Post-GWAS knowledge gap: the how, where, and when

Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding

Marker-assisted selection (MAS) plays a crucial role in crop breeding improving the speed and precision of conventional breeding programmes by quickly and reliably identifying and selecting plants with desired traits. However, the efficacy of MAS depends on several prerequisites, with precise phenotyping being a key aspect of any plant breeding programme. Recent advancements in high-throughput remote phenotyping, facilitated by unmanned aerial vehicles coupled to machine learning, offer a non-destructive and efficient alternative to traditional, time-consuming, and labour-intensive methods. Furthermore, MAS relies on knowledge of marker-trait associations, commonly obtained through genome-wide association studies (GWAS), to understand complex traits such as drought tolerance, including yield components and phenology. However, GWAS has limitations that artificial intelligence (AI) has been shown to partially overcome. Additionally, AI and its explainable variants, which ensure transparency and interpretability, are increasingly being used as recognised problem-solving tools throughout the breeding process. Given these rapid technological advancements, this review provides an overview of state-of-the-art methods and processes underlying each MAS, from phenotyping, genotyping and association analyses to the integration of explainable AI along the entire workflow. In this context, we specifically address the challenges and importance of breeding winter wheat for greater drought tolerance with stable yields, as regional droughts during critical developmental stages pose a threat to winter wheat production. Finally, we explore the transition from scientific progress to practical implementation and discuss ways to bridge the gap between cutting-edge developments and breeders, expediting MAS-based winter wheat breeding for drought tolerance.

Common genetic risk for Parkinson's disease and dysfunction of the endo-lysosomal system

Parkinson's disease is a progressive neurological disorder, characterized by prominent movement dysfunction. The past two decades have seen a rapid expansion of our understanding of the genetic basis of Parkinson's, initially through the identification of monogenic forms and, more recently, through genome-wide association studies identifying common risk variants. Intriguingly, a number of cellular pathways have emerged from these analysis as playing central roles in the aetiopathogenesis of Parkinson's. In this review, the impact of data deriving from genome-wide analyses for Parkinson's upon our functional understanding of the disease will be examined, with a particular focus on examples of endo-lysosomal and mitochondrial dysfunction. The challenges of moving from a genetic to a functional understanding of common risk variants for Parkinson's will be discussed, with a final consideration of the current state of the genetic architecture of the disorder. This article is part of a discussion meeting issue 'Understanding the endo-lysosomal network in neurodegeneration'.

Parkinson's disease risk enhancers in microglia

Genome-wide association studies have identified thousands of single nucleotide polymorphisms that associate with increased risk for Parkinson's disease (PD), but the functions of most of them are unknown. Using assay for transposase-accessible chromatin (ATAC) and H3K27ac chromatin immunoprecipitation (ChIP) sequencing data, we identified 73 regulatory elements in microglia that overlap PD risk SNPs. To determine the target genes of a "risk enhancer" within intron two of SNCA, we used CRISPR-Cas9 to delete the open chromatin region where two PD risk SNPs reside. The loss of the enhancer led to reduced expression of multiple genes including SNCA and the adjacent gene MMRN1. It also led to expression changes of genes involved in glucose metabolism, a process that is known to be altered in PD patients. Our work expands the role of SNCA in PD and provides a connection between PD-associated genetic variants and underlying biology that points to a risk mechanism in microglia.

The emergence of genotypic divergence and future precision medicine applications

Genotypic divergence is a term adapted from population genetics and intimately linked to evolution. We use divergence here to emphasize the differences that set individuals apart in any cohort. The history of genetics is filled with descriptions of genotypic differences, but causal inference of interindividual biological variation has been scarce. We suggest that the practice of precision medicine requires a divergent approach, an approach dependent on the causal interpretation of previous convergent (and preliminary) knowledge in the field. This knowledge has relied on convergent descriptive syndromology (lumping), which has overemphasized a reductionistic gene determinism on the quest of seeking associations without causal understanding. Regulatory variants with small effect and somatic mutations are some of the modifying factors that lead to incomplete penetrance and intrafamilial variable expressivity often observed in apparently monogenic clinical disorders. A truly divergent approach to precision medicine requires splitting, that is, the consideration of different layers of genetic phenomena that interact causally in a nonlinear fashion. This chapter reviews convergences and divergences in genetics and genomics, aiming to discuss what can be causally understood to approximate the as-yet utopian lands of Precision Medicine for patients with neurodegenerative disorders.

Innate and adaptive immune abnormalities underlying autoimmune diseases: the genetic connections

With the exception of an extremely small number of cases caused by single gene mutations, most autoimmune diseases result from the complex interplay between environmental and genetic factors. In a nutshell, etiology of the common autoimmune disorders is unknown in spite of progress elucidating certain effector cells and molecules responsible for pathologies associated with inflammatory and tissue damage. In recent years, population genetics approaches have greatly enriched our knowledge regarding genetic susceptibility of autoimmunity, providing us with a window of opportunities to comprehensively re-examine autoimmunity-associated genes and possible pathways. In this review, we aim to discuss etiology and pathogenesis of common autoimmune disorders from the perspective of human genetics. An overview of the genetic basis of autoimmunity is followed by 3 chapters detailing susceptibility genes involved in innate immunity, adaptive immunity and inflammatory cell death processes respectively. With such attempts, we hope to expand the scope of thinking and bring attention to lesser appreciated molecules and pathways as important contributors of autoimmunity beyond the 'usual suspects' of a limited subset of validated therapeutic targets.

Big Data in Gastroenterology Research

Studying individual data types in isolation provides only limited and incomplete answers to complex biological questions and particularly falls short in revealing sufficient mechanistic and kinetic details. In contrast, multi-omics approaches to studying health and disease permit the generation and integration of multiple data types on a much larger scale, offering a comprehensive picture of biological and disease processes. Gastroenterology and hepatobiliary research are particularly well-suited to such analyses, given the unique position of the luminal gastrointestinal (GI) tract at the nexus between the gut (mucosa and luminal contents), brain, immune and endocrine systems, and GI microbiome. The generation of 'big data' from multi-omic, multi-site studies can enhance investigations into the connections between these organ systems and organisms and more broadly and accurately appraise the effects of dietary, pharmacological, and other therapeutic interventions. In this review, we describe a variety of useful omics approaches and how they can be integrated to provide a holistic depiction of the human and microbial genetic and proteomic changes underlying physiological and pathophysiological phenomena. We highlight the potential pitfalls and alternatives to help avoid the common errors in study design, execution, and analysis. We focus on the application, integration, and analysis of big data in gastroenterology and hepatobiliary research.

Whole-Exome Sequencing Study of Consanguineous Parkinson's Disease Families and Related Phenotypes: Report of Twelve Novel Variants

Parkinson's disease (PD) is a common progressive neurodegenerative disorder with motor and nonmotor symptoms. Recent studies demonstrate various susceptibility loci and candidate genes for familial forms of the disease. However, the genetic basis of the familial form of early-onset PD (EOPD) is not widely studied in the Iranian population. Therefore, the present study aimed to investigate the possible causative genetic variants responsible for developing EOPD among Iranian patients. Iranian patients with a clinical diagnosis of Parkinson's disease were evaluated, and 12 consanguineous families with at least two affected individuals with early-onset PD (EOPD) were chosen to enroll in the present study. An expert neurologist group examined these families. Whole-exome sequencing (WES) was performed on PD patients, and the possible causative genetic variants related to the development of PD were reported. Exome sequencing (WES) was performed on every PD patient and revealed that patients had novel genetic variants in PRKN, PARK7, and PINK1 genes. All the genetic variants were in homozygous status and none of these variants were previously reported in the literature. Moreover, these genetic variants were "pathogenic" based on bioinformatic studies and according to the American College of Medical Genetics (ACMG). The present research revealed some novel variants for EOPD among the Iranian population. Further functional studies are warranted to confirm the pathogenicity of these novel variants and establish their clinical application for the early diagnosis of EOPD.

Identification of Novel Metabolic Subtypes Using Multi-Trait Limited Mixed Regression in the Chinese Population

The aggregation and interaction of metabolic risk factors leads to highly heterogeneous pathogeneses, manifestations, and outcomes, hindering risk stratification and targeted management. To deconstruct the heterogeneity, we used baseline data from phase II of the Fangshan Family-Based Ischemic Stroke Study (FISSIC), and a total of 4632 participants were included. A total of 732 individuals who did not have any component of metabolic syndrome (MetS) were set as a reference group, while 3900 individuals with metabolic abnormalities were clustered into subtypes using multi-trait limited mixed regression (MFMR). Four metabolic subtypes were identified with the dominant characteristics of abdominal obesity, hypertension, hyperglycemia, and dyslipidemia. Multivariate logistic regression showed that the hyperglycemia-dominant subtype had the highest coronary heart disease (CHD) risk (OR: 6.440, 95% CI: 3.177-13.977) and that the dyslipidemia-dominant subtype had the highest stroke risk (OR: 2.450, 95% CI: 1.250-5.265). Exome-wide association studies (EWASs) identified eight SNPs related to the dyslipidemia-dominant subtype with genome-wide significance, which were located in the genes APOA5, BUD13, ZNF259, and WNT4. Functional analysis revealed an enrichment of top genes in metabolism-related biological pathways and expression in the heart, brain, arteries, and kidneys. Our findings provide directions for future attempts at risk stratification and evidence-based management in populations with metabolic abnormalities from a systematic perspective.

Druggable transcriptomic pathways revealed in Parkinson's patient-derived midbrain neurons

Complex genetic predispositions accelerate the chronic degeneration of midbrain substantia nigra neurons in Parkinson’s disease (PD). Deciphering the human molecular makeup of PD pathophysiology can guide the discovery of therapeutics to slow the disease progression. However, insights from human postmortem brain studies only portray the latter stages of PD, and there is a lack of data surrounding molecular events preceding the neuronal loss in patients. We address this gap by identifying the gene dysregulation of live midbrain neurons reprogrammed in vitro from the skin cells of 42 individuals, including sporadic and familial PD patients and matched healthy controls. To minimize bias resulting from neuronal reprogramming and RNA-seq methods, we developed an analysis pipeline integrating PD transcriptomes from different RNA-seq datasets (unsorted and sorted bulk vs. single-cell and Patch-seq) and reprogramming strategies (induced pluripotency vs. direct conversion). This PD cohort’s transcriptome is enriched for human genes associated with known clinical phenotypes of PD, regulation of locomotion, bradykinesia and rigidity. Dysregulated gene expression emerges strongest in pathways underlying synaptic transmission, metabolism, intracellular trafficking, neural morphogenesis and cellular stress/immune responses. We confirmed a synaptic impairment with patch-clamping and identified pesticides and endoplasmic reticulum stressors as the most significant gene-chemical interactions in PD. Subsequently, we associated the PD transcriptomic profile with candidate pharmaceuticals in a large database and a registry of current clinical trials. This study highlights human transcriptomic pathways that can be targeted therapeutically before the irreversible neuronal loss. Furthermore, it demonstrates the preclinical relevance of unbiased large transcriptomic assays of reprogrammed patient neurons.

Establishing gene regulatory networks from Parkinson's disease risk loci

The latest meta-analysis of genome-wide association studies identified 90 independent variants across 78 genomic regions associated with Parkinson's disease, yet the mechanisms by which these variants influence the development of the disease remains largely elusive. To establish the functional gene regulatory networks associated with Parkinson's disease risk variants, we utilized an approach combining spatial (chromosomal conformation capture) and functional (expression quantitative trait loci) data. We identified 518 genes subject to regulation by 76 Parkinson's variants across 49 tissues, whicih encompass 36 peripheral and 13 CNS tissues. Notably, one-third of these genes were regulated via trans-acting mechanisms (distal; risk locus-gene separated by >1 Mb, or on different chromosomes). Of particular interest is the identification of a novel trans-expression quantitative trait loci-gene connection between rs10847864 and SYNJ1 in the adult brain cortex, highlighting a convergence between familial studies and Parkinson's disease genome-wide association studies loci for SYNJ1 (PARK20) for the first time. Furthermore, we identified 16 neurodevelopment-specific expression quantitative trait loci-gene regulatory connections within the foetal cortex, consistent with hypotheses suggesting a neurodevelopmental involvement in the pathogenesis of Parkinson's disease. Through utilizing Louvain clustering we extracted nine significant and highly intraconnected clusters within the entire gene regulatory network. The nine clusters are enriched for specific biological processes and pathways, some of which have not previously been associated with Parkinson's disease. Together, our results not only contribute to an overall understanding of the mechanisms and impact of specific combinations of Parkinson's disease variants, but also highlight the potential impact gene regulatory networks may have when elucidating aetiological subtypes of Parkinson's disease.

Genetic Elements at the Alpha-Synuclein Locus

Genome-wide association studies have consistently shown that the alpha-synuclein locus is significantly associated with Parkinson's disease. The mechanism by which this locus modulates the disease pathology and etiology remains largely under-investigated. This is due to the assumption that SNCA is the only driver of the functional aspects of several single nucleotide polymorphism (SNP) risk-signals at this locus. Recent evidence has shown that the risk associated with the top GWAS-identified variant within this locus is independent of SNCA expression, calling into question the validity of assigning function to the nearest gene, SNCA. In this review, we examine additional genes and risk variants present at the SNCA locus and how they may contribute to Parkinson's disease. Using the SNCA locus as an example, we hope to demonstrate that deeper and detailed functional validations are required for high impact disease-linked variants.

Uncommon Noninvasive Biomarkers for the Evaluation and Monitoring of the Etiopathogenesis of Alzheimer's Disease

Background:

Alzheimer´s disease (AD) is the most widespread dementia in the world, followed by vascular dementia. Since AD is a heterogeneous disease that shows several varied phenotypes, it is not easy to make an accurate diagnosis, so it arises when the symptoms are clear and the disease is already very advanced. Therefore, it is important to find out biomarkers for AD early diagnosis that facilitate treatment or slow down the disease. Classic biomarkers are obtained from cerebrospinal fluid and plasma, along with brain imaging by positron emission tomography. Attempts have been made to discover uncommon biomarkers from other body fluids, which are addressed in this update.

Objective:

This update aims to describe recent biomarkers from minimally invasive body fluids for the patients, such as saliva, urine, eye fluid or tears.

Methods:

Biomarkers were determined in patients versus controls by single tandem mass spectrometry, and immunoassays. Metabolites were identified by nuclear magnetic resonance, and microRNAs with genome-wide high-throughput real-time polymerase chain reaction-based platforms.

Results:

Biomarkers from urine, saliva, and eye fluid were described, including peptides/proteins, metabolites, and some microRNAs. The association with AD neuroinflammation and neurodegeneration was analyzed, highlighting the contribution of matrix metalloproteinases, the immune system and microglia, as well as the vascular system.

Conclusion:

Unusual biomarkers have been developed, which distinguish each stage and progression of the disease, and are suitable for the early AD diagnosis. An outstanding relationship of biomarkers with neuroinflammation and neurodegeneration was assessed, clearing up concerns of the etiopathogenesis of AD.

Alpha-synuclein negatively controls cell proliferation in dopaminergic neurons

As researchers grapple with the mechanisms and implications of alpha-synuclein (α-syn) in neuropathology, it is often forgotten that the function(s) of α-syn in healthy cells remain largely elusive. Previous work has relied on observing α-syn localization in the cell or using knockout mouse models. Here, we address the specific role of α-syn in human dopaminergic neurons by disrupting its gene (SNCA) in the human dopaminergic neuron cell line, LUHMES. SNCA-null cells were able to differentiate grossly normally and showed modest effects on gene expression. The effects on gene expression were monodirectional, resulting primarily in the significant decrease of expression for 401 genes, implicating them as direct, or indirect positive targets of α-syn. Gene ontological analysis of these genes showed enrichment in terms associated with proliferation, differentiation, and synapse activity. These results add to the tapestry of α-syn biological functions. SIGNIFICANCE STATEMENT: The normal functions of α-syn have remained controversial, despite its clear importance in Parkinson's Disease pathology, where it accumulates in Lewy bodies and contributes to neurodegeneration. Its name implies synaptic and nuclear functions, but how it participates at these locations has not been resolved. Via knock-out experiments in dopaminergic neurons, we implicate α-syn as a functional participant in synapse activity and in proliferation/differentiation, the latter being novel and provide insight into α-syn's role in neuronal development.

Parkinson's disease risk genes act in glia to control neuronal α-synuclein toxicity

Idiopathic Parkinson's disease is the second most common neurodegenerative disease and is estimated to be approximately 30% heritable. Genome wide association studies have revealed numerous loci associated with risk of development of Parkinson's disease. The majority of genes identified in these studies are expressed in glia at either similar or greater levels than their expression in neurons, suggesting that glia may play a role in Parkinson's disease pathogenesis. The role of individual glial risk genes in Parkinson's disease development or progression is unknown, however. We hypothesized that some Parkinson's disease risk genes exert their effects through glia. We developed a Drosophila model of α-synucleinopathy in which we can independently manipulate gene expression in neurons and glia. Human wild type α-synuclein is expressed in all neurons, and these flies develop the hallmarks of Parkinson's disease, including motor impairment, death of dopaminergic and other neurons, and α-synuclein aggregation. In these flies, we performed a candidate genetic screen, using RNAi to knockdown 14 well-validated Parkinson's disease risk genes in glia and measuring the effect on locomotion in order to identify glial modifiers of the α-synuclein phenotype. We identified 4 modifiers: aux, Lrrk, Ric, and Vps13, orthologs of the human genes GAK, LRRK2, RIT2, and VPS13C, respectively. Knockdown of each gene exacerbated neurodegeneration as measured by total and dopaminergic neuron loss. Knockdown of each modifier also increased α-synuclein oligomerization. These results suggest that some Parkinson's disease risk genes exert their effects in glia and that glia can influence neuronal α-synuclein proteostasis in a non-cell-autonomous fashion. Further, this study provides proof of concept that our novel Drosophila α-synucleinopathy model can be used to study glial modifier genes, paving the way for future large unbiased screens to identify novel glial risk factors that contribute to PD risk and progression.

The enigma and implications of brain hemispheric asymmetry in neurodegenerative diseases

The lateralization of the human brain may provide clues into the pathogenesis and progression of neurodegenerative diseases. Though differing in their presentation and underlying pathologies, neurodegenerative diseases are all devastating and share an intriguing theme of asymmetrical pathology and clinical symptoms. Parkinson’s disease, with its distinctive onset of motor symptoms on one side of the body, stands out in this regard, but a review of the literature reveals asymmetries in several other neurodegenerative diseases. Here, we review the lateralization of the structure and function of the healthy human brain and the common genetic and epigenetic patterns contributing to the development of asymmetry in health and disease. We specifically examine the role of asymmetry in Parkinson’s disease, Alzheimer’s disease, amyotrophic lateral sclerosis, and multiple sclerosis, and interrogate whether these imbalances may reveal meaningful clues about the origins of these diseases. We also propose several hypotheses for how lateralization may contribute to the distinctive and enigmatic features of asymmetry in neurodegenerative diseases, suggesting a role for asymmetry in the choroid plexus, neurochemistry, protein distribution, brain connectivity and the vagus nerve. Finally, we suggest how future studies may reveal novel insights into these diseases through the lens of asymmetry.

Quantitative neurogenetics: applications in understanding disease

Neurodevelopmental and neurodegenerative disorders (NNDs) are a group of conditions with a broad range of core and co-morbidities, associated with dysfunction of the central nervous system. Improvements in high throughput sequencing have led to the detection of putative risk genetic loci for NNDs, however, quantitative neurogenetic approaches need to be further developed in order to establish causality and underlying molecular genetic mechanisms of pathogenesis. Here, we discuss an approach for prioritizing the contribution of genetic risk loci to complex-NND pathogenesis by estimating the possible impacts of these loci on gene regulation. Furthermore, we highlight the use of a tissue-specificity gene expression index and the application of artificial intelligence (AI) to improve the interpretation of the role of genetic risk elements in NND pathogenesis. Given that NND symptoms are associated with brain dysfunction, risk loci with direct, causative actions would comprise genes with essential functions in neural cells that are highly expressed in the brain. Indeed, NND risk genes implicated in brain dysfunction are disproportionately enriched in the brain compared with other tissues, which we refer to as brain-specific expressed genes. In addition, the tissue-specificity gene expression index can be used as a handle to identify non-brain contexts that are involved in NND pathogenesis. Lastly, we discuss how using an AI approach provides the opportunity to integrate the biological impacts of risk loci to identify those putative combinations of causative relationships through which genetic factors contribute to NND pathogenesis.