Implementing Whole Genome Sequencing (WGS) in Clinical Practice: Advantages, Challenges, and Future Perspectives

Cells, Год журнала: 2024, Номер 13(6), С. 504 - 504

Опубликована: Март 13, 2024

The integration of whole genome sequencing (WGS) into all aspects modern medicine represents the next step in evolution healthcare. Using this technology, scientists and physicians can observe entire human comprehensively, generating a plethora new data. Modern computational analysis entails advanced algorithms for variant detection, as well complex models classification. Data science machine learning play crucial role processing interpretation results, using enormous databases statistics to discover support current genotype–phenotype correlations. In clinical practice, technology has greatly enabled development personalized medicine, approaching each patient individually accordance with their genetic biochemical profile. most propulsive areas include rare disease genomics, oncogenomics, pharmacogenomics, neonatal screening, infectious genomics. Another application WGS lies field multi-omics, working towards complete biomolecular Further technological technologies led birth third fourth-generation sequencing, which long-read single-cell nanopore sequencing. These technologies, alongside continued implementation medical research show great promise future medicine.

Язык: Английский

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Whole-genome sequencing analysis identifies rare, large-effect noncoding variants and regulatory regions associated with circulating protein levels

Nature Genetics, Год журнала: 2025, Номер unknown

Опубликована: Фев. 24, 2025

Abstract The contribution of rare noncoding genetic variation to common phenotypes is largely unknown, as a result historical lack population-scale whole-genome sequencing data and the difficulty categorizing variants into functionally similar groups. To begin addressing these challenges, we performed cis association analysis using data, consisting 1.1 billion variants, 123 million aggregate-based tests 2,907 circulating protein levels in ~50,000 UK Biobank participants. We identified 604 independent single-variant associations with levels. Unlike protein-coding variation, was almost likely increase or decrease Rare aggregate testing 357 conditionally associated regions. Of these, 74 (21%) were not detectable by alone. Our findings have important implications for identification, role, human phenotypes, including importance aggregates variants.

Язык: Английский

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Whole genome sequencing analysis identifies rare, large-effect non-coding variants and regions associated with circulating protein levels

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Ноя. 5, 2023

Abstract The role of non-coding rare variation in common phenotypes is largely unknown, due to a lack whole-genome sequence data, and the difficulty categorising variants into biologically meaningful regulatory units. To begin addressing these challenges, we performed cis association analysis using consisting 391 million 1,450 circulating protein levels ∼20,000 UK Biobank participants. We identified 777 independent single associated with ( P <1×10 -9 ), after conditioning on protein-coding variants. Rare aggregate testing 108 conditionally regions. Unlike variation, genetic was almost as likely increase decrease levels. regions overlapped predicted tissue-specific enhancers more than promoters, suggesting they represent Our results have important implications for identification, role, human phenotypes.

Язык: Английский

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Modern approaches to the assessment of individual risk of CHD development: status, problems, prospects

Ateroscleroz, Год журнала: 2024, Номер 20(2), С. 154 - 161

Опубликована: Июль 4, 2024

Cardiovascular diseases are the leading cause of non-violent deaths in world. Criteria for formation high-risk groups necessary primary prevention disease development. This was reason research on development riskmeters. A brief description history creation CHD The review provides a current challenges assessing individual risk CHD. main approaches to riskmeters have not changed significantly several decades. increase size study and number molecular genetic markers undoubtedly give certain results. However, order move from population level level, it is take into account many more factors assessment. That is, learn how analyze most complex set data one person (genome, transcriptome, proteome, maybe even microbiome) only with deep understanding mechanisms its functioning (from conception death), but also possible disorders, based available features. And this purpose rely so much statistical data, maximally similar sets (first all, relatives). It seems that similarity should be evaluated by an artificial intelligence system trained colossal array data.

Язык: Английский

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<b>Characterizing common and rare variations in non-traditional glycemic biomarkers using multivariate approaches on multi-ancestry ARIC study</b>

Опубликована: Июнь 13, 2024

<p dir="ltr"><b>ABSTRACT</b> (249 words)</p><p dir="ltr">Genetic studies of non-traditional glycemic biomarkers, glycated albumin and fructosamine, can shed light on unknown aspects type 2 diabetes genetics biology. We performed a multi-phenotype GWAS fructosamine from 7,395 White 2,016 Black participants in the Atherosclerosis Risk Communities (ARIC) study common variants genotyped/imputed data. discovered genome-wide significant loci, one mapping to known gene (<i>ARAP1/STARD10</i>) another novel region (<i>UGT1A</i> complex genes) using multi-omics gene-mapping strategies diabetes-relevant tissues. identified additional loci that were ancestry- sex-specific (e.g., <i>PRKCA</i> African ancestry, <i>FCGRT</i> European <i>TEX29</i> males). Further, we implemented gene-burden tests whole-exome sequence data 6,590 2,309 ARIC participants. Ten variant sets annotated genes across different aggregation exome-wide only multi-ancestry analysis, which <i>CD1D</i>, <i>EGFL7/AGPAT2</i> <i>MIR126</i> had notable enrichment rare predicted loss function ancestry despite smaller sample sizes. Overall, 8 out 14 implicated influence these biomarkers via pathways, most them not previously diabetes. This illustrates improved locus discovery potential effector by leveraging joint patterns related entire allele frequency spectrum analysis. Future investigation potentially acting through pathways may help us better understand risk developing diabetes.</p><p><br></p><p dir="ltr"><b>ARTICLE HIGHLIGHTS</b> (100 dir="ltr">· Glycated are reflecting process hemoglobin or blood glucose levels. Thus, they biology.</p><p leveraged array-based exome individuals US discover yet-unidentified genes.</p><p associated with and/or some have been Locus-specific effects at vary sex. Some associations unique either ancestry.</p>

Язык: Английский

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<b>Characterizing common and rare variations in non-traditional glycemic biomarkers using multivariate approaches on multi-ancestry ARIC study</b>

Опубликована: Июнь 13, 2024

<p dir="ltr"><b>ABSTRACT</b> (249 words)</p><p dir="ltr">Genetic studies of non-traditional glycemic biomarkers, glycated albumin and fructosamine, can shed light on unknown aspects type 2 diabetes genetics biology. We performed a multi-phenotype GWAS fructosamine from 7,395 White 2,016 Black participants in the Atherosclerosis Risk Communities (ARIC) study common variants genotyped/imputed data. discovered genome-wide significant loci, one mapping to known gene (<i>ARAP1/STARD10</i>) another novel region (<i>UGT1A</i> complex genes) using multi-omics gene-mapping strategies diabetes-relevant tissues. identified additional loci that were ancestry- sex-specific (e.g., <i>PRKCA</i> African ancestry, <i>FCGRT</i> European <i>TEX29</i> males). Further, we implemented gene-burden tests whole-exome sequence data 6,590 2,309 ARIC participants. Ten variant sets annotated genes across different aggregation exome-wide only multi-ancestry analysis, which <i>CD1D</i>, <i>EGFL7/AGPAT2</i> <i>MIR126</i> had notable enrichment rare predicted loss function ancestry despite smaller sample sizes. Overall, 8 out 14 implicated influence these biomarkers via pathways, most them not previously diabetes. This illustrates improved locus discovery potential effector by leveraging joint patterns related entire allele frequency spectrum analysis. Future investigation potentially acting through pathways may help us better understand risk developing diabetes.</p><p><br></p><p dir="ltr"><b>ARTICLE HIGHLIGHTS</b> (100 dir="ltr">· Glycated are reflecting process hemoglobin or blood glucose levels. Thus, they biology.</p><p leveraged array-based exome individuals US discover yet-unidentified genes.</p><p associated with and/or some have been Locus-specific effects at vary sex. Some associations unique either ancestry.</p>

Язык: Английский

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Systemic identification of functionally conserved lncRNA metabolic regulators in human and mouse livers

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Авг. 10, 2024

Abstract BACKGROUND & AIMS Unlike protein-coding genes, the majority of human long non-coding RNAs (lncRNAs) lack conservation based on their sequences, posing a challenge for investigating role in pathophysiological context clinical translation. This study explores hypothesis that non-conserved lncRNAs and mouse livers may share similar metabolic functions, giving rise to functionally conserved lncRNA regulators (fcLMRs). METHODS We developed sequence-independent strategy select putative fcLMRs, performed extensive analysis determine functional similarities LMR pairs (h/mLMRs). RESULTS found several fcLMRs functions regulating gene expression. further demonstrated pair h/mLMR1, robustly regulated triglyceride levels by modulating expression set lipogenic genes. Mechanistically, h/mLMR1 binds PABPC1, regulator protein translation, via short motifs either with divergent sequences but structures. interaction inhibits activating an amino acid-mTOR-SREBP1 axis regulate Intriguingly, PABPC1-binding each fully rescued corresponding LMRs opposite species. Given elevated humans mice hepatic steatosis, motif hLMR1 emerges as potential drug target whose can be validated physiologically relevant setting before studies. CONCLUSIONS Our supports represent novel prevalent biological phenomenon, deep phenotyping genetic mLMR models constitutes powerful approach understand liver.

Язык: Английский

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Harnessing the Power of Statistics and Machine Learning in the Era of Biobank-Scale Whole-Genome Sequencing and Multi-Omics Studies

XRDS Crossroads The ACM Magazine for Students, Год журнала: 2023, Номер 30(2), С. 28 - 33

Опубликована: Дек. 1, 2023

Researchers are developing new statistical and machine learning methods to effectively integrate biobank-scale whole-genome sequencing multi-omics electronic health records data better understand the molecular basis of complex human diseases.

Язык: Английский

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