Genetic influence on within-person longitudinal change in anthropometric traits in the UK Biobank.

The causes of temporal fluctuations in adult traits are poorly understood. Here, we investigate the genetic determinants of within-person trait variability of 8 repeatedly measured anthropometric traits in 50,117 individuals from the UK Biobank. We found that within-person (non-directional) variability had a SNP-based heritability of 2-5% for height, sitting height, body mass index (BMI) and weight (P ≤ 2.4 × 10-3). We also analysed longitudinal trait change and show a loss of both average height and weight beyond about 70 years of age. A variant tracking the Alzheimer's risk APOE- E 4 allele (rs429358) was significantly associated with weight loss ( ß = -0.047 kg per yr, s.e. 0.007, P = 2.2 × 10-11), and using 2-sample Mendelian Randomisation we detected a relationship consistent with causality between decreased lumbar spine bone mineral density and height loss (bxy = 0.011, s.e. 0.003, P = 3.5 × 10-4). Finally, population-level variance quantitative trait loci (vQTL) were consistent with within-person variability for several traits, indicating an overlap between trait variability assessed at the population or individual level. Our findings help elucidate the genetic influence on trait-change within an individual and highlight disease risks associated with these changes.

Loci on chromosome 20 interact with rs16969968 to influence cigarettes per day in European ancestry individuals.

BACKGROUND: The understanding of the molecular genetic contributions to smoking is largely limited to the additive effects of individual single nucleotide polymorphisms (SNPs), but the underlying genetic risk is likely to also include dominance, epistatic, and gene-environment interactions. METHODS: To begin to address this complexity, we attempted to identify genetic interactions between rs16969968, the most replicated SNP associated with smoking quantity, and all SNPs and genes across the genome. RESULTS: Using the UK Biobank European subsample, we found one SNP, rs1892967, and two genes, PCNA and TMEM230, that showed a significant genome-wide interaction with rs16969968 for log10 CPD and raw CPD, respectively, in a sample of 116 442 individuals who self-reported currently or previously smoking. We extended these analyses to individuals of South Asian descent and meta-analyzed the combined sample of 117 212 individuals of European and South Asian ancestry. We replicated the gene findings in a meta-analysis of five Finnish samples (N=40 140): FinHealth, FINRISK, Finnish Twin Cohort, GeneRISK, and Health-2000-2011. CONCLUSIONS: To our knowledge, this represents the first reliable epistatic association between single nucleotide polymorphisms for smoking behaviors and provides a novel direction for possible future functional studies related to this interaction. Furthermore, this work demonstrates the feasibility of these analyses by pooling multiple datasets across various ancestries, which may be applied to other top SNPs for smoking and/or other phenotypes.

Non-random Mating Patterns in Education, Mental, and Somatic Health: A Population Study on Within- and Cross-Trait Associations.

Partners resemble each other on many traits, such as health and education. The traits are usually studied one by one in data from established couples and with potential participation bias. We studied all Norwegian parents who had their first child between 2016 and 2020 (N=187,926) and the siblings of these parents. We analysed grade point averages (GPA), educational attainment (EA), and medical records with prospective diagnostic data on 10 mental and 10 somatic health conditions measured 10 to 5 years before childbirth. We found stronger partner similarity in mental (median r=0.14) than in somatic health conditions (median r=0.04), with ubiquitous cross-trait correlations for mental health conditions (median r=0.13). GPA correlated 0.43 and EA 0.47 between partners. High GPA or EA was associated with better mental (median r=-0.16) and somatic (median r=-0.08) health in partners. Elevated correlations for mental health (median r=0.25) in established couples indicated convergence. Analyses of data on siblings and in-laws revealed deviations from direct assortment, suggesting instead indirect assortment based on related traits. GPA and EA accounted for 30-40% of the partner correlations in health. This has implications for the distribution of risk factors among children and for studies of intergenerational transmission.

The challenges of sociogenomics make it more, not less, worthy of careful and innovative investigation.

Influences on social traits involve a tangled interplay of genetic, social, and environmental factors. Moreover, there is increasing awareness that gene-environment correlations are real and potentially measurable. Such gene-environment correlations can mislead if they are uncontrolled and genetic associations are interpreted as being purely because of direct genetic effects. This complexity is cause for more and better investigation, not a reason to refrain from researching one of the potentially important factors (genetics) influencing trait variation.

Evidence of correlations between human partners based on systematic reviews and meta-analyses of 22 traits and UK Biobank analysis of 133 traits.

Positive correlations between mates can increase trait variation and prevalence, as well as bias estimates from genetically informed study designs. While past studies of similarity between human mating partners have largely found evidence of positive correlations, to our knowledge, no formal meta-analysis has examined human partner correlations across multiple categories of traits. Thus, we conducted systematic reviews and random-effects meta-analyses of human male-female partner correlations across 22 traits commonly studied by psychologists, economists, sociologists, anthropologists, epidemiologists and geneticists. Using ScienceDirect, PubMed and Google Scholar, we incorporated 480 partner correlations from 199 peer-reviewed studies of co-parents, engaged pairs, married pairs and/or cohabitating pairs that were published on or before 16 August 2022. We also calculated 133 trait correlations using up to 79,074 male-female couples in the UK Biobank (UKB). Estimates of the 22 mean meta-analysed correlations ranged from rmeta = 0.08 (adjusted 95% CI = 0.03, 0.13) for extraversion to rmeta = 0.58 (adjusted 95% CI = 0.50, 0.64) for political values, with funnel plots showing little evidence of publication bias across traits. The 133 UKB correlations ranged from rUKB = -0.18 (adjusted 95% CI = -0.20, -0.16) for chronotype (being a 'morning' or 'evening' person) to rUKB = 0.87 (adjusted 95% CI = 0.86, 0.87) for birth year. Across analyses, political and religious attitudes, educational attainment and some substance use traits showed the highest correlations, while psychological (that is, psychiatric/personality) and anthropometric traits generally yielded lower but positive correlations. We observed high levels of between-sample heterogeneity for most meta-analysed traits, probably because of both systematic differences between samples and true differences in partner correlations across populations.

Genetic risk shared across 24 chronic pain conditions: identification and characterization with genomic structural equation modeling.

ABSTRACT: Chronic pain conditions frequently co-occur, suggesting common risks and paths to prevention and treatment. Previous studies have reported genetic correlations among specific groups of pain conditions and reported genetic risk for within-individual multisite pain counts (≤7). Here, we identified genetic risk for multiple distinct pain disorders across individuals using 24 chronic pain conditions and genomic structural equation modeling (Genomic SEM). First, we ran individual genome-wide association studies (GWASs) on all 24 conditions in the UK Biobank ( N ≤ 436,000) and estimated their pairwise genetic correlations. Then we used these correlations to model their genetic factor structure in Genomic SEM, using both hypothesis- and data-driven exploratory approaches. A complementary network analysis enabled us to visualize these genetic relationships in an unstructured manner. Genomic SEM analysis revealed a general factor explaining most of the shared genetic variance across all pain conditions and a second, more specific factor explaining genetic covariance across musculoskeletal pain conditions. Network analysis revealed a large cluster of conditions and identified arthropathic, back, and neck pain as potential hubs for cross-condition chronic pain. Additionally, we ran GWASs on both factors extracted in Genomic SEM and annotated them functionally. Annotation identified pathways associated with organogenesis, metabolism, transcription, and DNA repair, with overrepresentation of strongly associated genes exclusively in brain tissues. Cross-reference with previous GWASs showed genetic overlap with cognition, mood, and brain structure. These results identify common genetic risks and suggest neurobiological and psychosocial mechanisms that should be targeted to prevent and treat cross-condition chronic pain.

Declining autozygosity over time: An exploration in over 1 million individuals from three diverse cohorts.

Previous studies have hypothesized that autozygosity is decreasing over generational time. However, these studies were limited to relatively small samples (n < 11,000) lacking in diversity, which may limit the generalizability of their findings. We present data that partially support this hypothesis from three large cohorts of diverse ancestries, two from the US (All of Us, n = 82,474; the Million Veteran Program, n = 622,497) and one from the UK (UK Biobank, n = 380,899). Our results from a mixed-effect meta-analysis demonstrate an overall trend of decreasing autozygosity over generational time (meta-analyzed slope = -0.029, SE = 0.009, p = 6.03e-4). On the basis of our estimates, we would predict FROH to decline 0.29% for every 20-year increase in birth year. We determined that a model including an ancestry-by-country interaction term fit the data best, indicating that ancestry differences in this trend differ by country. We found further evidence to suggest a difference between the US and UK cohorts by meta-analyzing within country, observing a significant negative estimate in the US cohorts (meta-analyzed slope = -0.058, SE = 0.015, p = 1.50e-4) but a non-significant estimate in the UK (meta-analyzed slope = -0.001, SE = 0.008, p = 0.945). The association between autozygosity and birth year was substantially attenuated when accounting for educational attainment and income (meta-analyzed slope = -0.011, SE = 0.008, p = 0.167), suggesting they may partially account for decreasing autozygosity over time. Overall, we demonstrate decreasing autozygosity over time in a large, modern sample and speculate that this trend can be attributed to increases in urbanization and panmixia and differences in sociodemographic processes lead to country-specific differences in the rate of decline.

Genome-wide Association Study Shows That Executive Functioning Is Influenced by GABAergic Processes and Is a Neurocognitive Genetic Correlate of Psychiatric Disorders.

BACKGROUND: Deficits in executive functions (EFs), cognitive processes that control goal-directed behaviors, are associated with psychopathology and neurologic disorders. Little is known about the molecular bases of individual differences in EFs. Prior candidate gene studies have been underpowered in their search for dopaminergic processes involved in cognitive functioning, and existing genome-wide association studies of EFs used small sample sizes and/or focused on individual tasks that are imprecise measures of EFs. METHODS: We conducted a genome-wide association study of a common EF (cEF) factor score based on multiple tasks in the UK Biobank (n = 427,037 individuals of European descent). RESULTS: We found 129 independent genome-wide significant lead variants in 112 distinct loci. cEF was associated with fast synaptic transmission processes (synaptic, potassium channel, and GABA [gamma-aminobutyric acid] pathways) in gene-based analyses. cEF was genetically correlated with measures of intelligence (IQ) and cognitive processing speed, but cEF and IQ showed differential genetic associations with psychiatric disorders and educational attainment. CONCLUSIONS: Results suggest that cEF is a genetically distinct cognitive construct that is particularly relevant to understanding the genetic variance in psychiatric disorders.

Disentangling the influences of parental genetics on offspring's cognition, education, and psychopathology via genetic and phenotypic pathways.

BACKGROUND: Specific pathways of intergenerational transmission of behavioral traits remain unclear. Here, we aim to investigate how parental genetics influence offspring cognition, educational attainment, and psychopathology in youth. METHODS: Participants for the discovery sample were 2,189 offspring (aged 6-14 years), 1898 mothers and 1,017 fathers who underwent genotyping, psychiatric, and cognitive assessments. We calculated polygenic scores (PGS) for cognition, educational attainment, attention-deficit hyperactivity disorder (ADHD), and schizophrenia for the trios. Phenotypes studied included educational and cognitive measures, ADHD and psychotic symptoms. We used a stepwise approach and multiple mediation models to analyze the effect of parental PGS on offspring traits via offspring PGS and parental phenotype. Significant results were replicated in a sample of 1,029 adolescents, 363 mothers, and 307 fathers. RESULTS: Maternal and paternal PGS for cognition influenced offspring general intelligence and executive function via offspring PGS (genetic pathway) and parental education (phenotypic pathway). Similar results were found for parental PGS for educational attainment and offspring reading and writing skills. These pathways fully explained associations between parental PGS and offspring phenotypes, without residual direct association. Associations with maternal, but not paternal, PGS were replicated. No associations were found between parental PGS for psychopathology and offspring specific symptoms. CONCLUSIONS: Our findings indicate that parental genetics influences offspring cognition and educational attainment by genetic and phenotypic pathways, suggesting the expression of parental phenotypes partially explain the association between parental genetic risk and offspring outcomes. Multiple mediations might represent an effective approach to disentangle distinct pathways for intergenerational transmission of behavioral traits.

The estimation of environmental and genetic parental influences.

Parents share half of their genes with their children, but they also share background social factors and actively help shape their child's environment - making it difficult to disentangle genetic and environmental causes of parent-offspring similarity. While adoption and extended twin family designs have been extremely useful for distinguishing genetic and nongenetic parental influences, these designs entail stringent assumptions about phenotypic similarity between relatives and require samples that are difficult to collect and therefore are typically small and not publicly shared. Here, we describe these traditional designs, as well as modern approaches that use large, publicly available genome-wide data sets to estimate parental effects. We focus in particular on an approach we recently developed, structural equation modeling (SEM)-polygenic score (PGS), that instantiates the logic of modern PGS-based methods within the flexible SEM framework used in traditional designs. Genetically informative designs such as SEM-PGS rely on different and, in some cases, less rigid assumptions than traditional approaches; thus, they allow researchers to capitalize on new data sources and answer questions that could not previously be investigated. We believe that SEM-PGS and similar approaches can lead to improved insight into how nature and nurture combine to create the incredible diversity underlying human behavior.

Potential bias in genetic correlations.

Mating patterns across two traits can inflate estimates of genetic overlap.

Exploring the Relationships Between Autozygosity, Educational Attainment, and Cognitive Ability in a Contemporary, Trans-Ancestral American Sample.

Previous studies have found significant associations between estimated autozygosity - the proportion of an individual's genome contained in homozygous segments due to distant inbreeding - and multiple traits, including educational attainment (EA) and cognitive ability. In one study, estimated autozygosity showed a stronger association with parental EA than the subject's own EA. This was likely driven by parental EA's association with mobility: more educated parents tended to migrate further from their hometown, and because of the strong correlation between ancestry and geography in the Netherlands, these individuals chose partners farther from their ancestry and therefore more different from them genetically. We examined the associations between estimated autozygosity, cognitive ability, and parental EA in a contemporary sub-sample of adolescents from the Adolescent Brain Cognitive Development Study℠ (ABCD Study®) (analytic N = 6,504). We found a negative association between autozygosity and child cognitive ability consistent with previous studies, while the associations between autozygosity and parental EA were in the expected direction of effect (with greater levels of autozygosity being associated with lower EA) but the effect sizes were significantly weaker than those estimated in previous work. We also found a lower mean level of autozygosity in the ABCD sample compared to previous autozygosity studies, which may reflect overall decreasing levels of autozygosity over generations. Variation in spousal similarities in ancestral background in the ABCD study compared to other studies may explain the pattern of associations between estimated autozygosity, EA, and cognitive ability in the current study.

Multivariate GWAS of psychiatric disorders and their cardinal symptoms reveal two dimensions of cross-cutting genetic liabilities.

Understanding which biological pathways are specific versus general across diagnostic categories and levels of symptom severity is critical to improving nosology and treatment of psychopathology. Here, we combine transdiagnostic and dimensional approaches to genetic discovery for the first time, conducting a novel multivariate genome-wide association study of eight psychiatric symptoms and disorders broadly related to mood disturbance and psychosis. We identify two transdiagnostic genetic liabilities that distinguish between common forms of psychopathology versus rarer forms of serious mental illness. Biological annotation revealed divergent genetic architectures that differentially implicated prenatal neurodevelopment and neuronal function and regulation. These findings inform psychiatric nosology and biological models of psychopathology, as they suggest that the severity of mood and psychotic symptoms present in serious mental illness may reflect a difference in kind rather than merely in degree.

An evolutionary investigation of depressed mood: The relationship between daily stressors and patterns of depressive symptoms.

BACKGROUND AND OBJECTIVES: The situation-symptom congruence hypothesis (SSCH; (Keller & Nesse, 2006), grounded in evolutionary theory, argues that different types of adversity should lead to distinct patterns of depressive symptoms that help individuals deal with adaptive challenges. Situation-symptom congruence hypotheses were tested in this study using experience sampling methodology. METHODS: Two hundred and sixty-five individuals, including 54% who scored at least 16 on the Center for Epidemiologic Studies Revised Depression Scale, responded to text prompts daily for up to 9 days, reporting depressive symptoms as well as the most stressful event or issue they had experienced or focused on within the past 24 h. RESULTS: Multilevel modeling analyses indicated that the relationships between stressors and depressive symptom patterns were largely consistent with SSCH predictions. All stressors were significantly associated with symptoms hypothesized to be adaptive in response to those stressors. Moreover, in separate analyses, nine of the ten symptoms examined were either predicted by the stressors hypothesized to lead to that symptom or negatively related to stressors hypothesized to not elicit those symptoms. LIMITATIONS: It is unclear whether the results generalize to those diagnosed with a major depressive disorder; the study did not assess actual life events. CONCLUSIONS: Findings suggest that depressive symptoms may, in part, be adaptations that have evolved through natural selection to help individuals cope with adverse situations.

Predicting causal genes from psychiatric genome-wide association studies using high-level etiological knowledge.

Genome-wide association studies have discovered hundreds of genomic loci associated with psychiatric traits, but the causal genes underlying these associations are often unclear, a research gap that has hindered clinical translation. Here, we present a Psychiatric Omnilocus Prioritization Score (PsyOPS) derived from just three binary features encapsulating high-level assumptions about psychiatric disease etiology - namely, that causal psychiatric disease genes are likely to be mutationally constrained, be specifically expressed in the brain, and overlap with known neurodevelopmental disease genes. To our knowledge, PsyOPS is the first method specifically tailored to prioritizing causal genes at psychiatric GWAS loci. We show that, despite its extreme simplicity, PsyOPS achieves state-of-the-art performance at this task, comparable to a prior domain-agnostic approach relying on tens of thousands of features. Genes prioritized by PsyOPS are substantially more likely than other genes at the same loci to have convergent evidence of direct regulation by the GWAS variant according to both DNA looping assays and expression or splicing quantitative trait locus (QTL) maps. We provide examples of genes hundreds of kilobases away from the lead variant, like GABBR1 for schizophrenia, that are prioritized by all three of PsyOPS, DNA looping and QTLs. Our results underscore the power of incorporating high-level knowledge of trait etiology into causal gene prediction at GWAS loci, and comprise a resource for researchers interested in experimentally characterizing psychiatric gene candidates.

Assortative mating biases marker-based heritability estimators.

Many traits are subject to assortative mating, with recent molecular genetic findings confirming longstanding theoretical predictions that assortative mating induces long range dependence across causal variants. However, all marker-based heritability estimators implicitly assume mating is random. We provide mathematical and simulation-based evidence demonstrating that both method-of-moments and likelihood-based estimators are biased in the presence of assortative mating and derive corrected heritability estimators for traits subject to assortment. Finally, we demonstrate that the empirical patterns of estimates across methods and sample sizes for real traits subject to assortative mating are congruent with expected assortative mating-induced biases. For example, marker-based heritability estimates for height are 14% - 23% higher than corrected estimates using UK Biobank data.

Genomic partitioning of inbreeding depression in humans.

Across species, offspring of related individuals often exhibit significant reduction in fitness-related traits, known as inbreeding depression (ID), yet the genetic and molecular basis for ID remains elusive. Here, we develop a method to quantify enrichment of ID within specific genomic annotations and apply it to human data. We analyzed the phenomes and genomes of ∼350,000 unrelated participants of the UK Biobank and found, on average of over 11 traits, significant enrichment of ID within genomic regions with high recombination rates (>21-fold; p < 10-5), with conserved function across species (>19-fold; p < 10-4), and within regulatory elements such as DNase I hypersensitive sites (∼5-fold; p = 8.9 × 10-7). We also quantified enrichment of ID within trait-associated regions and found suggestive evidence that genomic regions contributing to additive genetic variance in the population are enriched for ID signal. We find strong correlations between functional enrichment of SNP-based heritability and that of ID (r = 0.8, standard error: 0.1). These findings provide empirical evidence that ID is most likely due to many partially recessive deleterious alleles in low linkage disequilibrium regions of the genome. Our study suggests that functional characterization of ID may further elucidate the genetic architectures and biological mechanisms underlying complex traits and diseases.

Twin studies to GWAS: there and back again.

The field of human behavioral genetics has come full circle. It began by using twin/family studies to estimate the relative importance of genetic and environmental influences. As large-scale genotyping became cost-effective, genome-wide association studies (GWASs) yielded insights about the nature of genetic influences and new methods that use GWAS data to estimate heritability and genetic correlations invigorated the field. Yet these newer GWAS methods have not replaced twin/family studies. In this review, we discuss the strengths and weaknesses of the two approaches with respect to characterizing genetic and environmental influences, measurement of behavioral phenotypes, and evaluation of causal models, with a particular focus on cognitive neuroscience. This discussion highlights how twin/family studies and GWAS complement and mutually reinforce one another.