Background: It is plausible that maternal pregnancy metabolism influences the risk of offspring congenital heart disease (CHD). We sought to explore this through a systematic approach using different methods and data. Methods: We undertook multivariable logistic regression of the odds of CHD for 923 mass spectrometry (MS)-derived metabolites in a sub-sample of a UK birth cohort (Born in Bradford (BiB); N = 2605, 46 CHD cases). We considered metabolites reaching a p-value threshold <0.05 to be suggestively associated with CHD. We sought validation of our findings, by repeating the multivariable regression analysis within the BiB cohort for any suggestively associated metabolite that was measured by nuclear magnetic resonance (NMR) or clinical chemistry , and by using genetic risk scores (GRS: weighted genetic risk scores of single nucleotide polymorphisms (SNPs) that were associated with any suggestive metabolite) in Mendelian randomization (MR) analyses. The MR analyses were performed in BiB and two additional European birth cohorts . Results: In the main multivariable analyses, we identified 44 metabolites suggestively associated with CHD, including those from the following super pathways: amino acids, lipids, co-factors and vitamins, xenobiotics, nucleotides, energy, and several unknown molecules. Of these 44, isoleucine and leucine were available in the larger BiB cohort (NMR), and for these the results were validated. The MR analyses were possible for 27/44 metabolites and for 11 there was consistency with the multivariable regression results. Conclusions: In summary, we have used complimentary data sources and statistical techniques to construct layers of evidence. We found that pregnancy amino acid metabolism, androgenic steroid lipids, and levels of succinylcarnitine could be important contributing factors for CHD. Congenital heart diseases (CHDs) are the most common congenital anomaly affecting approximately 6–8 per 1000 live births and 10% of stillbirths. They are the leading cause of death from congenital anomalies . The cauMetabolomics technologies have enabled the quantification of a large number of metabolites in a biological sample. Metabolites are small-molecule intermediates and products of metabolism. The metabolome, the complete set of metabolites in biological tissues/fluids, is influenced by both genotype and the environment, and dynamically responds to environmental influences. Analyses of maternal metabolomic profiles could identify causal mechanisms leading to CHDs . BecauseStudies have explored maternal biomarkers and found that the offspring of women with low 25-hydroxyvitamin D (<50 nmol/L compared to >75 nmol/L)  and dyslOne previous study has explored metabolomics in maternal serum including 27 CHD cases compared with 59 controls . A totalp < 0.05) and (ii) had summary data in the most recent metabolomic genome-wide association study (GWAS).The aim of this study was to gain insights into the potential molecular mechanisms for CHD. To address this aim, we explored associations of maternal metabolites quantified by an untargeted mass spectrometry (MS) platform and the odds of CHD in offspring. We searched for relevant studies within The LifeCycle Project-EU Child Cohort Network  to identp-value < 0.05 within the BiB study where equivalent  measures to any on the MS platform were available from other sources. BiB is a population-based prospective birth cohort, including 12,453 women across 13,776 pregnancies who were recruited at their oral glucose tolerance test (OGTT) at approximately 26–28 weeks’ gestation [p-value threshold of <0.05 to take associations forward into further validation analyses was appropriate as an initial screen, for a relatively rare outcome, to avoid missing potential causal effects. All the BiB participants included in the Metabolon multivariable regression analyses (N = 2605) (Step 1 (A schematic overview of the study design is illustrated in estation . The eli (Step 1 ) were al (Step 1 ), and 21To be included in the MR analyses, the studies and participants had to have genome-wide data in the mothers and CHD data in their offspring. Three cohorts contributed to the MR analyses: BiB, the Avon Longitudinal Study of Parents and Children (ALSPAC) and the Norwegian Mother, Father and Child Cohort Study (MoBa). ALSPAC is a UK prospective birth cohort study which was devised to investigate the environmental and genetic factors of health and development ,21,22. POf the 13,776 pregnancies in the BiB cohort, 11,480 had a fasting blood sample taken during the OGTT . The samples were taken by trained phlebotomists working in the antenatal clinic of the Bradford Royal Infirmary and sent immediately to the hospital laboratory. The metabolomics data in the BiB cohort has previously been described in detail . In brieIn multivariable regression analyses in BiB, we adjusted for the following maternal characteristics based on their known or plausible influence on maternal metabolites and on CHD: age, ethnicity, parity, residential neighbourhood index of multiple deprivation (IMD), body mass index (BMI), smoking, and alcohol consumption. Details of the methods for how the confounders were assessed are provided in the In the BiB cohort, the cases were identified from either the Yorkshire and Humber congenital anomaly register database, which tends to pick up most cases that were diagnosed antenatally and in the early postnatal period of life, or through linkage to primary care (up until age 5), which will have picked up any additional cases, in particular those that might have been less severe and not identified antenatally/in early life . All theThe rationale for performing the MR analyses was to further explore potential causal mechanisms by using a method, namely, MR, which has different key sources of bias to multivariable regression. Metabolites are affected by multiple disease processes as well as numerous environmental exposures; therefore, understanding the metabolic pathways implicated in CHD is nontrivial. Genetic variants are less likely to be confounded by the socioeconomic and environmental factors that might bias causal estimates in conventional multivariable regression , but mayThe ALSPAC mothers were genotyped using an Illumina Human 660K-Quad single nucleotide polymorphism (SNP) chip, and the ALSPAC children were genotyped using the Illumina Human Hap 550 Quad genome-wide SNP genotyping platform. Genotype data for both the ALSPAC mothers and children were imputed against the Haplotype Reference Consortium v1.1 reference panel, after performing the QC procedure  ≥1%, a call rate ≥95%, in Hardy–Weinberg equilibrium (HWE), correct sex assignment, no evidence of cryptic relatedness, and of European descent). The samples of the BiB cohort (mothers and offspring) were processed on three different types of Illumina chips: HumanCoreExome12v1.0, HumanCoreExome12v1.1 and HumanCoreExome24v1.0. The genotype data were imputed against the UK10K + 1000 Genomes reference panel, after a similar QC procedure . In MoBa, blood samples were obtained from both parents during pregnancy and from the mothers and children  at birth . Genotypp-value < 0.05 (referred to throughout as “suggestively associated”) in the multivariable regression analyses using BiB data. To do this, we cross-referenced our suggestive associations with the relevant GWAS and we used summary data from two GWAS. In the first, the authors explored the genetic associations with 174 metabolites (compared with the 923 included in our study) [p < 5 × 10−8). Independent SNPs were defined as linkage disequilibrium (LD) r2 ≥ 0.001 assessed at a distance of 10,000 kb, with only one SNP from a correlated (clumped) group retained  . To ensu package ). In the package  and INTE package . A totalp < 0.05 from the multivariable regression analyses and removing indels, the selected SNPs were extracted from the imputed genotype data in dosage format using QCTOOL (v2.0) and VCF tools (v 0.1.12b) in ALSPAC and BiB, respectively. PLINK (v1.9) was then used to construct the GRS for each exposure coded so that an increased score was associated with increased levels in metabolites. In MoBa, we constructed the GRSs from the QC’d data in PLINK format. If a SNP was missing, a proxy SNP was used where available based on a r2 > 0.8 using the European reference panel in the LDLink R package [GRSs were calculated using SNPs previously associated in largescale GWAS with the metabolites (described above) by adding up the number of metabolite-increasing alleles among the selected SNPs after weighting each SNP by its effect on the corresponding metabolite: package .Analyses were performed in R version 4.0.2 . An analysis plan was written and uploaded to the Open Science Framework before the analyses commenced, where any subsequent changes to the analyses were documented along with the rationale . We usedp < 0.05  for the metabolite being suggestively associated with CHD in the offspring . We took these metabolites forward to the MR analyses.We used logistic regression to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) of any CHD per SD higher metabolite, with and without an adjustment for confounders. As we are interested in the potential causal effects, we present confounder adjusted results throughout. The analyses were completed separately in the two BiB datasets and the results pooled using fixed-effects meta-analyses. Given that CHD is rare and binary, we accepted an uncorrected The MS-platform used in BiB includes the measures of xenobiotics which are synthetic chemicals that are not synthesised by humans. Their presence in the circulation usually reflects endogenous exposures, such as medications and supplements. Given that these metabolites would not be present in all participants (and therefore have high missingness), many were removed (86/154 (56%)) from the dataset given the metabolite inclusion criteria of 440 unique observations mentioned above. Consequently, we performed an exploratory additional analysis using xenobiotics (N = 154) as binary variables . We present adjusted ORs of these binary variables (any presence vs. none) with CHDs.We sought to internally validate any of the metabolites suggestively associated with CHDs that were also measured in BiB in larger numbers using different methods. After matching the suggestive associations, we used data from the NMR platform (N = 2 metabolites) and did not use any data from the clinical chemistry measurements. More information on the BiB NMR data including the methods, QC and participant information has been described in detail previously .We undertook MR in each of the 3 cohorts, including all BiB, ALSPAC, and MoBa participants with maternal genetic data and offspring CHD data. Logistic regression was used to estimate the OR of CHD per SD change in GRS, with an adjustment for the first 10 genetic principal components (PCs) with additional adjustments for the genetic chip, genetic batch, and imputation batch in MoBa.The key assumptions for MR are: (i) Relevance assumption—the genetic instruments are robustly and strongly statistically associated with the exposure and relevant to the population being studied . We tested the association of the GRS of each metabolite with the metabolite levels during pregnancy in BiB dataset 2 using linear regression. (ii) Independence assumption—the IV outcome association is not confounded. Such confounding could occur as a result of population stratification. To minimise this, we adjusted the GRS-CHDs associations for the first 10 genetic PCs. We also repeated the MR analyses without the inclusion of BiB, given that BiB has a unique ethnic structure of South Asians and White Europeans. (iii) Exclusion restriction criteria—the genetic variant is not related to the outcome other than via its association with the exposure. We assessed pleiotropy by estimating the variance explained in all metabolites by each of the GRSs by undertaking the linear regression of every metabolite measured in BiB on each GRS. If the variance explained in other metabolites was similar or greater than to that explained in the candidate risk metabolite, this would suggest that there was low metabolite-specificity for the GRS and potential horizontal pleiotropic bias via the other metabolite(s). Importantly, however, this approach of testing the GRS specificity does not distinguish between vertical pleiotropy  and horizontal pleiotropy . We also checked the consistency of the MR results when additionally adjusting for foetal genotype . We perfp < 0.05, in the confounder adjusted pooled analyses  with several amino acids, lipids and co-factors and vitamins. There were also suggestive effects for two xenobiotics, one nucleotide, one energy metabolite and some partially characterised and unknown metabolites   per SD increase in metabolite: 1.73 95% CI ) and 5 were negatively associated (decreased odds) ). All but one (N-Acetylcarnosine) of the 10 amino acid-related metabolites were negatively associated with CHDs ). Three of the four co-factors and vitamins were negatively associated, whereas one (biliverdin) was positively associated ). One nucleotide was negatively associated (inosine 5′-Monophosphate (Imp): aOR 0.59 ) and one energy related metabolite was positively associated (succinylcarnitine (C4): aOR 1.42 ). Benzoate and Saccharin were the two xenobiotics associated with CHDs in the main analyses with both showing positive associations. The results for the associations of all metabolites  in the unadjusted and confounder adjusted analyses from the pooled datasets, and each dataset separately are provided in Of the 923 metabolites quantified in both BiB datasets, 44 (4.8%) were associated with any CHD, at analyses . We obseabolites . None ofIn the analysis treating the xenobiotics as binary variables, after removal of the metabolites with no exposed cases, there were six xenobiotic metabolites suggestively associated with offspring CHDs . Two outIt was possible to explore 2 of the 44 metabolites suggestively associated with CHDs in the larger BiB sample. In the comparable confounder adjusted analyses, the NMR measured amino acids isoleucine and leucine were available on 7296 mothers, with 87 having an offspring with CHD. The results for these two amino acids were highly consistent between the two samples/assay methods  vs. 0.65  for the NMR isoleucine, and aOR per SD increase in the MS leucine 0.69  vs. 0.67  for NMR leucine).2 values ranging from 0.3% to 34%), but for the remaining three, the associations were imprecise with wide confidence intervals that included the null  effect of higher levels of the amino acids leucine, indolelactate and isoleucine on CHD , but for the other amino acids, the MR results were either very close to the null or in the opposite direction . Seven oMaternal metabolism is important for healthy foetal growth and development. To our knowledge, no previous study has examined the association of detailed maternal metabolites with the risk of CHD within a causal framework. In this study, we found 44 metabolites (of 923) suggestively associated with CHD. These included metabolites related to amino acids, lipids, co-factors and vitamins, unknown molecules, xenobiotics, nucleotides and energy. In separate xenobiotics analyses, there was some evidence that metabolites related to aspirin and saccharine may increase the odds of CHD, whereas metabolites related to plant food components may reduce the odds. Two of the amino acids, where it was possible to explore replication, were replicated in BiB in larger numbers using an alternative metabolomics platform. In the MR analyses, we analysed the relationship of maternal genetic variants that are associated with metabolite levels with offspring CHD. We completed this for 27 metabolites that showed evidence of association with offspring CHD in multivariable regression analyses and that we had GWAS summary data on. There was directional consistency for 11 out of the 27 metabolites that could be explored in the MR analyses. Overall, we found that maternal amino acid metabolism during pregnancy, several lipids , and levels of succinylcarnitine could be important contributing factors to offspring CHD risk.Nine out of the ten amino acids suggestively associated with CHDs were negatively associated, suggesting that deficiencies in certain amino acids during pregnancy could contribute to offspring CHDs. Previous research found that amino acid concentrations measured in amniotic fluid were lower in patients with CHDs , which iEighteen of the forty-four maternal metabolites suggestively associated with CHD were part of the lipid super pathway. Previous work reported that an abnormal lipid profile (defined as elevated cholesterol and apolipoprotein B) , abnormaLevels of bilirubin and biliverdin were positively associated with CHDs. MR analyses were only possible for biliverdin but were inconclusive with wide confidence intervals; thus, these two compounds, which are involved in heme catabolism, should be further investigated for a possible role in CHD development. Levels of succinylcarnitine were also positively associated with CHDs and we found good replications in the MR analyses with consistent directions of effect and a GRS that appeared highly specific for succinylcarnitine. Succinylcarnitine is an acylcarnitine, which is a group of metabolites responsible for the beta oxidation of fatty acids and mitochondrial function . It is wp-value threshold < 0.05, i.e., not taking account of multiple testing. When we applied a Bonferroni corrected threshold (p < 0.0001), none of the associations passed this  of each of the 27 GRSs with all other metabolites available in the BiB dataset 2. Stronger or similar associations with other metabolites would suggest that the GRS was not a specific instrument for the metabolite that we were using it for. In this case this could be because of known biological relations. For example, we know biologically that many of the lipid metabolites are related to each other, and we saw this with similar proportions of variation explained by the GRS of the androgenic steroid lipid metabolites with other androgenic steroid lipid metabolites. As such, we would interpret the results for these metabolites as supporting an effect of maternal androgenic steroid metabolites on CHD, but we could not be specific about which ones were driving this. A similar or stronger variation of a GRS for other metabolites could be related to vertical pleiotropy, i.e., the metabolite for which the GRS is instrumenting strongly influences other metabolites that are related to CHD with the other metabolites partly mediating the effect of the focused metabolite. This would not bias the result; however, this could also occur with horizontal pleiotropy where the GRS, independently of the metabolite of interest, influences other metabolites that are risk factors for CHD. With our current data, we are not able to distinguish between these two.A further limitation of this study is that the maternal plasma/serum metabolomics data were derived at a single timepoint around 26–28 weeks’ gestation. Foetal cardiac development starts early in pregnancy and much of the development occurs in the first trimester . Here, wIn summary, we have used metabolomics and genetic data obtained during pregnancy to explore how the maternal metabolome may contribute to offspring CHDs. We found evidence that markers of maternal pregnancy amino acids and androgenic steroids during pregnancy, as well as higher levels of succinylcarnitine could be important contributing factors to offspring CHD risk. It was not possible to undertake genetic MR analyses on 17 of the potential associations from multivariable regression and further multivariable regression and/or MR in larger independent samples, but these would be valuable. Our analysis pipeline, which involved seeking replication of metabolite associations by harnessing large-scale GWAS data, provides scope to address this and to obtain more precise estimates for the 27 associations we could undertake MR on, as more cohorts with genomic and metabolomic data become available. Metabolomics could prove to be an important tool for identifying the biological pathways that may lead to the identification of prevention targets to decrease the disease burden of CHDs. To do this, future research will require the international collaboration of more and larger studies with detailed metabolomics data in pregnancy, ideally with some of these having repeat measures across pregnancy and offspring CHD data.Ethical approval for ALSPAC was obtained from the ALSPAC Law and Ethics committee and local research ethics committees (NHS Haydock REC: 10/H1010/70). Informed consent for the use of data collected via questionnaires and clinics was obtained from the participants following the recommendations of the ALSPAC Ethics and Law Committee at the time. At age 18, the study’s children were sent ‘fair processing’ materials describing ALSPAC’s intended use of their health and administrative records and were given clear means to consent or object via a written form. Data were not extracted for the participants who objected, or who were not sent fair processing materials. For BiB, Ethics approval has been obtained for the main platform study and all of the individual sub studies from the Bradford Research Ethics Committee. Written consent was obtained from all participants. The establishment of MoBa and the initial data collection was based on a license from the Norwegian Data Protection Agency and approval from The Regional Committees for Medical and Health Research Ethics. The MoBa cohort is now based on regulations related to the Norwegian Health Registry Act.http://www.bristol.ac.uk/alspac/researchers/data-access/documents/alspac-data-management-plan.pdf), describes in detail the policy on data sharing, which is through a system of managed open access. Scientists are encouraged to make use of the BiB study data, which are available through a system of managed open access. Please note that the study website contains details of all the data that is available through a fully searchable data dictionary and variable search tool and reference the following webpage: http://www.bristol.ac.uk/alspac/researchers/our-data/). The ALSPAC data management plan (https://borninbradford.nhs.uk/research/guidance-for-collaborators/). MoBa data are used by researchers and research groups at both the Norwegian Institute of Public Health and other research institutions nationally and internationally. The research must adhere to the aims of MoBa and the participants’ given consent. All use of data and biological material from MoBa is subject to Norwegian legislation. More information can be found on the study website (https://www.fhi.no/en/studies/moba/for-forskere-artikler/research-and-data-access/).Before you contact the BiB study, please make sure you have read the Guidance for Collaborators:


On the basis of three main drawn settings, built in order to control both the effect of the sample size and the genetic structure, we have identified some signals common to those obtained in a previous work employing only taurine cattle. In particular, using the top 1% Fst approach, we detected a candidate region (22.6–23.8 megabases) on chromosome 14 in which genes related to pigmentation have been already documented. In addition, when we constructed a phylogenetic tree using the significant markers identified in this study and including also the genotyping data at these loci of both the grey taurine and the extinct wild auroch, we found a topological repartition consistent with breed colour pattern rather than with the known bovine evolutionary history. Thus, on the basis of this evidence, together with the geographical distribution of the current taurine grey cattle, an ancestral indicine origin for the grey phenotype would seem to be a conceivable interpretation. In this context, a higher thermo-tolerance and less UV-induced damage of the grey phenotype might have favoured the retention of advantageous genes into the taurine genome during the post-Neolithic human-mediated cattle expansions.Shades of grey and brown are a dominant component in mammal coat colours, representing a fundamental trait involved in a great number of processes including cryptism, sexual selection and signalling. The genetic mechanisms of the grey colouration in mammals are very complex and controlled by hundreds of genes whose effects and interactions are still largely unclear. In this study, we adopted a robust multi-cohort F The great majority of coat colours in mammals are shades of grey and brown as a consequence of both cryptism and concealment . HoweverSTX17 gene has been shown to cause a different life cycle in the hair follicle and melanocytes which promotes gradual whitening. However, such a recessive genotype has been also shown to be more prone to the incidence of melanoma, suggesting complex pleiotropic effects of genes involved in the grey coloration [D locus in a wide spectrum of dog breeds, however this phenotype does not seem to be related with pleiotropic effects increasing the incidence of melanoma [In domestic animals, strong artificial selection has led to a wide range of coat colours, where grey and brown pigmentation still remain dominant. Up to now, the genetic mechanisms of pigmentation in mammals are still largely unknown due to the complex inheritance, consisting of more than 150 genes related to the synthesis, distribution and density of eumelanin and pheomelanin pigments and its age-dependent expression ,5. In liloration ,7,8. In melanoma .ASIP (agouti), TYR , TYRP1 (brown), KIT , KITLG (roan), PMEL (dilution), MC1R (extension) and MITF (white spotting) [st-outlier approach has suggested several potential candidate genes when contrasting taurine grey cattle with taurine cattle displaying a non-grey phenotype [In cattle, coat colour is controlled by few known genes and their corresponding alleles, such as: potting) ,11,12. Wpotting) ,15,16,17Bos taurus makes the general framework particularly appealing, since all extant indicine and taurine breeds belong to Bos taurus taurus and Bos taurus indicus, respectively, whose ancestors diverged approximately 250 KYA [st-outlier analysis contrasting seven indicine cattle breeds with four taurine breeds displaying a non-grey phenotype using either single comparisons or using breeds as meta-populations. In addition, we also compared the seven grey indicine breeds with the non-grey Sahiwal indicine breed in order to mitigate a possible impact of the genetic differentiation between taurine and indicine breeds. The present work provides new insight into the comprehension of the molecular mechanisms underlying coat colour in cattle.In the wave of the aforementioned study, here we expanded the above methodology to indicine grey breeds with the aim of validating the previously detected candidate genes associated with the grey phenotype and/or to identify new putative ones. The evolutionary context of  250 KYA ,19. ThusA total of 62 individuals belonging to seven indicine breeds and 112 individuals belonging to four taurine cattle breeds were used in this study  and missing genotype call rate (--geno 0.1) using the software PLINK . SNPs weA multi-comparison approach has been used here to infer possible outlier loci using the Bayesian algorithm as implemented in the software BayeScan, and by using the default parameters . This mest values. Thus, all loci passing these criteria in multiple comparisons were retained to investigate the presence of relevant genes in neighbouring (±250 kilobases) regions. We recall here that the q-value of a given locus is the minimum false discovery rate, i.e., the proportion of false positives expected among outlier markers, at which this locus may become significant. The choice to adopt, in this study, the same method for selection signatures detection as in Senczuk et al. [st-outlier approach) was prompted by the need of assuring the comparability between our results and the previously published results by Senczuk et al. [Concerning the outlier detection, we used two different approaches to retain SNPs putatively under selection. We kept loci displaying either a q-value < 0.05 or falling within the top 1% distribution of the Fk et al.   that we found to be common in this study and the one by Senczuk et al. . MoreoveAfter filtering for missing call rates and minor allele frequencies we obtained a final dataset consisting of 23,313 SNPs.Bos taurus chromosomes (BTA) 2, 5, 10, 11, 14, 19, 21 and 26), highlighted in red in Considering the first scenario, no SNP reached the significant q-values (<0.05), except for nine loci in the Tharparkar vs. Limousin contrast , highlighted in red in st and retaining as significant only the SNPs falling in the top 1% distribution, the two SNPs previously mentioned also displayed, in all the seven tested breeds, the highest Fst values, while none of the remaining loci falling in the top 1% distribution were observed in all the seven comparisons. Moreover, the top Fst loci also included, for each corresponding scenario, all the loci that displayed a significant q-value in the meta-population  each against an indicine “not-grey” breed  as reference  in all the four tested scenarios , notably for four, six, six and six “grey” indicine breeds, respectively. Also, the two upstream neighbouring loci (BTB-01532239 and BTB-01530788) were significant in two (Charolais and Holstein) and three  scenarios, respectively. Finally, when adopting the same approach in the meta-population, all the loci identified in the taurine “grey” cattle [Based on the good reproducibility of results obtained in the above settings using both the q-value and the top 1% SNPs parameters, we then adopted the latter approach in the contrasts involving the seven indicine “grey” cattle breeds and the four taurine reference cattle breeds. Several regions were shared by at least four indicine breeds in at least three out of the four reference breeds . In addi” cattle , fell wiThe neighbor-net graph based on Reynolds distances  showed tTMEM8C and PML, respectively. While the TMEM8 gene does not seem to have any role in the melanogenetic processes being involved in muscle development, the PML gene is notoriously known to cause a dilution in pigmentation in several cattle breeds [In a previous study , we invee breeds ,26,27.st variance as a consequence of contrasting breeds with high genetic differentiation and to find possible convergent signals between taurine and indicine breeds. Within this scenario, the results appeared to be consistent among contrasts, as well as between the two criteria adopted for considering loci under significant differential selection , supporting our initial hypothesis that both a reduced sample size and the strong genetic divergence  between taurine and indicine breeds may have remarkably reduced the statistical power in the first scenario. In particular, two consecutive SNPs in BTA6 (Hapmap48464-BTA-77646 and Hapmap54435-rs29022514) showed both significant q-values and the highest Fst values, falling within a region which has been previously shown to contain genes related to carcass traits, fatty acid, lipid metabolism, milk fat secretion, eye area pigmentation and the immune system [XKR4, TRNAT-AGU, TMEM68, TGS1, LYN, CHCHD7, SDR16C5, SDR16C6, PENK, LOC112449660, IMPAD1; XKR4, TMEM68 and SDR16C5) were found in a candidate region for differential selection detected by contrasting three Korean cattle breeds displaying different coat colour phenotypes [XKR4, TMEM68, TGS1, LYN, CHCHD7, SDR16C5 and PENK were found in several selection signatures in cattle with various indicine introgression levels [Finally in the third scenario, we repeated the analysis, contrasting each of the seven indicine grey cattle breeds with a single non-grey cattle breed  of indicine origin. This setting was built to avoid false positive outliers due to the increased Fe system . Howevere system  in taurin levels ,37,38.LYN gene, encoding a non-receptor tyrosine protein kinase. LYN belongs to the Src family tyrosine kinases (SFK) which are known to be involved in the modulation and regulation of many different processes such as proliferation, differentiation, migration, metabolism and apoptosis, responses to DNA damage and genotoxic agents. Interestingly, LYN has been shown to play a role in the Wnt/β-catenin and in the NF-kappa B signaling pathways [LYN has also been shown to interact with and induce the autophosphorylation of KIT, and to play a role in the control of KIT expression and intracellular localisation [KIT signalling being well known to be involved in the regulation of melanogenesis. Finally, LYN has been shown to be a key mediator of the effect of oestrogens (at least on osteoclastogenesis) [Among the loci mapped in the candidate region of BTA14, the most interesting one appears to be the pathways ,40,41, wlisation , with thgenesis)  and to bgenesis) , which cSDR16C5, which encodes a member of the short-chain alcohol dehydrogenase/reductase superfamily of proteins and is involved in the oxidation of retinol to retinaldehyde, one of the steps necessary for conversion of retinol into retinoic acid, the major bioactive form of vitamin A, which influences a broad spectrum of physiological processes during embryogenesis and adulthood, regulating the expression of over 500 genes. In particular, retinoic acid has been shown to be involved in the differentiation, proliferation and maturation of melanocytes, also inducing ectopic melanocyte stem cell differentiation in the hair follicle niche [Another interesting candidate gene is le niche .PENK encodes a protein that is processed to generate endogenous opioid peptides . Activation of opioid receptors in the skin by opioid peptides can regulate keratinocyte and melanocyte activities. In addition, endogenous opioids can modulate, in the skin, effects due to environmental stresses such as the solar radiation. Furthermore, PENK has been detected in sexually dimorphic hypothalamic regions [Bos indicus-influenced composite cattle breed) [ regions  and it he breed) .All the above evidences is in strong agreement with the features of the grey phenotype in the considered cattle breeds, such as the presence of early developmental events responsible for the formation of a pale brown ventral pattern, an altered modulation of the melanogenesis explaining the premature hair graying of the fawn coat colour observed in calves, and the appearance of a sexually dimorphic coat colour after puberty, likely as a response to steroid hormones, characterised by the onset of eumelanic pigment production, mainly in males, and, possibly through recruiting pools of melanocyte stem cells from the hair follicle niche . Future st could be a reasonable approach especially in all those cases in which we do not have a priori information about which loci are under selection or not, as in our case study [Concerning the choice of the statistical methods to identify outliers, it has been shown that the ranking approach based on loci that show extreme values in terms of Fse study . This apse study ,49,50 anThe neighbor-net graph constructed using the most significant SNPs  showed aThese results are of a certain relevance, not only because they corroborate, and thus validate, the previously published differential selection signals (likely related with the genetic architecture of premature hair greying) detected in taurine cattle  in an in


In this issue, there are seven original articles and one publication in the category of clinical genomics. In this editorial, I would like to focus on two original articles about genome-wide association studies (GWAS). There have been great successes in large-scale GWAS to identify novel single-nucleotide polymorphisms (SNPs) associated with traits of interest. By combining independent, large-scale GWAS, meta-analyses have revealed additional SNPs. These SNPs have now been utilized for Mendelian randomization analyses for causal inference. There continue to be challenging methodological issues in GWAS, such as the analysis of longitudinal genetic data, gene-gene interactions, and gene-environment interactions.Dr. Wonil Chung , and his collaborators considered a Bayesian mixed model for longitudinal genetic data. In their earlier work, they demonstrated that their Bayesian method showed a higher statistical power than cross-sectional analysis for detecting SNP-time interactions. Through an analysis of Korea Association Resource (KARE) data for various longitudinal obesity traits, they demonstrated how to apply their Bayesian method in a more effective way. They conducted a two-stage analysis. In the first stage, they performed GWAS analyses of cross-sectional traits and applied a meta-analysis to identify candidate SNPs. In the second stage, they applied the Bayesian method to a subset of SNPs selected in the first stage. The main objective of the Bayesian method was to discover SNPs associated with traits of interest and SNP–time interactions. The application of their Bayesian method to KARE data successfully identified several novel SNPs associated with longitudinal obesity-related traits and significant SNP-time interactions.Dr. Mira Park  and her collaborators proposed an entropy-based gene-gene interaction analysis. In genetic association studies, using entropy-based mutual information is advantageous in that it does not depend on parametrization. For binary traits, both entropy and conditional entropy can be easily derived. For quantitative traits, however, these values cannot easily be obtained because quantitative traits require an exact evaluation of entropy by estimating the probability density function. Dr. Park and her collaborators proposed a method of combining the kernel density estimation and the entropy estimation with the probability density function. Through extensive simulation studies, they showed that the proposed method performed better or comparably well in detecting gene-gene interactions compared to existing methods, such as multifactor dimensionality reduction.New research topics have continued to emerge in GWAS. For example, polygenic risk scores have been popularly used in building prediction models using SNPs. I believe that the Bayesian method of Dr. Chung can be easily extended to estimate more accurate polygenic risk scores. Furthermore, Dr. Park’s work on gene-gene interactions can also be utilized in deriving polygenic risk scores that have both marginal genetic effects and interaction effects.


Musa spp.) face the problem of drought and black sigatoka disease (BSD) that restrict their production. The conventional plant breeding is experiencing difficulties, particularly phenotyping costs and long generation interval. To overcome these difficulties, GS in plant breeding is explored as an alternative with a great potential for reducing costs and time in selection process. So far, GS does not have the same success in polyploid plants as with diploid plants because of the complexity of their genome. In this review, we present the main constraints to the application of GS in polyploid plants and the prospects for overcoming these constraints. Particular emphasis is placed on breeding for BSD and drought—two major threats to banana production—used in this review as a model of polyploid plant. It emerges that the difficulty in obtaining markers of good quality in polyploids is the first challenge of GS on polyploid plants, because the main tools used were developed for diploid species. In addition to that, there is a big challenge of mastering genetic interactions such as dominance and epistasis effects as well as the genotype by environment interaction, which are very common in polyploid plants. To get around these challenges, we have presented bioinformatics tools, as well as artificial intelligence approaches, including machine learning. Furthermore, a scheme for applying GS to banana for BSD and drought has been proposed. This review is of paramount impact for breeding programs that seek to reduce the selection cycle of polyploids despite the complexity of their genome.Genomic selection (GS) in plant breeding is explored as a promising tool to solve the problems related to the biotic and abiotic threats. Polyploid plants like bananas ( Musa species, which offers the possibility of implementing genomic selection (GS) on a large population.Several efforts have been undertaken in the improvement of polyploid plants. This improvement involves the creation and the selection of new varieties for the traits of interest and requires crossbreeding in conventional approaches. However, these efforts quickly encountered obstacles because of the complexity of polyploid genomes . Indeed,GS approach developed by a priori distort the quality of SNPs and therefore the genetic breeding value of individuals.Although GS is making progress in diploid plants, it encounters difficulties in polyploid plants due to factors that marker-assisted effect cannot properly control in these species. These factors include allelic dominance, epistatic effects, and the high structuring observed in the germplasm of polyploids. In addition to these factors, the GxE (genotype x environment) interactions have an impact on GS and can be problematic to estimate. Thus, these factors make it difficult to carry out all of the important steps in GS ranging from genotyping to genomic prediction, including the variant calling. Likewise, the initial GS models were developed on biallelic Single Nucleotide Polymorphisms (SNP) approach that considers that the genome of candidates is diploid and the vast majority of variant calling tools are organized in this logic. Applying these approaches to polyploids will GS selection work on polyploids would do better to explore possible solutions to overcome limits encountered so far. Mycosphaerella fijiensis attacks the plant at the leaf level, and young leaves show early symptoms of infection. In principle, BSD does not develop much in dry and high-altitude areas. However, it is increasingly observed that new strains adapt to these hostile locations  in Nigeria, by the International Institute of Tropical Agriculture (IITA) in the French West Indies, by the Centre for Cooperation in Agronomic Research for Development (CIRAD) in Cameroon, and by the African Center for Research on Banana and Plantain (CARBAP) and in Brazil L’EMBRAPA-CNPMF  .FHIA is certainly the oldest of the banana improvement programs, aiming to select improved diploids that can be used to generate edible triploids. It has developed many varieties, the best known of which are FHIA-01, FHIA-02, FHIA-03, and FHIA-21. Varieties FHIA-01, FHIA-02, and FHIA-03 have shown resistance to BSD.IITA through its centers in Cameroon, Nigeria, and Uganda aims to create valuable quality cultivars. Problems of disease are the main concern with the presence of BSD. In Cameroon, work is particularly concentrated on cultivars of the Plantain group, which is very popular locally .CARBAP is a research center specializing in sweet bananas and plantains but with a particular focus on the cultivars of the Plantains group. The evaluation of some exotic cultivars in Cameroon showed that the varieties IDN 077, Lagun Vunalir, and Pisang Kelat were particularly resistant to BSD . Dwarf hCIRAD has developed an original improvement strategy based on the creation of triploids from natural or improved diploid plant Figure 2M. balbisiana are more drought tolerant than those solely based on the genome A of M. acuminata. This genetic advantage is exploited to incorporate better characteristics into elite varieties in banana. The cultivars Bluggoe (ABB) and Fougamou (ABB) show the greatest differences in leaf conductance between morning and afternoon, and the cultivars Bobby T (ABB) tend to maintain their stomatal conductance throughout the day  parent. C292 and F568 hybrids (Plantain group) developed by CARBAP were evaluated under drought condition through many regions in Cameroon, and the results allowed to better understand the role of drought in banana plant growth  . Similart growth . This stt growth .Biotechnology tools involving tissue culture, genetic engineering, and, more recently, genome editing technology have, meanwhile, made it possible to overcome the difficulties of crossing in bananas in conventional breeding . If the M. fijiensis interaction. Indeed, it showed that the major loci controlling parthenocarpy and resistance to BSD and the level of ploidy acted together on growth and yield of banana–plantain. M. fijiensis processes tools capable of overcoming resistance in the host when the two interact. Functional orthologs of the Avr4 and Ecp2 genes of Cladosporium fulvum, a tomato pathogen, are present in M. fijiensis  leaves from plants submitted to temperature stress, M. acuminata cv. Matti (AA) transformation by Agrobacterium strain EHA105 has shown that transgenic plants overexpressed salinity-induced pathogenesis-related class 10 proteins from Arachis hypogaea AhSIPR10 in comparison to the wild banana, suggesting the role of AsSIPR10 in better tolerance of salt stress and drought conditions (The mechanism of BSD resistance in banana host is based on the interaction between the major locus (bs) carrying the recessive allele and at least two other genes (bsr) with additive effect . These dijiensis . M. fjiecultivar . Knowlednditions . Similarnditions .The knowledge of genomic mechanisms involved in banana plant facing drought and BSD threats is a good perspective for GS, which aims to select varieties based on information carried by the genome. Moreover, the influence of environmental factors in the variations of resistance or susceptibility of banana cultivars underlines the importance of integrating the GxE interaction in GS models.Musa–M. fjiensis pathosystem  . As a prhosystem . RE et aArabidopsis thaliana (The advances in next-generation sequencing (NGS) have allowed sequencing many genomes and the first sequencing performed on thaliana . In planthaliana .rTBV,GEBV) . This accuracy comes into play in the breeder’s equation:GS is based on a dense marking of the genome by molecular markers and the use of specific statistical methods to select individuals. In general, the mixed model or Bayesian methods are capable of simultaneously valuing the information of all markers to estimate the genomic estimated breeding value (GEBV) of the candidates for selection . The effR is the genetic gain over a generation, i is the selection intensity, r is the selection accuracy, and σa is the additive genetic standard deviation.where In practice, the GS accuracy is strongly influenced by several factors that deserve to be well managed. These factors include particularly the training population (TP) size and structure, the linkage disequilibrium (LD) in the population, the heritability of traits, the type of marker and marker density use for genotyping, and GxE. Although, today, it is possible to successfully manipulate these factors for the GS in diploid plants, their implementation in polyploid plants remains a challenge due to the complexity of their genome.Musa spp. AAA) showed that 72% of the seeds appeared normal and only 59% contained embryos, of which 9%  germinated  has already been reported to have enormous potential especially for complex organisms. ML is an artificial intelligence program designed to simultaneously process data for several parameters using associated algorithms. The implication of computer science that uses algorithms and existing samples to capture the characteristics of well-defined patterns in ML is thus a great advantage in the context of GS in polyploids . In geneIn addition, deep learning (DL) is a set of non-parametric ML methods known to be adapted on situations with very large complex inputs and with complex patterns as expected in polyploids. Although polyploids have the potential to adapt to multiple environments, GS models must integrate the MxE (marker x environment) interaction so as not to bias the predictions. vice versa. However, most GS studies have focused on species with the same level of ploidy and on a population from the same species. In banana, cultivars are derived from two parental species, M. acuminata and M. balbisiana, which make the population structure complex and require understanding. Thus, the high-throughput genotyping required in GS to generate the SNPs necessary for a study of population structure must always involve the reference genomes of both M. acuminata and M. balbisiana. According to The diversity at the ploidy and genomic level of the banana is an aspect that deserves to be explored in depth because it could have a great impact on the efficiency of GS. If it is obvious that the allele dosage approach in banana depends on the ploidy level as reported by Furthermore, if several studies have shown that selection accuracy increases with TP size before reaching a constant value , then thMusa is found mostly in natural population found in the banana germplams. This important diversity assumes that obtaining an ideal population for GS is possible. The ideal in GS is to have a wide diversity within the population for the traits of interest because precision is obtained when the phenotypes and genotypes to be tested are within the same range of variation  of the European Union at the University of Abomey-Calavi (Republic of Benin).We gratefully acknowledge the European Union and the African Union for supporting our study through the GENES Intra-Africa Academic Mobility project.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Multilocus analysis on a set of single nucleotide polymorphisms (SNPs) pre-assigned within a gene constitutes a valuable complement to single-marker analysis by aggregating data on complex traits in a biologically meaningful way. However, despite the existence of a wide variety of SNP-set methods, few comprehensive comparison studies have been previously performed to evaluate the effectiveness of these methods.P value combination methods  behaved very well under the sparse genetic architecture in simulations and real-data applications to common and rare variant association analyses as well as in expression quantitative trait loci weighted integrative analysis. We also assessed the scalability of these approaches by recording computational time and found that all these methods can be scalable to biobank-scale data although some might be relatively slow.We herein sought to fill this knowledge gap by conducting a comprehensive empirical comparison for 22 commonly-used summary-statistics based SNP-set methods. We showed that only seven methods could effectively control the type I error, and that these well-calibrated approaches had varying power performance under the simulation scenarios. Overall, we confirmed that the burden test was generally underpowered and score-based variance component tests  were much powerful under the polygenic genetic architecture in both common and rare variant association analyses. We further revealed that two linkage-disequilibrium-free https://github.com/biostatpzeng/.In conclusion, we hope that our findings can offer an important guidance on how to choose appropriate multilocus association analysis methods in post-GWAS era. All the SNP-set methods are implemented in the R package called MCA, which is freely available at The online version contains supplementary material available at 10.1186/s12859-022-04897-3. Over the past two decades, genome-wide association studies (GWASs) have successfully identified a large number of genetic loci associated with many complex traits/diseases by examining millions of single nucleotide polymorphisms (SNPs) across the whole genome –4. HowevCompared to the conventional single-marker analysis, SNP-set analysis has several statistical and biological advantages. First, susceptibility genes may contain multiple independent pathogenic variants; SNP-set inference can hence substantially increase power by gathering different signals within the gene. The potential of improving power also results from the reduced burden of multiple comparisons. Second, SNP-set analysis can solve the problem of allelic heterogeneity , produciDue to the usefulness, distinct SNP-set methods have been recently developed , 40–51, In the present work, we sought to fill this knowledge gap by conducting a comprehensive comparison for 22 commonly-used summary-statistics based SNP-set methods in the hope that our results could serve as an important guidance for practitioners on how to choose appropriate SNP-set analysis methods in post-GWAS era. In the following, we first evaluated the performance of these various methods in type I error control. We further assessed the power of these SNP-set methods which could maintain well-calibrated control of type I error under various simulation scenarios including common variant association analysis, rare variant association analysis and expression quantitative trait loci (eQTL) weighted integrative association analysis. We also assessed the scalability of these SNP-set approaches by recording computational time in simulation studies. Finally, corresponding to the three main simulation scenarios above, we applied these well-calibrated SNP-set methods to common variant summary statistics of six psychiatric disorders available from the Psychiatric Genomics consortium (PGC) , 60, rarP value of the aggregated test statistic. Particularly, these approaches include LD-dependent linear or quadratic combination of Z-scores with an additional correlation matrix accounting for dependence among SNPs ) [P value combination methods which might be robust against correlation between SNPs ) [As a flexible and powerful strategy alternative to single-marker analysis in association studies, many SNP-set methods have been developed over the past few years , 66–74, (SKATO)) , 45, 51, (ACAT)) , 75.TablP value in an analytical way  [P value via a simulation-based algorithm  [Z-scores  and LD matrix, some methods additionally require tuning parameters to first remove potentially null SNPs which have large P values [On the other hand, some methods efficiently obtain their and HMP) , 54, 75,, GATES) , which wP values . From a P values , 76, 77,P values , 24.https://github.com/biostatpzeng/MCA. It needs to first point out that we here did not consider some other SNP-set methods as they enjoy the similar principle of approaches described in the present work. For instance, fastBAT [An overview of the 22 SNP-set methods with their corresponding modeling characteristic is summarized in Table  fastBAT  and MAGM fastBAT  were conG the standardized genotypes matrix of a given gene, and n the sample size of the reference panel. Intuitively, the empirical LD, GTG/(n − 1), can be used, which however is in general not well-conditioned in the sense that the smaller eigenvalues of n is often not sufficiently large [I is the identify matrix. We set δ to 0.95 throughout our analyses following prior studies [In general, the LD matrix required in some of the these SNP-set methods  is computed with genotypes of ancestry-matched individuals from an external reference panel such as the 1000 Genomes Project . Denote ly large . As a rely large . We here studies , 84, 85.Y) based on real genotypes of 4901 individuals available from the Wellcome Trust Case Control Consortium (WTCCC) study [CEPT1 on chr1. Note that, the selection of this gene was to some extent arbitrary. To generate the genotype matrix (G), we randomly selected M  continuous SNPs to maintain LD structure, and simulated Y via a linear model Y = Gβ + ε, with β the vector of effect sizes and ε the vector of normally distributed residual errors.To evaluate the performance of each SNP-set method, we first conducted numerical studies to investigate their behaviors in type I error control and power with common SNPs  ≥ 0.05). To make our numerical studies as realistic as possible, we produced the phenotype (C) study . To thisM selected SNPs, only 5%, 20% or 50% were at random selected to have substantial impacts on Y while the remaining had zero effects, corresponding the sparse setting where only a fraction of genetic variants were causal; the non-zero effect sizes were distributed in terms of a standard normal distribution; (ii) polygenic case: all SNPs had non-zero effects on Y and their effects sizes following a standard normal distribution, or a standard double exponential distribution, or a standard t-distribution, corresponding the polygenic setting where the effect sizes of SNPs might have distinct distributions; (iii) mixed case: all SNPs had relatively small non-zero impacts on Y with their effects sizes following a standard normal distribution, but 5%, 20% or 50% SNPs were randomly selected to have additional influences, corresponding the hybrid modeling assumption made by Bayesian sparse linear mixed model [To assess power for every method, we made three diverse scenarios of modeling assumptions on effect sizes: (i) sparse case: among these ed model  and lateed model .Y and residual errors so that the phenotypic variance explained (PVE) by genetic component was 0.3%, 0.5% or 1%; where PVE = var(Gβ)/(var(Gβ) + var(ε)) [M SNPs to obtain their marginal Z-scores or P values using a linear regression model [β = 0 and run 105 replications when assessing the type I error control, with the type I error primarily evaluated via the ratio between the empirical type I error and the given significance level. We repeated our numerical study 103 times for power evaluation, with the power calculated by the proportion of P values less than a given significance level α of 10–5.In all scenarios, we re-scaled the simulated SNP effect sizes on  var(ε)) . Afterwaon model . These sSUSD2 on chr22 from 337,198 independent individuals of European descent in the UK Biobank cohort [Z-scores or P values for each rare variant. Following previous work [Some SNP-set methods  were specially designed for analyzing rare variants although they were also often used for common variant association analysis as we assessed above; we hence performed a simulation to examine the performance of these methods in rare variant association study. First, we obtained a set of 759 rare variants (MAF < 0.05) located within the gene k cohort . Then, wous work , 45, we For multilocus association analysis, it generally incorporates other types of omics data or functional annotation as weights, which is often more powerful than using GWAS summary statistics alone and can provide more biologically meaningful results , 31–33. w) and simulated gene expression (e) using genotypes (G1) of 465 individuals from the Geuvadis project [E(e) = G1w with G1 the genotypes of SNPs around the gene CEPT1 on chr1. In the second stage of TWAS, we produced the phenotype (Y) based on genotypes (G2) of CEPT1 from WTCCC; that is Y = (G2w)θ + ε, with ε the residual simulated from a standard normal distribution and θ = 0.10 or 0.20.Therefore, we here conducted an additional simulation within the two-stage TWAS framework. The detail of simulation setting was described in our previous work . For simY = (G2w)θ + G2b + ε, where b was considered random effect following a normal distribution with mean zero and variance 0.05. The setting of other parameters was the same as the case without horizontal pleiotropy.The above simulation of TWAS explicitly assumed the absence of direct cis-SNP effects , which mw) for the simulated transcriptome data in the first stage, and used the linear regression model to obtain marginal Z-scores or P values for the GWAS data in the second stage [We applied the maximum likelihood method through the computationally efficient PX-EM algorithm –101 to end stage . Then, tN = 53,293), ASD , BIP , CU , MDD , and SCZ .Psychiatric disorders are one of the most enigmatic maladies in medicine ; althougWe defined the set of SNPs located within a gene according to the annotation file provided by VAGIS , in whicUsing these SNP-set methods, we here performed rare variant SNP-set association analysis for four plasma lipid traits , ulcerative colitis , Crohn’s disease , systemic lupus erythematosus , PBC , primary sclerosing cholangitis , rheumatoid arthritis , multiple sclerosis , and OST . Details with regards to these data can be found in the original papers and the quality control procedure for data processing was described in our previous studies [We finally applied these SNP-set approaches under the TWAS context. Following our prior work , 64, we  studies , 63, 64.P value combination methods, we found that only HMP, ACAT, minP and Simes generated a well-calibrated type I error control. SimpleM was conservative; in contrast, FCP, TPM, RTP, ART, ART-A, GM, and gene-based association test that uses extended Simes procedure (GATES) were inflated.We first evaluated the performance of type I error control for all these compared methods with common variants Table  and rareWe also observed that not all the LD-dependent methods could behave well in controlling type I error. For example, BJ and HC, as well as their generalized versions , were inflated under our simulation scenarios, while DOT, multiple linear regression (MLP) and functional multiple linear regression model (FLM) were much conservative. Three methods  could effectively maintain the control of type I error. Because some of these methods failed to control the type I error at a nominal level (inflation or much conservativeness), we therefore only considered seven well-calibrated methods, including BT, SKATO, SKAT, Simes, minP, HMP and ACAT in our subsequent analyses.M) and (or) the proportion of causal SNPs (denoted by prop) were small; that is, HMP and ACAT outperform other methods when there were very less effective SNPs. However, SKAT and SKATO were better than HMP and ACAT as the increase in M and (or) prop. For example, the powers of SKAT and SKATO were 0.155 and 0.154 respectively when prop = 5% and M = 50, which were lower than HMP (0.171) and ACAT (0.171); whereas the powers of SKAT and SKATO were 0.020 and 0.033 respectively when prop = 5% and M = 500, which were more powerful than HMP (0.016) and ACAT (0.016). The similar patterns were consistently observed under the polygenic and mixed cases. Second, unlike prior studies [When comparing the power of the rest seven methods . Under the case of no horizontal pleiotropy, ACAT, HMP, SKAT and SKATO behaved comparably Fig. A; while 3 replications are shown in Table We here compared the running time of the seven SNP-set methods based on an Intel(R) Xeon(R) Gold 5118 CPU 2.30 GHz) with 125 GB of RAM. The total computation times across 10.30 GHz wP value < 0.05)  genes associated with these disorders (Bonferroni-corrected P < 5 × 10–8) for each of the 531 unique genes  than that of HMP as the increase of the proportion of significant cis-SNPs of an associated gene (the genetic architecture of a gene becomes from sparsity to polygenicity), which is consistent with the finding described in the simulation study above.In order to further compare HMP and SKAT in our application of psychiatric disorders, we created a bar plot for the proportion of significant cis-SNPs , 198 for low-density-lipoprotein cholesterol (LDL), 209 for triglyceride (TG), and 252 for total cholesterol (TC), respectively  were associated with all the four lipid traits, which were confirmed by previous studies [When applying these SNP-set methods to rare variants of four plasma lipid traits Fig. B, we fouly Table , which i studies , 121. In studies . Neverth studies –125.P value < 0.05)   in TWAS when multiple gene expression prediction models were employed to construct weights for expression quantitative trait loci [As part of great efforts to explain more heritability of phenotypes and enhance power in association studies by integrating other types of omics data , SNP-setait loci . In addiParticularly, among these compared methods, we only identified seven methods which could correctly control type I error, including BT, SKATO, SKAT, Simes, minP, HMP and ACAT. In total, these well-calibrated methods had varying performance in power evaluation. For example, prior studies showed minP was powerful in the case in which the association signals were extremely sparse , 129. HoBy contrast, in many cases we found that integrating individual genetic variants  together might be a more suitable manner for SNP-set analysis , 130–132P values of HMP and ACAT based on the right tail area of the respective approximate null distributions. Under regularity conditions, their performance is robust with respect to the number of SNPs, the weights, as well as the correlation among SNPs [Furthermore, we revealed that two LD-free methods ) and VEGAS ) did not have much advantage over other methods. Third, because there were too many distinct genetic backgrounds needed to study; to be simple we only focused limited settings in our simulations. Some methods might be powerful in other uncovered scenarios. For instance, GBJ exhibited excellent single-gene effect separation but showed slight inflation in our simulation settings. In addition, DOT [Finally, although we showed that some of these methods might be relatively slow, as all methods can be applied using GWAS summary statistics, they can be thus scalable to biobank-scale data. In summary, we evaluated 22 SNP-set methods using simulations and real data applications, and compared the robustness and effectiveness of these methods under diverse genetic architectures of phenotypes. However, our study had several limitations. First, in the real data analysis of six psychiatric disorders, we detected a number of significant genes and further showed that the identified genes may be functionally important for these disorders. However, there is no gold standard to accurately assess these methods in our real data application as the true associations of these discovered genes with the disorders are unknown; further follow-up studies are needed. Second, because of being extremely computationally expensive, we did not compared some computation-intensive SNP-set approaches  and the mixed case (B) with a significance level α of 10−5. Here, PVE = 0.3%, 0.5% or 1% at the right side, the number of causal SNPs (prop) = 0.05, 0.20 or 0.50 or the distribution of effect size including double, normal and t on the top, the number of the total analyzed SNPs = 50, 200 or 500 on the x-axis. The power was estimated across 103 replications. Figure S2. Rank of power for the seven SNP-set methods under the sparse case (A), the polygenic case (B), and the mixed case (C) with a significance level α of 10−5. The number in each cell represents − log(P). normal: SNP effect sizes followed a standard normal distribution; double: SNP effect sizes followed a standard double exponential distribution; t: SNP effect sizes followed a standard t-distribution. Figure S3. Estimated power for the seven SNP-set methods in the case of rare variant association study under the polygenic case (A) and the mixed case (B) with a significance level α of 10−5. Here, PVE = 0.3%, 0.5% or 1% at the right side, the number of causal SNPs (prop) = 0.05, 0.20 or 0.50 or the distribution of effect size including double, normal and t on the top, the number of the total analyzed SNPs = 50, 200 or 500 on the x-axis. The power was estimated across 103 replications. Figure S4. Upset plot to illustrate the number of identified genes shared across seven SNP-set methods for six psychiatric disorders. Figure S5.A Enrichment of differentially expressed pleiotropic genes associated with the six psychiatric disorders in terms of expression level across the 54 GTEx tissues. P values are shown in the y-axis with a scale of − log10. The bar in red represents significant enrichment after Bonferroni’s adjustment for multiple hypothesis tests; B Top 10 significant types of pathways in terms of the GO and KEGG enrichment analyses. BP Biological process, CC Cellular component, MF Molecular function. Figure S6. Bar plot of 531 unique genes associated with the six psychiatric disorders. The red color in the heatmap represents the rank of P values of SKAT and HMP; prop: the proportion of significant cis-SNPs (P < 5 × 10−8) within each associated gene. Figure S7.A Enrichment of differentially expressed pleiotropic genes related to the four plasma lipid traits in terms of expression level across the 54 GTEx tissues. P values are shown in the y-axis with a scale of − log10. The bar in red represents significant enrichment after Bonferroni’s adjustment for multiple hypothesis tests; B Top 10 significant types of pathways in terms of the GO and KEGG enrichment analyses. BP Biological process, CC Cellular component, MF Molecular function. Figure S8.A Enrichment of differentially expressed pleiotropic genes associated with the nine immune-related diseases in terms of expression level across the 54 GTEx tissues. P values are shown in the y-axis with a scale of − log10. The bar in red represents significant enrichment after Bonferroni’s adjustment for multiple hypothesis tests; B Top 10 significant types of pathways in terms of the GO and KEGG enrichment analyses. BP Biological process, CC Cellular component, MF Molecular function. Table S1. Summary information of the six psychiatric disorders, four plasma lipid traits and nine immune-related diseases. Table S2. Ratio between the empirical type I error and the given significance level estimated over 105 simulations under rare variants. Table S3. Estimated power over 103 simulations with common variants. Table S4. Total running time of 103 simulations for the seven SNP-set methods under various simulation settings. Table S5. Identified genes associated with the six psychiatric disorders. Table S6. Identified genes associated with the four plasma lipid traits. Table S7. Identified genes associated with the nine immune-related diseases.


Identifying genomic features responsible for genome-wide association study (GWAS) signals has proven to be a difficult challenge; many researchers have turned to colocalization analysis of GWAS signals with expression quantitative trait loci (eQTL) and splicing quantitative trait loci (sQTL) to connect GWAS signals to candidate causal genes. The ColocQuiaL pipeline provides a framework to perform these colocalization analyses at scale across the genome and returns summary files and locus visualization plots to allow for detailed review of the results. As an example, we used ColocQuiaL to perform colocalization between a recent type 2 diabetes GWAS and Genotype-Tissue Expression (GTEx) v8 single-tissue eQTL and sQTL data.https://github.com/bvoightlab/ColocQuiaL.ColocQuiaL is primarily written in R and is freely available on GitHub:  Genome-wide association studies (GWAS) conducted on large populations have identified a plethora of associations between genetic variation and complex traits and diseases in humans  of colocalization analyses between signals for one (or more) complex traits and the catalog of available human tissue QTL data . As suchth PLINK .https://github.com/bvoightlab/ColocQuiaL. These procedures should also apply to any other eQTL or sQTL dataset for which summary statistics are available.The majority of these output files are deposited in lead SNP specific directories. The COLOC results and intermediary files for each colocalization analysis at a lead SNP will all be saved to the directory specific to the lead SNP. This directory will also include regional association plots for each QTL-tissue signal involved in a colocalization analysis and the GWAS trait signal at the locus. These regional association plots are similar to those generated by the popular tool LocusZoom, but are generated as part of the ColocQuiaL code  Pruim e. The ColP-value ≤ 5 × 10−8) lead SNPs reported in KCNJ11, had both an eQTL and an sQTL signal that colocalized with T2D signals (PLEKHA1, AP3S2, HMG20A, and 16 of the 31 genes from HMBS, PCBD1, and USP36 (As a use case, we used ColocQuiaL to perform colocalization analysis of all reported independent T2D genome-wide significant signals reported in There are a number of ways ColocQuiaL could be used for colocalization analyses that we have not explicitly discussed here. One that we would like to point out to users interested in multi-trait GWAS is that a user can simply run the pipeline once for each trait in a multi-trait analysis in order to assess the evidence that the traits share a causal QTL variant at a locus. There are also a number of other features we plan to add to the ColocQuiaL software over time, including compatibility with other QTL data types and the use of other colocalization methods, such as COLOC-SuSiE to account for loci with multiple causal variants and HyPrColoc to allow for rapid colocalization of three or more traits at a locus . In summThis work was supported by the American Heart Association [20PRE35120109 to W.P.B.] and National Institutes of Health [DK101478 and DK126194 to B.F.V.].Conflict of Interest: M.D.R. is on the scientific advisory board for Goldfinch Bio and Cipherome. The remaining authors declare no conflicts of interest.