GW-SEM 2.0: Efficient, Flexible, and Accessible Multivariate GWAS

Leveraging pleiotropy for the improved treatment of psychiatric disorders

Over 90% of drug candidates fail in clinical trials, while it takes 10-15 years and one billion US dollars to develop a single successful drug. Drug development is more challenging for psychiatric disorders, where disease comorbidity and complex symptom profiles obscure the identification of causal mechanisms for therapeutic intervention. One promising approach for determining more suitable drug candidates in clinical trials is integrating human genetic data into the selection process. Genome-wide association studies have identified thousands of replicable risk loci for psychiatric disorders, and sophisticated statistical tools are increasingly effective at using these data to pinpoint likely causal genes. These studies have also uncovered shared or pleiotropic genetic risk factors underlying comorbid psychiatric disorders. In this article, we argue that leveraging pleiotropic effects will provide opportunities to discover novel drug targets and identify more effective treatments for psychiatric disorders by targeting a common mechanism rather than treating each disease separately.

Research Review: A review of the past decade of family and genomic studies on adolescent mental health

BACKGROUND

Mental health problems and traits capturing psychopathology are common and often begin during adolescence. Decades of twin studies indicate that genetic factors explain around 50% of individual differences in adolescent psychopathology. In recent years, significant advances, particularly in genomics, have moved this work towards more translational findings.

METHODS

This review provides an overview of the past decade of genetically sensitive studies on adolescent development, covering both family and genomic studies in adolescents aged 10-24 years. We focus on five research themes: (1) co-occurrence or comorbidity between psychopathologies, (2) stability and change over time, (3) intergenerational transmission, (4) gene-environment interplay, and (5) psychological treatment outcomes.

RESULTS

First, research shows that much of the co-occurrence of psychopathologies in adolescence is explained by genetic factors, with widespread pleiotropic influences on many traits. Second, stability in psychopathology across adolescence is largely explained by persistent genetic influences, whereas change is explained by emerging genetic and environmental influences. Third, contemporary twin-family studies suggest that different co-occurring genetic and environmental mechanisms may account for the intergenerational transmission of psychopathology, with some differences across psychopathologies. Fourth, genetic influences on adolescent psychopathology are correlated with a wide range of environmental exposures. However, the extent to which genetic factors interact with the environment remains unclear, as findings from both twin and genomic studies are inconsistent. Finally, a few studies suggest that genetic factors may play a role in psychological treatment response, but these findings have not yet been replicated.

CONCLUSIONS

Genetically sensitive research on adolescent psychopathology has progressed significantly in the past decade, with family and twin findings starting to be replicated at the genomic level. However, important gaps remain in the literature, and we conclude by providing suggestions of research questions that still need to be addressed.

Genome-wide association insights into the genomic regions controlling vegetative and oil production traits in Acrocomia aculeata

BACKGROUND

Macauba (Acrocomia aculeata) is a non-domesticated neotropical palm that has been attracting attention for economic use due to its great potential for oil production comparable to the commercially used oil palm (Elaeis guineensis). The discovery of associations between quantitative trait loci and economically important traits represents an advance toward understanding its genetic architecture and can contribute to accelerating macauba domestication. Pursuing this advance, this study performs single-trait and multi-trait GWAS models to identify candidate genes associated with vegetative and oil production traits in macauba. Eighteen phenotypic traits were evaluated from 201 palms within a native population. Genotyping was performed with SNP markers, following the protocol of genotyping-by-sequencing. Given that macauba lacks a reference genome, SNP calling was performed using three different strategies: using i) de novo sequencing, ii) the Elaeis guineenses Jacq. reference genome and iii) the macauba transcriptome sequences. After quality control, we identified a total of 27,410 SNPs in 153 individuals for the de novo genotypic dataset, 10,444 SNPs in 158 individuals using the oil palm genotypic dataset, and 4,329 SNPs in 167 individuals using the transcriptome genotypic dataset. The GWAS analysis was then performed on these three genotypic datasets.

RESULTS

Statistical phenotypic analyses revealed significant differences across all studied traits, with heritability values ranging from 63 to 95%. This indicates that the population contains promising genotypes for selection and the initiation of breeding programs. Genetic correlations between the 18 traits ranged from -0.47 to 0.99. The total number of significant SNPs in the single-trait and multi-trait GWAS was 92 and 6 using the de novo genotypic dataset, 19 and 11 using the oil palm genotypic dataset, and 1 and 2 using the transcriptome genotypic dataset, respectively. Gene annotation identified 12 candidate genes in the single-trait GWAS and four in the multi-trait GWAS, across the 18 phenotypic traits studied, in the three genotypic datasets. Gene mapping of the macauba candidate genes revealed similarities with Elaeis guineensis and Phoenix dactylifera. The candidate genes detected are responsible for metal ion binding and transport, protein transportation, DNA repair, and other cell regulation biological processes.

CONCLUSIONS

We provide new insights into genomic regions that map candidate genes associated with vegetative and oil production traits in macauba. These potential candidate genes require confirmation through targeted functional analyses in the future, and multi-trait associations need to be scrutinized to investigate the presence of pleiotropic or linked genes. Markers linked to traits of interest could serve as valuable resources for the development of marker-assisted selection in macauba for its domestication and pre-breeding.

Genetic associations between non-cognitive skills and academic achievement over development

Non-cognitive skills, such as motivation and self-regulation, are partly heritable and predict academic achievement beyond cognitive skills. However, how the relationship between non-cognitive skills and academic achievement changes over development is unclear. The current study examined how cognitive and non-cognitive skills are associated with academic achievement from ages 7 to 16 years in a sample of over 10,000 children from England and Wales. The results showed that the association between non-cognitive skills and academic achievement increased across development. Twin and polygenic scores analyses found that the links between non-cognitive genetics and academic achievement became stronger over the school years. The results from within-family analyses indicated that non-cognitive genetic effects on academic achievement could not simply be attributed to confounding by environmental differences between nuclear families, consistent with a possible role for evocative/active gene-environment correlations. By studying genetic associations through a developmental lens, we provide further insights into the role of non-cognitive skills in academic development.

Using Alternative Definitions of Controls to Increase Statistical Power in GWAS

Genome-wide association studies (GWAS) are often underpowered due to small effect sizes of common single nucleotide polymorphisms (SNPs) on phenotypes and extreme multiple testing thresholds. The most common approach for increasing statistical power is to increase sample size. We propose an alternative strategy of redefining case-control outcomes into ordinal case-subthreshold-asymptomatic variables. While maintaining the clinical case threshold, we subdivide controls into two groups: individuals who are symptomatic but do not meet the clinical criteria for diagnosis (subthreshold) and individuals who are effectively asymptomatic. We conducted a simulation study to examine the impact of effect size, minor allele frequency, population prevalence, and the prevalence of the subthreshold group on statistical power to detect genetic associations in three scenarios: a standard case-control, an ordinal, and a case-asymptomatic control analysis. Our results suggest the ordinal model consistently provides the greatest statistical power while the case-control model the least. Power in the case-asymptomatic control model reflects the case-control or ordinal model depending on the population prevalence and size of the subthreshold category. We then analyzed a major depression phenotype from the UK Biobank to corroborate our simulation results. Overall, the ordinal model improves statistical power in GWAS consistent with increasing the sample size by approximately 10%.

Selective sweep and GWAS provide insights into adaptive variation of Populus cathayana leaves

Leaf morphology plays a crucial role in predicting the productivity and environmental adaptability of forest trees, making it essential to understand the genetic mechanisms behind leaf variation. In natural populations of Populus cathayana, leaf morphology exhibits rich intraspecific variation due to long-term selection. However, there have been no studies that systematically reveal the genetic mechanisms of leaf variation in P. cathayana. To fill this gap and enhance our understanding of leaf variation in P. cathayana, we collected nine leaf traits from the P. cathayana natural population, consisting of 416 accessions, and conducted the preliminary classification of leaf types with four categories. Subsequently, we conducted an analysis of selective sweep and genome-wide association studies (GWAS) to uncover the genetic basis of leaf traits variation. Most of the leaf traits displayed significant correlations, with broad-sense trait heritability ranging from 0.38 to 0.74. In total, three selective sweep methods ultimately identified 278 positively selected candidate regions and 493 genes associated with leaf size. Single-trait and multi-trait GWAS methods detected 13 and 59 genes, respectively. By integrating the results of selective sweep and GWAS, we further identified a total of nine overlapping genes. These genes may play a role in the leaf development process and are closely associated with leaf size. In particular, the gene CBSCBSPB3 (Pca07G009100) located on chromosome 7, was associated with the response to light stimulation. This study will deepen our understanding of the genetic mechanism of leaf adaptive variation in P. cathayana and provide valuable gene resources.

The genetic architecture of youth anxiety: a study protocol

BACKGROUND

Anxiety disorders are the most common psychiatric problems among Canadian youth and typically have an onset in childhood or adolescence. They are characterized by high rates of relapse and chronicity, often resulting in substantial impairment across the lifespan. Genetic factors play an important role in the vulnerability toward anxiety disorders. However, genetic contribution to anxiety in youth is not well understood and can change across developmental stages. Large-scale genetic studies of youth are needed with detailed assessments of symptoms of anxiety disorders and their major comorbidities to inform early intervention or preventative strategies and suggest novel targets for therapeutics and personalization of care.

METHODS

The Genetic Architecture of Youth Anxiety (GAYA) study is a Pan-Canadian effort of clinical and genetic experts with specific recruitment sites in Calgary, Halifax, Hamilton, Toronto, and Vancouver. Youth aged 10-19 (n = 13,000) will be recruited from both clinical and community settings and will provide saliva samples, complete online questionnaires on demographics, symptoms of mental health concerns, and behavioural inhibition, and complete neurocognitive tasks. A subset of youth will be offered access to a self-managed Internet-based cognitive behavioral therapy resource. Analyses will focus on the identification of novel genetic risk loci for anxiety disorders in youth and assess how much of the genetic risk for anxiety disorders is unique or shared across the life span.

DISCUSSION

Results will substantially inform early intervention or preventative strategies and suggest novel targets for therapeutics and personalization of care. Given that the GAYA study will be the biggest genomic study of anxiety disorders in youth in Canada, this project will further foster collaborations nationally and across the world.

Using Alternative Definitions of Controls to Increase Statistical Power in GWAS

Genome-wide association studies (GWAS) are underpowered due to small effect sizes of single nucleotide polymorphisms (SNPs) on phenotypes and extreme multiple testing thresholds. The most common approach for increasing statistical power is to increase sample size. We propose an alternative strategy of redefining case-control outcomes into ordinal case-subthreshold-asymptomatic variables. While maintaining the clinical case threshold, we subdivide controls into two groups: individuals who are symptomatic but do not meet the clinical criteria for diagnosis (subthreshold) and individuals who are effectively asymptomatic. We conducted a simulation study to examine the impact of effect size, minor allele frequency, population prevalence, and the prevalence of the subthreshold group on statistical power to detect genetic associations in three scenarios: a standard case-control, an ordinal, and a case-asymptomatic control analysis. Our results suggest the ordinal model consistently provides the most statistical power while the case-control model the least. Power in the case-asymptomatic control model reflects the case-control or ordinal model depending on the population prevalence and size of the subthreshold category. We then analyzed a major depression phenotype from the UK Biobank to corroborate our simulation results. Overall, the ordinal model improves statistical power in GWAS consistent with increasing the sample size by approximately 10%.

Genetic contributions of noncognitive skills to academic development

Noncognitive skills such as motivation and self-regulation, are partly heritable and predict academic achievement beyond cognitive skills. However, how the relationship between noncognitive skills and academic achievement changes over development is unclear. The current study examined how cognitive and noncognitive skills contribute to academic achievement from ages 7 to 16 in a sample of over 10,000 children from England and Wales. Noncognitive skills were increasingly predictive of academic achievement across development. Twin and polygenic scores analyses found that the contribution of noncognitive genetics to academic achievement became stronger over the school years. Results from within-family analyses indicated that associations with noncognitive genetics could not simply be attributed to confounding by environmental differences between nuclear families and are consistent with a possible role for evocative/active gene-environment correlations. By studying genetic effects through a developmental lens, we provide novel insights into the role of noncognitive skills in academic development.

CADM2 is implicated in impulsive personality and numerous other traits by genome- and phenome-wide association studies in humans and mice

Impulsivity is a multidimensional heritable phenotype that broadly refers to the tendency to act prematurely and is associated with multiple forms of psychopathology, including substance use disorders. We performed genome-wide association studies (GWAS) of eight impulsive personality traits from the Barratt Impulsiveness Scale and the short UPPS-P Impulsive Personality Scale (N = 123,509-133,517 23andMe research participants of European ancestry), and a measure of Drug Experimentation (N = 130,684). Because these GWAS implicated the gene CADM2, we next performed single-SNP phenome-wide studies (PheWAS) of several of the implicated variants in CADM2 in a multi-ancestral 23andMe cohort (N = 3,229,317, European; N = 579,623, Latin American; N = 199,663, African American). Finally, we produced Cadm2 mutant mice and used them to perform a Mouse-PheWAS ("MouseWAS") by testing them with a battery of relevant behavioral tasks. In humans, impulsive personality traits showed modest chip-heritability (~6-11%), and moderate genetic correlations (rg = 0.20-0.50) with other personality traits, and various psychiatric and medical traits. We identified significant associations proximal to genes such as TCF4 and PTPRF, and also identified nominal associations proximal to DRD2 and CRHR1. PheWAS for CADM2 variants identified associations with 378 traits in European participants, and 47 traits in Latin American participants, replicating associations with risky behaviors, cognition and BMI, and revealing novel associations including allergies, anxiety, irritable bowel syndrome, and migraine. Our MouseWAS recapitulated some of the associations found in humans, including impulsivity, cognition, and BMI. Our results further delineate the role of CADM2 in impulsivity and numerous other psychiatric and somatic traits across ancestries and species.

Research Review: A guide to computing and implementing polygenic scores in developmental research

The increasing availability of genotype data in longitudinal population- and family-based samples provides opportunities for using polygenic scores (PGS) to study developmental questions in child and adolescent psychology and psychiatry. Here, we aim to provide a comprehensive overview of how PGS can be generated and implemented in developmental psycho(patho)logy, with a focus on longitudinal designs. As such, the paper is organized into three parts: First, we provide a formal definition of polygenic scores and related concepts, focusing on assumptions and limitations. Second, we give a general overview of the methods used to compute polygenic scores, ranging from the classic approach to more advanced methods. We include recommendations and reference resources available to researchers aiming to conduct PGS analyses. Finally, we focus on the practical applications of PGS in the analysis of longitudinal data. We describe how PGS have been used to research developmental outcomes, and how they can be applied to longitudinal data to address developmental questions.

Using Genetic Marginal Effects to Study Gene-Environment Interactions with GWAS Data

Gene-environment interactions (GxE) play a central role in the theoretical relationship between genetic factors and complex traits. While genome wide GxE studies of human behaviors remain underutilized, in part due to methodological limitations, existing GxE research in model organisms emphasizes the importance of interpreting genetic associations within environmental contexts. In this paper, we present a framework for conducting an analysis of GxE using raw data from genome wide association studies (GWAS) and applying the techniques to analyze gene-by-age interactions for alcohol use frequency. To illustrate the effectiveness of this procedure, we calculate genetic marginal effects from a GxE GWAS analysis for an ordinal measure of alcohol use frequency from the UK Biobank dataset, treating the respondent's age as the continuous moderating environment. The genetic marginal effects clarify the interpretation of the GxE associations and provide a direct and clear understanding of how the genetic associations vary across age (the environment). To highlight the advantages of our proposed methods for presenting GxE GWAS results, we compare the interpretation of marginal genetic effects with an interpretation that focuses narrowly on the significance of the interaction coefficients. The results imply that the genetic associations with alcohol use frequency vary considerably across ages, a conclusion that may not be obvious from the raw regression or interaction coefficients. GxE GWAS is less powerful than the standard "main effect" GWAS approach, and therefore require larger samples to detect significant moderated associations. Fortunately, the necessary sample sizes for a successful application of GxE GWAS can rely on the existing and on-going development of consortia and large-scale population-based studies.

Integration of evidence across human and model organism studies: A meeting report

The National Institute on Drug Abuse and Joint Institute for Biological Sciences at the Oak Ridge National Laboratory hosted a meeting attended by a diverse group of scientists with expertise in substance use disorders (SUDs), computational biology, and FAIR (Findability, Accessibility, Interoperability, and Reusability) data sharing. The meeting's objective was to discuss and evaluate better strategies to integrate genetic, epigenetic, and 'omics data across human and model organisms to achieve deeper mechanistic insight into SUDs. Specific topics were to (a) evaluate the current state of substance use genetics and genomics research and fundamental gaps, (b) identify opportunities and challenges of integration and sharing across species and data types, (c) identify current tools and resources for integration of genetic, epigenetic, and phenotypic data, (d) discuss steps and impediment related to data integration, and (e) outline future steps to support more effective collaboration-particularly between animal model research communities and human genetics and clinical research teams. This review summarizes key facets of this catalytic discussion with a focus on new opportunities and gaps in resources and knowledge on SUDs.

Clarifying the Genetic Influences on Nicotine Dependence and Quantity of Use in Cigarette Smokers

Nicotine dependence and smoking quantity are both robustly associated with the CHRNA5-A3-B4 gene cluster in the 15q25 region, and SNP rs16969968 in particular. The purpose of this paper is to use structural equation modeling techniques (SEM) to disentangle the complex pattern of relationships between rs16969968, nicotine quantity (as measured by the number of cigarettes an individual smokes per day; CPD) and nicotine dependence (as measured by the Fagerström Test for Nicotine Dependence; FTND). CPD is an indicator, but also a potential cause, of FTND, complicating the interpretation of associations between these constructs and requires a more detailed investigation than standard GWAS or general linear regression models can provide. FTND items and genotypes were collected in four samples, with a combined sample size of 5,373 respondents. A mega-analysis was conducted using a multiple group SEM approach to test competing hypotheses regarding the relationships between the SNP rs16969968, FTND and CPD. In the best fitting model, the FTND items loaded onto two correlated factors. The first, labeled "maintenance," assesses the motivation to maintain constant levels of nicotine through out the day. The second was labeled "urgency" as its items concern the urgency to restore nicotine levels after abstinence. We focus our attention on the "maintenance" factor, of which CPD was an indicator. The best fitting model included a negative feedback loop between the Maintenance factor and CPD. Accordingly, the motivation to maintain higher levels of nicotine increased the quantity of nicotine consumed, which subsequently decreases the maintenance motivation. The fact that the Maintenance-CPD feedback model fits the data best implies that there are at least two biological pathways that lead from rs16969968 to smoking behaviors. The model is consistent with a supply and demand system, which allows individuals to achieve a homeostatic equilibrium for their nicotine concentration.