Systems genetics approaches for understanding complex traits with relevance for human disease

Merritt-Putnam Symposium | Developmental and Epileptic Encephalopathies-Current Concepts and Novel Approaches

Developmental and epileptic encephalopathies (DEEs) are among the most severe and difficult to treat epilepsies. Two broad strategies for understanding the etiology and impacts of DEEs include genetic and complex adaptive systems approaches. This review, inspired by the 2024 Merritt-Putnam Symposium, describes current perspectives of DEE, identifies limitations of current views, and discusses potential novel ways forward. First, we discuss the rationale for a reevaluation of the role of seizures in the pathogenesis of cognitive and behavioral impairments in DEE. Second, we discuss newly emerging methods employing neural organoids to study brain development and DEE in vitro. Third, we present recent precision therapy approaches for the clinical treatment of DEE. Lastly, we discuss computational systems approaches to understanding the genetic landscape of DEE. The severe and multifaceted impacts of DEE and associated comorbidities underscore the necessity of novel interdisciplinary approaches to produce an improved understanding of etiology and more effective treatment strategies.

Biomarker identification for Alzheimer's disease through integration of comprehensive Mendelian randomization and proteomics data

BACKGROUND

Alzheimer's disease (AD) is the main cause of dementia with few effective therapies. We aimed to identify potential plasma biomarkers or drug targets for AD by investigating the causal association between plasma proteins and AD by integrating comprehensive Mendelian randomization (MR) and multi-omics data.

METHODS

Using two-sample MR, cis protein quantitative trait loci (cis-pQTLs) for 1,916 plasma proteins were used as an exposure to infer their causal effect on AD liability in individuals of European ancestry, with two large-scale AD genome-wide association study (GWAS) datasets as the outcome for discovery and replication. Significant causal relationships were validated by sensitivity analyses, reverse MR analysis, and Bayesian colocalization analysis. Additionally, we investigated the causal associations at the transcriptional level with cis gene expression quantitative trait loci (cis-eQTLs) data across brain tissues and blood in European ancestry populations, as well as causal plasma proteins in African ancestry populations.

RESULTS

In those of European ancestry, the genetically predicted levels of five plasma proteins (BLNK, CD2AP, GRN, PILRA, and PILRB) were causally associated with AD. Among these five proteins, GRN was protective against AD, while the rest were risk factors. Consistent causal effects were found in the brain for cis-eQTLs of GRN, BLNK, and CD2AP, while the same was true for PILRA in the blood. None of the plasma proteins were significantly associated with AD in persons of African ancestry.

CONCLUSIONS

Comprehensive MR analyses with multi-omics data identified five plasma proteins that had causal effects on AD, highlighting potential biomarkers or drug targets for better diagnosis and treatment for AD.

Establishing the hybrid rat diversity program: a resource for dissecting complex traits

Rat models have been a major model for studying complex disease mechanisms, behavioral phenotypes, environmental factors, and for drug development and discovery. Inbred rat strains control for genetic background and allow for repeated, reproducible, cellular and whole animal phenotyping. The Hybrid Rat Diversity Panel (HRDP) was designed to be a powerful panel of inbred rats with genomic, physiological, and behavioral data to serve as a resource for systems genetics. The HRDP consists of 96-98 inbred rat strains aimed to maximize power to detect specific genetic loci associated with complex traits while maximizing the genetic diversity among strains. The panel consists of 32-34 genetically diverse inbred strains and two panels of recombinant inbred panels. To establish the HRDP program, embryo resuscitation and breeding were done to establish colonies for distribution. Whole genome sequencing was performed to achieve 30X coverage. Genomic, phenotype, and strain information is available through the Hybrid Rat Diversity Panel Portal at the Rat Genome Database ( http://rgd.mcw.edu ).

Multi-omics approaches for understanding gene-environment interactions in noncommunicable diseases: techniques, translation, and equity issues

Non-communicable diseases (NCDs) such as cardiovascular diseases, chronic respiratory diseases, cancers, diabetes, and mental health disorders pose a significant global health challenge, accounting for the majority of fatalities and disability-adjusted life years worldwide. These diseases arise from the complex interactions between genetic, behavioral, and environmental factors, necessitating a thorough understanding of these dynamics to identify effective diagnostic strategies and interventions. Although recent advances in multi-omics technologies have greatly enhanced our ability to explore these interactions, several challenges remain. These challenges include the inherent complexity and heterogeneity of multi-omic datasets, limitations in analytical approaches, and severe underrepresentation of non-European genetic ancestries in most omics datasets, which restricts the generalizability of findings and exacerbates health disparities. This scoping review evaluates the global landscape of multi-omics data related to NCDs from 2000 to 2024, focusing on recent advancements in multi-omics data integration, translational applications, and equity considerations. We highlight the need for standardized protocols, harmonized data-sharing policies, and advanced approaches such as artificial intelligence/machine learning to integrate multi-omics data and study gene-environment interactions. We also explore challenges and opportunities in translating insights from gene-environment (GxE) research into precision medicine strategies. We underscore the potential of global multi-omics research in advancing our understanding of NCDs and enhancing patient outcomes across diverse and underserved populations, emphasizing the need for equity and fairness-centered research and strategic investments to build local capacities in underrepresented populations and regions.

Causal effects of circulating inflammatory proteins on oral phenotypes: Deciphering immune-mediated profiles in the host-oral axis

BACKGROUND

Oral manifestations function as precursors to potential systemic pathologies, signaling early indicators of underlying health complications or immunological dysfunctions. Within these dynamics, circulating inflammatory proteins are recognized as critical mediators in immunopharmacology, bridging holistic health, immune response, and oral health.

METHODS

We employed genetic data from genome-wide association studies to perform comprehensive Mendelian randomization (MR) analyses on 91 circulating inflammatory proteins and 17 oral phenotypes. Six MR algorithms and five auxiliary control measures were utilized to estimate the causal effects. Subsequently, the MR-Bayesian model averaging (MR-BMA) approach was conducted to elucidate the priorities in host-oral communication, followed by network analyses to explore the interactions among phenotypes.

RESULTS

After multiple corrections, MR identified five genetically predicted proteins associated with oral phenotypes. Specifically, FGF21 was correlated with Nteeth and DMFS; hGDNF with gingival pain; CCL4 with stomatitis; and S100A12 with denture use. The causal associations remained robust in sensitivity analyses. Nine protein-phenotype clusters were prioritized using MR-BMA. Among these, S100A12, FGF19, FGF21, and CCL4 exhibited extensive correlations with various oral phenotypes.

CONCLUSIONS

Our study offers novel genetic insights into the causal relationships, prioritizations, and connections between circulating inflammatory proteins and oral phenotypes. These findings comprehensively depict immune-mediated proteomic profiles underlying the host-oral axis, providing significant implications for clinical practice, public health, and immunopharmacology.

Scrutinizing neurodegenerative diseases: decoding the complex genetic architectures through a multi-omics lens

Neurodegenerative diseases present complex genetic architectures, reflecting a continuum from monogenic to oligogenic and polygenic models. Recent advances in multi-omics data, coupled with systems genetics, have significantly refined our understanding of how these data impact neurodegenerative disease mechanisms. To contextualize these genetic discoveries, we provide a comprehensive critical overview of genetic architecture concepts, from Mendelian inheritance to the latest insights from oligogenic and omnigenic models. We explore the roles of common and rare genetic variants, gene-gene and gene-environment interactions, and epigenetic influences in shaping disease phenotypes. Additionally, we emphasize the importance of multi-omics layers including genomic, transcriptomic, proteomic, epigenetic, and metabolomic data in elucidating the molecular mechanisms underlying neurodegeneration. Special attention is given to missing heritability and the contribution of rare variants, particularly in the context of pleiotropy and network pleiotropy. We examine the application of single-cell omics technologies, transcriptome-wide association studies, and epigenome-wide association studies as key approaches for dissecting disease mechanisms at tissue- and cell-type levels. Our review introduces the OmicPeak Disease Trajectory Model, a conceptual framework for understanding the genetic architecture of neurodegenerative disease progression, which integrates multi-omics data across biological layers and time points. This review highlights the critical importance of adopting a systems genetics approach to unravel the complex genetic architecture of neurodegenerative diseases. Finally, this emerging holistic understanding of multi-omics data and the exploration of the intricate genetic landscape aim to provide a foundation for establishing more refined genetic architectures of these diseases, enhancing diagnostic precision, predicting disease progression, elucidating pathogenic mechanisms, and refining therapeutic strategies for neurodegenerative conditions.

Unlocking metabolic insights with mouse genetic diversity

As part of EMBO Journal’s 2024 metabolism methods series, this commentary revisits the impact of genetics on metabolic studies, enabling dissection of novel mechanisms and phenotypes.

A PLURIPOTENT STEM CELL PLATFORM FOR IN VITRO SYSTEMS GENETICS STUDIES OF MOUSE DEVELOPMENT

The directed differentiation of pluripotent stem cells (PSCs) from panels of genetically diverse individuals is emerging as a powerful experimental system for characterizing the impact of natural genetic variation on developing cell types and tissues. Here, we establish new PSC lines and experimental approaches for modeling embryonic development in a genetically diverse, outbred mouse stock (Diversity Outbred mice). We show that a range of inbred and outbred PSC lines can be stably maintained in the primed pluripotent state (epiblast stem cells -- EpiSCs) and establish the contribution of genetic variation to phenotypic differences in gene regulation and directed differentiation. Using pooled in vitro fertilization, we generate and characterize a genetic reference panel of Diversity Outbred PSCs (n = 230). Finally, we demonstrate the feasibility of pooled culture of Diversity Outbred EpiSCs as "cell villages", which can facilitate the differentiation of large numbers of EpiSC lines for forward genetic screens. These data can complement and inform similar efforts within the stem cell biology and human genetics communities to model the impact of natural genetic variation on phenotypic variation and disease-risk.

Genetics unravels protein-metabolite relationships

Integrating molecular traits into genetic studies enhances our understanding of how DNA variation influences complex clinical and physiological phenotypes. In a recent article, Benson and colleagues apply this systems genetics approach with proteomics and metabolomics data in plasma from humans to identify and validate several previously unrecognized causal protein-metabolite associations.

Leveraging the transcriptome to further our understanding of GWAS findings: eQTLs associated with genes related to LDL and LDL subclasses, in a cohort of African Americans

Background: GWAS discoveries often pose a significant challenge in terms of understanding their underlying mechanisms. Further research, such as an integration with expression quantitative trait locus (eQTL) analyses, are required to decipher the mechanisms connecting GWAS variants to phenotypes. An eQTL analysis was conducted on genes associated with low-density lipoprotein (LDL) cholesterol and its subclasses, with the aim of pinpointing genetic variants previously implicated in GWAS studies focused on lipid-related traits. Notably, the study cohort consisted of African Americans, a population characterized by a heightened prevalence of hypercholesterolemia. Methods: A comprehensive differential expression (DE) analysis was undertaken, with a dataset of 17,948 protein-coding mRNA transcripts extracted from the whole-blood transcriptomes of 416 samples to identify mRNA transcripts associated with LDL, with further granularity delineated between small LDL and large LDL subclasses. Subsequently, eQTL analysis was conducted with a subset of 242 samples for which whole-genome sequencing data were available to identify single-nucleotide polymorphisms (SNPs) associated with the LDL-related mRNA transcripts. Lastly, plausible functional connections were established between the identified eQTLs and genetic variants reported in the GWAS catalogue. Results: DE analysis revealed 1,048, 284, and 94 mRNA transcripts that exhibited differential expression in response to LDL, small LDL, and large LDL, respectively. The eQTL analysis identified a total of 9,950 significant SNP-mRNA associations involving 6,955 SNPs including a subset 101 SNPs previously documented in GWAS of LDL and LDL-related traits. Conclusion: Through comprehensive differential expression analysis, we identified numerous mRNA transcripts responsive to LDL, small LDL, and large LDL. Subsequent eQTL analysis revealed a rich landscape of eQTL-mRNA associations, including a subset of eQTL reported in GWAS studies of LDL and related traits. The study serves as a testament to the important role of integrative genomics in unraveling the enigmatic GWAS relationships between genetic variants and the complex fabric of human traits and diseases.

Special Issue "Deployment of Proteomics Approaches in Biomedical Research"

Many angles of personalized medicine, such as diagnostic improvements, systems biology [...].