However, there is an ongoing debate whether executive functioning (EF)–another key predictor of cognitive health and performance, is separable from general g. To provide an analytical review on existing findings on genetic influences on the relationship between g and EF, we re-analysed a subset of genome-wide association studies (GWAS) from the GWAS catalogue that used measures of g and EF as outcomes in non-clinical populations. We identified two sets of single nucleotide polymorphisms (SNPs) associated with g , and EF (300 SNPs across 5 studies) at p<5x10-6. A comparative analysis of GWAS-identified g and EF SNPs in high linkage disequilibrium (LD), followed by pathway enrichment analyses suggest that g and EF are overlapping but separable at genetic variant and molecular pathway levels, however more evidence is required to characterize the genetic overlap/distinction between the two constructs. While not without limitations, these findings may have implications for navigating further research towards translatable genetic findings for cognitive remediation, enhancement, and augmentation.Understanding the genomic architecture and molecular mechanisms of cognitive functioning in healthy individuals is critical for developing tailored interventions to enhance cognitive functioning, as well as for identifying targets for treating impaired cognition. There has been substantial progress in uncovering the genetic composition of the general cognitive ability ( The heritability of cognitive performance was recognised through twin and family studies long before the development of high-throughput genotyping and genome-wide association study (GWAS) methodology , 2. At tg [g captures about 25 to 40% of the total variance when a battery of multiple cognitive tests is administered to a sample with a good range of cognitive ability [g, translatability of these findings to specific performance tasks is limited.The recent success in identifying genetic associations of general cognitive ability, g , a cogni ability , 8. HoweOn the other hand, Executive Functioning (EF)—a set of high-level mental processes that are fundamental to cognitive control of behaviour —is one oDefining and measuring EF is a challenging task that has been a topic of debate in many subdisciplines of psychological science. While there are several reasons for measurement difficulty, including poorly established construct validity and low internal and/or test–retest reliability of complex executive tasks, the major issue is the task impurity problem . Systemag and EF constructs [g, some authors have found no correlation between g and EF [g at the phenotypic level and predicts behaviour above and beyond g [g and EF using classical twin study design suggests that EF is moderately to highly correlated with g (rG = .5-.9) [g using LD-regression modeling and identified 129 genome-wide significant lead variants associated with EF. They concluded that the two constructs are overlapping but genetically separable at the aggregate level with correlation estimates ranging r = .7-.8, which was comparable to what twin studies were suggesting earlier. While these findings have important implications for further research, given the aggregate level of this estimation, is it not clear what specific genetic variants are common and for g and EF, or whether EF and g can be distinguished at the individual gene or molecular pathway level. This is especially evident considering inconsistent GWAS hits that were found associated with g or EF in previous studies.Given the importance of EF in overall cognitive functioning, it is reasonable to assume that there is a substantial overlap between general nstructs . In an og and EF –22, whilbeyond g , 23–26. = .5-.9) , 27, 28.= .5-.9) . The autg and EF, by considering genetic markers found to be associated with either intelligence or executive functions, or both, at individual SNP level across multiple studies. To provide a comprehensive overview on the biological underpinning of phenotypic relationships between general intelligence and executive function, we analytically reviewed a subset of genome-wide association studies (GWAS) that used measures of g and EF as outcomes. We first identified all genetic variants that were found associated with either g or EF. Then, performed functional characterization and pathway enrichment analyses of the candidate SNPs and those in high LD for both g and EF to examine whether biological pathways associated with g and EF variants converge/diverge. Finally, we compared structural (genetic variants) and functional  results for g and EF. Our study contributes to the ongoing debate on whether executive function constitutes an aspect of general intelligence or could be considered as independent cognitive abilities and adds on a better understanding of differences and similarities of g and EF genetic architectures at the individual SNP level.The aim of the current study is to extend the analysis of the relationship and characterize the molecular overlap and distinction between g, we (1) identified all cognition-related studies using GWAS catalog search engine (data release on 2020-03-08); (2) selected studies that measured EF as an outcome using the definition proposed by Snyder, Miyake [g as an outcome, we chose studies claiming to have calculated Spearman’s g based on tests of verbal-numerical reasoning, a collection of various cognitive tests, Wechsler’s IQ test, and Scholastic Assessment Test. Selection of both EF and g studies was verified by three authors who are experts in cognitive psychology . In order to capture all relevant g and EF data, no published studies were excluded on the basis of participant demographics such as age or sex.To identify GWAS studies of interest, we used the most comprehensive NHGRI-EBI GWAS catalog that provides a publicly available curated resource of all published human GWAS findings . To sele, Miyake , we chosg (p < = 5 × 10−6). To ensure that common variants are independent, we removed SNPs with LD and Minor Allele Frequency (MAF) at the commonly accepted threshold  in g and EF SNP lists using LDlink tool 5.1 Release [2> = 0.6, and identified SNPs that are in high LD (r2 > = 0.8) for both g or EF  using SNiPA v3.4 . Although we began by searching all GWAS studies, we found no GWAS hits in no-European populations, hence the use of SNiPA genome annotation for European population is justified.To identify genetic variants of interest, we selected SNPs associated with EF or  Release   using the hypergeometric over-representation pathway analyses were conducted in SNPnexus tool; SNPnexus uses the Reactome knowledgebase to link the gene variants specific to, or shared by the cognitive domains in this analysis with their biological pathways [g and EF genetic variants, we scanned the GWAS catalog for any association of the SNPs of interest with other physical, psychological, and neuropsychiatric traits at p < 5 × 10−8.To provide in-silico functional annotation of genetic correlates of sentinel and proxy EF and pathways , 33. Stapathways –38. To eNon-random association of alleles of different loci in a given population. Loci are considered to be in LD when the frequency of association of their different alleles is higher or lower than what would be expected if the loci were independent and associated randomly .Frequency at which the second most common allele occurs in a given population. MAF is widely used in GWAS because it provides information to differentiate between common (MAF>0.05) and rare (MAF<0.05) variants in the population .Statistical technique that help to gain mechanistic insight into gene/SNP lists generated from genome-scale experiments. This method identifies biological pathways that are enriched in a gene list more than would be expected by chance.For further reading on GWAS methodology, please refer to the .g (n = 12), or executive functioning (EF) as an outcome (n = 5). The reason for excluding studies on clinical populations is our attempt to provide a picture of relatively healthy cognition, as genetic variants associated with cognitive functioning in clinical groups may represent illness-specific associations. However, it is worth noting that some studies included in this work utilized a population study design, without specific screening for psychopathology, therefore, we cannot rule out a possibility of some pathology in our sample. This sample is characterised by a population level demographics, i.e., both sexes with age range from as early as 6 years old in a Childhood Intelligence Consortium, CHIC [Our initial search for the term ‘cognition’  and an additional 11 cognition-related terms that were relevant for cognitive functioning  identifium, CHIC  to 102-yum, CHIC . More chg, across 12 studies (-6) across 5 studies ,  studies , and 351 studies . Given ts for EF  Tables. g and EF, we used a threshold of r2> = 0.6. Out of a total of g and EF-associated 1,672 common variants, 76 SNPs were within this threshold, suggesting a 4.6% overlap between g and EF. The estimated overlap was not uniform across chromosomes. Thus, there was no overlap observed for chromosomes 9, 11, 12, 14, 15, 21. For other chromosomes the overlap ranged from 1.4% (Chr 4) to 10.6% (Chr 20). More details on the estimated per chromosome overlap between g and EF is available in g and EF. Characteristics of g and EF common variants are in To estimate LD-based genetic overlap between g index variants at FDR<0.05. These pathways are known to be involved in neuronal system synaptic activity , developmental biology (EPHA-mediated growth cone collapse), immune system (Butyrophilin (BTN) family interactions), and cell-to-cell communication (Adherens junctions interactions)  . No statg and EF common SNPs with other traits revealed that 8 SNPs have been previously associated with biological , psychopocampal , schizoppocampal , 66, fampocampal ) cognitipocampal , alcoholpocampal , sedentapocampal , househopocampal )  (2>0.8)   and for  (2>0.8) .g and EF pathways at various nominal thresholds. Thus, under a relaxed p<0.05 we found 104 g-associated pathways and 74 EF-associated pathways with 7.8% (n = 14) being common for both constructs (p<0.01 there were 75 and 24 g and EF pathways respectively with only 2 common pathways between the two (2.9%). At the most stringent threshold of p<0.002 we found 12 g-related and only one EF-related pathway (MECP2 regulates transcription factors) with no common pathways identified . These results should be treated under an assumption of statistical interactions [g and EF, we can expect to identify more individual markers in the future with more high-quality studies of sufficient power and refined phenotypes, which will inevitably enrich findings reported here. Furthermore, the contribution of rare variants not tagged by SNP arrays is a further potential source of underestimation in existing GWASs. Factoring into g and EF biological pathways, we observed a 7.8% overlap defined as a proportion of common pathways at nominal p<0.05. The small increase in biological pathways overlap compared to genetic overlap between g and EF can be partially explained by pleiotropic effects of identified genetic variants on the downstream molecular signaling and cellular functioning. This might also be partially attributed to increase of genetic information fed into pathways enrichment algorithm by proxy variants in high LD at r2> = 0.8, however, we argue that by limiting our analyses to sentinel GWAS SNPs, we are likely missing downstream biological effects of proxy variants that despite proximity and high correlation with sentinel variants, might have different functional consequences.While some earlier studies failed to find correlation between intelligence and executive functioning, more recent findings suggest both overlapping and separable aspects of ractions , which wg and EF (at p<0.05) were involved in basic neuronal and cellular functioning processes, such as signal transduction, gene expression (transcription), and metabolism, which one could expect given the common biological grounds for g and EF; however, the role for Circadian clock and Class I MHC mediated antigen processing & presentation (adaptive immune system response) pathways in g and EF is less obvious.Most of the biological pathways that were common for g and EF.Many studies have examined the relationship between circadian clock and cognition, as well as between cognition and the immune system, however, the exact molecular mechanism linking all three: circadian clock, immune system response, and cognitive functioning has not been established yet. It has been previously shown that MHC class I immune proteins are critical for hippocampus-dependent memory formation  and for g and EF constructs, Chromatin organization and Chromatin modifying enzymes, that remained statistically significant at both relaxed p<0.05 and more stringent p<0.01, are of particular interest. For decades, research has implicated epigenetic mechanisms, such as histone modifications in regulating chromatin compaction necessary for experience-dependent changes to gene expression and cell function during memory formation [The two common biological pathways for ormation , 79, howormation .g or EF, to suggest molecular mechanisms that distinguish the two constructs. We found that that mitochondria-specific autophagy (mitophagy), a fundamental process that contributes to mitochondrial quality control by selectively eliminating dysfunctional mitochondria [g. Previous research supports the role of mitochondrial functioning in cognition without suggesting specific domains [plasma lipoprotein pathway, integral to energy and cholesterol metabolism in cells, including neurons, and NR1H2 and NR1H3-mediated signaling, that regulates gene expression linked to cholesterol transport and efflux, appear to be EF-specific. Although this is consistent with previous clinical observations suggesting a link between executing functioning and cholesterol [cell cycle pathways were prominent in g and not EF, given their central role in the neuronal life cycle [g GWAS included in this analysis, potentially larger EF-based studies may find similar relationships. Studies in clinical populations have found associations between cell cycle genes and EF [MECP2 regulates transcription factors (p = 0.0013) and is known to be a critical regulator of chromatin in neurodevelopment and adult brain function [MECP2 was also associated with g at the p = 0.02.Apart from common g and EF pathways we also looked to identify those specific for chondria , was ass domains –84. The lesterol , 86, morfe cycle , and thes and EF  , household income, and others are less intuitive. However, a new line of research on the effects of the genetic contribution to cognitive functioning is emerging. Thus, a recent study estimated the association of types of screen time  with intelligence after accounting for screen type, socioeconomic status, and genetic predisposition for intelligence. Surprisingly, gaming and watching was shown positively influence the amount of change in intelligence so that children who played more video games at 9–10 years of age showed the more gains in intelligence two years later [Consistent with patterns typical for complex traits , we obsers later . Furtherg and EF phenotypes by selecting studies that used comparable cognitive measures, the opportunistic nature of phenotyping in different studies influences the replicability of GWAS results and, therefore, our conclusions. Second, while there is agreement on how to define g, the definition of EF is more debatable. Previous studies have used single tasks to measure EF, however, this approach leads to the inclusion of task specific variance, when factor analysis across multiple tasks may more accurately distil a core measure. Task specific variance may explain the difference between the derived sumstats from single task studies and the approach taken by Hatoum et al. [g and EF appear to be overlapping but largely separable at an individual SNP level, the estimation of aggregated genetic effects on g that are correlated with genetic effects on EF independently of the heritability estimates of the two traits, derived from the genetic analysis of covariance (such as LD Score Regression) [g and EF studies here were selected based on different representations of the constructs, while [g and EF. We suggest that the inconsistency in EF-related pathways we observed is complementary to the aforementioned findings, with the two distinct methodologies producing different perspectives. EF is a highly complex phenotype, and it is unlikely that only several pathways are involved in its underlying processes. We suggest that with increased power in GWAS and improved methodology we will be able to identify even more pathways involved in EF.This study has several limitations. First, it shares the common limitations of the included GWAS studies. While we attempted to ‘clean’ the m et al.  who usedression) , shows as, while  used a sg and EF in GWAS studies, where the same measures used for both constructs [g and EF. Furthermore, given the limited statistical power for the majority of EF studies to date, our results should be treated with caution as with better-powered EF studies the relationship between g and EF might change.Despite the numerous theoretical models, large inconsistencies in defining nstructs , remain To overcome these limitations, it is important to examine genetic and functional relationships between the smallest units of measurement characterising cognitive functioning–individual cognitive test metrics. Such examination should enable a biology-informed measurement model of cognitive functioning. We view this approach as a comprehensive mapping of broad and specific cognitive functions to their genetic correlates, which might enable uncovering of a hierarchical structure of cognition and suggest novel targets for functional interventions with translation potential.g and EF and their functional consequences by employing recent GWAS findings and pathways enrichment analyses. We found that while some genetic variants are common for g and EF, executive functions appear to be separable from general intelligence at both structural and functional levels; however more evidence is required to characterize the genetic overlap/distinction between g and EF. To the best of our knowledge, this study is the first to systematically compare structural and functional genetic correlates of general intelligence and executive function at an individual SNP level across multiple studies. It provides biologically tractable evidence to inform cognitive enhancement programs focused on modifiable executive functions and can serve as a guide for future research in the field.The question of separability of executive function from general intelligence has been an ongoing debate for decades. In this study, we examine biological underpinnings of these cognitive constructs and analyse high level insights into genetic architectures of S1 Table(DOCX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.


The identification and understanding of gene-environment interactions can provide insights into the pathways and mechanisms underlying complex diseases. However, testing for gene-environment interaction remains a challenge since a.) statistical power is often limited and b.) modeling of environmental effects is nontrivial and such model misspecifications can lead to false positive interaction findings. To address the lack of statistical power, recent methods aim to identify interactions on an aggregated level using, for example, polygenic risk scores. While this strategy can increase the power to detect interactions, identifying contributing genes and pathways is difficult based on these relatively global results. Here, we propose RITSS (Robust Interaction Testing using Sample Splitting), a gene-environment interaction testing framework for quantitative traits that is based on sample splitting and robust test statistics. RITSS can incorporate sets of genetic variants and/or multiple environmental factors. Based on the user’s choice of statistical/machine learning approaches, a screening step selects and combines potential interactions into scores with improved interpretability. In the testing step, the application of robust statistics minimizes the susceptibility to main effect misspecifications. Using extensive simulation studies, we demonstrate that RITSS controls the type 1 error rate in a wide range of scenarios, and we show how the screening strategy influences statistical power. In an application to lung function phenotypes and human height in the UK Biobank, RITSS identified highly significant interactions based on subcomponents of genetic risk scores. While the contributing single variant interaction signals are weak, our results indicate interaction patterns that result in strong aggregated effects, providing potential insights into underlying gene-environment interaction mechanisms. The understanding of gene-environment interactions provides potential insights into the pathways and mechanisms underlying complex diseases, but they are hard to detect since effect sizes are expected to be small. To facilitate the detection of such interactions in quantitative traits, we propose a robust and flexible approach called RITSS that can incorporate sets of genetic variants and multiple environmental factors. RITSS can utilize any suitable machine/statistical learning approach to screen for interactions and rigorously tests these aggregated signals using sample splitting and robust test statistics. We demonstrate the validity and power of our approach in extensive simulation studies. Furthermore, in an application to lung function and height data in the UK Biobank, RITSS discovers highly significant interactions based on subcomponents of genetic risk scores. Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex diseases and traits . HoweverMany of the methodological approaches for gene-environment interaction testing have focused on scenarios in which a single genetic variant and a single environmental variable are tested for potential interaction. Methods have been developed for case-control data, case-only data, and quantitative trait studies, summarized in detail by Gauderman et al. .Since most gene-environment interaction effect sizes are expected to be small and statistical power is consequently limited , power-iCurrent data analyses observed that genetic risk scores, (weighted) sums of genetic risk variants, interact with smoking exposure in Coronary heart disease  and lungBesides the power limitation, another caveat of gene-environment interaction testing is that the misspecification of the marginal main effect models can lead to false positive findings in interaction testing ,33. ThisIn this communication, we propose RITSS, a robust and flexible framework for gene-environment interaction testing in quantitative traits. The general idea is to derive an interaction score comprised of the (weighted) sum of individual genetic variant/environmental factor product pairs, such that the combination of these signals increases the power while maintaining biological interpretability. Our approach utilizes a sample splitting strategy and test statistics that are robust against misspecifications of the main effects. The general form of RITSS allows the incorporation of user-specified screening/learning approaches in constructing candidate interaction scores while providing valid statistical inference without restrictive assumptions. In extensive simulation studies, we demonstrate the robustness and power of RITSS in various scenarios. We also applied RITSS to lung function phenotypes and human height in the UK Biobank, incorporating sex information and smoking exposure.This research was conducted by using the UK Biobank resource under application number 20915. All participants provided written informed consent, and study protocols were approved by the North West Multi-centre Research Ethics Committee and ethical procedures were controlled by the UK Biobank Ethics Advisory Committee.In this section, we describe the RITSS framework and the corresponding algorithm.i, we denote the quantitative trait by Yi, the m-dimensional genotype information by Xi, and the d-dimensional environmental information by Ei. Also, we define an additional p-dimensional covariate vector by Zi that includes, for example, age, study indicators, and genetic ancestry principal components  = 0. The unknown function μ describes the main effect of the environmental factors Ei and other covariates Zi. The genetic contribution of variant j is modeled by πjXij, where πj is an unknown function. The null hypothesis of no gene-environment interaction is described by πj0 is an unknown function depending on Zi only. Therefore, we implicitly assume the absence of gene-gene-interactions but, in general, allow for interactions between the genetic variants and the covariates Zi under the null hypothesis.For sample mponents . The genThe rationale of RITSS is to incorporate sets of genetic variants and/or multiple environmental factors to increase power and detect interactions based on aggregated signals. This step is motivated by the observation that single variant approaches are often underpowered. There are two critical aspects that RITSS aims to address: 1.) testing of aggregated interaction signals can increase power, but the results are more difficult to interpret since individual factor contributions are unobserved, and 2.) recent results demonstrated that the misspecification of main effects in interaction analyses can lead to false positive interaction findings ,33.Ui = ∑j∑lπjlXijEil that combine signals adaptively while maintaining biological interpretability by keeping the number of involved factors of moderate size. RITSS uses sample splitting since screening and testing, in general, cannot be performed using the same data.Given the input set of genetic and environmental factors, RITSS derives interaction scores of the form Xi, Ei, and Zi, that is m, d, and p, in our setting are of substantial size. The potential high dimensionality of the models leads to slower convergence rates so that standard results might not apply here. Furthermore, considering RITSS as a general framework, we aim to derive an algorithm that can incorporate any other suitable future approach. Therefore, we utilize a concept based on recent theoretical results in causal inference  = 0.In all scenarios, we simulated a sample size of z→N. The type 1 error simulations aim to demonstrate the accuracy of this asymptotic approximation in realistic scenarios.Under the null hypothesis of no gene-environment interaction, the overall RITSS z-score is asymptotical normal with As described in the methods section, the screening strategies of RITSS1 and RITSS2 assume linear environmental main effects. We included scenarios in which the true environmental main effect is non-linear and gene-environment correlation is present. In these scenarios, the screening strategies, therefore, construct interaction scores that are reflecting false positive interaction findings ,33. BesiIn addition, we investigated if the type 1 error rate is inflated in the case where the genetic variants for the analysis were selected based on GWAS results in the same dataset, i.e., based on their marginal association p-values (scenario notated as SELECT:yes/no). This is of particular interest since many reported genetic associations were identified based on the large-scale datasets that are suitable for gene-environment interaction testing. In total, this resulted in 10 different type 1 error scenarios .The specific details of the implementations of these scenarios are described in μXE and standard deviation σXE of interaction effects, as well as the density pXE of these interaction effects. The interaction model incorporates the first component Ei1 only. The detailed implementation is described in In addition to the type I error simulations, we also included a set of power simulations. In the power simulations, we considered scenario 1 with SELECT:no from the type 1 error simulations but incorporated a gene-environment interaction component. The interaction contribution is controlled by the mean K and the splitting fractions c. While the asymptotic theory, that is the asymptotic standard normality of the RITSS z-score under the null hypothesis, applies for any fixed choice, the finite sample performance in practice as well as the power can be influenced by these choices. Choosing these parameters faces specific tradeoffs. For a fixed K, splitting fractions c that allocate more sample size to the screening strategy are expected to have higher power, while the decreased sample size for the main effect and transformation estimation might lead to less accurate model fits and therefore inflated type 1 error rates. Since every sample contributes exactly once to the test statistic for each fixed K, increasing K (moderately) is expected to increase power since a bigger fraction of the overall sample is used for screening. At the same time, a larger K can lead to increased computational burden, depending on how the screening and main effect model estimation tasks scale with sample size. We considered the four combinations of K = 3, 4 and the splitting fractions c =  and c = . In Table B in RITSS can be applied using different choices for c =  leads to the most stable results; this is in line with the intuition described above. Furthermore, D1 and D2 do not control the type 1 error rates, demonstrating that the robust test statistics are needed to provide valid results. The power results in Table C in K = 4 has slightly more power than K = 3, as intuitively described above, but the overall power differences between the four combinations are small. For the full type 1 error and power simulations, we considered the combination of K = 4 and c = .The results in Table B in We report the quantile-quantile-plots (qq-plots) for scenarios 1–5 with SELECT:no in −10 in the plots for better visualization. The inflated results are expected since GESAT does not account for heteroscedastic errors (scenarios 3 and 5) and the linear main effect model implies a mis specification in scenarios 2–5. GAMsv, which models the environmental main effects similar to RITSS, shows valid type 1 error rates for scenarios 1, 2, 4, and 5. In scenario 3, the heteroscedastic errors lead to inflation. In summary, our type 1 error simulations demonstrate the necessity of flexible main effect models to ensure robustness and that RITSS provides valid type 1 error rates in all investigated scenarios.Overall, the simulations demonstrate that the results for SELECT:no and SELECT:yes are similar. RITSS1 and RITSS2 provide controlled type 1 error rates across all five scenarios. GESAT demonstrates valid results for scenario 1 but is highly inflated in the scenarios 2–5. We note that we cut off the GESAT p-values at 10pXE, for different effect size distributions. The significance level was set to α = 0.005.The results of the power simulations are reported in Figs μXE = 0.05 in We consider the results for σXE = 0.01 and increasing pXE. The power difference is smaller for σXE = 0.1. With larger effect sizes and sparse signals, GAMsv shows advantages since the single variant tests are powerful enough to circumvent the multiple testing burden in this case.RITSS1 outperforms GESAT and GAMsv in most scenarios, especially for μXE = 0.1 are similar, but the power advantages of RITSS1 are smaller or vanish (σXE = 0.1). Overall, the power simulations demonstrate the interplay between the screening strategy implemented in RITSS and the resulting power. Especially for dense signals with small effect sizes, RITSS1 shows substantial power advantages. We hypothesize that these signal architectures approximate the signals observed in recent interaction studies based on polygenic risk scores.The results in 1), forced vital capacity (FVC), and the ratio FEV1/FVC) and height. Here, for our analyses, we restricted the input to previously reported GWAS associations for the respective trait. This step reduces the dimensionality of Xi, denoted by m, to a range between 383 and 3368. We note that this choice can be too restrictive since interaction effects do not need to be accompanied by main effects. Details about the study population as well as the extraction of genetic, environmental, and phenotypic data are described in We applied RITSS to UK Biobank data to analyze gene-environment interactions in lung function  were mean-centered before computing the squared variable. Sex was coded as male = 1 and female = 0 in this analysis. For the lung function traits, we also included height in Ei as a potentially interacting variable. We note that, given the coding of sex in this analysis, height and sex are (strongly) positively correlated. The lung function measurements were analyzed on the original scale and not transformed, as robustness of the approach against non-normal errors was demonstrated in the type 1 error simulations. After quality control, we split the resulting 254,033 samples (European ancestry) in two parts: 180,000 randomly selected samples for the main analysis using RITSS and 74,033 samples that serve as an independent dataset to validate the analysis results of RITSS. We applied RITSS using the same two different implementations described in the simulation studies (RITSS1 and RITSS2). For both RITSS1 and RITSS2, we chose K = 4 and Ei was tested for interaction separately, resulting in a total of 32 tests. In Xi, Ei, and Zi using standard linear regression. The density plots are based on the 180,000 samples in the main analysis.1/FVC [n = 32 tests at a significance level of α = 0.05.Furthermore, in line with previous results in the literature, we observed a significant interaction with pack-years of smoking in FEV1/FVC ,31 that K = 4 different interaction scores, we investigated the overlap between the genetic variants included in the sets m(Ik), k = 1,…,4. We denote all genetic variants that were in all four scores by m4 and all genetic variants included in exactly three scores by m3. For the validation analysis, we considered all Yi/Eit pairs that had a Bonferroni corrected significant interaction p-value at a significance level of α = 0.05 and non-empty m4 or m3 sets; this included RITSS1 results only.Based on the Xi, Ei, and Zi. We tested two interaction scores, the first based on the variants in the corresponding m4 set and the second based on variants in m3. Here, the genetic main effects for the genetic risk scores were estimated based on the 180,000 samples in the main analysis. The regression interaction p-values were evaluated based on model-based standard errors as well as standard errors obtained from a robust sandwich estimator.The validation analysis was based on the remaining and independent 74,033 samples and a standard regression interaction test while fitting The results of our validation analysis are described in m4/m3 with the respective environmental factor in the validation dataset. This analysis was based on a linear regression that fits Xi, Ei, and Zi, and the respective interaction term. p<5×10−8, as well as the number of significant single variant interactions using a Bonferroni correction adjusting for the number of variants in the corresponding combined set m4/m3 at a significance level of α = 0.05. Both numbers are either 0 or very small, for all trait/environmental factor combinations. This suggests that the interaction effects at the single variant level are weak. However, we made an interesting observation by investigating the empirical distribution of directions of effect across the single variant interaction tests. Xij×Eit and b.) the weighted terms m4/m3 between the sex and height interactions in lung function highly overlap, which is in line with the observation that height and sex are strongly positively correlated. However, if we test the height interaction separately in males and females in the validation dataset, the signal vanished or was strongly diminished. Therefore, we hypothesize that the observed interactions are a result of sex-differential effects, but further analyses are required to disentangle the exact mechanisms. For example, investigating the impact of sitting or thoracic height, instead of standing height [m4/m3 across the three lung function phenotypes and height were not significantly associated with sex, but replication of the interaction signal in an independent cohort is preferable. Detailed information about the selected variants in the gene-by-sex interactions for lung function and height can be found in To disentangle these findings in more detail, we examined the single variant interaction p-values for the genetic variants in the combined set g height . In thisg height , the genEi and Zi as covariates) based on 1.) all genetic variants in the analysis and 2.) all genetic variants in the corresponding combined m4/m3 set. For all traits, these tests were at least nominal significant (p<0.05), and we obtained similar p-values in the two analyses 1.)/2.) (except for FVC): FEV1: p = 1.81×10−11/p = 8.92×10−7, FVC: p = 2.71×10−14/p = 2.29×10−4, FEV1/FVC: p = 2.61×10−2/p = 2.29×10−2, height: p = 1.08×10−3/p = 6.19×10−3. These results add further evidence that RITSS1 identified a fine mapped set of interacting genetic variants.We also applied GESAT in the validation analysis. For each of the four traits, we applied GESAT to test for interaction with sex . This restriction is related to the required assumption that the main effects and transformations can be estimated accurate enough given the sample size. The computational burden, depending on the specific implementation of the screening strategy, also increases with m. Therefore, an important question is how to pre-select the set of genetic variants for RITSS testing. It is possible to integrate an external screening approach. For a quantitative trait, one can screen for potential interaction signals based on marginal effects or variance-effects [Our approach has the following limitations. A significant interaction p-value of the aggregated score does not imply that all included pairs of genetic variants/environmental factors necessarily truly contribute to the interaction signal. Another limitation is that the set of genetic variants needs to be pre-selected and, as described in the Methods section, the size -effects . Based oUi = ∑j∑lπjlXijEil. However, a significant contribution by Ui does not imply interaction between Ei and Xi without assumptions regarding no unmeasured confounding/omitted variables and the sampling design [In general, the interpretation of significant RITSS p-values requires careful consideration. RITSS aims to identify deviations from the null hypothesis model g design ,45. MoreRITSS. We note that RITSS can also be used to analyze gene-gene-interactions and interaction models can be based on more general, non-linear approaches. In addition, RITSS can be applied to other omics data layers, such as DNA methylation, transcriptomics, or metabolomics. The R package RITSS as well as the code for the simulation studies is available at https://github.com/julianhecker/RITSS.Future directions include more detailed follow-up analyses of the observed gene-by-sex interactions, the incorporation of other screening techniques, such as, for example, LASSO  or RaSE S1 AppendixThis appendix contains additional information regarding the ACE algorithm and RITSS main effects, the screening strategies, the implementation of GAMsv, simulation details, additional simulation study results, and the UK Biobank data.(PDF)Click here for additional data file.S2 Appendixm4/m3 information for the sex-interaction analysis in the UK Biobank.These tables contain all genetic variants as well as the (XLSX)Click here for additional data file.S1 Fig1: forced expiratory volume in 1 second, FVC: forced vital capacity.FEV(TIF)Click here for additional data file.


PELP1) gene may affect the expression of PELP1 alternative splices. We infer that the CNVR may influence IMF content by regulating the alternative splicing of the PELP1 gene and ultimately affects the structure of the PELP1 protein. These findings suggest a novel mechanistic approach for meat quality improvement in animals and the potential treatment of insulin resistance in human beings.Copy number variation (CNV) is a type of variant that may influence meat quality of, for example intramuscular fat (IMF). In this study, a genome-wide association study (GWAS) was then performed between CNVs and IMF in a pig F2 resource population. A total of 19 CNVRs were found to be significantly associated with IMF. RNA-seq and qPCR validation results indicated that CNV150, which is located on the 3′UTR end of the proline, as well as glutamate and the leucine rich protein 1 (PELP1) gene may affect the expression of PELP1 alternative splices. Sequence alignment and Alphafold2 structure prediction results indicated that the two alternative splices of PELP1 have a 23 AA sequence variation and a helix-fold structure variation. This region is located in the region of interaction between PELP1 and other proteins which have been reported to be significantly associated with fat deposition or insulin resistance. We infer that the CNVR may influence IMF content by regulating the alternative splicing of the PELP1 gene and ultimately affects the structure of the PELP1 protein. In conclusion, we found some CNVRs, especially CNV150, located in PELP1 that affect IMF. These findings suggest a novel mechanistic approach for meat quality improvement in animals and the potential treatment of insulin resistance in human beings.Intramuscular fat (IMF) is a key meat quality trait. Research on the genetic mechanisms of IMF decomposition is valuable for both pork quality improvement and the treatment of obesity and type 2 diabetes. Copy number variations (CNVs) are a type of variant that may influence meat quality. In this study, a total of 1185 CNV regions (CNVRs) including 393 duplicated CNVRs, 432 deleted CNVRs, and 361 CNVRs with both duplicated and deleted status were identified in a pig F2 resource population using next-generation sequencing data. A genome-wide association study (GWAS) was then performed between CNVs and IMF, and a total of 19 CNVRs were found to be significantly associated with IMF. QTL colocation analysis indicated that 3 of the 19 CNVRs overlapped with known QTLs. RNA-seq and qPCR validation results indicated that CNV150, which is located on the 3′UTR end of the proline, as well as glutamate and the leucine rich protein 1 ( Intramuscular fat (IMF) refers to the total amount of fat located between muscle fibers and as lipid droplets in the muscle cells. In humans, extra IMF deposition has been reported to be associated with type 2 diabetes and insulin resistance . In animhttp://www.animalgenome.org/cgi-bin/QTLdb/SS/index, accessed on 23 August 2021) a total of 842 IMF-related QTLs have been reported  an an5] andreported . Althougreported ,8.KIT were reported to be associated with coat color [Copy number variations (CNVs) are the mutation type with the widest coverage of the genome and are one of the variation types most likely to explain “missing inheritance” beyond the SNP effect. In pigs, there have been several studies revealing CNVs or CNV regions (CNVRs) associated with economic traits. In the research of Rubin et al., CNVs of at color . Fowler at color . In the at color ,12,13,14at color . The preIn this study, we used genome-wide CNV markers identified and genotyped via next-generation sequencing to perform genome-wide association analysis on IMF. Moreover, function analyses of these CNVs were also performed to analyze the potential effects and mechanisms of the CNVs on IMF. We aimed to identify the candidate genes and causal mutation sites of IMF and provide a research basis for quality pig meat breeding.Longissimus dorsi muscles (LDM) were collect for IMF content measurement and DNA sequencing for F2 individuals. IMF content was measured using an ether extraction method .The pigs used in the experiment were all from the Large white pig × Min F2 resource population, raised in the Changping pig farm of the Institute of Animal Science, Chinese Academy of Agricultural Sciences. All 592 F2 individuals were raised to market age (240 ± 7 days) and slaughtered for commercial purposes. The F2 individuals were gilts or barrows and have good health status. All of the F0 individuals were raised as normal sows, and ear tissues were collected for DNA extraction when they were one-day old. DNA of LDM tissue  and ear tissue  was extracted using the phenol imitation method. The NANODROP 1000 was used to detect the concentration and quality of DNA . An Illumina Hi-seq2500 was used for paired-end sequencing, and the sequencing depth was 5–7× for F2 individuals, 30× for F0 individuals.CNVcaller software was used to detect and genotype all individual CNVs. First, the reference genome  is segmented into overlapping sliding windows of 500 bp . Second,y is the vector of phenotypic values; b is a vector of fixed effects; and u represents breeding values. X and Z were design matrices for b and u, respectively; e represents the residual error vector.CNV-based GWAS was performed using the GLM, mixed linear model (MLM), and FarmCPU model approaches with rMVP software . In the y is the vector of phenotypic values; T is a matrix of fixed effects, iw; jP is the genotype matrix of j pseudo quantitative trait nucleotides (QTNs), which was used as the fixed effect; and jq is the corresponding effect. km is a vector of genotypes for the kth marker to be tested, and kh is the corresponding effect. e is the residual effect vector.In the FarmCPU method, we used principle component analysis (PCA) as a fixed effect. The model can be written as follows:y is the vector of phenotypes of each pig, a is the vector of fixed effects, b is the vector of the SNP substitution effects, and c is the vector of random additive genetic effects with a~N. W, Z, and S are the incidence matrices for b, a, and c, respectively.In the method of MLM, we also used PCA as a fixed effect, and the MLM model can be written as follows:The selected threshold for genomic level significance was −log10(0.05/N), where N was the total number of CNVRs after quality control.longissimus dorsi muscle tissue were used for RNA expression analysis, as in our previous research [Six RNA-seq datasets of research . These dGCG) was used as the single-copy control. RNA extract from five individuals’ LDMs in each CNVR genotype group was used for expression validation. The glyceraldehyde-3-phosphate dehydrogenase (GAPDH) gene was used as the housekeeping gene. Copy number and relative expression were calculated using the 2−ΔΔCT method [−ΔΔCT stood for the comparison of the ΔCT value of the samples with CNV to those without CNV. The PCR cycle was as follows: 3 min at 50 °C, 10 min at 95 °C, 40 cycles of 15 s at 95 °C, and 1 min at 60 °C. The primer sequences are shown in DNA of five individuals in each CNVR genotype group were used for qPCR amplification for CNVR validation. The primers were designed using the Primer 6 software. The glucagon gene  was compared using a t-test in SAS software (version 9.2). p < 0.05 was considered to indicate a significant difference.In the CNV’s validation analysis, four CNVRs which were CNV11, CNV148, CNV150, and CNV223, were compared independently between the normal and Duplicate/Deleted group using a T-test in SAS software (version 9.2). In the https://version11.string-db.org/cgi/network.pl?taskId=NzfSlslTYWvr, accessed on 12 August 2021) [For the combined analysis of CNVRs and QTLs, QTLs were retrieved from the PigQTLdb . BedtoolConsensus sequences and alignment of A0A5G2R420 and F1RFT3 were built using ClustalX in MEGA X ; the strFor the 592 F2-generation individuals of the Large white pigs × Min pig resource population, the average slaughter weight (240 ± 7 d) was 109.13 kg, and the average carcass weight (left side) was 40.38 kg. The average IMF content of these pigs was 2.87%, the maximum was 12.70%, the minimum was 0.73%, the standard deviation was 1.85%, and the coefficient of variation was 0.65.CNVRs were separated by calling and genotype in the F0 and F2 population, and all the CNVRs were screened with the silhouette_score > 0.6 in this study. CNVRs could be investigated in both the F0 and F2 population we selected for further research. Finally, a total of 1185 CNV regions (CNVRs) were identified, including 393 duplicated CNVRs, 432 deleted CNVRs, and 361 CNVRs with both duplicated and deleted status . The lenAll 592 pigs were used in a genome-wide association study (GWAS) between CNVs and IMF content. The GWAS results are shown in p > 0.05) and normal distribution (p > 0.05). As shown in In this study, DNA qPCR validation was performed to confirm the existence of the identified CNVRs. Data of each group could meet the assumptions of homogeneity of variance  genes. The expression of one of the PELP1 alternative splices (ASs), ENSSSCT00000019597, was significantly different between different individual CNVRs , we also detected the copy numbers of F0 individuals to confirm the relationship between IMF and CNVR150. From Beyond the RNA-seq data, we also detected the expression of the two PELP1 alternative splices (ENSSSCT00000075280 and ENSSSCT00000019507) using the qPCR method. As shown in In order to mine the mechanism of CNV150 in depth, we analyzed the interaction network of PELP1. From the protein–protein interaction (PPI) networks , a totalIn order to study whether the protein structure variation affected the interaction between PELP1 and its interactive proteins, we aligned the AA of A0A5G2R420 and F1RFT3 and found there was a 23 AA (83–105) difference between these two proteins A. Alpha The Min is a native Chinese pig breed with an average IMF of > 4.0%, and the Large white is a commercial pig breed with an average IMF of ≤2.0%. The Large white × Min F2 separated population is an ideal population in which to investigate the candidate genes or QTLs for IMF. There is a huge variation in the IMF in our population (0.73%~12.7%). As we known, the “segregation variance” can often be observed in the F2 generation, and the magnitude of segregation variance depends on the extent of population differentiation and the genetic base ,26. In tIn our study, only FarmCPU detected serval significant signals; the other two methods detected no signal or only one significant signal. FarmCPU could split MLM into a fixed effect model (FEM) and a random effect model (REM), using them iteratively to increase the power for detecting candidate genes associated with the population structure. Association tests in FarmCPU are validated by FEM with the same computing efficiency as GLM, while the statistical power surpasses that of MLM at the same level of type I errors. In much research, FarmCPU could detect more significant signals than MLM and GLM could in our research ,17,30.QTL co-location is a method to analyze the potential functions of unknown variations or regulation transcriptional factors . In ordePELP1 is an ESR coregulator protein [PELP1 gene, duplicated CNVs also exist [SPATA22) is a sex-related gene associated with infertility and related traits [In order to identify the potential function of CNVRs, the function of the known genes in which significant CNVRs were located was retrieved. Among the nine genes,  protein  and has  protein . In the so exist ,34. Homod traits . The SH3d traits ,37. The d traits . Other kd traits ,40,41,42PELP1 ASs, named ENSSSCT00000019597, was significantly differently expressed in CNV150-variant individuals. We then validated the differential expression using qPCR and the results were positive. Hence, we inferred that this CNV150 may affect PELP1 alternative splicing.As CNVRs usually work through regulation effects or dose effects ,44, we aPELP1 and its ASs.In order to confirm the function of CNV150, we then analyzed the read depth of CNV 150 in F0-generation individuals and found that the copy number of CNV150 was normal in Min pigs and duplicate in Large white pigs. As shown in PELP1 directly or indirectly affects IMF. In the PPI networks, about half of the proteins had been reported as being related to IMF or insulin resistance. Among these genes or proteins, AR and ESR1 can regulate leptin transcript accumulation and protein secretion in adipocytes [First, we studied whether ipocytes . The NR3ipocytes . NR4A1 cipocytes . RB1 hasipocytes . RPL11 hipocytes . SRC andipocytes . Thus, wIn previous research, the interacting regions of PELP1 and ESR, AR, GR, RB, and STAT3 were amino acids 1–400, LXXLL motifs, amino acids 1–600, or amino acids 1–330 . Hence, In this study, a CNV-based GWAS was performed between CNVs and IMF, and a total of 19 CNVRs were found to be significantly associated with IMF. Some CNVRs, such as the three CNVRs overlapped with known QTLs, may be useful candidate markers for IMF selection. CNV150, which was located on the 3′UTR of PELP1, may influence IMF content by regulating the alternative splicing of the PELP1 gene and the structure of the PELP1 protein. These findings suggest a novel mechanistic approach for meat quality improvement in animals and the potential treatment of insulin resistance in human beings.


There is strong evidence showing that joint analysis of multiple phenotypes in genome-wide association studies (GWAS) can increase statistical power when detecting the association between genetic variants and human complex diseases. We previously developed the Clustering Linear Combination (CLC) method and a computationally efficient CLC (ceCLC) method to test the association between multiple phenotypes and a genetic variant, which perform very well. However, both of these methods require individual-level genotypes and phenotypes that are often not easily accessible. In this research, we develop a novel method called sCLC for association studies of multiple phenotypes and a genetic variant based on GWAS summary statistics. We use the LD score regression to estimate the correlation matrix among phenotypes. The test statistic of sCLC is constructed by GWAS summary statistics and has an approximate Cauchy distribution. We perform a variety of simulation studies and compare sCLC with other commonly used methods for multiple phenotype association studies using GWAS summary statistics. Simulation results show that sCLC can control Type I error rates well and has the highest power in most scenarios. Moreover, we apply the newly developed method to the UK Biobank GWAS summary statistics from the XIII category with 70 related musculoskeletal system and connective tissue phenotypes. The results demonstrate that sCLC detects the most number of significant SNPs, and most of these identified SNPs can be matched to genes that have been reported in the GWAS catalog to be associated with those phenotypes. Furthermore, sCLC also identifies some novel signals that were missed by standard GWAS, which provide new insight into the potential genetic factors of the musculoskeletal system and connective tissue phenotypes. At the same time, a vast majority of GWAS summary statistics obtained from single-trait tests are publicly available, which contain the estimated marginal effect sizes, the corresponding standard deviations, 6. On the other hand, because multiple related phenotypes are often measured as indicators for one specific trait, considering the correlated structure between multiple phenotypes and jointly analyzing these phenotypes may increase statistical power in association studies12.Over the last decades, genome-wide association studies (GWAS) have been very successful in detecting genetic variants associated with human complex traits or diseases13 contains two separate tests (Hom and Het), where Hom is more powerful when the genetic variant has homogeneous effects on the phenotypes; Het is more powerful when heterogeneous effects are present, whereas Monte-Carlo simulations are needed to calculate the p-value of Het when the number of traits is large, which is computationally intensive. SSU15 is a test statistic based on the sum of squared 16 has the test statistic that combines all p-values of independent principal components using Fisher’s method, where allocates larger weights to PCs with smaller eigenvalues. The classical Wald test16 uses the 17 builds a group of multi-phenotype association tests (MATs) that may have good performance in a specific scenario and then integrates the testing results adaptively.Recently, many multiple phenotype association tests based on GWAS summary statistics have been proposed. CPASSOC18 and a computationally efficient CLC (ceCLC) method19 to test the association between multiple phenotypes and a genetic variant based on individual level genotypes and phenotypes. Both of these methods perform very well compared with other multiple phenotypes association tests especially for phenotypes that have natural grouping. In this research, we develop a novel approach called CLC based on GWAS summary statistic (sCLC). In sCLC, we use the LD score regression21 to estimate the correlation matrix among phenotypes. It has been shown that the LD score regression which has been commonly used in recent years can control the potential confounders such as population stratification, unknown sample overlap, cryptic relatedness, and so forth22. In our simulation studies, we consider a range of simulation settings and compare sCLC with other five commonly used methods for multiple phenotype association studies using GWAS summary statistics to evaluate the performance of sCLC. The simulation results show that sCLC can control the Type I error rate well and has the highest power in most scenarios. We also apply the sCLC method to UK Biobank GWAS summary statistics for 70 related musculoskeletal system and connective tissue phenotypes in the XIII category of UK Biobank. The results show that sCLC identifies the most number of significant SNPs, and most of these SNPs can be matched to the genes that have been reported in the GWAS catalog to be associated with the phenotypes in the XIII category. Furthermore, sCLC also identifies some novel signals that were missed by standard GWAS. The new identified signals may provide new insight into the potential genetic factors of the musculoskeletal system and connective tissue phenotypes.In our previous studies, we developed the Clustering Linear Combination (CLC) methodWe consider a GWAS with 21 to estimate the correlation matrix among phenotypes, denoted by 20 regresses the pairwise product of 21; and 20 can be applied to each pair of phenotypes, and the estimated intercepts 21 for each phenotype and the estimated intercepts are used to estimate the diagonal elements of 23. Moreover, LDSC can control potential confounders such as population stratification, unknown sample overlap, cryptic relatedness, and so forth22.Firstly, sCLC uses the LD score regression (LDSC)18, we use the hierarchical clustering approach with similarity matrix 24. Because we consider a single SNP Secondly, similar to CLC26 to integrate the p-values obtained from the second step for all possible number of clusters, Finally, we use Cauchy combination26 showed that a weighted sum of “correlated” standard Cauchy variables still has an approximately Cauchy tail, and the influence of the correlated structure on the tail is quite limited because of the heaviness of the Cauchy tail. Therefore, Under the null hypothesis, 15, Hom13, PCFisher16, Wald16, and aMAT17. Below, we briefly summarize these five methods, where To better demonstrate the performance of the sCLC approach, we compare sCLC with other five methods for multiple phenotype association studies using GWAS summary statistics: SSUThe test statistic of SSU is Assume that there are summary statistics of GWASs from 16ncp) of 16 combines p-values of all Assume that the spectral decomposition of 16.The test statistic of Wald test is defined as 17.The method was developed to deal with potential (near) singularity problem of 27, we generate 28 for 40 phenotypes, where Based on a widely used simulation procedure17, we consider two cases in the 70 phenotypic correlation matrix structure. In the first case, we suppose that To investigate how the estimation error of To evaluate Type I error rate of sCLC, we generate Scenario 1: Generate Scenario 2: Generate Scenario 3: Generate Scenario 4: Generate Table Power comparison results of the six methods under four scenarios with the phenotypic correlation matrix mentclass2pt{minimmentclass2pt{minim31, such as Systemic Lupus Erythematosus (SLE), Sjögren Syndrome (SS), and Rheumatoid Arthritis (RA), may influence the physical activity or movement of patients. These kinds of diseases seriously affect the quality of life of people and have been reported to be potentially affected by genetic factors32. In this paper, we consider the GWAS summary statistics in the XIII category of UK Biobank with 70 musculoskeletal system and connective tissue phenotypes to detect potential genetic factors.Connective tissue dysplasia (CTD) and musculoskeletal disorders33. Sequenced genotypes for 488,377 participants with 784,256 variants in autosomal chromosomes were extracted by UK Biobank dataset34. Similar to Liang et al.28, we first perform quality controls (QCs) on genotypes and individuals by using PLINK 1.935. We remove SNPs with missing rates larger than 5%, p-values from Hardy–Weinberg equilibrium exact test less than The UK Biobank is a large long-term biobank study which has recruited almost half a million participants in the UK, enrolled at ages from 40 to 69http://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=41202) to define Electronic Health Record (EHR)-derived phenotypes. When the individual has the truncated ICD-10 code recorded for a specific phenotype, the corresponding EHR-derived phenotype for that individual will be coded as 1, otherwise it will be 0 (1 for cases and 0 for controls). In the XIII category, we only consider phenotypes with more than 200 cases and there are a total of 72 unique phenotypes, such as rheumatoid arthritis (M06.9) and Systemic Lupus Erythematosus (M32.9). Table 28, we also exclude individuals who are marked as outliers for heterozygosity, and have been identified to have more than ten third-degree relatives or closer, etc. The final dataset includes On the other hand, the phenotypes that coded by International Classification of Diseases, the 10th Revision (ICD-10) codes are considered in our study. We truncate the full ICD-10 code to the UK Biobank ICD-10 level 3 code  and M67.8 (Other specified disorders of synovium and tendon) are excluded in this procedure because the estimators of their heritability are out of bounds. Therefore, there are a total of 70 phenotypes in the simulation studies and real data analysis. The phenotypic correlation matrix only needs to be estimated once for all SNPs. Finally, we apply our proposed sCLC method and the other five methods to test the association between each of 288,647 SNPs and 70 phenotypes, and the commonly used genome-wide significant level To apply our method, we first calculate the GWAS summary statistics for the 72 phenotypes based on https://hgdownload-test.gi.ucsc.edu/goldenPath/hg19/bigZips/genes/). Each gene has a position interval. A SNP can be mapped to a gene if its position is within the interval or 20 kb downstream or 20 kb upstream from the interval. These 969 SNPs can be mapped to 235 genes. From the results, we find that 746 out of 969 SNPs can be matched to the genes that have been reported to be associated with the Chapter XIII phenotypes in GWAS catalog. Moreover, among 318 SNPs only identified by sCLC, 229 SNPs can be mapped to the genes that have been reported to be associated with those phenotypes.Among all the six methods, sCLC identifies the largest number of SNPs (969), where Hom identifies 74 SNPs, SSU identifies 872 SNPs, Wald test identifies 654 SNPs, aMAT identifies 622 SNPs, and PCFisher identifies 585 SNPs. Figure 38 platform to obtain independent lead SNPs and distinct risk loci. Here, the independent lead SNPs are defined as 38, Hom has no lead SNPs. Figure 39 greater than 20, which means having a high observed probability of a deleterious variant effect. In addition, we compare the p-values of the 13 independent lead SNPs obtained by sCLC with the minimum p-value (MinP) among 70 p-values for testing the association between a SNP and each of the 70 phenotypes. Table However, SNPs within the same LD block are highly correlated and are more likely to be mapped to the same gene. For example, 205 out of 969 identified SNPs are mapped to gene TSBP1-AS1, which is associated with 10 phenotypes in the XIII category; other genes such as NOTCH4, HLA-DRA, and HLA-DRB1 also have many identified SNPs mapped on them. Hence, we are also interested in the independent lead SNPs associated with those phenotypes. We use the Functional Mapping and Annotation (FUMA)40, we use DAVID functional annotation software for the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis42. There are 29 significantly enriched pathways identified by sCLC with FDR < 0.05 and enriched gene count > 2 , 20 genes in the four core histones (H2A(6): H2AC6, H2AC13, H2AC14, H2AC15, H2AC16, H2AC17; H2B(6): H2BC3, H2BC4, H2BC13, H2BC14, H2BC15, H2BC17; H3(4): H3C3, H3C10, H3C11, H3C12; H4(4): H4C3, H4C11, H4C12, H4C13), as well as four genes . For the rheumatoid arthritis pathway, sCLC identifies 104 SNPs mapped to 11 genes that are enriched in this pathway, including HLA-DMA, HLA-DMB, ATP6V1G2, HLA-DRA, LTB, TNF, HLA-DOB, HLA-DQA2, HLA-DRB1, HLA-DQA1, and HLA-DQB1.In order to better understand the biological meaning behind 235 mapped genes identified by sCLC, similar to Cao et al.43. This is a limitation of the proposed method in identifying pleiotropic effects. Recently, some methods45 are proposed to evaluate pleiotropic effects. For example, Schaid et al.43 proposed a new statistical method to evaluate pleiotropy using a sequential testing framework. This approach can determine the number of phenotypes associated with a genetic variant and which phenotypes are associated, while accounting for correlations among the phenotypes. SHAHER44, a novel framework for analysis of the shared genetic background of correlated phenotypes, can identify genetic factors common for all analyzed phenotypes and specific genetic factors for each phenotype using genetic correlations between phenotypes. PolarMorphism46 is a summary-statistic-based framework to map and interpret pleiotropic loci in a joint analysis of multiple phenotypes. It identifies horizontally pleiotropic SNPs by converting the trait-specific SNP effect sizes to polar coordinates.In this paper, we propose a multiple-phenotype association test strategy called sCLC which is based on GWAS summary statistics. Through a variety of simulation studies and an application to the UK Biobank XIII category summary statistics, we observed that sCLC is a valid and powerful approach. Specially, sCLC detected some novel signals associated with the musculoskeletal system and connective tissue phenotypes, which provides more evidence to show that those diseases are potentially affected by genetic factors. The sCLC method is also computationally efficient. Since the estimation of the phenotypic correlation matrix 21 can also be used in the hierarchical clustering. Furthermore, considering only the phenotypes with a significant non-zero heritability in the estimation of the genetic correlation matrix may also improve the statistical power in the multiple phenotype association studies. Therefore, we would like to consider using the genetic correlation matrix estimated by the LDSC regression20 or using network-based approaches to cluster phenotypes based on shared genetic architectures in our further work47.On the other hand, the hierarchical clustering approach in sCLC is applied to cluster multiple phenotypes based on the phenotypic correlation matrix Supplementary Information.


ATP5F1B)). These genes are involved in processes that influence muscle pigmentation and postmortem flesh coloration. Other identified genes are involved in the maintenance of muscle structural integrity (kelch protein 41b (klh41b), collagen α-1(XXVIII) chain (COL28A1), and cathepsin K (CTSK)) and protection against lipid oxidation , sestrin-1, Ubiquitin carboxyl-terminal hydrolase-10 (USP10)). A-to-G single-nucleotide polymorphism in β,β-carotene 15,15′-dioxygenase, and USP10 result in isoleucine-to-valine and proline-to-leucine non-synonymous amino acid substitutions, respectively. Our observation confirms that fillet color is a complex trait regulated by many genes involved in carotenoid metabolism, myoglobin homeostasis, protection against lipid oxidation, and maintenance of muscle structural integrity. The significant SNPs identified in this study could be prioritized via genomic selection in breeding programs to improve fillet color in rainbow trout.The visual appearance of the fish fillet is a significant determinant of consumers’ purchase decisions. Depending on the rainbow trout diet, a uniform bright white or reddish/pink fillet color is desirable. Factors affecting fillet color are complex, ranging from the ability of live fish to accumulate carotenoids in the muscle to preharvest environmental conditions, early postmortem muscle metabolism, and storage conditions. Identifying genetic markers of fillet color is a desirable goal but a challenging task for the aquaculture industry. This study used weighted, single-step GWAS to explore the genetic basis of fillet color variation in rainbow trout. We identified several SNP windows explaining up to 3.5%, 2.5%, and 1.6% of the additive genetic variance for fillet redness, yellowness, and whiteness, respectively. SNPs are located within genes implicated in carotenoid metabolism  and myoglobin homeostasis (ATP synthase subunit β, mitochondrial ( The aquaculture industry produces food fish to satisfy a growing US and worldwide demand. Rainbow trout is the most cultivated, cool, freshwater fish in the United States . AquaculUse of genomic information in breeding programs offers a faster and more accurate method of achieving genetic progress. Achieving this goal requires understanding the genetic architecture underlying the variability in these traits. Genome-wide association (GWA) studies can identify genome regions associated with desired traits. GWA studies take advantage of linkage disequilibrium between SNP markers and genetic loci controlling a trait of interest. GWA studies have been conducted in the rainbow trout breeding program at the National Center for Cool- and Cold-Water Aquaculture (NCCCWA) for growth , muscle Fillet color is an important quality trait, usually influencing consumers’ satisfaction and point-of-purchase decisions. There are two markets for rainbow trout fillets—one for a bright reddish/pink fillet and one for bright white fillets. Consumers usually reject or downgrade pale yellowish fillets. Factors affecting fillet color range from genetics to environmental factors and to harvest, handling, and storage conditions . PostmorAstaxanthin, a carotenoid added to the salmonid feed to improve the reddish color of the fillet, is an expensive feed ingredient, accounting for up to 30% of the feed cost. Therefore, development of genetically improved rainbow trout strains that more efficiently incorporate carotenoids into the muscle will benefit the aquaculture industry by improving profitability and consumer satisfaction. When fed unpigmented diets, genetically improved strains will also use naturally occurring carotenoids in the feed ingredients.PyBCO-1 in Scallop 1/2 1/2 . The datThe 878 fish were genotyped with the 50k transcribed SNP-chip developed and described before  was usede is the residual effect. X, Z1, and Z2 are incidence matrices for the effects contained in b, a, and w, respectively.The mean and standard deviation values of each fillet color phenotype were calculated. Heritability was estimated as the ratio of additive genetic variance to total phenotypic variance. Variance components were estimated using the restricted maximum likelihood method found in AIREML in BLUPF90 software  using thThe weighted single-step GBLUP (wssGBLUP) approach proposed by Wang et al.  was usede is the residual effect. X, Z1, and Z2 are incidence matrices for the effects contained in b, a, and w, respectively. The fixed effects used in this study are fish age, harvest group, and hatch-year. The assumptions are that a~N were analyzed using the single-trait animal model in wssGBLUP according to the model below:d matrix , while Ianalysis .H−1=A−1AIREMLF90 was used to estimate variance components supplied to BLUPF90 to predict genomic estimated breeding values (GEBV). The inbreeding coefficient was calculated from the pedigree data of 1420 fish by RENUMF90 using the method of Meuwissen and Luo .BLUPF90 was used to predict breeding values using a weighted genomic relationship matrix (G). The SNP marker effect and new weights were then computed with POSTGSF90  using 50ia is the genetic value of the i-th window consisting of 50 adjacent SNPs, σ2 is the total genetic variance, jz is a vector genotype of the j-th SNP for all animals, and j-th SNP within the i-th window.The percentage of additive genetic variance explained by each SNP window was calculated as:The qqman package  was usedGenomic windows explaining at least 1% of the genetic variance were selected as possible genetic regions associated with the fillet color traits. The 1% threshold was set based on the literature obtained ,42,43. TPITA, miRanda, and TargetSpy) from the sRNAToolbox (http://bioinfo5.ugr.es/srnatoolbox). The rainbow trout microRNA repertoire was obtained from Juanchich et al. [SNPs located in the 3’UTR of genes associated with fillet color in this study were investigated if their 3’UTR served as a target site for rainbow trout microRNAs. MicroRNA targets were predicted using three algorithms , whiteness and redness (a*) was 0.28, whiteness and yellowness (b*) was 0.67, and redness (a*) and yellowness (b*) was 0.45 (There was more variation in the redness (54%) and yellowness (30%) in comparison to lightness and whiteness (6%) . The herwas 0.45 .A weighted single-step GBLUP approach was implemented in the BLUPF90 family of programs  to identFor redness (a*), chromosome 7 harbors the majority (33%) of the SNPs (80), followed by chromosome 9 (67 SNPs) , Table 2For the yellowness trait (b*), most of the SNPs (66) are resident in chromosome 6 (41%), followed by 46 SNPs on chromosome 4 (29%). The peak SNP window, resident on chromosome 6, explains up to ~2.5% of the genetic variance for this trait , Table 2Lightness (L*) and whiteness are similar in their genetic architecture, with peak SNP on chromosome 8 explaining only 1.6% of the genetic variance for this trait , Table 3ANKH (ANKH inorganic pyrophosphate transport regulator), RETRIG1 (reticulophagy regulator 1), and HSPB1  genes are target sites for the omy-mir-1388-3p, omy-mir-219-5p, and omy-miR-724-5p microRNAs, respectively. An A-to-T single-nucleotide substitution at the target site of omy-mir-1388-3p causes a loss of its miRNA target site. Likewise, a C-to-T transition at the 3’UTR of HSPB1 resulted in a loss of the target site for the omy-miR-724-5p miRNA. Single-nucleotide substitution at the target site of omy-mir-219-5p does not lead to a loss of the target site.Our results revealed that the 3’UTR region of Fillet color is an important quality trait in salmonids influencing consumers’ purchasing decisions. Therefore, the industry is interested in selecting rainbow trout with superior genetic merit in their ability to produce a bright red or white fillet. Understanding the trait’s genetic architecture is required to determine the best genetic improvement approach. In this study, a genome-wide association investigation identifies regions of the genome influencing variability in fillet color traits in rainbow trout.There is more variation in redness (a*) and yellowness (b*) compared to whiteness. The estimated heritability for fish in this population is low to moderate, similar to an estimate of 0.27 obtained for a rainbow trout fillet color score by Gjerde and Schaeffer . A heritThe SNP windows explaining the highest genetic variance are found on chromosomes 7, 6, and 8 for fillet redness, yellowness, and whiteness, respectively. The SNP-harboring genes were classified according to their function and relevance to fillet color into the following categories.BCO1) and its paralogue β-carotene oxygenase-1 (BCO1L) as two probable causal genes influencing flesh color in Atlantic salmon. They supported their findings with functional studies of mRNA and protein expression, which pointed to BCO1L as the most likely of the two genes to influence flesh color variation. Several studies have identified single-nucleotide polymorphism within BCO1 that was associated with breast meat color in chicken [BCO1 in fish) was responsible for carotenoid metabolism and subsequent muscle coloration [β, betacarotene 15,15-dioxygenase and retinol dehydrogenase are involved in carotenoid metabolism ,48,49. F chicken ,48,49. Iloration .β,β-carotene 15,15′-dioxygenase on chromosome 6 explains 2.2% of the phenotypic variance for yellowness, while retinol dehydrogenase-7 found on chromosome 7 explains 2.9% of the variation in the redness trait . A-G SNPATP5F1B, methylmalonyl-CoA mutase, ABC11, calsequestrin, cytochrome b5 (CYB5), ubiquitin carboxyl-terminal hydrolase 10 (USP10), peroxiredoxin, superoxide dismutase 2 (SOD2), sestrin-1, myosin X, and protein PRRC2C are genes that were found to affect fillet color in the study (he study . They arATP5F1B) on chromosome 7 is another identified gene explaining over 3.5% of the genetic variability of fillet redness in rainbow trout  on chromosome 7 explains ~2.9% of the genetic variation in fillet redness (ATP5F1B) gene. Blay et al. [Bile salt export pump  redness . This gey et al.  identifiCYB5), on chromosome 6, explained up to 2.3% of the genetic variability for fillet yellowness  is a member of the deubiquitinating enzyme family known as deubiquitinases, which include ubiquitin C-terminal hydroxylase-1 (UCH-L1) [USP10 and ubiquitin carboxyl-terminal hydrolase 47 (both on chromosome 6), respectively, explain 2.3% and ~2.0% of genetic variance associated with the yellowness phenotype in this study  . USP10 ais study . UCH-L1  in pigs  and sheel muscle  and playl muscle . PolymorPRDX6), on chromosome 4, explains 2.1% of genetic variance in fillet yellowness in the present fish population  function in the homeostatic regulation of the myoglobin redox state, protecting oxymyoglobin against oxidation and thereby enhancing the reddish coloration of the fillet.The peroxiredoxin family is a group of proteins capable of detoxifying peroxides and protecting cells against oxidation . Peroxirpulation . Proteompulation , has beeeductase ,90. It iSOD2) encodes for muscle antioxidant enzyme. This enzyme reduces the damage caused by superoxide anion radicals [SOD2 on chromosome 8 explained 1.5% of the genetic variance in fillet whiteness  on chromosome 8 explains ~1.4% of the genetic variance in fillet whiteness  when in the presence of myosin compared to when myosin is absent in Tuna fish and Sardine [MYO10) on chromosome 8 explains 1.3% of the genetic variance for fillet whiteness , collagen α-1(XXVIII) chain (COL28A1), myocilin (MYOC), F-actin-methionine sulfoxide oxidase (MICAL2), and cathepsin K (CTSK) are genes that are found to affect fillet color in the study. They are known to be involved in the maintenance of muscle structural integrity. KLH4LB on chromosome 7 explains up to 3.4% of this study’s variance in the redness trait  harbors the SNP marker for muscle color in broiler chicken [Collagen α-1(XXVIII) chain ( chicken . The sam chicken . The rel chicken .CTSK) explains up to 2.8% of this study’s genetic variance in fillet redness  encodes the protein myocilin, which is involved in regulating the actin cytoskeleton [Myocilin skeleton . It explMICAL2) encodes methionine monooxygenase, which promotes depolymerization of F-actin by mediating the oxidation of residues on actin to form methionine-sulfoxide, resulting in actin filament disassembly and preventing repolymerization [F-actin-methionine sulfoxide oxidase  gene encodes a protein binding heparin and glycosaminoglycans and is involved in regulating the innate immune system [The cysteine-rich secretory protein LCCL domain-containing 2 (e system . It was e system . It explMicroRNAs (miRNAs) are short non-coding RNAs between 20 and 24 nucleotides in length and can regulate gene expression post-transcriptionally by binding to the 3’UTR of its target mRNA ,122. ThiANKH (ANKH inorganic pyrophosphate transport regulator), RETRIG1 (reticulophagy regulator 1), and HSPB1  genes are target sites for omy-mir-1388-3p, omy-mir-219-5p, and omy-miR-724-5p microRNAs, respectively (HSPB1 resulted in a loss of the target site for the omy-miR-724-5p miRNA. Single-nucleotide substitution at the target site of omy-mir-219-5p does not lead to loss of the target site.The 3’UTR region of ectively . An A-toHSPB1 encodes for a heat-shock protein that can protect against oxidative stress. Diet supplementation with antioxidant vitamins resulted in a significant drop in HSPB1 expression in athletes after an exercise period compared with athletes fed an un-supplemented diet [HSPB1 has been shown to improve stress resistance (including oxidative stress) [HSPB1 target site facilitates the translation of the gene and induces resistance against oxidative stress.Heat-shock proteins are common effectors of the cellular stress response. Thermal, environmental, or oxidative stress can trigger the transcription of genes encoding heat-shock proteins ,128,129.ted diet . Over-ex stress) ,133,134.ANKH (ANKH inorganic pyrophosphate transport regulator). The gene encodes a protein that controls the extracellular level of pyrophosphate [Another gene that exhibits a loss of the miRNA target site under single-nucleotide polymorphism is the hosphate . Inorganhosphate ,137,138.We used weighted single-step GWAS to identify genetic variants associated with variability in fillet color traits in rainbow trout. Our result confirms that fillet color is a complex trait with no major gene but many SNP variants contributing to its regulation. We established that regulatory genes are involved in maintaining muscle structural integrity, carotenoid metabolism, or protection against myoglobin and lipid oxidation. An isoleucine-to-valine non-synonymous amino acid substitution mutation in β,β-carotene 15,15′-dioxygenase explained 2.2% of the phenotypic variance for yellowness, while SNP variants in retinol dehydrogenase-7 explained 2.9% of the variance in the muscle redness.