HASE: Framework for efficient high-dimensional association analyses

The blood metabolome of cognitive function and brain health in middle-aged adults - influences of genes, gut microbiome, and exposome

Increasing evidence suggests the involvement of metabolic alterations in neurological disorders, including Alzheimer's disease (AD), and highlights the significance of the peripheral metabolome, influenced by genetic factors and modifiable environmental exposures, for brain health. In this study, we examined 1,387 metabolites in plasma samples from 1,082 dementia-free middle-aged participants of the population-based Rotterdam Study. We assessed the relation of metabolites with general cognition (G-factor) and magnetic resonance imaging (MRI) markers using linear regression and estimated the variance of these metabolites explained by genes, gut microbiome, lifestyle factors, common clinical comorbidities, and medication using gradient boosting decision tree analysis. Twenty-one metabolites and one metabolite were significantly associated with total brain volume and total white matter lesions, respectively. Fourteen metabolites showed significant associations with G-factor, with ergothioneine exhibiting the largest effect (adjusted mean difference = 0.122, P = 4.65×10-7). Associations for nine of the 14 metabolites were replicated in an independent, older cohort. The metabolite signature of incident AD in the replication cohort resembled that of cognition in the discovery cohort, emphasizing the potential relevance of the identified metabolites to disease pathogenesis. Lifestyle, clinical variables, and medication were most important in determining these metabolites' blood levels, with lifestyle, explaining up to 28.6% of the variance. Smoking was associated with ten metabolites linked to G-factor, while diabetes and antidiabetic medication were associated with 13 metabolites linked to MRI markers, including N-lactoyltyrosine. Antacid medication strongly affected ergothioneine levels. Mediation analysis revealed that lower ergothioneine levels may partially mediate negative effects of antacids on cognition (31.5%). Gut microbial factors were more important for the blood levels of metabolites that were more strongly associated with cognition and incident AD in the older replication cohort (beta-cryptoxanthin, imidazole propionate), suggesting they may be involved later in the disease process. The detailed results on how multiple modifiable factors affect blood levels of cognition- and brain imaging-related metabolites in dementia-free participants may help identify new AD prevention strategies.

A comprehensive study of genetic regulation and disease associations of plasma circulatory microRNAs using population-level data

BACKGROUND

MicroRNAs (miRNAs) are small non-coding RNAs that post-transcriptionally regulate gene expression. Perturbations in plasma miRNA levels are known to impact disease risk and have potential as disease biomarkers. Exploring the genetic regulation of miRNAs may yield new insights into their important role in governing gene expression and disease mechanisms.

RESULTS

We present genome-wide association studies of 2083 plasma circulating miRNAs in 2178 participants of the Rotterdam Study to identify miRNA-expression quantitative trait loci (miR-eQTLs). We identify 3292 associations between 1289 SNPs and 63 miRNAs, of which 65% are replicated in two independent cohorts. We demonstrate that plasma miR-eQTLs co-localise with gene expression, protein, and metabolite-QTLs, which help in identifying miRNA-regulated pathways. We investigate consequences of alteration in circulating miRNA levels on a wide range of clinical conditions in phenome-wide association studies and Mendelian randomisation using the UK Biobank data (N = 423,419), revealing the pleiotropic and causal effects of several miRNAs on various clinical conditions. In the Mendelian randomisation analysis, we find a protective causal effect of miR-1908-5p on the risk of benign colon neoplasm and show that this effect is independent of its host gene (FADS1).

CONCLUSIONS

This study enriches our understanding of the genetic architecture of plasma miRNAs and explores the signatures of miRNAs across a wide range of clinical conditions. The integration of population-based genomics, other omics layers, and clinical data presents opportunities to unravel potential clinical significance of miRNAs and provides tools for novel miRNA-based therapeutic target discovery.

Machine Learning to Advance Human Genome-Wide Association Studies

Machine learning, including deep learning, reinforcement learning, and generative artificial intelligence are revolutionising every area of our lives when data are made available. With the help of these methods, we can decipher information from larger datasets while addressing the complex nature of biological systems in a more efficient way. Although machine learning methods have been introduced to human genetic epidemiological research as early as 2004, those were never used to their full capacity. In this review, we outline some of the main applications of machine learning to assigning human genetic loci to health outcomes. We summarise widely used methods and discuss their advantages and challenges. We also identify several tools, such as Combi, GenNet, and GMSTool, specifically designed to integrate these methods for hypothesis-free analysis of genetic variation data. We elaborate on the additional value and limitations of these tools from a geneticist's perspective. Finally, we discuss the fast-moving field of foundation models and large multi-modal omics biobank initiatives.

Evidence for involvement of the alcohol consumption WDPCP gene in lipid metabolism, and liver cirrhosis

Biological pathways between alcohol consumption and alcohol liver disease (ALD) are not fully understood. We selected genes with known effect on (1) alcohol consumption, (2) liver function, and (3) gene expression. Expression of the orthologs of these genes in Caenorhabditis elegans and Drosophila melanogaster was suppressed using mutations and/or RNA interference (RNAi). In humans, association analysis, pathway analysis, and Mendelian randomization analysis were performed to identify metabolic changes due to alcohol consumption. In C. elegans, we found a reduction in locomotion rate after exposure to ethanol for RNAi knockdown of ACTR1B and MAPT. In Drosophila, we observed (1) a change in sedative effect of ethanol for RNAi knockdown of WDPCP, TENM2, GPN1, ARPC1B, and SCN8A, (2) a reduction in ethanol consumption for RNAi knockdown of TENM2, (3) a reduction in triradylglycerols (TAG) levels for RNAi knockdown of WDPCP, TENM2, and GPN1. In human, we observed (1) a link between alcohol consumption and several metabolites including TAG, (2) an enrichment of the candidate (alcohol-associated) metabolites within the linoleic acid (LNA) and alpha-linolenic acid (ALA) metabolism pathways, (3) a causal link between gene expression of WDPCP to liver fibrosis and liver cirrhosis. Our results imply that WDPCP might be involved in ALD.

BOSO: A novel feature selection algorithm for linear regression with high-dimensional data

With the frenetic growth of high-dimensional datasets in different biomedical domains, there is an urgent need to develop predictive methods able to deal with this complexity. Feature selection is a relevant strategy in machine learning to address this challenge. We introduce a novel feature selection algorithm for linear regression called BOSO (Bilevel Optimization Selector Operator). We conducted a benchmark of BOSO with key algorithms in the literature, finding a superior accuracy for feature selection in high-dimensional datasets. Proof-of-concept of BOSO for predicting drug sensitivity in cancer is presented. A detailed analysis is carried out for methotrexate, a well-studied drug targeting cancer metabolism.

We present BOSO (Bilevel Optimization Selector Operator), a novel method to conduct feature selection in linear regression models. In machine learning, feature selection consists of identifying the subset of input variables (features) that are correctly associated with the response variable that is aimed to be predicted. An adequate feature selection is particularly relevant for high-dimensional datasets, commonly encountered in biomedical research questions that rely on -omics data, e.g. predictive models of drug sensitivity, resistance or toxicity, construction of gene regulatory networks, biomarker selection or association studies. The need of feature selection is emphasized in many of these complex problems, since the number of features is greater than the number of samples, which makes it harder to obtain accurate and general predictive models. In this context, we show that the models derived by BOSO make a better combination of accuracy and simplicity than competing approaches in the literature. The relevance of BOSO is illustrated in the prediction of drug sensitivity of cancer cell lines, using RNA-seq data and drug screenings from GDSC (Genomics of Drug Sensitivity in Cancer) database. BOSO obtains linear regression models with a similar level of accuracy but involving a substantially lower number of features, which simplifies the interpretation and validation of predictive models.

GenNet framework: interpretable deep learning for predicting phenotypes from genetic data

Applying deep learning in population genomics is challenging because of computational issues and lack of interpretable models. Here, we propose GenNet, a novel open-source deep learning framework for predicting phenotypes from genetic variants. In this framework, interpretable and memory-efficient neural network architectures are constructed by embedding biologically knowledge from public databases, resulting in neural networks that contain only biologically plausible connections. We applied the framework to seventeen phenotypes and found well-replicated genes such as HERC2 and OCA2 for hair and eye color, and novel genes such as ZNF773 and PCNT for schizophrenia. Additionally, the framework identified ubiquitin mediated proteolysis, endocrine system and viral infectious diseases as most predictive biological pathways for schizophrenia. GenNet is a freely available, end-to-end deep learning framework that allows researchers to develop and use interpretable neural networks to obtain novel insights into the genetic architecture of complex traits and diseases.

van Hilten and colleagues present GenNet, a deep-learning framework for predicting phenotype from genetic data. This framework generates interpretable neural networks that provide insight into the genetic basis of complex traits and diseases.

Three-Dimensional Stereophotogrammetry in the Evaluation of Craniosynostosis: Current and Potential Use Cases

ABSTRACT

Three-dimensional (3D) stereophotogrammetry is a novel imaging technique that has gained popularity in the medical field as a reliable, non-invasive, and radiation-free imaging modality. It uses optical sensors to acquire multiple 2D images from different angles which are reconstructed into a 3D digital model of the subject's surface. The technique proved to be especially useful in craniofacial applications, where it serves as a tool to overcome the limitations imposed by conventional imaging modalities and subjective evaluation methods. The capability to acquire high-dimensional data in a quick and safe manner and archive them for retrospective longitudinal analyses, provides the field with a methodology to increase the understanding of the morphological development of the cranium, its growth patterns and the effect of different treatments over time.This review describes the role of 3D stereophotogrammetry in the evaluation of craniosynostosis, including reliability studies, current and potential clinical use cases, and practical challenges. Finally, developments within the research field are analyzed by means of bibliometric networks, depicting prominent research topics, authors, and institutions, to stimulate new ideas and collaborations in the field of craniofacial 3D stereophotogrammetry.We anticipate that utilization of this modality's full potential requires a global effort in terms of collaborations, data sharing, standardization, and harmonization. Such developments can facilitate larger studies and novel deep learning methods that can aid in reaching an objective consensus regarding the most effective treatments for patients with craniosynostosis and other craniofacial anomalies, and to increase our understanding of these complex dysmorphologies and associated phenotypes.

The single-cell eQTLGen consortium

In recent years, functional genomics approaches combining genetic information with bulk RNA-sequencing data have identified the downstream expression effects of disease-associated genetic risk factors through so-called expression quantitative trait locus (eQTL) analysis. Single-cell RNA-sequencing creates enormous opportunities for mapping eQTLs across different cell types and in dynamic processes, many of which are obscured when using bulk methods. Rapid increase in throughput and reduction in cost per cell now allow this technology to be applied to large-scale population genetics studies. To fully leverage these emerging data resources, we have founded the single-cell eQTLGen consortium (sc-eQTLGen), aimed at pinpointing the cellular contexts in which disease-causing genetic variants affect gene expression. Here, we outline the goals, approach and potential utility of the sc-eQTLGen consortium. We also provide a set of study design considerations for future single-cell eQTL studies.

Full exploitation of high dimensionality in brain imaging: The JPND working group statement and findings

Advances in technology enable increasing amounts of data collection from individuals for biomedical research. Such technologies, for example, in genetics and medical imaging, have also led to important scientific discoveries about health and disease. The combination of multiple types of high-throughput data for complex analyses, however, has been limited by analytical and logistic resources to handle high-dimensional data sets. In our previous EU Joint Programme-Neurodegenerative Disease Research (JPND) Working Group, called HD-READY, we developed methods that allowed successful combination of omics data with neuroimaging. Still, several issues remained to fully leverage high-dimensional multimodality data. For instance, high-dimensional features, such as voxels and vertices, which are common in neuroimaging, remain difficult to harmonize. In this Full-HD Working Group, we focused on such harmonization of high-dimensional neuroimaging phenotypes in combination with other omics data and how to make the resulting ultra-high-dimensional data easily accessible in neurodegeneration research.

Heritability and genome-wide associations studies of cerebral blood flow in the general population

Cerebral blood flow is an important process for brain functioning and its dysregulation is implicated in multiple neurological disorders. While environmental risk factors have been identified, it remains unclear to what extent the flow is regulated by genetics. Here we performed heritability and genome-wide association analyses of cerebral blood flow in a population-based cohort study. We included 4472 persons free of cortical infarcts who underwent genotyping and phase-contrast magnetic resonance flow imaging (mean age 64.8 ± 10.8 years). The flow rate, cross-sectional area of the vessel, and flow velocity through the vessel were measured in the basilar artery and bilateral carotids. We found that the flow rate of the basilar artery is most heritable (h2 (SE) = 24.1 (9.8), p-value = 0.0056), and this increased over age. The association studies revealed two significant loci for the right carotid artery area (rs12546630, p-value = 2.0 × 10^-8) and velocity (rs2971609, p-value = 1.4 × 10^-8), with the latter showing a concordant effect in an independent sample (N = 1350, p-value = 0.057, meta-analyzed p-value = 2.5 × 10^-9). These loci were also associated with other cerebral blood flow parameters below genome-wide significance, and rs2971609 lies in a known migraine locus. These findings establish that cerebral blood flow is under genetic control with potential relevance for neurological diseases.

Decentralized Analysis of Brain Imaging Data: Voxel-Based Morphometry and Dynamic Functional Network Connectivity

In the field of neuroimaging, there is a growing interest in developing collaborative frameworks that enable researchers to address challenging questions about the human brain by leveraging data across multiple sites all over the world. Additionally, efforts are also being directed at developing algorithms that enable collaborative analysis and feature learning from multiple sites without requiring the often large data to be centrally located. In this paper, we propose two new decentralized algorithms: (1) A decentralized regression algorithm for performing a voxel-based morphometry analysis on structural magnetic resonance imaging (MRI) data and, (2) A decentralized dynamic functional network connectivity algorithm which includes decentralized group ICA and sliding-window analysis of functional MRI data. We compare results against those obtained from their pooled (or centralized) counterparts on the same data i.e., as if they are at one site. Results produced by the decentralized algorithms are similar to the pooled-case and showcase the potential of performing multi-voxel and multivariate analyses of data located at multiple sites. Such approaches enable many more collaborative and comparative analysis in the context of large-scale neuroimaging studies.

Maximizing the Potential of Longitudinal Cohorts for Research in Neurodegenerative Diseases: A Community Perspective

Despite a wealth of activity across the globe in the area of longitudinal population cohorts, surprisingly little information is available on the natural biomedical history of a number of age-related neurodegenerative diseases (ND), and the scope for intervention studies based on these cohorts is only just beginning to be explored. The Joint Programming Initiative on Neurodegenerative Disease Research (JPND) recently developed a novel funding mechanism to rapidly mobilize scientists to address these issues from a broad, international community perspective. Ten expert Working Groups, bringing together a diverse range of community members and covering a wide ND landscape [Alzheimer's, Parkinson's, frontotemporal degeneration, amyotrophic lateral sclerosis (ALS), Lewy-body and vascular dementia] were formed to discuss and propose potential approaches to better exploiting and coordinating cohort studies. The purpose of this work is to highlight the novel funding process along with a broad overview of the guidelines and recommendations generated by the ten groups, which include investigations into multiple methodologies such as cognition/functional assessment, biomarkers and biobanking, imaging, health and social outcomes, and pre-symptomatic ND. All of these were published in reports that are now publicly available online.

Gray matter heritability in family-based and population-based studies using voxel-based morphometry

BACKGROUND

The combination of genetics and imaging has improved their understanding of the brain through studies of aggregate measures obtained from high-resolution structural imaging. Voxel-wise analyses have the potential to provide more detailed information of genetic influences on the brain. Here they report a large-scale study of the heritability of gray matter at voxel resolution (1 × 1 × 1 mm).

METHODS

Validated voxel-based morphometry (VBM) protocols were applied to process magnetic resonance imaging data of 3,239 unrelated subjects from a population-based study and 491 subjects from two family-based studies. Genome-wide genetic data was used to estimate voxel-wise gray matter heritability of the unrelated subjects and pedigree-structure was used to estimate heritability in families. They subsequently associated two genetic variants, known to be linked with subcortical brain volume, with most heritable voxels to determine if this would enhance their association signals.

RESULTS

Voxels significantly heritable in both estimates mapped to subcortical structures, but also voxels in the language areas of the left hemisphere were found significantly heritable. When comparing regional patterns of heritability, family-based estimates were higher than population-based estimates. However, regional consistency of the heritability measures across study designs was high (Pearson's correlation coefficient = 0.73, P = 2.6 × 10^-13 ). They further show enhancement of the association signal of two previously discovered genetic loci with subcortical volume by using only the most heritable voxels.

CONCLUSION

Gray matter voxel-wise heritability can be reliably estimated with different methods. Combining heritability estimates from multiple studies is feasible to construct reliable heritability maps of gray matter voxels. Hum Brain Mapp 38:2408-2423, 2017. © 2017 Wiley Periodicals, Inc.

Heritability of the shape of subcortical brain structures in the general population

The volumes of subcortical brain structures are highly heritable, but genetic underpinnings of their shape remain relatively obscure. Here we determine the relative contribution of genetic factors to individual variation in the shape of seven bilateral subcortical structures: the nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen and thalamus. In 3,686 unrelated individuals aged between 45 and 98 years, brain magnetic resonance imaging and genotyping was performed. The maximal heritability of shape varies from 32.7 to 53.3% across the subcortical structures. Genetic contributions to shape extend beyond influences on intracranial volume and the gross volume of the respective structure. The regional variance in heritability was related to the reliability of the measurements, but could not be accounted for by technical factors only. These findings could be replicated in an independent sample of 1,040 twins. Differences in genetic contributions within a single region reveal the value of refined brain maps to appreciate the genetic complexity of brain structures.