A method to customize population-specific arrays for genome-wide association testing

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.

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.

Integrative Multi-omics Analysis of Childhood Aggressive Behavior

This study introduces and illustrates the potential of an integrated multi-omics approach in investigating the underlying biology of complex traits such as childhood aggressive behavior. In 645 twins (cases = 42%), we trained single- and integrative multi-omics models to identify biomarkers for subclinical aggression and investigated the connections among these biomarkers. Our data comprised transmitted and two non-transmitted polygenic scores (PGSs) for 15 traits, 78,772 CpGs, and 90 metabolites. The single-omics models selected 31 PGSs, 1614 CpGs, and 90 metabolites, and the multi-omics model comprised 44 PGSs, 746 CpGs, and 90 metabolites. The predictive accuracy for these models in the test (N = 277, cases = 42%) and independent clinical data (N = 142, cases = 45%) ranged from 43 to 57%. We observed strong connections between DNA methylation, amino acids, and parental non-transmitted PGSs for ADHD, Autism Spectrum Disorder, intelligence, smoking initiation, and self-reported health. Aggression-related omics traits link to known and novel risk factors, including inflammation, carcinogens, and smoking.

A comprehensive evaluation of polygenic score and genotype imputation performances of human SNP arrays in diverse populations

Regardless of the overwhelming use of next-generation sequencing technologies, microarray-based genotyping combined with the imputation of untyped variants remains a cost-effective means to interrogate genetic variations across the human genome. This technology is widely used in genome-wide association studies (GWAS) at bio-bank scales, and more recently, in polygenic score (PGS) analysis to predict and stratify disease risk. Over the last decade, human genotyping arrays have undergone a tremendous growth in both number and content making a comprehensive evaluation of their performances became more important. Here, we performed a comprehensive performance assessment for 23 available human genotyping arrays in 6 ancestry groups using diverse public and in-house datasets. The analyses focus on performance estimation of derived imputation (in terms of accuracy and coverage) and PGS (in terms of concordance to PGS estimated from whole-genome sequencing data) in three different traits and diseases. We found that the arrays with a higher number of SNPs are not necessarily the ones with higher imputation performance, but the arrays that are well-optimized for the targeted population could provide very good imputation performance. In addition, PGS estimated by imputed SNP array data is highly correlated to PGS estimated by whole-genome sequencing data in most cases. When optimal arrays are used, the correlations of PGS between two types of data are higher than 0.97, but interestingly, arrays with high density can result in lower PGS performance. Our results suggest the importance of properly selecting a suitable genotyping array for PGS applications. Finally, we developed a web tool that provides interactive analyses of tag SNP contents and imputation performance based on population and genomic regions of interest. This study would act as a practical guide for researchers to design their genotyping arrays-based studies. The tool is available at: https://genome.vinbigdata.org/tools/saa/ .

LmTag: functional-enrichment and imputation-aware tag SNP selection for population-specific genotyping arrays

Despite the rapid development of sequencing technology, single-nucleotide polymorphism (SNP) arrays are still the most cost-effective genotyping solutions for large-scale genomic research and applications. Recent years have witnessed the rapid development of numerous genotyping platforms of different sizes and designs, but population-specific platforms are still lacking, especially for those in developing countries. SNP arrays designed for these countries should be cost-effective (small size), yet incorporate key information needed to associate genotypes with traits. A key design principle for most current platforms is to improve genome-wide imputation so that more SNPs not included in the array (imputed SNPs) can be predicted. However, current tag SNP selection methods mostly focus on imputation accuracy and coverage, but not the functional content of the array. It is those functional SNPs that are most likely associated with traits. Here, we propose LmTag, a novel method for tag SNP selection that not only improves imputation performance but also prioritizes highly functional SNP markers. We apply LmTag on a wide range of populations using both public and in-house whole-genome sequencing databases. Our results show that LmTag improved both functional marker prioritization and genome-wide imputation accuracy compared to existing methods. This novel approach could contribute to the next generation genotyping arrays that provide excellent imputation capability as well as facilitate array-based functional genetic studies. Such arrays are particularly suitable for under-represented populations in developing countries or non-model species, where little genomics data are available while investment in genome sequencing or high-density SNP arrays is limited. $\textrm{LmTag}$ is available at: https://github.com/datngu/LmTag.

Genome-wide association study of frontotemporal dementia identifies a C9ORF72 haplotype with a median of 12-G4C2 repeats that predisposes to pathological repeat expansions

Genetic factors play a major role in frontotemporal dementia (FTD). The majority of FTD cannot be genetically explained yet and it is likely that there are still FTD risk loci to be discovered. Common variants have been identified with genome-wide association studies (GWAS), but these studies have not systematically searched for rare variants. To identify rare and new common variant FTD risk loci and provide more insight into the heritability of C9ORF72-related FTD, we performed a GWAS consisting of 354 FTD patients (including and excluding N = 28 pathological repeat carriers) and 4209 control subjects. The Haplotype Reference Consortium was used as reference panel, allowing for the imputation of rare genetic variants. Two rare genetic variants nearby C9ORF72 were strongly associated with FTD in the discovery (rs147211831: OR = 4.8, P = 9.2 × 10^-9, rs117204439: OR = 4.9, P = 6.0 × 10^-9) and replication analysis (P < 1.1 × 10^-3). These variants also significantly associated with amyotrophic lateral sclerosis in a publicly available dataset. Using haplotype analyses in 1200 individuals, we showed that these variants tag a sub-haplotype of the founder haplotype of the repeat expansion that was previously found to be present in virtually all pathological C9ORF72 G₄C₂ repeat lengths. This new risk haplotype was 10 times more likely to contain a C9ORF72 pathological repeat length compared to founder haplotypes without one of the two risk variants (~22% versus ~2%; P = 7.70 × 10^-58). In haplotypes without a pathologic expansion, the founder risk haplotype had a higher number of repeats (median = 12 repeats) compared to the founder haplotype without the risk variants (median = 8 repeats) (P = 2.05 × 10^-260). In conclusion, the identified risk haplotype, which is carried by ~4% of all individuals, is a major risk factor for pathological repeat lengths of C9ORF72 G₄C₂. These findings strongly indicate that longer C9ORF72 repeats are unstable and more likely to convert to germline pathological C9ORF72 repeat expansions.

Japonica Array NEO with increased genome-wide coverage and abundant disease risk SNPs

Ethnic-specific SNP arrays are becoming more important to increase the power of genome-wide association studies in diverse population. In the Tohoku Medical Megabank Project, we have been developing a series of Japonica Arrays (JPA) for genotyping participants based on reference panels constructed from whole-genome sequence data of the Japanese population. Here, we designed a novel version of the SNP array for the Japanese population, called Japonica Array NEO (JPA NEO), comprising a total of 666,883 markers. Among them, 654,246 tag SNPs of autosomes and X chromosome were selected from an expanded reference panel of 3,552 Japanese, 3.5KJPNv2, using pairwise r² of linkage disequilibrium measures. Additionally, 28,298 markers were included for the evaluation of previously identified disease risk markers from the literature and databases, and those present in the Japanese population were extracted using the reference panel. Through genotyping 286 Japanese samples, we found that the imputation quality r² and INFO score in the minor allele frequency bin >2.5–5% were >0.9 and >0.8, respectively, and >12 million markers were imputed with an INFO score >0.8. From these results, JPA NEO is a promising tool for genotyping the Japanese population with genome-wide coverage, contributing to the development of genetic risk scores.

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.

Onset of Preclinical Alzheimer Disease in Monozygotic Twins

Objective

The present work was undertaken to study the genetic contribution to the start of Alzheimer's disease (AD) with amyloid and tau biomarkers in cognitively intact older identical twins.

Methods

We studied in 96 monozygotic twin‐pairs relationships between amyloid‐beta (Aβ) aggregation as measured by the Aβ1–42/1–40 ratio in cerebrospinal fluid (CSF; n = 126) and positron emission tomography (PET, n = 194), and CSF markers for Aβ production (beta‐secretase 1, Aβ1–40, and Aβ1–38) and CSF tau. Associations among markers were tested with generalized estimating equations including a random effect for twin status, adjusted for age, gender, and apolipoprotein E ε4 genotype. We used twin analyses to determine relative contributions of genetic and/or environmental factors to AD pathophysiological processes.

Results

Twenty‐seven individuals (14%) had an abnormal amyloid PET, and 14 twin‐pairs (15%) showed discordant amyloid PET scans. Within twin‐pairs, Aβ production markers and total‐tau (t‐tau) levels strongly correlated (r range = 0.73–0.86, all p < 0.0001), and Aβ aggregation markers and 181‐phosphorylated‐tau (p‐tau) levels correlated moderately strongly (r range = 0.50–0.64, all p < 0.0001). Cross‐twin cross‐trait analysis showed that Aβ1–38 in one twin correlated with Aβ1–42/1–40 ratios, and t‐tau and p‐tau levels in their cotwins (r range = −0.28 to 0.58, all p < .007). Within‐pair differences in Aβ production markers related to differences in tau levels (r range = 0.49–0.61, all p < 0.0001). Twin discordance analyses suggest that Aβ production and tau levels show coordinated increases in very early AD.

Interpretation

Our results suggest a substantial genetic/shared environmental background contributes to both Aβ and tau increases, suggesting that modulation of environmental risk factors may aid in delaying the onset of AD pathophysiological processes. ANN NEUROL 2021;89:987–1000

Genetic Liability for Depression, Social Factors and Their Interaction Effect in Depressive Symptoms and Depression Over Time in Older Adults

OBJECTIVES

The objectives of this study were to investigate the effect of genetic and social factors on depressive symptoms and depression over time and to test whether social factors moderate the relationship between depressive symptoms and its underlying genetics in later life.

METHODS

The study included 2,279 participants with a mean follow-up of 15 years from the Longitudinal Aging Study Amsterdam with genotyping data. The personal genetic loading for depression was estimated for each participant by calculating a polygenic risk scores (PRS-D), based on 23,032 single nucleotide polymorphisms associated with major depression in a large genome-wide association study. Partner status, network size, received and given emotional support were assessed via questionnaires and depressive symptoms were assessed using the CES-D Scale. A CES-D Scale of 16 and higher was considered as clinically relevant depression.

RESULTS

Higher PRS-D was associated with more depressive symptoms whereas having a partner and having a larger network size were independently associated with less depressive symptoms. After extra adjustment for education, cognitive function and functional limitations, giving more emotional support was also associated with less depressive symptoms. No evidence for gene-environment interaction between PRS-D and social factors was found. Similar results were found for clinically relevant depression.

CONCLUSION

Genetic and social factors are independently associated with depressive symptoms over time in older adults. Strategies that boost social functioning should be encouraged in the general population of older adults regardless of the genetic liability for depression.

The Netherlands Twin Register: Longitudinal Research Based on Twin and Twin-Family Designs

The Netherlands Twin Register (NTR) is a national register in which twins, multiples and their parents, siblings, spouses and other family members participate. Here we describe the NTR resources that were created from more than 30 years of data collections; the development and maintenance of the newly developed database systems, and the possibilities these resources create for future research. Since the early 1980s, the NTR has enrolled around 120,000 twins and a roughly equal number of their relatives. The majority of twin families have participated in survey studies, and subsamples took part in biomaterial collection (e.g., DNA) and dedicated projects, for example, for neuropsychological, biomarker and behavioral traits. The recruitment into the NTR is all inclusive without any restrictions on enrollment. These resources - the longitudinal phenotyping, the extended pedigree structures and the multigeneration genotyping - allow for future twin-family research that will contribute to gene discovery, causality modeling, and studies of genetic and cultural inheritance.

Genetic Similarity Assessment of Twin-Family Populations by Custom-Designed Genotyping Array

Twin registries often take part in large collaborative projects and are major contributors to genome-wide association (GWA) meta-analysis studies. In this article, we describe genotyping of twin-family populations from Australia, the Midwestern USA (Avera Twin Register), the Netherlands (Netherlands Twin Register), as well as a sample of mothers of twins from Nigeria to assess the extent, if any, of genetic differences between them. Genotyping in all cohorts was done using a custom-designed Illumina Global Screening Array (GSA), optimized to improve imputation quality for population-specific GWA studies. We investigated the degree of genetic similarity between the populations using several measures of population variation with genotype data generated from the GSA. Visualization of principal component analysis (PCA) revealed that the Australian, Dutch and Midwestern American populations exhibit negligible interpopulation stratification when compared to each other, to a reference European population and to globally distant populations. Estimations of fixation indices (FST values) between the Australian, Midwestern American and Netherlands populations suggest minimal genetic differentiation compared to the estimates between each population and a genetically distinct cohort (i.e., samples from Nigeria genotyped on GSA). Thus, results from this study demonstrate that genotype data from the Australian, Dutch and Midwestern American twin-family populations can be reasonably combined for joint-genetic analysis.

The Longitudinal Aging Study Amsterdam: cohort update 2019 and additional data collections

The Longitudinal Aging Study Amsterdam (LASA) is a prospective cohort study of older adults in the Netherlands, initially based on a nationally representative sample of people aged 55-84 years. The study has been ongoing since 1992, and focuses on the determinants, trajectories and consequences of physical, cognitive, emotional and social functioning. Strengths of the LASA study include its multidisciplinary character, the availability of over 25 years of follow-up, and the cohort-sequential design that allows investigations of longitudinal changes, cohort differences and time trends in functioning. The findings from LASA have been reported in over 600 publications so far (see www.lasa-vu.nl). This article provides an update of the design of the LASA study and its methods, on the basis of recent developments. We describe additional data collections, such as additional nine-monthly measurements in-between the regular three-yearly waves that have been conducted among the oldest old during 2016-2019, and the inclusion of a cohort of older Turkish and Moroccan migrants.

Glioma risk associated with extent of estimated European genetic ancestry in African Americans and Hispanics

Glioma incidence is highest in non-Hispanic Whites, and to date, glioma genome-wide association studies (GWAS) to date have only included European ancestry (EA) populations. African Americans and Hispanics in the US have varying proportions of EA, African (AA) and Native American ancestries (NAA). It is unknown if identified GWAS loci or increased EA is associated with increased glioma risk. We assessed whether EA was associated with glioma in African Americans and Hispanics. Data were obtained for 832 cases and 675 controls from the Glioma International Case-Control Study and GliomaSE Case-Control Study previously estimated to have <80% EA, or self-identify as non-White. We estimated global and local ancestry using fastStructure and RFMix, respectively, using 1,000 genomes project reference populations. Within groups with ≥40% AA (AFR_≥0.4 ), and ≥15% NAA (AMR_≥0.15 ), genome-wide association between local EA and glioma was evaluated using logistic regression conditioned on global EA for all gliomas. We identified two regions (7q21.11, p = 6.36 × 10^-4 ; 11p11.12, p = 7.0 × 10^-4 ) associated with increased EA, and one associated with decreased EA (20p12.13, p = 0.0026) in AFR_≥0.4 . In addition, we identified a peak at rs1620291 (p = 4.36 × 10^-6 ) in 7q21.3. Among AMR_≥0.15 , we found an association between increased EA in one region (12q24.21, p = 8.38 × 10^-4 ), and decreased EA in two regions (8q24.21, p = 0. 0010; 20q13.33, p = 6.36 × 10^-4 ). No other significant associations were identified. This analysis identified an association between glioma and two regions previously identified in EA populations (8q24.21, 20q13.33) and four novel regions (7q21.11, 11p11.12, 12q24.21 and 20p12.13). The identifications of novel association with EA suggest regions to target for future genetic association studies.

The EMIF-AD PreclinAD study: study design and baseline cohort overview

Background

Amyloid pathology is the pathological hallmark in Alzheimer’s disease (AD) and can precede clinical dementia by decades. So far it remains unclear how amyloid pathology leads to cognitive impairment and dementia. To design AD prevention trials it is key to include cognitively normal subjects at high risk for amyloid pathology and to find predictors of cognitive decline in these subjects. These goals can be accomplished by targeting twins, with additional benefits to identify genetic and environmental pathways for amyloid pathology, other AD biomarkers, and cognitive decline.

Methods

From December 2014 to October 2017 we enrolled cognitively normal participants aged 60 years and older from the ongoing Manchester and Newcastle Age and Cognitive Performance Research Cohort and the Netherlands Twins Register. In Manchester we included single individuals, and in Amsterdam monozygotic twin pairs. At baseline, participants completed neuropsychological tests and questionnaires, and underwent physical examination, blood sampling, ultrasound of the carotid arteries, structural and resting state functional brain magnetic resonance imaging, and dynamic amyloid positron emission tomography (PET) scanning with [¹⁸F]flutemetamol. In addition, the twin cohort underwent lumbar puncture for cerebrospinal fluid collection, buccal cell collection, magnetoencephalography, optical coherence tomography, and retinal imaging.

Results

We included 285 participants, who were on average 74.8 ± 9.7 years old, 64% female. Fifty-eight participants (22%) had an abnormal amyloid PET scan.

Conclusions

A rich baseline dataset of cognitively normal elderly individuals has been established to estimate risk factors and biomarkers for amyloid pathology and future cognitive decline.

Electronic supplementary material

The online version of this article (10.1186/s13195-018-0406-7) contains supplementary material, which is available to authorized users.

Establishing a Twin Register: An Invaluable Resource for (Behavior) Genetic, Epidemiological, Biomarker, and ‘Omics’ Studies

Twin registers are wonderful research resources for research applications in medical and behavioral genetics, epidemiology, psychology, molecular genetics, and other areas of research. New registers continue to be launched all over the world as researchers from different disciplines recognize the potential to boost and widen their research agenda. In this article, we discuss multiple aspects that need to be taken into account when initiating a register, from its preliminary sketch to its actual development. This encompasses aspects related to the strategic planning and key elements of research designs, promotion and management of a twin register, including recruitment and retaining of twins and family members of twins, phenotyping, database organization, and collaborations between registers. We also present information on questions unique to twin registers and twin-biobanks, such as the assessment of zygosity by SNP arrays, the design of (biomarker) studies involving related participants, and the analyses of clustered data. Altogether, we provide a number of basic guidelines and recommendations for reflection when planning a twin register.

Establishment of the Avera Twin Register in the Midwest USA

The Avera Twin Register (ATR) aims to study environmental and genetic influences on health and disease using a longitudinal repository of biological specimens, survey data, and health information provided by multiples and their family members. The ATR is located in Sioux Falls, South Dakota, which is a rural and frontier area in the Midwestern United States with a density of four people per square kilometer. The target area of the ATR is South Dakota and the four surrounding states: Minnesota, Iowa, North Dakota, and Nebraska. Enrollment of twins and higher-order multiples of all ages and their family members started on May 18, 2016. A description of the first 13 months of enrollment in this longitudinal register will be provided. The ATR will collect longitudinal data on lifestyle, including diet and activity levels, aging, complex traits, and diseases. Upon registration, all participants are genotyped on the Illumina Global Screening Array (GSA) and twins and higher order multiples receive information on their zygosity. The ATR aims to contribute to large international GWAS consortia and collaborates closely with the Netherlands Twin Register, allowing for the comparison of collected data and analyses of results. In addition, the ATR will address twin-specific questions.