mechanistic analyses or integrative methods for data from different sources could prove key to understand microbiome–host and microbiome–disease interactions. Here, we set out to investigate whether our in-house-developed ML platform Synomics Insights was better at identifying cues that could demonstrate the action of specific sets of host SNPs or genes on rumen bacteria responsible for methane production and RFI in cattle. Therefore, our assumption at the onset of the study is that changes in the abundance of certain bacteria drive differences in methane emission and RFI, and these changes are modulated by a finite and detectable number of host SNPs and genes.2 values, and their abundance levels, into the core combinatorial analysis engine of Synomics Insights. We showed that Synomics Insights can identify taxonomy-abundance combinations in microbiome data that can classify case/control sample groups significantly better than other publicly available tools. Additionally, this combinatorial methodology can identify significant differences in the abundance of relevant microbial taxa that are difficult to detect. For methane emission, the study identified 26 order-level taxa shared by high and low emitters that are part of networks our core engine reported as significantly enriched. A Z-test on proportions found 21/26 (81%) of these taxa were differentially enriched between high and low methane emitters . In particular, the phylum of Proteobacteria and the order Desulfovibrionales were found enriched in high emitters, and the order Veillonellales was found more abundant in low emitters as previously reported for cattle  was significantly enriched among high RFI samples. At the genus level, several taxa were found in networks albeit with relatively low counts. Among these, the most abundant Lactobacillus was enriched among low RFI samples . Lactobacilli are found associated with food efficiency in swine (Synomics Insights was utilized to analyze both microbiome and host genotype data. For microbiome analysis, we fed a large number of microbial taxa, some of which showed significant hr cattle . Importar cattle . The anain swine , and thein swine .The second phase of our analysis dealt with finding host SNPs significantly associated with changes in heritable rumen bacteria, some of which with clear links to methanogenesis. There are no reports in literature of what genetic factors in ruminants modulate the rumen bacteria responsible for methanogenesis, and most of the studies available only quantify the amount of variation in methane emission or microbiota structure explained by host genetics. We compared Synomics Insights technology with GWAS, the mainstream methodology for association studies. Here, we show that, through the testing of millions of SNP:genotype combinations, Synomics Insights identifies predictors of rumen bacterial abundance as both individual SNPs and networks of SNPs that would otherwise be discarded as poor predictors when considered individually, for example, in a GWAS approach. Indeed, 1,290 additional SNPs that were not found by GWAS were significantly associated to changes in heritable bacterial taxa by Synomics Insights. We did not observe SNPs located in QTLs previously linked to feed efficiency traits  or in geThe advantages of ML and feature network-based methods over classical statistical-bioinformatics methods in the handling and analysis of multidimensional data are numerous, especially with regards to the inference of relationships between variables for automatic pattern discovery and biomarker detection. These methods have only recently started to be applied to microbiome data with very promising results . In thisBy harnessing its advanced omics feature discovery method and its computational power designed to test hundreds of millions of potential high order interactions among features, for example, the Synomics Insights platform could help unlock the host’s genetic as well as microbiome potential in precision management and genetic selection programs. This study shows specific application of this technology to make beneficial changes in complex traits, such as methane production by rumen microbes.


Left–right hemispheric asymmetry is an important aspect of healthy brain organization for many functions including language, and it can be altered in cognitive and psychiatric disorders. No mechanism has yet been identified for establishing the human brain’s left–right axis. We performed multivariate genome-wide association scanning of cortical regional surface area and thickness asymmetries, and subcortical volume asymmetries, using data from 32,256 participants from the UK Biobank. There were 21 significant loci associated with different aspects of brain asymmetry, with functional enrichment involving microtubule-related genes and embryonic brain expression. These findings are consistent with a known role of the cytoskeleton in left–right axis determination in other organs of invertebrates and frogs. Genetic variants associated with brain asymmetry overlapped with those associated with autism, educational attainment and schizophrenia. Comparably large datasets will likely be required in future studies, to replicate and further clarify the associations of microtubule-related genes with variation in brain asymmetry, behavioural and psychiatric traits. Genes associated with brain asymmetry are involved with the internal skeletons of cells, and are especially active in the embryo. Some of these genes are associated with handedness and risk for psychiatric disorders. Many brain functions are also lateralized, including hand motor control and language which show left-hemisphere dominance in roughly 85% of people13. Altered brain or behavioural asymmetries have been reported in various cognitive and psychiatric disorders17, which suggests that population-typical asymmetries are linked to optimal human brain function.The human brain is characterized by various population-level asymmetries on its left–right axis20, which indicates an early genetic-developmental programme of brain left–right axis formation22. Studies of visceral organ development  have revealed that the generation of population-level asymmetry requires at least three important steps in the early embryo24: (1) the breaking of bilateral symmetry to create a left–right axis in a consistent orientation relative to the anterior–posterior and dorsal–ventral axes, (2) the triggering of different patterns of gene expression on the left and right sides of early embryonic structures and (3) the translation of asymmetric gene expression into lateralized morphology and organ placement.Behavioural and anatomical brain asymmetries are already apparent in uteroL-form amino acids rather than the mirror D-form, and this chirality carries through to the macrostructure and movement of primary cilia26, which help to create the left–right axis of the visceral organs in embryos25. However, hemispheric dominances for language and hand motor control do not typically reverse in people with situs inversus of the viscera, that is, reversal of the visceral organs on the left–right axis, when caused by mutations in genes that encode primary ciliary components30. This observation indicates that there are distinct and possibly organ-intrinsic mechanisms at play in brain development, but such mechanisms remain unidentified. Therefore the genetic origins of human brain asymmetry remain unknown.In principle, establishing an embryonic left–right axis requires chirality at some level, that is, key biomolecules or cellular structures that exist in only one of two possible mirror forms. Life on Earth is based on 33. However, these studies were necessarily based on data from just handfuls of samples, because of limited availability arising from appropriate ethical, legal and practical concerns. An alternative approach to identify genetic influences on brain asymmetry is to relate genomic variation in large population datasets to variation in adult brain asymmetry. Only three loci have previously been reported at a genome-wide significant level to affect variation in adult human brain asymmetries, in studies targeted at single features of temporal lobe anatomy, and they did not yield broader insights into biological pathways35. Here we made use of the unprecedented sample size available through the 2020 release of the UK Biobank magnetic resonance imaging (MRI) data, in combination with genome-wide genotype data, to perform the first multivariate, brain-wide genetic analysis of human brain anatomical asymmetry. The results from this analysis also allowed us to test whether genetic polymorphisms that are associated with variation in brain asymmetry are also associated with neurodevelopmental disorders or other behavioural and psychological traits, using publicly available genome-wide association scan (GWAS) summary statistics for these traits.Post mortem studies that contrasted gene expression between the left and right sides of the embryonic central nervous system have identified molecular pathways that may be involved, including that the two sides may transition through developmental stages slightly out of synchrony with each otherP < 0.05, Supplementary Fig. 6. For example, some language-related regions showed greater average left than right surface areas, including superior temporal and supramarginal cortex, and pars opercularis.For each of 32,256 participants with post-quality-control MRI and genetic data , and eac36 software was used to estimate the single-nucleotide polymorphism (SNP)-based heritability (h2) for each AI, that is, the extent to which variance in each AI was linked to common genetic variation over the entire genome (P < 0.05), that is, 28 of the surface area AIs, 8 cortical thickness AIs and 6 subcortical volume AIs  indicated overlapping genetic contributions to some of the AIs Fig. –10. With37, with the 42 AIs that had significant SNP-based heritability. This analysis tested each SNP separately for its simultaneous associations with all 42 AIs (38 software was used to clump mvGWAS results on the basis of linkage disequilibrium (LD), and identify lead SNPs  at each associated locus. There were 21 distinct genomic loci at the 5 × 10−8 significance level associated with different aspects of brain asymmetry GWAS for 9,803,522 SNPs, using meta-canonical correlation analysis as implemented in MetaPhatl 42 AIs . A multi37 identified the ‘central’ AIs that contributed to its multivariate association  had five central AIs: the minor allele was associated with a leftward shift of surface area asymmetry for two lateral temporal regions, a rightward shift of surface area asymmetry for two medial temporal regions and a leftward shift of cortical thickness asymmetry in the inferior temporal gyrus . On 2p23.3, rs62130503 is intronic to MAPRE3 (microtubule associated protein RP/EB family member 3a), and rs12617392 is a brain eQTL39 of MAPRE3. Located between these two SNPs on 2p23.3 is also AGBL5, which is a post-translational modifier of tubulin41. On 2q34, rs368536282 is close to MAP2 (microtubule associated protein 2), a well-known dendrite-specific marker of neurons42, previously implicated in left-handedness by GWAS analysis45. On 6p21.33, rs7781 is in the 3′ untranslated region (UTR) of TUBB (tubulin beta class I). On 12q13.12, rs10783306 is close to the alpha tubulin gene TUBA1B. This variant is also in high LD with a handedness-associated variant, rs11168884 (r2 = 0.89)44. On 14q23.1, two lead variants for two independent genomic loci, rs160459 and rs201816193, showed evidence for cross-locus chromatin interaction via the promoters of nearby genes in foetal cortex46 44. On 17q21.31 there were five independent lead SNPs: rs35908989 is intronic to MAPT which encodes microtubule-associated protein tau, and rs55938136, rs35853889 and rs568039055 are brain eQTLs52 of MAPT, while rs80103986 is in high LD (r2 = 0.91) with handedness-associated variant rs5597401444. On 19p13.3, rs11672092 is intronic to the tubulin gene TUBB4A, and in high LD with rs66479618 (r2 = 0.88), another handedness-associated SNP44.Ten of the loci had annotations involving cytoskeleton-related genes: On 1p33, rs2279829 is a cortical eQTL39 of ZIC4, which is involved in visual and auditory pathway development53. rs9307052 on 4q22.1 is in high LD (r2 = 0.99) with the handedness-associated variant rs2865828244. On 5q15, rs869219775 is close to NR2F1, which is involved in neural activity during cortical patterning54. On 6q22.31-q22.32, rs9385385 is close to NCOA7, a nuclear receptor co-activator with its most abundant expression in the brain55. On 7p14.3, rs6947352 is intronic to BBS9, which causes Bardet–Biedl syndrome when mutated, involving retinopathy and intellectual disability57. On 9q22.33, rs911934 is located in a region having a chromatin interaction with TRIM14 in adult cortex46 . This observation supports the validity of our mvGWAS association findings, as random noise would not lead to such functional clustering. Microtubule-related genes  linked different clusters together in the largest protein interaction network  showed significant enrichment  and ‘microtubule_based_process’ (P = 2.36 × 10−6), was also found when using the list of single closest genes  and early–mid-prenatal (P = 9.37 × 10−4) stages, from 9 (P = 1.84 × 10−3) to 24 (P = 7.36 × 10−3) post-conceptional weeks   Fig. . This is73 to perform genetic overlap analysis with our mvGWAS results in relation to GWAS summary statistics from neurodevelopmental disorders, behavioural and psychological traits which have been reported to associate phenotypically with aspects of structural and/or functional brain asymmetry: attention deficit hyperactivity disorder77, autism spectrum disorder82, educational attainment84, handedness45, intelligence88 and schizophrenia93. There was evidence for genetic overlap between brain asymmetries and autism (P = 0.005), educational attainment (P = 0.001) and schizophrenia (P = 0.002) which remained significant at Bonferroni-corrected P < 0.05 . In addition, we found no overlap between our mvGWAS results and those from a previous GWAS of intracranial volume in 32,438 participants94 . The UK95 . After aP values < 0.05 on multivariate testing in the replication set , which we confirmed by permutation with respect to 10,000 repeat random samplings of 17 SNPs from the whole genome in the replication set (permutation-based P = 4 × 10−4). This indicates that the limited sample size of the replication set, compared with the discovery set, did not provide adequate power to replicate at the level of some individual SNPs, but that in combination there was evidence for replication. Moreover, among the 17 SNPs that showed P > 0.05 on multivariate testing in the replication set, some showed association P < 0.05 on univariate testing of the specific central traits identified for those SNPs in the discovery mvGWAS (Supplementary Table 44 (see above for details), which is an additional form of validation with respect to a phenotype related to brain asymmetry. As also mentioned above, the high degree of functional clustering of genes identified through gene-based association testing is another form of support for the association results in the mvGWAS.Ten of the 27 independent lead SNPs from the discovery mvGWAS showed association Despite the importance of asymmetry as an organizing feature of the human brain, the early developmental processes which establish its left–right axis are unknown. In this study, we carried out multivariate GWAS analysis which identified 21 genetic loci associated with different aspects of adult brain anatomical asymmetry. Functional annotation implicated genes particularly involved in microtubule organization and prenatal brain development. Our study therefore sheds new light on the molecular genetic foundations of human brain asymmetry. In addition, at the genome-wide level, SNPs associated with brain asymmetry overlapped with those associated with educational attainment, autism and schizophrenia, while five specific loci that are associated with both brain asymmetry and handedness were identified.102. Cellular chirality refers to directional biases in cellular morphology, position, rotation or migration, which arise because of the inherent chirality of intracellular macromolecules such as those composing the cytoskeleton96, and also manifest in terms of the intracellular distributions of organelles103. For example, during early cell divisions in Xenopus (frog) embryos, the cytoskeleton has been reported to mediate asymmetric intracellular protein localization, as protein transport molecules move along cytoskeletal tracks within cells104. Thus the cytoskeleton can provide a directionally consistent, organ-intrinsic bias during embryonic development that acts as a determinant of morphological asymmetry105, arising from fundamental aspects of molecular and cellular biology.Previous studies in invertebrates and frog embryos have shown that the cytoskeleton plays a role in determining cellular chirality and asymmetrical patterning of other organs105, that is, distinct from other pathways that establish broader aspects of body asymmetry.As our study associated cytoskeletal and embryonically expressed genes with variation in adult human brain asymmetry, it is possible that these genes are involved in the establishment of left–right asymmetry during early brain development, through a mechanism involving cellular chirality. As mentioned in 37. A single set of genome-wide association results, pertaining simultaneously to multiple aspects of brain asymmetry, was then taken forward into functional annotation and downstream analyses, such as testing for genetic overlaps with other traits. The multivariate approach therefore helped to detect and interpret key aspects of the genetic architecture of brain asymmetry, without the noise inherent in repeat univariate testing. An important challenge remained to identify the particular brain traits that drove the multivariate associations at each locus, which was achieved in MetaPhat37 by decomposing associations into sets of ‘central’ traits on the basis of the Bayesian information criterion and canonical correlation P values.In this study, we identified genetic loci that are associated with 42 heritable aspects of brain asymmetry through a multivariate, brain-wide approach. A multivariate approach can boost statistical power while achieving data reduction, compared with separate univariate analyses of individual brain traits106. Therefore, we used iSECA73 to explore the genetic overlap of brain asymmetry with neurodevelopmental disorders, behavioural and psychological traits. This analysis was based on SNP-wise P values of association over the whole genome, that is, a genetic overlap was found when the SNPs tending to show low P values in our mvGWAS for brain asymmetry also tended to show low P values in publicly available GWAS summary statistics for a given disorder or trait. We found significant genetic overlaps of brain asymmetry with autism, schizophrenia and educational attainment, which suggests that genes affecting brain asymmetry also influence these traits. This is in line with literature that has shown phenotypic associations between altered brain asymmetry and these traits .Further research, for example, using Mendelian randomization112. A cytoskeleton-based origin of brain asymmetry would fit this scenario, as the cytoskeleton is essential for various fundamental functions in cellular biology, beyond axis formation114. Previous, twin- and family-based analyses6 have reported heritabilities up to roughly 25% for some of the same asymmetry measures that we analysed in the present study, with an overall similar regional pattern. Higher heritabilities were found particularly for regions that are important in the language system  and limbic system . Twin-based heritability is often measured to be higher than SNP-based heritability, which may be expected because SNPs are just one class of genetic variation, and also because twin studies can overestimate heritability when certain assumptions are not fully met115. As twin studies have not indicated effects of shared environment on brain aymmetries6, early developmental randomness is likely to cause most variation116.Many of the brain asymmetries were strong and directional at the population level, but their heritabilities were generally low, ranging up to 10%. This suggests that developmental mechanisms for brain asymmetry are tightly constrained and largely genetically invariant in the population, and that environmental factors and/or developmental randomness are responsible for most variability117 . Handedness cannot therefore be treated safely as a confound variable when analysing brain asymmetry. We were interested in any genetic effects associated with brain asymmetry, regardless of whether they might also be shared with other traits such as handedness. Having identified genetic variants associated with brain asymmetry, we then queried post hoc whether they have been reported as significant in previous GWAS of handedness in over 1 million people44. We did not observe a significant genetic overlap between structural brain asymmetry and handedness at the genome-wide level, which again may be due to the relatively low SNP-based heritabilities of these traits, in combination with limited statistical power in the present sample size for this kind of analysis. However, five individual SNPs associated with both brain asymmetry and handedness were identified, which suggests that a significant genome-wide overlap might be detected when using a larger dataset in the future.We did not correct for handedness as a covariate in our genetic analyses, as it is generally not advisable to correct for covariates which are themselves partly heritable. This is because biased genetic effects can be measured with respect to the target traitP value in the replication set was 3.3 × 10−4, indicating that a larger dataset would have supported replication at the level of more individual SNPs. Functional clustering of the closest genes to the 27 independent lead SNPs, according to microtubule-related biology and protein–protein interactions, also supported the validity of the mvGWAS findings, as this was unlikely to occur by chance. In addition, four of the SNPs that did not replicate individually at P < 0.05 , and all of their procedures were performed in accordance with the World Medical Association guidelines. Informed consent was obtained for all participants. We used the brain imaging data released in February 2020, and data availability and processing (described below) resulted in a final sample of 32,256 participants of white British ancestry for the primary GWAS analysis, together with the structural MRI data and genotype data from the same participants. The age range of these participants was 45 to 81 years (mean 63.77 years), 15,288 were male and 16,968 were female. An independent replication dataset of 3,600 individuals was also drawn from the UK Biobank, who self-identified as white, but not British, or did not cluster genetically with the bulk of the ‘white British’ ancestry participants (see below). The age range of these participants was 45 to 80 years (mean 62.89 years), 1,574 were male and 2,026 were female.95. The primary analyses were restricted to participants with ‘white British ancestry’, which was defined by Bycroft et al. (‘in.white.British.ancestry.subset’)95, using a combination of self-report and cluster analysis on the basis of the first six principal components which capture genetic ancestry from the genome-wide genotype data. We randomly excluded one from each pair of remaining individuals who had a kinship coefficient >0.0442, as defined by Bycroft et al.95. Next, QCTOOL (v.2.0.6) and PLINK119 were used to perform genotype quality control: excluding SNPs with minor allele frequency <1%, Hardy–Weinberg equilibrium test P value <1 × 10−7 and imputation INFO score <0.7 (a measure of genotype imputation confidence). We also excluded multi-allelic SNPs because most of the downstream software (below) could not handle them. This resulted in 9,803,522 bi-allelic variants.We downloaded imputed SNP genotype data from the UK Biobank data portal . We first excluded subjects with a mismatch of their self-reported and genetically inferred sex, with putative sex chromosome aneuploidies, or who were outliers on the basis of heterozygosity (principle-component-corrected heterozygosity >0.19) and genotype missingness (missing rate >0.05) as calculated by Bycroft et al.95. We also imposed the extra requirement that participants in the replication set should not show a kinship coefficient >0.0442 with any individual in the primary discovery dataset.The same process was applied to derive the independent replication dataset of 3,600 individuals, except that these participants did not self-identify as British , or they did not fall within the bounds of the ‘white British ancestry’ cluster as defined by Bycroft et al.http://biobank.ndph.ox.ac.uk/showcase/refer.cgi?id=2367). Briefly, in vivo whole-brain T1-weighted MRI scans were used to perform cortical parcellation into 34 regions per hemisphere with the Desikan–Killiany atlas120, and 7 subcortical structural segmentations. Surface area was measured at the grey–white matter boundary, and thickness was measured as the average distance in a region between the white matter and pial surfaces. Details of the image quality control and processing are described elsewhere121. Given that the data for the temporal pole were reported as unreliable121, we only used 33 surface area, 33 cortical thickness and 7 subcortical volume measures in each hemisphere  released by the UK Biobank Imaging Study  , scanner position parameters , T1 signal-to-noise ratio (field 25734-2.0) and T1 contrast-to-noise ratio (field 25735-2.0), plus categorical covariates which were: assessment centre (field 54-2.0), genotype measurement batch (field 22000-0.0) and sex (field 31-0.0).We also made use of continuous variables as covariates in heritability estimation and genome-wide association analysis (below), which were: age when attended assessment centre (UK Biobank fields 21003-2.0), nonlinear age, that is (age − mean_age)P > 1 × 10−7 were used to build a genetic relationship matrix using GCTA36 (version 1.93.0beta). Specifically for analyses using GCTA, we further excluded one random participant from each pair having a kinship coefficient higher than 0.025 based on the calculated genetic relationship matrix , resulting in 30,315 participants. Genome-based restricted maximum likelihood (GREML)36 analyses were performed to estimate the SNP-based heritability for each AI, controlling for the above-mentioned covariates, and applying FDR 0.05 across the 73 AIs to define significantly heritable AIs. Bivariate GREML124 analysis was used to estimate genetic correlations between pairs of AIs, separately for cortical surface area, cortical thickness and subcortical volume AIs, with FDR correction at 0.05 for multiple testing.From the primary dataset, 9,516,074 autosomal variants with minor allele frequencies >1%, INFO score >0.7 and Hardy–Weinberg equilibrium 37 software to perform mvGWAS analysis across asymmetries for cortical surface area, cortical thickness and subcortical volume, including only the 42 AIs that had shown significant SNP-based heritability. MetaPhat performs meta-canonical correlation analysis, and uses univariate GWAS summary statistics as input from each separate AI, which were derived under an additive genetic model while controlling for the above-mentioned covariates, using BGENIE software (v1.2)95. Thus our mvGWAS tested effectively for association with 42 traits. This approach estimates the linear combination of traits that is maximally associated with genotype, which can differ for each SNP, while maintaining a correct false-positive rate. A total of 9,803,522 SNPs (see further above) were used for mvGWAS, spanning all autosomes and chromosome X. Statistically significant SNPs were considered as those with P < 5 × 10−8 in mvGWAS, which is a widely used threshold to account for multiple testing over the whole genome, in the context of LD in European-descent populations126.In mvGWAS, a single association test is performed for each SNP in relation to multiple traits simultaneously. We used MetaPhatP values and the Bayesian information criterion37. For the lead SNPs at genome-wide significant loci (see below for how these were defined), we also performed post hoc analysis in which we examined their separate left and right hemispheric associations, using traits corresponding to the central AIs that were involved in the multivariate associations , and independent lead SNPs were also defined among them as having low LD (r2 < 0.1) with any others. If LD blocks of significant SNPs are located within 250 kb of each other (default parameter), they are merged into one genomic locus. Therefore, some genomic loci could include one or more independent lead SNPs (Table FUMA version v1.3.6).3.638, a128 categories, Combined Annotation-Dependent Depletion129 scores, RegulomeDB130 scores and chromatin state132:ANNOVAR categories identify SNPs on the basis of their locations with respect to genes, such as exonic, intronic and intergenic, using Ensembl gene definitions.133.Combined Annotation-Dependent Depletion scores predict deleteriousness, with scores higher than 12.37 suggesting potential pathogenicityRegulomeDB scores integrate regulatory information from eQTL and chromatin marks, and range from 1a to 7, with lower scores representing more importance for regulatory function.132, which were H3K4me3, H3K4me1, H3K36me3, H3K27me3 and H3K9me3. For each SNP, FUMA calculated the minimum chromatin state across 127 tissue/cell-type in the Roadmap Epigenomics Project132. Categories 1–7 are considered as open chromatin states.Chromatin states show the accessibility of genomic regions, and were labelled by 15 categorical states on the basis of five chromatin marks for 127 epigenomes in the Roadmap Epigenomics ProjectFunctional annotations were applied by matching chromosome location, base-pair position, reference and alternate alleles to databases containing known functional annotations, which were ANNOVARP < 5 × 10−5) in the National Human Genome Research Institute–European Bioinformatics Institute catalogue134.We also used FUMA to annotate independent significant SNPs and their candidate SNPs according to previously reported phenotype associations (P = 1.62 × 10−10), as the single lead SNP to represent this locus, and annotated it manually.For a significant mvGWAS association in the major histocompatibility complex region Table , we tookPositional mapping was used to map SNPs to protein-coding genes on the basis of physical distance (within 10 kb) in the human reference assembly (GRCh37/hg19).52, CommonMind Consortium39, BRAINEAC51 and GTEx v8 Brain135. FUMA applied a FDR of 0.05 within each analysis to identify significant eQTL associations.eQTL mapping was used to annotate SNPs to genes . eQTL mapping was carried out in relation to genes up to 1 Mb away on the basis of four brain-expression data repositories: PsychENCORE52, PsychENCORE promoter anchored loops39, HiC adult cortex46, HiC foetal cortex46, HiC (GSE87112) dorsolateral prefrontal cortex60, HiC (GSE87112) hippocampus60 and HiC (GSE87112) neural progenitor cell60. We further selected only those genes for which one or both regions involved in the chromatin interaction overlapped with a predicted enhancer or promoter region (250 bp up- and 500 bp downstream of the transcription start site) in any of the brain-related repositories from the Roadmap Epigenomics Project132, that is, E053 (neurospheres) cortex, E054 (neurospheres) ganglion eminence, E067 (brain) angular gyrus, E068 (brain) anterior caudate, E069 (brain) cingulate gyrus, E070 (brain) germinal matrix, E071 (brain) hippocampus middle, E072 (brain) inferior temporal lobe, E073 (brain) dorsolateral prefrontal cortex, E074 (brain) substantia nigra, E081 (brain) foetal brain male, E082 (brain) foetal brain female, E003 embryonic stem (ES) H1 cells, E008 ES H9 cells, E007 (ES-derived) H1 derived neuronal progenitor cultured cells, E009 (ES-derived) H9 derived neuronal progenitor cultured cells and E010 (ES-derived) H9 derived neuron cultured cells. A FDR of 1 × 10−6 was applied to identify significant interactions (default parameter), separately for each analysis.Chromatin interaction mapping was performed to map SNPs to genes on the basis of seven brain-related Hi-C chromatin conformation capture datasets: PsychENCORE EP link (one way)SNP-to-gene mapping at significant mvGWAS loci was performed using the default FUMA processes for these three strategies:65, using default parameters implemented in FUMA (SNP-wide mean model). This process examines the joint association signals of all SNPs within a given gene (including 50 kb upstream to 50 kb downstream of the gene), while considering the LD between the SNPs. SNPs were mapped to 20,146 protein-coding genes on the basis of National Center for Biotechnology Information build 37.3 gene definitions, and each gene was represented by at least one SNP. We applied a Bonferroni correction for the number of tested genes .Genome-wide gene-based association analysis was performed using mvGWAS summary statistics as input into MAGMA (v1.08)65, again with default settings as implemented in FUMA, to test for enrichment of association within predefined gene sets. This process tests whether gene-based P values among all 20,146 genes are lower for those genes within pre-defined functional sets than the rest of the genes in the genome, while correcting for other gene properties such as the number of SNPs. A total of 7,343 gene sets, defined according to Gene Ontology biological processes, were tested from MSigDB version 7.069. In the main text we report the gene sets with P values that met Bonferroni correction for multiple testing .We used MAGMA38, which is appropriate for gene lists.In addition, we used the list of single closest genes to the 27 lead SNPs arising from mvGWAS Table  as inputP values for brain asymmetry were associated with differential expression levels across cell types, using Bonferroni correction within each separate analysis with respect to each cell-type expression dataset included in FUMA.Finally, we used the CELL TYPE function (as implemented within FUMA) to test whether lower gene-based association http://string-db.org)68 for protein network analysis, using as input the names of 57 genes identified through gene-based association analysis, as described above. The STRING dataset includes protein–protein interaction information from numerous sources, including experimental data, publications and computational prediction methods. Only links with medium confidence or higher  were retained.We used the Search Tool for the Retrieval of Interacting Genes/Proteins  to examine whether lower gene-based P values tended to be found for genes showing relatively higher expression in BrainSpan71 gene expression data from any particular ages compared with all other ages, separately for 29 different age groups ranging from 8 postconceptional weeks to 40 years old, and 11 defined developmental stages from early prenatal to middle adulthood. We corrected for multiple testing through a FDR of 0.05 .Using the gene-based association 73 toolbox that can test for genetic overlap on the basis of per-SNP association P values only . We tested for genetic overlap in relation to traits previously reported to associate phenotypically with different aspects of brain structural asymmetry 66, handedness 45, intelligence 67, autism spectrum disorder 78, attention deficit hyperactivity disorder 74 and schizophrenia 89. We also tested for genetic overlap in relation to brain intracranial volume 94. After LD-based filtering and clumping using default parameters, iSECA tests for pleiotropy between two sets of GWAS results using an exact binomial statistical test at each of 12 P value levels: P ≤ . The analysis compares the expected and observed overlap in the subsets of SNPs at these levels from two GWAS . In other words, iSECA iterates through each of the 12 P value levels and counts the number of overlapping variants between two GWAS at each P value threshold, and compares that number with the number expected under the null hypothesis of no genetic overlap, using the exact binomial test. iSECA then counts up the number of comparisons with evidence of overlap at a nominally significant level of P ≤ 0.05. To assess the significance level of overlap, we generated 1000 datasets through permutations (default parameter), which contained all the possible combinations for a pair of traits, and determined whether the number of levels with nominally significant genetic overlap was significantly more than expected by chance. Finally, Bonferroni correction <0.05 was applied for multiple testing of seven traits. Additionally, iSECA generated Q–Q plots for asymmetry mvGWAS P values conditioned on the other trait P values  to visualize whether there is an excess of pleiotropic SNPs, which should be visible as a leftward shift of the curve as the P value threshold becomes tighter (Supplementary Fig. We applied the iSECAsymmetry , using GFurther information on research design is available in the Supplementary InformationList of Supplementary Tables and Supplementary Figures 1–9.Reporting SummaryPeer Review InformationSupplementary TablesSupplementary Tables 1–20 in one Excel file with multiple tabs.


Traditional methods for single-variant genome-wide association study (GWAS) incur a substantial multiple-testing burden because of the need to test for associations with a vast number of single-nucleotide polymorphisms (SNPs) simultaneously. Further, by ignoring more complex joint effects of nearby SNPs within a given region, these methods fail to consider the genomic context of an association with the outcome.To address these shortcomings, we present a more powerful method for GWAS, coined ‘Wavelet Screening’ (WS), that greatly reduces the number of tests to be performed. This is achieved through the use of a sliding-window approach based on wavelets to sequentially screen the entire genome for associations. Wavelets are oscillatory functions that are useful for analyzing the local frequency and time behavior of signals. The signals can then be divided into different scale components and analyzed separately. In the current setting, we consider a sequence of SNPs as a genetic signal, and for each screened region, we transform the genetic signal into the wavelet space. The null and alternative hypotheses are modeled using the posterior distribution of the wavelet coefficients. WS is enhanced by using additional information from the regression coefficients and by taking advantage of the pyramidal structure of wavelets. When faced with more complex genetic signals than single-SNP associations, we show via simulations that WS provides a substantial gain in power compared to both the traditional GWAS modeling and another popular regional association test called SNP-set (Sequence) Kernel Association Test (SKAT). To demonstrate feasibility, we applied WS to a large Norwegian cohort (N=8006) with genotypes and information available on gestational duration.WS is a powerful and versatile approach to analyzing whole-genome data and lends itself easily to investigating various omics data types. Given its broader focus on the genomic context of an association, WS may provide additional insight into trait etiology by revealing genes and loci that might have been missed by previous efforts. The main objective of a genetic association study is to identify the genes and loci that are associated with a phenotype of interest. Although the human genome is very similar across individuals, it is interspersed with single base-pair differences called single-nucleotide polymorphisms (SNPs) that collectively account for the observed differences across individuals. One of the most common approaches to genetic association testing is to conduct a genome-wide association study (GWAS), where the significance of the effect of each SNP on a phenotype is assessed in a sequential fashion. Despite its many successes, this approach has two important limitations: (i) it incurs a substantial multiple-testing burden due to the large number of tests carried out simultaneously, and (ii) it ignores the genomic context of an association by failing to exploit the typically dense microarray-based genotyping of the genome. As larger regions of the genome are more likely to contribute to the phenotype , consideThe issue of multiple testing can be resolved using a regularization method such as Fused Lasso , which aDespite an increased interest in penalized regressions within the broader statistical community, they remain elusive in the top-tiered genetic publications. Penalized regression has recently been incorporated into PLINK , one of Several regional tests have been developed for GWAS, including the Burden test , 6, C-alTo address these shortcomings, we developed a new approach called Wavelet Screening (WS) for analyzing genome-wide genotype data by leveraging key insights from functional modeling , 10. SpeThe use of reverse regression to search for genetic associations is becoming more widespread in the genetic literature  . Our priOur method transforms the raw genotype data similarly to the widely used ‘Gene- or Region-Based Aggregation Tests of Multiple Variants’ method  Fig. 1)1). Like k at physical position bp (base pair), and let iid) over individuals, with bp can be interpreted as a function of the imputation quality IQ(bp), which has a value in bp. As the value of a SNP is in We code a SNP 0 if an individual is homozygous for the reference allele , 1 if heterozygous, and 2 if homozygous for the alternative allele . This is the standard way of coding alleles in an additive genetic model . Let \doputation , the disbp in the interval lb, ub), with some error due to the genome-wide imputation process [k at position d and c coefficients as sequences of length We define a genetic region  process . For nowlgorithm .k, all values For the coefficients at the highest scale  and the right part.The wavelet l for an individual represents the discrepancy between an individual’s genotypes and the reference genotypes coded as s, l.The wavelet The main rationale behind this modeling is that, if a genetic locus has an effect on the phenotype, then the association is likely to be spread across genomic regions of a given size  at different positions (locations). By using wavelet transform to perform a position/size  decomposition and then regressing the wavelet coefficients on the phenotype, one can visualize where (location) and how  the genetic signal influences the phenotype.In essence, the coefficients obtained after performing wavelet transform on a genomic region can be viewed as local ‘scores’ of the genotype, with the following interpretations:c coefficients specifically. c coefficients are easier to interpret than d coefficients. For instance, in case of completely observed genotypes, c coefficients correspond to the sum of minor alleles, similar to the Burden test [In the rest of this article, we use ‘wavelet coefficients’ to refer to den test .n positions We use the method of Kovac and Silverman  to handlsee the “” sectionN distribution. As we use the quantile-transformed wavelet coefficient as the endogenous variable  using least squares estimation [In essence, our approach to modeling aims at detecting regions containing sub-regions associated with a trait/phenotype of interest. We localize these sub-regions to ease the interpretation of the output. We first need to assess whether certain scales are associated with the phenotype at different locations to estimate the effect of a genetic region on the phenotype of interest. Within a genetic region, let r models . To adjutimation .Under Under To help identify a subset of X is the design matrix with the phenotype k is of the order of 100.For a given locus, a genetic signal might be assumed to occur in only a subset of the regression coefficients. Thus, the After obtaining the estimates, we compute—for each beta coefficient—the posterior probability ohnstone , we defiFinally, we compute the average evidence towards the alternative byStephens , where lentclass1pt{minimaS is the maximum depth of analysis. We summarize the proportion of associations vertically, and for each average, we consider the positions that overlap with the sub-regions. For example, the first coefficient at scale 1 contributes half of the sub-region average of association.To improve the separation of the null and alternative distributions, we extract two additional pieces of information from the Our goal is to find the right balance between having as large of a entclass1pt{minimant Figs. . We are As wavelet transform induces a correlation between the mentclass2pt{minimHigh LD Simulations: we computed the correlation structure (LD) of the above-described locus and identified 28 small LD-blocks. In this simulation set-up, all the SNPs used in constructing the phenotype are selected within high LD regions. These simulations are engineered to mimic a diluted effect of SNPs within different LD blocks, also known as “block polygenic effect”, where each variant has a small additive effect.Random LD Simulations: all the SNPs (from 1 to 28) used in constructing the phenotype are taken uniformly at random from within the considered locus. These simulations are engineered to mimic a diluted effect of SNPs regardless of the LD structure, where each variant has a small additive effect.We performed simulations catering to a complex genetic signal by combining real genetic data with a simulated phenotype. We used a previously identified locus for gestational age in the Norwegian HARVEST dataset  and mateMono-directional model (MD): for each iteration, we randomly selected 1 to 28 SNPs, and for each individual, we summed up their SNP dosages within the selected set of SNPs to construct a score. On top of the individual scores, we added normally-distributed noise, scaled so that the genetic score explains Random direction model (RD): the same setting as above, but the sign of the effect (positive/negative) for each SNP is random. In the mono-directional simulations, any additional variant may increase the level of the phenotype. This is not necessarily the case with the random direction model. Taken together, these simulations showcase the sensitivity of WS to the direction of the SNP coding.The variance explained by a single SNP varies between We provide an example of such a phenotype in Fig. Further, we performed ize see “” sectionentclass1pt{minimaWe also compared our method with one of the most popular regional methods for association testing called SNP-set (Sequence) Kernel Association Test (SKAT) . For eacNA stands for ‘not applicable’. As the standard single-SNP GWAS does not take LD structure and local polygenicity into account, the only effect modeled here is the dilution effect. As we obtained similar results, we chose not to display them in Tables Model with NA for SKAT. Overall, the results in Tables Random LD Simulation but increases in the case of High LD Simulation. The power of SKAT increases with increasing number of causal SNPs. Compared to WS, SKAT is more powerful for large sample sizes and when a large number of variants affect the phenotype. Conversely, WS has higher power especially for smaller sample sizes and when a moderate number of SNPs affect the phenotype. For In Tables mentclass2pt{minimRandom LD Simulation, Table For entclass1pt{minimaHigh LD Simulation is within an LD block, which is not necessarily the case with the Random LD Simulation. As it is rare that only a single SNP is responsible for the association observed with the phenotype, in a general setting WS would be more powerful than the standard GWAS based on single-variant modeling. More importantly, our simulations show that WS has higher power for detecting an association in case of moderate ‘local polygenicity’. Overall, our simulations show that the type I error is well-controlled by WS for the proposed simulation settings  a chromosome-wide association study of human gestational duration using real data from a Norwegian cohort , and 2) EBF1). Using the same exclusion criteria for SNPs and individuals as in Zhang et al.  [Gestational duration is a complicated phenotype to study because of large measurement errors (}≈7 days ) and typ1.5 days , 27). We1.5 days  to see i1.5 days  in the h et al.  , the lowAlthough a typical GWAS can now interrogate millions of SNPs at a time, several chromosomal regions are still difficult to analyze due to poor marker density, particularly regions located near telomeres and centromeres, regions containing highly repetitive DNA, and regions of low imputation quality. Most SNPs with low imputation quality are routinely discarded during quality control. Given that we pre-processed the gestational duration data using an interpolation, we tried to avoid analyzing purely interpolated regions by including an additional criterion in the pre-processing step to exclude these types of regions. We propose studying regions of size 1 Mb, with a maximum distance of 10 kb between any two adjacent SNPs. Furthermore, we defined overlapping regions to avoid having a signal located at the very boundary of two regions. By applying these additional criteria, we excluded Besides avoiding fully-interpolated regions, we also need to choose an appropriate depth of analysis for the wavelet decomposition. The precision of the wavelet coefficient depends on the number of non-interpolated points in a given region . As a ruEBF1 detected by WS.We applied WS to the gestational duration dataset described above. We simulated The classic pyramidal wavelet decomposition representation was used to display the  see Eq. , we highR package statgenGWAS.Grain yield in maize is a complex trait and there is a growing literature aimed at elucidating its etiology , 28. TheAs with the analysis of gestational age, we also excluded regions of poor marker density in the maize dataset. This was particularly important because the data provided by Millet and colleagues  have a let al. study [We applied WS to the grain yield dataset and simulated l. study .WS identified 44 significantly associated loci. One of these loci corresponds to the genome-wide significant locus on chromosome 6 reported by Millet and colleagues . We provThis paper introduces WS as a novel and powerful alternative to the classic GWAS methodology. It offers a more flexible modeling scheme than the standard single-SNP association approach and enhances the discovery rate in case of moderate local polygenicity. We acknowledge the empirical nature of this article, as most of the simulations indicate that WS is slightly over-conservative, which might be due to our calibration criterion for ed tests .S is the number of cohorts and s. However, it may be more appropriate to do a meta-analysis at the level of coefficients and then compute a new test statistic for the meta-analysis.In future developments of WS, we plan to add new functionalities to enable GWAMAs based on the use of summary statistics from different participating cohorts, akin to what is routinely performed using the popular METAL software . If eachIn addition, we aim to adapt our method to include phenotypes on non-ordered scales, e.g., blood types or psychiatric phenotypes. These phenotypes are usually analyzed in a case-control fashion and not by multinomial regression due to computational and power issues. By exploiting reverse regression, we can include such phenotypes in the predictor matrix by coding them in a similar way as in ANOVA. The modeling of the two hypotheses can be done using multivariate Gaussian, with one dimension per coefficient, instead of using simple univariate Gaussian. Furthermore, by exploiting reverse regression, we can also easily adapt this method to the multiple-phenotype GWAS setting known as phenome-wide association studies or PheWAS . HoweverThe complexity of the test statistics makes it difficult to infer directly how power would be influenced by different parameters, such as the distance between SNPs and their LD structure. Future work should focus on exploring power behavior under different settings, including varying sample size, different percentage of variance explained, and unequally distributed effects between SNPs. The wavelet-based methodology presented here is both powerful and versatile, and lends itself easily to the analysis of other ‘omics’ data types. In future developments, we will investigate the feasibility of extending WS by adding one more level of hierarchy to enable multi-omics data analyses.R package. In addition to the code, the package contains a data visualization tool to scrutinize any associations detected by WS. The R package is available at https://github.com/william-denault/WaveletScreening. We also provide a detailed example of a typical WS run by using simulated data. Additional details are provided in the help function wavelet_screening. Further, we show how to simulate plot_WS function to visualize the output of wavelet_screening, as exemplified in Fig. WS is distributed as an


Verticillium dahliae , is a soil-borne disease affecting sunflower worldwide. A single dominant locus, known as V1, was formerly effective in controlling North-American Vd races, whereas races from Argentina, Europe and an emerging race from USA overcome its resistance. This emphasizes the need for identifying broad-spectrum genetic resistance (BSR) sources. Here we characterize two sunflower mapping populations (MPs) for SVW resistance: a biparental MP and the association MP from the National Institute of Agricultural Technology (INTA), under field growing conditions. Nine field-trials (FTs) were conducted in highly infested fields in the most SVW-affected region of Argentina. Several disease descriptors (DDs), including incidence and severity, were scored across four phenological stages. Generalized linear models were fitted according to the nature of each variable, adjusting mean phenotypes for inbred lines across and within FTs. Comparison of these responses allowed the identification of novel BSR sources. Furthermore, we present the first report of SVW resistance heritability, with estimates ranging from 35 to 45% for DDs related to disease incidence and severity, respectively. This study constitutes the largest SVW resistance characterization reported to date in sunflower, identifying valuable genetic resources for BSR-breeding to cope with a pathogen of increasing importance worldwide.Sunflower Verticillium Wilt and Leaf Mottle (SVW), caused by  Verticillium dahliae (Kleb.). Fungal inoculum consists of long-lasting microsclerotia, which remain infective in soil from 10 to 15 years1. V. dahliae (Vd) is a polyphagous pathogen affecting over 350 dicotyledonous hosts, thus making its management difficult through common agricultural practices5. The wilting caused by SVW exerts up to 30% yield reductions in susceptible commercial sunflower hybrids7 and up to 73% in susceptible materials grown in highly infested fields8. Yield losses have a direct relationship with symptom level (i.e. foliage necrotic surface)9.Sunflower Verticillium Wilt and Leaf Mottle (SVW) is a monocyclic vascular disease whose causative agent is the soil-borne fungal pathogen 10. Historically, this has been the most prevalent disease in Argentina and it has high impact on extensive regions of Canada and USA, where new pathogenic races have arisen12. Moreover, it has recently become a serious threat for European sunflower producing countries of temperate regions with increasing prevalence rates in France, Italy, Spain and countries around the Black Sea14.SVW occurs in most sunflower producing areas of the world16, Vd is an endemic pathogen with a variety of local races19. Vd inoculum is spread over 1.2 million hectares, affecting over 70% of the country’s sunflower growing region20. In the south of Buenos Aires Province, the main area for sunflower production in Argentina, the SVW-prevalence levels have shown an average of 45% (± 14%) over the last 8 years21.In Argentina, the third sunflower producer and edible-oil exporter worldwide4, genetic resistance is still the most effective strategy to cope with this affection. SVW resistance was first reported as a qualitative trait governed by a single dominant locus22. Later contributions have described different inbred lines (ILs) with dominant, additive and recessive sources of resistance to North American races24. Since then, the V1 locus, which was identified on the maintainer IL HA8925, has been the main source of resistance for hybrid development worldwide12.Despite the relevance of the pathogen, the disease-management tools available to date are limited. Although no-tilling is known to be a useful tool for lowering disease incidenceVd races overcoming V1 locus resistance, first in Argentina and later in the USA and Europe27. In Argentina, two local races, VArg1 and VArg2, were identified among the isolates affecting sunflower18. Consequently, researchers of the private sector have reported pairs of differential inbred lines bearing specific resistance to these races, giving rise to SVW-resistant hybrids28. However, further reports described less-frequent new races overcoming these resistance sources as well19.During the last 40 years, various studies have reported Even though some commercial hybrids currently display SVW-resistance, the lack of public research on this subject has prevented disentangling the genetic architecture of the trait. Exploring large sets of germplasm and characterizing their behavior against SVW are the first steps towards identifying novel resistance sources to contribute to sunflower breeding. However, no large-scale surveys have been conducted and little is known about the importance of the different DDs  or their heritability, up to this date.32. The AMP was extended for this study by incorporating ILs from the INTA breeding program with good behavior for SVW-resistance formerly identified by breeders on the EEA-INTA-Pergamino using artificial inoculation33. The AMP contains a representative sample of the genetic diversity held in INTA’s sunflower breeding program and comprises 164 ILs. The genetic diversity of the AMP is comparable to that of other sunflower inbred panels from public breeding programs from France, Canada and USA, while containing the singularities of the Argentinian germplasm32.In this study, we evaluated 301 ILs of two mapping populations (MPs) developed by the INTA for SVW Broad Spectrum Resistance (BSR) against Argentinian local races. The first MP is the result of the biparental crossing between two restorer public-ILs PAC2 × RHA439 . The second is the sunflower association mapping panel of INTA (AMP)The aims of the present study were: (1) to assess the diversity held in INTA´s genetic resources for SVW-resistance, (2) to identify inbred lines representing new SVW-resistance sources, (3) to understand the inheritance and (iv) to estimate the heritability of the trait.The phenotypic evaluation for SVW resistance of the two MPs retrieved robust results for 162 ILs from the AMP  and 139 RILs from the BMP (4 FTs in one location). SVW was scored at four Evaluation Dates (EDs). The DDs recorded to evaluate ILs at each ED were: disease incidence (DI), disease severity (DS), binomial disease severity (bDS) and disease intensity (DInt). Estimation of the Area under the Disease Progress Curve for disease incidence (AUDPC.DI) and for disease intensity (AUPDC.DInt) integrated all EDs. The combination of DDs and EDs rendered 18 SVW-DDs to be modeled in the statistical analysis. We selected four of those to illustrate IL’s behavior on the basis of their potential impact on yield components. The selected combinations were DI at flowering (DI.Flw or DI.3), AUDPC.DI, DS at grain filling (DS.Gf or DS.4) and bDS at grain filling (bDS.Gf or bDS.4).The analysis of the raw DI.Flw and DS.Gf values showed similar overall patterns in both MPs, with DI.Flw and DS.Gf ranging from 1.6 to 7.3 plants per plot (p.p.p.) and from 2.9 to 3.9 symptomatic p.p.p., respectively  held values of DI.Flw and AUDPC.DI markedly above the overall mean in season 2016/17 and below it in 2014/15 and 2015/16. Altogether, AUDPC.DI and DI.Flw showed a similar pattern , except for DInt at the first evaluation date in the AMP (DInt.1). The selected model for further analysis was the Row-Column design with Genotype × Season interaction  or DS  derived descriptors  ranked in the bottom ten of the four DDs. The highest level of DI.Flw and bDS.Gf occurred in IL PMA70 . As seen in the resistant group, some ILs exclusively ranked at the bottom of a particular DD. None of the parental lines of the BMP, both present in the AMP, were ranked on the extremes of any DD , we calculated Pearson’s correlation coefficients between Best Linear Unbiased Predictors (BLUPs) of individual FTs, and between these and the G × E adjusted BLUPs, for the main four DDs , whereas the lowest was between the first and the last FTs (0.541)  of the 18 DDs scored in this study, we computed Pearson’s correlations among the IL’s G×E adjusted means on each MP. All coefficients were significant, positive and rather high. Values were always above 0.5 or 0.6 for the AMP and BMP, respectively. Coefficients above 0.95 occurred between some contiguous evaluation dates for DI, DInt and bDS  explained 86.1% of the total variance  in both cases. Further hierarchical and non-hierarchical clustering analyses based on distances and cluster number allowed the distinction of a fourth phenotypic group that was not apparent when analyzing the data on a single variable basis (DGC procedure detected between 2 and 3 classes). The K-mean method, presented here, generated more evenly represented groups for both MPs . In comparison to the remaining DDs, DI.Flw showed the lowest average H2. Furthermore, DS.Gf (H2 = 53.2) and AUDPC.DI (H2 = 43.87) had the highest heritability coefficients for the AMP and for the BMP, respectively. As expected, the AMP showed higher levels for each H2 estimate and larger standard deviations than the BMP. The BMP denoted a progressive increase in the H2 estimates for most of the variables along FTs. In the AMP, this response was evident only for DI.Flw, considering only FTs performed in Balcarce. In general, the H2 estimates obtained at Coronel Suarez were lower than those of Balcarce on the same year. For most DDs, the environments with higher H2 coefficients were FT-2016 and FT-2017B for the AMP, and FT-2015 and FT-2016 for the BMP.The study of broad sense heritability . Since the critical stages for potential grain-yield definition are flowering and grain filling onset, three DDs focused on these stages assessing DI, DS and the combination of both relative to a tolerance threshold . AUDPC.DI, while integrating all DI scores through time, gives a cumulative resistance profile from early stages of each IL, giving higher values to ILs presenting symptomatic plants earlier. The bDS.Gf descriptor seemed to be most suitable for quick disease assessment since it provided a weighted estimation of DS.Vd pathogenic races have underscored the need for BSR sources in sunflower41. The inoculum load measured in the infested field of EEA INTA Balcarce . This notion supports the BSR displayed by the best performing ILs of the AMP. Despite the significant G×E effects, the high correlation coefficients observed among FTs indicate that the ranking of ILs was consistent across years for both MPs. Furthermore, the alternation of the groups of ILs occupying the top ranks for each DDs  described in Filippi et al.32, with the addition of 29 new accessions, thus reaching 164 ILs , from the growing seasons 2013/14 (F6) to 2016/17 (F10). The AMP was evaluated in five FTs, four at EEA-INTA Balcarce (2014/15 to 2017/18) and a fifth (2017/2018) at “El Cencerro” seed company, Coronel Suárez, Buenos Aires .ILs from both MPs were grown under field conditions in artificially infested fields containing a diverse set of Vd inoculum titter of EEA-INTA Balcarce’s infested field was estimated around 900 CFU per gram of soil in season 2015/16  infections. Mechanical and manual controls were implemented for weed management.Plots of each FT consisted of a single 5 m row (± 0.5 m) disposed at an inter-row spacing of 0.7 m with 20 ± 5 plants. Each plot corresponds to an experimental unit. An Alpha-Lattice design in two replications was implemented for all FTsThe DDs recorded to evaluate ILs were DI, DS, bDS, DInt, AUDPC.DI and AUPDC.DInt. DI and DS were scored at individual plant level. DI was assessed as the overall count of symptomatic plants per plot, whereas DS was scored using an ordinal scale of six levels, as follows: “0”, non-symptomatic plant, “1” plant with symptoms at basal leaves , “2” symptoms below middle leaves (20–40% t.l.a.), “3” symptoms reaching middle leaves (40–60% t.l.a.), “4” symptoms in upper leaves (60–80% t.l.a.) and “5”: a totally wilted plant  corresponds to the time elapsed between two observations (days).DInt, bDS and AUDPCs were also calculated according to DI and DS scores. DInt is the per-plot weighted mean resulting from the multiplication of the frequencies obtained for each severity level and the score-level. The descriptor bDS accounts for infected plants with DS above “2” and any plant below this threshold is considered tolerant. AUDPC is the integration of the scores of a particular variable across the evaluation dates, according to the formula described by Shaner and Finney60 Eq. .1\docum39. Therefore, each ED represents a particular phenological stage as follows: ED-1 = Floral initiation to Early floral bud growth (R1–R2); ED-2 = Floral growth to Pre-Flowering (R3–R4); ED-3 = Flowering ; ED-4 = Grain filling 39. DDs are named both with number or acronym indistinctly (i.e.: DI.3 = DI.Flw). These combinations yielded 18 variables: 16 DD × ED combinations and two AUDPCs (for DI and DInt).Disease evaluation was performed weekly from early reproductive stages to grain filling onset, covering four evaluations per FT. Each ED was centered on the most frequent phenological stage among ILs at the time61. The selection of the right linking function when modelling was determined according to the statistical properties of each DDs. DI and bDS are discrete binomial variables and were modeled by logistic linear regressions. DS was transformed to a continuous variable by the Normal Scores method62, and modelled as a normally distributed variable by Linear Mixed Models (LMM). DInt and AUDPCs are continuous variables that were converted to relative proportions of the maximum score reached per plot and per FT, respectively, and modeled as gamma distributed variables. Several models were employed for obtaining IL’s adjusted phenotypic means and BLUPS63 for each DD, in both single and combined FTs . These approaches allowed the comparison between them and the variance components analysis of random-effect models for DDs’ H2.For the estimation of IL’s mean phenotype for each SVW-DD, the statistical analysis at each evaluation date was boarded by Generalized Linear Mixed Models (GLMM)GS” (Genotype-Season) interaction factor. The same models were tested for Alpha-Lattice design, which was obtained by excluding the factor “Column” (C) on (2) and (3).All of these models were fitted after the best model for each DD was found by comparing four models considering two experimental designs, Alpha-Lattice or Row–Column, and the inclusion of a random effect factor term considering G × E interaction under each design. For the Row–Column design, models were compared without (2) and with (3) the “i, in Season j, under Replicate k, within Block m and Column n; μ is the overall mean of observations; Gi is the Inbred Line fixed effect (as random for BLUPs and H2 determination); Sj is the random effect of Season j; k within Season j; m within Replicate k; Cn is the random effect of Column n within Replicate k; and Models 1 and 2 are defined as follows:35. BLUPs were calculated for each IL on every DD both, on each FT and across FTs, to evaluate the phenotypic stability of ILs across FTs and locations. Pearson’s correlation coefficients were used to compare BLUPs among FTs and models. Broad-sense heritability estimates were assessed for each DD considering the individual FT data. For binomial variables, residual variance components were estimated as described in Snijders and Bosker64. LMM and GLMM were built with the lme4 package65 for the R statistical software66. Pearson’s correlations were estimated and visualized using the GGpairs R-package67.The adjusted means were then subjected to multiple comparison tests using the DGC procedureOf the combination of DDs and EDs modeled, mainly four were considered to characterize ILs regarding SVW-BSR in a single-variant fashion. The criterion for selecting them accounted for agronomical issues and took into consideration SVW impact on yield components and the maximization of each DD descriptive capability. These four DDs were: DI.Flw (DI.3), AUDPC.DI, DS.Gf (DS.4) and bDS.Gf (bDS.4).prcomp function of the stats package for the R statistical software66. Both MPs were analyzed with 18 DDs . Results were visualized using the ggfortify R-package68.PCA were carried out using the standardized adjusted means of DDs for each mapping population and the get_dist of package vegan for R software69. The most likely number of clusters was defined using the function fviz_nbclust from the factoextra R-package70. Non-hierarchical clusters were estimated for each MP using the function “kmeans” of the R-package stats66. Finally, a hierarchical approach was performed by using the “hclus” function from the same package, by implementing different linkage methods and comparing them by their co-phenetic correlation coefficients with original distance matrices.The clustering analysis was performed by estimating distance matrices for each MP from the adjusted means of DDs across FTs using the function Supplementary Information 1.Supplementary Information 2.