Population structure, migration, and diversifying selection in the Netherlands

Exploring first-degree family history in a cohort of Portuguese Alzheimer's disease patients: population evidence for X-chromosome linked and recessive inheritance of risk factors

BACKGROUND

Alzheimer's disease (AD) heritability is estimated to be around 70-80%. Yet, much of it remains to be explained. Studying transmission patterns may help in understanding other factors contributing to the development of AD.

OBJECTIVE

In this study, we aimed to search for evidence of autosomal recessive or X- and Y-linked inheritance of risk factors in a large cohort of Portuguese AD patients.

METHODS

We collected family history from patients with AD and cognitively healthy controls over 75 years of age. We compared the proportions of maternal and paternal history in male and female patients and controls (to search for evidence of X-linked and Y-linked inherited risk factors). We compared the risk of developing AD depending on parents' birthplace (same vs. different), as a proxy of remote consanguinity. We performed linear regressions to study the association of these variables with different endophenotypes.

RESULTS

We included 3090 participants, 2183 cognitively healthy controls and 907 patients with AD. Men whose mother had dementia have increased odds of developing AD comparing to women whose mother had dementia. In female patients with a CSF biomarker-supported diagnosis of AD, paternal history of dementia is associated with increased CSF phosphorylated Tau levels. People whose parents are from the same town have higher risk of dementia. In multivariate analysis, this proxy is associated with a lower age of onset and higher CSF phosphorylated tau.

CONCLUSIONS

Our study gives evidence supporting an increased risk of developing AD associated with an X-linked inheritance pattern and remote consanguinity.

Intergenerational transmission of genetic risk for hyperactivity and inattention. Direct genetic transmission or genetic nurture?

Background

Hyperactivity and inattention, the symptoms of ADHD, are marked by high levels of heritability and intergenerational transmission. Two distinct pathways of genetic intergenerational transmission are distinguished: direct genetic transmission when parental genetic variants are passed to the child's genome and genetic nurture when the parental genetic background contributes to the child's outcomes through rearing environment. This study assessed genetic contributions to hyperactivity and inattention in childhood through these transmission pathways.

Methods

The sample included 415 families from the Quebec Newborn Twin Study. Twins' hyperactivity and inattention were assessed in early childhood by parents and in primary school by teachers. The polygenic scores for ADHD (ADHD-PGS) and educational attainment (EA-PGS) were computed from twins' and parents' genotypes. A model of intergenerational transmission was developed to estimate (1) the contributions of parents' and children's PGS to the twins' ADHD symptoms and (2) whether these variances were explained by genetic transmission and/or genetic nurture.

Results

ADHD-PGS explained up to 1.6% of the variance of hyperactivity and inattention in early childhood and primary school. EA-PGS predicted ADHD symptoms at both ages, explaining up to 1.6% of the variance in early childhood and up to 5.5% in primary school. Genetic transmission was the only significant transmission pathway of both PGS. The genetic nurture channeled through EA-PGS explained up to 3.2% of the variance of inattention in primary school but this association was non-significant.

Conclusions

Genetic propensities to ADHD and education predicted ADHD symptoms in childhood, especially in primary school. Its intergenerational transmission was driven primarily by genetic variants passed to the child, rather than by environmentally mediated parental genetic effects. The model developed in this study can be leveraged in future research to investigate genetic transmission and genetic nurture while accounting for parental assortative mating.

No gene by stressful life events interaction on individual differences in adults' self-control

Background

Difficulty with self-control, or the ability to alter impulses and behavior in a goal-directed way, predicts interpersonal conflict, lower socioeconomic attainments, and more adverse health outcomes. Etiological understanding, and intervention for low self-control is, therefore, a public health goal. A prominent developmental theory proposes that individuals with high genetic propensity for low self-control that are also exposed to stressful environments may be most at-risk of low levels of self-control. Here we examine if polygenic measures associated with behaviors marked by low self-control interact with stressful life events in predicting self-control.

Methods

Leveraging molecular data from a large population-based Dutch sample (N = 7,090, Mage = 41.2) to test for effects of genetics (i.e., polygenic scores for ADHD and aggression), stressful life events (e.g., traffic accident, violent assault, financial problems), and a gene-by-stress interaction on self-control (measured with the ASEBA Self-Control Scale).

Results

Both genetics (β =.03 -.04, p <.001) and stressful life events (β = .11 -.14, p <.001) were associated with individual differences in self-control. We find no evidence of a gene-by-stressful life events interaction on individual differences in adults' self-control.

Conclusion

Our findings are consistent with the notion that genetic influences and stressful life events exert largely independent effects on adult self-control. However, the small effect sizes of polygenic scores increases the likelihood of null results. Genetically-informed longitudinal research in large samples can further inform the etiology of individual differences in self-control from early childhood into later adulthood and its downstream implications for public health.

Adjusting for principal components can induce spurious associations in genome-wide association studies in admixed populations

Principal component analysis (PCA) is widely used to control for population structure in genome-wide association studies (GWAS). Top principal components (PCs) typically reflect population structure, but challenges arise in deciding how many PCs are needed and ensuring that PCs do not capture other artifacts such as regions with atypical linkage disequilibrium (LD). In response to the latter, many groups suggest performing LD pruning or excluding known high LD regions prior to PCA. However, these suggestions are not universally implemented and the implications for GWAS are not fully understood, especially in the context of admixed populations. In this paper, we investigate the impact of pre-processing and the number of PCs included in GWAS models in African American samples from the Women's Women's Health Initiative SNP Health Association Resource and two Trans-Omics for Precision Medicine Whole Genome Sequencing Project contributing studies (Jackson Heart Study and Genetic Epidemiology of Chronic Obstructive Pulmonary Disease Study). In all three samples, we find the first PC is highly correlated with genome-wide ancestry whereas later PCs often capture local genomic features. The pattern of which, and how many, genetic variants are highly correlated with individual PCs differs from what has been observed in prior studies focused on European populations and leads to distinct downstream consequences: adjusting for such PCs yields biased effect size estimates and elevated rates of spurious associations due to the phenomenon of collider bias. Excluding high LD regions identified in previous studies does not resolve these issues. LD pruning proves more effective, but the optimal choice of thresholds varies across datasets. Altogether, our work highlights unique issues that arise when using PCA to control for ancestral heterogeneity in admixed populations and demonstrates the importance of careful pre-processing and diagnostics to ensure that PCs capturing multiple local genomic features are not included in GWAS models.

Phylogeography, origin and population structure of the self-fertile emerging plant pathogen Phytophthora pseudosyringae

Phytophthora pseudosyringae is a self-fertile pathogen of woody plants, particularly associated with tree species from the genera Fagus, Notholithocarpus, Nothofagus and Quercus, which is found across Europe and in parts of North America and Chile. It can behave as a soil pathogen infecting roots and the stem collar region, as well as an aerial pathogen infecting leaves, twigs and stem barks, causing particular damage in the United Kingdom and western North America. The population structure, migration and potential outcrossing of a worldwide collection of isolates were investigated using genotyping-by-sequencing. Coalescent-based migration analysis revealed that the North American population originated from Europe. Historical gene flow has occurred between the continents in both directions to some extent, yet contemporary migration is overwhelmingly from Europe to North America. Two broad population clusters dominate the global population of the pathogen, with a subgroup derived from one of the main clusters found only in western North America. Index of association and network analyses indicate an influential level of outcrossing has occurred in this preferentially inbreeding, homothallic oomycete. Outcrossing between the two main population clusters has created distinct subgroups of admixed individuals that are, however, less common than the main population clusters. Differences in life history traits between the two main population clusters should be further investigated together with virulence and host range tests to evaluate the risk each population poses to natural environments worldwide.

Integrative multi-omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention-Deficit/Hyperactivity Disorder

The evolving field of multi-omics combines data and provides methods for simultaneous analysis across several omics levels. Here, we integrated genomics (transmitted and non-transmitted polygenic scores [PGSs]), epigenomics, and metabolomics data in a multi-omics framework to identify biomarkers for Attention-Deficit/Hyperactivity Disorder (ADHD) and investigated the connections among the three omics levels. We first trained single- and next multi-omics models to differentiate between cases and controls in 596 twins (cases = 14.8%) from the Netherlands Twin Register (NTR) demonstrating reasonable in-sample prediction through cross-validation. The multi-omics model selected 30 PGSs, 143 CpGs, and 90 metabolites. We confirmed previous associations of ADHD with glucocorticoid exposure and the transmembrane protein family TMEM, show that the DNA methylation of the MAD1L1 gene associated with ADHD has a relation with parental smoking behavior, and present novel findings including associations between indirect genetic effects and CpGs of the STAP2 gene. However, out-of-sample prediction in NTR participants (N = 258, cases = 14.3%) and in a clinical sample (N = 145, cases = 51%) did not perform well (range misclassification was [0.40, 0.57]). The results highlighted connections between omics levels, with the strongest connections between non-transmitted PGSs, CpGs, and amino acid levels and show that multi-omics designs considering interrelated omics levels can help unravel the complex biology underlying ADHD.

Genomics Research of Lifetime Depression in the Netherlands: The BIObanks Netherlands Internet Collaboration (BIONIC) Project

In this cohort profile article we describe the lifetime major depressive disorder (MDD) database that has been established as part of the BIObanks Netherlands Internet Collaboration (BIONIC). Across the Netherlands we collected data on Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) lifetime MDD diagnosis in 132,850 Dutch individuals. Currently, N = 66,684 of these also have genomewide single nucleotide polymorphism (SNP) data. We initiated this project because the complex genetic basis of MDD requires large population-wide studies with uniform in-depth phenotyping. For standardized phenotyping we developed the LIDAS (LIfetime Depression Assessment Survey), which then was used to measure MDD in 11 Dutch cohorts. Data from these cohorts were combined with diagnostic interview depression data from 5 clinical cohorts to create a dataset of N = 29,650 lifetime MDD cases (22%) meeting DSM-5 criteria and 94,300 screened controls. In addition, genomewide genotype data from the cohorts were assembled into a genomewide association study (GWAS) dataset of N = 66,684 Dutch individuals (25.3% cases). Phenotype data include DSM-5-based MDD diagnoses, sociodemographic variables, information on lifestyle and BMI, characteristics of depressive symptoms and episodes, and psychiatric diagnosis and treatment history. We describe the establishment and harmonization of the BIONIC phenotype and GWAS datasets and provide an overview of the available information and sample characteristics. Our next step is the GWAS of lifetime MDD in the Netherlands, with future plans including fine-grained genetic analyses of depression characteristics, international collaborations and multi-omics studies.

Genetic Distinctness and Diversity of American Aberdeen Cattle Compared to Common Beef Breeds in the United States

American Aberdeen (AD) cattle in the USA descend from an Aberdeen Angus herd originally brought to the Trangie Agricultural Research Centre, New South Wales, AUS. Although put under specific selection pressure for yearling growth rate, AD remain genomically uncharacterized. The objective was to characterize the genetic diversity and structure of purebred and crossbred AD cattle relative to seven common USA beef breeds using available whole-genome SNP data. A total of 1140 animals consisting of 404 purebred (n = 8 types) and 736 admixed individuals (n = 10 types) was used. Genetic diversity metrics, an analysis of molecular variance, and a discriminant analysis of principal components were employed. When linkage disequilibrium was not accounted for, markers influenced basic diversity parameter estimates, especially for AD cattle. Even so, intrapopulation and interpopulation estimates separate AD cattle from other purebred types (e.g., Latter's pairwise FST ranged from 0.1129 to 0.2209), where AD cattle were less heterozygous and had lower allelic richness than other purebred types. The admixed AD-influenced cattle were intermediate to other admixed types for similar parameters. The diversity metrics separation and differences support strong artificial selection pressures during and after AD breed development, shaping the evolution of the breed and making them genomically distinct from similar breeds.

Recent natural selection conferred protection against schizophrenia by non-antagonistic pleiotropy

Schizophrenia is a debilitating psychiatric disorder associated with a reduced fertility and decreased life expectancy, yet common predisposing variation substantially contributes to the onset of the disorder, which poses an evolutionary paradox. Previous research has suggested balanced selection, a mechanism by which schizophrenia risk alleles could also provide advantages under certain environments, as a reliable explanation. However, recent studies have shown strong evidence against a positive selection of predisposing loci. Furthermore, evolutionary pressures on schizophrenia risk alleles could have changed throughout human history as new environments emerged. Here in this study, we used 1000 Genomes Project data to explore the relationship between schizophrenia predisposing loci and recent natural selection (RNS) signatures after the human diaspora out of Africa around 100,000 years ago on a genome-wide scale. We found evidence for significant enrichment of RNS markers in derived alleles arisen during human evolution conferring protection to schizophrenia. Moreover, both partitioned heritability and gene set enrichment analyses of mapped genes from schizophrenia predisposing loci subject to RNS revealed a lower involvement in brain and neuronal related functions compared to those not subject to RNS. Taken together, our results suggest non-antagonistic pleiotropy as a likely mechanism behind RNS that could explain the persistence of schizophrenia common predisposing variation in human populations due to its association to other non-psychiatric phenotypes.

Genetic Ancestry Estimates within Dutch Family Units and Across Genotyping Arrays: Insights from Empirical Analysis Using Two Estimation Methods

Accurate inference of genetic ancestry is crucial for population-based association studies, accounting for population heterogeneity and structure. This study analyzes genome-wide SNP data from the Netherlands Twin Register to compare genetic ancestry estimates. The focus is on the comparison of ancestry estimates between family members and individuals genotyped on multiple arrays (Affymetrix 6.0, Affymetrix Axiom, and Illumina GSA). Two conventional methods, principal component analysis and ADMIXTURE, were implemented to estimate ancestry, each serving its specific purpose, rather than for direct comparison. The results reveal that as the degree of genetic relatedness decreases, the Euclidean distances of genetic ancestry estimates between family members significantly increase (empirical p < 0.001), regardless of the estimation method and genotyping array. Ancestry estimates among individuals genotyped on multiple arrays also show statistically significant differences (empirical p < 0.001). Additionally, this study investigates the relationship between the ancestry estimates of non-identical twin offspring with ancestrally diverse parents and those with ancestrally similar parents. The results indicate a statistically significant weak correlation between the variation in ancestry estimates among offspring and differences in ancestry estimates among parents (Spearman's rho: 0.07, p = 0.005). This study highlights the utility of current methods in inferring genetic ancestry, emphasizing the importance of reference population composition in determining ancestry estimates.

Phylogeography and population structure of the global, wide host-range hybrid pathogen Phytophthora × cambivora

Invasive, exotic plant pathogens pose a major threat to native and agricultural ecosystems. Phytophthora × cambivora is an invasive, destructive pathogen of forest and fruit trees causing severe damage worldwide to chestnuts (Castanea), apricots, peaches, plums, almonds and cherries (Prunus), apples (Malus), oaks (Quercus), and beech (Fagus). It was one of the first damaging invasive Phytophthora species to be introduced to Europe and North America, although its origin is unknown. We determined its population genetic history in Europe, North and South America, Australia and East Asia (mainly Japan) using genotyping-by-sequencing. Populations in Europe and Australia appear clonal, those in North America are highly clonal yet show some degree of sexual reproduction, and those in East Asia are partially sexual. Two clonal lineages, each of opposite mating type, and a hybrid lineage derived from these two lineages, dominated the populations in Europe and were predominantly found on fagaceous forest hosts (Castanea, Quercus, Fagus). Isolates from fruit trees (Prunus and Malus) belonged to a separate lineage found in Australia, North America, Europe and East Asia, indicating the disease on fruit trees could be caused by a distinct lineage of P. × cambivora, which may potentially be a separate sister species and has likely been moved with live plants. The highest genetic diversity was found in Japan, suggesting that East Asia is the centre of origin of the pathogen. Further surveys in unsampled, temperate regions of East Asia are needed to more precisely identify the location and range of the centre of diversity.

Quality Control Procedures for Genome-Wide Association Studies

Genome-wide association studies (GWAS) are being conducted at an unprecedented rate in population-based cohorts and have increased our understanding of the pathophysiology of many complex diseases. Regardless of the context, the practical utility of this information ultimately depends upon the quality of the data used for statistical analyses. Quality control (QC) procedures for GWAS are constantly evolving. Here, we enumerate some of the challenges in QC of genotyped GWAS data and describe the approaches involving genotype imputation of a sample dataset along with post-imputation quality assurance, thereby minimizing potential bias and error in GWAS results. We discuss common issues associated with QC of the GWAS data (genotyped and imputed), including data file formats, software packages for data manipulation and analysis, sex chromosome anomalies, sample identity, sample relatedness, population substructure, batch effects, and marker quality. We provide detailed guidelines along with a sample dataset to suggest current best practices and discuss areas of ongoing and future research. © 2022 Wiley Periodicals LLC.

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.

Gene-environment correlations across geographic regions affect genome-wide association studies

Gene-environment correlations affect associations between genetic variants and complex traits in genome-wide association studies (GWASs). Here we showed in up to 43,516 British siblings that educational attainment polygenic scores capture gene-environment correlations, and that migration extends these gene-environment correlations beyond the family to broader geographic regions. We then ran GWASs on 56 complex traits in up to 254,387 British individuals. Controlling for geographic regions significantly decreased the heritability for socioeconomic status (SES)-related traits, most strongly for educational attainment and income. For most traits, controlling for regions significantly reduced genetic correlations with educational attainment and income, most significantly for body mass index/body fat, sedentary behavior and substance use, consistent with gene-environment correlations related to regional socio-economic differences. The effects of controlling for birthplace and current address suggest both passive and active sources of gene-environment correlations. Our results show that the geographic clustering of DNA and SES introduces gene-environment correlations that affect GWAS results.

Estimating effects of parents' cognitive and non-cognitive skills on offspring education using polygenic scores

Understanding how parents' cognitive and non-cognitive skills influence offspring education is essential for educational, family and economic policy. We use genetics (GWAS-by-subtraction) to assess a latent, broad non-cognitive skills dimension. To index parental effects controlling for genetic transmission, we estimate indirect parental genetic effects of polygenic scores on childhood and adulthood educational outcomes, using siblings (N = 47,459), adoptees (N = 6407), and parent-offspring trios (N = 2534) in three UK and Dutch cohorts. We find that parental cognitive and non-cognitive skills affect offspring education through their environment: on average across cohorts and designs, indirect genetic effects explain 36-40% of population polygenic score associations. However, indirect genetic effects are lower for achievement in the Dutch cohort, and for the adoption design. We identify potential causes of higher sibling- and trio-based estimates: prenatal indirect genetic effects, population stratification, and assortative mating. Our phenotype-agnostic, genetically sensitive approach has established overall environmental effects of parents' skills, facilitating future mechanistic work.

The Socioeconomic Gradient in Epigenetic Ageing Clocks: Evidence from the Multi-Ethnic Study of Atherosclerosis and the Health and Retirement Study

Epigenetic clocks have been widely used to predict disease risk in multiple tissues or cells. Their success as a measure of biological ageing has prompted research on the connection between epigenetic pathways of ageing and the socioeconomic gradient in health and mortality. However, studies examining social correlates of epigenetic ageing have yielded inconsistent results. We conducted a comprehensive, comparative analysis of associations between various dimensions of socioeconomic status (SES) (education, income, wealth, occupation, neighbourhood environment, and childhood SES) and eight epigenetic clocks in two well-powered US ageing studies: The Multi-Ethnic Study of Atherosclerosis (MESA) (n = 1,211) and the Health and Retirement Study (HRS) (n = 4,018). In both studies, we found robust associations between SES measures in adulthood and the GrimAge and DunedinPoAm clocks (Bonferroni-corrected p-value < 0.01). In the HRS, significant associations with the Levine and Yang clocks were also evident. These associations were only partially mediated by smoking, alcohol consumption, and obesity, which suggests that differences in health behaviours alone cannot explain the SES gradient in epigenetic ageing in older adults. Further analyses revealed concurrent associations between polygenic risk for accelerated intrinsic epigenetic ageing, SES, and the Levine clock, indicating that genetic risk and social disadvantage may contribute additively to faster biological aging.

Estimating SNP heritability in presence of population substructure in biobank-scale datasets

Single nucleotide polymorphism heritability of a trait is measured as the proportion of total variance explained by the additive effects of genome-wide single nucleotide polymorphisms. Linear mixed models are routinely used to estimate single nucleotide polymorphism heritability for many complex traits, which requires estimation of a genetic relationship matrix among individuals. Heritability is usually estimated by the restricted maximum likelihood or method of moments approaches such as Haseman-Elston regression. The common practice of accounting for such population substructure is to adjust for the top few principal components of the genetic relationship matrix as covariates in the linear mixed model. This can get computationally very intensive on large biobank-scale datasets. Here, we propose a method of moments approach for estimating single nucleotide polymorphism heritability in presence of population substructure. Our proposed method is computationally scalable on biobank datasets and gives an asymptotically unbiased estimate of heritability in presence of discrete substructures. It introduces the adjustments for population stratification in a second-order estimating equation. It allows these substructures to vary in their single nucleotide polymorphism allele frequencies and in their trait distributions (means and variances) while the heritability is assumed to be the same across these substructures. Through extensive simulation studies and the application on 7 quantitative traits in the UK Biobank cohort, we demonstrate that our proposed method performs well in the presence of population substructure and much more computationally efficient than existing approaches.

Interrogating Associations Between Polygenic Liabilities and Electroconvulsive Therapy Effectiveness

BACKGROUND

Electroconvulsive therapy (ECT) is the most effective treatment for severe major depressive episodes (MDEs). Nonetheless, firmly established associations between ECT outcomes and biological variables are currently lacking. Polygenic risk scores (PRSs) carry clinical potential, but associations with treatment response in psychiatry are seldom reported. Here, we examined whether PRSs for major depressive disorder, schizophrenia (SCZ), cross-disorder, and pharmacological antidepressant response are associated with ECT effectiveness.

METHODS

A total of 288 patients with MDE from 3 countries were included. The main outcome was a change in the 17-item Hamilton Depression Rating Scale scores from before to after ECT treatment. Secondary outcomes were response and remission. Regression analyses with PRSs as independent variables and several covariates were performed. Explained variance (R²) at the optimal p-value threshold is reported.

RESULTS

In the 266 subjects passing quality control, the PRS-SCZ was positively associated with a larger Hamilton Depression Rating Scale decrease in linear regression (optimal p-value threshold = .05, R² = 6.94%, p < .0001), which was consistent across countries: Ireland (R² = 8.18%, p = .0013), Belgium (R² = 6.83%, p = .016), and the Netherlands (R² = 7.92%, p = .0077). The PRS-SCZ was also positively associated with remission (R² = 4.63%, p = .0018). Sensitivity and subgroup analyses, including in MDE without psychotic features (R² = 4.42%, p = .0024) and unipolar MDE only (R² = 9.08%, p < .0001), confirmed the results. The other PRSs were not associated with a change in the Hamilton Depression Rating Scale score at the predefined Bonferroni-corrected significance threshold.

CONCLUSIONS

A linear association between PRS-SCZ and ECT outcome was uncovered. Although it is too early to adopt PRSs in ECT clinical decision making, these findings strengthen the positioning of PRS-SCZ as relevant to treatment response in psychiatry.

Expanding the environmental scope: an environment-wide association study for mental well-being

BACKGROUND

Identifying modifiable factors associated with well-being is of increased interest for public policy guidance. Developments in record linkage make it possible to identify what contributes to well-being from a myriad of factors. To this end, we link two large-scale data resources; the Geoscience and Health Cohort Consortium, a collection of geo-data, and the Netherlands Twin Register, which holds population-based well-being data.

OBJECTIVE

We perform an Environment-Wide Association Study (EnWAS), where we examine 139 neighbourhood-level environmental exposures in relation to well-being.

METHODS

First, we performed a generalized estimation equation regression (N = 11,975) to test for the effects of environmental exposures on well-being. Second, to account for multicollinearity amongst exposures, we performed principal component regression. Finally, using a genetically informative design, we examined whether environmental exposure is driven by genetic predisposition for well-being.

RESULTS

We identified 21 environmental factors that were associated with well-being in the domains: housing stock, income, core neighbourhood characteristics, livability, and socioeconomic status. Of these associations, socioeconomic status and safety are indicated as the most important factors to explain differences in well-being. No evidence of gene-environment correlation was found.

SIGNIFICANCE

These observed associations, especially neighbourhood safety, could be informative for policy makers and provide public policy guidance to improve well-being. Our results show that linking databases is a fruitful exercise to identify determinants of mental health that would remain unknown by a more unilateral approach.

Runs of homozygosity in Swiss goats reveal genetic changes associated with domestication and modern selection

Background

The domestication of goat (Capra hircus) started 11,000 years ago in the fertile crescent. Breed formation in the nineteenth century, establishment of herd books, and selection for specific traits resulted in 10 modern goat breeds in Switzerland. We analyzed whole-genome sequencing (WGS) data from 217 modern goats and nine wild Bezoar goats (Capra aegagrus). After quality control, 27,728,288 biallelic single nucleotide variants (SNVs) were used for the identification of runs of homozygosity (ROH) and the detection of ROH islands.

Results

Across the 226 caprine genomes from 11 populations, we detected 344 ROH islands that harbor 1220 annotated genes. We compared the ROH islands between the modern breeds and the Bezoar goats. As a proof of principle, we confirmed a signature of selection, which contains the ASIP gene that controls several breed-specific coat color patterns. In two other ROH islands, we identified two missense variants, STC1:p.Lys139Arg and TSHR:p.Ala239Thr, which might represent causative functional variants for domestication signatures.

Conclusions

We have shown that the information from ROH islands using WGS data is suitable for the analysis of signatures of selection and allowed the detection of protein coding variants that may have conferred beneficial phenotypes during goat domestication. We hypothesize that the TSHR:p.Ala239Thr variant may have played a role in changing the seasonality of reproduction in modern domesticated goats. The exact functional significance of the STC1:p.Lys139Arg variant remains unclear and requires further investigation. Nonetheless, STC1 might represent a new domestication gene affecting relevant traits such as body size and/or milk yield in goats.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-022-00695-w.

Using population-specific add-on polymorphisms to improve genotype imputation in underrepresented populations

Genome-wide association studies rely on the statistical inference of untyped variants, called imputation, to increase the coverage of genotyping arrays. However, the results are often suboptimal in populations underrepresented in existing reference panels and array designs, since the selected single nucleotide polymorphisms (SNPs) may fail to capture population-specific haplotype structures, hence the full extent of common genetic variation. Here, we propose to sequence the full genomes of a small subset of an underrepresented study cohort to inform the selection of population-specific add-on tag SNPs and to generate an internal population-specific imputation reference panel, such that the remaining array-genotyped cohort could be more accurately imputed. Using a Tanzania-based cohort as a proof-of-concept, we demonstrate the validity of our approach by showing improvements in imputation accuracy after the addition of our designed add-on tags to the base H3Africa array.

Genome-wide association studies, which study the association between genetic variants and various phenotypes, typically rely on genotyping arrays. Only a small proportion of genetic variants within the genome are typed on genotyping arrays. Untyped variants are statistically inferred through a process known as genotype imputation, where correlations between variants (haplotypes) observed in external reference panels are leveraged to infer untyped variants in the study population. However, for study populations that are underrepresented in existing reference panels, the quality of imputation is often sub-optimal. This is because typed variants incorporated on existing genotyping arrays can be unsuitable for the study population, and haplotype structures can be different between the reference and the study population. Here, we illustrate an approach to select a custom set of population-specific typed variants to improve genotype imputation in such underrepresented populations.

Is there still evolution in the human population?

It is often claimed that humanity has stopped evolving because modern medicine erased all selection on survival. Even if that would be true, and it is not, there would be other mechanisms of evolution which could still led to changes in allelic frequencies. Here I show, by applying basic evolutionary genetics knowledge, that we expect humanity to evolve. The results from genome sequencing projects have repeatedly affirmed that there are still recent signs of selection in our genomes. I give some examples of such adaptation. Then I briefly discuss what our evolutionary future has in store for us.

Gene flow influences the genomic architecture of local adaptation in six riverine fish species

Understanding how gene flow influences adaptive divergence is important for predicting adaptive responses. Theoretical studies suggest that when gene flow is high, clustering of adaptive genes in fewer genomic regions would protect adaptive alleles from recombination and thus be selected for, but few studies have tested it with empirical data. Here, we used restriction site-associated sequencing to generate genomic data for six fish species with contrasting life histories from six reaches of the Upper Mississippi River System, USA. We used four differentiation-based outlier tests and three genotype-environment association analyses to define neutral single nucleotide polymorphisms (SNPs) and outlier SNPs that were putatively under selection. We then examined the distribution of outlier SNPs along the genome and investigated whether these SNPs were found in genomic islands of differentiation and inversions. We found that gene flow varied among species, and outlier SNPs were clustered more tightly in species with higher gene flow. The two species with the highest overall F_ST (0.0303-0.0720) and therefore lowest gene flow showed little evidence of clusters of outlier SNPs, with outlier SNPs in these species spreading uniformly across the genome. In contrast, nearly all outlier SNPs in the species with the lowest F_ST (0.0003) were found in a single large putative inversion. Two other species with intermediate gene flow (F_ST  ~ 0.0025-0.0050) also showed clustered genomic architectures, with most islands of differentiation clustered on a few chromosomes. Our results provide important empirical evidence to support the hypothesis that increasingly clustered architecture of local adaptation is associated with high gene flow.

Continuity of Genetic Risk for Aggressive Behavior Across the Life-Course

We test whether genetic influences that explain individual differences in aggression in early life also explain individual differences across the life-course. In two cohorts from The Netherlands (N = 13,471) and Australia (N = 5628), polygenic scores (PGSs) were computed based on a genome-wide meta-analysis of childhood/adolescence aggression. In a novel analytic approach, we ran a mixed effects model for each age (Netherlands: 12-70 years, Australia: 16-73 years), with observations at the focus age weighted as 1, and decaying weights for ages further away. We call this approach a 'rolling weights' model. In The Netherlands, the estimated effect of the PGS was relatively similar from age 12 to age 41, and decreased from age 41-70. In Australia, there was a peak in the effect of the PGS around age 40 years. These results are a first indication from a molecular genetics perspective that genetic influences on aggressive behavior that are expressed in childhood continue to play a role later in life.

Genome-wide CNV investigation suggests a role for cadherin, Wnt, and p53 pathways in primary open-angle glaucoma

BACKGROUND

To investigate whether copy number variations (CNVs) are implicated in molecular mechanisms underlying primary open-angle glaucoma (POAG), we used genotype data of POAG individuals and healthy controls from two case-control studies, AGS (n = 278) and GLGS-UGLI (n = 1292). PennCNV, QuantiSNP, and cnvPartition programs were used to detect CNV. Stringent quality controls at both sample and marker levels were applied. The identified CNVs were intersected in CNV region (CNVR). After, we performed burden analysis, CNV-genome-wide association analysis, gene set overrepresentation and pathway analysis. In addition, in human eye tissues we assessed the expression of the genes lying within significant CNVRs.

RESULTS

We reported a statistically significant greater burden of CNVs in POAG cases compared to controls (p-value = 0,007). In common between the two cohorts, CNV-association analysis identified statistically significant CNVRs associated with POAG that span 11 genes (APC, BRCA2, COL3A1, HLA-DRB1, HLA-DRB5, HLA-DRB6, MFSD8, NIPBL, SCN1A, SDHB, and ZDHHC11). Functional annotation and pathway analysis suggested the involvement of cadherin, Wnt signalling, and p53 pathways.

CONCLUSIONS

Our data suggest that CNVs may have a role in the susceptibility of POAG and they can reveal more information on the mechanism behind this disease. Additional genetic and functional studies are warranted to ascertain the contribution of CNVs in POAG.

Dissecting polygenic signals from genome-wide association studies on human behaviour

Genome-wide association studies on human behavioural traits are producing large amounts of polygenic signals with significant predictive power and potentially useful biological clues. Behavioural traits are more distal and are less directly under biological control compared with physical characteristics, which makes the associated genetic effects harder to interpret. The results of genome-wide association studies for human behaviour are likely made up of a composite of signals from different sources. While sample sizes continue to increase, we outline additional steps that need to be taken to better delineate the origin of the increasingly stronger polygenic signals. In addition to genetic effects on the traits themselves, the major sources of polygenic signals are those that are associated with correlated traits, environmental effects and ascertainment bias. Advances in statistical approaches that disentangle polygenic effects from different traits as well as extending data collection to families and social circles with better geographical coverage will probably contribute to filling the gap of knowledge between genetic effects and behavioural outcomes.

The genetic structure of Norway

The aim of the present study was to describe the genetic structure of the Norwegian population using genotypes from 6369 unrelated individuals with detailed information about places of residence. Using standard single marker- and haplotype-based approaches, we report evidence of two regions with distinctive patterns of genetic variation, one in the far northeast, and another in the south of Norway, as indicated by fixation indices, haplotype sharing, homozygosity, and effective population size. We detect and quantify a component of Uralic Sami ancestry that is enriched in the North. On a finer scale, we find that rates of migration have been affected by topography like mountain ridges. In the broader Scandinavian context, we detect elevated relatedness between the mid- and northern border areas towards Sweden. The main finding of this study is that despite Norway's long maritime history and as a former Danish territory, the region closest to mainland Europe in the south appears to have been an isolated region in Norway, highlighting the open sea as a barrier to gene flow into Norway.

Genetic evidence for a large overlap and potential bidirectional causal effects between resilience and well-being

Resilience and well-being are strongly related. People with higher levels of well-being are more resilient after stressful life events or trauma and vice versa. Less is known about the underlying sources of overlap and causality between the constructs. In a sample of 11.304 twins and 2.572 siblings from the Netherlands Twin Register, we investigated the overlap and possible direction of causation between resilience (i.e. the absence of psychiatric symptoms despite negative life events) and well-being (i.e. satisfaction with life) using polygenic score (PGS) prediction, twin-sibling modelling, and the Mendelian Randomization Direction of Causality (MR-DoC) model. Longitudinal twin-sibling models showed significant phenotypic correlations between resilience and well-being (.41/.51 at time 1 and 2). Well-being PGS were predictive for both well-being and resilience, indicating that genetic factors influencing well-being also predict resilience. Twin-sibling modeling confirmed this genetic correlation (0.71) and showed a strong environmental correlation (0.93). In line with causality, both genetic (51%) and environmental (49%) factors contributed significantly to the covariance between resilience and well-being. Furthermore, the results of within-subject and MZ twin differences analyses were in line with bidirectional causality. Additionally, we used the MR-DoC model combining both molecular and twin data to test causality, while correcting for pleiotropy. We confirmed the causal effect from well-being to resilience, with the direct effect of well-being explaining 11% (T1) and 20% (T2) of the variance in resilience. Data limitations prevented us to test the directional effect from resilience to well-being with the MR-DoC model. To conclude, we showed a strong relation between well-being and resilience. A first attempt to quantify the direction of this relationship points towards a bidirectional causal effect. If replicated, the potential mutual effects can have implications for interventions to lower psychopathology vulnerability, as resilience and well-being are both negatively related to psychopathology.

Multilevel Twin Models: Geographical Region as a Third Level Variable

The classical twin model can be reparametrized as an equivalent multilevel model. The multilevel parameterization has underexplored advantages, such as the possibility to include higher-level clustering variables in which lower levels are nested. When this higher-level clustering is not modeled, its variance is captured by the common environmental variance component. In this paper we illustrate the application of a 3-level multilevel model to twin data by analyzing the regional clustering of 7-year-old children’s height in the Netherlands. Our findings show that 1.8%, of the phenotypic variance in children’s height is attributable to regional clustering, which is 7% of the variance explained by between-family or common environmental components. Since regional clustering may represent ancestry, we also investigate the effect of region after correcting for genetic principal components, in a subsample of participants with genome-wide SNP data. After correction, region no longer explained variation in height. Our results suggest that the phenotypic variance explained by region might represent ancestry effects on height.

Efficient toolkit implementing best practices for principal component analysis of population genetic data

Motivation

Principal component analysis (PCA) of genetic data is routinely used to infer ancestry and control for population structure in various genetic analyses. However, conducting PCA analyses can be complicated and has several potential pitfalls. These pitfalls include (i) capturing linkage disequilibrium (LD) structure instead of population structure, (ii) projected PCs that suffer from shrinkage bias, (iii) detecting sample outliers and (iv) uneven population sizes. In this work, we explore these potential issues when using PCA, and present efficient solutions to these. Following applications to the UK Biobank and the 1000 Genomes project datasets, we make recommendations for best practices and provide efficient and user-friendly implementations of the proposed solutions in R packages bigsnpr and bigutilsr.

Results

For example, we find that PC19–PC40 in the UK Biobank capture complex LD structure rather than population structure. Using our automatic algorithm for removing long-range LD regions, we recover 16 PCs that capture population structure only. Therefore, we recommend using only 16–18 PCs from the UK Biobank to account for population structure confounding. We also show how to use PCA to restrict analyses to individuals of homogeneous ancestry. Finally, when projecting individual genotypes onto the PCA computed from the 1000 Genomes project data, we find a shrinkage bias that becomes large for PC5 and beyond. We then demonstrate how to obtain unbiased projections efficiently using bigsnpr. Overall, we believe this work would be of interest for anyone using PCA in their analyses of genetic data, as well as for other omics data.

Availability and implementation

R packages bigsnpr and bigutilsr can be installed from either CRAN or GitHub (see https://github.com/privefl/bigsnpr). A tutorial on the steps to perform PCA on 1000G data is available at https://privefl.github.io/bigsnpr/articles/bedpca.html. All code used for this paper is available at https://github.com/privefl/paper4-bedpca/tree/master/code.

Supplementary information

Supplementary data are available at Bioinformatics online.

Genetic underpinnings of schizophrenia-related electroencephalographical intermediate phenotypes: A systematic review and meta-analysis

Although substantial research into genetics of psychotic disorders has been conducted, a large proportion of their genetic architecture has remained unresolved. Electroencephalographical intermediate phenotypes (EIP) have the potential to constitute a valuable tool when studying genetic risk loci for schizophrenia, in particular P3b amplitude, P50 suppression, mismatch negativity (MMN) and resting state power spectra of the electroencephalogram (EEG). Here, we systematically reviewed studies investigating the association of single nucleotide polymorphisms (SNPs) with these EIPs and meta-analysed them when appropriate. We retrieved 45 studies (N = 34,971 study participants). Four SNPs investigated in more than one study were genome-wide significant for an association with schizophrenia and three were genome-wide suggestive, based on a lookup in the influential 2014 GWAS (Ripke et al., 2014). However, in our meta-analyses, rs1625579 failed to reach a statistically significant association with p3b amplitude decrease and rs4680 risk allele carrier status was not associated with p3b amplitude decrease or with impaired p50 suppression. In conclusion, evidence for SNP associations with EIPs remains limited to individual studies. Careful selection of EIPs and SNPs, combined with consistent reporting of effect sizes, directions of effect and p-values would aid future meta-analyses.

Introduction to Population Genomics Methods

High-throughput sequencing technologies have provided an unprecedented opportunity to study the different evolutionary forces that have shaped present-day patterns of genetic diversity, with important implications for many directions in plant biology research. To manage such massive quantities of sequencing data, biologists, however, need new additional skills in informatics and statistics. In this chapter, our objective is to introduce population genomics methods to beginners following a learning-by-doing strategy in order to help the reader to analyze the sequencing data by themselves. Conducted analyses cover several main areas of evolutionary biology, such as an initial description of the evolutionary history of a given species or the identification of genes targeted by natural or artificial selection. In addition to the practical advices, we performed re-analyses of two cases studies with different kind of data: a domesticated cereal (African rice) and a non-domesticated tree species (sessile oak). All the code needed to replicate this work is publicly available on github ( https://github.com/ThibaultLeroyFr/Intro2PopGenomics/ ).

Dutch population structure across space, time and GWAS design

Previous genetic studies have identified local population structure within the Netherlands; however their resolution is limited by use of unlinked markers and absence of external reference data. Here we apply advanced haplotype sharing methods (ChromoPainter/fineSTRUCTURE) to study fine-grained population genetic structure and demographic change across the Netherlands using genome-wide single nucleotide polymorphism data (1,626 individuals) with associated geography (1,422 individuals). We identify 40 haplotypic clusters exhibiting strong north/south variation and fine-scale differentiation within provinces. Clustering is tied to country-wide ancestry gradients from neighbouring lands and to locally restricted gene flow across major Dutch rivers. North-south structure is temporally stable, with west-east differentiation more transient, potentially influenced by migrations during the middle ages. Despite superexponential population growth, regional demographic estimates reveal population crashes contemporaneous with the Black Death. Within Dutch and international data, GWAS incorporating fine-grained haplotypic covariates are less confounded than standard methods.

The shared genetic architecture of schizophrenia, bipolar disorder and lifespan

Psychiatric disorders such as Schizophrenia (SCZ) and Bipolar Disorder (BD) represent an evolutionary paradox, as they exhibit strong negative effects on fitness, such as decreased fecundity and early mortality, yet they persist at a worldwide prevalence of approximately 1%. Molecular mechanisms affecting lifespan, which may be widely common among complex diseases with fitness effects, can be studied by the integrated analysis of data from genome-wide association studies (GWAS) of human longevity together with any disease of interest. Here, we report the first of such studies, focusing on the genetic overlap-pleiotropy-between two psychiatric disorders with shortened lifespan, SCZ and BD, and human parental lifespan (PLS) as a surrogate of life expectancy. Our results are twofold: first, we demonstrate extensive polygenic overlap between SCZ and PLS and to a lesser extent between BD and PLS. Second, we identified novel loci shared between PLS and SCZ (n = 39), and BD (n = 8). Whereas most of the identified SCZ (66%) and BD (62%) pleiotropic risk alleles were associated with reduced lifespan, we also detected some antagonistic protective alleles associated to shorter lifespans. In fact, top-associated SNPs with SCZ seems to explain longevity variance explained (LVE) better than many other life-threatening diseases, including Type 2 diabetes and most cancers, probably due to a high overlap with smoking-related pathways. Overall, our study provides evidence of a genetic burden driven through premature mortality among people with SCZ, which can have profound implications for understanding, and potentially treating, the mortality gap associated with this psychiatric disorder.

Empirical comparisons of multiple Mendelian randomization approaches in the presence of assortative mating

BACKGROUND

Mendelian randomization (MR) is widely used to unravel causal relationships in epidemiological studies. Whereas multiple MR methods have been developed to control for bias due to horizontal pleiotropy, their performance in the presence of other sources of bias, like non-random mating, has been mostly evaluated using simulated data. Empirical comparisons of MR estimators in such scenarios have yet to be conducted. Pleiotropy and non-random mating have been shown to account equally for the genetic correlation between height and educational attainment. Previous studies probing the causal nature of this association have produced conflicting results.

METHODS

We estimated the causal effect of height on educational attainment in various MR models, including the MR-Egger and the MR-Direction of Causation (MR-DoC) models that correct for, or explicitly model, horizontal pleiotropy.

RESULTS

We reproduced the weak but positive association between height and education in the Netherlands Twin Register sample (P= 3.9 × 10-6). All MR analyses suggested that height has a robust, albeit small, causal effect on education. We showed via simulations that potential assortment for height and education had no effect on the causal parameter in the MR-DoC model. With the pleiotropic effect freely estimated, MR-DoC yielded a null finding.

CONCLUSIONS

Non-random mating may have a bearing on the results of MR studies based on unrelated individuals. Family data enable tests of causal relationships to be conducted more rigorously, and are recommended to triangulate results of MR studies assessing pairs of traits leading to non-random mate selection.

Illicit drug use and the genetic overlap with Cannabis use

BACKGROUND

The use of illicit substances is correlated, meaning that individuals who use one illicit substance are more likely to also use another illicit substance. This association could (partly) be explained by overlapping genetic factors. Genetic overlap may indicate a common underlying genetic predisposition, or can be the result of a causal association.

METHODS

Polygenic scores for lifetime cannabis use were generated in a sample of Dutch participants (N = 8348). We tested the association of a PGS for cannabis use with ecstasy, stimulants and a broad category of illicit drug use. To explore the nature of the relationship: (1) these analyses were repeated separately in cannabis users and non-users and (2) monozogytic twin pairs discordant for cannabis use were compared on their drug use.

RESULTS

The lifetime prevalence was 24.8 % for cannabis, 6.2 % for ecstasy, 6.5 % for stimulants and 7.1 % for any illicit drug use. Significant, positive associations were found between PGS for cannabis use with ecstasy use, stimulants and any illicit drug use. These associations seemed to be stronger in cannabis users compared to non-users for both ecstasy and stimulant use, but only in people born after 1968 and not significant after correction for multiple testing. The discordant twin pair analyses suggested that cannabis use could play a causal role in drug use.

CONCLUSIONS

The genetic liability underlying cannabis use significantly explained variability in ecstasy, stimulant and any illicit drug use. Further research should further explore the underlying mechanism to understand the nature of the association.

An Independent Locus Upstream of ASIP Controls Variation in the Shade of the Bay Coat Colour in Horses

Novel coat colour phenotypes often emerge during domestication, and there is strong evidence of genetic selection for the two main genes that control base coat colour in horses-ASIP and MC1R. These genes direct the type of pigment produced, red pheomelanin (MC1R) or black eumelanin (ASIP), as well as the relative concentration and the temporal-spatial distribution of melanin pigment deposits in the skin and hair coat. Here, we describe a genome-wide association study (GWAS) to identify novel genic regions involved in the determination of the shade of bay. In total, 126 horses from five different breeds were ranked according to the extent of the distribution of eumelanin: spanning variation in phenotype from black colour restricted only to the extremities to the presence of some black pigment across nearly all the body surface. We identified a single region associated with the shade of bay ranking spanning approximately 0.5 MB on ECA22, just upstream of the ASIP gene (p = 9.76 × 10-15). This candidate region encompasses the distal 5' end of the ASIP transcript (as predicted from other species) as well as the RALY gene. Both loci are viable candidates based on the presence of similar alleles in other species. These results contribute to the growing understanding of coat colour genetics in the horse and to the mapping of genetic determinants of pigmentation on a molecular level. Given pleiotropic phenotypes in behaviour and obesity for ASIP alleles, especially those in the 5' regulatory region, improved understanding of this new Shade allele may have implications for health management in the horse.

Survey of Sustainable Regeneration of Historic and Cultural Cores of Cities

The united nations educational, scientific and cultural organization (UNESCO) considers the historic urban landscapes as the world heritages. Managing historic city centers and maintaining historic cores are the emerging challenges for sustainable urban planning. Today, the historic cores form an important part of the economic, social, environmental, and physical assets and capacities of contemporary cities, and play a strategic role in their development. One of the most important approaches to the development of central textures, especially in historical and cultural cities, is the sustainable urban regeneration approach, which encompasses all aspects of sustainability, such as the economic, social, cultural and environmental aspects. To maintain sustainability and regeneration of historic cores of cities, it is necessary to provide insight into the underlying characteristics of the local urbanization. Furthermore, the fundamental assets are to be investigated as indicators of sustainable regeneration and drivers of urban development. In the meantime, a variety of research and experience has taken place around the world, all of which has provided different criteria and indicators for the development of strategies for the historic cores of cities. The present study, through a meta-analytic and survey method, analyzing the experience and research reported in 139 theoretical and empirical papers in the last twenty years, seeks to provide a comprehensive conceptual model taking into account the criteria and indices of sustainable regeneration in historic cores of cities. The quality of the survey has been ensured using the preferred reporting items for systematic reviews and meta-analysis (PRISMA).

Performing Highly Efficient Genome Scans for Local Adaptation with R Package pcadapt Version 4

R package pcadapt is a user-friendly R package for performing genome scans for local adaptation. Here, we present version 4 of pcadapt which substantially improves computational efficiency while providing similar results. This improvement is made possible by using a different format for storing genotypes and a different algorithm for computing principal components of the genotype matrix, which is the most computationally demanding step in method pcadapt. These changes are seamlessly integrated into the existing pcadapt package, and users will experience a large reduction in computation time (by a factor of 20–60 in our analyses) as compared with previous versions.

Genetic Vulnerability for Smoking and Cannabis Use: Associations With E-Cigarette and Water Pipe Use

INTRODUCTION

Cigarette smoking and cannabis use are heritable traits and share, at least in part, a common genetic substrate. In recent years, the prevalence of alternative methods of nicotine intakes, such as electronic cigarette (e-cigarette) and water pipe use, has risen substantially. We tested whether the genetic vulnerability underlying cigarettes smoking and cannabis use explained variability in e-cigarette and water pipe use phenotypes, as these vaping methods are alternatives for smoking tobacco cigarettes and joints.

METHODS

On the basis of the summary statistics of the International Cannabis Consortium and the Tobacco and Genetics Consortium, we generated polygenic risk scores (PRSs) for smoking and cannabis use traits, and used these to predict e-cigarette and water pipe use phenotypes in a sample of 5025 individuals from the Netherlands Twin Register.

RESULTS

PRSs for cigarettes per day were positively associated with lifetime e-cigarette use and early initiation of water pipe use, but only in ex-smokers (odds ratio = 1.43, R2 = 1.56%, p = .011) and never cigarette smokers (odds ratio = 1.35, R2 = 1.60%, p = .013) respectively.

CONCLUSIONS

Most associations of PRSs for cigarette smoking and cannabis use with e-cigarette and water pipe use were not significant, potentially due to a lack of power. The significant associations between genetic liability to smoking heaviness with e-cigarette and water pipe phenotypes are in line with studies indicating a common genetic background for substance-use phenotypes. These associations emerged only in nonsmokers, and future studies should investigate the nature of this observation.

IMPLICATIONS

Our study showed that genetic vulnerability to smoking heaviness is associated with lifetime e-cigarette use and age at initiation of water pipe use. This finding has implications for the current debate on whether alternative smoking methods, such as usage of vaping devices, predispose to smoking initiation and related behaviors.

Substance use: Interplay between polygenic risk and neighborhood environment

BACKGROUND

Tobacco, alcohol, and cannabis use are prevalent behaviors that pose considerable health risks. Genetic vulnerability and characteristics of the neighborhood of residence form important risk factors for substance use. Possibly, these factors do not act in isolation. This study tested the interaction between neighborhood characteristics and genetic risk (gene-environment interaction, GxE) and the association between these classes of risk factors (gene-environment correlation, rGE) in substance use.

METHODS

Two polygenic scores (PGS) each (based on different discovery datasets) were created for smoking initiation, cigarettes per day, and glasses of alcohol per week based on summary statistics of different genome-wide association studies (GWAS). For cannabis initiation one PGS was created. These PGS were used to predict their respective phenotype in a large population-based sample from the Netherlands Twin Register (N = 6,567). Neighborhood characteristics as retrieved from governmental registration systems were factor analyzed and resulting measures of socioeconomic status (SES) and metropolitanism were used as predictors.

RESULTS

There were (small) main effects of neighborhood characteristics and PGS on substance use. One of the 14 tested GxE effects was significant, such that the PGS was more strongly associated with alcohol use in individuals with high SES. This was effect was only significant for one out of two PGS. There were weak indications of rGE, mainly with age and cohort covariates.

CONCLUSION

We conclude that both genetic and neighborhood-level factors are predictors for substance use. More research is needed to establish the robustness of the findings on the interplay between these factors.

Measurement and genetic architecture of lifetime depression in the Netherlands as assessed by LIDAS (Lifetime Depression Assessment Self-report)

BACKGROUND

Major depressive disorder (MDD) is a common mood disorder, with a heritability of around 34%. Molecular genetic studies made significant progress and identified genetic markers associated with the risk of MDD; however, progress is slowed down by substantial heterogeneity as MDD is assessed differently across international cohorts. Here, we used a standardized online approach to measure MDD in multiple cohorts in the Netherlands and evaluated whether this approach can be used in epidemiological and genetic association studies of depression.

METHODS

Within the Biobank Netherlands Internet Collaboration (BIONIC) project, we collected MDD data in eight cohorts involving 31 936 participants, using the online Lifetime Depression Assessment Self-report (LIDAS), and estimated the prevalence of current and lifetime MDD in 22 623 unrelated individuals. In a large Netherlands Twin Register (NTR) twin-family dataset (n ≈ 18 000), we estimated the heritability of MDD, and the prediction of MDD in a subset (n = 4782) through Polygenic Risk Score (PRS).

RESULTS

Estimates of current and lifetime MDD prevalence were 6.7% and 18.1%, respectively, in line with population estimates based on validated psychiatric interviews. In the NTR heritability estimates were 0.34/0.30 (s.e. = 0.02/0.02) for current/lifetime MDD, respectively, showing that the LIDAS gives similar heritability rates for MDD as reported in the literature. The PRS predicted risk of MDD (OR 1.23, 95% CI 1.15-1.32, R2 = 1.47%).

CONCLUSIONS

By assessing MDD status in the Netherlands using the LIDAS instrument, we were able to confirm previously reported MDD prevalence and heritability estimates, which suggests that this instrument can be used in epidemiological and genetic association studies of depression.

The genetic history of France

The study of the genetic structure of different countries within Europe has provided significant insights into their demographic history and population structure. Although France occupies a particular location at the western part of Europe and at the crossroads of migration routes, few population genetic studies have been conducted so far with genome-wide data. In this study, we analyzed SNP-chip genetic data from 2184 individuals born in France who were enrolled in two independent population cohorts. Using FineSTRUCTURE, six different genetic clusters of individuals were found that were very consistent between the two cohorts. These clusters correspond closely to geographic, historical, and linguistic divisions of France, and contain different proportions of ancestry from Stone and Bronze Age populations. By modeling the relationship between genetics and geography using EEMS, we were able to detect gene flow barriers that are similar across the two cohorts and correspond to major rivers and mountain ranges. Estimations of effective population sizes also revealed very similar patterns in both cohorts with a rapid increase of effective population sizes over the last 150 generations similar to other European countries. A marked bottleneck is also consistently seen in the two datasets starting in the 14^th century when the Black Death raged in Europe. In conclusion, by performing the first exhaustive study of the genetic structure of France, we fill a gap in genetic studies of Europe that will be useful to medical geneticists, historians, and archeologists.

Heritability estimates for 361 blood metabolites across 40 genome-wide association studies

Metabolomics examines the small molecules involved in cellular metabolism. Approximately 50% of total phenotypic differences in metabolite levels is due to genetic variance, but heritability estimates differ across metabolite classes. We perform a review of all genome-wide association and (exome-) sequencing studies published between November 2008 and October 2018, and identify >800 class-specific metabolite loci associated with metabolite levels. In a twin-family cohort (N = 5117), these metabolite loci are leveraged to simultaneously estimate total heritability (h2total), and the proportion of heritability captured by known metabolite loci (h2Metabolite-hits) for 309 lipids and 52 organic acids. Our study reveals significant differences in h2Metabolite-hits among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation have higher h2Metabolite-hits estimates than phosphatidylcholines with low degrees of unsaturation. This study highlights the importance of common genetic variants for metabolite levels, and elucidates the genetic architecture of metabolite classes.

Integrating Clinical Data and Imputed Transcriptome from GWAS to Uncover Complex Disease Subtypes: Applications in Psychiatry and Cardiology

Classifying subjects into clinically and biologically homogeneous subgroups will facilitate the understanding of disease pathophysiology and development of targeted prevention and intervention strategies. Traditionally, disease subtyping is based on clinical characteristics alone, but subtypes identified by such an approach may not conform exactly to the underlying biological mechanisms. Very few studies have integrated genomic profiles (e.g., those from GWASs) with clinical symptoms for disease subtyping. Here we proposed an analytic framework capable of finding complex diseases subgroups by leveraging both GWAS-predicted gene expression levels and clinical data by a multi-view bicluster analysis. This approach connects SNPs to genes via their effects on expression, so the analysis is more biologically relevant and interpretable than a pure SNP-based analysis. Transcriptome of different tissues can also be readily modeled. We also proposed various evaluation metrics for assessing clustering performance. Our framework was able to subtype schizophrenia subjects into diverse subgroups with different prognosis and treatment response. We also applied the framework to the Northern Finland Birth Cohort (NFBC) 1966 dataset and identified high and low cardiometabolic risk subgroups in a gender-stratified analysis. The prediction strength by cross-validation was generally greater than 80%, suggesting good stability of the clustering model. Our results suggest a more data-driven and biologically informed approach to defining metabolic syndrome and subtyping psychiatric disorders. Moreover, we found that the genes "blindly" selected by the algorithm are significantly enriched for known susceptibility genes discovered in GWASs of schizophrenia or cardiovascular diseases. The proposed framework opens up an approach to subject stratification.

Genetic correlates of social stratification in Great Britain

Human DNA polymorphisms vary across geographic regions, with the most commonly observed variation reflecting distant ancestry differences. Here we investigate the geographic clustering of common genetic variants that influence complex traits in a sample of ~450,000 individuals from Great Britain. Of 33 traits analysed, 21 showed significant geographic clustering at the genetic level after controlling for ancestry, probably reflecting migration driven by socioeconomic status (SES). Alleles associated with educational attainment (EA) showed the most clustering, with EA-decreasing alleles clustering in lower SES areas such as coal mining areas. Individuals who leave coal mining areas carry more EA-increasing alleles on average than those in the rest of Great Britain. The level of geographic clustering is correlated with genetic associations between complex traits and regional measures of SES, health and cultural outcomes. Our results are consistent with the hypothesis that social stratification leaves visible marks in geographic arrangements of common allele frequencies and gene-environment correlations.

Efficient analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr

Motivation

Genome-wide datasets produced for association studies have dramatically increased in size over the past few years, with modern datasets commonly including millions of variants measured in dozens of thousands of individuals. This increase in data size is a major challenge severely slowing down genomic analyses, leading to some software becoming obsolete and researchers having limited access to diverse analysis tools.

Results

Here we present two R packages, bigstatsr and bigsnpr, allowing for the analysis of large scale genomic data to be performed within R. To address large data size, the packages use memory-mapping for accessing data matrices stored on disk instead of in RAM. To perform data pre-processing and data analysis, the packages integrate most of the tools that are commonly used, either through transparent system calls to existing software, or through updated or improved implementation of existing methods. In particular, the packages implement fast and accurate computations of principal component analysis and association studies, functions to remove single nucleotide polymorphisms in linkage disequilibrium and algorithms to learn polygenic risk scores on millions of single nucleotide polymorphisms. We illustrate applications of the two R packages by analyzing a case–control genomic dataset for celiac disease, performing an association study and computing polygenic risk scores. Finally, we demonstrate the scalability of the R packages by analyzing a simulated genome-wide dataset including 500 000 individuals and 1 million markers on a single desktop computer.

Availability and implementation

https://privefl.github.io/bigstatsr/ and https://privefl.github.io/bigsnpr/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Testing Familial Transmission of Smoking With Two Different Research Designs

Introduction

Classical twin studies show that smoking is heritable. To determine if shared family environment plays a role in addition to genetic factors, and if they interact (G×E), we use a children-of-twins design. In a second sample, we measure genetic influence with polygenic risk scores (PRS) and environmental influence with a question on exposure to smoking during childhood.

Methods

Data on smoking initiation were available for 723 children of 712 twins from the Netherlands Twin Register (64.9% female, median birth year 1985). Children were grouped in ascending order of risk, based on smoking status and zygosity of their twin-parent and his/her co-twin: never smoking twin-parent with a never smoking co-twin; never smoking twin-parent with a smoking dizygotic co-twin; never smoking twin-parent with a smoking monozygotic co-twin; and smoking twin-parent with a smoking or never smoking co-twin. For 4072 participants from the Netherlands Twin Register (67.3% female, median birth year 1973), PRS for smoking were computed and smoking initiation, smoking heaviness, and exposure to smoking during childhood were available.

Results

Patterns of smoking initiation in the four group children-of-twins design suggested shared familial influences in addition to genetic factors. PRS for ever smoking were associated with smoking initiation in all individuals. PRS for smoking heaviness were associated with smoking heaviness in individuals exposed to smoking during childhood, but not in non-exposed individuals.

Conclusions

Shared family environment influences smoking, over and above genetic factors. Genetic risk of smoking heaviness was only important for individuals exposed to smoking during childhood, versus those not exposed (G×E).

Implications

This study adds to the very few existing children-of-twins (CoT) studies on smoking and combines a CoT design with a second research design that utilizes polygenic risk scores and data on exposure to smoking during childhood. The results show that shared family environment affects smoking behavior over and above genetic factors. There was also evidence for gene-environment interaction (G×E) such that genetic risk of heavy versus light smoking was only important for individuals who were also exposed to (second-hand) smoking during childhood. Together, these findings give additional incentive to recommending parents not to expose their children to cigarette smoking.

The Dutch Y-chromosomal landscape

Previous studies indicated existing, albeit limited, genetic-geographic population substructure in the Dutch population based on genome-wide data and a lack of this for mitochondrial SNP based data. Despite the aforementioned studies, Y-chromosomal SNP data from the Netherlands remain scarce and do not cover the territory of the Netherlands well enough to allow a reliable investigation of genetic-geographic population substructure. Here we provide the first substantial dataset of detailed spatial Y-chromosomal haplogroup information in 2085 males collected across the Netherlands and supplemented with previously published data from northern Belgium. We found Y-chromosomal evidence for genetic–geographic population substructure, and several Y-haplogroups demonstrating significant clinal frequency distributions in different directions. By means of prediction surface maps we could visualize (complex) distribution patterns of individual Y-haplogroups in detail. These results highlight the value of a micro-geographic approach and are of great use for forensic and epidemiological investigations and our understanding of the Dutch population history. Moreover, the previously noted absence of genetic-geographic population substructure in the Netherlands based on mitochondrial DNA in contrast to our Y-chromosome results, hints at different population histories for women and men in the Netherlands.

The moderating role of SES on genetic differences in educational achievement in the Netherlands

Parental socioeconomic status (SES) is a strong predictor of children's educational achievement (EA), with an increasing effect throughout development. Inequality in educational outcomes between children from different SES backgrounds exists in all Western countries. It has been proposed that a cause of this inequality lies in the interplay between genetic effects and SES on EA, which might depend on society and the equality of the education system. This study adopted two approaches, a classical twin design and polygenic score (PGS) approach, to address the effect of parental SES on EA in a large sample of 12-year-old Dutch twin pairs (2479 MZ and 4450 DZ twin pairs with PGSs for educational attainment available in 2335 children) from the Netherlands Twin Register (NTR). The findings of this study indicated that average EA increased with increasing parental SES. The difference in EA between boys and girls became smaller in the higher SES groups. The classical twin design analyses based on genetic covariance structure modeling pointed to lower genetic, environmental, and thus phenotypic variation in EA at higher SES. Independent from a child's PGS, parental SES predicted EA. However, the strength of the association between PGS and EA did not depend on parental SES. In a within-family design, the twin with a higher PGS scored higher on EA than the co-twin, demonstrating that the effect of the PGS on EA was at least partly independent from parental SES. To conclude, EA depended on SES both directly and indirectly, and SES moderated the additive genetic and environmental components of EA. Adding information from PGS, in addition to parental SES, improved the prediction of children's EA.