Most are concentrated in a small subset (4%) of loci with genome-wide significant main effects, and 44% replicate (p < 0.05) in the Women’s Genome Health Study . Next, we test each locus-biomarker pair for interaction across 2380 exposures, identifying 847 significant interactions (p < 2.4×10−7), of which 132 are independent (p < 0.05) after accounting for correlation between exposures. Specific examples demonstrate interaction of triglyceride-associated variants with distinct body mass- versus body fat-related exposures as well as genotype-specific associations between alcohol consumption and liver stress at the ADH1B gene. Our catalog of variance-quantitative trait loci and gene-environment interactions is publicly available in an online portal.Gene-environment interactions represent the modification of genetic effects by environmental exposures and are critical for understanding disease and informing personalized medicine. These often induce differential phenotypic variance across genotypes; these variance-quantitative trait loci can be prioritized in a two-stage interaction detection strategy to greatly reduce the computational and statistical burden and enable testing of a broader range of exposures. We perform genome-wide variance-quantitative trait locus analysis for 20 serum cardiometabolic biomarkers by multi-ancestry meta-analysis of 350,016 unrelated participants in the UK Biobank, identifying 182 independent locus-biomarker pairs ( Understanding how our genes interact with the environment is critical to improving health. Using a large-scale discovery pipeline, here the authors investigate synergies between genetic variants and a broad range of environmental factors impacting cardiometabolic health. Genome-wide interaction studies have begun to more systematically explore these interactions across the genome4, and across multiple exposures5, but have focused primarily on body mass index (BMI).Despite advances in identifying the genetic and environmental determinants of common complex diseases like cardiovascular disease and type 2 diabetes, the variability in the penetrance of genetic effects and the role played by environmental factors across populations are not fully understood. Part of this variability is due to gene-environment interactions (GEIs), in which genetic and non-genetic exposures synergistically affect disease-related traits. Understanding how exposures, including demographic, physiological, and lifestyle, modify genetic effects can spur new biological insights and therapies. Conversely, clarifying the ways in which one’s genetic background alters the effect of environmental exposures is a key step toward genome-guided precision medicine. There has been a substantial amount of hypothesis-driven research into GEIs for cardiometabolic traits6. Meanwhile, the massive number of GEI tests involved renders such an endeavor computationally infeasible and statistically underpowered, compounding known power limitations due to typically modest GEI effect sizes and low breadth and precision of exposure measurements7. Many screening procedures have been proposed to circumvent these computational and statistical limitations by reducing the genetic search space. These strategies prioritize specific sets of variants for GEI testing, such as those with main effects on the outcome8 or the exposure9.Comprehensive mapping of cardiometabolic GEIs across all genome-wide genetic variants and possible exposures carries practical, computational, and statistical challenges. Practically, it is difficult to collect and examine the thousands of exposures necessary for an “exposome-wide” approach, though recent software tools have made the process of high-dimensional phenotype processing substantially easier16. vQTLs can be identified in genome-wide scans analogous to those testing for phenotypic mean differences in typical genome-wide association studies (GWAS). Though direct GEI tests are more powerful when the environmental factor is measured accurately, vQTL tests may be advantageous when the relevant exposure is unknown or poorly measured, or when multiple exposures have aligned GEI effects at a locus. Such scenarios are quite common in practice due to the high dimensionality, dense correlation structure, and poor measurement of typical environmental exposures17. Recent studies in the UK Biobank (UKB) have demonstrated that a genome-wide vQTL discovery approach for anthropometric and lung function-related traits prioritizes variants enriched for GEI effects16. However, it is unclear whether vQTL effects are observed more broadly for cardiometabolic traits and to what degree the specific underlying GEI relationships can be uncovered using a more comprehensive array of environmental exposures.GEIs may induce differences in the variance of continuous phenotypes across genotypes. Thus, tests for genetic markers associated with this variance, termed variance-quantitative trait loci (vQTLs), represent an alternate strategy to identify loci harboring underlying GEIs for quantitative traitsN = 350,016) in stage one, followed by an exposome-wide interaction study (EWIS), incorporating 2380 exposures, to identify the specific underlying GEIs in stage two. We found 136 vQTLs, many of which were pleiotropic across biomarkers and largely overlapped with known GWAS loci. Using our EWIS approach, we then identified >800 specific GEIs with numerous correlated exposures underlying 43 of our vQTLs. Our study develops novel genetic maps of variance effects on a panel of cardiometabolic biomarkers, greatly increases the breadth of exposures tested for GEI, and introduces a publicly available catalog of vQTLs and GEIs that can inform precision medicine related to cardiometabolic health.Our objective was to identify genetic variants associated with the variance of a series of cardiometabolic blood biomarkers and to leverage these associations to efficiently detect underlying GEIs at an exposome-wide scale. We conducted a multi-ancestry, genome-wide vQTL scan to prioritize genetic loci across 20 biomarkers in UKB . The meta-analysis identified 184 vQTL-biomarker pairs at 136 independent loci after distance-based pruning . This locus has strong main effects on the same biomarkers and is known to interact with lifestyle factors in determining cardiovascular disease risk19.In stage one, a study-wide Bonferroni significance threshold of ing Fig. . The loc15. Therefore, downstream analysis focuses on the meta-analysis vQTL findings.P-values from the vQTL meta-analysis tracked closely with those from the European subset  method21 (see Methods).To understand these vQTLs in the context of genetic main effects (MEs), standard GWAS were also conducted for the same adjusted biomarker phenotypes. The resulting genetic MEs showed strong overlap with the identified vQTLs Fig. . The majp > 0.05) rather than clustering just above the Bonferroni significance threshold. Thus, some of these loci may truly lack genetic main effects, especially given the greater power of standard linear regression compared to Levene’s test11. Upon deeper investigation, we note that these 17 vQTLs were evenly spread across both chromosomes and biomarkers. While there were more vQTLs with at least one GEI among those that also had a ME (28%) compared to a vQTL effect only (18%), this difference was not significant (Chi-square test p = 0.55) and does not provide evidence that vQTLs without a ME represent spurious associations. To induce a vQTL effect in the absence of a significant ME, genotypic effects on phenotype may have opposite signs in some subset of the population. In some instances, this may be explained by an underlying qualitative interaction , while in other instances the relevant stratifying trait may remain unknown. Thus, rather than being spurious, at least some subset of these 17 vQTLs appears to truly lack overall MEs.The 17 vQTL-biomarker pairs without corresponding main effects deserve specific consideration. First, we note that they had a wide distribution of p-values (including eight with g) analysis, using bivariate LD-score regression, to understand whether specific pairs of biomarkers were notably more or less similar in their genetic architecture when measured using MEs (standard) or vQTLs. We observed similar genetic correlations between biomarkers when using vQTL  or main-effect  summary statistics ; therefore, while the associated significances of rg,ME tended to be substantially stronger, likely due to the known larger standard errors in variance estimation compared to mean estimation16, the genetic correlation magnitudes tended to be similar. For three biomarker pairs, the ρg,vQTL estimate was substantially different  than their fixed genetic effects.We next conducted a genetic correlation 22. Of 60 significant vQTL-biomarker pairs for which replication was possible, nominal replication (p < 0.05) was seen for 27 (45%) in spite of the much smaller sample size  we identified 847 significant interaction effects at 49 of the 184 vQTL-biomarker pairs, altogether representing 30 loci  of vQTL-biomarker-exposure triplets had a significant GEI versus 0.02%  of ME-biomarker-exposure triplets (Chi-square test p < 10−300). This 10-fold discovery enrichment remained after collapsing exposures and comparing variant-biomarker pairs; 26.6% (49/184) of vQTL-biomarker pairs had at least one significant GEI, versus just 3.3% (145/4413) of ME-biomarker pairs (Chi-square test p = 1.8 × 10−52). Of note, the EWIS using ME loci used the same Bonferroni-adjusted significance threshold as the EWIS on vQTL signals; if using MEs in practice, the more stringent Bonferroni adjustment would further reduce the number of GEIs identified.To understand the power gained for GEI discovery with a vQTL screening approach versus the more conventional approach prioritizing ME loci, we conducted a full EWIS using the same analytical pipeline on the larger set of stage one ME signals and compared GEI enrichment. Replicating findings from previous studiesp < 2.35 × 10−7) for “secondary signals”, which increased to 132 GEIs when using a nominal threshold (p < 0.05) for these secondary signals. However, this dense correlation structure also provides a unique opportunity to isolate specific relevant exposures with greater precision.The 847 GEIs were unevenly distributed across biomarkers, with greater than one hundred for ALT, TG, and AST and none for albumin, creatinine, cystatin C, random glucose, total bilirubin, or urea. Many of the participating exposures were from highly correlated categories such as anthropometric measures. To address this, we iteratively conditioned GEI tests on the most-significant exposure interaction at each locus to estimate the number of independent GEIs. We found 66 independent GEIs when retaining the same strict Bonferroni threshold (p < 0.05) was seen for five interactions  many significant GEIs implicated BMI or another anthropometric trait (89%), (2) BMI is an objective and standard quantitative measurement that is easily compared across studies, and (3) BMI has strong biological links to the majority of biomarkers assessed in this study. Of 11 interactions involving BMI and one of the 10 available biomarkers in WGHS, nominal replication  using inverse-normal transformed (INT) biomarkers again in the European subset to confirm that the vQTLs were not artifacts of skewed biomarker distributions. While a substantial number of these relationships were attenuated, 47% remained significant at the Bonferroni level . Thus, we performed a sensitivity analysis based on the results of the primary analysis, residualizing each pre-processed biomarker on the much smaller set of 88 significant GEI exposures (separately in each ancestry) and re-performing vQTL tests plus meta-analysis at the loci of interest. In these exposure-adjusted analyses, 140 out of the original 184 vQTL-biomarker pairs (76%) remained significant . This result means that most of these vQTLs would have passed on to stage two for interaction testing even after explicit removal of this potential bias in the two-stage testing procedure. As additional support for the robustness of our interaction results in false positives, we did not observe substantial systematic inflation of interaction p values across the entire set of EWIS tests . The nine variants showed distinct patterns of variation across exposures: two interacted more strongly with body fat measures (FADS1–2 and LIPC), three interacted more strongly with body mass measures , and four were balanced across anthropometric exposures  . Focusing on TG specifically as a biomarker with known complex relationships with obesity and cardiometabolic disease risk, we extracted GEI A3) Fig. . Given sA3) Fig. . These PA3) Fig. .Fig. 4ExLIPC) gene interacted almost exclusively with body fat-related measures. Hepatic lipase is a glycoprotein in the triacylglycerol lipase family that is important for the metabolism of lipoproteins including intermediate-density lipoprotein (IDL) and HDL24. The insertion (AF = 79% in the European subset) is associated with increased expression of LIPC  and decreased serum TG . Its GEI effect with BMI represents a small but significant increase in the positive association between BMI and serum TG (p interaction = 6.05 × 10−9). To further pinpoint relevant biological mechanisms, we further tested GEIs with BMI and the two anthropometric PCs for their effects on TG-containing lipid subfractions measured by nuclear magnetic resonance in the UKB  as the most likely causal variant for TG in this locus25.The intronic indel rs139566989 in the hepatic lipase  had a strong interaction with body mass PC1 on TG levels  but none with body fat PC2. These three genes are involved in the production or regulation of lipoprotein lipase, which cleaves TG from circulating lipoproteins24. These body mass-specific interactions may reflect (1) biological processes in skeletal muscle rather than adipose, or (2) body mass-associated behavioral characteristics such as total caloric intake, which is likely better measured by body mass than from self-reported questionnaires.A group of three variants  but did not have a significant main effect (p = 0.031) in the meta-analysis for ALT , confirming that this original vQTL signal was not simply an artifact of the alcohol-ALT relationship. This interaction suggests that alcohol consumption may be of greater concern for potential liver damage in individuals homozygous for the major allele at rs1229984. We note that mean alcohol intake was much lower in T allele carriers, producing a decrease in intake variability that may contribute to the lack of alcohol-ALT relationship in these individuals.One of our lead vQTLs with a clear underlying GEI was the combined effect of SNP rs1229984 and alcohol consumption on ALT, a biomarker of liver stress. SNP rs1229984 is a missense variant in the ALT Fig. , highligWe conducted a two-stage analysis to first identify vQTLs impacting cardiometabolic traits then conduct a systematic search for underlying GEIs. Using this strategy, we identified 136 loci associated with the variance of one or more of 20 cardiometabolic serum biomarkers, subsequently uncovering 847 GEIs impacting those biomarkers at 30 independent loci.15. Examining anthropometric and lung function traits, they found a similar average number of vQTL relationships per effective phenotype (15 versus 16.6 in our study), indicating a similar level of polygenicity of vQTL effects. They found a somewhat smaller enrichment of vQTLs in ME loci (1.7% of ME loci contained vQTLs versus 3.7% in our study), suggesting that the genetic control of cardiometabolic biomarkers may be particularly susceptible to modulation by genetic background or environmental exposures compared to traits like height or forced vital capacity.Results from our study largely align with previous results from Wang and colleagues, which utilized analogous methods in the same population (European-only subset of UKB)8, but we note that (1) the presence of a vQTL further increases the likelihood of identifying an underlying interaction16, (2) in our analysis, the use of vQTLs reduced the genetic search space by more than an order of magnitude compared to genetic MEs, and (3) the alcohol-ADH1B interaction is an example of a strong interaction found via vQTLs that would not have been explored using only ME-based screening. Second, the correlation between vQTL and ME strengths could theoretically suggest that the vQTLs are statistical artifacts unrelated to underlying interactions. Non-normally distributed variables have a mean-variance relationship and thus some vQTL tests are susceptible to false positives in the presence of genetic MEs and can be sensitive to trait transformations27. However, previous studies have demonstrated reasonable robustness of Levene’s median-based test to non-normality15 and our sensitivity analyses demonstrate that inverse-normal transformed phenotypes, for which the correlation between mean and variance effects is reduced, produce largely similar vQTL results. Third, it highlights the weight of prior probability in agnostic analyses: loci that have already demonstrated a role in biology (by being implicated in MEs) are more likely to be relevant in GEI searches as well.We observed that vQTLs were predominantly found in ME loci and that their strengths of association were generally correlated with the strengths of corresponding main effects. This observation has three important implications. First, it impacts the value of vQTLs as a screening tool. Screening that simply uses genetic main effects is a common and viable strategy5 GEI tests, compared to the 4.8 × 1011 tests that would have been necessary to exhaustively explore all genetic variants and biomarkers. Beyond the clear practical and computational benefits of this reduction in the genetic search space, the statistical value in this two-stage analysis is dependent on both the power of Levene’s test and the extent to which it decreases the multiple testing penalty in the second stage . While it is well-established that vQTL loci are more enriched than ME loci for underlying GEIs16, the power of Levene’s test may be affected by the strength of the underlying exposure-outcome relationship and may be compromised in cases where a complex set of underlying interactions exist without consistent directions of effect11. Given this fact, it is not surprising that many of our significant GEIs involved straightforward and high-impact exposures such as body size  and alcohol intake as compared to more-complex traits like socioeconomic status indicators or dietary behaviors.Our two-stage approach required 4.4 × 106, but here we leveraged it to create a library of exposures to be used in GEI tests. Many of its motivations apply equally in this context—non-normally distributed continuous variables and highly imbalanced binary variables can create instability and bias in GEI tests (as exposures) just as in standard genetic main effect tests (as outcomes). PHESANT enabled the high-throughput pre-processing of 2380 variables for our exposome-wide analysis, which would be impractical to prepare one-by-one. We note that the semi-automated nature of this process means that there may be more appropriate quality control and coding strategies for specific exposures of interest in follow-up studies.The PHESANT tool, which processes thousands of phenotypes in an automated way, was developed to facilitate phenome-wide association studies30. However, the scale of this population also means that spurious GEIs induced by even small statistical artifacts or biological confounders may reach statistical significance, as demonstrated by Tyrell and colleagues31. Thus, the catalog of GEIs described here should be treated as hypothesis-generating rather than confirmatory.Our approach identified 847 significant GEIs, many of which involved a set of highly correlated exposures (discussed further below). The large sample size of UKB provides the statistical power needed to identify interactions and has supported genome-wide GEI discovery in investigations of anthropometric and cardiometabolic phenotypes32. The dense correlation of these exposures reduces the number of effective discoveries, but this more-comprehensive coverage of the exposure space allowed us to tease apart underlying mechanisms. Focusing on TG as an outcome, we found that nine relevant variants tended to fall into three groups, interacting with anthropometric measures of either body mass, body fat, or both. We further validated this observation using representative principal component summary variables. For one variant (rs139566989 in LIPC), body fat interactions affected TG in specific lipoprotein subfractions such as IDL and very large HDL, consistent with the well-established role of hepatic lipase in lipoprotein remodeling 24. Lipoprotein-related GEIs involving LIPC variants have been previously reported for relevant exposures such as dietary fat composition in weight loss trials34 and observational cohorts35. In general, our fine-grained exposure analysis provides insight into lipoprotein biology and highlights the importance of comprehensive and precise phenotyping.Many of the significant GEIs involved a set of highly correlated anthropometric exposures . These are not typically used as GEI exposures because they are not behavioral or environmental, but they can nonetheless play a role in modifying genetic associations with cardiometabolic biomarkers and outcomesA primary strength of this investigation is the comprehensiveness of cardiometabolic biomarkers and exposures examined. However, the pipeline relies on many self-reported exposure measurements; follow-up of specific interactions should employ more objective and precise methodologies  as well as more comprehensive sensitivity analyses to refine interaction estimates. Additionally, we were only able to assess replication of vQTLs and GEIs for half of the biomarkers of interest. While the replication analysis provides confidence in the general robustness of our approach across populations, many signals from UKB, such as those for liver enzymes, bilirubin, and random glucose, were not able to be tested in WGHS. The WGHS also contains only women, which may particularly impact replication for sex-differentiated biomarkers such as HDL-C. Finally, while multiple ancestries were analyzed here as available, the meta-analysis results primarily reflect the effects on European individuals due to the major sample size discrepancy. Future work should incorporate more ancestry-balanced data sets, explore the heterogeneity of interaction effects across ancestries, and pursue follow-up of some of the many ancestry-specific vQTLs and interactions described here.https://hugeamp.org/research.html?pageid=UKB-vQTL-GxE). These catalogs can generate hypotheses for future research and enable lookups to better characterize findings from GWAS and other genetic studies. Our findings extend previous proofs-of-concept of the vQTL strategy to the realm of cardiometabolic biomarkers, establish a set of loci to be prioritized for further GEI analysis, and highlight specific GEIs that can inform cardiometabolic precision medicine strategies.The vQTLs and GEIs described here have been made publicly available in the AMP Common Metabolic Disease Knowledge Portal  during central genetic data pre-processing37. Furthermore, only genetic variants with minor allele frequency (MAF) > 0.005 in the full sample were included in the present analysis (in addition to subsequent ancestry-specific MAF filters). Ultimately, 9,487,345 variants were used for analysis in the European-ancestry subset, with slightly different numbers in the three other ancestry groups based on the MAF filter. For the vQTL and GWAS analyses, imputed genotypes within 0.1 of an integer value  were converted to hard-calls using PLINK238 and all other values were set to missing. Work was conducted on genetic data release version 3, with imputation to both Haplotype Reference Consortium and 1000 Genomes Project (1KGP), under UK Biobank application 27892.This work was conducted under a Not Human Subjects Research determination for UKB data analysis (NHSR-4298 at the Broad Institute of MIT and Harvard). UKB is a large prospective cohort with both deep phenotyping and molecular data, including genome-wide genotyping, on over 500,000 individuals ages 40–69 living throughout the UK between 2006–201039. Each component mean was defined by selecting data points randomly without replacement at initialization. We built the Gaussian mixture models using the Scikit-learn Python package and tested 10-fold cross-validation on 40 models with different initialization states. We used the adjusted rand index score40 to evaluate the similarity between predicted and true clustering using 1KGP principal components and their 1KGP ancestry labels. This approach was used to refine the subpopulation clustering of 1KGP principal components. We then projected UKB principal components onto the 1KGP principal component space and used the Gaussian mixture model with the highest adjusted Rand index score to cluster individuals into ancestry subgroups.UKB samples were grouped into four primary ancestry groups: African (AFR), East Asian (EAS), European (EUR), and South Asian (SAS). We defined ancestry groups using a Gaussian mixture model on the principal components of 1000 Genomes project (1KGP) phase 3 data. The Gaussian mixture model is a probabilistic model for representing normally distributed subpopulations within an overall population, by assigning data points to the multivariate normal components that maximize the component posterior probability given the data using the expectation-maximization algorithmhttps://biobank.ctsu.ox.ac.uk/crystal/crystal/docs/serum_biochemistry.pdf).We focused on 20 serum biomarkers related to cardiovascular disease and metabolism, including but not limited to lipids, liver enzymes, glycemic parameters, and kidney function markers  that had been empirically estimated by Sinnott-Armstrong and colleagues in the same population. Following residualization, outliers  were set to missing. Finally, residuals were z-score normalized to mean zero and variance one.Phenotype data processing and other downstream analyses were conducted using R version 3.6.043, pre-processed biomarkers were collected into a single dataset across ancestries and principal components analysis was performed (prcomp function with standardized variables). The number of effective biomarkers was then calculated from the principal component variances Due to the substantial correlation between the 20 cardiometabolic biomarkers, a smaller number of “effective” biomarkers was calculated to inform multiple hypothesis testing corrections. Using a previously-described approach44. Differential variance tests used the median-based Levene’s test, which is equivalent to a one-way analysis of variance (ANOVA) for absolute deviations from the median biomarker value. The test statistic, which is described in detail elsewhere15, is:th genotype, F distribution with vQTL analysis was performed using the vQTL module from the OSCA suite45. Stouffer’s method (SAMPLESIZE mode) was used to perform fixed-effects meta-analysis based on p-values, sample sizes, and effect directions. While Levene’s test does not natively produce effect sizes or directions, they are derived by the OSCA tool based on a method described by Zhu and colleagues46 and previously implemented for vQTLs15. An effect direction is determined by regression of absolute deviations from the median on additively-coded genotypes, then this sign is combined with the Levene’s test p-value to derive an effect size and standard error through back-transformation: b is the vQTL effect size, z is a z-statistic derived from the p value and effect direction, p is the minor allele frequency of the variant, and n is the sample size.Genome-wide vQTL analysis was performed separately for each of the 20 biomarkers .For each genome-wide vQTL scan (20 biomarkers × 4 ancestries), we performed an analogous standard GWAS testing for genetic effects on the mean of the phenotype using theGenome-wide summary statistics were pruned by removing variants within 500 kb of each index variant. This distance-based procedure was chosen to be reasonably conservative and avoid the need for separate LD matrices per ancestry. To identify overlapping variants across biomarkers, ancestries, and analysis types (vQTL vs. ME), relevant pruned summary statistics were combined and a second clumping procedure (similarly defined based on 1MB windows) was performed. For example, to identify overlapping vQTL loci across ancestries for ALT, all ancestry-specific, pruned summary statistic matrices for ALT  were stacked and then subject to a similar iterative clumping procedure as was used for the initial pruning.48 for both vQTL and ME results from the European-ancestry subset. For each pair of biomarkers, summary statistics were retrieved and analyzed along with a European-ancestry linkage disequilibrium reference dataset from the 1000 Genomes Project .Genetic correlation analysis was performed using bivariate LD-score regression21. LOLA calculates enrichment of a set of target regions of the genome for enrichment in a set of epigenomic annotations compared to a background “universe” set of loci. The basic “LOLA core” set of epigenomic annotation collections was used. The background locus set was chosen to be the union of the vQTL and ME loci such that the resulting significance tests directly compared enrichment between the two groups for any given epigenomic annotation. Significance was assessed after the application of a Bonjamini–Hochberg false discovery rate correction.Differential enrichment for epigenomic features between vQTL and ME loci was assessed using the Locus Overlap Analysis (LOLA) strategy and associated R package6) and later expanded (https://github.com/astheeggeggs/PHESANT). This PHESANT procedure relies on files describing variable input types  and data coding schemes. Based on this input, it then conducts automated pre-processing of the phenotypic dataset, including removal of variables with excessive missingness or insufficient heterogeneity, conversion of categorical variables to ordinal and binary variables, and inverse-rank normal transformation of continuous variables.In order to conduct the GEI analysis in an exposome-wide manner, we needed to collect and clean a large set of relevant exposure variables. For this purpose, we used a program for automated phenotype pre-processing initially developed for phenome-wide association studies , lifestyle , psychosocial , medical , and other types of traits. Based on the data dictionary available from UKB and additional manual curation, phenotypes were placed into one of eight exposure categories. Exposure group summary counts and variable examples are available in Supplementary Data 49 using robust standard errors. As with the vQTL and GWAS analyses, these interaction tests were performed separately in each ancestry group, followed by the inverse variance-weighted, fixed-effect meta-analysis45. Principal components analysis using prcomp in R was conducted on a set of anthropometric exposures in individuals of European ancestry in UK Biobank to include as additional body size-related exposures in GEI testing.For each combination of vQTL-biomarker pair (from Stage I) and exposure (from PHESANT), a GEI test was performed using the following simple model:p < 0.05) and using our stricter EWIS Bonferroni threshold.Because of the substantial correlation between many tested exposures, we conducted a follow-up analysis to determine the number of independent GEIs. For each vQTL-biomarker pair participating in a significant GEI from the primary EWIS (and thus significant at the strict Bonferroni threshold), we re-tested for GEI with each exposure. Then, we iteratively re-tested while additionally conditioning on the GEI effect for the most-significant exposure. We report the number of total independent GEIs, counting secondary signals using both a nomninal significance threshold , this analysis was identical to the primary EWIS (including the use of the same Bonferroni threshold).Anthropometric exposures were defined as those with a “Level 3 Category” of “Anthropometry” in the UKB data dictionary. Initially, GEI summary statistics from the EWIS were collected for this set of exposures with TG as the outcome and including nine genetic variants passing the EWIS significance threshold for at least one of these 31 exposures. These nine variants were manually annotated to likely causal genes based on known lipid biology. To create a set of anthropometric summary variables for GEI analysis, PCA was performed on this set of exposures in the European ancestry subset. The first two principal components were determined to represent body mass and body fat based on inspecting trait loadings. These two components were extracted and used for GEI testing.Triglyceride concentrations in various lipoprotein subfractions, based on nuclear magnetic resonance (NMR) spectroscopy metabolomics data, were retrieved for ~90,000 European ancestry individuals (differing numbers per metabolite based on missingness). Quality control was performed using measures provided by the UKBB, removing values lower than the limit of detection and samples with likely contamination or degradation issues. Metabolite measurements were inverse-normal transformed prior to modeling. GEI testing was performed for each anthropometric exposure-TG subfraction pair , with additional adjustment for age, sex, 10 genetic PCs, NMR batch, and spectrometer.22. The biomarkers available in WGHS included ApoA, ApoB, hsCRP, TC, creatinine, HbA1c, HDL-C, LDL-C, LipA, and TG, and were pre-processed and analyzed similarly to biomarkers in UKB . The same pre-processed biomarkers were used for replicating the BMI GEIs, adjusting for age and genetic PCs. Effect directions were not considered when assessing vQTL replication in WGHS.The WGHS cohort was used to replicate vQTLs and BMI GEIs. WGHS is a prospective US-based cohort of healthy adult females 45 years or olderFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1-11Reporting Summary


TGM3, CACNB4, ANKS1B, CSMD1 and SYNE1) associated with measures of working memory, processing speed and social behavior. Our results thus identify novel associations, including previously unreported parent-of-origin associations with relevant genes, and our top results illustrate new potential gene → endophenotype → disorder pathways.Many psychiatric and neurodevelopmental disorders are known to be heritable, but studies trying to elucidate the genetic architecture of such traits often lag behind studies of somatic traits and diseases. The reasons as to why relatively few genome-wide significant associations have been reported for such traits have to do with the sample sizes needed for the detection of small effects, the difficulty in defining and characterizing the phenotypes, partially due to overlaps in affected underlying domains , and the complex genetic architectures of the phenotypes, which are not wholly captured in traditional case–control GWAS designs. We aimed to tackle the last two issues by performing GWASs of eight quantitative neurocognitive, motor, social-cognitive and social-behavioral traits, which may be considered endophenotypes for a variety of psychiatric and neurodevelopmental conditions, and for which we employed models capturing both general genetic association and parent-of-origin effects, in a family-based sample comprising 402 children and their parents (mostly family trios). We identified 48 genome-wide significant associations across several traits, of which 3 also survived our strict study-wide quality criteria. We additionally performed a functional annotation of implicated genes, as most of the 48 associations were with variants within protein-coding genes. In total, our study highlighted associations with five genes (The online version contains supplementary material available at 10.1186/s12993-022-00198-0. As a species, humans are adept at using communication , mental facilities, social interaction abilities and fine motor skills in their everyday lives. These aptitudes mature during neurodevelopment. Some individuals, however, have non-typical neurodevelopment, which is associated with cognitive, motor, behavioral and/or social-cognitive impairments. Disorders characterized by these impairment are collectively known as neurodevelopmental disorders, and they often exhibit high comorbidity . Many ofCACNA1C) gene, which is a known susceptibility gene for several psychiatric disorders including schizophrenia , they are all inherently relevant to neurodevelopment in their own right. Moreover, a recent article examining the history of the use of endophenotypes in psychiatry proposed to expand the definition to include transdiagnostic traits, which are not necessarily associated with only one disorder . In thisThe sample used in this study is part of the Danish High Risk and Resilience Study—VIA 7 (hereafter the VIA 7 study) . The VIAMABC , 2nd edition ; SSR  [WISC Arithmetic [score  from the Arithmetic subtest of the Danish version of the WISC-IV]; WISC Letter-Number Sequencing [score  from the Letter-Number Sequencing subtest of the Danish version of the WISC-IV]; RIST Index ; WISC S—Revised , based o (SRS-2) , 2nd edit (RIST) ].hist and density functions. We also calculated the pairwise Pearson’s correlation coefficients across the traits in the sample of children with genotypes used in this study, which are shown in Fig. shapiro.test function in R.The WISC Arithmetic, WISC Letter-Number Sequencing, RIST Index and MABC scores were age-standardized based on the norms from the manual of each respective test. Where norms were not available for some tests or subtests e.g., when we used the versions of the WISC-IV Coding and Symbol Search subtests for children aged 8 to 16, or when there were no norms (SSR scores), the raw scores were rescaled into Z-scores in SPSS v25.0.0.2 using the mean of the population control subset of VIA 7 children, who were age-matched to the rest of the cohort. The SRS total T-score was not adjusted for age, as this score was not associated with age in children aged 7–15 . More deAll traits deviated from normality to some degree. However, as the effective sample for the majority of our tests depends on the parental genotypes, it varies greatly across genetic markers . Therefore, different subsets of children were used for different markers, and it is not practical to try to transform the scores so as to force them to have a normal distribution, when each transformation will not necessarily work for more than one marker. Moreover, transforming scores in this way would hinder the interpretation of the results, as the spaces between scores would have been changed unevenly, and, therefore, the interpretation of the effect sizes would be problematic. We discuss this at length in a previous paper, where we also examined the difference normalization had made for our top result . Lastly,–6 was employed for markers, as well as a minor allele frequency (MAF) threshold of 1% (in founders). Markers with a significant HWE p-value based on the above threshold or MAF below 1% were excluded. We removed one marker per pair in case of pairs of markers with identical positions included in the PsychChip, either using PLINK --list-duplicate-vars suppress-first, if the allele codes matched, otherwise prioritizing markers with rsIDs. The number of individuals removed during these steps was 64, including 3 duplicate samples . In total, 1094 genotyped individuals and 299,604 autosomal markers passed these QC steps, from which we further removed 125 indels, for a total of 299,479 autosomal markers. Only this dataset of genotyped markers was used in this study. Positions in the text and tables are in genome build hg19. A minority of markers on the array had positions in the Illumina manifest file which differed slightly from the ones in dbSNP for the same major build. For that reason we checked all the probes from the manifest file for our array for the top hits in this study, as for the top hit in our previous study [We had DNA samples from a subset of the VIA 7 study sample, and these were genotyped on the Illumina PsychChip v1-1_15073391_C, which had a loci count of 603,144 (according to the information lines in the Illumina manifest file for this array). The dataset has been described in detail in our previous studies , 54. Brius study . This waqtdt -at), a paternal parent-of-origin total test of association, in which only paternally inherited alleles were used (-at -op) and a maternal parent-of-origin total test of association, in which only maternally inherited alleles were used (-at -om). The total association model  is not a TDT, and it was used because it is more powerful in the absence of population stratification [2 statistic, which can be used to compute a one-sided p-value from the χ2 distribution. The tests in this study had one additional free parameter in the full model as compared with the null model, and so the χ2 statistic was evaluated with 1 degree of freedom. We included variance components in both models (-wega), incorporating an environmental component, a polygenic component and an additive major locus component. This allowed for the use of families with multiple children, although only 11 families included a sibling. Age was taken into account in the scoring of the phenotypes, as explained earlier. For all traits, a covariate for sex was added to both the null model and the full model.https://github.com/stephenturner/qqman/bloqb/v0.0.0/qqman.r). Regional association plots were generated with LocusZoom [In the GWAS stage, we used QTDT (quantitative transmission-disequilibrium test)  v2.6.1 ffication . In thisocusZoom , after cqtdt -at -ot). In this test, the null model has X, and the full model has both X and Xmat. It is not possible to include Xpat instead of Xmat in the full model ; therefore, as a precaution, we tested both parameterizations for a known POE using a different program, EMIM [When a POE is detected with one parent, it does not mean that the other parent’s transmissions are significantly different. It could be that a child effect is significant and appears as such also when looking at paternally inherited alleles or only at maternally inherited alleles separately. Therefore, it is necessary to test for a difference between these parental allele transmissions. This can be achieved by controlling for risk parameters other than the POE parameter by including them in both the null and the full models. QTDT does not allow a free choice of parameters in the null and the full models, but it incorporates a test for the difference in the effects between the paternal and maternal allelic transmissions , the baseline risk was 0.1, the significance level was 0.05, and the risk allele frequency was 0.3. When strong POEs operate and are included in the model, some methods achieved power of ~ 90% with as few as 100 case-parents trios [p = 2.5 or Im = 2.5, a significance level of 0.05, a baseline risk of 0.05 or 0.01 and a risk allele frequency of 0.3 or 0.1, for 20% and 80% of the population, respectively. With regards to quantitative traits, as relevant to this study, we considered published reports of simulations estimating the power of various QTDT models. For example, in the original QTDT paper, assuming a maximum D’, h2 of 0.1, a risk allele frequency of 0.5, a significance level of 0.001 and including parental genotypes, a sample of 480 children  resulted in a power estimate of 97.4% [2 of 0.1, and a risk allele frequency of 0.3 [The power of a QTDT analysis depends on several factors, including: the marker allele frequencies, the effect size, the linkage disequilibrium between the marker and the quantitative trait locus, the number of child genotypes in the analysis and the parental genotypes. Studies which evaluated family-based association methods used simulations of models with the above parameters to estimate the power of those methods. For dichotomous traits, for example, 300 case-mother duos offered reasonable power for detection of child genetic effects , when thts trios , with Ipof 97.4% . In anoty of 0.3 . We can y of 0.3 :\documenal model .Regarding the sample sizes in the various tests, for the general test, all children who had non-missing genotypes and IBD information for a given marker and non-missing phenotypes were used in the test for that marker. In the POE tests, two groups of children are included: (i) children whose both parents are genotyped and where one parent is homozygous, or whose mother and father have different genotypes , and (ii) all children with at least one homozygous parent, even if the other parent has a missing genotype . This ma–8), (ii) for POE associations, they have P ≤ 0.0008 in the test of difference between paternal and maternal alleles, which was calculated as the conventional threshold (0.05) Bonferroni-corrected for the number of post hoc tests for POE associations which met the first criterion (n = 63). We then prioritize associations that, in addition to meeting the above two criteria, also meet the following criteria: (iii) they have a p-value (in the GWAS) equal to or below the conventional significance threshold (0.05) Bonferroni-corrected for the actual number of tests performed across all GWASs , i.e., P ≤ 7 × 10–9; and (iv) at least 30 children had the minor allele for the associated marker , it could highlight associations for which the effect size is less likely to be biased due to one of the alleles being relatively rare). Associations surviving all four criteria are discussed in more detail in the Results section. For these associations, we also repeated the relevant association test while adding a covariate for the high risk status (HRS) of the family  for whether the child is from a family with a parent with schizophrenia or bipolar disorder, or from a family in which neither parent has either of these diagnosis). Additionally, we used EMIM v3.22 [2 = R12. In this analyses we used both case and control family subsets, but we did not use controls without parents, since EMIM does not distinguish between controls and individuals with an unknown disease status . The p-values for this test are derived from the χ2 distribution with one degree of freedom (since only one risk parameter was freely estimated in the full model), and the test statistic comes from twice the difference in the log-likelihoods of a null model (in which the multiplicative risk parameter is fixed to 1) and a full model, in which it is estimated from the data.We employed the following strategy for correction for multiple testing in this study: in the GWASs, we present all the associations that met the following two criteria: (i) they pass the conventional genome-wide significance threshold  as follows: the median of the observed χ2 distribution from each GWAS divided by qchisq.QTDT does not output standard errors (SEs) for the estimates it computes. In order to obtain SEs for the observed effect in the top associations in our results we used the following approach: using the χuivalent . Lastly,For functional annotation of variants, we used the eQTLGen  portal a–8), of which 25 were highlighted in the general test and the rest were highlighted in the POE tests. Additional file Across all 24 GWASs, 88 associations achieved genome-wide significance (P ≤ 5 × 10TGM3) and rs7604835  in the Calcium Channel, Voltage-Dependent, Beta 4 Subunit gene (CACNB4). Marker rs214831 was strongly associated with the expression of the gene it was located in, namely, TGM3, on eQTLGen (P = 5.72 × 10–34), whereby the A allele was associated with higher expression of the gene; in our study, the effect allele (G) was associated with a lower test score, suggesting that lower expression would be associated with a lower score. It was also associated with the expression of PTPRA in the basal ganglia on GTEx (P = 0.000022), with allele G being associated with lower expression. This marker remained at least nominally significant when adding a covariate for high risk status (i.e. for whether the child comes from a high risk family or a control family) to the model (P = 0.0312). Marker rs7604835, which showed a maternal POE in our study, was associated with multiple DNA methylation and histone modification sites on PhenoScanner (minimum P = 1.19 × 10–45), based on evidence from two different studies [–9). The last association which met all four criteria, namely, between rs6117457 and WISC Arithmetic in the general test, did not implicate any protein-coding gene, and we could not find any relevant prior association with it in the literature or functional databases. This marker did not remain significant when adding a covariate for high risk status (P = 0.0765). Translating the effects of the top markers into PVEs, we get: 0.078, 0.08 and 0.11 for rs6117457, rs214831 and rs7604835, respectively. The associations adjusted for HRS were in the same direction as before in all cases. It should be noted, however, that the interpretation of the models with the covariate for HRS can be difficult: both WISC Arithmetic and SRS are associated with the child’s schizophrenia family status in the VIA 7 study [Of the 48 associations that were genome-wide significant and, where applicable, showed a significant difference between paternal and maternal alleles, only 3 met our extra conditions pertaining to the study-wide significance level and a minimum number of 30 probands with the minor allele. Regional association plots for these 3 markers are shown in Fig. en P = 5. × 10–34, studies . This pr 7 study , 40; sin 7 study , 76. ThuANKS1B, ATP11A, CACNB4, CPLX2, CSMD1, EFCAB1, FRK, KIF13B, PRKCE, SIRPA, SYNE1, TGM3, TMEM2, TSACC and ZSWIM6. Given that the associations in Table ANKS1B), and Synaptic Nuclear Envelope Protein 1 (SYNE1). Additional file CSMD1), and the gene with the highest average disease causing likelihood was the aforementioned CACNB4.Most of the associated markers in Table Our study investigated eight neurocognitive, motor and social-cognitive and social-behavioral functions using a family-based GWAS design, including a general association test as well as tests of parent-of-origin effect tests. We have identified 48 genome-wide significant associations, of which 3 met our study-wide significance threshold. Our results highlighted several protein-coding genes, some of which have been implicated in prior genetic analyses of relevant phenotypes.TGM3 and CACNB4. The association with TGM3 was further supported by the marker’s being an eQTL for the gene. This gene is involved in terminal epidermal differentiation and has been implicated in some cancers [TGM3 [PTPRA, a gene which is important for hippocampal neuronal migration; mice deficient for the PTPRA protein exhibit impairments in learning and short-term memory [CACNB4 was with a maternal POE. This marker was also associated with methylation and histone modifications sites, providing further support for a POE. The gene encodes a member of the beta subunit family of voltage-dependent calcium channels, and it belongs to a family of genes which has been implicated in several psychiatric and neurodevelopmental disorders, including autism spectrum disorder, across many studies [CACNB4, specifically, is highly expressed in the brain and is prominent in the cerebellum [Two genes were highlighted through associations which met all four of our significance criteria:  cancers , 78. In rs [TGM3 . Genes ors [TGM3 . Also ofm memory . The ass studies . The subrebellum . A recenrebellum . When adCSMD1, ANKS1B and SYNE1. CSMD1 is of particular interest because it has been implicated in schizophrenia [CSMD1 was associated with spatial working memory [ANKS1B was implicated through the association between the marker psy_rs10860381 and social responsiveness in the maternal POE test. This gene encodes an activity dependent postsynaptic effector protein highly expressed in the brain, and it has been implicated in a wide array of neurodevelopmental phenotypes [SYNE1 was implicated through the associations between several markers and processing speed (WISC Symbol Search) in the general test. The gene encodes a protein that is involved in anchoring specialized myonuclei underneath neuromuscular junctions, but it is also expressed in the brain—predominantly in the cerebellum [SYNE1 mutations exhibit processing speed deficits compared with controls [SYNE1 and TGM3 have been highlighted in a study of de novo mutations in autism spectrum disorder [Among the other genes in Table ophrenia –87. Integ memory . This cog memory . ANKS1B enotypes . Importaenotypes . Even moenotypes . SYNE1 wrebellum . It has rebellum . Interescontrols , which idisorder .MYOM2 on eQTLGen (P = 3.2717 × 10–310) and GTEx (P = 9.5 × 10–20) in whole blood. Interestingly, the mouse ortholog of this gene, Myom2, was significantly upregulated and had the fifth largest fold change among upregulated genes in the hippocampus of memory-enhanced mice in one study [Some of the other associations in Table ne study , which iTGM3 → working memory → ADHD and CACNB4 → social responsiveness → ASD, through the top genetic associations we identified. Similarly, memory impairments, including verbal working memory impairment, are common feature of schizophrenia [TGM3, CSMD1 and PTPRA and schizophrenia through the working memory endophenotype. The highlighted associations in Table The traits implicated by the top results in our study, namely, SRS  and WISC Arithmetic (working memory), had been proposed as endophenotypes for ASD and ADHD, respectively , 98–100.ophrenia , suggestOur results should be evaluated in the light of several potential limitations. Firstly, our study sample was a family-based sample, and, as such, not a very large one. While this has the advantage of our being able to have a deeply phenotyped sample, it can be detrimental to genetic association studies. While, as shown in previous simulations studies of QTDT models, our sample should be large enough to detect some effects, it is to expected that only strong effects could be detected in our sample, which can explain why the majority of our genome-wide significant associations were intragenic. It should also be emphasized that some of the effect sizes could be overestimated due to confounding. As it is difficult to determine the appropriateness of the adjustment for high risk status, it should be borne in mind that the effects for some associations might not be accurate. However, since the GWASs were performed with the goal of discovering new genetic associations for downstream analyses and not for estimating their effects, we adopted this approach rather than potentially over-adjust the models, as discussed earlier. Another limitation is that we did not have a suitable replication sample which included the same phenotypes and genetic data from children and parents. Although our candidate genes have been highlighted in previous studies of related traits, providing more credibility to their association with our traits, the associations with specific variants need to be replicated in an independent sample.It has been shown that the heritability of cognitive ability increases from childhood to young adulthood . InteresOur study identified several candidate genes for social-behavioral and neurocognitive functions, implicated either through a general test, or a test of POEs; associations in the latter test were also supported by external studies which had identified methylation or histone modification sites associated with the relevant marker. Importantly, most of our genome-wide significant associations were within protein-coding genes, and many of these had previously been implicated in studies of related traits and disorders, although many of these previous associations were with rare and/or deleterious mutations. Our study provides further evidence to the effect that common variants may influence related traits in individuals not diagnosed with severe mental disorders, and it further supports a role for the highlighted genes in the studied traits, which can be seen as a replication of those genes’ implications in the previous studies. We did not identify significant associations for traits in some of the other functions/domains included in this study; this could be the result of the lower heritability of those traits, as well as potentially smaller effects that could not be discovered in the VIA sample. Our results also illustrate the usefulness of modeling POEs in human genetic studies, and, while previous studies focused on an array of quantitative non-social-cognitive, non-social-behavioral, and non-neurocognitive traits, our study highlights the presence of potential POEs in several of these traits studied in a systematic way, thus providing further evidence for this phenomenon in humans.Additional file 1. Archive file containing std_qtdt, which can parse and tabulate the QTDT output.Additional file 2: Figure S1. Manhattan plots for all 24 GWASs.Additional file 3: Figure S2. QQ plots for all 24 GWASs.Additional file 4: Table S1. Direct associations from VarElect for genes in Table 1.


Recently, the rate at which provisional draft genomes are generated has increased substantially, giving new insights into ecological processes and interactions. From a genotype perspective, the wide availability of genome-centric data requires new data synthesis approaches that place organismal genomes center stage in the study of environmental roles and functional performance. Extraction of ecologically relevant traits from microbial genomes will be essential to the future of microbial ecological research. Here, we present microTrait, a computational pipeline that infers and distills ecologically relevant traits from microbial genome sequences. microTrait maps a genome sequence into a trait space, including discrete and continuous traits, as well as simple and composite. Traits are inferred from genes and pathways representing energetic, resource acquisition, and stress tolerance mechanisms, while genome-wide signatures are used to infer composite, or life history, traits of microorganisms. This approach is extensible to any microbial habitat, although we provide initial examples of this approach with reference to soil microbiomes.Remote sensing approaches have revolutionized the study of macroorganisms, allowing theories of population and community ecology to be tested across increasingly larger scales without much compromise in resolution of biological complexity. In microbial ecology, our remote window into the ecology of microorganisms is through the lens of genome sequencing. For microbial organisms, recent evidence from genomes recovered from metagenomic samples corroborate a highly complex view of their metabolic diversity and other associated traits which map into high physiological complexity. Regardless, during the first decades of this  To streamline the process of going from genome sequences to putative ecological traits, we developed microTrait, a set of tools to efficiently discover and distill the trait-based representation of a microbial genome.The rapid adoption of high-throughput microbial sequencing is leading to accumulation of microbial genomes at an ever-increasing rate. These genomes represent instances from not only isolated microbes but also microbial populations in their native environmental context as metagenome-assembled genomes (MAGs) or single-cell amplified genomes (SAGs). We believe that an ability to efficiently predict ecological traits directly from primary sequence data is a necessary interface between microbial Linking microbiome structure and dynamics to ecosystem functioning globally in a predictive way and in face of global change has been a long-standing goal of microbial ecology . EffortsMicrobial-biogeochemical models are crucial tools in linking microbiome dynamics, environmental responses, and ecosystem processes across scales. Wide-spread availability of taxon-centric microbial measurements have naturally popularized taxon-centric models including few species or functional groups dominant at the local scale of interest. The upward scalability of such models would be limited given the fact that no single taxa would dominate at larger scales and with a limited number of parameter sets, the model would have poor adaptive capability both across scales and environmental conditions. Moreover, trying to approach the complexity of real systems at larger scales by adding more taxa or functional groups lead to increasingly complex models with a continuous demand for more parameters. Given these limitations of taxon-centric approaches in modeling the diversity and activity of microbes globally and with changing environmental conditions, trait-based representation of microbes is becoming increasingly popular.Trait-based approaches represent an intermediate approach to modeling complex populations while also preserving key mechanistic properties that determine fitness in dynamic systems. The trait-based framework represents microbes with traits that can be summarized by few parameters and that are constrained by environmentally-dependent trade-offs. These approaches were developed in the field of plant ecology , and hav‘omic approaches  or through physiological studies  or they may be inferred by manipulation experiments such as stable-isotope tracing with substrates at various concentrations to determine relative affinities. The paradigm shift from a taxa-to a trait-centric representation of microbiomes is partly stimulated by the wide-use of omic technologies to illuminate the functional potential of environmental microbial communities and their interactions with each other, higher organisms, and their environment  pairwise sequence alignments and database search , 2) statmicroTrait, that provides a conceptual framework and associated pipelines to translate a microbial genome into a suite of potential fitness traits. microTrait maps a genome sequence into a hierarchical trait space that covers energetic, resource acquisition, stress tolerance, and life history traits that underlie microbial strategies describing environmental microbes , which can then be acquired through uptake (substrate uptake) via membrane transporters . Thus, oRedox reactions underlie all biological energy metabolism and redox chemistry provides an organizing principle to connect microscale to global scale processes . Genes wStress may be induced by physical, chemical, or biological conditions that adversely affect microbial growth and survival. Microbes that use stress tolerance strategies respond to a variety of stressors using several physiological and evolutionary mechanisms. Though the specific stress response depends on the particular suboptimal conditions, common traits with genomic underpinnings have been broadly identified . These include increasing the concentration of some molecular chaperones (stress proteins/heat-shock proteins) to combat biomolecular damage in response to stress. This is a universal feature across all domains of life but the relative importance of genetic  or regulatory  processes under different stressors is less clear .Genomic bases of microbial traits that underlie stress tolerance to specific physiochemical and chemical factors have also been identified: 1) Temperature stress: a suite of heat shock genes serving as chaperones and proteases are involved in the protection, repair, and degradation of denatured/misfolded proteins. Response to cold shock involves adaptation of the membrane via an increase in the proportion of unsaturated fatty acids and activation of chaperone cold shock proteins to restore mRNA functionality. 2) Desiccation, osmotic, salt stress: Known molecular strategies to tolerate drought and freezing include production or uptake of osmolytes like trehalose and glycine betaine to reduce water potential and maintain hydration or synthesis of extracellular polymeric substances . 3) OxidEcological and evolutionary processes leave their signatures in overall microbial genome content and organization. A key dimension of any ecological strategy is growth. Optimal growth characteristics of microbes are key to understand how the key traits regarding resource acquisition, resource use, and stress tolerance are realized to adapt to a particular environmental niche. Traits that concern these characteristics are classified as life history traits. Codon usage bias and ribosomal RNA (rRNA) operon copy number are linked to maximum growth rate, a life history trait constraining all other functional traits . AnothermicroTrait-HMM) to model the diversity of protein families based on sequences from genomes and metagenomes with independently established accuracy to detect genetic loci (microTrait rules) encoded in predicate logic to infer traits from presence and absence of the set of loci modeled in microTrait-HMM (. The model-based detection of genetic loci ensures decreased run-times and interoperability across datasets (given model and scoring cutoff). The rule-based framework to infer traits from primary features gives the user the flexibility for redefinition and refinement.The computational pipeline to infer traits from primary genome sequences has two major components : 1) a datic loci , 2) a serait-HMM . The modmicroTrait-HMM reflect the most recent sequence diversity from both cultured and uncultured microbes and therefore should have improved accuracy over existing methods to detect genes underlying traits covered in microTrait. To ensure interoperability of the microTrait pipeline with the existing HMM databases and relevant sequence database resources, for each gene model we provide database cross references to KEGG  in KEGG orthologs database (when the loci was represented in KEGG)  had high sensitivity (≥75%) and low FPR , with 92% of HMMs having an F-score >=0.8 (We assessed the performance of each in KEGG) . A test in KEGG) . The smare >=0.8 .microTrait is a genome sequence (.fa) or the corresponding protein coding genes (.faa) in FASTA format. When genomic rather than protein coding gene sequences are supplied, Prodigal is used to predict open reading frames  or other rules to specific traits   and the microTrait allows the investigation of this modularity across a set of genomes. The resulting information can be used by the user to define custom logical rules to assign traits based on the protein family content.A challenge in assigning traits to genomes based on the protein family signatures is the modularity of the underlying pathways. This modularity might be truly reflecting the genomic variation within a set of isolates, MAGs or SAGs but also be an apparent manifestation of incomplete and noisy genomic information. Starting with genomic sequences, microTrait-rules recovered the validated culturable taxa for different microbial functional groups. For each functional group, we first matched the taxonomic names from literature, primarily genus/species names but also extending to higher ranks for certain functional groups, to canonical NCBI taxonomic names. All available genomes from environmental ecosystems with the respective taxonomic affiliation were considered as a “positive” for that functional group according to the Faprotax approach , dark iron oxidation (10/16), iron respiration (19/86), aerobic nitrite oxidation (6/13), chlorate reducers (3/6), dark sulfide oxidation (49/93), anoxygenic photoautotrophy Fe oxidizing (9/16), dark sulfur oxidation (71/124), sulfur respiration (82/139), thiosulfate respiration (88/145). A close examination of the taxonomic identity of the genomes “missed” by microTrait suggested a variety of explanations for the functional groups with poor recall.Among 41 functional groups, 29 had a recall rate over 70%. Functional groups for which P. mendocina had corresponding genomes in the isolate set (n = 7) and none of those had the genomic features for anammox suggesting that this is a strain specific trait for P. mendocina. Similarly, for dark iron oxidation, genome features suggested that the trait can be strain specific. Among 15 R. palustris and 2 M. ferrooxydans, a limited number (9 and 1 genome respectively) was genome-supported to carry the trait. There were also cases where the genomic evidence suggested that trait conservation was limited to deep taxonomic levels so a taxonomic inference at genus or family level would have impacted the accuracy of Faprotax method. For instance, methanotrophy is associated with Methylocystaceae (family) and Methylocapsa (genus) yet the trait was specific to subfamily/subgenus. Among 7 Methylocystaceae genera with genome representatives, 2 genera (Methylocystis and Methylosinus) had genome support for the trait. Similarly, 2 out 3 Methylocapsa species with genomes had evidence for the trait.A primary advantage of inferring microbial traits directly from genomic sequences rather than by taxonomic names is the ability to resolve diversity (species or strain level), which increases the prediction accuracy. We have observed that for many functional groups defined in Faprotax, the genomes that were assigned to the taxonomic clades lacked the required genetic repertoire for the metabolic function in question. Some prominent examples are for the “anammox” and “dark iron oxidation”. For anammox, among the diversity of taxa (genus and species), only Ferrimonas and Shewanella .It should be noted that, there were also cases for which the absence of the genomic signal reflected limited knowledge for the genetic underpinnings of the trait. A typical example was for iron respiration, a trait for which current evidence suggests that electron transport for iron reduction proceeds in a different and unknown mechanism in acidophiles compared with ewanella . Anotherewanella  which immicroTrait to publicly available isolate genomes and MAGs. The datasets we used included 1) isolate genomes from environmental ecosystems from IMG/M , 2) MAGs from an aquifer system   , 3) MAGs= 2,545) , 4) MAGs= 2,545) , and 5) = 2,545) . This comicroTrait on a machine with a 2.3 GHz 16-core Intel Xeon Processor E5-2,698. When run using a single core, with a single genome processed using that core, microTrait processed that genome in 3.94 ± 2.59 min, with an average of 1.11 min/Mb of genome sequence ) mapping genomes to separate logical processors. In our tests, when run in a 64 processor compute node, the processing of the compendium of 20,062 genomes  took 12.47 h.We tested sequence . From thmicroTrait outputs a trait matrix of “genomes x traits” with three types of qualitative variables. Binary trait variables are calculated as presence/absence of a specific functional capacity and span 1) energy generation via specific electron acceptors/donors, 2) capacity to degrade, assimilate, or acquire specific substrates. Continuous trait variables are of two groups. The first group of continuous traits are calculated starting from counts of specific functional capacities in the genome and span 1) acquisition of chemical classes of substrates with transporters or via extracellular breakdown, 2) investment in extracellular polysaccharides and osmolytes. For each genome, the counts are normalized by genome size. The second group represent life history traits and include 1) minimum generation time (unit: h−1) predicted based on indices of codon-usage bias in ribosomal protein genes (a proxy for highly expressed genes)   (Weissmad genes) , 2) optid genes) .microTrait in refining functional guilds, we explored how denitrifier guilds can be defined based on the genomic distribution of denitrification traits in the isolate genomes from our compendium of genomes. Denitrification is a key biologically catalyzed process by which nitrogen available to plants is transformed to the atmospheric nitrogen pool as gaseous forms of nitrogen as molecular N2 or as an oxide of N. Denitrification occurs as a step-wise reduction of nitrogen oxides with gaseous products. Four reductases are involved in the denitrification, NAR, NIR, NOR and N2OR, sequentially catalyzing the reductions of NO3 - → NO2 - → NO →N2O →N2. Several previous studies reported both genomic and phenotypic evidence for truncated versions of the denitrification pathway but a global genomic analysis is not currently available , or multiple end products with missing steps (e.g. guilds 11–13). The default trait matrix in microTrait defines denitrification traits by the end products of denitrification  for a total of 4,116 genomes of organisms isolated from terrestrial environments (ST9) was computed using microTrait. A non-parametric rank-order correlation metric was used to estimate the degree of relatedness between all trait pairs, visualized as a correlation matrix and reordered to elucidate the potential hidden structure and pattern in the matrix  suggesting that t traits . On the microTrait.Metagenomics allow the recovery of the genomes of all detectable members of an ecosystem along extensive spatiotemporal gradients. The genomes then provide support for co-occurrence of ecologically relevant traits of the members that together underlie the ecosystem function. A typical genome-centric microbiome study involves the analysis of hundreds to thousands of genomes leading to trait matrices of high genomic dimensionality. This high dimensionality poses a particular problem for statistical analyses . FurthermicroTrait can be used to discover and define functional guilds, parameterize the defined guilds with life history traits , and reduce the dimensionality of the trait space in a quantifiable way. microTrait encompasses both continuous and binary traits, the similarity between genomes are measured using a distance metric suitable for mixed data types . All traits except “anaerobic ammonia oxidation (anammox)” were detected at least once in the dataset resulting in a trait matrix of dimensionality 3,430 genomes X 190 traits. To date no pure culture isolates of anammox organisms have been obtained  were each enriched in specific traits under resource acquisition and resource use strategies (ST16). Guild 23 compared to guild 3, and 4 was marked by enrichment of the ability to assimilate simple C compounds, use 2 C compounds in the absence of glucose via glyoxylate cycle, uptake a variety of N compounds  as well aromatic acids and biopolymers, and fix elemental nitrogen for biomass. On the other hand, compared to guild 23, guild 3, and 4 represent a different strategy for incorporation of N compounds into biomass through assimilatory nitrate reduction and a unique ability to assimilate P compounds. Notably, although all three guilds were enriched in the capacity to utilize glucose, guilds 23 and guilds 3, and 4 differed in their preferred glycolytic pathways  pathway in guilds 3, and 4 vs. less common Entner–Doudoroff (ED) pathway in guild 23) reflecting differing preferences in balancing production of ATP (energy yield) and cost of protein synthesis to achieve maximum fitness . Across microTrait, our goal was to provide an extendable toolset and computational pipeline to infer microbial traits from genomic data and show how the resulting information can be used to define microbial guilds with varying parameters.Genome sequencing, from a data perspective, now provides a primary window into the traits that regulate fitness and function across Earth’s microbiomes. Genomes are increasingly recognized as a fundamental unit in the study of microorganisms, however, the integration of this information is required to understand how such genome units relate to ecologically coherent behavior. Exploration of feedbacks between microorganisms and their environments requires numerical modeling approaches, and the assimilation of genomic information has substantially lagged its generation. This assimilation of microbiome information into numerical models in an automated fashion remains a significant challenge as microbial communities are ultra-diverse, physiologically plastic, and dynamically adaptive. Trait-based approaches to microbial ecology provide a framework to represent microbial diversity in a way that facilitates prediction, integration and generalization  and the microTrait-HMMs have been trained were selected from an extensive sequence database  that nomicroTrait-HMM, we used the corresponding genes from the corresponding KO (KEGG Orthology) group. While this approach makes a systematic assessment of model accuracy possible by balancing model precision and recall, it should be noted that the computed thresholds may be overly strict for certain applications. Sequences in the KO database correspond to a highly curated set of sequences with a limited phylogenetic scope, this may lead to high precision and low recall with respect to the true labels especially for phylogenetically divergent or novel genomes not well represented in KEGG . In https://microbiomedata.org/) towards higher quality metadata standards and metadata enrichment will be much needed.Finally, a trait-based microbial ecology framework has the potential to integrate ecological and genomic data. For this promise to be achieved however, the availability of metadata on the provenance and biogeochemical/ecological identification of the underlying biological samples is essential. Environmental metadata give essential context for genome data but current isolation of metadata resources  based on archaeal and bacterial sequence diversity from 1) genomes of cultured organisms, 2) single cell genomes, 3) metagenome-assembled genomes, and 4) metagenomes from environmental, host associated and engineered microbiome samples. For each gene loci, a profile HMM was trained as follows. Seed protein sequences were collected from the non-redundant IMG/M database (img_core_v400) based on “EC Number”, “Gene Symbol”, and “IMG Term and Synonym”  as a function of the number of clusters. This allows the user the option to pick the number of guilds capturing a given level of trait variance across the dataset, and vice versa. Given a threshold for a trait variance or a number of guilds, we then assign each genome to a guild based on the corresponding tree cut from hierarchical clustering. Finally, we visualize the trait profiles for the defined guilds using trait positivity as a metric.Ecological guilds were inferred from ble data  as impleble data  for mixe


The genomic selection program for dairy cattle in the United States has doubled the rate of genetic gain. Since 2010, the average annual increase in net merit has been $85 compared to $40 during the previous 5 years. The number of genotypes has been rapidly increasing both domestically and internationally and reached over 6.5 million in 2022 with 1,134,593 submitted in 2021. Evaluations are calculated for over 50 traits. Feed efficiency , heifer and cow livability, age at first calving, six health traits, and gestation length have been added in recent years to represent the economic value of selection candidates more accurately; work is underway to develop evaluations for hoof health. Evaluations of animals with newly submitted genotypes are calculated weekly. In April 2019, evaluations were extended to crossbreds; to support that effort, evaluations are initially calculated on an all-breed base and then blended by an estimated breed composition. For animals that are less than 90% of one breed, the evaluation is calculated by weighting contributions of each of the five major dairy breeds evaluated  by the breed proportion. Nearly 200,000 animals received blended evaluations in July 2022. Pedigree is augmented by using haplotype matching to discover maternal grandsires and great-grandsires. Haplotype analysis is also used to discover undesirable recessive conditions. In many cases, the causative variant has been identified, and results from a gene test or inclusion on a genotyping chip improves the accuracy of those determinations for the current 27 conditions reported. Recently discovered recessive conditions include neuropathy with splayed forelimbs in Jerseys, early embryonic death in Holsteins, and curly calves in Ayrshires. Techniques have been developed to support rapid searches for parent-progeny relationships and identical genotypes among all likely genotypes, which substantially reduces processing time. Work continues on using sequence data to discover additional informative single nucleotide polymorphisms and to incorporate those previously discovered. Adoption of genotyping by sequencing is expected to improve flexibility of marker selection. The success of the Council on Dairy Cattle Breeding in conducting the genetic evaluation program is the result of close cooperation with industry and research groups, including the United States Department of Agriculture, breed associations, genotyping laboratories, and artificial-insemination organizations. Genomic selection has revolutionized dairy cattle breeding by doubling the rate of genetic gain primarily through halving the generation interval. In the United States, the Council on Dairy Cattle Breeding (CDCB) conducts a genetic evaluation program that includes genotypes from all over the world. The number of genotypes in the collection has been rapidly increasing and reached 6.6 million in August 2022 with 1,134,593 submitted in 2021 . ContinuThe first official genomic evaluations were released in January 2009 for Holsteins and Jerseys. The genotyping chips used for dairy cattle also have evolved. To reduce the cost of genotyping, chips with fewer single nucleotide polymorphism (SNP) markers were introduced after the initial 50 K chip . As technology advanced, higher density chips were offered. Typically, bulls that are marketed, have two genotypes: the first to determine if they rank high enough to be marketable and the second with higher density to maximize the accuracy of their evaluation by minimizing imputation errors. Generation interval is the average age of parents when offspring are born and impacts genetic improvement. The shorter the generation interval, the faster progress can be made so long as accuracy is not compromised excessively. A few years after genomic evaluations became official in 2009, parent ages for bulls began to drop dramatically and are now near the biological minimum .Genomic selection has produced a large increase in genetic trend as indicated by the average net merit of marketed Holstein bulls in the United States . The $85The national genetic evaluations calculated by the CDCB currently include over 50 traits . ProductLifetime genetic-economic indices are provided to the dairy industry for net merit, fluid merit, cheese merit, and grazing merit . Those iGenomic evaluations are based on estimation of allele substitution effects for 78,964 SNPs selected considering minor allele frequency, distribution across the genome, linkage to genes of particular interest, and reliability considering call rate and Mendelian consistency. The sum of SNP effects, called direct genomic value, is combined with an estimate of polygenetic effects and the traditional evaluation to create the genomic evaluation . This isBreed composition is estimated with the same set of 78,964 SNPs used to determine the direct genomic values for other traits. The predictor population is purebred bulls, and the data are defined as one for the animal’s breed and 0 otherwise. Solutions (as percentages) are forced to add to 100, and portions of less than 2% are distributed to remaining breeds. The percentages are called breed base representation (BBR). They are used to validate the breed of the identification data for an animal and weight individual breed contribution to the evaluations of crossbreds.Because SNP effects differ by breed, genomic evaluations are calculated separately by breed. To provide evaluations for crossbreds, SNP effects from the individual breeds are combined. Animals with a highest BBR of less than 89.5% are evaluated as crossbreds, and their evaluations are a blend of their direct genomic values weighted by their BBRs for each breed. This blending is possible for traits that are initially evaluated on an all-breed base. Type traits and traits that are not calculated for all breeds are not blended; therefore, the animal receives an evaluation for the breed with the highest BBR. For first-generation crosses, the breed from the preferred identification determines the breed of evaluation. Nearly 200,000 animals received blended evaluations in July 2022.Each genotype is compared with the genotypes of parents and then with all existing genotypes that might have a parent-progeny relationship or be identical. To limit the time required, birth date limits are imposed. If both parents are confirmed, the search is limited to genotypes of animals born no more than 5 years before. That limit is increased to 12 years for other animals. Genotypes of bulls born more than 5 years ago without progeny born in the preceding 5 years are skipped unless their genotype was added to the evaluation system within the last year. No animals with genotyped progeny are skipped. Animals with conflicting parents or discovered relationships are not evaluated until the conflicts are resolved. In general, for a conflicting pair, the genotype with the less reliable information is the one designated as not usable. If a parent is not genotyped or not confirmed, the likelihood of the grandsire is determined. If the grandsire is unlikely, the animal is not evaluated.Discovery of maternal grandsires (MGS) and maternal great-grandsires (MGGS) is done as part of the evaluation based on haplotypes in common. An imputation process is used to create genotypes with 78,964 SNPs from incoming genotypes of various lengths. The genome is divided into intervals and maternal or paternal origin is determined. Those haplotypes are compared, and bulls are designated as discovered ancestors based on the percentage of haplotypes in common. Crossing over reduces the expected haplotypes in common to 45% for MGS and 20% for MGGS. A bull’s percentage must exceed the next highest bull’s by 15% and have a percent matches greater than 35% for MGS and 15% for MGGS to be designated as discovered. The age of the bull at the birth of the grand progeny/great grand progeny is considered. The discoveries are used to remove an unlikely grandsire designation if the discovered grandsire is the same as the pedigree grandsire. If no pedigree information on a dam or granddam was provided, the discovered MGS and MGGS are added to the pedigree. Similarly, if the connecting dam is unknown, identification data will be constructed so that a pedigree record can be created to store the MGS or MGGS information.To speed discovery, a set of 3,552 SNPs that are present on most genotyping chips and have high call rate and good Mendelian consistency were selected. Comparisons are ordered so that checking stops after 96 or 1,000 SNPs if the percentage of conflicts exceeds that likely for a parent-progeny relationship. The discovered closely related pairs are stored using a unique genotype identification so that the identity of close relatives is not affected by genotype reassignment.The CDCB provides information on the genomic relationships between potential dams and currently marketed bulls. Those data report the actual portion of genetic variants  in common, in contrast to pedigree analysis, which can only give the average based on relationships. This allows avoiding inbreeding more precisely.The CDCB also provides predictions for a number of recessive conditions so that likely carrier-to-carrier matings can be avoided even without testing an animal. The haplotypes that affect fertility are conditions discovered through genomics and can now be considered in matings. Currently, 27 conditions are reported . RecentlFuture mating programs may also consider the effects of dominance, which causes some sire-MGS grandsire combinations to do better than expected and others to do worse.Genomic evaluation relies on having enough DNA markers to track the segments of chromosomes associated with high performance across generations. The SNPs are used as markers because of their reliability and low cost. However, crossing over (recombination) can disrupt the associations between a marker and the causative variant, which results in a decay in the linkage between SNPs and genomic regions associated with high performance. To counteract this, new data are needed so that the SNP effects can be re-estimated to maintain or improve evaluation accuracy. Now that the cost of whole-genome sequencing has fallen and thousands of animals have been sequenced, research is focused on finding SNPs that are more closely associated with the causative genetic variants (or even the variants themselves). The closer the marker is to the causative variant, the lower the likelihood that recombination will disrupt the linkage.For traditional genetic evaluations, an animal could only receive an evaluation for a trait that was observed for the animal or its offspring. Evaluation were limited to traits where large scale collection of data was possible such and milk and fat yield and type traits collected by breed associations. With genomics, evaluations can be generated for all genotyped animals if enough animals have genotypes and traditional evaluations for the trait (the reference population) to give reasonably accurate estimates of the SNP effects. Feed efficiency is an example of a small population of animals with feed intake measured providing the basis for feed saved evaluations for all genotyped animals. Efforts to collect data for more traits are ongoing. Foot health and milking speed are currently in the research phase. Mid-infrared spectroscopy of milk samples is expected to provide data related to traits of economic importance.Early in the development of the US genomic evaluation system, arrangements were made to share genotypes between countries to increase the size of the reference population . From thHerds with high levels of genotyping generally have fewer misidentified sires in their data that contribute to traditional evaluations. With better data, less information is lost from the extensive checks done by the CDCB to eliminate unreliable and inconsistent data. Genotypes are checked against all other genotypes to ensure they are assigned to the correct animal and that the parents are correctly identified. Data problems can include submitting the same identification number for different cows, not documenting that sexed semen was used for an insemination and reporting the transfer of an embryo to a recipient as a normal breeding. Greater knowledge of data usage should help providers better understand the importance and benefit of accurate data collection.In addition to data accuracy, comprehensive reporting is important. Although genomics can be used to provide evaluations for animals without an observed trait, continued submission of phenotypic data still is needed to maintain accuracy. In recognition of the value of data, the CDCB makes payments to dairy records processing centers for providing data, supports the collection of feed efficiency data, and structures the fees for genomic evaluation to give a discount to data contributors.The CDCB has a quality assurance program for the genotyping laboratories and nominators that includes monthly report cards and annual reviews. To become certified, the organizations must demonstrate the ability to provide data in the required formats. The monthly reports for labs include SNP accuracy and completeness as well as the percentage of genotypes with nomination and animal genotypes with a low call rate. The labs receive reports on those characteristics for each submission for possible correction before submissions are to added to the database.The popularity of the genomic evaluation program in the United States has resulted in a rapid growth in the genotyping of dairy cattle. When genomic evaluation began in 2008, the focus was on bulls, but genotyping of females has grown rapidly in recent years. Many dairy producers genotype all their heifers so that they can select among a range of breeding and management strategies.Dairy genetics in the United States and worldwide have been transformed by the use of genomic information. Genomic evaluations determine the value of animals at a much earlier age and have contributed to a dramatic increase in the rate of genetic improvement. Bulls are used widely as sires based on the analysis of their DNA before they have any milking daughters. A continuing stream of improvements are planned to increase accuracy and comprehensiveness of genomic evaluations. Success requires a partnership between data suppliers and users to generate the most effective information for all.


As in the previous volume, this collection presents several research articles that address statistical methodology, computing, and resources for genome-wide association studies (GWAS). The research interest covers association studies of longitudinal data, multiple testing, computationally efficient analysis of binary phenotypes and more.Li et al. propose a function-on-function regression method, a longitudinal functional data association test, which not only models the developmental process but also takes advantage of gene-based testing, and shows that their method performs well for the identification of genetic variants switching in the growth and development stage.Phenotypic data is often longitudinal in plant science. Genetic analysis of longitudinal phenotypic data can treat data at different time points separately or fit a growth curve that depicts the developmental process. The latter is commonly practiced at single genetic variants while gene-based GWAS are often of interest. p-value cutoff is commonly used for statistical inference. However, the genome size and the SNP distribution often vary from study to study, and it is thus desirable to determine data-driven significance thresholds. Many computationally efficient methods have been proposed for the derivation of genome-wide thresholds, including Bonferroni correction based on an estimated effective number of tests and Brown’s method, which take the SNP dependency into account to achieve a higher power while properly controlling the type I error rate. Cinar and Viechtbauer survey several gene-based testing methods and evaluate their performance via simulation studies. Their work provides valuable information to those who seek computationally efficient methods for significance threshold estimation in GWAS.Proper controlling of the type I error rate is an important issue in GWAS. While the permutation test is regarded as the gold standard, its extensive computation is prohibitive in genetic studies which often include thousands of samples and millions of single nucleotide polymorphisms (SNPs). In human genetics, a broadly-accepted Gurinovich et al. empirically evaluate several known software programs such as SAIGE and fastGWA-GLMM and provide food for thought for researchers who consider software programs that perform GWAS analysis with binary phenotypes.Categorical phenotypes are not rare in genetic studies. Genome-wide association studies of categorical phenotypes are computationally challenging due to the incorporation of genetic relatedness matrices (GRM). The most common categorical phenotypes are binary. There have been enormous efforts to address the computational issues in GWAS with binary phenotypes. Strategies include the use of spare GRM plus Saddle Point Approximation. With a focus on computational efficiency and the control of false positives, Courbariaux et al. study phenotypic data that is longitudinal and arises from multiple sources, and propose a sparse mixture-of-experts model that incorporates genetic information for disease subtyping. The authors claim two advantages of their methods over others. First, their model-based clustering utilizes original data without transformation and thus facilitates the interpretation of results. Second, they address the large-scale problem due to massive genetic data by model selection. This collection also touches on two popular problems, heritability estimation and polygenic risk scores (PRS). Heritability is often reported in GWAS. Zhang and Sun show that heritability tends to be overestimated in the presence of Hardy–Weinberg disequilibrium. Such information should be useful for those who are interested in heritability estimation. As genomic prediction, PRS concern estimation of genetic values underlying a trait of interest. Zhang et al. compare methods that utilize sex-specific PRS and share useful information with interested readers.It is known that many diseases are heritable. Genetic data not only plays an important role in the identification of genetic factors underlying diseases but also is helpful for determining disease subtypes. To sum up, this volume highlights several research interests in statistical methods, computing, and resources for GWAS which we hope contribute to the society of genetic studies.