Molecular Genetic Contributions to Social Deprivation and Household Income in UK Biobank

Deciphering the influence of socioeconomic status on brain structure: insights from Mendelian randomization

Socioeconomic status (SES) influences physical and mental health, however its relation with brain structure is less well documented. Here, we examine the role of SES on brain structure using Mendelian randomisation. First, we conduct a multivariate genome-wide association study of SES using educational attainment, household income, occupational prestige, and area-based social deprivation, with an effective sample size of N = 947,466. We identify 554 loci associated with SES and distil these loci into those that are common across those four traits. Second, using an independent sample of ~35,000 we provide evidence to suggest that SES is protective against white matter hyperintensities as a proportion of intracranial volume (WMHicv). Third, we find that differences in SES still afford a protective effect against WMHicv, independent of that made by cognitive ability. Our results suggest that SES is a modifiable risk factor, causal in the maintenance of cognitive ability in older-age.

Intergenerational transmission of complex traits and the offspring methylome

The genetic makeup of parents can directly or indirectly affect their offspring phenome through genetic transmission or via the environment that is influenced by parental heritable traits. Our understanding of the mechanisms by which indirect genetic effects operate is limited. Here, we hypothesize that one mechanism is via the offspring methylome. To test this hypothesis, polygenic scores (PGSs) for schizophrenia, smoking initiation, educational attainment (EA), social deprivation, body mass index (BMI), and height were analyzed in a cohort of 1528 offspring and their parents (51.5% boys, mean [SD] age = 10 [2.8] years). We modelled parent and offspring PGSs on offspring buccal-DNA methylation, accounting for the own PGS of offspring, and found significant associations between parental PGSs for schizophrenia, EA, BMI, and height, and offspring buccal methylation sites, comprising 16, 2, 1, and 6 sites, respectively (alpha = 2.7 × 10-5). More DNA methylation sites were associated with maternal than paternal PGSs, possibly reflecting the maternal pre- and periconceptional environment or stronger maternal involvement in shaping the offspring's environment during early childhood.

Associations between common genetic variants and income provide insights about the socio-economic health gradient

We conducted a genome-wide association study on income among individuals of European descent (N = 668,288) to investigate the relationship between socio-economic status and health disparities. We identified 162 genomic loci associated with a common genetic factor underlying various income measures, all with small effect sizes (the Income Factor). Our polygenic index captures 1-5% of income variance, with only one fourth due to direct genetic effects. A phenome-wide association study using this index showed reduced risks for diseases including hypertension, obesity, type 2 diabetes, depression, asthma and back pain. The Income Factor had a substantial genetic correlation (0.92, s.e. = 0.006) with educational attainment. Accounting for the genetic overlap of educational attainment with income revealed that the remaining genetic signal was linked to better mental health but reduced physical health and increased risky behaviours such as drinking and smoking. These findings highlight the complex genetic influences on income and health.

Contribution of copy number variations to education, socioeconomic status and cognition from a genome-wide study of 305,401 subjects

Educational attainment (EA), socioeconomic status (SES) and cognition are phenotypically and genetically linked to health outcomes. However, the role of copy number variations (CNVs) in influencing EA/SES/cognition remains unclear. Using a large-scale (n = 305,401) genome-wide CNV-level association analysis, we discovered 33 CNV loci significantly associated with EA/SES/cognition, 20 of which were novel (deletions at 2p22.2, 2p16.2, 2p12, 3p25.3, 4p15.2, 5p15.33, 5q21.1, 8p21.3, 9p21.1, 11p14.3, 13q12.13, 17q21.31, and 20q13.33, as well as duplications at 3q12.2, 3q23, 7p22.3, 8p23.1, 8p23.2, 17q12 (105 kb), and 19q13.32). The genes identified in gene-level tests were enriched in biological pathways such as neurodegeneration, telomere maintenance and axon guidance. Phenome-wide association studies further identified novel associations of EA/SES/cognition-associated CNVs with mental and physical diseases, such as 6q27 duplication with upper respiratory disease and 17q12 (105 kb) duplication with mood disorders. Our findings provide a genome-wide CNV profile for EA/SES/cognition and bridge their connections to health. The expanded candidate CNVs database and the residing genes would be a valuable resource for future studies aimed at uncovering the biological mechanisms underlying cognitive function and related clinical phenotypes.

Polygenic prediction of occupational status GWAS elucidates genetic and environmental interplay in intergenerational transmission, careers and health in UK Biobank

Socioeconomic status (SES) impacts health and life-course outcomes. This genome-wide association study (GWAS) of sociologically informed occupational status measures (ISEI, SIOPS, CAMSIS) using the UK Biobank (N = 273,157) identified 106 independent single-nucleotide polymorphisms of which 8 are novel to the study of SES. Genetic correlations with educational attainment (rg = 0.96-0.97) and income (rg = 0.81-0.91) point to a common genetic factor for SES. We observed a 54-57% reduction in within-family predictions compared with population-based predictions, attributed to indirect parental effects (22-27% attenuation) and assortative mating (21-27%) following our calculations. Using polygenic scores from population predictions of 5-10% (incremental R2 = 0.023-0.097 across different approaches and occupational status measures), we showed that (1) cognitive and non-cognitive traits, including scholastic and occupational motivation and aspiration, link polygenic scores to occupational status and (2) 62% of the intergenerational transmission of occupational status cannot be ascribed to genetic inheritance of common variants but other factors such as family environments. Finally, links between genetics, occupation, career trajectory and health are interrelated with parental occupational status.

Investigating cross-sectional and longitudinal relationships between brain structure and distinct dimensions of externalizing psychopathology in the ABCD sample

Externalizing psychopathology in childhood is a predictor of poor outcomes across the lifespan. Children exhibiting elevated externalizing symptoms also commonly show emotion dysregulation and callous-unemotional (CU) traits. Examining cross-sectional and longitudinal neural correlates across dimensions linked to externalizing psychopathology during childhood may clarify shared or distinct neurobiological vulnerability for psychopathological impairment later in life. We used tabulated brain structure and behavioural data from baseline, year 1, and year 2 timepoints of the Adolescent Brain Cognitive Development Study (ABCD; baseline n = 10,534). We fit separate linear mixed effect models to examine whether baseline brain structures in frontolimbic and striatal regions (cortical thickness or subcortical volume) were associated with externalizing symptoms, emotion dysregulation, and/or CU traits at baseline and over a two-year period. The most robust relationships found at the cross-sectional level was between cortical thickness in the right rostral middle frontal gyrus and bilateral pars orbitalis was positively associated with CU traits (β = |0.027-0.033|, pcorrected = 0.009-0.03). Over the two-year follow-up period, higher baseline cortical thickness in the left pars triangularis and rostral middle frontal gyrus predicted greater decreases in externalizing symptoms ((F = 6.33-6.94, pcorrected = 0.014). The results of the current study suggest that unique regions within frontolimbic and striatal networks may be more strongly associated with different dimensions of externalizing psychopathology. The longitudinal findings indicate that brain structure in early childhood may provide insight into structural features that influence behaviour over time.

Exome sequencing identifies genes for socioeconomic status in 350,770 individuals

Socioeconomic status (SES) is a critical factor in determining health outcomes and is influenced by genetic and environmental factors. However, our understanding of the genetic structure of SES remains incomplete. Here, we conducted a large-scale exome study of SES markers (household income, occupational status, educational attainment, and social deprivation) in 350,770 individuals. For rare coding variants, we identified 56 significant associations by gene-based collapsing tests, unveiling 7 additional SES-associated genes (NRN1, CCDC36, RHOB, EP400, NCAM1, TPTEP2-CSNK1E, and LINC02881). Exome-wide single common variant analysis revealed nine lead single-nucleotide polymorphisms (SNPs) associated with household income and 34 lead SNPs associated with EduYears, replicating previous GWAS findings. The gene-environment correlations had a substantial impact on the genetic associations with SES, as indicated by the significantly increased P values in several associations after controlling for geographic regions. Furthermore, we observed the pleiotropic effects of SES-associated genetic factors on a wide range of health outcomes, such as cognitive function, psychosocial status, and diabetes. This study highlights the contribution of coding variants to SES and their associations with health phenotypes.

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.

Principled distillation of UK Biobank phenotype data reveals underlying structure in human variation

Data within biobanks capture broad yet detailed indices of human variation, but biobank-wide insights can be difficult to extract due to complexity and scale. Here, using large-scale factor analysis, we distill hundreds of variables (diagnoses, assessments and survey items) into 35 latent constructs, using data from unrelated individuals with predominantly estimated European genetic ancestry in UK Biobank. These factors recapitulate known disease classifications, disentangle elements of socioeconomic status, highlight the relevance of psychiatric constructs to health and improve measurement of pro-health behaviours. We go on to demonstrate the power of this approach to clarify genetic signal, enhance discovery and identify associations between underlying phenotypic structure and health outcomes. In building a deeper understanding of ways in which constructs such as socioeconomic status, trauma, or physical activity are structured in the dataset, we emphasize the importance of considering the interwoven nature of the human phenome when evaluating public health patterns.

Comparing the stability and reproducibility of brain-behavior relationships found using canonical correlation analysis and partial least squares within the ABCD sample

Canonical correlation analysis (CCA) and partial least squares correlation (PLS) detect linear associations between two data matrices by computing latent variables (LVs) having maximal correlation (CCA) or covariance (PLS). This study compared the similarity and generalizability of CCA- and PLS-derived brain-behavior relationships. Data were accessed from the baseline Adolescent Brain Cognitive Development (ABCD) dataset (N > 9,000, 9-11 years). The brain matrix consisted of cortical thickness estimates from the Desikan-Killiany atlas. Two phenotypic scales were examined separately as the behavioral matrix; the Child Behavioral Checklist (CBCL) subscale scores and NIH Toolbox performance scores. Resampling methods were used to assess significance and generalizability of LVs. LV1 for the CBCL brain relationships was found to be significant, yet not consistently stable or reproducible, across CCA and PLS models (singular value: CCA = .13, PLS = .39, p < .001). LV1 for the NIH brain relationships showed similar relationships between CCA and PLS and was found to be stable and reproducible (singular value: CCA = .21, PLS = .43, p < .001). The current study suggests that stability and reproducibility of brain-behavior relationships identified by CCA and PLS are influenced by the statistical characteristics of the phenotypic measure used when applied to a large population-based pediatric sample.

An epidemiological study of season of birth, mental health, and neuroimaging in the UK Biobank

Environmental exposures during the perinatal period are known to have a long-term effect on adult physical and mental health. One such influential environmental exposure is the time of year of birth which affects the amount of daylight, nutrients, and viral load that an individual is exposed to within this key developmental period. Here, we investigate associations between season of birth (seasonality), four mental health traits (n = 137,588) and multi-modal neuroimaging measures (n = 33,212) within the UK Biobank. Summer births were associated with probable recurrent Major Depressive Disorder (β = 0.026, pcorr = 0.028) and greater mean cortical thickness in temporal and occipital lobes (β = 0.013 to 0.014, pcorr<0.05). Winter births were associated with greater white matter integrity globally, in the association fibers, thalamic radiations, and six individual tracts (β = -0.013 to -0.022, pcorr<0.05). Results of sensitivity analyses adjusting for birth weight were similar, with an additional association between winter birth and white matter microstructure in the forceps minor and between summer births, greater cingulate thickness and amygdala volume. Further analyses revealed associations between probable depressive phenotypes and a range of neuroimaging measures but a paucity of interactions with seasonality. Our results suggest that seasonality of birth may affect later-life brain structure and play a role in lifetime recurrent Major Depressive Disorder. Due to the small effect sizes observed, and the lack of associations with other mental health traits, further research is required to validate birth season effects in the context of different latitudes, and by co-examining genetic and epigenetic measures to reveal informative biological pathways.

Obesity and hypertension mediate the effect of education on deep intracerebral hemorrhage: A Mendelian randomization study

BACKGROUND

Educational attainment (EA) as a stable indicator of socioeconomic status has been confirmed to affect intracerebral hemorrhage (ICH), but the mechanism relating EA and ICH is still unknown.

AIM

To explore the causal relationship between EA and ICH through a bidirectional and two-step Mendelian randomization (MR) study.

METHODS

Using summary-level Genome-wide Association Study using GWAS data FROM CASES AND CONTROLS of European ancestry, we performed bidirectional and two-step MR analyses to explore the causal relationship between educational attainment and ICH to understand the mediating influence of risk factors in this process. We also carried out subgroup analysis according to the different sites (deep and lobar) of ICH. A set of sensitivity analyses were performed to test valid MR assumptions.

RESULTS

Bidirectional MR analysis consistently demonstrated a unidirectional causal effect, revealing that higher EA had a protective influence on ICH. Each additional 1-standard deviation (SD) increase in genetically predicted years of schooling was associated with a reduced risk of all ICH (inverse variance weighted (IVW) OR: 0.381 [95 %CI: 0.264-0.549]), deep ICH (OR: 0.334 [95 %CI: 0.216-0.517]), and lobar ICH (OR: 0.422 [95 %CI: 0.261-0.682]). The mediating effect of EA on all ICH was mediated via systolic blood pressure (SBP) (6.93 % [1.20-13.45 %]) and body mass index (BMI) (17.87 % [3.92-34.64 %]), and the mediating effect of EA on deep ICH was also mediated via SBP (7.85 % [1.55-15.07 %]) and BMI (18.63 % [4.02-36.26 %]).

CONCLUSION

This study provides robust genetic evidence for supporting the protective effect of EA on ICH risk, with further evidence that the effect of EA on deep ICH is partially mediated through hypertension and obesity. Further validation is needed to ascertain whether these findings are applicable to other racial or general population groups.

Distinct genomic signatures and modifiable risk factors underly the comorbidity between major depressive disorder and cardiovascular disease

Major depressive disorder (MDD) and cardiovascular disease (CVD) are often comorbid, resulting in excess morbidity and mortality. Using genomic data, this study elucidates biological mechanisms, key risk factors, and causal pathways underlying their comorbidity. We show that CVDs share a large proportion of their genetic risk factors with MDD. Multivariate genome-wide association analysis of the shared genetic liability between MDD and atherosclerotic CVD (ASCVD) revealed seven novel loci and distinct patterns of tissue and brain cell-type enrichments, suggesting a role for the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic, and psychosocial/lifestyle risk factors. Finally, we found support for causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and demonstrated that the causal effects were partly explained by metabolic and psychosocial/lifestyle factors. The distinct signature of MDD-ASCVD comorbidity aligns with the idea of an immunometabolic sub-type of MDD more strongly associated with CVD than overall MDD. In summary, we identify plausible biological mechanisms underlying MDD-CVD comorbidity, as well as key modifiable risk factors for prevention of CVD in individuals with MDD.

Distinguishing happiness and meaning in life from depressive symptoms: A GWAS-by-subtraction study in the UK Biobank

Hedonic (happiness) and eudaimonic (meaning in life) well-being are negatively related to depressive symptoms. Genetic variants play a role in this association, reflected in substantial genetic correlations. We investigated the overlap and differences between well-being and depressive symptoms, using results of Genome-Wide Association studies (GWAS) in UK Biobank. Subtracting GWAS summary statistics of depressive symptoms from those of happiness and meaning in life, we obtained GWASs of respectively "pure" happiness (neffective  = 216,497) and "pure" meaning (neffective  = 102,300). For both, we identified one genome-wide significant SNP (rs1078141 and rs79520962, respectively). After subtraction, SNP heritability reduced from 6.3% to 3.3% for pure happiness and from 6.2% to 4.2% for pure meaning. The genetic correlation between the well-being measures reduced from 0.78 to 0.65. Pure happiness and pure meaning became genetically unrelated to traits strongly associated with depressive symptoms, including loneliness, and psychiatric disorders. For other traits, including ADHD, educational attainment, and smoking, the genetic correlations of well-being versus pure well-being changed substantially. GWAS-by-subtraction allowed us to investigate the genetic variance of well-being unrelated to depressive symptoms. Genetic correlations with different traits led to new insights about this unique part of well-being. Our results can be used as a starting point to test causal relationships with other variables, and design future well-being interventions.

Educational Influences on Late-Life Health: Genetic Propensity and Attained Education

OBJECTIVES

The educational gradient in late-life health is well established. Despite this, there are still ambiguities concerning the role of underlying confounding by genetic influences and gene-environment (GE) interplay. Here, we investigate the role of educational factors (attained and genetic propensities) on health and mortality in late life using genetic propensity for educational attainment (as measured by a genome-wide polygenic score, PGSEdu) and attained education.

METHODS

By utilizing genetically informative twin data from the Swedish Twin Registry (n = 14,570), we investigated influences of the educational measures, familial confounding as well as the possible presence of passive GE correlation on both objective and subjective indicators of late-life health, that is, the Frailty Index, Multimorbidity, Self-rated health, cardiovascular disease, and all-cause mortality.

RESULTS

Using between-within models to adjust for shared familial factors, we found that the relationship between educational level and health and mortality later in life persisted despite controlling for familial confounding. PGSEdu and attained education both uniquely predicted late-life health and mortality, even when mutually adjusted. Between-within models of PGSEdu on the health outcomes in dizygotic twins showed weak evidence for passive GE correlation (prGE) in the education-health relationship.

DISCUSSION

Both genetic propensity to education and attained education are (partly) independently associated with health in late life. These results lend further support for a causal education-health relationship but also raise the importance of genetic contributions and GE interplay.

Socioeconomic status and severe mental disorders: a bidirectional multivariable Mendelian randomisation study

BACKGROUND

Despite the evidence supporting the relationship between socioeconomic status (SES) and severe mental disorders (SMD), the directionality of the associations between income or education and mental disorders is still poorly understood.

OBJECTIVE

To investigate the potential bidirectional causal relationships between genetic liability to the two main components of SES (income and educational attainment (EA)) on three SMD: schizophrenia, bipolar disorder (BD) and depression.

METHODS

We performed a bidirectional, two-sample univariable Mendelian randomisation (UVMR) and multivariable Mendelian randomisation (MVMR) study using SES phenotypes (income, n=397 751 and EA, n=766 345) and SMD (schizophrenia, n=127 906; BD, n=51 710 and depression, n=500 119) genome-wide association studies summary-statistics to dissect the potential direct associations of income and EA with SMD.

FINDINGS

UVMR showed that genetic liability to higher income was associated with decreased risk of schizophrenia and depression, with a smaller reverse effect of schizophrenia and depression on income. Effects were comparable after adjusting for EA in the MVMR. UMVR showed bidirectional negative associations between genetic liability to EA and depression and positive associations between genetic liability to EA and BD, with no significant effects on schizophrenia. After accounting for income, MVMR showed a bidirectional positive direction between genetic liability to EA and BD and schizophrenia but not with depression.

CONCLUSIONS

Our results suggest a heterogeneous link pattern between SES and SMD. We found a negative bidirectional association between genetic liability to income and the risk of schizophrenia and depression. On the contrary, we found a positive bidirectional relationship of genetic liability to EA with schizophrenia and BD, which only becomes apparent after adjusting for income in the case of schizophrenia.

CLINICAL IMPLICATIONS

These findings shed light on the directional mechanisms between social determinants and mental disorders and suggest that income and EA should be studied separately in relation to mental illness.

One versus two biological parents with mental disorders: Relationship to educational attainment in the next generation

BACKGROUND

Both maternal and, separately, paternal mental illness are associated with diminished academic attainment among children. However, the differential impacts of diagnostic type and degree of parental burden (e.g. one v. both parents affected) on these functional outcomes are unknown.

METHODS

Using the Swedish national patient (NPR) and multi-generation (MGR) registers, 2 226 451 children (1 290 157 parental pairs), born 1 January 1973-31 December 1997, were followed through 31 December 2013. Diagnostic status of all cohort members was defined for eleven psychiatric disorders, and families classed by exposure: (1) parents affected with any disorder, (2) parents affected with a disorder group (e.g. neuropsychiatric disorders), and (3) parents affected with a specific disorder (e.g. ADHD). Pairs were further defined as 'unaffected,' 'single-affected,', or 'dual-affected.' Among offspring, the study evaluated fulfillment of four academic milestones, from compulsory (primary) school through University (college). Sensitivity analyses considered the impact of child's own mental health, as well as parental education, on main effects.

RESULTS

Marked reductions in the odds of achievement were observed, emerging at the earliest levels of schooling for both single-affected [adjusted odds ratio (aOR), 0.50; 95% CI 0.49-0.51] and dual-affected (aOR 0.29, 95% CI 0.28-0.30) pairs and persisting thereafter [aOR range (single), 0.52-0.65; aOR range (dual), 0.30-0.40]. This pattern was repeated for analyses within diagnosis/diagnostic group. Main results were robust to adjustment for offspring mental health and parent education level.

CONCLUSIONS

Parental mental illness is associated with profound reductions in educational attainment in the subsequent generation, with children from dual-affected families at uniquely high risk.

Correlates of infant pointing frequency in the first year

This study examines the emergence of concurrent correlates of infant pointing frequency with the aim of contributing to its ontogenetic theories. We measured monthly from 8 to 12 months infants' (N = 56) index-finger pointing frequency along with several candidate correlates: (1) family socioeconomic status (SES), (2) mothers' pointing production, and (3) infants' point following to targets in front of and behind them. Results revealed that (1) infants increased their pointing frequency across age, but high-SES infants had a steeper increase, and a higher pointing frequency than low-SES infants from 10 months onward, (2) maternal pointing frequency was not associated with infant pointing frequency at any age, (3) infants' point following abilities to targets behind their visual fields was positively associated with their pointing frequency at 12 months, after pointing had already emerged around 10 months. Findings suggest that family SES impacts infants' pointing development more generally, not just through maternal pointing. The association between pointing and following points to targets behind, but not in front, suggests that a higher level of referential understanding emerges after, and perhaps through the production of pointing.

Heritable Traits and Lung Cancer Risk: A Two-Sample Mendelian Randomization Study

INTRODUCTION

Lung cancer is a complex polygenic disorder. Analysis with Mendelian randomization (MR) allows for genetically predicted risks to be estimated between exposures and outcomes.

METHODS

We analyzed 345 heritable traits from the United Kingdom Biobank and estimated their associated effects on lung cancer outcomes using two sample MR. In addition to estimating effects with overall lung cancer, adenocarcinoma, small cell lung cancer, and squamous cell lung cancers, we performed conditional effect modeling with multivariate MR (MVMR) and the traits of alcohol use, smoking initiation, average pre-tax income, and educational attainment.

RESULTS

Univariate MR provided evidence for increased age at first sexual intercourse (OR, 0.55; P = 6.15 × 10-13), educational attainment (OR, 0.24; P = 1.07 × 10-19), average household income (OR, 0.58; P = 7.85 × 10-05), and alcohol usually taken with meals (OR, 0.19; P = 1.06 × 10-06) associating with decreased odds of overall lung cancer development. In contrast, a lack of additional educational attainment (OR, 8.00; P = 3.48 × 10-12), body mass index (OR, 1.28; P = 9.00 × 10-08), pack years smoking as a proportion of life span (OR, 9.93; P = 7.96 × 10-12), and weekly beer intake (OR, 3.48; P = 4.08 × 10-07) were associated with an increased risk of overall lung cancer development.

CONCLUSIONS

Many heritable traits associated with an increased or inverse risk of lung cancer development. Effects vary based on histologic subtype and conditional third trait exposures.

IMPACT

We identified several heritable traits and presented their genetically predictable impact on lung cancer development, providing valuable insights for consideration.

Identification and analysis of individuals who deviate from their genetically-predicted phenotype

Findings from genome-wide association studies have facilitated the generation of genetic predictors for many common human phenotypes. Stratifying individuals misaligned to a genetic predictor based on common variants may be important for follow-up studies that aim to identify alternative causal factors. Using genome-wide imputed genetic data, we aimed to classify 158,951 unrelated individuals from the UK Biobank as either concordant or deviating from two well-measured phenotypes. We first applied our methods to standing height: our primary analysis classified 244 individuals (0.15%) as misaligned to their genetically predicted height. We show that these individuals are enriched for self-reporting being shorter or taller than average at age 10, diagnosed congenital malformations, and rare loss-of-function variants in genes previously catalogued as causal for growth disorders. Secondly, we apply our methods to LDL cholesterol (LDL-C). We classified 156 (0.12%) individuals as misaligned to their genetically predicted LDL-C and show that these individuals were enriched for both clinically actionable cardiovascular risk factors and rare genetic variants in genes previously shown to be involved in metabolic processes. Individuals whose LDL-C was higher than expected based on the genetic predictor were also at higher risk of developing coronary artery disease and type-two diabetes, even after adjustment for measured LDL-C, BMI and age, suggesting upward deviation from genetically predicted LDL-C is indicative of generally poor health. Our results remained broadly consistent when performing sensitivity analysis based on a variety of parametric and non-parametric methods to define individuals deviating from polygenic expectation. Our analyses demonstrate the potential importance of quantitatively identifying individuals for further follow-up based on deviation from genetic predictions.

Weighing psychosocial factors in relatives for the risk of psychopathology: a study of patients with depressive and anxiety disorders and their siblings

PURPOSE

Siblings of probands with depressive and anxiety disorders are at increased risk for psychopathology, but little is known about how risk factors operate within families to increase psychopathology for siblings. We examined the additional impact of psychosocial risk factors in probands-on top of or in combination with those in siblings-on depressive/anxious psychopathology in siblings.

METHODS

The sample included 636 participants (Mage = 49.7; 62.4% female) from 256 families, each including a proband with lifetime depressive and/or anxiety disorders and their sibling(s) (N = 380 proband-sibling pairs). Sixteen psychosocial risk factors were tested. In siblings, depressive and anxiety disorders were determined with standardized psychiatric interviews; symptom severity was measured using self-report questionnaires. Analyses were performed with mixed-effects models accounting for familial structure.

RESULTS

In siblings, various psychosocial risk factors (female gender, low income, childhood trauma, poor parental bonding, being single, smoking, hazardous alcohol use) were associated with higher symptomatology and likelihood of disorder. The presence of the same risk factor in probands was independently associated (low income, being single) with higher symptomatology in siblings or moderated (low education, childhood trauma, hazardous alcohol use)-by reducing its strength-the association between the risk factor and symptomatology in siblings. There was no additional impact of risk factors in probands on likelihood of disorder in siblings.

CONCLUSION

Our findings demonstrate the importance of weighing psychosocial risk factors within a family context, as it may provide relevant information on the risk of affective psychopathology for individuals.

Patterns of item nonresponse behaviour to survey questionnaires are systematic and associated with genetic loci

Response to survey questionnaires is vital for social and behavioural research, and most analyses assume full and accurate response by participants. However, nonresponse is common and impedes proper interpretation and generalizability of results. We examined item nonresponse behaviour across 109 questionnaire items in the UK Biobank (N = 360,628). Phenotypic factor scores for two participant-selected nonresponse answers, 'Prefer not to answer' (PNA) and 'I don't know' (IDK), each predicted participant nonresponse in follow-up surveys (incremental pseudo-R2 = 0.056), even when controlling for education and self-reported health (incremental pseudo-R2 = 0.046). After performing genome-wide association studies of our factors, PNA and IDK were highly genetically correlated with one another (rg = 0.73 (s.e. = 0.03)) and with education (rg,PNA = -0.51 (s.e. = 0.03); rg,IDK = -0.38 (s.e. = 0.02)), health (rg,PNA = 0.51 (s.e. = 0.03); rg,IDK = 0.49 (s.e. = 0.02)) and income (rg,PNA = -0.57 (s.e. = 0.04); rg,IDK = -0.46 (s.e. = 0.02)), with additional unique genetic associations observed for both PNA and IDK (P < 5 × 10-8). We discuss how these associations may bias studies of traits correlated with item nonresponse and demonstrate how this bias may substantially affect genome-wide association studies. While the UK Biobank data are deidentified, we further protected participant privacy by avoiding exploring non-response behaviour to single questions, assuring that no information can be used to associate results with any particular respondents.

Exploring the bidirectional causal link between household income status and genetic susceptibility to neurological diseases: findings from a Mendelian randomization study

Objectives

Observational studies have revealed that socioeconomic status is associated with neurological disorders and aging. However, the potential causal effect between the two remains unclear. We therefore aimed to investigate the causal relationship between household income status and genetic susceptibility to neurological diseases using a bidirectional Mendelian randomization (MR) study.

Methods

An MR study was conducted on a large-sample cohort of the European population pulled from a publicly available genome-wide association study dataset, using a random-effects inverse-variance weighting model as the main standard. MR-Egger regression, weighted median, and maximum likelihood estimation were also performed concurrently as supplements. A sensitivity analysis, consisting of a heterogeneity test and horizontal pleiotropy test, was performed using Cochran's Q, MR-Egger intercept, and MR-PRESSO tests to ensure the reliability of the conclusion.

Results

The results suggested that higher household income tended to lower the risk of genetic susceptibility to Alzheimer's disease (odds ratio [OR]: 0.740, 95% confidence interval [CI] = 0.559-0.980, p-value = 0.036) and ischemic stroke (OR: 0.801, 95% CI = 0.662-0.968, p-value = 0.022). By contrast, higher household income tended to increase the risk of genetic susceptibility to Parkinson's disease (OR: 2.605, 95% CI = 1.413-4.802, p-value = 0.002). No associations were evident for intracranial hemorrhage (OR: 1.002, 95% CI = 0.607-1.653, p-value = 0.993), cerebral aneurysm (OR: 0.597, 95% CI = 0.243-1.465, p-value = 0.260), subarachnoid hemorrhage (OR: 1.474, 95% CI = 0.699-3.110, p-value = 0.308), or epilepsy (OR: 1.029, 95% CI = 0.662-1.600, p-value = 0.899). The reverse MR study suggested no reverse causal relationship between neurological disorders and household income status. A sensitivity analysis verified the reliability of the results.

Conclusion

Our results revealed that the populations with a superior household income exhibit an increased predisposition of genetic susceptibility to Parkinson's Disease, while demonstrating a potential decreased genetic susceptibility to ischemic stroke and Alzheimer's disease.

Socioeconomic status may affect association of vegetable intake with risk of ischemic cardio-cerebral vascular disease: a Mendelian randomization study

Background

Previous studies found that increasing vegetable intake benefits are reduced after adjustment for socioeconomic factors. Using genetic variation as an instrumental variable for vegetable intake and socioeconomic status, we investigated the relationship between vegetable intake and ischemic cardio-cerebral vascular diseases and focused on whether socioeconomic status was a possible confounder.

Methods

From three independent genome-wide association studies, we extracted instrumental variables reflecting raw and cooked vegetable intake, which were used to perform Mendelian randomization analysis. To evaluate the effects of socioeconomic factors on vegetable intake, univariate and multivariate Mendelian randomization analyses were performed using single nucleotide polymorphisms representing education attainment and household income reported in the literature. We also performed outlier assessment and a series of sensitivity analyses to confirm the results.

Results

Genetically predicted raw and cooked vegetable intake were not associated with any ischemic cardio-cerebral vascular diseases and lipid components after Bonferroni correction. Univariate Mendelian randomized analysis revealed that raw vegetable intake was positively correlated with education attainment (β = 0.04, p = 0.029) and household income (β = 0.07, p < 0.001). Multivariate Mendelian randomized model showed a positive correlation between household income and raw vegetable intake (β = 0.06, p = 0.004). Socioeconomic status was closely associated with eating habits and lifestyle related to the risk of cardiovascular diseases.

Conclusion

Genetically determined raw and cooked vegetable intake was not associated with significant benefits in terms of ischemic cardio-cerebral vascular diseases while genetically determined socioeconomic status may have an impact on vegetable intake. Socioeconomic status, which was closely associated with other eating habits and lifestyle, may affect the association between vegetable intake and ischemic cardio-cerebral vascular diseases.

Exploring the relationship between socioeconomic deprivation index and Alzheimer's disease using summary-level data: From genetic correlation to causality

Patients with Alzheimer's disease (AD) are markedly increasing as population aging and no disease-modifying therapies are currently available for AD. Previous studies suggested a broad link between socioeconomic status and a variety of disorders, including mental illness and cognitive abilities. However, the association between socioeconomic deprivation and AD has been unknown. We here employed Townsend deprivation index (TDI) to explore such relation and found a positive genetic correlation (r̂_g=0.211, P = 8.00 × 10^-4) between the two traits with summary statistics data (N = 455,258 for TDI and N = 455,815 for AD). Then, we performed pleiotropy analysis at both variant and gene levels using a powerful method called PLACO and detected 87 distinct pleiotropic genes. Functional analysis demonstrated these genes were significantly enriched in pancreas, liver, heart, blood, brain, and muscle tissues. Using Mendelian randomization methods, we further found that one genetically predicted standard deviation elevation in TDI could lead to approximately 18.5% (95% confidence intervals 1.6- 38.2%, P = 0.031) increase of AD risk, and that the identified causal association was robust against used MR approaches, horizontal pleiotropy, and instrumental selection. Overall, this study provides deep insight into common genetic components underlying TDI and AD, and further reveals causal connection between them. It is also helpful to develop a more suitable plan for ameliorating inequities, hardship, and disadvantage, with the hope of improving health outcomes among economically disadvantaged people.

Comparing the stability and reproducibility of brain-behaviour relationships found using Canonical Correlation Analysis and Partial Least Squares within the ABCD Sample

Introduction

Canonical Correlation Analysis (CCA) and Partial Least Squares Correlation (PLS) detect associations between two data matrices based on computing a linear combination between the two matrices (called latent variables; LVs). These LVs maximize correlation (CCA) and covariance (PLS). These different maximization criteria may render one approach more stable and reproducible than the other when working with brain and behavioural data at the population-level. This study compared the LVs which emerged from CCA and PLS analyses of brain-behaviour relationships from the Adolescent Brain Cognitive Development (ABCD) dataset and examined their stability and reproducibility.

Methods

Structural T1-weighted imaging and behavioural data were accessed from the baseline Adolescent Brain Cognitive Development dataset (N > 9000, ages = 9-11 years). The brain matrix consisted of cortical thickness estimates in different cortical regions. The behavioural matrix consisted of 11 subscale scores from the parent-reported Child Behavioral Checklist (CBCL) or 7 cognitive performance measures from the NIH Toolbox. CCA and PLS models were separately applied to the brain-CBCL analysis and brain-cognition analysis. A permutation test was used to assess whether identified LVs were statistically significant. A series of resampling statistical methods were used to assess stability and reproducibility of the LVs.

Results

When examining the relationship between cortical thickness and CBCL scores, the first LV was found to be significant across both CCA and PLS models (singular value: CCA = .13, PLS = .39, p < .001). LV1 from the CCA model found that covariation of CBCL scores was linked to covariation of cortical thickness. LV1 from the PLS model identified decreased cortical thickness linked to lower CBCL scores. There was limited evidence of stability or reproducibility of LV1 for both CCA and PLS. When examining the relationship between cortical thickness and cognitive performance, there were 6 significant LVs for both CCA and PLS (p < .01). The first LV showed similar relationships between CCA and PLS and was found to be stable and reproducible (singular value: CCA = .21, PLS = .43, p < .001).

Conclusion

CCA and PLS identify different brain-behaviour relationships with limited stability and reproducibility when examining the relationship between cortical thickness and parent-reported behavioural measures. However, both methods identified relatively similar brain-behaviour relationships that were stable and reproducible when examining the relationship between cortical thickness and cognitive performance. The results of the current study suggest that stability and reproducibility of brain-behaviour relationships identified by CCA and PLS are influenced by characteristics of the analyzed sample and the included behavioural measurements when applied to a large pediatric dataset.

Identification and analysis of individuals who deviate from their genetically-predicted phenotype

Findings from genome-wide association studies have facilitated the generation of genetic predictors for many common human phenotypes. Stratifying individuals misaligned to a genetic predictor based on common variants may be important for follow-up studies that aim to identify alternative causal factors. Using genome-wide imputed genetic data, we aimed to classify 158,951 unrelated individuals from the UK Biobank as either concordant or deviating from two well-measured phenotypes. We first applied our methods to standing height: our primary analysis classified 244 individuals (0.15%) as misaligned to their genetically predicted height. We show that these individuals are enriched for self-reporting being shorter or taller than average at age 10, diagnosed congenital malformations, and rare loss-of-function variants in genes previously catalogued as causal for growth disorders. Secondly, we apply our methods to LDL cholesterol. We classified 156 (0.12%) individuals as misaligned to their genetically predicted LDL cholesterol and show that these individuals were enriched for both clinically actionable cardiovascular risk factors and rare genetic variants in genes previously shown to be involved in metabolic processes. Individuals whose LDL-C was higher than expected based on the genetic predictor were also at higher risk of developing coronary artery disease and type-two diabetes, even after adjustment for measured LDL-C, BMI and age, suggesting upward deviation from genetically predicted LDL-C is indicative of generally poor health. Our results remained broadly consistent when performing sensitivity analysis based on a variety of parametric and non-parametric methods to define individuals deviating from polygenic expectation. Our analyses demonstrate the potential importance of quantitatively identifying individuals for further follow-up based on deviation from genetic predictions.

Author Summary

Human genetics is becoming increasingly useful to help predict human traits across a population owing to findings from large-scale genetic association studies and advances in the power of genetic predictors. This provides an opportunity to potentially identify individuals that deviate from genetic predictions for a common phenotype under investigation. For example, an individual may be genetically predicted to be tall, but be shorter than expected. It is potentially important to identify individuals who deviate from genetic predictions as this can facilitate further follow-up to assess likely causes. Using 158,951 unrelated individuals from the UK Biobank, with height and LDL cholesterol, as exemplar traits, we demonstrate that approximately 0.15% & 0.12% of individuals deviate from their genetically predicted phenotypes respectively. We observed these individuals to be enriched for a range of rare clinical diagnoses, as well as rare genetic factors that may be causal. Our analyses also demonstrate several methods for detecting individuals who deviate from genetic predictions that can be applied to a range of continuous human phenotypes.

Education plays a crucial role in the pathway from poverty to smoking: a Mendelian randomization study

BACKGROUND AND AIMS

Disproportionately high rates of smoking have been found in low-income communities, but the causal direction and role of education in this relationship remains less well understood. Here, we used bidirectional Mendelian randomization (MR) to measure the causal relationships between smoking, income and education.

DESIGN

Two-sample univariable and multivariable MR analyses were conducted to evaluate the total and direct effect of income and education on tobacco smoking. The effects of smoking on education and income were explored with reverse MR analysis.

SETTING

European ancestry.

PARTICIPANTS

The most recent large-scale genome-wide association study (GWAS) summary data on educational attainment, household income and smoking (n = 143 210-766 345).

MEASUREMENTS

Genetic variants for exposures including income, education and smoking.

FINDINGS

Both income and education had protective effects against smoking, especially for smoking initiation (education: β = -0.447, 95% CI = -0.508 to -0.387, P < 0.001; income: β = -0.290, 95% CI = -0.43 to -0.149, P < 0.001) and cessation (education: β = -0.364, 95% CI = -0.429 to -0.298, P < 0.001; income: β = -0.323, 95% CI = -0.448 to -0.197, P < 0.001). Here, higher scores in cessation indicated a lower likelihood of quitting according to the coding scheme. There was little evidence that income influenced smoking once education was controlled for, whereas education could significantly affect smoking behaviours independently of income (P = 3.40 × 10^-10 -0.0272). The reverse MR results suggested that smoking may result in a loss of years of schooling (β = -0.190, 95% CI = -0.297 to -0.083, P < 0.001) and reduced earnings (β = -0.204, 95% CI = -0.347 to -0.060, P = 0.006).

CONCLUSIONS

Education appears to play an important role in the relationship between income and smoking. There is a bidirectional association of smoking with socioeconomic status.

The shared genetic architecture of suicidal behaviour and psychiatric disorders: A genomic structural equation modelling study

Background: Suicidal behaviour (SB) refers to behaviours, ranging from non-fatal suicidal behaviour, such as suicidal ideation and attempt, to completed suicide. Despite recent advancements in genomic technology and statistical methods, it is unclear to what extent the spectrum of suicidal behaviour is explained by shared genetic aetiology. Methods: We identified nine genome-wide association statistics of suicidal behaviour (sample sizes, n, ranging from 62,648 to 125,844), ten psychiatric traits [n up to 386,533] and collectively, nine summary datasets of anthropometric, behavioural and socioeconomic-related traits [n ranging from 58,610 to 941,280]. We calculated the genetic correlation among these traits and modelled this using genomic structural equation modelling, identified shared biological processes and pathways between suicidal behaviour and psychiatric disorders and evaluated potential causal associations using Mendelian randomisation. Results: Among populations of European ancestry, we observed strong positive genetic correlations between suicide ideation, attempt and self-harm (rg range, 0.71-1.09) and moderate to strong genetic correlations between suicidal behaviour traits and a range of psychiatric disorders, most notably, major depression disorder (rg = 0.86, p = 1.62 × 10^-36). Multivariate analysis revealed a common factor structure for suicidal behaviour traits, major depression, attention deficit hyperactivity disorder (ADHD) and alcohol use disorder. The derived common factor explained 38.7% of the shared variance across the traits. We identified 2,951 genes and 98 sub-network hub genes associated with the common factor, including pathways associated with developmental biology, signal transduction and RNA degradation. We found suggestive evidence for the protective effects of higher household income level on suicide attempt [OR = 0.55 (0.44-0.70), p = 1.29 × 10^-5] and while further investigation is needed, a nominal significant effect of smoking on suicide attempt [OR = 1.24 (1.04-1.44), p = 0.026]. Conclusion: Our findings provide evidence of shared aetiology between suicidal behaviour and psychiatric disorders and indicate potential common molecular mechanisms contributing to the overlapping pathophysiology. These findings provide a better understanding of the complex genetic architecture of suicidal behaviour and have implications for the prevention and treatment of suicidal behaviour.

The association of obesity-related traits on COVID-19 severity and hospitalization is affected by socio-economic status: a multivariable Mendelian randomization study

BACKGROUND

Due to its large impact on human health, socio-economic status (SES) could at least partially influence the established association between obesity and coronavirus disease 2019 (COVID-19) severity. To estimate the independent effect of body size and SES on the clinical manifestations of COVID-19, we conducted a Mendelian randomization (MR) study.

METHODS

Applying two-sample MR approaches, we evaluated the effects of body mass index (BMI, n = 322 154), waist circumference (WC, n = 234 069), hip circumference (n = 213 019) and waist-hip ratio (n = 210 088) with respect to three COVID-19 outcomes: severe respiratory COVID-19 (cases = 8779, controls = 1 000 875), hospitalized COVID-19 (cases = 17 992, controls = 1 810 493) and COVID-19 infection (cases = 87 870, controls = 2 210 804). Applying a multivariable MR (MVMR) approach, we estimated the effect of these anthropometric traits on COVID-19 outcomes accounting for the effect of SES assessed as household income (n = 286 301).

RESULTS

BMI and WC were associated with severe respiratory COVID-19 [BMI: odds ratio (OR) = 1.51, CI = 1.24-1.84, P = 3.01e-05; WC: OR = 1.48, 95% CI = 1.15-1.91, P = 0.0019] and hospitalized COVID-19 (BMI: OR = 1.50, 95% CI = 1.32-1.72, P = 8.83e-10; WC: OR = 1.41, 95% CI = 1.20-1.67, P = 3.72e-05). Conversely, income was associated with lower odds of severe respiratory (OR = 0.70, 95% CI = 0.53-0.93, P = 0.015) and hospitalized COVID-19 (OR = 0.78, 95% CI = 0.66-0.92, P = 0.003). MVMR analyses showed that the effect of these obesity-related traits on increasing the odds of COVID-19 negative outcomes becomes null when accounting for income. Conversely, the association of income with lower odds of COVID-19 negative outcomes is not affected when including the anthropometric traits in the multivariable model.

CONCLUSION

Our findings indicate that SES contributes to the effect of obesity-related traits on COVID-19 severity and hospitalization.

Association between the timing of family socioeconomic deprivation and adolescence hair cortisol among adolescent twins: A study of the genetic and environmental processes involved

BACKGROUND

While evidence shows that lower socioeconomic status (SES) is related to dysregulated hair cortisol concentration (HCC), the genetic and environmental processes underlying this association remain understudied.

OBJECTIVES

(1) to examine whether childhood and adolescence SES are phenotypically related to late adolescence HCC and to what extent these associations are explained by common underlying genetic factors (2) to estimate whether the genetic and environmental etiology of HCC varies according to SES and the timing of these experiences.

METHODS

Participants were 422 twin pairs for whom SES was measured in childhood (ages 0-5 years) and adolescence (age 14 years). Hair cortisol was assessed at age 19.

RESULTS

Additive genetic factors explained 39% of variability in HCC, whereas nonshared environmental factors accounted for the remaining 61%. A significant negative association emerged between HCC and family SES assessed in adolescence (β=-.11,p = .02), which was entirely explained by common underlying environmental influences. We also found evidence of stronger genetic contributions to HCC among youths who lived in more disadvantaged households during childhood in comparison to those from wealthier backgrounds.

CONCLUSIONS

This study provides first-time evidence that the association between adolescence SES and HCC is environmentally-explained and that genetic influences underlying HCC are not uniformly distributed across the family SES continuum measured during childhood. These findings may pave the way for a fuller understanding of the impact of early adversity on HPA axis activity.

What next for eating disorder genetics? Replacing myths with facts to sharpen our understanding

Substantial progress has been made in the understanding of anorexia nervosa (AN) and eating disorder (ED) genetics through the efforts of large-scale collaborative consortia, yielding the first genome-wide significant loci, AN-associated genes, and insights into metabo-psychiatric underpinnings of the disorders. However, the translatability, generalizability, and reach of these insights are hampered by an overly narrow focus in our research. In particular, stereotypes, myths, assumptions and misconceptions have resulted in incomplete or incorrect understandings of ED presentations and trajectories, and exclusion of certain patient groups from our studies. In this review, we aim to counteract these historical imbalances. Taking as our starting point the Academy for Eating Disorders (AED) Truth #5 "Eating disorders affect people of all genders, ages, races, ethnicities, body shapes and weights, sexual orientations, and socioeconomic statuses", we discuss what we do and do not know about the genetic underpinnings of EDs among people in each of these groups, and suggest strategies to design more inclusive studies. In the second half of our review, we outline broad strategic goals whereby ED researchers can expand the diversity, insights, and clinical translatability of their studies.

Low income and schizophrenia risk: a narrative review

Despite decades of research, the precise etiology of schizophrenia is not fully understood. Ample evidence indicates that the disorder derives from a complex interplay of genetic and environmental factors during vulnerable stages of brain maturation. Among the plethora of risk factors investigated, stress, pre- and perinatal insults, and cannabis use have been repeatedly highlighted as crucial environmental risk factors for schizophrenia. Compelling findings from population-based longitudinal studies suggest low income as an additional risk factor for future schizophrenia diagnosis, but underlying mechanisms remain unclear. In this narrative review, we 1) summarize the literature in support of a relationship between low (parental) income and schizophrenia risk, and 2) explore the mediating role of chronic stress, pre- and perinatal factors, and cannabis use as established risk factors for schizophrenia. Our review describes how low income facilitates the occurrence and severity of these established risk factors and thus contributes to schizophrenia liability. The broadest influence of low income was identified for stress, as low income was found to be associated with exposure to a multitude of severe psychological and physiological stressors. This narrative review adds to the growing literature reporting a close relationship between income and mental health.

Sexually antagonistic selection on educational attainment and body size in Estonian children

Natural selection is a key mechanism of evolution, which results from the differential reproduction of phenotypes. We describe fecundity selection at different parity transitions on 15 anthropometric traits and educational attainment in Estonian children sampled in the middle of 20th century. The direction of selection on educational attainment and bodily traits was sexually antagonistic, and it occurred via different parity transitions in boys and girls. Compared to boys with primary education, obtaining tertiary education was associated with 3.5 times and secondary education two times higher odds of becoming a father. Transition to motherhood was not related to educational attainment, while education above primary was associated with lower odds (OR = 0.5-0.7) to progression to parities above one and two. Selection on anthropometric traits occurred almost exclusively via childlessness in boys, while among the girls, most of the traits that were associated with becoming a mother were additionally associated with a transition from one child to higher parities. Male (but not female) fitness was thus primarily determined by traits related to mating success. Selection favored stronger and larger boys and smaller girls. Selection on girls favored some traits that associate with perceived femininity, while other feminine traits were selected against.

Pattern learning reveals brain asymmetry to be linked to socioeconomic status

Socioeconomic status (SES) anchors individuals in their social network layers. Our embedding in the societal fabric resonates with habitus, world view, opportunity, and health disparity. It remains obscure how distinct facets of SES are reflected in the architecture of the central nervous system. Here, we capitalized on multivariate multi-output learning algorithms to explore possible imprints of SES in gray and white matter structure in the wider population (n ≈ 10,000 UK Biobank participants). Individuals with higher SES, compared with those with lower SES, showed a pattern of increased region volumes in the left brain and decreased region volumes in the right brain. The analogous lateralization pattern emerged for the fiber structure of anatomical white matter tracts. Our multimodal findings suggest hemispheric asymmetry as an SES-related brain signature, which was consistent across six different indicators of SES: degree, education, income, job, neighborhood and vehicle count. Hence, hemispheric specialization may have evolved in human primates in a way that reveals crucial links to SES.

Mendelian randomization

Mendelian randomization (MR) is a term that applies to the use of genetic variation to address causal questions about how modifiable exposures influence different outcomes. The principles of MR are based on Mendel's laws of inheritance and instrumental variable estimation methods, which enable the inference of causal effects in the presence of unobserved confounding. In this Primer, we outline the principles of MR, the instrumental variable conditions underlying MR estimation and some of the methods used for estimation. We go on to discuss how the assumptions underlying an MR study can be assessed and give methods of estimation that are robust to certain violations of these assumptions. We give examples of a range of studies in which MR has been applied, the limitations of current methods of analysis and the outlook for MR in the future. The difference between the assumptions required for MR analysis and other forms of non-interventional epidemiological studies means that MR can be used as part of a triangulation across multiple sources of evidence for causal inference.

Mendelian Randomization: Concepts and Scope

Mendelian randomization (MR) is a method of studying the causal effects of modifiable exposures (i.e., potential risk factors) on health, social, and economic outcomes using genetic variants associated with the specific exposures of interest. MR provides a more robust understanding of the influence of these exposures on outcomes because germline genetic variants are randomly inherited from parents to offspring and, as a result, should not be related to potential confounding factors that influence exposure-outcome associations. The genetic variant can therefore be used as a tool to link the proposed risk factor and outcome, and to estimate this effect with less confounding and bias than conventional epidemiological approaches. We describe the scope of MR, highlighting the range of applications being made possible as genetic data sets and resources become larger and more freely available. We outline the MR approach in detail, covering concepts, assumptions, and estimation methods. We cover some common misconceptions, provide strategies for overcoming violation of assumptions, and discuss future prospects for extending the clinical applicability, methodological innovations, robustness, and generalizability of MR findings.

The P-factor and its genomic and neural equivalents: an integrated perspective

Different psychiatric disorders and symptoms are highly correlated in the general population. A general psychopathology factor (or "P-factor") has been proposed to efficiently describe this covariance of psychopathology. Recently, genetic and neuroimaging studies also derived general dimensions that reflect densely correlated genomic and neural effects on behaviour and psychopathology. While these three types of general dimensions show striking parallels, it is unknown how they are conceptually related. Here, we provide an overview of these three general dimensions, and suggest a unified interpretation of their nature and underlying mechanisms. We propose that the general dimensions reflect, in part, a combination of heritable 'environmental' factors, driven by a dense web of gene-environment correlations. This perspective calls for an update of the traditional endophenotype framework, and encourages methodological innovations to improve models of gene-brain-environment relationships in all their complexity. We propose concrete approaches, which by taking advantage of the richness of current large databases will help to better disentangle the complex nature of causal factors underlying psychopathology.

Genetic effects on educational attainment in Hungary

INTRODUCTION

Educational attainment is a substantially heritable trait, and it has recently been linked to specific genetic variants by genome-wide association studies (GWASs). However, the effects of such genetic variants are expected to vary across environments, including countries and historical eras.

METHODS

We used polygenic scores (PGSs) to assess molecular genetic effects on educational attainment in Hungary, a country in the Central Eastern European region where behavioral genetic studies are in general scarce and molecular genetic studies of educational attainment have not been previously published.

RESULTS

We found that the PGS is significantly associated with the attainment of a college degree as well as the number of years in education in a sample of Hungarian study participants (N = 829). PGS effect sizes were not significantly different when compared to an English (N = 976) comparison sample with identical measurement protocols. In line with previous Estonian findings, we found higher PGS effect sizes in Hungarian, but not in English participants who attended higher education after the fall of Communism, although we lacked statistical power for this effect to reach significance.

DISCUSSION

Our results provide evidence that polygenic scores for educational attainment have predictive value in culturally diverse European populations.

Mendelian randomization of genetically independent aging phenotypes identifies LPA and VCAM1 as biological targets for human aging

Length and quality of life are important to us all, yet identification of promising drug targets for human aging using genetics has had limited success. In the present study, we combine six European-ancestry genome-wide association studies of human aging traits-healthspan, father and mother lifespan, exceptional longevity, frailty index and self-rated health-in a principal component framework that maximizes their shared genetic architecture. The first principal component (aging-GIP1) captures both length of life and indices of mental and physical wellbeing. We identify 27 genomic regions associated with aging-GIP1, and provide additional, independent evidence for an effect on human aging for loci near HTT and MAML3 using a study of Finnish and Japanese survival. Using proteome-wide, two-sample, Mendelian randomization and colocalization, we provide robust evidence for a detrimental effect of blood levels of apolipoprotein(a) and vascular cell adhesion molecule 1 on aging-GIP1. Together, our results demonstrate that combining multiple aging traits using genetic principal components enhances the power to detect biological targets for human aging.

Genetic variation, brain, and intelligence differences

Individual differences in human intelligence, as assessed using cognitive test scores, have a well-replicated, hierarchical phenotypic covariance structure. They are substantially stable across the life course, and are predictive of educational, social, and health outcomes. From this solid phenotypic foundation and importance for life, comes an interest in the environmental, social, and genetic aetiologies of intelligence, and in the foundations of intelligence differences in brain structure and functioning. Here, we summarise and critique the last 10 years or so of molecular genetic (DNA-based) research on intelligence, including the discovery of genetic loci associated with intelligence, DNA-based heritability, and intelligence's genetic correlations with other traits. We summarise new brain imaging-intelligence findings, including whole-brain associations and grey and white matter associations. We summarise regional brain imaging associations with intelligence and interpret these with respect to theoretical accounts. We address research that combines genetics and brain imaging in studying intelligence differences. There are new, though modest, associations in all these areas, and mechanistic accounts are lacking. We attempt to identify growing points that might contribute toward a more integrated 'systems biology' account of some of the between-individual differences in intelligence.

A polygenic score for educational attainment partially predicts voter turnout

Twin and adoption studies have shown that individual differences in political participation can be explained, in part, by genetic variation. However, these research designs cannot identify which genes are related to voting or the pathways through which they exert influence, and their conclusions rely on possibly restrictive assumptions. In this study, we use three different US samples and a Swedish sample to test whether genes that have been identified as associated with educational attainment, one of the strongest correlates of political participation, predict self-reported and validated voter turnout. We find that a polygenic score capturing individuals' genetic propensity to acquire education is significantly related to turnout. The strongest associations we observe are in second-order midterm elections in the United States and European Parliament elections in Sweden, which tend to be viewed as less important by voters, parties, and the media and thus present a more information-poor electoral environment for citizens to navigate. A within-family analysis suggests that individuals' education-linked genes directly affect their voting behavior, but, for second-order elections, it also reveals evidence of genetic nurture. Finally, a mediation analysis suggests that educational attainment and cognitive ability combine to account for between 41% and 63% of the relationship between the genetic propensity to acquire education and voter turnout.

Pathfinder: a gamified measure to integrate general cognitive ability into the biological, medical, and behavioural sciences

Genome-wide association (GWA) studies have uncovered DNA variants associated with individual differences in general cognitive ability (g), but these are far from capturing heritability estimates obtained from twin studies. A major barrier to finding more of this 'missing heritability' is assessment--the use of diverse measures across GWA studies as well as time and the cost of assessment. In a series of four studies, we created a 15-min (40-item), online, gamified measure of g that is highly reliable (alpha = 0.78; two-week test-retest reliability = 0.88), psychometrically valid and scalable; we called this new measure Pathfinder. In a fifth study, we administered this measure to 4,751 young adults from the Twins Early Development Study. This novel g measure, which also yields reliable verbal and nonverbal scores, correlated substantially with standard measures of g collected at previous ages (r ranging from 0.42 at age 7 to 0.57 at age 16). Pathfinder showed substantial twin heritability (0.57, 95% CIs = 0.43, 0.68) and SNP heritability (0.37, 95% CIs = 0.04, 0.70). A polygenic score computed from GWA studies of five cognitive and educational traits accounted for 12% of the variation in g, the strongest DNA-based prediction of g to date. Widespread use of this engaging new measure will advance research not only in genomics but throughout the biological, medical, and behavioural sciences.

Recursive Path Model for Health Literacy: The Effect of Social Support and Geographical Residence

Background: The public health relevance of health literacy is highlighted by the fact that its higher levels can improve health outcomes and reduce health inequities. In order to design effective interventions for improving health literacy, the relationship between health literacy and other factors such as sociodemographic variables, subjective health and social support must be understood. Objective: Our aim was to test a socioecological model of the determinants of health literacy with a special focus on the effect of residence. Our study investigated geographical differences regarding the levels of health literacy and its determinants as this was not investigated before in European nationwide surveys. Methods: Data was collected by a polling company in a sample (n = 1,200) of the Hungarian adult population nationally representative by age, gender, and permanent residence in 2019 January. The questionnaire included items on sociodemographic data, subjective well-being, social support, and two health literacy scales. A recursive path model was used to outline the mediating effect of social support between sociodemographic variables and health literacy where both direct and indirect effects of the explanatory variables and multiple relationships among the variables were analyzed simultaneously. Multiple-group analysis was applied to the three pre-set categories of permanent residence (capital city, urban and rural). Results: There was no statistically significant difference by residence regarding levels of health literacy. Social support and educational attainment were the most important determinants of health literacy after adjusting for the effect of other sociodemographic variables. However, the magnitude of effect of social support and educational attainment is different between types of settlements, the strongest being in rural areas. Conclusion: Social support seems to mediate the effect of socioeconomic position on health literacy which could be taken into account when designing interventions to improve health literacy, especially in rural areas. Further studies would be needed especially in rural communities to see whether improvement of social support could be utilized in projects to increase the level of health literacy.

Genetically determined low income modifies Alzheimer's disease risk

Background

Socioeconomic status (SES) is considered to be associated with the prevalence of Alzheimer’s disease (AD). However, the causal association remain unclear. Here, we determining whether income has a causal protective effect on the risk of developing AD using Mendelian randomization (MR).

Methods

Single-nucleotide polymorphisms (SNPs) that are strongly associated with household income levels (P<5×10⁻⁸) from the UK Biobank (UKB) (n=286,301) were selected as instrumental variables for this study. Confounding instruments were removed through data set browsing. Selected SNPs were then harmonized with results from an AD genome-wide meta-analysis (71,880 cases, 383,378 controls) including both case-control and proxy cases. The analysis was conducted using MR methods, and multiple sensitivity analyses were applied for testing of potential bias.

Results

After confounding instrument removal and clumping, 9 SNPs associated with household income level identified by the UKB were left for the MR analysis. Our results demonstrated that higher household income level was causally related with a lower risk of AD (odds ratio 0.78, 95% confidence interval: 0.69–0.89; P<0.001). Multiple sensitivity analyses suggested no obvious evidence for heterogeneity or pleiotropy of the results.

Conclusions

Under MR assumptions, our results suggest robust evidence of a causal association between household income and AD risk, which may provide potential prevention strategies for this devastating disease.

Contribution of birth weight to mental health, cognitive and socioeconomic outcomes: two-sample Mendelian randomisation

BACKGROUND

Low birth weight is associated with adult mental health, cognitive and socioeconomic problems. However, the causal nature of these associations remains difficult to establish owing to confounding.

AIMS

To estimate the contribution of birth weight to adult mental health, cognitive and socioeconomic outcomes using two-sample Mendelian randomisation, an instrumental variable approach strengthening causal inference.

METHOD

We used 48 independent single-nucleotide polymorphisms as genetic instruments for birth weight (genome-wide association studies' total sample: n = 264 498) and considered mental health (attention-deficit hyperactivity disorder (ADHD), autism spectrum disorder, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, post-traumatic stress disorder (PTSD), schizophrenia, suicide attempt), cognitive (intelligence) and socioeconomic (educational attainment, income, social deprivation) outcomes.

RESULTS

We found evidence for a contribution of birth weight to ADHD (OR for 1 s.d. unit decrease (~464 g) in birth weight, 1.29; 95% CI 1.03-1.62), PTSD (OR = 1.69; 95% CI 1.06-2.71) and suicide attempt (OR = 1.39; 95% CI 1.05-1.84), as well as for intelligence (β = -0.07; 95% CI -0.13 to -0.02) and socioeconomic outcomes, i.e. educational attainment (β = -0.05; 95% CI -0.09 to -0.01), income (β = -0.08; 95% CI -0.15 to -0.02) and social deprivation (β = 0.08; 95% CI 0.03-0.13). However, no evidence was found for a contribution of birth weight to the other examined mental health outcomes. Results were consistent across a wide range of sensitivity analyses.

CONCLUSIONS

These findings support the hypothesis that birth weight could be an important element on the causal pathway to mental health, cognitive and socioeconomic outcomes.

Genetic correlates of socio-economic status influence the pattern of shared heritability across mental health traits

Epidemiological studies show high comorbidity between different mental health problems, indicating that individuals with a diagnosis of one disorder are more likely to develop other mental health problems. Genetic studies reveal substantial sharing of genetic factors across mental health traits. However, mental health is also genetically correlated with socio-economic status (SES), and it is therefore important to investigate and disentangle the genetic relationship between mental health and SES. We used summary statistics from large genome-wide association studies (average N ~ 160,000) to estimate the genetic overlap across nine psychiatric disorders and seven substance use traits and explored the genetic influence of three different indicators of SES. Using genomic structural equation modelling, we show significant changes in patterns of genetic correlations after partialling out SES-associated genetic variation. Our approach allows the separation of disease-specific genetic variation and genetic variation shared with SES, thereby improving our understanding of the genetic architecture of mental health.

Genetic correlates of socio-economic status influence the pattern of shared heritability across mental health traits

Epidemiological studies show high comorbidity between different mental health problems, indicating that individuals with a diagnosis of one disorder are more likely to develop other mental health problems. Genetic studies reveal substantial sharing of genetic factors across mental health traits. However, mental health is also genetically correlated with socio-economic status (SES), and it is therefore important to investigate and disentangle the genetic relationship between mental health and SES. We used summary statistics from large genome-wide association studies (average N ~ 160,000) to estimate the genetic overlap across nine psychiatric disorders and seven substance use traits and explored the genetic influence of three different indicators of SES. Using genomic structural equation modelling, we show significant changes in patterns of genetic correlations after partialling out SES-associated genetic variation. Our approach allows the separation of disease-specific genetic variation and genetic variation shared with SES, thereby improving our understanding of the genetic architecture of mental health.

Genetic underpinnings of risky behaviour relate to altered neuroanatomy

Previous research points to the heritability of risk-taking behaviour. However, evidence on how genetic dispositions are translated into risky behaviour is scarce. Here, we report a genetically informed neuroimaging study of real-world risky behaviour across the domains of drinking, smoking, driving and sexual behaviour in a European sample from the UK Biobank (N = 12,675). We find negative associations between risky behaviour and grey-matter volume in distinct brain regions, including amygdala, ventral striatum, hypothalamus and dorsolateral prefrontal cortex (dlPFC). These effects are replicated in an independent sample recruited from the same population (N = 13,004). Polygenic risk scores for risky behaviour, derived from a genome-wide association study in an independent sample (N = 297,025), are inversely associated with grey-matter volume in dlPFC, putamen and hypothalamus. This relation mediates roughly 2.2% of the association between genes and behaviour. Our results highlight distinct heritable neuroanatomical features as manifestations of the genetic propensity for risk taking.