Genetic associations between alcohol phenotypes and life satisfaction: a genomic structural equation modelling approach.

Alcohol use (i.e., quantity, frequency) and alcohol use disorder (AUD) are common, associated with adverse outcomes, and genetically-influenced. Genome-wide association studies (GWAS) identified genetic loci associated with both. AUD is positively genetically associated with psychopathology, while alcohol use (e.g., drinks per week) is negatively associated or NS related to psychopathology. We wanted to test if these genetic associations extended to life satisfaction, as there is an interest in understanding the associations between psychopathology-related traits and constructs that are not just the absence of psychopathology, but positive outcomes (e.g., well-being variables). Thus, we used Genomic Structural Equation Modeling (gSEM) to analyze summary-level genomic data (i.e., effects of genetic variants on constructs of interest) from large-scale GWAS of European ancestry individuals. Results suggest that the best-fitting model is a Bifactor Model, in which unique alcohol use, unique AUD, and common alcohol factors are extracted. The genetic correlation (rg) between life satisfaction-AUD specific factor was near zero, the rg with the alcohol use specific factor was positive and significant, and the rg with the common alcohol factor was negative and significant. Findings indicate that life satisfaction shares genetic etiology with typical alcohol use and life dissatisfaction shares genetic etiology with heavy alcohol use.

Missingness adapted group informed clustered (MAGIC)-LASSO: a novel paradigm for phenotype prediction to improve power for genetic loci discovery.

Introduction: The availability of large-scale biobanks linking genetic data, rich phenotypes, and biological measures is a powerful opportunity for scientific discovery. However, real-world collections frequently have extensive missingness. While missing data prediction is possible, performance is significantly impaired by block-wise missingness inherent to many biobanks. Methods: To address this, we developed Missingness Adapted Group-wise Informed Clustered (MAGIC)-LASSO which performs hierarchical clustering of variables based on missingness followed by sequential Group LASSO within clusters. Variables are pre-filtered for missingness and balance between training and target sets with final models built using stepwise inclusion of features ranked by completeness. This research has been conducted using the UK Biobank (n > 500 k) to predict unmeasured Alcohol Use Disorders Identification Test (AUDIT) scores. Results: The phenotypic correlation between measured and predicted total score was 0.67 while genetic correlations between independent subjects was high >0.86. Discussion: Phenotypic and genetic correlations in real data application, as well as simulations, demonstrate the method has significant accuracy and utility for increasing power for genetic loci discovery.

Sex differences in sleep quality and psychological distress: Insights from a middle-aged twin sample from Spain.

There is a moderate association between poor sleep and psychological distress. There are marked sex differences in the prevalence of both variables, with females outnumbering males. However, the origin of these sex differences remains unclear. The objectives of this study were to: (1) study genetic and environmental influences on the relationship between poor sleep quality and psychological distress; and (2) test possible sex differences in this relationship. The sample comprised 3544 participants from the Murcia Twin Registry. Univariate and multivariate twin models were fitted to estimate the magnitude of genetic and environmental influences on both individual variance and covariance between poor sleep quality and psychological distress. Sleep quality and psychological distress were measured using the Pittsburgh Sleep Quality Index and the EuroQol five-dimensions questionnaire, respectively. The results reveal a strong genetic association between poor sleep quality and psychological distress, which accounts for 44% (95%CI: 27%-61%) of the association between these two variables. Substantial genetic (rA = 0.50; 95%CI: 0.32, 0.67) and non-shared environmental (rE = 0.41; 95%CI: 0.30, 0.52) correlations were also found, indicating a moderate overlap between genetic (and non-shared environmental) factors influencing both phenotypes. Equating sexes in sex-limitation models did not result in significant decreases in model fit. Despite the remarkable sex differences in the prevalence of both poor sleep quality and psychological distress, there were no sex differences in the genetic and environmental influences on these variables. This suggests that genetic factors play a similar role for men and women in explaining individual differences in both phenotypes and their relationship.

Determining the stability of genome-wide factors in BMI between ages 40 to 69 years.

Genome-wide association studies (GWAS) have successfully identified common variants associated with BMI. However, the stability of aggregate genetic variation influencing BMI from midlife and beyond is unknown. By analysing 165,717 men and 193,073 women from the UKBiobank, we performed BMI GWAS on six independent five-year age intervals between 40 and 72 years. We then applied genomic structural equation modeling to test competing hypotheses regarding the stability of genetic effects for BMI. LDSR genetic correlations between BMI assessed between ages 40 to 73 were all very high and ranged 0.89 to 1.00. Genomic structural equation modeling revealed that molecular genetic variance in BMI at each age interval could not be explained by the accumulation of any age-specific genetic influences or autoregressive processes. Instead, a common set of stable genetic influences appears to underpin genome-wide variation in BMI from middle to early old age in men and women alike.

Alcohol use and alcohol use disorder differ in their genetic relationships with PTSD: A genomic structural equation modelling approach.

PURPOSE: Posttraumatic Stress Disorder (PTSD) is associated with increased alcohol use and alcohol use disorder (AUD), which are all moderately heritable. Studies suggest the genetic association between PTSD and alcohol use differs from that of PTSD and AUD, but further analysis is needed. BASIC PROCEDURES: We used genomic Structural Equation Modeling (genomicSEM) to analyze summary statistics from large-scale genome-wide association studies (GWAS) of European Ancestry participants to investigate the genetic relationships between PTSD (both diagnosis and re-experiencing symptom severity) and a range of alcohol use and AUD phenotypes. MAIN FINDINGS: When we differentiated genetic factors for alcohol use and AUD we observed improved model fit relative to models with all alcohol-related indicators loading onto a single factor. The genetic correlations (rG) of PTSD were quite discrepant for the alcohol use and AUD factors. This was true when modeled as a three-correlated-factor model (PTSD-AUD rG:.36, p < .001; PTSD-alcohol use rG: -0.17, p < .001) and as a Bifactor model, in which the common and unique portions of alcohol phenotypes were pulled out into an AUD-specific factor (rG with PTSD:.40, p < .001), AU-specific factor (rG with PTSD: -0.57, p < .001), and a common alcohol factor (rG with PTSD:.16, NS). PRINCIPAL CONCLUSIONS: These results indicate the genetic architecture of alcohol use and AUD are differentially associated with PTSD. When the portions of variance unique to alcohol use and AUD are extracted, their genetic associations with PTSD vary substantially, suggesting different genetic architectures of alcohol phenotypes in people with PTSD.

Are Different Individuals Sensitive to Different Environments? Individual Differences in Sensitivity to the Effects of the Parent, Peer and School Environment on Externalizing Behavior and its Genetic and Environmental Etiology.

Externalizing behavior is substantially affected by genetic effects, which are moderated by environmental exposures. However, little is known about whether these moderation effects differ depending on individual characteristics, and whether moderation of environmental effects generalizes across different environmental domains. With a large sample (N = 1,441 individuals) of early adolescent twins (ages 11 and 13), using a longitudinal multi-informant design, we tested interaction effects between negative emotionality and both positive and negative aspects of three key social domains: parents, peers, and schools, on the phenotypic variance as well as the etiology of externalizing. Negative emotionality moderated some of the environmental effects on the phenotypic, genetic, and environmental variance in externalizing, with adolescents at both ends of the negative emotionality distribution showing different patterns of sensitivity to the tested environmental influences. This is the first use of gene-environment interaction twin models to test individual differences in environmental sensitivity, offering a new approach to study such effects.

Combining Structural-Equation Modeling with Genomic-Relatedness-Matrix Restricted Maximum Likelihood in OpenMx.

There is a long history of fitting biometrical structural-equation models (SEMs) in the pregenomic behavioral-genetics literature of twin, family, and adoption studies. Recently, a method has emerged for estimating biometrical variance-covariance components based not upon the expected degree of genetic resemblance among relatives, but upon the observed degree of genetic resemblance among unrelated individuals for whom genome-wide genotypes are available-genomic-relatedness-matrix restricted maximum-likelihood (GREML). However, most existing GREML software is concerned with quickly and efficiently estimating heritability coefficients, genetic correlations, and so on, rather than with allowing the user to fit SEMs to multitrait samples of genotyped participants. We therefore introduce a feature in the OpenMx package, "mxGREML", designed to fit the biometrical SEMs from the pregenomic era in present-day genomic study designs. We explain the additional functionality this new feature has brought to OpenMx, and how the new functionality works. We provide an illustrative example of its use. We discuss the feature's current limitations, and our plans for its further development.

Does parental education influence child educational outcomes? A developmental analysis in a full-population sample and adoptee design.

Children's educational outcomes are strongly correlated with their parents' educational attainment. This finding is often attributed to the family environment-assuming, for instance, that parents' behavior and resources affect their children's educational outcomes. However, such inferences of a causal role of the family environment depend on the largely untested assumption that such relationships do not simply reflect genes shared between parent and child. We examine this assumption with an adoptee design in full-population cohorts from Danish administrative data. We test whether parental education predicts children's educational outcomes in both biological and adopted children, looking at four components of the child's educational development: (I) the child's conscientiousness during compulsory schooling, (II) academic performance in those same years, (III) enrollment in academically challenging high schools, and (IV) graduation success. Parental education was a substantial predictor of each of these child outcomes in the full population. However, little intergenerational correlation in education was observed in the absence of genetic similarity between parent and child-that is, among adoptees. Further analysis showed that what links adoptive parents' education did have with later-occurring components such as educational attainment (IV) and enrollment (III) appeared to be largely attributable to effects identifiable earlier in development, namely early academic performance (II). The primary nongenetic mechanisms by which education is transmitted across generations may thus have their effects on children early in their educational development, even as the consequences of those early effects persist throughout the child's educational development. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Using Multimodel Inference/Model Averaging to Model Causes of Covariation Between Variables in Twins.

OBJECTIVE: To explore and apply multimodel inference to test the relative contributions of latent genetic, environmental and direct causal factors to the covariation between two variables with data from the classical twin design by estimating model-averaged parameters. METHODS: Behavior genetics is concerned with understanding the causes of variation in phenotypes and the causes of covariation between two or more phenotypes. Two variables may correlate as a result of genetic, shared environmental or unique environmental factors contributing to variation in both variables. Two variables may also correlate because one or both directly cause variation in the other. Furthermore, covariation may result from any combination of these sources, leading to 25 different identified structural equation models. OpenMx was used to fit all these models to account for covariation between two variables collected in twins. Multimodel inference and model averaging were used to summarize the key sources of covariation, and estimate the magnitude of these causes of covariance. Extensions of these models to test heterogeneity by sex are discussed. RESULTS: We illustrate the application of multimodel inference by fitting a comprehensive set of bivariate models to twin data from the Virginia Twin Study of Psychiatric and Substance Use Disorders. Analyses of body mass index and tobacco consumption data show sufficient power to reject distinct models, and to estimate the contribution of each of the five potential sources of covariation, irrespective of selecting the best fitting model. Discrimination between models on sample size, type of variable (continuous versus binary or ordinal measures) and the effect size of sources of variance and covariance. CONCLUSIONS: We introduce multimodel inference and model averaging approaches to the behavior genetics community, in the context of testing models for the causes of covariation between traits in term of genetic, environmental and causal explanations.

Mendelian randomization study of maternal influences on birthweight and future cardiometabolic risk in the HUNT cohort.

There is a robust observational relationship between lower birthweight and higher risk of cardiometabolic disease in later life. The Developmental Origins of Health and Disease (DOHaD) hypothesis posits that adverse environmental factors in utero increase future risk of cardiometabolic disease. Here, we explore if a genetic risk score (GRS) of maternal SNPs associated with offspring birthweight is also associated with offspring cardiometabolic risk factors, after controlling for offspring GRS, in up to 26,057 mother-offspring pairs (and 19,792 father-offspring pairs) from the Nord-Trøndelag Health (HUNT) Study. We find little evidence for a maternal (or paternal) genetic effect of birthweight associated variants on offspring cardiometabolic risk factors after adjusting for offspring GRS. In contrast, offspring GRS is strongly related to many cardiometabolic risk factors, even after conditioning on maternal GRS. Our results suggest that the maternal intrauterine environment, as proxied by maternal SNPs that influence offspring birthweight, is unlikely to be a major determinant of adverse cardiometabolic outcomes in population based samples of individuals.

DNA methylation associated with postpartum depressive symptoms overlaps findings from a genome-wide association meta-analysis of depression.

BACKGROUND: Perinatal depressive symptoms have been linked to adverse maternal and infant health outcomes. The etiology associated with perinatal depressive psychopathology is poorly understood, but accumulating evidence suggests that understanding inter-individual differences in DNA methylation (DNAm) patterning may provide insight regarding the genomic regions salient to the risk liability of perinatal depressive psychopathology. RESULTS: Genome-wide DNAm was measured in maternal peripheral blood using the Infinium MethylationEPIC microarray. Ninety-two participants (46% African-American) had DNAm samples that passed all quality control metrics, and all participants were within 7 months of delivery. Linear models were constructed to identify differentially methylated sites and regions, and permutation testing was utilized to assess significance. Differentially methylated regions (DMRs) were defined as genomic regions of consistent DNAm change with at least two probes within 1 kb of each other. Maternal age, current smoking status, estimated cell-type proportions, ancestry-relevant principal components, days since delivery, and chip position served as covariates to adjust for technical and biological factors. Current postpartum depressive symptoms were measured using the Edinburgh Postnatal Depression Scale. Ninety-eight DMRs were significant (false discovery rate < 5%) and overlapped 92 genes. Three of the regions overlap loci from the latest Psychiatric Genomics Consortium meta-analysis of depression. CONCLUSIONS: Many of the genes identified in this analysis corroborate previous allelic, transcriptomic, and DNAm association results related to depressive phenotypes. Future work should integrate data from multi-omic platforms to understand the functional relevance of these DMRs and refine DNAm association results by limiting phenotypic heterogeneity and clarifying if DNAm differences relate to the timing of onset, severity, duration of perinatal mental health outcomes of the current pregnancy or to previous history of depressive psychopathology.

Genetic and environmental risk factors in the non-medical use of over-the-counter or prescribed analgesics, and their relationship to major classes of licit and illicit substance use and misuse in a population-based sample of young adult twins.

BACKGROUND AND AIMS: The non-medical use of over-the-counter or prescribed analgesics (NMUA) is a significant public health problem. Little is known about the genetic and environmental etiology of NMUA and how these risks relate to other classes of substance use and misuse. Our aims were to estimate the heritability NMUA and sources of genetic and environmental covariance with cannabis and nicotine use, cannabis and alcohol use disorders and nicotine dependence in Australian twins. DESIGN: Biometrical genetic analyses or twin methods using structural equation univariate and multivariate modeling. SETTING: Australia. PARTICIPANTS: A total of 2007 young adult twins [66% female; µage  = 25.9, standard deviation (SD) = 3.6, range = 18-38] from the Brisbane Longitudinal Twin Study retrospectively assessed between 2009 and 2016. MEASUREMENTS: Self-reported NMUA (non-opioid or opioid-based), life-time nicotine, cannabis and opioid use, DSM-V cannabis and alcohol use disorders and the Fagerström Test for Nicotine Dependence. FINDINGS: Life-time NMUA was reported by 19.4% of the sample. Univariate heritability explained 46% [95% confidence interval (CI) = 0.29-0.57] of the risks in NMUA. Multivariate analyses revealed that NMUA is moderately associated genetically with cannabis (rg  = 0.41) and nicotine (rg  = 0.45) use and nicotine dependence (rg  = 0.34). In contrast, the genetic correlations with cannabis (rg  = 0.15) and alcohol (rg  = 0.07) use disorders are weak. CONCLUSIONS: In young male and female adults in Australia, the non-medical use of over-the-counter or prescribed analgesics appears to have moderate heritability. NMUA is moderately associated with cannabis and nicotine use and nicotine dependence. Its genetic etiology is largely distinct from that of cannabis and alcohol use disorders.

An examination of the etiologic overlap between the genetic and environmental influences on insomnia and common psychopathology.

BACKGROUND: Insomnia is comorbid with internalizing and externalizing psychiatric disorders. However, the extent to which the etiologic influences on insomnia and common psychopathology overlap is unclear. There are limited genetically informed studies of insomnia and internalizing disorders and few studies of overlap exist with externalizing disorders. METHODS: We utilized twin data from the Virginia Adult Twin Studies of Psychiatric and Substance Use Disorders (total n = 7,500). Insomnia, internalizing disorders (major depressive disorder [MDD], generalized anxiety disorder [GAD]), and alcohol abuse or dependence (AAD) were assessed at two time points, while antisocial personality disorder (ASPD) was assessed once. Cholesky decompositions were performed in OpenMx and longitudinal measurement models were run on available phenotypes to reduce measurement error. RESULTS: The latent additive genetic influences on insomnia overlapped significantly (56% for females, 74% for males) with MDD and were shared completely (100%) with GAD. There was significant overlap of latent unique environmental influences, with overlap ranging from 38 to 100% across disorders. In contrast, there was less genetic overlap between insomnia and externalizing disorders, with 18% of insomnia's heritability shared with AAD and 23% with ASPD. Latent unique environmental overlap between insomnia and both externalizing disorders was negligible. CONCLUSIONS: The evidence for substantial genetic overlap between insomnia and stable aspects of both internalizing disorders suggests that there may be few insomnia-specific genes and investigation into unique environmental factors is important for understanding insomnia development. The modest overlap between insomnia and externalizing disorders indicates that these disorders are genetically related, but largely etiologically distinct.

Applying Multivariate Discrete Distributions to Genetically Informative Count Data.

We present a novel method of conducting biometric analysis of twin data when the phenotypes are integer-valued counts, which often show an L-shaped distribution. Monte Carlo simulation is used to compare five likelihood-based approaches to modeling: our multivariate discrete method, when its distributional assumptions are correct, when they are incorrect, and three other methods in common use. With data simulated from a skewed discrete distribution, recovery of twin correlations and proportions of additive genetic and common environment variance was generally poor for the Normal, Lognormal and Ordinal models, but good for the two discrete models. Sex-separate applications to substance-use data from twins in the Minnesota Twin Family Study showed superior performance of two discrete models. The new methods are implemented using R and OpenMx and are freely available.

OpenMx 2.0: Extended Structural Equation and Statistical Modeling.

The new software package OpenMx 2.0 for structural equation and other statistical modeling is introduced and its features are described. OpenMx is evolving in a modular direction and now allows a mix-and-match computational approach that separates model expectations from fit functions and optimizers. Major backend architectural improvements include a move to swappable open-source optimizers such as the newly written CSOLNP. Entire new methodologies such as item factor analysis and state space modeling have been implemented. New model expectation functions including support for the expression of models in LISREL syntax and a simplified multigroup expectation function are available. Ease-of-use improvements include helper functions to standardize model parameters and compute their Jacobian-based standard errors, access to model components through standard R $ mechanisms, and improved tab completion from within the R Graphical User Interface.

A Longitudinal Twin Study of Insomnia Symptoms in Adults.

OBJECTIVE: Twin modeling was used to conduct a genetically informative longitudinal analysis of insomnia symptoms in both sexes. METHOD: Data from the Virginia Adult Twin Studies of Psychiatric and Substance Use Disorders (n = 7,500) were used. Past-month insomnia symptoms were assessed at two time points with the shortened version of the Symptom Checklist-90. A composite score for the insomnia items (trouble falling asleep, restless or disturbed sleep, early morning awakenings) was computed. Twin modeling on the composite score was conducted in OpenMx to decompose the phenotypic variance, to examine the longitudinal stability of etiologic influences on insomnia symptoms, and to test for sex differences. RESULTS: Insomnia symptoms were most commonly endorsed at a mild severity level (composite score mean = 2.24, standard deviation = 2.51). There was no evidence for sex effects in either of the univariate models, and insomnia symptoms were found to be modestly heritable (~25% at Time 1 and ~22% at Time 2). In the longitudinal measurement model, which accounts for error of measurement, the heritability for the latent factor of insomnia symptoms increased substantially, and demonstrated quantitative sex differences. The heritability of the latent insomnia factor was ~59% in females and ~38% in males. CONCLUSIONS: Genetic factors influence insomnia symptoms in adults, moreso for females than males, and these influences are largely stable over time. When taking into account measurement error, heritability estimates are substantial, but unique environmental factors continue to account for a large amount of variance in insomnia symptoms.

Genome-Wide Association Study of Behavioral Disinhibition in a Selected Adolescent Sample.

Behavioral disinhibition (BD) is a quantitative measure designed to capture the heritable variation encompassing risky and impulsive behaviors. As a result, BD represents an ideal target for discovering genetic loci that predispose individuals to a wide range of antisocial behaviors and substance misuse that together represent a large cost to society as a whole. Published genome-wide association studies (GWAS) have examined specific phenotypes that fall under the umbrella of BD (e.g. alcohol dependence, conduct disorder); however no GWAS has specifically examined the overall BD construct. We conducted a GWAS of BD using a sample of 1,901 adolescents over-selected for characteristics that define high BD, such as substance and antisocial behavior problems, finding no individual locus that surpassed genome-wide significance. Although no single SNP was significantly associated with BD, restricted maximum likelihood analysis estimated that 49.3 % of the variance in BD within the Caucasian sub-sample was accounted for by the genotyped SNPs (p = 0.06). Gene-based tests identified seven genes associated with BD (p ≤ 2.0 × 10(-6)). Although the current study was unable to identify specific SNPs or pathways with replicable effects on BD, the substantial sample variance that could be explained by all genotyped SNPs suggests that larger studies could successfully identify common variants associated with BD.

Replication of a gene-environment interaction Via Multimodel inference: additive-genetic variance in adolescents' general cognitive ability increases with family-of-origin socioeconomic status.

The present study of general cognitive ability attempts to replicate and extend previous investigations of a biometric moderator, family-of-origin socioeconomic status (SES), in a sample of 2,494 pairs of adolescent twins, non-twin biological siblings, and adoptive siblings assessed with individually administered IQ tests. We hypothesized that SES would covary positively with additive-genetic variance and negatively with shared-environmental variance. Important potential confounds unaddressed in some past studies, such as twin-specific effects, assortative mating, and differential heritability by trait level, were found to be negligible. In our main analysis, we compared models by their sample-size corrected AIC, and base our statistical inference on model-averaged point estimates and standard errors. Additive-genetic variance increased with SES-an effect that was statistically significant and robust to model specification. We found no evidence that SES moderated shared-environmental influence. We attempt to explain the inconsistent replication record of these effects, and provide suggestions for future research.

Results of a "GWAS plus:" general cognitive ability is substantially heritable and massively polygenic.

We carried out a genome-wide association study (GWAS) for general cognitive ability (GCA) plus three other analyses of GWAS data that aggregate the effects of multiple single-nucleotide polymorphisms (SNPs) in various ways. Our multigenerational sample comprised 7,100 Caucasian participants, drawn from two longitudinal family studies, who had been assessed with an age-appropriate IQ test and had provided DNA samples passing quality screens. We conducted the GWAS across ∼ 2.5 million SNPs (both typed and imputed), using a generalized least-squares method appropriate for the different family structures present in our sample, and subsequently conducted gene-based association tests. We also conducted polygenic prediction analyses under five-fold cross-validation, using two different schemes of weighting SNPs. Using parametric bootstrapping, we assessed the performance of this prediction procedure under the null. Finally, we estimated the proportion of variance attributable to all genotyped SNPs as random effects with software GCTA. The study is limited chiefly by its power to detect realistic single-SNP or single-gene effects, none of which reached genome-wide significance, though some genomic inflation was evident from the GWAS. Unit SNP weights performed about as well as least-squares regression weights under cross-validation, but the performance of both increased as more SNPs were included in calculating the polygenic score. Estimates from GCTA were 35% of phenotypic variance at the recommended biological-relatedness ceiling. Taken together, our results concur with other recent studies: they support a substantial heritability of GCA, arising from a very large number of causal SNPs, each of very small effect. We place our study in the context of the literature-both contemporary and historical-and provide accessible explication of our statistical methods.

Low-Frequency Copy-Number Variants and General Cognitive Ability: No Evidence of Association.

Although twin, family, and adoption studies have shown that general cognitive ability (GCA) is substantially heritable, GWAS has not uncovered a genetic polymorphism replicably associated with this phenotype. However, most polymorphisms used in GWAS are common SNPs. The present study explores use of a different class of genetic variant, the copy-number variant (CNV), to predict GCA in a sample of 6,199 participants, combined from two longitudinal family studies. We aggregated low-frequency (<5%) CNV calls into eight different mutational burden scores, each reflecting a different operationalization of mutational burden. We further conducted three genome-wide association scans, each of which utilized a different subset of identified low-frequency CNVs. Association signals from the burden analyses were generally small in effect size, and none were statistically significant after a careful Type I error correction was applied. No signal from the genome-wide scans significantly differed from zero at the adjusted Type I error rate. Thus, the present study provides no evidence that CNVs underlie heritable variance in GCA, though we cannot rule out the possibility of very rare or small-effect CNVs for this trait, which would require even larger samples to detect. We interpret these null results in light of recent breakthroughs that aggregate SNP effects to explain much, but not all, of the heritable variance in some quantitative traits.