Brassica Infinium® single nucleotide polymorphism (SNP) array was utilized to genotype an association panel with 520 diverse accessions. A genome-wide association study was performed to determine the genetic architecture of branch angle, and 56 loci were identified as being significantly associated with the branch angle trait via three models, including a robust, novel, nonparametric Anderson-Darling (A-D) test. Moreover, these loci explained 51.1% of the phenotypic variation when a simple additive model was applied. Within the linkage disequilibrium (LD) decay ranges of 53 loci, we observed plausible candidates orthologous to documented Arabidopsis genes, such as LAZY1, SGR2, SGR4, SGR8, SGR9, PIN3, PIN7, CRK5, TIR1, and APD7. These results provide insight into the genetic basis of the branch angle trait in rapeseed and might facilitate marker-based breeding for improvements in plant architecture.The rapeseed branch angle is an important morphological trait because an adequate branch angle enables more efficient light capture under high planting densities. Here, we report that the average angle of the five top branches provides a reliable representation of the average angle of all branches. Statistical analyses revealed a significantly positive correlation between the branch angle and multiple plant-type and yield-related traits. The 60 K  Brassica napus L., 2n = 38, genome AACC) is one of the most important oilseed crops worldwide. Branch angle, or the adaxial angle of the branch to the stem, is a key morphological trait that shapes the canopy architecture and thus influences yield. This notion is best exemplified through the continuous increase in grain yield of US corn over the past century, which reflects the adaptation of hybrid upper leaf angles that continually increase plant densitiesRapeseed  angle gene that was first identified in riceIGT gene family as LAZY1TAC1 control branch angles in Arabidopsis and peachArabidopsis showed that mutants with defects in auxin homeostasis or auxin response genes, such as wei8 tar2, tir1-1, afb4-2 afb5-5, and arf10-3 arf16-2, have altered branch anglesBranch angles are primarily regulated through shoot gravitropism, which is also referred to as the gravitropic set-point angle (GSA)p-values for most traits19A genome-wide association study (GWAS) provides a methodical analysis of the genetic architecture of complex traits in crops. GWAS identifies the underlying QTLs at a relatively high resolution, taking full advantage of ancient recombination events11141617Brassica Infinium® SNP array, and the corresponding phenotype was evaluated in four environments. GWAS was performed with 520 diverse accessions to identify underlying QTLs that contribute to rapeseed branch angle variations. A total of 56 loci that were significantly associated with the branch angle trait were identified by three association methods: MLM, the general linear model (GLM) and the A-D test. Considerable candidate genes were identified based on LD decay range of these loci, including multiple orthologues of well-characterized Arabidopsis genes. This study demonstrates that GWAS can be used as an effective approach for dissecting complex quantitative traits in rapeseed.In the present study, we investigated correlations between the angles of branches at different positions and between these angles and other important agronomic traits. Genome-wide SNPs of this panel were assessed using the 60K High positive correlations were observed among the angles of different branches, indicating similar genetic control . In geneWe collected phenotypic data for the association panel in four environments; the trial performed at the Changsha farm was the only trial with phenotypic data across two growing seasons . ExtensiThe branch angles of the association panel in the four environments exhibited significantly positive correlations with each other, indicating the reliability and repeatability of these phenotypic data . AnalysiWe then analysed the relationship between the branch angle and other important agronomic traits (only data for 2012/2013 and 2013/2014 at Changsha were available for these traits). Notably, branch angle was significantly positively correlated with plant height, which had the highest coefficient , followed by branch number  were retained. After excluding SNPs lacking clearly defined clusters or with multiple loci in the genome, 19,167 high-quality SNPs (36.7%) genotyped across 520 rapeseed accessions were utilized for association mapping. The genotyping scores for all polymorphic SNPs are presented in The Illumina The population structure of the association panel was calculated for the 19,167 SNPs using STRUCTURE, and the parameters LnP(D) and Delta K suggested that the 520 genotypes could be assigned to two groups. A probability of membership threshold of 0.60 was used, and 65 and 398 lines were assigned to Groups 1 and 2, respectively, with the remaining 57 lines classified into a mixed group . In addi2 = 0.1, seven chromosomes (A01 to A07) exhibited comparatively modest LD, with distances ranging from 708 to 873 kb  = 4.3); these sites correspond to 12 loci located on chromosomes A03, A04, A09, C04, C07 and C08  = 4.3 for GLM and −log10(p) = 5.6 for the A-D test  and 24 (A-D test) loci significantly associated with BLUP and individual environmental data at the corresponding Bonferroni threshold, values of −logA-D test , Fig. 2.A-D test . A totalA-D test . AltogetA-D test . ApproxiArabidopsis orthologue information and published gravistimulation microarray data, we further predicted candidate causal genes for loci significantly associated with the branch angle trait within the observed LD decay (r2 > 0.1), with 77 plausible candidate genes predicted for 53 loci. Due to the low rate of LD decay (776.8 kb on average when r2 = 0.1), more than one-third of the GWAS loci (20/53) have at least two candidate genes  and genes with auxin transmembrane transporter activity (GO:0080161), were found to enrich in the LD decay ranges of significant loci sgr9 mutant, interaction between statoliths and actin filaments is perturbed, resulting in attenuated statolith sedimentationSGR4, BnaA04g09380 and BnaC04g31610, are located at 8.4 Mb on A04 and 33.4 Mb on C04, 205.4 kb downstream from the peak SNP Bn-A04-p6929056 and 370.9 kb upstream from the peak SNP Bn-scaff_16876_1-p1162532, respectively  = 4.6 < 5.6, −log10(p) = 4.8 < 5.6).In Arabidopsis, ectively . We also-p450133 . In addi17414621 . NotablyLAZY1 is a well-characterized gene that modulates branch (tiller) angles in plants. The Arabidopsis mutant lazy1 exhibits a dramatically increased branch angle of up to 81°, as compared to the 42° branch angle of wild-type plantsBnaA10g19550 and BnaC03g06250, two orthologues of LAZY1 in rapeseed, were identified at 13.9 Mb on A10 and 3.0 Mb on C03, respectively, in close proximity  to their corresponding peak SNPs Bn-A10-p13818569 and Bn-scaff_18936_1-p472353  = 2.8 < 4.3, −log10(p) = 4.0 < 5.6).-p472353 , Fig. 2.Arabidopsis showed that mutants with defects in auxin homeostasis or auxin response genes, such as wei8 tar2, tir1-1, afb4-2 afb5-5, arf10-3 arf16-2, have altered branch anglesBnaA07g19520, which is orthologous to the auxin receptor TIR1, was identified at 15.6 Mb on A07, 74.7 kb downstream from the peak SNP Bn-A07-p13662635  that provide the most reliable representation of the branch angle phenotype. High correlation (r = 0.93) was observed between the average angle of five branches from the top and the average angle of all branches, and measuring only these five branches rather than all branches would reduce the time required to perform such an evaluation and preclude confounding due to the variable branch numbers of different plants. Our statistical analyses revealed that plant height and branch angle are positively correlated, suggesting that shorter accessions tend to accompany a more compact canopy architecture. In addition, we also observed positive correlations between plant-type traits and yield-related traits. These results are informative to breeders attempting to adapt branch angles to achieve the ideal canopy architecture and high yields.2 = 0.1, which can be explained by three possible reasons. First, the often cross-pollination habit of rapeseed  could partially account for this phenomenon, as limited recombination from inadequate outcrossing is insufficient to break strong LD. The similar phenomenon has been observed in crops with low natural outcrossing rate, such as rice, which showed residual LD at a distance of 2,000 kbet al., B. napus was first introduced to China from Japan and Europe in the 1930–1940sB. napus populations3435The association panel examined in the present study exhibited a strong LD of up to 6,660 kb  at a cut-off value of rThe 56 GWAS loci in the present study cumulatively explained up to 51.1% of the phenotypic variation when using a simple additive model. Interestingly, despite the numerous significant SNPs identified, none of them explained more than 7.0% of the phenotypic variation. The significant SNPs identified by GLM and MLM explained 2.6–6.6% and 3.2–6.0% of the phenotypic variation, respectively . SimilarBnaC08g09050 and BnaC08g44370 , which are orthologous to well-known Arabidopsis SGR25PTASE13p-value overcorrections do not occur when the method is applied to traits that are correlated with population structureBnaC04g31610 and BnaC07g15160 . Compared with GLM, MLM is more stringent because it involves familial relationships (kinship) to further reduce p-value inflation, which caused false negative results in the present study. For example, the loci harbouring BnaA07g19520, BnaA10g19550 and BnaA10g26980 failed to reach the significance threshold in MLM but not in GLM, even though the orthologues of these genes in Arabidopsis, TIR1LAZY1SGR9In the present study, three methods  were collectively applied to dissect the genetic architecture of branch angle. Considerable GWAS loci (described in the Results) overlapped between the methods; however, we also observed variations in the power and applicable scenarios of these three methods. Compared with GLM and particularly MLM, the A-D test showed two advantages. First, because the A-D test is a nonparametric test, it is more robust, particularly for traits with abnormal phenotypic distributions and controlled by moderate effects or rare variationsLAZY1 on C03 was detected in two environments (2012–2013 Nanjing and 2013–2014 Wuhan) by using the A-D test but not by using GLM.Despite the large number of loci associated with the branch angle trait, few loci were consistently detected in all environments and a considerable number of loci were only detected in one environment . There a4142Arabidopsis auxin signalling genes, including TIR1, AFB4, AXL1, RUB1, ARF9, ARF10 and IAA13, were collectively identified as candidate genes  representing a plant architecture trait that is primarily governed by plant gravitropism; however, the precise mechanisms underlying branch angle maintenance and development remain poorly understood. Interestingly, a recent study proposed a model to address this issuete genes . AlthougA set of 530 diverse rapeseed inbred accessions, including landraces and elite varieties, was collected to construct the association panel, a subset of which was reported in a previous study of flowering time44.In 2011/2012 in Wuhan, we measured the branch angles of 30 randomly selected lines at three weeks after the final flowering stage . A smallThe association panel was grown in the 2012/2013 and 2013/2014 growing season using a randomized complete block design with three replications on experimental farms at Changsha , Wuhan  and Nanjing  China. Meteorological data for the three locations are presented in 44. Subsequently, an R script based on a linear model was used to obtain the broad-sense heritability and best linear unbiased prediction (BLUP) of the multi-environment phenotypes for each accession44.Correlation analysis and analysis of variance (ANOVA) of branch angle for the association panel across different environments was performed in RLeaf tissue samples from the entire association panel were obtained from bulk of at least four individuals for each accession at the seedling stage. DNA was extracted using a modified CTAB procedure according to Murray and ThompsonBrassica 60K Infinium® SNP array according to the manufacturer’s instructions (http://www.illumina.com/technology/infinium_hd_ assay.ilmn). SNP data were clustered and automatically called using Illumina Genome Studio genotyping software. First, accessions with a Call Rate < 0.7 were excluded and all SNPs were reclustered. Next, SNPs with Call Freq < 0.75, Minor Freq < 0.05, AA or BB frequencies < 0.03 or GenTrain Scores < 0.5 were excluded. The remaining SNPs were manually reassessed, and those that did not show three clearly defined clusters were also excluded. Because heterozygous SNPs cannot be distinguished from hemi-SNPs or false calls, heterozygous calls were treated as missing values.SNP genotyping was performed using the Illumina B.napus genome database (http://www.genoscope.cns.fr/brassicanapus/). Using an e-value threshold of e−12, SNPs corresponding to multiple loci in the genome were excluded, and only the top blast hits were retained for further analysis.Fifty base pair sequences of retained SNPs after filtration were used to perform a BLASTNad hoc statistic Delta K, which is based on the rate of change of LnP(D) between successive K-values. The cluster membership coefficient matrices of five independent runs from STRUCTURE were integrated to obtain a Q matrix by the CLUMPP software44. Relative kinship coefficients (K) were calculated using the SPADeGi software package2 was used to estimate linkage disequilibrium (LD) of A and C subgenome chromosomes via TASSEL5.0The filtered SNP dataset for selected accessions with a Call Rate > 0.7 was entered into STRUCTURE V2.3.3p-valuesp ≤ 5.2 × 10−5 (−log10(p) = 4.3). The A-D test for branch angle was conducted by using the R package ADGWAS 1.0p ≤ 2.6 × 10−5 (−log10(p) = 5.6).Trait-SNP association analysis was performed using three methods. Both GLM and MLM were implemented in TASSEL 5.02 of the best fitting multiple regression model was referred to as the phenotypic variation explained by the significant SNPs.To better understand the explanatory power of the significant SNPs, we used the SNP genotypes at candidate loci as predictor variables in multiple linear models fitted to the phenotypic variables and subsequently ran a model comparison analysis (stepwise AIC procedure implemented as the R function “stepAIC”)2. Genes within the observed LD decay were annotated using the software Blast2GO v3.3.5 with the default settingsArabidopsis genome to determine whether candidate SNP-tagged genome regions contain genes orthologous to Arabidopsis genes with established roles in shoot gravitropism. Additionally, we exploited the Arabidopsis “Electronic Fluorescent Pictograph” (eFP) browser55562 = 0.1) were considered linked to a more distant gene, and the closest one of these genes was considered the most likely candidate.To define regions of interest containing potential candidate genes, local LD decay was calculated within flanking regions up to 12,000 kb on either side of significant SNPs using TASSEL5.0How to cite this article: Sun, C. et al. Genome-Wide Association Study Dissecting the Genetic Architecture Underlying the Branch Angle Trait in Rapeseed (Brassica napus L.). Sci. Rep.6, 33673; doi: 10.1038/srep33673 (2016).


Third, we investigated to what extent polygenic risk scores for schizophrenia predicted residence in deprived neighborhoods during late adulthood using the TwinGene sample (n=6796). Sibling data suggested that living in deprived neighborhoods was substantially heritable; 65% : 60–71%) of the variance was attributed to genetic influences. Although the correlation between schizophrenia and neighborhood deprivation was moderate in magnitude , it was entirely explained by genetic influences. We replicated these findings in the TCHAD sample. Moreover, the association between polygenic risk for schizophrenia and neighborhood deprivation was statistically significant . Our findings are primarily consistent with a genetic selection interpretation where genetic liability for schizophrenia also predicts subsequent residence in socioeconomically deprived neighborhoods. Previous studies may have overemphasized the relative importance of environmental influences in the social drift of schizophrenia patients. Clinical and policy interventions will therefore benefit from the future identification of potentially causal pathways between different dimensions of cognitive functions and socioeconomic trajectories derived from studies adopting family-based research designs.Neighborhood influences in the etiology of schizophrenia have been emphasized in a number of systematic reviews, but causality remains uncertain. To test the social drift hypothesis, we used three complementary genetically informed Swedish cohorts. First, we used nationwide Swedish data on approximately 760 000 full- and half-sibling pairs born between 1951 and 1974 and quantitative genetic models to study genetic and environmental influences on the overlap between schizophrenia in young adulthood and subsequent residence in socioeconomically deprived neighborhoods. Schizophrenia diagnoses were ascertained using the National Patient Registry. Second, we tested the overlap between childhood psychotic experiences and neighborhood deprivation in early adulthood in the longitudinal Twin Study of Child and Adolescent Development (TCHAD;  Despite considerable efforts to understand the nature of this ecological association, its potential causal mechanisms (both in terms of the direction of the effects as well as the underlying etiology) remain largely uncertain.The pioneering work of Faris and Dunham5 In contrast, the drift interpretation suggests that the association goes in the opposite direction; namely that psychotic patients tend to gradually undergo downward social mobility (or to ‘drift') into deprived neighborhoods following employment difficulties associated with experiencing psychotic-like symptoms and declining cognitive functioning.7Proponents of the social causation interpretation have proposed a number of hypotheses postulating that the development of schizophrenia results from early exposure to adverse neighborhood stressors, accumulated over time, particularly in individuals with genetic liabilities.9 However, the evidence is limited and inconclusive; most of the findings are either based on small and selected clinical samples, ecological study designs with inadequate adjustment for individual-level confounders or population-based studies with genetically uninformed designs. Large-scale, eco-epidemiological studies that simultaneously account for individual, familial and social contextual determinants are therefore integral in clarifying underlying mechanisms.11 The only genetically informed study using nationwide longitudinal registry data failed to support the causal interpretation; the authors concluded that familial confounding fully explained the impact of neighborhood characteristics , measured from birth up to adolescence, on later schizophrenia.12Systematic reviews have generally claimed support for the social causation interpretation.13 Notably, a recent Scottish twin study14 estimated the heritability of deprived neighborhood residence to 71% , which is similar in magnitude to that of schizophrenia.15 What remains unknown is to what degree the genetic influences that contribute to the liability of schizophrenia overlap with those of neighborhood deprivation. In contrast to studies on poor social functioning outcomes in individuals with schizophrenia that tend to focus on environmental explanations ,20 a shared genetic etiology between schizophrenia and socioeconomic deprivation would imply that such associations are, at least partly, due to genetic confounding.Much less is known about the magnitude and etiology of drift mechanisms in patients with schizophrenia. Quantitative and molecular genetic studies have consistently found that indicators of socioeconomic status are considerably influenced by genetic influences.To estimate the contributions of genetic and environmental influences on adulthood deprived neighborhood residence.To estimate such influences on the overlap between schizophrenia and subsequent residence in deprived neighborhoods.To explore the association between subclinical measures of psychotic experiences during childhood and adolescence with neighborhood deprivation during early adulthood.To examine whether polygenic risk scores for schizophrenia predict neighborhood deprivation in late adulthood.We adopted multiple genetically informed designs to address the following aims:Statistics Sweden maintains all nationwide Swedish longitudinal registries with routinely gathered governmental data. Individual data can be linked across registers via a unique civic registration number assigned to all Swedish residents at birth or upon immigration. To ensure confidentiality, we were granted access to anonymized data after approval from the Regional Research Ethics Committee at Karolinska Institutet.We used the Total Population Register to identify all individuals born in Sweden between 1951 and 1974 and linked their data with the Multi-Generation Register to identify all biological full and half siblings in the sample and generate family pedigree structures. Censoring information on mortality and migration dates was obtained from the Cause of Death and Migration Registries, respectively. We identified individuals with hospital discharges and associated schizophrenia diagnoses using the National Patient Register that encompasses data on psychiatric inpatient care since 1973  as well as specialist  outpatient care since 2001 (ICD-10). The Small Area Marketing Statistics Register provided data on annual neighborhood residence between 1982 and 2009. Neighborhood characteristics data were aggregated from the Total Population Register, census registries, as well as the National Crime Register, which includes all criminal convictions in lower general court in Sweden since 1973.n=41 440) or emigrated (n=163 868) before age 35 years to have sufficient follow-up time. Further, we excluded those who could not be assigned to residential areas between ages 31 to 35 (n=37 315) resulting in a sample of 90.8 percent (n=2 386 008) of the original cohort. From this population sample, we subsequently identified all biological full siblings (n=1 615 719), maternal half siblings (n=232 720) and paternal half siblings (n=279 758). In families with multiple offspring, we selected the oldest two siblings born within 5 years from one another to reduce bias due to violation of the equal environment assumption for siblings in our analyses. The final sample included 759 536 sibling pairs .Between 1951 and 1974, a total of 2 628 631 live births that could be linked to two biological parents were registered in Sweden. We excluded individuals who died  on at least two separate occasions, irrespective of any psychiatric comorbidity, before age 31 years. Although single-episode hospitalizations of schizophrenia have been validated in the Swedish Patient Registry;23 Annual neighborhood deprivation scores were generated on the basis of the following aggregated indicators; proportion of residents aged 25–64 years with no secondary school qualifications, immigrant status, divorced and the neighborhood crime rate.12 Despite Sweden's comprehensive welfare policies, previous studies suggest stark socioeconomic differences24 and the occurrence of psychotic disorders has been found to be strongly nonlinearly distributed across neighborhoods with the highest concentrations being observed in the most deprived areas.25 To account for such nonlinear effects, we used a binary measure indicating whether the study participant had ever resided in a deprived neighborhood, defined as the 95th percentile of the continuous distribution, between ages 31 and 35. A total of 260 957 study participants, corresponding to approximately 11 percent of the sample, fulfilled this criterion.We defined neighborhoods according to the Small Area Marketing Statistics classification system that delineates very small and socioeconomically homogenous residential areas,26In our initial analyses, we estimated odds ratios (ORs) for living in a deprived neighborhood in siblings of a proband with schizophrenia compared with sibling of an individual without schizophrenia using the binary logistic regression model. For genetically influenced traits, larger ORs for full rather than half siblings are expected given that they share on average 50 and 25 percent of their co-segregating genes, respectively. Similarly, for traits to be influenced by shared childhood environmental factors, we expected the ORs for maternal half siblings to be larger than those for paternal half siblings owing to the assumption that the former share their childhood environment to a substantially greater extent.27 that explicitly estimate the relative importance of three sources of variation in schizophrenia and neighborhood deprivation; additive genetic effects, shared environmental effects and unique environmental effects, the latter of which also includes measurement error. We estimated these sources of variation by constraining sibling correlations in the variance components to their expected values; additive genetic correlations between full siblings was set to 0.5 and half siblings to 0.25, respectively, while the shared environmental correlation was set to 1.0 for full siblings, 0.83 for maternal half siblings and 0.03 for paternal half siblings. The latter two assumptions were empirically chosen on the basis of a study demonstrating that 83 percent of maternal half siblings and 3 percent of paternal half siblings were registered to live in the same household in the 1960–1990 Swedish national censuses.28 We accounted for potential gender and cohort effects by adjusting for sex and birth year as covariates in all the models.We subsequently fitted univariate, quantitative genetic structural equation models29 we estimated the bivariate correlation between schizophrenia and neighborhood deprivation and stratified the correlation by genetic and environmental influences. We additionally specified a liability-threshold model to account for the binary nature of our phenotypes30 but relaxed the equal thresholds assumption because we observed differences in the prevalence of the phenotypes between full and half siblings. All the models were fitted in R/OpenMx 1.3.31By implementing the Cholesky decomposition method,32 is a population-based longitudinal twin study including all twins born in Sweden between May 1985 and December 1986 who were alive and Swedish residents at the end of 1994 (n=2960). The study participants and their parents were surveyed via mailed questionnaires at three different time points . We excluded study participants whose parents had decided not to participate and those who lacked information on their residential areas in adulthood (n=240). Although participation rates were substantially lower in ethnically heterogeneous residential areas, the attrition rates at follow-up did not vary by reported psychotic symptoms.33 The final sample included 1355 twin pairs, distributed over 533 monozygotic twin pairs and 822 dizygotic twin pairs. The determination of zygosity was based on a validated algorithm derived from questionnaire data measuring the physical similarities between the twins as well as the extent to which others tend to confuse them for one another.32The TCHAD (Twin Study of Child and Adolescent Development)34 Measures of auditory hallucinations, especially when reported by parents,35 have been found to strongly predict psychotic experiences in general.36We classified children whose parents reported that their child had experienced auditory hallucinations according to the Child Behavior Checklist anytime throughout the studied period as having had psychotic experiences.Neighborhood deprivation was measured during 2 years (2008 and 2009) for the TCHAD sample and using a wider definition (75th percentile) owing to the small sample size and relatively fewer participants from the most deprived neighborhoods.Quantitative genetic twin modelsWe fitted quantitative genetic models to the TCHAD data equivalent to those used for siblings as described above. Genetic correlations between monozygotic and dizygotic twins were set to 1.0 and 0.5, respectively, and we assumed that both groups shared childhood environments with their co-twins.37 One twin from each pair was randomly selected for inclusion to yield a data set of 6796 unrelated individuals, avoiding potential biases from analyses of genetically correlated subjects.The TwinGene study includes a total of 22 390 twins born 1958 or earlier, invited to participate between 2004 and 2008. Overall, 12 614 twins (56.3%) consented to participate and donated blood samples, which were genotyped using the Illumina OmniExpress chip containing probes for single-nucleotide polymorphisms (SNPs). The details on the data collection procedures have been described elsewhere.Owing to the TwinGene study design, the study participants were increasingly economically affluent and healthy as compared with population controls. Therefore, we decided to study the relative differences in their exposure to neighborhood deprivation by averaging their continuous deprivation scores between 1999 and 2004.38 and comprised 97 046 SNPs in relative linkage equilibrium. The weightings for each SNP based on the strength of its association in the discovery genome-wide association study were applied to the target sample, TwinGene, to calculate aggregated polygenic risk scores for each subject using PLINK v1.07.39 The scores were generated for P-value thresholds from 0.01 to 1. We fitted linear regression models to assess the proportion of variance in neighborhood deprivation attributed to the polygenic risk scores, incorporating 10 principal components accounting for population substructure and estimated the R2 by subtracting the effects due to the covariates alone.The discovery SNP set was generated from the results of the genome-wide association study of schizophrenia from the Psychiatric Genomics ConsortiumWe estimated that 0.4% of the full-sibling pairs had at least one occurrence of schizophrenia while the equivalent estimate in half siblings was 0.6% . Moreove15 we found a high familial recurrence risk for schizophrenia , which was replicated in the twin study . The partitioning of the phenotypic correlations into genetic and environmental influences demonstrated that genetic influences almost entirely accounted for these correlations. The unique environmental influences were not significantly different from zero . Complete model parameters are presented in The bivariate quantitative genetic models  revealedP-value thresholds for the SNPs (P<0.05) and increasing as a function of the thresholds. The polygenic risk scores for schizophrenia explained approximately 0.15% of the variance in neighborhood deprivation when using all SNPs.Last, we found an association between polygenic risk scores for schizophrenia and living in deprived neighborhoods in mid-to-late adulthood across the the SNPs . All of 9 we found that individuals diagnosed with schizophrenia, as well as children with subclinical psychotic experiences, were more likely to live in deprived neighborhoods as compared with population controls. However, in contrast to these reviews, we found limited support for the interpretation that environmental factors causes the social drift into deprived neighborhoods because the associations were fully confounded by genetic influences. Complementary analyses using molecular genetic data revealed, for the first time to our knowledge, that polygenic risk for schizophrenia in nonclinical participants predicted living in deprived neighborhoods in late adulthood.We investigated the influences of schizophrenia and psychotic experiences on subsequent residence in deprived neighborhoods. We found that living in deprived neighborhoods in adulthood was a substantially heritable trait with genetic influences accounting for 65% of the observed variation. Consistent with recent systematic reviews,14 we found that genetic influences seem to explain 65 percent of the variance in adulthood residence in deprived neighborhoods measured over a period of 5 years. Together, these findings indicate that there is genetic liability that increases the likelihood of living in deprived neighborhoods in the entire population and not only among individuals diagnosed with schizophrenia and their relatives. It is important to note, however, that environmental influences, and in particular those that are not shared within a family, remain important and account for approximately a third of the variation in deprived neighborhood residence.Consistent with a recent Scottish twin study,40 Our twin data suggested a similar mechanism for the likelihood of early adulthood residence in deprived neighborhoods among children experiencing auditory hallucinations. Importantly, this link was also apparent on the molecular level when we observed statistically significant associations between polygenic risk scores for schizophrenia among non-patients and neighborhood deprivation in late adulthood. The shared genetic influences likely target a broader spectrum of problems, including psychotic symptoms, cognitive impairments and language difficulties that are more common in families with individuals with non-affective psychotic disorders.42 These problems have previously been linked to poor vocational outcomes , particularly in patients diagnosed with schizophrenia,43 but our findings suggest that the elevated rates of these problems also extend to residents of deprived neighborhoods.Our next three aims focused specifically on understanding the underlying mechanisms that might explain why individuals with psychotic disorders tend to live in deprived neighborhoods. We found that the association between schizophrenia and adulthood neighborhood deprivation, albeit moderate in magnitude, was fully explained by shared genetic influences. In other words, the excess likelihood of adulthood residence in deprived neighborhoods in schizophrenia patients was explained by genetic factors that also increased their liability to be diagnosed with schizophrenia.44Our results should be interpreted in light of some methodological limitations. First, we relied on national patient registry data for the ascertainment of schizophrenia cases and we adopted a narrow definition of the disorder to achieve a high level of specificity. Sensitivity tests using a broader measure of non-affective psychosis  was neve11 Although assortative mating in schizophrenia is established,45 a violation of this assumption will likely contribute to a downward bias of the genetic influences.46 Half siblings tend to experience higher prevalence of both phenotypes, which is why we allowed for the estimation of different thresholds across sibling types. The specification of a constant shared environmental correlation for Swedish half siblings may be problematic as the correlation has been shown to vary across birth cohorts  as well as with age , particularly in paternal half siblings.47 We conducted, therefore, two sets of complementary sensitivity analyses to test the stability of the presented parameter estimates. First, we specified a number of alternative and plausible shared environmental correlations for the half siblings (n=8830) and dizygotic twins (n=23 030) in the population sample. We subsequently re-fitted the quantitative genetic models on the sample of twins and full siblings. The results matched the presented findings; a moderately sized correlation  that was entirely attributed to common genetic influences .Last, quantitative genetic models involve a number of assumptions, including, but not limited to; no assortative mating, equal exposure across sibling types and a constant shared environmental correlation for each sibling type.siblings  but only49 The development of clinical and policy interventions will, therefore, benefit from future identification of potential pathways between different dimensions of cognitive functions and socioeconomic trajectories.Our findings suggest that the association between schizophrenia and later residence in deprived neighborhoods is consistent with a genetic selection interpretation where the overlap between the phenotypes is primarily explained by common genetic influences. Considering recent developments within psychiatric genetics to incorporate environmental risk factors into molecular genetic investigations of psychiatric disorders , the findings of this study highlight the importance of combining such endeavors with family-based research designs to further our understanding of the underlying causal mechanisms.


PCK1 c.2456C>A) has been reported. In contrast, identification of polymorphisms related to fat composition has been more successful. For instance, our group has described a variant in the stearoyl-coA desaturase (SCD) gene that improves the desaturation index of fat without affecting overall fatness or growth. Identification of mutations in candidate genes can be a tedious and costly process. Genome-wide association studies can help in narrowing down the number of candidate genes by highlighting those which contribute most to the genetic variation of the trait. Results from our group and others indicate that fat content and composition are highly polygenic and that very few genes explain more than 5% of the variance of the trait. Moreover, as the complexity of the genome emerges, the role of non-coding genes and regulatory elements cannot be disregarded. Prediction of breeding values from genomic data is discussed in comparison with conventional best linear predictors of breeding values. An example based on real data is given, and the implications in phenotype prediction are discussed in detail. The benefits and limitations of using large SNP sets versus a few very informative markers as predictors of genetic merit of breeding candidates are evaluated using field data as an example.Among the large number of attributes that define pork quality, fat content and composition have attracted the attention of breeders in the recent years due to their interaction with human health and technological and sensorial properties of meat. In livestock species, fat accumulates in different depots following a temporal pattern that is also recognized in humans. Intramuscular fat deposition rate and fatty acid composition change with life. Despite indication that it might be possible to select for intramuscular fat without affecting other fat depots, to date only one depot-specific genetic marker ( Meat quality has always been important to consumers and critical to guarantee returning customers. However, meat quality is difficult to define as it is a complex concept that includes appearance, sensorial, hygienic and nutritional attributes. In addition, a number of technological features, such as fat melting point, water holding capacity or pH, are crucial for the meat processing industry. Moreover, consumer preferences are specific to each particular market, which means that optimal values on certain attributes such as marbling score, color, or ageing flavor can vary widely across countries.Regarding the sensory traits, the two attributes more important for meat consumers are tenderness and flavor. The former is directly related to muscle fiber morphology  and, most importantly, to fiber type composition. The contractile and metabolic properties of muscle are differentiated by muscle fiber types. According to the main isoforms of myosin heavy chain (MHC) that are present, muscle fibers are classified as type I MHC-1), type IIa MHC-2a), type IIb (MHC-2b), and type IIx (MHC-2x). The type I and IIb fibers, also known as slow-oxidative and fast-glycolytic, respectively, represent two extreme metabolic profiles, while the type IIa and IIx fibers show intermediate properties. Muscles rich in type I fibers are darker, richer in intramyocytic lipids and have a lower glycolytic potential than type IIb fibers [, type IIa, type I2 ≅ 0.4–0.7), which means animals can be selected for more favorable content and composition and bred to improve these traits in the next generation  than vegetable fats. While consumers will favor meat cuts with little rind fat, a minimum amount of IMF is needed to ensure tenderness and flavor. Fatty acid composition of fat varies between muscles, and with the diet, live weight or fatness, age, sex, and genotype. In pigs and other monogastric animals, the fatty acid composition of the fat tissue (particularly of the subcutaneous fat) can be readily manipulated by modifying the proportions of dietary fatty acids, which are absorbed intact from the small intestine and incorporated directly into fat tissue. Thus, fat composition can be manipulated to make it less saturated, which is beneficial from a nutritional point of view. Intramuscular fat content and composition also have an important genetic component  gene, which is the lowest in IMF [Despite sharing a common physiological basis, adipocytes from different fat depots display distinct regulation of cellular activities. Functional studies indicate that IMF cells have a delayed maturation and display differences in both metabolic and secretory functions . The delt in IMF . Distinct in IMF . Expresst in IMF ). In addAs a result of all the above, fatty acid composition in IMF is different from other fat depots. As shown in As stated above, fat content and composition are clearly dependent on the genetic background of each pig population. There are marked differences between breeds and even between lines within breed ,17,18,19Experiments so far provide evidence that IMF responds to selection, but at the expense of increasing fatness, which is expected given the positive correlation that exists between both traits. For instance, after six generations of direct phenotypic selection on IMF, Schwab et al.  found thThere is little experimental evidence on the changes in fatty acid composition due to selection. In line with their correlation, Burkett et al.  found thThe development of molecular markers in the context of the PigMap project in the 1990s allowed conducting global analysis of genomic regions associated with complex quantitative traits such as meat quality-related traits. Measuring intramuscular fat composition is costly, which may explain the relatively low number of studies which have focused on this trait, compared to studies on intramuscular fat content. Early studies with low density marker panels already evidenced that IMF and MUFA are polygenic traits and that the chromosomal regions associated with them are very variable between breeds and even between muscles ,34,35. DAPOA2 gene, [ELOVL6, [The appearance of pig-specific high-density genotyping tools marked a turning point in the efficiency of genome scans. The use of these novel marker arrays reduced the genotyping time and improved the resolution of genome-wide association studies (GWAS). The large linkage disequilibrium rate in the pig genome allowed the definition of haploblocks and the selection of representative tag-SNPs in each one of them. Overall, results confirmed early observations, i.e., the breed- and muscle-dependence of the associated regions and the pleiotropic nature of the metabolism/transport pathways. The efforts from the Pig Genome Sequencing Consortia in assembling the pig genome materialized in a new and efficient tool in the form of genome browsers that can help to identify positional candidate genes underlying these relevant genomic regions. The latest GWAS studies in IMF composition have confirmed the relevance of regions in SSC4, 8 and 14 across most breeds and identified other breed-specific fragments ,40,41. OA2 gene, ) or for [ELOVL6, ,44). AltOverall, there is a large overlap between genomic regions associated with IMF and fattening, particularly with backfat thickness. This reflects the positive genetic correlation that exists between IMF and backfat. In other words, mutations resulting on higher IMF content will also result in increased overall fatness. This limits the application of these markers in a pig breeding system that has lean content, among other traits, as a selection objective. Similarly, genomic regions associated with fat composition are mostly shared between muscle and fat traits , indicatSince the genetic correlation between IMF and backfat is not complete, there is the possibility to find markers that will improve IMF content without compromising the lean content of the carcass. Functional studies on adipocytes from different fat depots indicate that IMF cells have a delayed maturation and display differences in both metabolic and secretory functions . UnderstMC4R), the (heart) fatty acid binding protein 3 (FABP3) and the leptin receptor (LEPR), although causality has not been proved for any of them.In this regard, several markers have been reported in association with IMF content in pigs, particularly within functional or positional candidate genes. However, most of them are not specific for IMF. Instead, they promote overall fatness and, although an improvement in IMF content is achieved, this is counterbalanced by worse lean content. Many of these mutations have only been validated in a number of pig breeds and often they only explain a small percentage of genetic variation. In general, marker associations are influenced by the genetic background of the animals and magnitude of the observed effect are normally population specific. Three of the mutation with a more consistent effect on IMF content across studies are the melanocortin 4 receptor  is active in many peripheral tissues. Besides the endocrine role in regulating food intake in the central neural system, leptin also exerts important paracrine and autocrine functions in muscle and fat cells regulating fatty acid uptake and oxidation. In pigs, a missense mutation in exon 14 of the LEPR (g.1987C>T) has been reported to affect overall fatness, in all breeds tested so far [A missense substitution in the exon 1 of the sp298Asn ) was onesp298Asn . The MC4sp298Asn . Severalsp298Asn ,51 and Psp298Asn  but not sp298Asn . Similarorkshire  while muorkshire . This pa content ,57. Knowd so far ,57,58,59PCK1 gene has represented a major breakthrough [The link between muscular IMF and obesity-related genes is well reported ,61. In ckthrough . In croskthrough  are valiELOVL6) gene have been associated with C16 (16:0 and 16:1) to C18 (18:0 and 18:1) content in longissimus dorsi muscle and backfat [APOA2) gene associated with the content of several PUFA in muscle [Regarding IMF fatty acid composition, mutations in a small number of genes have been found to be related to variation in some of the fatty acid fractions. For instance, several promoter mutations in the ELOVL fatty acid elongase 6 . Current annotation in pigs has identified similar number of protein-coding genes, but fewer non-coding transcripts than in humans but it is highly likely that, when the pig genome annotation is more advanced, similar numbers are encountered. In this regard, most porcine microarrays have been designed to target protein-coding genes, which limits the information that can be obtained from the RNA samples. In contrast, massive parallel sequencing methodologies are more flexible on defining the target. Library preparation protocols can be accommodated to include (or restrict) a wide variety of RNA populations. Moreover, RNA-seq has other additional advantages including the identification of novel transcript variants and the detection of sequence polymorphisms. Therefore, this technology gives currently the most comprehensive screening of key genes and processes participating in these complex phenotypes and, more importantly, it represents a more powerful approximation to detect population-specific markers associated with the traits.Complex questions need to be tackled from more than one perspective. In the context of traits with a complex genetic background, structural genomic experiments (as seen in the last section) have been complemented with functional approaches, involving the study of the transcriptome of target tissues. The interest in comparing patterns of active and inactive genes between breeds differing in fattening (and growth) potential was developed from even before there were pig-specific microarrays (reviewed in ). HoweveA number of studies have compared RNA-seq transcriptome data from breeds with contrasting growing and fattening abilities. Most of these experiments compared lean, fast growing breeds such as Yorkshire or Landrace with indigenous Chinese or Korean breeds ,70,71,72SRA (steroid receptor RNA activator) gene which encodes the SRA protein but is also transcribed into an lncRNA which is a transcriptional coactivator of steroid and non-steroid receptors. During pre-adipocyte differentiation SRA expression is induced by 2-fold and it promotes differentiation partly via coactivation of PPARγ [Recent studies have shown that non-coding genes are crucial for adipocyte differentiation and function. Generally, non-coding RNAs (ncRNAs) can be sorted into housekeeping and regulatory ncRNAs. Housekeeping ncRNAs include transfer RNAs (tRNAs), ribosomal RNAs (rRNAs), small nuclear RNAs (snRNAs), small nucleolar RNAs (snoRNAs) and are usually expressed constitutively, while regulatory RNAs consists of piwi-interacting RNAs (piRNAs), microRNAs (miRNAs), and long non-coding RNAs (lncRNAs) . Interacof PPARγ . Thus faAnother novel area of research is the study of adipose-muscle cross-information by extracellular vesicles ,80. ThesFrom efforts from the human ENCODE project it has become evident that much of the genomic variation that affects phenotypes is not in coding regions  but in regulatory regions (which might account for up to 80% of the genome sequences). A network of as many as one million enhancers have been described in the human genome , which rIncluding IMF and fatty acid composition in the selection objectives of breeding companies has several drawbacks, such as that the measurement is costly and usually unavailable on the selection candidates. Consequently, in traditional breeding schemes, the genetic evaluation for fatty acid content is more likely to be based on phenotypes of slaughtered littermates and other relatives of the selection candidates. Genotyping cost is one of the main factors limiting the implementation of genomic selection in pigs because, despite improving the prediction accuracy of the estimated breeding value (EBV), the value of any individual pig is lower and the generation interval is shorter than in cattle . TherefoSCD and LEPR) with strong and consistent effects on IMF and fatty acid composition [SCD promoter  could b1987C>T; .SCD and LEPR. The correlation between genomic EBV (GEBV) and the adjusted phenotypic values of the testing dataset was used as a measure of the prediction accuracy. The correlations are shown in We compared three different methods for genetic evaluation of IMF and fatty acid traits in our Duroc resource population. The first method was genomic prediction using a Bayes B model using high-throughput genotypic data, the second method was a traditional best linear unbiased prediction (BLUP) estimation without genotypic data, and the third method was BLUP with the two genetic markers at We genotyped 135 purebred Duroc barrows using the PorcineSNP60 v2 Genotyping BeadChip . After data quality control, the remaining data comprised 36,432 SNPs. We used 65 animals born in 2002–2003 as training data to estimate the SNP effects and then to predict the GEBV of the 70 animals born in 2009–2010. The substitution effect of each SNP was estimated using the Bayes B approach , which iSCD and LEPR loci were the most influential on IMF and fatty acids in the population studied. Markers at SCD and LEPR were selected as representative of these two associations and genotyped independently. Then, both training and prediction were performed using only one or both of these two SNPs  as testing set. The amount and quality of the phenotypic data greatly affected the accuracies achieved. In unitrait (U) models, only the studied trait was evaluated. In practice, traits such as body weight and backfat thickness are measured on routine performance tests at age of 180 days and they have large amounts of records that are used in genetic evaluations, including phenotypes for the selection candidates . In multitrait (M) models, IMF and the fatty acid compositional traits were evaluated conjointly with body weight and backfat thickness. All models used 936 records of IMF and fatty acids from pigs born from 2002 to 2007 (phenotypes with a low relationship degree), and some models additionally used 196 records from littermates of the testing set .Unitrait BLUP without genotypic information provided moderate correlations only when records from littermates were available, ranging from 0.31 to 0.39 for all traits except SFA and 18:1/18:0 ratio. With multitrait BLUP, great increments in correlation can be achieved for those traits that are more correlated to body weight and backfat thickness. For example, in our resource Duroc population, the genetic correlations of IMF with body weight and backfat thickness were 0.29 and 0.44, respectively, while 18:1/18:0 ratio did not improve because it was genetically uncorrelated to body weight and backfat thickness  (own unpublished data). The littermate phenotypes on IMF content and fatty acid composition became less beneficial for the multitrait model.SCD) and 803 for NM_001024587:g.1987C>T (LEPR). In this case, the breeding value of each animal was calculated as the genotypic value of the SCD and LEPR markers plus a polygenic effect. This method provided the best accuracies. With univariate models and no records from littermates, accuracies from BLUP with genetic markers were similar to those predicted with only the SCD and LEPR markers. By adding records from littermates and on body weight and backfat thickness, the correlations were 0.47–0.48 for IMF and 18:1, 0.53–0.55 for MUFA and 18:1/18:0, and as high as 0.62–0.70 for SFA, PUFA, and SFA/PUFA.Instead of genotyping a few animals with a high-density SNP chip (Method A), in Method C we genotyped a much larger group of animals for two SNPs with validated effect on IMF and fatty acid composition. We genotyped 915 pigs for AY487830:g.2228T>C , is becoming available to cope with this need with very promising results .SCD and LEPR loci in our Duroc population, may enhance the accuracy of BLUP-based breeding values for some meat quality traits, especially when the phenotypic data available are insufficient for accurate predictions. If additional markers are incorporated, custom low-density SNP panels could be developed [Selected genetic markers, such as eveloped  with whiOur results show no benefit of using high-density SNP panels over phenotypic data and singled-out genetic markers. However, we used a relatively small training set and it is possible that larger training sets allow for a more accurate estimation of marker effects and better predictions with the whole SNP chip, which in turn could be used for the prediction of other traits in the selection objectives. While genotyping costs are a concern, according to a simulation by Tribout et al. , only a Meat quality cannot be defined by a single meat attribute. However, IMF content and composition affect important quality attributes such as tenderness, juiciness, flavor, nutritional value, fat melting point and rancidity potential. Given the incomplete genetic correlation of IMF and fatty acid composition with total fat content, they can both be improved through selection independently from overall fatness. However, their measurement is costly and not easy, usually requiring determinations performed on meat samples taken at slaughter. Therefore, selection strategies would benefit from the use of molecular markers. Meat quality is a complex polygenic trait and, unfortunately, with one exception, no IMF-specific markers have been identified and only a few have been associated to fatty acid composition. However, as we develop a better understanding of how the genome works it is becoming clear that both coding and non-coding genes influence polygenic traits. Moreover, most of the genome has a regulatory function which makes identification of causal mutations even more difficult. However, it is possible to have good predictive ability of breeding values even with a small number of molecular markers as long as they explain a sufficient fraction of the genetic variance of the traits. As shown in our field data, this information can very effectively complement pedigree-based genetic predictions using phenotypic data from relatives. Altogether, this advocates for the development of population specific low-density marker arrays, particularly in populations with genotyping budget constraints.


Understanding mediation is useful for identifying intermediates lying between an exposure and an outcome which, when intervened upon, will block  the causal pathway between the exposure and outcome. Mediation approaches used in conventional epidemiology have been adapted to understanding the role of molecular intermediates in situations of high-dimensional omics data with varying degrees of success. In particular, the limitations of observational epidemiological study including confounding, reverse causation and measurement error can afflict conventional mediation approaches and may lead to incorrect conclusions regarding causal effects. Solutions to analysing mediation which overcome these problems include the use of instrumental variable methods such as Mendelian randomization, which may be applied to evaluate causality in increasingly complex networks of omics data. New technologies permit the genotyping and profiling of gene expression, epigenetics and metabolites, allowing the collection of high-dimensional molecular phenotype data on a large number of individuals. This “omics revolution” ,2 offersRecent applications of omics technologies within large-scale population-based studies present new opportunities for identifying novel biomarkers for both risk factors and disease. Furthermore, different forms of omic data can be combined with increasingly complex models  and may However, as these molecular intermediates are influenced by both endogenous and exogenous factors and by disease processes, they are prone to the many limitations of observational epidemiological study including confounding, bias and reverse causation  5). We . We 5). A mediator (M) is a variable that is on the causal path from an exposure (E) to an outcome variable (Y). It causes the outcome and is itself caused by the exposure. There are a variety of statistical methods that have been introduced for analysing mediation, from simple regression-based systems and structural equation models to more novel parametric and semi-parametric methods , and theUnderstanding mediation is useful for identifying potential modifiable risk factors lying between an exposure and an outcome which, when intervened upon, will block  the causal pathway between the exposure and outcome. For example, elevated levels of non-fasting remnant and LDL cholesterol levels are modifiable intermediates of cardiovascular disease. These may be intervened upon to alter the downstream risk of cardiovascular disease, when underlying risk factors are either difficult, as in the case of adiposity , or indeMediation approaches have been adapted to understanding the role of molecular intermediates in causal pathways, using high-dimensional omics data ,11–18. HOne of the most widely cited approaches for evaluating mediation in an epidemiological setting is that originally outlined by Baron and Kenny . This reE is associated with YE is associated with MM is associated with Y after adjusting for EE is independent of Y after adjusting for MThe Sobel test may then be used to indicate whether the decrease in the effect of E on Y after adjusting for M is “statistically significant”. If this test provides evidence for mediation, the proportion of the effect of E on Y that is mediated by M can be calculated.While this approach is widely implemented, it is known to be problematic because it is highly dependent on a number of strong assumptions, the measurement characteristics of the variables and on reliable identification of causal effects. Some such often overlooked assumptions are that (i) both Y and M are continuous; (ii) there are no unmeasured confounders of E and Y or of M and Y; (iii) E must not cause a confounder of the M-Y association; (iv) the correct functional form has been specified for each model (e.g. linearity); (v) there are no interactions between E and M on Y; and (vi) there is no measurement error . Here, mFurther methods have been developed to allow much more flexible modelling than the traditional Baron and Kenny approach and allow for a more general outcomes framework, distribution-free estimates of mediated effects, interactions and intermediate confounding ,21. SuchAHRR region have shown the largest signals of differential methylation in both these EWASs. These findings have driven subsequent analyses to investigate whether environmentally modified DNA methylation play an important role in the aetiology of cancer, through the use of mediation analysis.Nonetheless, some of these approaches have been readily applied in the setting of the molecular mediation without much consideration being given to the underlying assumptions and thus may have led to spurious results and interpretations. For example, large epigenome-wide association studies (EWAS) have identified associations between smoking and DNA methylation , and lunAHRR methylation in the association between smoking and lung cancer  . This teThis approach has typically been applied to understand the extent to which molecular processes mediate the effect of quantitative trait loci (QTLs) on the risk of a particular disease. By focusing on the assessment of the genetic component of the molecular intermediate, this avoids limitations in the observational epidemiology setting of potential confounding from environmental factors on the intermediate – outcome relationship and also the influence of reverse causation, whereby changes in the outcome may influence the intermediate factor. In addition, some other qualities of genetic variants which make them useful in the causal inference analysis are that they are not influenced by reporting bias and are subject to relatively little measurement error.By using a genetic variant as a causal “anchor” to dissect causal relationships, the causal inference test has close links with Mendelian randomization (MR) ,32, a meFurthermore, this approach is also vulnerable to measurement error, which can be in either the mediator or the outcome, in those steps 2) to 4) of the causal inference test (see above) which involve adjustment in the regression models.In particular, the presence of measurement error can make it hard to delineate a situation of true mediation from that of horizontal pleiotropy  genetic variation, this provides the opportunity to use genetic proxies to probe causality between DNA methylation and particular outcomes using MR  in a known exposure-outcome relationship (e.g. smoking-lung cancer), it may be possible to obtain causal estimates from MR studies for all steps in the chain, e.g. from smoking to DNA methylation, and from DNA methylation to lung cancer, in a two-step Mendelian randomization approach . Here thusing MR ,42–44.FWhile the two-step MR method was initially posed for the delineation of mediation by specific epigenetic processes between environmental exposures and disease, it may equally be applied to a full range of potential mediators, such as transcriptomics, proteomics and metabolomics . In addiIn addition, with regards to asserting mediation in an exposure-outcome setting, the two-step MR approach could be combined with the two-sample approach to powerfully and efficiently examine the extent of mediation in causal networks . First, As with the causal inference test, complexity of associations between omic level intermediates and inadequate biological knowledge of the genetic variants associated with them pose a challenge to Mendelian randomization. Arguably, the biggest challenge to overcome is that of potential pleiotropy of the genetic instrument . ApproacIn situations of reverse causation whereby a genetic variant may be causing the outcome which in turn causes the molecular phenotype, rather than vice versa, bidirectional Mendelian randomization using well characterized genetic variants for both the molecular intermediate and the outcome may be used to distinguish between these causal models ,38. AlteMediation analysis and causation are linked concepts and the former cannot be successfully applied without some consideration given to the latter . Care muMendelian randomization approaches hold promise for investigating mediation without relying on such strict assumptions ,43,45. FBox 1. Identifying genetic proxies for ‘omics measuresDevelopments in genomics have driven the identification of many genetic variants associated with a wide range of exposures which have potential utility in MR analysis ,52. As mBox 2. Consequences of pleiotropy and potential solutions for Mendelian randomization analysisPleiotropy is the phenomenon by which a genetic variant may affect more than one phenotypic characteristic . There aOne potential means of investigating potential pleiotropy is with the use of multiple genetic instruments. With an amassing number of independent instruments, it would be increasingly improbable that they would result in the same conclusion regarding a causal effect if they were all pleiotropic variants. In particular, the finding that all genetic variants have an effect on the outcome to the extent expected given their effect on the exposure can be used to support an assertion of no horizontal pleiotropy ,61. If sA further approach used to separate independent effects of risk factors when multiple phenotypes are correlated with a particular genetic variant or set of variants is with multi-variable Mendelian randomization analysis  which prWhile MR is a helpful solution in some circumstances when considering molecular mediation, it is not a global solution and its application can be restricted due to the availability of genetic instruments. In addition, while MR makes less strict assumptions about confounding and measurement error, it does make other assumptions ,70,71 wh


Many complex diseases like diabetes, hypertension, metabolic syndrome, et cetera, are measured by multiple correlated phenotypes. However, most genome-wide association studies (GWAS) focus on one phenotype of interest or study multiple phenotypes separately for identifying genetic variants associated with complex diseases. Analyzing one phenotype or the related phenotypes separately may lose power due to ignoring the information obtained by combining phenotypes, such as the correlation between phenotypes. In order to increase statistical power to detect genetic variants associated with complex diseases, we develop a novel method to test a weighted combination of multiple phenotypes (WCmulP). We perform extensive simulation studies as well as real data (COPDGene) analysis to evaluate the performance of the proposed method. Our simulation results show that WCmulP has correct type I error rates and is either the most powerful test or comparable to the most powerful test among the methods we compared. WCmulP also has an outstanding performance for identifying single-nucleotide polymorphisms (SNPs) associated with COPD-related phenotypes. Genome-wide association studies (GWAS) aim to discover genetic variants associated with complex diseases , 2. In GThere are three commonly used strategies to detect genetic associations between a genetic variant and multiple correlated phenotypes. The first one is combining test statistics  from univariate analysis. This strategy first tests an association between each phenotype and a genetic variant individually and then combines the univariate analysis results, i.e. test statistics or p-values, by using different approaches. The O’Brien’s method , sample In this article, we developed a novel allele-based method for testing association between multiple phenotypes and a genetic variant. First, we take the allele at the genetic variant as the response variable and the multiple phenotypes as predictors. Then, we present a new multivariate method that we refer to as WCmulP (Weighted Combination of multiple Phenotypes), inspired by TOW  procedure proposed by Sha et al.  for raren unrelated individuals. Each individual has K possibly correlated phenotypes. Let Yi,k denote the kth phenotype of the ith individual. We propose to use an allele-based logistic regression model to test the association between a variant of interest and multiple phenotypes. For a genetic variant with two alleles, we use xi−12 and xi2 to denote the coding of the two alleles of the ith individual such that we use x1 and x2 to code the two alleles of the first individual, use x3 and x4 to code the two alleles of the second individual, and so on. For a variant with two alleles A and a, if the genotype of the ith individual is AA, we define xi−12 = xi2 = 1; if the genotype is aa, we define xi−12 = xi2 = 0; and if the genotype is Aa, we define xi−12 = 1; and xi2 = 0. We define the kth phenotype corresponding to the two alleles xi−12 and xi2 of the ith individual as yi−1,k2 and yi,k2, where yi−1,k2 = yi,k2 = Yi,k. Hence, the total number of observations in the allele-based data is 2n. We model the relationship between alleles and multiple phenotypes using the inverse logistic regression modelπj = PrT), α is the intercept, and β = T is a K-dimention vector of parameters. To test the association between multiple phenotypes and the variant is equivalent to test the null hypothesis H0: β = 0 under Eq  in Eq  to univariate T is our proposed test statistic to test the effect of the Weighted Combination of multiple Phenotypes (WCmulP).We propose to use the optimal weights proposed by Sha et al. , that isK) in Eq . With then in Eq  becomes(xj−x¯)2,where y¯(xj−x¯)2,where y¯ that Eq . Since wp covariates. Let Zi,l denote the lth covariate of the ith individual. We define the lth covariate corresponding to the two alleles xi−12 and xi2 of the ith individual as zi−1,l2 and zi,l2, where zi−1,l2 = zi,l2 = Zi,l. We then adjust the phenotype value yj,k for the covariates by applying linear regressions. That is,yj,k in the linear regression. We incorporate the covariate effects in WCmulP by replacing yj,k in Eq : the test statistic of Score is given by Eq (en by Eq .OB (O’Brien’s method) [eTΣ−1Tuni, is a linear combination of univariate test statistics, and it is the most powerful test among a class of test statistics that are linear combination of Tuni, where Tuni is the vector of the univariate test statistics, Σ is the covariance matrix of Tuni, and e = T is a 1’s vector with length K (the number of phenotypes). method) : the tesMultiPhen (Joint model of Multiple Phenotypes) [notypes) : it usesTATES (Trait-based Association Test that uses Extended Simes procedure) [pk) sorted p-value in ascending order, me and me(k) are the effective numbers of independent p-values of all K phenotypes and k specified phenotypes, respectively. The effective numbers can be calculated from the correlation matrix of p-values.ocedure) : it combCCA  [nalysis) : it extrSHet (Test for Heterogeneous genetic effects) [SHet, is based on SHom, which is the most powerful test statistic when the genetic effect is homogeneous. Both SHom and SHet are quadratic combinations of the univariate test statistics. The test statistic of SHom is R is the correlation matrix of Tuni, W is a diagonal matrix of weights for the univariate test statistics, and e is a 1’s vector with length K (number of phenotypes). SHet can be viewed as the maximum of SHom’s satisfying different thresholds. More specifically, given a threshold, only test statistics with absolute values that are greater than the threshold are used, R and W are therefore partially used corresponding to the selected test statistics. The p-values of SHet can be evaluated by simulation.effects) : The tesn unrelated individuals with total K phenotypes according to a variety of simulation scenarios. Specifically, genotype data at a genetic variant are simulated according to the minor allele frequency (MAF) under the assumption of Hardy-Weinberg equilibrium. We generate K phenotypes by the factor modely = T; x is the genotype score at the variant of interest; λ =  is the vector of effect sizes of the genetic variant on the K phenotypes; f = T ∼ MVN, Σ = (1−ρ)I + ρA, R is the number of factors, A is a matrix with elements of 1, I is the identity matrix, and ρ is the correlation between fi and fj for i ≠ j; γ is a K by R matrix; c is a constant number; and ε = T is a vector of residuals, ε1,…,εK are independent, and εk ∼ N for k = 1,…,K. Based on Eq T, and γ = T.Model 2: There are two factors and genotype has an impact on two factors with opposite effects. That is, R = 2, γ = bdiag, where i = 1,2, “bdiag” indicates the block diagonal matrix.Model 3: There are two factors and genotype has an impact on one factor. That is, R = 2, γ = bdiag, where i = 1,2.Model 4: There are four factors and genotype has an impact on one factor. That is, R = 4, γ = bdiag, where i = 1,…,4.Model 5: There are four factors and genotype has an impact on two factors with opposite effects. That is, R = 4, γ = bdiag, where i = 1,…,4.Model 6: There are four factors and genotype has an impact on three factors with effects of different directions. That is, R = 4, γ = bdiag, where i = 1,…,4.c2 and the between-factor correlation is ρc2. Table A in γ and cov(y|x) for different numbers of factors  when the number of phenotypes is 8.In the six models, the within-factor correlation is z1 is a continuous random variable generated from a standard normal distribution, z2 is a binary random variable taking values of 0 and 1 with a probability of 0.5, and e is a K-dimensional vector with all elements being 1’s. To evaluate type I error rates and powers, we consider n = 1,000 unrelated individuals, MAF = 0.3, and different numbers of phenotypes K = 8,16. To evaluate the type I error rates of all methods, we generate all phenotypes independent of genotypes by setting β = 0. We evaluate type I error rates at significance levels α = 0.001 and 0.01 for all methods. To evaluate powers, we vary the values of β (within-factor correlation c2 = 0.5 and between-factor correlation ρc2 = 0.1) and vary the values of within-factor correlation c2  .We also generate phenotypes with covariates effects. We refer to Sha et al.  and Sun K = 8) and K = 16). From these tables, we can see that all estimated type I error rates of WCmulP are within 95% CIs, which indicates that the proposed WCmulP is a valid test. The estimated type I error rates of SHet, Score, MultiPhen, TATES, CCA and OB are not significantly different from the nominal levels.To evaluate the type I error rates of WCmulP and other six methods, we consider different numbers of phenotypes, different significance levels, and different numbers of factors. In each simulation scenario, the p-values of WCmulP and SHet are estimated using 10,000 permutations, and the p-values of Score, MultiPhen, TATES, CCA and OB are estimated using their asymptotic distributions. The type I error rates of the seven methods are evaluated using 10,000 replicated samples. For 10,000 replicated samples, the 95% confidence intervals (CIs) for type I error rates of nominal levels 0.001 and 0.01 are  and , respectively. The estimated type I error rates of WCmulP and other six methods are summarized in β . However, OB reduces power significantly when genetic effects are heterogeneous, especially when opposite directions of the genetic effects exist  or when the genetic variant impacts only a small portion of phenotypes (model 4). This phenomenon was also observed by Zhu et al. .Power comparisons of TATES with tests in group 1 depend on the models. In general, TATES is more powerful than tests in group 1 when the genetic variant impacts on a portion of phenotypes (models 3 and 4).c2 increases, the powers of all methods decrease  WCmulP, Score, MultiPhen, and CCA have very similar powers in all six models  of the phenotypes instead of the original phenotypes to do power comparisons and the results are given in Figures A-D in −5  refers to a group of diseases that cause airflow blockage and breathing-related problems. The Genetic Epidemiology of COPD Study (COPDGene) is a multicenter observational study designed to identify genetic factors associated with COPD, to define and characterize disease-related phenotypes, and to assess the association of disease-related phenotypes with the identified susceptibility genes . 10,192 −6 of the total variation are excluded. In this way, one PC is excluded and there are 6 PCs left. Using the first few PCs is also a dimension reduction method. Thus, using PCs of the phenotypes, WCmulP uses two dimension reduction methods: using the first few PCs and the weighted combination of those PCs. To identify SNPs significantly associated with the 7 COPD-related phenotypes and the top 6 PCs of the phenotypes, we use the genome-wide significance threshold of 5 × 10−8. There are total 16 SNPs that are significant under at least one method  complex diseases are usually measured by multiple correlated phenotypes in genetic association studies; and (2) there is increasing evidence showing that studying multiple correlated phenotypes jointly may increase powers for detecting genetic variants that are associated with complex diseases. Our results show that WCmulP has correct type I error rates and is either the most powerful test or comparable to the most powerful tests among the seven tests we considered. None of the other methods showed consistent good performances under the simulation scenarios. OB is the most powerful test when the genetic effects are homogeneous, while it loses power dramatically when genetic effects are heterogeneous; especially when opposite directions of the genetic effects exist. SHet, Score, MultiPhen, and CCA have similar powers and they are less powerful than WCmulP in most scenarios. TATES is more powerful only when the genetic variant affects a portion of phenotypes. In addition, in the real data analysis, WCmulP identified 13 (out of 16) significant SNPs, 1 SNP less than the largest number of identified SNPs; using PCs of phenotypes, WCmulP is the only method that identified all 16 SNPs. The real data analysis results show that WCmulP has excellent performance in identifying SNPs associated with complex disease with multiple correlated phenotypes such as COPD.In the context of association studies, it is important to correct for population stratification (PS). PS refers to allele frequency differences between populations unrelated to the outcome of interest, but due to systematic ancestry differences. PS can cause seriously confounded associations if not adjusted properly , 44. TheOne disadvantage of WCmulP is that the test statistic does not have an asymptotic distribution and a permutation procedure is needed to calculate its p-value, which is time consuming compared to the methods whose test statistics have asymptotic distributions. The running time of WCmulP with 1,000 permutations on a data set with 5,000 individuals and 20 phenotypes on a laptop with 4 Intel(R) Cores(TM) i7-4790 CPU @ 3.6GHz and 4 GB memory is no more than 0.15s. To perform GWAS, we can first select genetic variants that show evidence of association based on a small number of permutations , and then a large number of permutations are used to test the selected significant genetic variants . FurtherIn our simulation studies, the numbers of phenotypes varied from 8 to 16 and the methods rely on all observations having fully observed phenotypes. However, in real data analysis, as the number of phenotypes increases the chance that missing at least one observation increases exponentially, especially in epidemiological and clinical research , 60. TheS1 File(PDF)Click here for additional data file.


P=3.32x10−5) based on analyses using the optimal sequence kernel association test. Variants along this pathway also showed evidence of replication using imputed data from the Avon Longitudinal Study of Parents and Children cohort (P=0.02). Subsequent analyses found that the strength of evidence diminished when analysing genes in this pathway individually, suggesting that they would have been overlooked in a conventional gene-based analysis. Future studies that adopt similar approaches to investigate polygenic effects should yield value in better understanding the genetic architecture of complex disease.Current endeavours in rare variant analysis are typically underpowered when investigating association signals from individual genes. We undertook an approach to rare variant analysis which utilises biological pathway information to analyse functionally relevant genes together. Conventional filtering approaches for rare variant analysis are based on variant consequence and are therefore confined to coding regions of the genome. Therefore, we undertook a novel approach to this process by obtaining functional annotations from the Combined Annotation Dependent Depletion (CADD) tool, which allowed potentially deleterious variants from intronic regions of genes to be incorporated into analyses. This work was undertaken using whole-genome sequencing data from the UK10K project. Rare variants from the KEGG pathway for arginine and proline metabolism were collectively associated with systolic blood pressure ( The analysis of these variants, potentially with larger effect sizes,1 may prove vital in understanding this missing heritability and uncovering the genetic architecture of complex disease.Genome-wide association studies (GWAS) have been successful in identifying novel susceptibility loci across a range of complex diseases. However, the underlying mechanisms by which associated variants influence disease or quantitative phenotypes are often undefined and the variance that they explain is almost always small. Next-generation sequencing (NGS) has allowed rarer genetic variants <1%), which may potentially contribute substantively to phenotypic variance,5 SKAT6) and are more robust when analysing rare variants that have conflicting directions of effect for a phenotype of interest (protective or deleterious). Despite this, rare variant analyses remain underpowered, owing to inadequate samples sizes and the manner in which the genome is partitioned. One way in which statistical power can potentially be increased is by analysing combinations of variants which have a similar functional impact from within genes that lie along the same molecular pathway.Study designs over the last decade have largely concerned the genetic contribution of common variants and thus lack power when applied to rare variants. In order to improve power when analysing these variants using NGS data, methodology based on their aggregation has been developed. Some of these techniques are based on dispersion  from participants involved in the UK10K project and cardiovascular phenotypes as measured in the Avon Longitudinal Study of Parents and Children (ALSPAC)The UK10K consortium has two main project arms. In this study, we have used data from the cohorts’ arm, which was designed to investigate the contribution of genome-wide genetic variation to a range of quantitative traits. This arm contains individuals from two intensively studied cohorts of European ancestry, ALSPAC and TwinsUK:12 .ALSPAC is a population-based cohort study investigating genetic and environmental factors that affect the health and development of children. The study methods are described in detail elsewhereEthical approval was obtained from the National Research Ethics Service (NRES) Committee, South East London, REC 2. Written informed consent was obtained from parents for all measurements made.11 Initially, only middle-aged women were recruited and as a result 83% of the registry is female. The registry currently contains 51% monozygotic and 49% dizygotic twins aged 18–103 years. Further details are available online (http://www.twinsuk.ac.uk/).The TwinsUK registry is a cohort of volunteer adult twins from all over the United Kingdom.Informed consent was obtained from participants before they entered the study and ethical approval was granted by the NRES Committee, Westminster, London.μg) was sheared to 100–1000 bp using a Covaris E210 or LE220 . Sheared DNA was size subjected to Illumina paired-end DNA library preparation. Following size selection (300–500 bp insert size), DNA libraries were sequenced using the Illumina HiSeq platform as paired-end 100 base reads according to manufacturer’s protocol.DNA Samples from 4030 UK10K study participants  were subjected to low coverage (6–8 × average read depth) WGS. Sequencing was performed at both the WTSI and the Beijing Genomics Institute (BGI). DNA  went through the variant calling procedure. Low-quality samples were identified by comparing the samples to their GWAS genotypes using ~20 000 sites on chromosome 20. A total of 112 samples (48 ALSPAC and 64 TwinsUK) were removed, leaving 3798 samples (1928 ALSPAC and 1870 TwinsUK) that were eligible for the genotype refinement phase.Data that passed quality control (QC) was aligned to the GRCh37 human reference used in phase 1 of the 1000 Genomes Project. Reads were aligned using BWA (v0.5.9-r16).14 with default parameters. Additional sample-level QC steps were carried out on refined genotypes, resulting in 17 samples (16 TwinsUK and 1 ALSPAC) being removed owing to either non-reference discordance with GWAS SNV data>5%, multiple relations to other samples or failed sex check. A principal components analysis was conducted using EIGENSTRAT15 to exclude participants of non-European ancestry after merging our data with a pruned 11 HapMap3 population data set.16 Forty-four subjects (12 TwinsUK and 32 ALSPAC) did not cluster to the European cluster and were removed. The final sample size for association analyses comprised of 3621 individuals (1754 TwinsUK and 1867 ALSPAC).Missing and low-confidence genotypes in the filtered VCFs were refined out using the imputation procedure in BEAGLE 417 Low-density lipoproteins were subsequently calculated using the Friedwald equation:18Body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP) and non-fasting blood samples were obtained from the age 9 clinic . The blood samples were used to determine lipids including total cholesterol (TC), triglycerides (TG) and high-density lipoproteins (HDL) using a modification of the standard Lipid Research Clinic Protocol.BMI, SBP, DBP and blood samples taken after at least 6 h of fasting were obtained from study participants . A colorimetric enzymatic method was used to determine TC, TG and HDL-c levels. The Friedewald equation was used to calculate LDL-c levels in subjects.Further details on phenotype measurements in the UK10K consortium are provided in the Genome-wide association (GWA) data was also available for 6557 ALSPAC samples who were not enroled in the UK10K project. This data was genotyped using the Illumina HumanHap550 quad genome-wide SNP genotyping platform  by the Wellcome Trust Sanger Institute  and the Laboratory Corporation of America . All individuals passed QC based on incorrect sex assignment; abnormal heterozygosity ; high missingness (>3%); cryptic relatedness (>10% identity by descent) and non-European ancestry .20 to use each reference panel to impute missing variants in their counterparts before ultimately combining them together. This meant that all loci involved in the discovery analysis were imputed in this sample. Further detail on this imputation process can be found in the publication by Timpson et al.20 All rare variants (MAF≤1%) were filtered to have Hardy–Weinberg equilibrium P>5 × 10−7. As accurate imputation of rare variants can be challenging, only variants with an imputation quality score of 0.4 or higher were used in this analysis. Phenotype measurements were collected as before for lipid traits, although for BMI, SBP and DBP we used measurements obtained from the focus at age 7 clinic  in order to maximise sample size.Variants discovered through WGS in the UK10K project were used for the development and use of a reference panel for this imputation within the ALSPAC and TwinsUK GWA data sets. These were combined with known variants taken from the 1000 Genomes reference panel. Novel functionality was developed in IMPUTE2http://www.bris.ac.uk/alspac/researchers/data-access/data-dictionary). Ethical approval was obtained from the ALSPAC Law and Ethics Committee and the Southmead, Frenchay, UBHT and Weston Research Ethics Committees. Written informed consent was obtained from parents for all measurements made.Please note that the study website contains details of all the data that are available through a fully searchable data dictionary (21 KEGG22 and Reactome23). This was achieved by downloading gene sets from the Broad institute’s molecular signatures database  which is used by the GSEA algorithm.24 All rare genetic variants (MAF≤1%) that survived QC were aggregated together across pathways according to UCSC reference genome hg19 definitions. We then filtered these sets to only include nonsynonymous variants according to the Ensembl Variant Effect Predictor (VEP).25 This process was repeated for a separate analysis except using variants resulting in a loss-of-function . In a final set of analyses, we used the Combined Annotation Dependent Depletion (CADD)26 resource to filter variants based on molecular functionality and pathogenicity. Based on simulations using network analyses surrounding candidate loci  from these regions were then analysed with each cardiovascular trait using the optimal sequence kernel association test (SKAT-O).analyses .P-values were evaluated as robust to permutation if no more than 10 permuted P-values were smaller than the P-values observed when phenotypes were not shuffled .Results that survived the conservative multiple testing threshold  were further evaluated by performing 100 000 permutation tests. For each test, the same set of genotype values was reanalysed as before except after randomly shuffling phenotype values among individuals. We followed up pathways that were robust to permutation testing by analysing all genes individually across the genome using the appropriate variant filtering technique and corresponding phenotype. Results were plotted using Manhattan plots, highlighting genes located along the pathway of interest.29 were used to visualise how these subnetworks interacted across the genome. Subnetworks of genes were analysed in a similar manner to before in order to isolate association signals across pathways.We also examined protein–protein interactions to elucidate subnetworks of genes within any pathways with strong evidence of association in the previous analysis. These subnetworks were determined due to the most connected genes in the pathway based on STRINGdb experimental data. Circos plots30 to verify whether there were any key SNVs in both sets of analyses. All other statistical analyses in this study were undertaken using R statistical software.31Using the imputed ALSPAC data set, we attempted to replicate evidence of association for pathways that survived the multiple testing threshold and were robust to permutation testing. All SNVs in pathways found to be associated in the UK10K data  were collapsed together as before and SKAT-O analyses were repeated using the respective trait. SNVs with an imputation quality score less than this were not included in this analysis. All SNVs in this analysis were also collapsed according to individual gene coordinates to see if there was any overlap with key genes identified in the discovery analysis. As before, analyses were adjusted for the top 10 principal components. Single variant analyses were also undertaken using PLINKv1.9P<4.57 × 10−5). Results can be located in P<5.57 × 10−5). However, all these signals were driven by a single rare variant located within the APOC3 gene region (rs138326449), which has been previously identified in the UK10K data set.20 These results can be located in In total, 1095 pathway definitions were obtained from the three pathway databases. We observed no strong evidence of association for any pathway results after filtering to only include nonsynonymous variants based on the multiple testing correction threshold , which was associated with SBP. This was the KEGG arginine and proline Metabolism pathway (P=3.32 × 10−5), referred to hereafter as the arginine and proline pathway. The top results from this analysis can be located in IGF1 pathway according to BioCarta, although this did not survive the threshold for multiple testing (P=2.88 × 10−4). The top results for this analysis and all other traits can be found in The following results for our study now solely concern the analysis based on filtering variants based on their CADD score. One pathway result survived the conservative correction for multiple testing ). This shows that a conventional gene-based approach would not have been able to detect the effect across rare variants in the arginine and proline pathway. We collapsed all rare variants (MAF≤1%) across the genome with a CADD C-Score threshold of ≥20 within individual gene regions according to coordinates based on UCSC reference genome hg19 definitions. Each gene-based set of variants was analysed using SKAT-O with SBP. 32 experimental data to identify subnetworks of genes within the arginine and proline pathway. There were three genes within this pathway , which had more interactions than any others and therefore subnetworks were based around all genes directly connected to them. 29 which illustrates interactions between subnetworks within this pathway. Variants within these subnetworks were analysed as before using SKAT-O with SBP. Two subnetworks (the CPS1 and GLUD1 subnetworks) provided stronger evidence of association over any individual gene analysis , however only when all genes in the network were analysed together did evidence become strong enough to survive the threshold for multiple testing in the initial analysis (P=4.65 × 10−5). We also examined protein–protein interactions according to StringdbP=0.02 from 71 variants, N=4380). Evidence of association appeared to be consistent from both the discovery and replication analyses for variants within the NOS1 gene , although there was little concordance between key individual variant results between the discovery and replication sets of analyses . We replicated evidence of association in the imputed data set between variants along the arginine and proline pathway and SBP  in the UK10K data set appeared to replicate in the imputed ALSPAC data set . There is evidence to suggest that both these amino acids can influence blood pressure, although arginine in particular has gained interest with several studies reporting a blood pressure lowering effect due to arginine dietary supplements.35 An important function of arginine is a necessary precursor for the synthesis of nitric oxide, where the impairment of nitric oxide bioavailability has been shown to be a risk factor for hypertension.36NOS1 was observed to be one of the key genes in both the discovery and replication analyses within this study. This gene belongs to the family of nitric oxide synthases and is involved in the synthesis of nitric oxide from arginine, making it an interesting candidate to explore the mechanisms within this pathway and its effect on blood pressure.The observed association between genes involved in the metabolism of arginine and proline and SBP . For future applications of this approach we advise implementing a strict variant filtering phase before analysis in order to remove as much statistical noise as possible. In our initial analysis when filtering variants by conventional methods based on variant consequence we did not observe any novel evidence of association in the UK10K data set. However, when incorporating annotations from CADD we were able to identify a previously undetected signal. Although other studies have incorporated non-coding annotations into their analyses to great effect,42 also makes it an attractive approach when analysing multiple genes in a pathway which can potentially have close proximity. As reported by other studies, analysing pathway sets of variants using SKAT-O, or with any other collapsing method, remains relatively underpowered.8 Power calculations from the replication analysis in this study also support this, as evaluations found observed power to diminish as the sample size and percentage of variants in the sample were reduced  would struggle to detect an over representation of genes along certain pathways. Our approach allows pathological mechanistic insights to be made which methods involving individual gene regions would not. This is also an advantage over individual variant methods, such as GWAS, where the majority of robustly associated findings lie in non-coding regions, leaving questions regarding the variants’ functional impact.48 As larger samples of low coverage sequence data become available, future studies should benefit from using this approach to uncover the genetic architecture of complex traits that contribute to disease.Success from rare variant association analyses has been limited thus far.