Functional characterization of human genomic variation linked to polygenic diseases

Fabo, Tania; Khavari, Paul A.

doi:10.1016/j.tig.2023.02.014

Cited by 13 publications

(5 citation statements)

References 222 publications

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“…While a subset of REs is evolutionarily conserved, many show evolutionary divergence [ 71 , 72 ], and several studies have suggested that RE evolution is an important driver of speciation, i.e., the difference in regulatory landscapes underlies differences between species [ 66 , 73 ]. Moreover, the inter-individual variations in the sequence of REs are also likely to importantly contribute to differences between individuals from the same species, including differences in traits and susceptibility to particular diseases [ 74 , 75 , 76 ]. Single genetic variants frequently affect binding of transcription factors, affecting local chromatin state and transcription, implicating these natural genetic variants in phenotypic heterogeneity [ 77 , 78 , 79 ].…”

Section: Regulatory Elementsmentioning

confidence: 99%

Role of Genetic Variation in Transcriptional Regulatory Elements in Heart Rhythm

Jonker,

Barnett,

Boink

et al. 2023

Cells

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Genetic predisposition to cardiac arrhythmias has been a field of intense investigation. Research initially focused on rare hereditary arrhythmias, but over the last two decades, the role of genetic variation (single nucleotide polymorphisms) in heart rate, rhythm, and arrhythmias has been taken into consideration as well. In particular, genome-wide association studies have identified hundreds of genomic loci associated with quantitative electrocardiographic traits, atrial fibrillation, and less common arrhythmias such as Brugada syndrome. A significant number of associated variants have been found to systematically localize in non-coding regulatory elements that control the tissue-specific and temporal transcription of genes encoding transcription factors, ion channels, and other proteins. However, the identification of causal variants and the mechanism underlying their impact on phenotype has proven difficult due to the complex tissue-specific, time-resolved, condition-dependent, and combinatorial function of regulatory elements, as well as their modest conservation across different model species. In this review, we discuss research efforts aimed at identifying and characterizing-trait-associated variant regulatory elements and the molecular mechanisms underlying their impact on heart rate or rhythm.

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Section: Regulatory Elementsmentioning

confidence: 99%

Role of Genetic Variation in Transcriptional Regulatory Elements in Heart Rhythm

Jonker,

Barnett,

Boink

et al. 2023

Cells

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“…Despite this success, significant hurdles are still faced in moving from the identified common variant association to a potential pathogenic effector gene [ 3 ]. This is principally because ~ 88.5% of NHGRI-EBI GWAS variants are non-coding [ 4 ] and are typically in high LD with numerous other co-inherited variants, any of which may be the critical cis -regulatory variant(s) influencing a downstream effector target gene(s). The integration of cell or tissue-specific epigenomic data with these initial GWAS findings is a highly informative step in achieving functional understanding [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Epigenomic insights into common human disease pathology

Bell

2024

Cell. Mol. Life Sci.

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The epigenome—the chemical modifications and chromatin-related packaging of the genome—enables the same genetic template to be activated or repressed in different cellular settings. This multi-layered mechanism facilitates cell-type specific function by setting the local sequence and 3D interactive activity level. Gene transcription is further modulated through the interplay with transcription factors and co-regulators. The human body requires this epigenomic apparatus to be precisely installed throughout development and then adequately maintained during the lifespan. The causal role of the epigenome in human pathology, beyond imprinting disorders and specific tumour suppressor genes, was further brought into the spotlight by large-scale sequencing projects identifying that mutations in epigenomic machinery genes could be critical drivers in both cancer and developmental disorders. Abrogation of this cellular mechanism is providing new molecular insights into pathogenesis. However, deciphering the full breadth and implications of these epigenomic changes remains challenging. Knowledge is accruing regarding disease mechanisms and clinical biomarkers, through pathogenically relevant and surrogate tissue analyses, respectively. Advances include consortia generated cell-type specific reference epigenomes, high-throughput DNA methylome association studies, as well as insights into ageing-related diseases from biological ‘clocks’ constructed by machine learning algorithms. Also, 3rd-generation sequencing is beginning to disentangle the complexity of genetic and DNA modification haplotypes. Cell-free DNA methylation as a cancer biomarker has clear clinical utility and further potential to assess organ damage across many disorders. Finally, molecular understanding of disease aetiology brings with it the opportunity for exact therapeutic alteration of the epigenome through CRISPR-activation or inhibition.

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Section: Introductionmentioning

confidence: 99%

“…Traditional genome-wide association studies (GWAS) have identified associations between more than 400,000 genetic variants and hundreds of human phenotypes, providing insights into the pathophysiology of complex diseases and traits (Andreassen et al ., 2023; Fabo & Khavari, 2023). Despite these successes, there are still several limitations to be considered (Fabo & Khavari, 2023). One of the most significant ones is the fact that the GWAS approach relies on statistical modelling, testing each single nucleotide polymorphism (SNP) separately, and the identified variants only account for a small proportion of the heritability (Mieth et al ., 2021).…”

Section: Introductionmentioning

confidence: 99%

New genetic loci discovery for Alzheimer’s disease using explainable deep neural networks

Gkertsou,

Parameshwarappa,

Shiyanbola

et al. 2023

Preprint

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BackgroundGenome-wide association studies (GWAS) have shed light on various complex diseases and traits, by detecting more than 400,000 associated genetic loci. This number is expected to drastically increase because of the use of novel artificial intelligence methods offering new ways to study the effects of variants. Deep learning using artificial neural networks (ANN) is a sub-field of artificial intelligence, which simulates how the human brain learns. We aimed at assessing the potential of deep learning in human genetic studies of Alzheimer’s Disease (AD) and how these compare to the traditional statistical methods used in GWAS, by simultaneously testing the two approaches on the same dataset, while discovering new genetic loci associated to AD.MethodsTo address this aim, phenotypic and genome-wide SNP data from the UK Biobank was analysed on a binary outcome, AD diagnosis, in two different data balance options, of one-to-one and one-to-two case-control datasets, using 2,764 cases vs 2,764 controls and 5,528 controls respectively matched on gender, age, ethnicity, PC1-20 and genotyping array. Genetic data handling and GWAS were performed using PLINK, whereas neural networks were trained using GenNet, a new ANN tool, with the same datasets, separated into training (60%), validation (20%) and test (20%) sets. Neural network layers were determined using biological knowledge, by annotating SNPs to genes and genes to AD related pathways, using ANNOVAR annotations followed by GeneSCF and KEGG.ResultsSignificant associations were detected between four SNPs linked to two different genes and AD for the 1 to 1 case-control study design and six SNPs linked to four different genes for the 1 to 2 case-control study design by using PLINK. All identified regions have been previously associated to AD. GenNet identified twelve SNPs on seven genes to be associated with AD, all with biological plausibility, achieving an AUC of 0.80 when using three biologically determined layers and 0.73 when using two layers at the neural networks. No common top SNPs were identified between the machine learning and GWAS models.ConclusionThis is one of the first studies attempting to compare the traditional GWAS to more sophisticated state-of-art methods for understanding the genetic architecture of complex phenotypes using the same dataset. More systematic comparisons with such approaches on real data are needed to enable best practises for machine learning in the analysis of genome-wide genetic data.

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Functional characterization of human genomic variation linked to polygenic diseases

Cited by 13 publications

References 222 publications

Role of Genetic Variation in Transcriptional Regulatory Elements in Heart Rhythm

Role of Genetic Variation in Transcriptional Regulatory Elements in Heart Rhythm

Epigenomic insights into common human disease pathology

New genetic loci discovery for Alzheimer’s disease using explainable deep neural networks

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