The availability of genome-wide genetic data on hundreds of thousands of people has led to an equally rapid growth in methodologies available to analyse these data. While the motivation for undertaking genome-wide association studies (GWAS) is identification of genetic markers associated with complex traits, once generated these data can be used for many other analyses. GWAS have demonstrated that complex traits exhibit a highly polygenic genetic architecture, often with shared genetic risk factors across traits. New methods to analyse data from GWAS are increasingly being used to address a diverse set of questions about the aetiology of complex traits and diseases, including psychiatric disorders. Here, we give an overview of some of these methods and present examples of how they have contributed to our understanding of psychiatric disorders. We consider: (i) estimation of the extent of genetic influence on traits, (ii) uncovering of shared genetic control between traits, (iii) predictions of genetic risk for individuals, (iv) uncovering of causal relationships between traits, (v) identifying causal single-nucleotide polymorphisms and genes or (vi) the detection of genetic heterogeneity. This classification helps organise the large number of recently developed methods, although some could be placed in more than one category. While some methods require GWAS data on individual people, others simply use GWAS summary statistics data, allowing novel well-powered analyses to be conducted at a low computational burden. The primary aim of these studies is to identify genetic polymorphisms associated with a quantitative trait or with an increased risk of disease. These genome-wide association studies (GWAS) have led to an important increase in understanding of the underpinnings of psychiatric and other disorders (Sullivan h2SNP), (ii) uncovering of shared genetic control between traits (Estimation of genetic correlation from using genome-wide SNPs), (iii) predictions of genetic risk for individuals (Polygenic risk prediction), (iv) uncovering of causal relationships between traits [Mendelian randomisation (MR)], (v) identifying causal SNPs and genes (Fine-mapping and gene prioritisation), or (vi) Detection of genetic heterogeneity. There is often an overlap between these applications: some methods can be applied for more than one purpose, and some of the available software implements more than one method. In this overview, it is not possible to be exhaustive, and we apologise to authors whose methods have not been included.Here, we present an overview of some recently developed methods utilising genome-wide genotype and phenotype data on large numbers of individuals and show how they can be applied to the research of psychiatric disorders. These new methods serve at least one of the following purposes: (i) estimation of the extent of genetic influence on traits (Estimation of proportion of variance attributable to genome-wide single-nucleotide polymorphisms (SNPs), SNP-heritability or p value of association and allele frequency of each SNP. While it has been shown that it is possible to infer whether an individual was part of a cohort using summary statistics, the power to do so is limited  data. Genomic annotation can give clues about the functional importance of a region in which a SNP resides, whereas eQTL data are the result of an association test where the phenotype analysed is expression of a gene.Here we review a range of different polygenic methods and highlight their aims and the input data they require . For eacet al.et al.Heritability is the proportion of phenotypic variance that can be attributed to genetic factors. It is a key quantity in genetics research as it summarises the role of causal inherited variation. Trait heritability can be estimated by comparing the phenotypic resemblance among family members to their coefficients of relationships. However, estimating heritability from relatives can result in upwards-biased estimates  for which it is difficult to collect the large samples needed to calculate heritability from estimates of increased risk in relatives of those affected. Both traditional-heritability and SNP-heritability estimates are presented on the liability scale (and depend on lifetime risk of disease in the population), and empirical data of the GWAS era , which includes a component for the construction of the genetic relationship matrix and a component for the actual REML algorithm. The steep increase in runtime with larger N makes it impractical for data sets with very large numbers of individuals. The software BOLT-LMM can estimate variance components through a stochastic approximation algorithm, which circumvents the costly calculation of a genetic relationship matrix and thus reduces the runtime to only O(MN1.5)  of estimates. The s.e. of GREML SNP-heritability estimates is accurately approximated as 316/N, where N is the total sample size  since only summary statistics are needed. Comparisons of estimates from GREML and LDSC show that the accuracy of estimates from LDSC are dependent on LD scores calculated from a population representative of the population used to estimate GWAS summary statistics   are of interest because they suggest a shared aetiology. However, misdiagnosis between two genetically uncorrelated diseases can generate significant estimates of rG . The power of bivariate GREML analyses to detect rG departing from 0 or from 1 depends on the population value of rG, the SNP-heritability of both traits, on the sample sizes, on whether the same or different samples are used for the two traits, and for disease traits, on the proportion of cases in the sample  and hence scale transformation is not needed . It has also been applied to two data sets of the same disease, where rG is expected to be one  Joyce, . Howeveret al.et al.LDSC has been applied to a full battery of GWAS summary statistics and report higher estimates of genetic correlations estimates between pairs of psychiatric disorders than between psychiatric- and non-psychiatric disorders  genetic architecture and size of the discovery sample. In the latest schizophrenia GWAS, the optimum p value threshold was identified as 0.05 (based on variance explained in out-of-sample prediction across many samples), although inclusion of all SNPs did not lower accuracy drastically . Prediction accuracy can be improved by using methods that provide estimates of SNP effects that are conditional on all other SNPs, thereby directly taking SNP LD correlations into account  into penalised, conditional SNP effects, by making use of an LD reference data set  utilising all these kinds of information has recently been applied to 21 autoimmune disorders and identified many putative causal variants by integrating information from different types of functional annotations, including epigenetic marks and gene expression information . Gene-based tests have a reduced multiple-testing burden (~20 000 independent tests) and give biological meaning to association results. In gene-based tests SNPs are aggregated into larger groups  method  sets of risk loci, and the genetic risk profile of one or more subgroups of cases departs from that of the rest. This may arise through misclassification of some cases, or through distinct aetiological pathways leading to the same diagnosis .Although the concept of identifying genetic sub-groups is intuitively appealing simulations suggest it is very difficult to find meaningful genetic groupings if each sub-group has a genetic architecture of a large number of loci with small effects. The large number of combinations of risk loci, their small effect sizes, the uncertainty about the size or even about the presence of genetically heterogeneous groups, and the challenging disentanglement from population stratification all contribute to the difficulty of this problem. As a result, even data sets comprising hundreds of thousands of individuals may not provide sufficient power for naïve approaches to detecting even the simplest scenarios of genetic heterogeneity  are correlated because the shared genetic risk factors are present in the whole sample (pleiotropy) or are confined to only a subgroup of individuals (identifiable genetic sub-type)  based on non-genetic data and then testing for genetic heterogeneity between these disease subtypes. A recent method follows this approach by jointly modelling the probability for each SNP of whether its frequency differentiates cases and controls and/or differentiates disease subgroups. Applied to type 1 diabetes, this method suggests that cases with and without autoantibodies exhibit a different genetic architecture for type 1 diabetes disease risk (Liley et al.The BUHMBOX method was used to investigate the shared genetic basis between MDD and schizophrenia, and found no evidence that suggested that a subset of MDD cases was genetically more similar to schizophrenia cases, implying that the genetic correlation estimated between the disorders reflect pleiotropy (Han et al.et al.p value threshold for almost all investigated traits. However, it is also becoming clear that the bulk of genetic risk factors remains hidden among those loci that do not achieve genome-wide significance. Many of the methods presented in this review leverage the large amount of information that is harboured by genetic variants, regardless of whether or not they achieve significance. While the focus of some methods is on individual SNPs or genes, other methods aggregate over a potentially large number of loci to answer questions such as ‘What is the combined genetic effect of all measurable SNPs on phenotypic variance?’, ‘Do these traits have a shared genetic aetiology?’ or ‘Do these traits causally influence one another?’. One thing that all of these methods have in common is that their utility crucially depends on the power to detect an association, which in turn depends on sample size. Larger sample sizes lead to a higher computational burden, but for most analytical questions, which have been presented here, there are methods which can utilise summary statistics and thus drastically reduce runtime and memory requirements.For many psychiatric disorders, genetic factors explain more variation in disease risk in the population than any other known risk factors (Sullivan et al.The literature on new methods is ever-growing and while we have tried to present an overview of key methods to help navigation of this complex field, it is difficult to be fully exhaustive. In particular, pathway analyses are important application of GWAS summary statistics, but these methods have been reviewed recently (Wang


Focusing on these correlations, we used two sample MRs to detect the causal relationships between trait pairs by treating single nucleotide polymorphisms as non-modifiable risk factors underlying both phenotypes. Significant bidirectional associations were detected  and the latent causal variable analyses did not support a causal relationship independent of the genetic correlations between these traits. This suggests that molecular pathways contribute to the genetic overlap between these traits. Dopamine transport enrichment  and DRD2 association  were detected in SCZ and CompGaming and support their negative correlative relationship. FOXP2 was significantly associated with ADHD (p = 9.32 × 10−7) and PhoneUse (p = 9.00 × 10−11) with effect directions concordant with their positive genetic correlation. Our study demonstrates that epidemiological associations between psychiatric disorders and CDUs are due, in part, to the molecular mechanisms shared between them rather than a causal relationship. Our findings imply that biological mechanisms underlying CDU contribute to the psychiatric phenotype manifestation.Computerized device use (CDU) is societally ubiquitous but its effects on mental health are unknown. We performed genetic correlation, Mendelian randomization, and latent causal variable analyses to identify shared genetic mechanisms between psychiatric disorders  and CDU . Using linkage disequilibrium score regression, we detected strong genetic correlations between “ The information revolution has contributed to humans’ reliance on technology to store and transmit ideas to the point where computerized device use (CDU) is societally ubiquitous . While tWe hypothesize that genetic information underlying CDU and psychiatric disorders explains the epidemiological relationships between them. We tested this hypothesis by investigating causal relationships, genetic overlap, and shared molecular mechanisms linking these traits using GWAS summary association data  generateFor approximately how many years have you been using a mobile phone at least once per week to make or receive calls? (UKB Field ID: 1110), Over the last three months, on average how much time per week did you spend making or receiving phone calls on a mobile phone? , Hands-free device/speakerphone use with mobile phone in the last three months (UKB Field ID: 1130), Difference in mobile phone use compared to two years previously (UKB Field ID: 1140), and Plays computer games . All phenotypes have been represented in the biobank as ordinal measures of CDU behavior and the specific categorical distributions for UKB participants of all ancestries are shown in https://github.com/hail-is/hail) including the first 20 ancestry principal components, sex, age, age2, sex × age, and sex × age2 as covariates. Additional details regarding quality control criteria and GWAS methods for the UKB and PGC are available at https://github.com/Nealelab/UK_Biobank_GWAS/tree/master/imputed-v2-gwas and https://www.med.unc.edu/pgc/results-and-downloads, respectively. Details regarding the original GWAS analyses and findings from PGC are described in previous articles [Summary statistics of five CDU measures and eight psychiatric disorders  to minimize correlation among genetic instruments, which may bias downstream causal estimates between phenotypes. The region encoding the major histocompatibility complex was removed from this process due to its complex linkage disequilibrium structure. Note that (1) outputs from PRSice v1.25 were not interpreted for predictability of an outcome trait using genetic data from an exposure trait, as is the typical application of PRSice v1.25 and (2) Trait 1 predicts Trait 2 differently than Trait 2 predicts Trait 1 and as such, the number and identity of the genetic instruments identified through these analyses may be different for each trait pair and each analysis direction for any given trait pair.PRSice v1.25  was usedp-value threshold from PRSice v1.25 . This approach has proven well-powered for overcoming the combination of high polygenicity and relatively few, and weakly associated, risk loci contributing to psychiatric disorders relative to a more traditional MR approach strictly using genome-wide significant loci in the genetic instruments [The causality between CDU and psychiatric disorders was evaluated using two-sample Mendelian randomization (MR) with genetic instruments included based on the best-fit truments ,32,33,34truments , median truments , and modtruments  with vartruments  and the truments ,40. Apprtruments , and MR truments . It shouMultivariable MR was performed using the MendelianRandomization R package ,43 and gz-score converted per-variant effects and regression weights for genome-wide summary statistics. LCV uses genome-wide data (rather than select genetic instruments) to evaluate whether causal estimates  are independent of the genetic correlations  between traits.The latent causal variable (LCV) model  was usedβ) causes, or protects against, a portion of the risk for exhibiting/developing Trait 2.It should be noted that MR and LCV causal estimates can be interpreted as Trait 1  using Multi-marker Analysis of GenoMic Annotation (MAGMA v1.06) [p < 5 × 10−8, minor allele frequency ≥ 0.01, and LD blocks merged at <250 kb for LD r2 ≥ 0.6 with lead variant. Z-tests were used to determine differences in gene set enrichment and genetic correlations. The SNPs underlying each trait were mapped to genes within a 10 kb physical proximity and were assessed for enrichment of ~17,000 gene sets.GWAS summary statistics were annotated for (1) physical proximity to genes (window size = 10 kb) and (2) enrichment of molecular pathways and gene ontology (GO) annotations (A v1.06) , implemeA v1.06)  with theh2z-scores greater than 4 indicating appropriate power for LDSC . MR studies using psychiatric phenotypes are prone to detecting relatively small effect estimates and therefore often meet levels of nominal significance [p-values from MR analyses, we selected the two trait pairs with highest genetic correlation p−values: (1) ADHD versus PhoneUse;  and (2) schizophrenia versus CompGaming . All CDUs were significantly heritable with for LDSC . A totalificance ,47,48,49PhoneUse in females  than in males .Sex−stratified genetic correlation  than the reverse relationship . These effects were significantly stronger for CDU→PsychiatricDisorder than the reverse direction . When stratified by sex, the female PhoneUse→ADHD relationship was nearly 4-fold greater than that in males .PRSice v1.25 was used to select genetic instruments with which to perform MR analyses . The PRSCompGaming and ADHD→PhoneUse relationships given the genetic correlations of other psychiatric disorders with the same CDU. When adjusted for ADHD diagnosis, only the negative causal estimate between ASDADHD→PhoneUse remained significant  SCZ and CompGaming, and (3) ASD and PhoneUse (selected due to multivariable MR significance detected above) were used to estimate the genetic causality proportion (gĉp) using regression weights via the LCV model . Partial causality estimates between ADHD, ASD, and PhoneUse were also not significant, so we concluded that the causal estimates between CDU traits and psychiatric disorders, as detected above with MR approaches, were not independent of the genetic correlations between them . Sex-stratified heritabilities were insufficient to reliably estimate the gĉp between trait pairs.Full GWAS summary data for the relationship between (1) ADHD and CV model . LCV resThe combination of (1) bidirectional causal estimates between trait pairs using MR and (2) lack of genome-wide causal estimates that are independent of genetic correlations  suggest that the trait pairs tested share underlying biological features  rather than causal relationships . These sCompGaming have a negative genetic correlation suggesting shared genetic architecture, perhaps with opposing effects, contributing to phenotype. Using a SNP-based approach, one variant (rs62512616) met genome-wide significance in both SCZ  and has opposite effects in SCZ . Using a gene-annotation approach, significant variants mapped to a total of 13 genes; one (rs2514218) and three  lead variants from the SCZ and CompGaming GWAS, respectively, mapped to the dopamine receptor D2 (DRD2) gene. In addition to DRD2 and TSNARE1, 11 other genes met genome-wide significance for both traits in a gene-based GWAS  and MAPK14 targets (p = 1.25 × 10−8); and the PID p38-gamma and p38-delta pathway (p = 1.96 × 10−8) and synapse structure, plasticity, function, and neurotransmitter signaling  p = 2.17 × 10−13; GO:0015872 dopamine transport p = 2.74 × 10−10; GO:0007186 G-protein coupled glutamate receptor signaling pathway p = 7.22 × 10−7; and GO:0006836 neurotransmitter transporter activity p = 2.40 × 10−6). The six and seven gene set enrichments surviving Bonferroni correction in SCZ and CompGaming, respectively, are provided in Twenty-two molecular pathways showed differential enrichments in line with the negative association between SCZ and CompGaming association, genetic correlation was calculated between 4085 phenotypic traits and SCZ/CompGaming (CompGaming in line with the negative association between these traits (CompGaming after Bonferroni correction (p < 1.22 × 10−5): “number of correct matches in round (pairs matching)” , “usual side of head for mobile phone use ” , and “reason for reducing amount of alcohol drunk: other reason” . Two hundred and sixty-three and 348 UKB traits were independently significantly associated with SCZ and CompGaming, respectively, after Bonferroni correction , females , and males . Using a SNP-based approach, there was no overlap in genomic risk loci or individual significant variants between ADHD ) in ADHD [HCRTR2) [PhoneUse) and language/speech development/impairment (Semaphorin 6D (SEMA6D) in ADHD [FOXP2) in ADHD and PhoneUse [FOXP2 was the only gene-based genome-wide significant result in both ADHD (p = 9.32 × 10−7) and PhoneUse (p = 9.00 × 10−11). Conversely, FOXP2 (p = 0.084) was not significant in the ASD GWAS , an observation in line with the independent associations of ADHD and ASD with PhoneUse (ADHD and  in ADHD  and hypo[HCRTR2)  in PhonePhoneUse ). FOXP2 PhoneUse . PhoneUse males were insufficiently powered to identify genome-wide significant variants so these data were explored using a suggestive threshold of p < 1 × 10−5 , “smoking status: never” , and “qualifications: college or university degree” . These genetic correlations were replicated in females and males . Differential gene set enrichments and genetic correlations between ASD and PhoneUse that survived multiple testing correction were observed but did not represent any singular molecular mechanism as was observed for dopamine enrichment between SCZ and CompGaming  also demonstrate several significant genetic correlations. We hypothesize that the overlap between the CDU traits genetically correlated with ADHD and ASD are likely due to the high genetic correlation between ADHD and ASD. The observations involving MDD were relatively weak in magnitude but had a very different correlated architecture than those of ADHD, ASD, and SCZ, which warrants additional future study.CompGaming and (2) ADHD and PhoneUse. The genetic overlap detected between SCZ and CompGaming suggests that these phenotypes share underlying molecular mechanisms, which contribute to the two phenotypes. For example, enrichment of dopamine transport was detected with several analytic procedures . Gene ontologies were significantly enriched in SCZ and CompGaming but had opposite directions of the effect, which suggests that genes contributing to dopamine transport work in opposing ways to produce the schizophrenia and computer gaming phenotypes. Gene-based GWAS analyses implicated DRD2 and TSNARE1 in the SCZ and CompGaming protective relationship. Our discussion focuses on this repeated detecting of dopamine processes. Genetic variation in DRD2 and imbalance of the dopamine transport system in various brain regions are readily observed in SCZ patients [2 dopamine receptors (D2R), the protein product of DRD2 [CompGaming negative relationship. TSNARE1 has recently shown synergistic effects with other putative SCZ risk loci but interactions between TSNARE1 and DRD2 remains unknown. Based on the findings of this study and the current literature, in SCZ patients lacking a secondary addiction diagnosis, the pairs-matching component of video and/or computer game playing may increase D2R density (perhaps through synergistic TSNARE1 mechanisms) in specific brain regions, thereby increasing grey matter density in the region over time and providing protective effects against SCZ symptoms.Among the trait pairs tested for causal inference, we detected bidirectional causality using MR methods and the LCV analyses did not support a causal relationship independent of the genetic correlations between these traits. We therefore focused our analyses on the underlying biology that may be responsible for strong genetic correlations between CDU and psychiatric disorders. Specifically, we investigated the two strongest genetic correlations: (1) SCZ and patients  and most of DRD2 . Studies of DRD2 . SCZ patPhoneUse and ADHD with sex-stratified differences. FOXP2 was the only gene overlapping the ADHD and PhoneUse GWASs. The FOXP2 locus encodes a brain-expressed transcription factor strongly implicated in the human ability to communicate via complex speech [PhoneUse in the combined and sex-stratified cohorts, including “age at first sexual intercourse” and “risk taking.” While these traits were detected by the PGC ADHD GWAS of both sexes combined [PhoneUse is perhaps best explored in the sex-stratified cohorts. The genetic correlation between males and females with ADHD is quite high, initially suggesting that the same set of common variants may contribute to ADHD in both sexes; however, this difference has previously been attributed to differences in ADHD prevalence between sexes [We observed strong evidence of genetic overlap between x speech . Additiocombined , the conen sexes . BecauseFOXP2 expression is dampened by androgen/testosterone expression and androgen receptor density is modulated by testosterone [FOXP2 also is expressed in brain regions in which androgen receptor density is highly responsive to androgen/testosterone [age at first sexual intercourse” and “risk taking” as genetic correlates herein and in additional studies also complements testosterone or related mechanisms with PhoneUse and ADHD [FOXP2 expression differences, resulting in observations of stronger genetic correlation estimates between ADHD and PhoneUse in females. Sex hormones are implicated in various aspects of human speech, including pitch, overall voice profile, and vocal structural anatomy, many of which are influenced by androgen/testosterone expression differences between males and females . In rateosterone . FOXP2 aosterone . The preand ADHD . These dPhoneUse). We did not test other potential mediating variables. This means that, although it seems unlikely due to the several sensitivity analyses conducted, an unaccounted mediator may have a causal effect on both psychiatric disorders and CDU phenotypes, generating the genetic correlation observed. Lastly, although we have corrected for multiple testing, selection of an appropriate p-value threshold with which to determine a result significant is perhaps confounded by lack of independence between genetic correlations and MR. With respect to our study, a Bonferroni threshold (p < 0.001) would not change our interpretation of the results presented. Future MR studies will need to address suitable multiple testing correction strategies given the high number of tests performed .Our study has several limitations. First, although sample sizes for large-scale genetic studies are capable of reducing the effects of psychiatric disorder heterogeneity on analogous epidemiological studies , GWAS are not devoid of the effects of diagnostic heterogeneity that may exist in the GWAS used for this study. A potential consequence of this heterogeneity may indeed be sufficient genome-wide noise to mask the strength of causal relationships, such as those not detected with the LCV approach using genome-wide variants rather than targeted variants. This may be especially true for use of computerized devices, which have substantial ascertainment differences between older and younger individuals. Therefore, our lack of detection of causal relationships between CDU and psychiatric disorders may be restricted to adults. It remains undetermined if similar observations are true in youths who have more ubiquitous CDU behavior across their lifetime. Second, the use of reduced genome-wide significance thresholds for selecting MR instrumental variables is an unconventional procedure. However, when accompanied by rigorous sensitivity testing of SNPs with effect size outliers, tests for horizontal pleiotropy, and causal inference tests specifically designed to overcome weak instruments, the approach employed here adheres to MR assumptions . AlthougOur findings demonstrate that CDUs have strong genetic overlap with psychiatric disorders , though


Infectious hematopoietic necrosis (IHN) is a disease of salmonid fish that is caused by the IHN virus (IHNV). Under intensive aquaculture conditions, IHNV can cause significant mortality and economic losses. Currently, there is no proven and cost-effective method for IHNV control. Clear Springs Foods, Inc. has been applying selective breeding to improve genetic resistance to IHNV in their rainbow trout breeding program. The goals of this study were to elucidate the genetic architecture of IHNV resistance in this commercial population by performing genome-wide association studies (GWAS) with multiple regression single-step methods and to assess if genomic selection can improve the accuracy of genetic merit predictions over conventional pedigree-based best linear unbiased prediction (PBLUP) using cross-validation analysis.Ten moderate-effect quantitative trait loci (QTL) associated with resistance to IHNV that jointly explained up to 42% of the additive genetic variance were detected in our GWAS. Only three of the 10 QTL were detected by both single-step Bayesian multiple regression (ssBMR) and weighted single-step GBLUP (wssGBLUP) methods. The accuracy of breeding value predictions with wssGBLUP (0.33–0.39) was substantially better than with PBLUP (0.13–0.24).Our comprehensive genome-wide scan for QTL revealed that genetic resistance to IHNV is controlled by the oligogenic inheritance of up to 10 moderate-effect QTL and many small-effect loci in this commercial rainbow trout breeding population. Taken together, our results suggest that whole genome-enabled selection models will be more effective than the conventional pedigree-based method for breeding value estimation or the marker-assisted selection approach for improving the genetic resistance of rainbow trout to IHNV in this population. IHNV iso salar) . Under io salar) , 7. Curr((O. mykiss) population [The additive genetic basis for IHNV resistance is evident from the moderate estimates of heritability for IHNV survival status entclass1pt{minimapulation . NonetheFew quantitative trait loci (QTL) mapping studies have been conducted to identify genetic polymorphisms that are associated with IHNV resistance in rainbow trout. A number of moderate-large-effect QTL associated with IHNV resistance were found on 12 rainbow trout chromosomes using linkage analysis , 10–12 aThere is ambiguity on the best computational algorithm when using multiple-regression based models in GWAS and genomic selection (GS) studies because the genetic architecture of the trait and the population structure can have a major impact on power to detect marker effects and on the accuracy of genomic predictions. Therefore, it is important to compare the results from the best available computational methods when elucidating the genetic architecture of a complex disease trait for the first time in a population. This approach will ensure the effective discovery of QTL underlying the genetic basis of the disease under study, and better control of the type I error rate, which is often high in GWAS.In multiple-regression based GWAS models that fit all single nucleotides polymorphisms (SNPs) with high quality genotypes, the genomic best linear unbiased prediction (GBLUP) method assumes that all SNPs have a non-zero contribution to the variance of the trait of interest, with equal variance for each SNP, and that the distribution of the SNP effects follows a normal distribution –21. In ap matrix , 23. Conp matrix –26. Basep matrix . More rep matrix , 28–30, p matrix , 29.The development of these GWAS methods, along with the development of the rainbow trout 57 K SNP array , a denseThis study used rainbow trout fin clips that were collected after controlled exposure to IHNV, as part of a selective breeding program at the Clear Springs Foods Inc. research facility. As farm animals used in a commercial breeding program, these fish are exempted from regulation under the US Animal Welfare Act and were therefore not subject to oversight by an Institutional Animal Care and Use Committee or other such ethics committee. This exemption is defined in US Code title 7, chapter 54, section 2132g. However, experimentation and handling were conducted in accordance with US government principles for the use and care of vertebrate animals used in testing, research, and training, which includes provisions to minimize animal suffering. Specific measures for amelioration of animal suffering during the fish pathogen testing  included minimization of handling, maintenance of optimal water temperature and oxygen saturation, and the fish were fed a standard fish meal diet to satiation daily. Fish near death with severe symptoms of infection during the observation period were removed and euthanized  before collection of fin tissue to minimize suffering. After the 3-week observation period, surviving fish were euthanized by immersion in a lethal dose of MS222 before sampling and disposal.Samples were collected from disease-naïve parents and their disease challenged offspring fish in brood years 2014 and 2016, respectively, by staff at the Clear Springs Foods Inc. research facility in Buhl, Idaho. Briefly, healthy fish from the previous generation were artificially spawned to produce ~ 5000 fertilized eggs from 100 year-class (YC) 2016 families. Fin tissue samples from each parent fish were collected at the time of spawning. The offspring were grown to ~ 1 g (62 days post-fertilization) and 50 fish per family were selected randomly for disease challenge and were infected with IHNV by immersion into a volume of water equivalent to 10× the total body weight of the fish in g containing 10,000 plaque-forming units of IHNV per mL for 1 h (IHNV isolate 220-90). After exposure, the fish were moved to 19-L tanks by family (50 fish/family/tank), because young and small fish cannot be labeled individually, and monitored for a 21-day period, with mortality recorded daily. Fin tissue samples were collected from mortalities during the 21-day monitoring period and survivor samples were taken at the end of the challenge. Fin clips from all fish  were individually kept in 95% ethanol until DNA was isolated using already described protocols .n = 4987 fish. The associated pedigree file included 6308 records.The fish used in the GWAS included 100 pedigreed full-sib (FS) families from year-class (YC) 2016 of the commercial breeding company Clear Springs Foods, Inc. (CSF). These 100 FS families included 41 paternal half-sib (HS) families and four maternal HS families and were generated using 78 sires and 98 dams. Fifty-nine families were generated by mating each of 59 sires with a single dam. Among the families that were generated by mating a sire with multiple dams: 17 sires were mated with two dams, one sire was mated with three dams, and another sire was mated with four dams. These YC 2016 families represented a commercial nucleus breeding population that was undergoing intensive selection for growth and IHNV resistance for the past eight generations, since 2000. The fish were evaluated for IHNV survival in the laboratory challenge, with one tank per family with an initial stocking of 50 fish per tank. The 100 families were evaluated by groups of 7 to 10 families at 11 challenge dates. After the IHNV challenge, the dataset included IHNV resistance phenotype records on The discrete IHNV resistance phenotype survival days (DAYS), i.e. the number of days post-challenge until the fish succumbed to the disease, was recorded for all mortalities, while survivors were assigned a value of 21. Each fish also had a binary survival status (STATUS) record. The resistance phenotype STATUS had two categories: 1 for fish that died during the 21 days post challenge evaluation period; and 2 for fish that survived for the duration of the challenge. The DAYS and STATUS records were analyzed separately using the univariate GWAS models described below.P < 0.05) and removed offspring fish that did not have matching genotypes with the parents given in the pedigree (i.e. that did not pass the pedigree check). After this initial genotype data QC, 42,045 SNPs were included in the raw chip genotype dataset.The fish sampled from the CSF population were genotyped using the Rainbow Trout Affymetrix 57K SNP array (Chip) following previously described procedures  by a comBefore conducting GWAS analyses, the raw marker genotype dataset was further QC filtered using the algorithms that are implemented in the software BLUPF90 . The QC n = 4987) were fit to an animal linear and a threshold model, respectively, to estimate genetic variance parameters for IHNV resistance phenotypes. The variance components analysis was performed using pedigree-based BLUP (PBLUP) and PBLUP with genomic data (ssGBLUP) under a Bayesian framework, using computer applications from the BLUPF90 family of programs [The phenotypes of DAYS and STATUS . For the next iterations , the weights are SNP-specific variances that are calculated using the estimate of the SNP allele-substitution effect from the preceding iteration and the corresponding SNP allele frequencies . Estimatmated as : \documedictions  and markdictions , 42, 43.We fitted mixed linear and threshold models for DAYS and STATUS, respectively, using the following animal model: r et al. , and itsr et al. , 39.The full-sib fish progeny from each family were allocated to one tank for IHNV challenge evaluation, so the tank and family effects were confounded. The 100 tested families were evaluated in 11 challenge dates (date), with 7 to 10 families per date. This nested random family/date effect was used to account for the common environment effect.The GWAS for DAYS and STATUS was also performed using Bayesian methods implemented in the BLUPF90 family of programs . The GWAWe performed GWAS for IHNV survival DAYS with a single-step Bayesian multiple regression (ssBMR) model using 1-Mb non-overlapping SNP windows , 29. TheIn GWAS for DAYS, we fitted this Bayesian linear mixed model: The GWAS for DAYS was performed with ssBMR using the Bayesian variable selection BayesB method (BayesB)  implemenve trait . In our on model . In thisMethod BayesB uses MCMC Gibbs sampling in the GWAS analysis . The GibThe results from the analyses conducted with wssGBLUP and ssBMR were used to identify 1-Mb SNP windows that are associated with IHNV resistance. A two-step approach was used to identify QTL associated with IHNV resistance. First, the 1-Mb windows that explained additive genetic variance (EGV) higher than 2% of the total were defined as associated with IHNV resistance, and 1-Mb windows with 1%  mapping –48 and G mapping  in rainbThe accuracy and bias of breeding value predictions for DAYS and STATUS were estimated using five-fold cross-validation (CV) analysis. Briefly, the fish with phenotype and genotype records  were detected using either the wssGBLUP or the ssBMR GWAS methods  were detected by both GWAS methods Table . InteresThe physical map coordinates of the identified QTL regions for IHNV resistance Table  were queThe 1-Mb windows that were detected with EGV ≥ 1% for IHNV resistance using wssGBLUP and ssBMR are compared in Table S3  and wssGBLUP (0.33–0.39) than for the pedigree-based PBLUP model (0.13–0.24)  for DAYS (0.27–0.50) than for STATUS (0.11–0.14) Table . For surIn this genome-wide association study, we detected 10 QTL regions that were associated with IHNV resistance and that together explained up to 42% of the additive genetic variance for IHNV resistance in a commercial rainbow trout breeding population. Only two of these QTL (17.1 and 25.1) were reported in previous studies  in terms of accuracy. These results highlight the potential utility of genome-enabled selection for genetic improvement of resistance against IHNV in this commercial rainbow trout breeding population.Estimates of heritability for IHNV resistance in our study were moderately 0.23–0.33) and somewhat lower than those reported previously using pedigree-based model in a different rainbow trout population 3–0.33 an, 54.In this study, we did not detect large-effect QTL for IHNV resistance, which suggests that marker-assisted selection is not likely to be an effective strategy for improving the genetic resistance of rainbow trout against IHNV in this specific commercial population. However, further fine-mapping of the detected IHNV-QTL regions and subsequent identification of putative candidate genes or causative mutations would be valuable for advancing the understanding of the mechanisms of genetic resistance to IHNV in rainbow trout and the underlying biology of host–pathogen interactions. This can be achieved by genotyping a large number of SNPs at positions within and near the major QTL regions, and by re-sequencing highly characterized IHNV resistant and susceptible fish, as was effectively done in the search for the IPNV resistance gene in Atlantic salmon . We genePrevious QTL mapping studies have reported 36 QTL for IHNV resistance in rainbow and steelhead trout backcross populations  associated with DAYS Fig.  than theis study , 29, incAcross the evaluated methods, the accuracy of breeding value predictions for STATUS were higher than those for DAYS Table . HoweverThe accuracy of pedigree-based EBV predictions for STATUS was 0.24 when performing CV analysis using IHNV records from the 2016 CSF families Table . RemarkaOverall, accuracies of genomic predictions for DAYS and STATUS with wssGBLUP (0.33–0.39) and ssGBLUP (0.30–0.34) were higher than those from the pedigree-based PBLUP model used with both CV analysis (0.13–0.24) and historical progeny performance data (0.25). Therefore, these results show the high potential for effective genetic improvement of IHNV genetic resistance using genome-enabled selective breeding in this commercial rainbow trout population.Our comprehensive genome-wide scan for QTL for IHNV resistance revealed that genetic resistance to IHNV in this commercial rainbow trout breeding population is controlled by the oligogenic inheritance of up to 10 QTL with moderate effects and many loci with small effects. Taken together, our results suggest that whole-genome-enabled breeding value prediction models will be more effective than the conventional pedigree-based prediction method or the marker-assisted selection approach for improving genetic resistance of rainbow trout against IHNV in this economically important breeding population.Additional file 1: Table S1. Summary of the genomic windows with an explained genetic variance (EGV) higher than 1% for IHNV resistance phenotypes (DAYS and STATUS) detected in the Clear Springs Foods, Inc. rainbow trout breeding population. The data provided represents all the genomic windows that had an EGV higher than 1% when performing GWAS for IHNV survival DAYS and STATUS using weighted single-step GBLUP (wssGBLUP) and single-step Bayesian multiple regression (ssBMR) in the Clear Springs Foods, Inc. rainbow trout breeding population.Additional file 2: Figure S1. Manhattan plot showing the association between 1-Mb sliding-windows and IHNV survival STATUS in Clear Springs Foods, Inc. rainbow trout breeding population: (a) GWAS using ssGBLUP. (b) GWAS using wssGBLUP. These plots represent the explained additive genetic variance for IHNV survival STATUS by each 1-Mb-sliding window that was tested along the rainbow trout genome with single-step GBLUP (ssGBLUP) and weighted single-step GBLUP (wssGBLUP) in the Clear Springs Foods, Inc. rainbow trout breeding population.Additional file 3: Table S2. List of the 774 genes that are located within the 1-Mb windows that are associated with IHNV resistance in the Clear Springs Foods, Inc. rainbow trout breeding population. This data represents a list of 774 genes that are located in the QTL windows associated with IHNV resistance generated by query of the rainbow trout reference genome sequence annotation (RefSeq Accession GCF_002163495.1).Additional file 4: Table S3. Comparison of genomic windows with an explained genetic variance (EGV) higher than 1% for IHNV survival DAYS and QTL regions detected with wssGBLUP and ssBMR in the Clear Springs Foods, Inc. rainbow trout breeding population. The data provided represent all the genomic windows that had an EGV higher than 1% when performing GWAS for IHNV survival DAYS using weighted single-step GBLUP (wssGBLUP) and single-step Bayesian multiple regression (ssBMR) in the Clear Springs Foods, Inc. rainbow trout breeding population. Table S3 was built to perform a fair comparison between wssGBLUP and ssBMR GWAS methods.Additional file 5: Table S4. Summary of QTL for IHNV resistance reported in previous studies in rainbow trout populations. The data provided represent a summary of all reported QTL for IHNV resistance in past QTL/GWAS studies performed in rainbow trout populations. This table provides information on experimental variables including sample size, number of markers, disease phenotype, method of analysis, software, location of reported QTL on genetic and physical maps, and statistics such as nominal P value and LOD score for the reported QTL.Additional file 6: Figure S2. Manhattan plot showing the association between 1-Mb exclusive-windows and IHNV survival DAYS using the BayesB method in Cold Springs Food, Inc. rainbow trout breeding population. These plots represent the explained additive genetic variance for IHNV survival STATUS by each 1-Mb-exclusive window when using the standard BayesB method with complete data  in the Clear Springs Foods, Inc. rainbow trout breeding population.Additional file 7: Table S5. Historical accuracy of pedigree-based EBV predictions for IHNV resistance in Clear Springs Foods, Inc. rainbow trout breeding population. These data represent the historical accuracy of animal merit predictions that were estimated with the pedigree-based PBLUP model using empirical progeny survival records from IHNV challenges performed in year-class 2008, 2010, 2012, 2014 and 2016 families in the Clear Springs Foods, Inc. rainbow trout breeding population. The accuracy of predicted EBV are estimated as the correlation coefficient of mid-parent, sire or dam EBV with their offspring or progeny survival rate.


Most studies, however, ignore phenotypical diversity by characterizing entire species considering only trait means rather than their variability. Phenotypical variability may arise from genotypical differences or from ecological factors , and these causes can usually not be separated in natural populations. We used a single genotype from a parthenogenetic model system (the oribatid mite Archegozetes longisetosus Aoki) to exclude genotypical differences. We investigated patterns of dietary (10 different food treatments) induced trait variation by measuring the response of nine different traits .Trait based functional and community ecology is Nutritional quality (approximated by carbon-to-nitrogen ratios) influenced all trait means and their variation. Some traits were more prone to variation than others. Furthermore, the “threshold elemental ratio”- rule of element stoichiometry applied to phenotypic trait variation. Imbalanced food  led to lower trait mean values, but also to a higher variation of traits.Imbalanced food led not only to lower trait value averages, but also to higher trait variability. There was a negative relationship between both parameters, indicating a direct link of both, average trait levels and trait variation to nutritional quality. Hence, variation of trait means may be a predictor for general food quality, and further indicate trade-offs in specific traits an animal must deal with while feeding on imbalanced diets.The online version of this article (10.1186/s12983-018-0297-2) contains supplementary material, which is available to authorized users. This  ecology , 8, and  ecology , 10. Nat ecology , 12. Fur ecology , 11. Tha ecology , 14).Theory , but alsArchegozetes longisetosus ran lineage, which reproduces via automixis with an inverted meiosis resulting in clonal offspring [Here, we harness the power of a unique model system – the all-female parthenogenetic and mono-genotypic oribatid mite ffspring , 33. Whiffspring –37). We Based on recent meta-analyses , 28 abouArchegozetes longesitosus ran [Spirulina powder , shiitake fungus powder , grinded dry yeast , Chlorella powder , hemp protein powder , sweet lupine flour , grinded mixed pollen grain  and wheat grass powder . A detailed list of macro-elemental and nutritional composition can be found in the Additional file osus ran  were reaosus ran . The tenAs traits we selected life-history, morphological and chemical characters Fig. , Table 1At the start of the experiment we selected young (approximately one week after eclosing) adult individuals from their original culture-plates. We directly used 130 specimens per resource  for chemical experiments, while 25 specimens per resource  were individually redistributed into smaller culture boxes  for further experiments.n = 10*30) were accomplished by immersing individuals in 50 μl hexane  for 3 min  and fed with the same resources mentioned above. After 28 days, specimens were individually extracted in hexane to measure the proportion of specimens that had regenerated oil-gland secretions  protocol which does not influence the mites’ life-history, but results in the complete depletion of the glands . Replicates where all offspring died before reaching adulthood were not used to calculate the developmental time (n = 72). In addition to the counted data  could lay eggs for ten days and the same food and water was provided ad libitum three times a week  were weighed into tin capsules. Total organic carbon and nitrogen contents were measured by an elemental analyzer . Acetanilide  was used as standard. Carbon and nitrogen amounts were calculated based on the standard and the initial dry weight and expressed as C/N ratios Table .Table 2CWe analyzed the univariate traits , using the lm-function in R Prior to this statistical analysis we had to adjust our data in several ways: first we transformed the trait data (see results section) to ensure the normality of residuals of every regression and to break potential mean-variance relationships; secondly, we rescaled all the transformed trait values between 0 and 1 using the minimum-maximum normalization (eq. 1)t is any transformed trait value, tmin and tmax are the lowest or highest expression level of a given trait, respectively and t’ is the min-max normalized trait value. The rescaling was necessary to plot all the optima curves simultaneously and elucidate potential trade-offs among the trait-resource combinations.To statistically access the optimality response of a certain trait to the resource quality parameter  we fitted a non-linear regression model (t’ ~ C/N + C/Nt’mean and the ln CV of the univariate traits (because multivariate traits do not have a mean t’). Because the different traits within one resource treatment are not independent from each other we used a linear mixed effect model (LMM) with trait variation as dependent, trait mean as independent and resource treatment as random variable using the R packages “lme4” and “car”. The model was checked for normality of residuals and variance homogeneity, and no further transformation was necessary.To quantify the trait Table  variation = 14 of 300); mothers which did not survive the egg laying period for life history data (n = 34 of 250); total biomass output lower than 1 μg ; non intact mothers from mass measurement (n = 8 of 250); damaged specimen for morphometric measures (n = 47 of 250). The raw data is deposited in Additional file The following replicates were not included in the statistical analyses: chemical samples with contaminations for secretion amounts n = 1 of 300; Some food resources were characterized by an extreme composition – e.g. blood meal had a very high C/N ratio, which corresponds to a high nitrogen content , while pollen was nitrogen poor, but comparatively carbon rich . Yet, most of the remaining resources had a quite equilibrated stoichiometry Table . Thus, tn = 286, df = 9, χ2 = 62.74, p <  0.0001; Table 9,276 = 11.25, p <  0.0001; Table N = 100, df = 9, χ 2 = 42.97, P <  0.0001; Table 9,90 = 2.99, P = 0.004; Table 2,283 = 16.84, r2 = 0.11) and fraction of regenerating individuals  showed C/N optima curves  as well as in multivariate dispersion  also differed across all resources. All life-history traits responded with optima curves .In total, we investigated four life-history traits, related to reproductive fitness and resource allocation Table . The dev2 = 89.06, p <  0.0001; Table 9,198 = 2.60, p = 0.007; Table 2,205 = 30.92, r2 = 0.23). The eleven morphometric characters . Life-history traits had a ln CVmean = − 0.78 , chemical traits responded with a ln CVmean = − 0.86  and the morphological traits showed a ln CVmean = − 1.40  across all ten feeding treatments. There were no differences among mean trait variations across resources . We also tested whether the mean total variation of one resource  is related to the C/N ratio of the food , while the traits showed no optimum related to C/N-ratios . Eventually, there was a negative relationship of mean and the variance of the different univariate traits .All traits Table  were infDaphnia, a full control of the genotype is not possible. He also suggested that an obligate asexual species would be a perfect model to study nutritional reaction norms of traits and their plasticity. That is why parthenogenetic generalists (like some oribatid mites), which can tolerate a wide range of environmental conditions .Dietary induced intraspecific reaction norms and variation are a mostly neglected topic in evolutionary ecology , 34. Oveenotype; ), are thMost studies on a single species so far focused on the relationship of nutrients to one or a distinct set of traits – often times related to fitness e.g., , 58, 59), 5958, 5variability potential allows to derive different hypothesis: a certain trait may react with higher variability to an environmental gradient, because it faces more trade-offs along this gradient than other characters; or the formation/development of a trait may be more “complex” and thus demands a stronger segregation of energy leading to more trade-offs. Consequently, lower trait variation may indicate less trade-offs (or selection) along a certain gradient. Also, a lower variability may be a signal for an inherently lower plasticity of a trait because it is less controlled by the phenotypes’ response or selection. For instance, in our experiments, the body size of the mites changed considerably along the C/N gradient, yet the overall body shape (morphometric measurements) only showed a weak response and low variability. This may indicate, that – besides selection or genetical conservation – the overall body size could be influenced by the phenotypic response to altered nutrients. Yet, the proportions of the body shape isometrically scaled with this phenotypic change, leading to low overall variability.Generally, there are no multicellular organisms without a certain plasticity, because intra-individual trade-offs as reactions towards environmental conditions like temperature, salinity or resource availability, but also biotic factors like predation will lead to variation of traits , 63, 65.Overall, we have shown that the threshold elemental ratio rule , 26, 60,Additional file 1:Raw data table. (XLSX 560 kb)Additional file 2:Morphometric data. (PDF 370 kb)Additional file 3:Trait data used for Fig. Additional file 4:Post-hoc test results. (PDF 94 kb)


Azospirillum brasilense colonization and to receive benefits from this association. Hence, our aim was to study the genetic control, heterosis effect and the prediction accuracy of the shoot and root traits of maize in response to A. brasilense. For that, we evaluated 118 hybrids under two contrasting scenarios: i) N stress (control) and ii) N stress plus A. brasilense inoculation. The diallel analyses were performed using mixed model equations, and the genomic prediction models accounted for the general and specific combining ability  and the presence or not of G×E effects. In addition, the genomic models were fitted considering parametric (G-BLUP) and semi-parametric (RKHS) kernels. The genotypes showed significant inoculation effect for five root traits, and the GCA and SCA were significant for both. The GCA in the inoculated treatment presented a greater magnitude than the control, whereas the opposite was observed for SCA. Heterosis was weakly influenced by the inoculation, and the heterozygosity and N status in the plant can have a role in the benefits that can be obtained from this Plant Growth-Promoting Bacteria (PGPB). Prediction accuracies for N stress plus A. brasilense ranged from 0.42 to 0.78, depending on the scenario and trait, and were higher, in most cases, than the non-inoculated treatment. Finally, our findings provide an understanding of the quantitative variation of maize responsiveness to A. brasilense and important insights to be applied in maize breeding aiming the development of superior hybrids for this association.Several studies have shown differences in the abilities of maize genotypes to facilitate or impede  Azospirillum sp. have been extensively studied for agricultural purposes due to the excellent potential for response in association with cereal crops, such as maize [Azospirillum sp. in cropping areas increase up to 30% the grain yield and reduce up to 25% in N fertilizer needs [In recent years, several Plant Growth-Promoting Bacteria (PGPB) have been isolated, and their beneficial effects on the production of phytohormones and biological nitrogen fixation (N) have been identified ,2. The pas maize . The useer needs ,5. Otherer needs ,7.Azospirillum sp. inoculation [The plant-PGPB association establishment involves complex mechanisms. In this sense, the genetic background of both host plant and bacteria plays a crucial role in the regulation of this partnership. Previous studies have shown differential responses among plant genotypes to culation ,9. Genotculation ,11.Knowledge about the genetic control and inheritance of this association could help breeders to establish selection strategies for the partnership-related plant traits, which will enable the development of new cultivars with better responsiveness to inoculation. In this context, the diallel mating designs can be a useful approach for these studies, once it determines the genetic control and the relative importance of additive and non-additive genetic effects associated to agronomically important traits –14.Azospirillum sp., is laborious and time-consuming. The use of genomic enabled approaches, as the genomic prediction [The evaluation of the maize responsiveness to PGPBs, as ediction , can be ediction –19. To oAzospirillum sp. inoculation in maize [Several studies have proposed the use of plant breeding to improve the interaction of plants with soil microorganisms –23. So fin maize ,24, no sin maize . In addiin maize ,27, litt(i) to identify the genetic control and inheritance of maize traits related to its responsiveness to Azospirillum brasilense under N stress, (ii) to verify the possible influence of heterosis and heterozygosity of maize on the benefits obtained from the association with this PGPB, and (iii) to compare the prediction accuracy of maize hybrids under inoculated and non-inoculated treatments through different prediction models.Hence, our objectives were A. brasilense was grown in dextrose yeast glucose sucrose (DYGS) liquid medium [−8 colony-forming units (CFU) mL-1 by dilutions with the respective liquid medium. About 30 minutes before sowing, the inoculant was applied in plastic bags containing the non-sterile seeds of each genotype individually.Nineteen inbred lines contrasting for Nitrogen Use Efficiency (NUE) were crossed in an incomplete diallel mating design , withoutd medium , while bA. brasilense inoculation. During the experiment, the average temperature was semi-controlled, with a maximum temperature of 33°C, and the water supply was provided individually per pot every other day or when necessary to maintain a well-watered condition. Supplementary luminosity was done with fluorescent lamps to establish a photoperiod of 12 hours of light. Parental inbred lines and hybrids were conducted under the same conditions but as individual experiments.Experiments were carried out in two years (2016 and 2017) under greenhouse conditions at Allogamous Plant Breeding Laboratory, “Luiz de Queiroz” College of Agriculture, University of São Paulo, Brazil . The maize plants were grown in 3-L plastic pots containing unsterilized loam soil . To achi3), and length of a serial of root diameter classes. Lateral root length  and axial root length  were considered as roots fragments with a diameter class less than or equal to 0.5 mm and root fragments with a diameter class greater than 0.5 mm, respectively [-1) and specific root surface area  were calculated through the division of the total root length and the superficial area by RDM, respectively. All the measures were recorded individually by plant, thereby resulting in six replications per genotype for a total of 1,644 analyzed roots.The phenotypes (in both years) were collected at the V7 stage of development (seven expanded leaves), about 35 days after emergence. Plant height  was measured, and the harvested shoot (leaves and stem) was dried in a forced draft oven at 60°C for 72 h to determinate the shoot dry mass . The roots were extracted and carefully rinsed with water to remove soil particles before being stored individually in plastic pots with a 25% ethanol solution for preservation. Images of each root were obtained using an Epson LA2400 scanner  and after were analyzed by the WinRHIZO software . This software provided measures of root average diameter , root volume , with G is the associated additive relationship matrix from the parental inbred lines; uH is a vector of random effects of specific combining ability (SCA), with uGE is the vector of random effects of GCA × year interaction, with uHE is the vector of random effects of SCA × year interaction, with uGI is the vector of random effects of GCA × inoculation interaction, with uHI is the vector of random effects of SCA × inoculation interaction, with ε is the vector of random residual effects, with XE, XB, XC, XI, XEI, ZG, ZH, ZGE, ZHE, ZGI, and ZHI are the respective incidence matrices and IH,IE,IH are identity matrices of appropriate dimensions. ⊗ denotes the Kronecker product of matrices.The diallel joint analysis across both treatments was performed for each trait by fitting the following model:A. brasilense inoculation were conducted employing the previous model disregarding the inoculation effect and its interactions effects with GCA and SCA. All analyses were carried out by the ASReml R package [G matrix by VanRaden method from the SNP set of inbred lines [H2) and narrow-sense heritability (h2) were estimated as:Individual diallel analyses for N stress and N stress plus  package . The syn package  was useded lines . This deed lines . The Waled lines . The ranectively . Conside follows :BR=2σG2x in accounting for the variability retained by PC1 and PC2 was expressed in percentage, according the following expression:C1 and C2 are the contribution of the variable x on PC1 and PC2 respectively; Eig1 and Eig2 are the amount of variation retained by PC1 and PC2, respectively. In addition, we dissected the relation between all measured traits, estimating the correlations among them. The igraph R package [Principal Components (PCs) from the SCA values were obtained by treatment using the pre-installed R function prcomp. The contribution of a variable  package  was usedy is the phenotypes of hybrids or inbred lines; βE is the vector of the fixed effects of year; βB is the vector of the block within year effect, considered as fixed; βC is the vector of the fixed effects of countertop within block and year; βI is the vector of fixed effects of inoculation; βIE is the vector of fixed effects of inoculation × year interactions; βG is the vector of fixed effects of the genotype; βGE is the fixed effects of genotype × year interaction; ε is the vector of random residual effects, with XE, XB, XC, XI, XEI, XG, and XGE being the respective incidence matrices.Adjusted means of the hybrids and inbred lines in each treatment were obtained using the following model:F1 is the mean performance of the hybrid; MP is the mid-parent value, given by (P1+P2)/2, where P1 and P2 are the mean performance of parental inbred line 1 and parental inbred line 2; BP is the mean performance of the better parental inbred line.Mid-parent heterosis (MPH) and high-parent heterosis (HPH) were calculated for each hybrid for those traits with significant inoculation effect in the diallel joint analysis as:A. brasilensis. Also, the heterozygosity was correlated with the difference of the hybrid performance in the two treatments (Δ), being Δ = T2−T1, where T1 and T2 are the hybrids adjusted means in N stress and N stress plus A. brasilense, respectively. Considering that the Δ in the biological sense is the change in the trait due to inoculation, this parameter for each hybrid was also correlated with the adjusted means in the N stress treatment.Furthermore, the individual heterozygosity level was estimated as the ratio between the number of heterozygous loci and the number of total markers from the genomic matrix. After, these values were correlated with the performance of the hybrids in the N stress and N stress plus A. brasilense inoculation scenarios. The fitted prediction models accounted for the genotype by the environment interaction effects  or not (across environments). For the genomic prediction of the hybrids performance, we used a two-stage approach [Parametric (G-BLUP) and semi-parametric (RKHS) prediction methods accounting for the general and specific combining abilities (GCA and SCA) were used to predict the performance of the single-crosses in the N stress and N stress plus approach , where, y is the vector of adjusted phenotypes; βE is the vector of environmental fixed effects; g is the vector of random effects of GCA with G are the variance component and a variance covariance matrix associated with the GCA effects, respectively; h is the vector of random effects of SCA with H are the variance component and the relationship matrix associated with the SCA effects, respectively; ZE,ZG, and ZH are the respective incidence matrices; and ε is the vector of the residuals with The model assumes the fixed effect of the environment and the random effects of the GCA of the parental inbred line and the SCA of the hybrid . Here, eZp1) and two (Zp2) were computed separately, and ZG was obtained as ZG = Zp1+Zp2.This model was initially proposed to account for the GCA effects from inbred lines of two distinct heterotic groups . HoweverGB = WW′/m, where W is the centered and standardized matrix of the molecular markers for the inbreed lines, and m is the number of markers [i and j; h is the bandwidth parameter. This parameter controls the decay rate of the GK values when the distance increases and in this study we considered h = 1. The use of semi-parametric kernels can be advantageous in genomic prediction models once potentially takes into account complex gene interactions ,which may increase the prediction accuracy[We considered two covariance matrices: 1)  markers ; hereaft accuracy.H was computed following the equation Hij)(i′j′)(i′j′) for group 1 and 2, respectively [For both kernels, the relationship matrix ectively ,46. × isuG is the vector of random interaction effects of GCA with the environment with VG are the relative variance component and the associated variance-covariance matrix; uH is the vector of random interaction effects of SCA with the environment with VH are the relative variance component and the associated variance-covariance matrix.We fitted the extended previous model by adding GCA and SCA × environment interactions, as proposed by Acosta-Pech et al.:y=ZEβE+VG and VH matrices were derived as GB+G×E) is equivalent to Acosta-Pech et al. [GK+G×E).The h et al. . FurtherGB+G×E) and (GK+G×E) models.All the variance components were estimated by fitting the models using the Bayesian Generalized Linear Regression (BGLR) R package . The resThe comparisons between the models were based on their prediction accuracies from cross-validation (CV) schemes simulating two prediction problems, as proposed by Burgueño et al. . First, A. brasilense), being, for most of the cases, the observed differences due changes to the correlation’s estimates magnitudes than to its direction. This is clear for the correlations between LRL and RV, ARL, RSR, and RDM, that were smaller in the inoculated treatment in comparison to the N stress condition. For both treatments, strong positive correlations were observed within the group of RDM, RV, RAD, and ARL. Additionally, the SRL and SRSA were positively correlated, however, these traits were negatively correlated with RDM, RV, and RAD.The different degrees of the genetic correlations among the traits were revealed by the network . Howeverα≤0.05) for most of the traits, indicating the importance of environmental control even in greenhouse conditions (Y×I effect (inoculation by year interaction) was not significant for any trait, suggesting that the responses of the genotypes to inoculation do not change differentially with the year. The A. brasilense inoculation effect was significant only for RDM, RV, RAD, SRL, and SRSA. Thus, all subsequent results and analyses were reported only for these five root traits.In the diallel joint analysis performed across both treatments, the fixed effects of year and experimental design (block and countertop) were significant  . On the A. brasilense than N stress  and high-parent (HPH) . The disA. brasilense treatment was relatively low for all the traits . For RDM and RV, the average prediction accuracy considering all the prediction models for N stress plus A. brasilense treatment were 0.66 and 0.77, respectively, and higher than N stress, for both CV methods. Additionally, a small increase in prediction accuracies were found for SRL on the inoculated treatment, with values ranging from 0.55 to 0.59. On the other hand, the prediction accuracies for RAD were reduced under N stress plus A. brasilense. For SRSA, the prediction accuracies varied according to the CV method. For example, in CV1 the prediction accuracies increased in the inoculated treatment, whereas in CV2 validation system, the results between the two treatments varied according to the prediction model.Results of the prediction accuracy varied according to the root traits and treatments , ranging from 0.65 (RDM) to 0.78 (RV). Conversely, no substantial differences were observed between parametric and semi-parametric prediction models. Also, there was no advantage in modeling the e models  Tables.A. brasilense. In addition, for the same treatment, the opposite was found when the relationship between individuals was modeled using Gaussian kernel.Concerning the cross-validations methods, for some traits  the prediction accuracies obtained in the CV2 validation approach slightly varied when compared to the CV1, which suggests that the recovered information among the environments was small. The highest gains in prediction accuracy when employing the CV2 over CV1 were found for the SRSA under N stress, especially in combination with GBLUP. However, surprisingly, we found a modest reduction of the prediction accuracy for the same trait when using the GBLUP model for prediction under N stress plus A. brasilense for a series of shoot and root related traits. From a total of ten traits, the RDM, RV, RAD, SRL, and SRSA were significantly affected by the bacterial inoculation. These results are consistent with those found under previous studies under similar growing conditions, indicating that the root growth promotion did not necessarily increase the shoot-related traits [A. brasilense capacity to modulate essential root traits by the production of phytohormones increasing the exploration of the soil and allow growth into deep soil layers, thus helping to mitigate stress conditions [A. brasilense inoculation should be specific for each stress condition.We performed individual and joint diallel analysis for 118 maize hybrids evaluated under N stress and N stress plus d traits . In turnnditions . Howevernditions . TherefoA. brasilense treatment in comparison to than N stress. Additive effects are inheritable and are a determinant factor for the evolutionary potential of species in natural conditions [A. brasilense interactions would enhance the plant’s ability to respond to environmental changes and persist over time. For example, in common bean, this PGPB can be vertically transmitted to successive plant generations demonstrating to be an effective inoculum in seed [A. brasilense inoculation could be more significant than N stress. However, we should be cautious with these results because in the diallel analysis is assumed absence of epistatic effects, and, it is known that epistasis can be manifested in several plant traits [A. brasilense.A key finding of our study is the big importance of additive genetic effects for the phenotypic expression of root associated traits in N stress plus nditions . In this in seed . Converst traits ,53. Moret traits , additivt traits ,56. In tA. brasilense association. This is consistent with the high number of genes that could be involved in the production of the root exudates, hormonal balance, and defense system that would modulate the plant bacterial colonization [A. brasilense inoculation.We also found low to moderate heritability estimates, which suggest a polygenic inheritance of the maize–nization ,24. AddiHeterosis, or hybrid vigor, is a phenomenon in which the hybrids often outperform their parents in yield, growth rate, biomass or stress tolerance . Mid-parAlthough present in a range of traits, the heterotic responses can display variable levels among them ,61. The The analyses of heterosis showed positive, negative, or the combination of both directions. Considering that an increase of root biomass and expansion is often the primary driver for plant performance ,64, the A. brasilense inoculation did not lead to pronounced variations in heterosis for most of the evaluated traits. However, heterotic responses in other maize traits, such as grain yield and leaf growth, can be correlated linearly with environmental quality [The enhancement of growing conditions by the  quality ,66. Unde quality . TherefoA. brasilense than under the N stress, similar heterosis levels were observed under both treatments. Advances in genetic and genomic studies have revealed that, in addition to traditional dominance and overdominance hypotheses, multiple causal mechanisms contribute to heterosis, including epistasis, epigenetic modification and small RNA activity [A. brasilense inoculation than dominance effects, such as observed in traits of bread wheat [Interesting, in both treatments (inoculated and non-inoculated) the heterosis were similar. Even though four of the five traits showed lower activity . As alread wheat .A. brasilense treatment. Most likely, the increase in the diversity of compounds released in the exudates because of high levels of heterozygosity results in an interaction with a more significant amount of soil microorganisms, leading to stimulation of other certain strains [A. brasilense and a wide range of other microorganisms that have little or no effect on the plant could have resulted in a lower benefit due to inoculation. In addition, regarding host-pathogen systems, the lower individual genetic diversity can increase the susceptibility to infection [A. brasilense, where certain heterozygous loci associated with plant immunity would actively control the extent of colonization by the bacteria and, consequently, the degree of beneficial results. Moreover, further studies are needed to better understand why some traits under inoculation treatment are more affected by heterozygosity level than other. We can speculate that the A. brasilense inoculation can stimulate certain heterozygous loci, which may affect differently the plant traits.Relations between genomic heterozygosity and plant fitness have been explored in several species, and, when studied at the individual level, these relations serve as a measure of individual genetic diversity ,69. Our  strains ,70. Hencnfection ,71. SimiA. brasilense and N stress treatments) for all evaluated traits. This correlation indicates that the average rate of increase due to inoculation tends to be higher in genotypes with worse performance under N stress effects. Thereby, traits related to NUE and the internal N status in the plant could be relevant for the development of more responsive maize hybrids to A. brasilense inoculation. Additionally, this observation reinforces the possible role of internal N metabolism regulating the association efficiency through the modulation of plant defense [A. brasilense and other PGPB.We also found negative correlations between performance under N stress and Δ  possibly resulted in high correlation among environments and led to a small occurrence of G×E interactions. In this sense, in our results, this modeling did not provide mainly an increase in the prediction accuracy for all traits and treatments. Our results reinforce the importance of prediction model-kernel combinations for both maize prediction under N stress plus A. brasilense and N stress, in addition to the possibility that they must be specific to each CV scheme for determined traits.Although predictions with medium-to-high accuracy were found for both treatments in all tested genomic models, no substantial differences were observed between the use of parametric and semi-parametric methods . However, the limitations due to the small number of hybrids and the relatedness among our parental set may have influenced our results. For instance, in studies with larger panels of hybrids, superior overall performance of the nonlinear GK model relative to GBLUP has been observed ,73. In tA. brasilense, and that both additive and dominance genetic effects are involved the genetic control of this association. Furthermore, the heterozygosity and N status in the plant could influence the regulation of A. brasilense benefits to the plant. Prediction accuracies showed moderate to high values indicating an opportunity for genomic selection of traits related to the maize- A. brasilense partnership in the early stages of the plant development. Finally, our results may support possible plant breeding strategies to explore the genetic variability among maize genotypes relative to their differential ability to allow the colonization by A. brasilense and take advantage of this beneficial interaction.We verified a quantitative inheritance of the maize responsiveness to S1 Fig(A) Scheme of obtaining from the crossing of 19 parental inbred lines in an incomplete diallel design , where the red squares indicate the hybrids evaluated and the gray squares represent the unrealized crosses. (B) Heatmap and dendogram of a kinship matrix estimated using Van Raden method based on 59,215 SNPs markers.(TIF)Click here for additional data file.S2 FigBased on 65,225 SNPs markers, (A) the first two principal components, and (B) heatmap and dendogram of the kinship matrix estimated using the Van Raden method.(TIF)Click here for additional data file.S3 FigPH: plant height, SDM: shot dry mass, RDM: root dry mass, LRL: lateral root length, ARL: axial root length, RV: root volume, RAD: root average diameter, SRL: specific root length, SRSA: specific root surface area, and RSR: root shoot ratio.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 TablePH: plant height, SDM: shot dry mass, RDM: root dry mass, LRL: lateral root length, ARL: axial root length, RV: root volume, RAD: root average diameter, SRL: specific root length, SRSA: specific root surface area, and RSR: root shoot ratio. Significant at 5% (*) or 1% (**) level.(DOCX)Click here for additional data file.S3 TablePH: plant height, SDM: shot dry mass, RDM: root dry mass, LRL: lateral root length, ARL: axial root length, RV: root volume, RAD: root average diameter, SRL: specific root length, SRSA: specific root surface area, and RSR: root shoot ratio. Significant at 5% (*) or 1% (**) level.(DOCX)Click here for additional data file.S4 TableRDM: root dry mass, RV: root volume, RAD: root average diameter, SRL: specific root length, and SRSA: specific root surface area.(DOCX)Click here for additional data file.S5 TableRDM: root dry mass, RV: root volume, RAD: root average diameter, SRL: specific root length, and SRSA: specific root surface area.(DOCX)Click here for additional data file.S6 TableGBLUP+G×E (GB+G×E), Gaussian Kernel (GK) and Gaussian Kernel + G×E (GK+G×E) models. The values must be multiplied by 10−3 to return to its correct magnitude.(DOCX)Click here for additional data file.S7 TableGBLUP+G×E (GB+G×E)), Gaussian Kernel (GK) and Gaussian Kernel + G×E (GK+G×E) models.(DOCX)Click here for additional data file.S8 TableGBLUP+G×E (GB+G×E), Gaussian Kernel (GK) and Gaussian Kernel + G×E (GK+G×E) models. The values must be multiplied by 10−4 to return to its correct magnitude.(DOCX)Click here for additional data file.S9 TableGBLUP+G×E (GB+G×E), Gaussian Kernel (GK) and Gaussian Kernel + G×E (GK+G×E) models.(DOCX)Click here for additional data file.S10 TableGBLUP+G×E (GB+G×E), Gaussian Kernel (GK) and Gaussian Kernel + G×E (GK+G×E) models.(DOCX)Click here for additional data file.S11 Table(DOCX)Click here for additional data file.


CCDC92S70C, previously associated with WHR, is associated specifically increased leg fat mass and reduced visceral but not subcutaneous central fat. The minor allele-carrying transcript of CCDC92 is constitutively more highly expressed in adipose tissue samples. In addition, we identify two coding variants in SPATA20 and UQCC1 that are associated with arm fat mass. SPATA20K422R is a low-frequency variant with a large effect on arm fat only, and UQCC1R51Q is a common variant reaching significance for arm but showing similar trends in other subcutaneous fat depots. Our findings support the notion that different fat compartments are regulated by distinct genetic factors.Waist-to-hip ratio (WHR) is a prominent cardiometabolic risk factor that increases cardio-metabolic disease risk independently of BMI and for which multiple genetic loci have been identified. However, WHR is a relatively crude proxy for fat distribution and it does not capture all variation in fat distribution. We here present a study of the role of coding genetic variants on fat mass in 6 distinct regions of the body, based on dual-energy X-ray absorptiometry imaging on more than 17k participants. We find that the missense variant  The obsSPATA20K422R is a rare variant (EAF 0.016), which has to our knowledge not been reported for any other phenotypes. This amino acid substitution is predicted to be benign and allele-specific qPCR on paired ASAT and GSAT cDNA samples of five Oxford Biobank participants heterozygous for rs62621401 did not reveal any association with expression of SPATA20   . ExpressUQCC1R51Q (EAF = 0.62) variant was also inversely associated with arm fat mass  is in LD with rs4911494 (r2>0.9) and reaches suggestive significance with arm fat mass in women  scan-derived visceral and subcutaneous fat mass also found associations at the CCDC92 locus with ZNF664 (rs1048497) and DNAH10 (rs1316952)[2 0.34 and 0.18, respectively) and may represent independent signals. No other previously reported loci associated with CT-derived visceral fat measures[2 = 0.96)[CCDC92-ZNF664 locus highlight this as an important region in regulating adipose tissue distribution. In addition, we report two coding variants associated with arm fat mass: a novel low-frequency variant in SPATA20 with a large effect size that seems to only affect arm fat mass and a common variant in UQCC1 that has additional but weaker effects on other subcutaneous fat depots. Overall, when comparing these data to exome chip analyses on standard anthropometric traits, such as WHR[CCDC92), highlighting the value of more defined regional fat measures.This study represents the largest exome chip meta-analysis on DXA-derived discrete fat depot masses to date. The value of better-defined fat depot regions is illustrated at the s1316952) but thes measures were repRegional fat depots have distinct physiological regulation with an impact on the whole body metabolic homeostasis, with distinct transcriptomes demonstrating functional differences and differences in origin. This sthttp://www.oxfordbiobank.org.uk) consists of an age-stratified random sample of men and women (aged 30 to 50 years) of European ancestry resident in Oxfordshire, UK, as described previously[The Oxford Biobank (OBB) cohort  and undeA total of 1,145 individuals from the Fenland cohort had both measures of fatmass with GE Lunar iDXA and IlluEPIC-Norfolk is one of the two UK-based centres of the European Prospective Investigation into Cancer and nutrition (EPIC) study–an ongoing multi-centre European cohort study set up to study the role of nutrition in cancer risk  The EPICFor all cohorts depot-specific fat mass was quantified using GE Lunar iDXA . As previously described these giSix data sets from three cohorts, Oxford Biobank, Fenlandhttp://genome.sph.umich.edu/wiki/RAREMETALWORKER). To account for cryptic relatedness, kinship matrices were first calculated and added into the analysis. Single-variant analysis was performed with, additive, recessive and dominant models.Principal component analysis was performed within each study on LD-pruned genotype data with MAF>5% obtained using GWAS arrays. Models were adjusted for the first 4 genetic principal components (PCs) in order to adjust for population stratification and relatedness, which is commonly done in exome and genome-wide association studies. All DXAp<10−7 and markers on Y chromosome or mitochondrial genome. Exome-wide significance for the single-variant analysis was set, based on the full ExomeChip content, as p<2×10−7. A suggestive significance was set to p<10−6.Meta-analysis was carried out centrally using RAREMETAL. VariantFor this analysis we focussed on non-synonymous variants only, therefore all non-coding variants and synonymous variants were filtered out post meta-analysis together with loci that had a total heterozygous count of less than 10. The exome-wide significant findings are presented in https://loschmidt.chemi.muni.cz/predictsnp1/). This allows for assessment across a number of different tools to generate a consensus assessment. For CCDC92 the S70C missense variant was assessed; for UQCC1 the R51Q was assessed and for SPATA20 three different proteins as products of different splice variants were assessed .For the three exome-wide significant loci the amino-acid substitutions was assessed for functional significance using the predictSNP online consensus tool , ZNF664 , DNAH10 , SPATA20 , UQCC1 (ABI assay hs00921074) and GDF5 .Six genes found within the three index SNP LD boundaries  were asset al.[PPIA, PGK1, PSMB6 and IPO8 were used. IPO8 was not used in a paired arm, ASAT and GSAT panel , ASAT and GSAT gene expression are presented in Data for a panel of 52 paired ASAT and GSAT biopsy samples was used to assess expression between sexes, between ASAT and GSAT fat depots, and between lean and obese individuals. Descriptives for this panel are presented in CCDC92, ZNF664, SPATA20, UQCC1 and GDF5. Data is presented in The setup of a human primary adipocyte differentiation time course is described elsewhere. RelativCCDC92 and the UQCC1 loci are associated with multiple eQTL signals. Whilst we only consider non-synonymous variants in this analysis this does not discount that the coding locus is also under the influence of an eQTL. To assess the available data from resources such as the GTEx portal and to assess any eQTL effect between ASAT and GSAT fat depots we used the combined resources available within the Oxford Biobank.Both the et al.[Allele specific qPCR was run essentially as described in Fogarty et al..CCDC92 the index SNP assay performed poorly so the Proxy SNP rs9863  was selected. To assess the nearby gene ZNF664 a SNP in high LD with the CCDC92 index SNP that fell within the ZNF664 transcript, rs1054852 , was selected. For SPATA20 the index SNP was used  as was for UQCC1 . As was previously stated neither DNAH10, nor GDF5 could be detected in whole adipose tissue panels. Therefore, allele specific qPCR could not be assessed for these two genes.Taqman genotyping assays (Applied Biosystems) were selected to fall within the transcripts under investigation. For CCDC92 and ZNF664 28 paired ASAT and GSAT samples were selected, for UQCC1 there were 34 and for SPATA20 there were 5. Genomic DNA (gDNA) for these individuals were also retrieved and diluted to 1.5ng/μl. The gDNA is used as the control comparison to the cDNA samples as there is an equal quantity of both alleles in heterozygous gDNA samples. By comparing the ratio of the Ct values from each allele  between cDNA and gDNA any allelic expression differences observed in the cDNA samples can be resolved. This is particularly relevant as technical variation exists with each genotyping assay; particularly pronounced in SPATA20  and minor allele (bb). Genomic DNAs are diluted to 1.5ng/μl then BB and bb homozygotes are combined to ratios 80:20, 60:40, 50:50,40:60,80:20. Following qPCR analysis using the dual-labelled TaqMan Genotyping assays the ratio of the B to b Ct values are calculated (Ct B minus Ct b) then plotted against the percentage of the minor allele in the dilution series. The linear regression statistic from this standard curve is then used to calculate the percentage minor allele expression of the unknown heterozygous individuals. The standard curves are presented in CCDC92, ZNF664 and UQCC1 there were sufficient cDNAs in the 200 panel, however for SPATA20 there were only 5 individuals. Therefore, to improve the accuracy of the SPATA20 analysis, each sample was run in triplicate 4x across the assay plate and the average of all 4 sets of triplicates calculated. A single outlier in the SPATA20 data was followed up in a second cDNA synthesis and persisted in both ASAT and GSAT samples. No phenotype differences were observed for this individual and no obvious genetic differences were found.For Statistical significance was assessed for each experiment in SPSS v24. For estimates of per-allele grams fat mass change, log phenotype data was analysed in a general linear model and adjusted for age, PC1-4 and total %fat mass then estimated marginal means were calculated .S1 FigCCDC92, ZNF664, UQCC1 and SPATA20 across a panel of paired abdominal subcutaneous adipose tissue (ASAT) and gluteal subcutaneous adipose tissue (GSAT) cDNA samples from the Oxford Biobank. The panel consisted of 25 male and 29 female healthy individuals selected for either high or low BMI . Data are shown as the mean ± SEM DDCt values  as described previously[mRNA expression of the genes reviously, 41. A mCCDC92, ZNF664 and UQCC1 between fat depots in lean individuals but this difference was lost and expression was significantly reduced, in obese individuals. This is in keeping with a general quiescent state observed in transcripts associated with adipocyte metabolic activity in obesity.There were small but significant differences in expression of (TIF)Click here for additional data file.S2 FigA:CCDC92, ZNF664, UQCC1 and SPATA20 and a selection of developmental HOX genes B:HOXA5, HOXB8, HOXC8, HOXC9 and HOXC11 were determined by real-time qPCR. Data are shown as the mean ±SEM DDCt values . A univariate general linear model was used to test for statistical significance between depots. P-values for the HOX genes in B are presented in the shaded box.mRNA expression of the candidate genes (TIF)Click here for additional data file.S3 FigCCDC92 (A), ZNF664 (B), SPATA20 (C) and UQCC1 (D). To quantify any allelic expression imbalance for the four genes a standard curve was generated from genomic DNA for individuals homozygous for the Major allele (BB) and Minor allele (bb). Genomic DNAs are diluted to 1.5ng/μl then BB and bb homozygotes were combined to ratios 80:20, 60:40, 50:50,40:60,80:20 to generate a standard curve. Following qPCR analysis using dual labelled TaqMan Genotyping assays the ratio of the B to b allele Ct values are calculated (Ct B minus Ct b) then plotted against the percentage of the minor allele in the dilution series. The linear regression statistic from this  is then used to calculate the percentage minor allele expression of our unknown individuals. For CCDC92 (A), ZNF664 (B) and UQCC1 (D) three different pairs of homozygote individuals were used to generate each standard curve and a Mean ± SEM plotted for each dilution . For SPATA20 only one genomic DNA homozygote minor allele individual was available so an error bar cannot be displayed.The standard curve and regression statistic used to calculate the percentage minor allele expression with allele-specific qPCR is shown above for CCDC92 and ZNF664 across different cDNA panels. To assess any correlation between these two genes within the samples, the allele-specific qPCR paired data points were plotted and regression statistic calculated (Graphs E and F). For both abdominal subcutaneous adipose tissue (ASAT) (E) and gluteal subcutaneous adipose tissue (GSAT) (F) there was a significant correlation, further supporting the co-regulatory pattern of expression.As discussed in the main text there was an observed co-regulatory pattern of expression between (TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table−6) is shown in bold. Exome-wide significant data (p<2E-7) are in bold and underlined.Detailed data on the three exome-wide significant loci described. DXA parameters are included for all measures and meta-analysis statistics for the additive model. DXA measures are arm fatmass (Arm), Total android fat mass (Android), Subcutaneous android fat mass (Subcut), Visceral android fat mass , Gluteal fat mass  and Leg fat mass (Leg). Effect size data for suggestive exome-wide significance (p< = 10a The impact of missense variants were assessed using the PREDICTsnp online consensus tool[https://loschmidt.chemi.muni.cz/predictsnp1/).nsus tool (https:/b Approximate fat mass (grams) changes per allele is shown where test reaches suggestive significance and were calculated as marginal means after adjusting for age, PCs1-4 and %fatmass as covariates in a general linear model, implemented in SPSS v24(DOCX)Click here for additional data file.S3 TableAdditional non-synonymous loci where statistical tests did not reach exome-wide significance but did reach a suggestive significance cut off of p< = 10–6 are included above.a Where it reaches suggestive significance the model is shown as Additive (add), Recessive(rec) or Dominant (Dom).b The impact of missense variants were assessed using the predictSNP online consensus tool[https://loschmidt.chemi.muni.cz/predictsnp1/).nsus tool (https:/c The cluster of 8 Missense SNPs found at the SPPL2C-MAPT-KANSL1 locus on chromosome 17 are part of a single haplotype that extends across ~400kb in this region containing >2300 SNPs (r2>0.9), rather than independent signals.(DOCX)Click here for additional data file.