scholarly journals Deep Learning/Artificial Intelligence and Blood-Based DNA Epigenomic Prediction of Cerebral Palsy

2019 ◽  
Vol 20 (9) ◽  
pp. 2075 ◽  
Author(s):  
Ray O. Bahado-Singh ◽  
Sangeetha Vishweswaraiah ◽  
Buket Aydas ◽  
Nitish Kumar Mishra ◽  
Chittibabu Guda ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cerebral Palsy ◽  
Neuronal Development ◽  
Brain Structures ◽  
Receiver Operating Curve ◽  
P Value ◽  
Neuron Development ◽  
Methylation Analysis ◽  
Genome Wide ◽  
Canonical Pathways

The etiology of cerebral palsy (CP) is complex and remains inadequately understood. Early detection of CP is an important clinical objective as this improves long term outcomes. We performed genome-wide DNA methylation analysis to identify epigenomic predictors of CP in newborns and to investigate disease pathogenesis. Methylation analysis of newborn blood DNA using an Illumina HumanMethylation450K array was performed in 23 CP cases and 21 unaffected controls. There were 230 significantly differentially-methylated CpG loci in 258 genes. Each locus had at least 2.0-fold change in methylation in CP versus controls with a FDR p-value ≤ 0.05. Methylation level for each CpG locus had an area under the receiver operating curve (AUC) ≥ 0.75 for CP detection. Using Artificial Intelligence (AI) platforms/Machine Learning (ML) analysis, CpG methylation levels in a combination of 230 significantly differentially-methylated CpG loci in 258 genes had a 95% sensitivity and 94.4% specificity for newborn prediction of CP. Using pathway analysis, multiple canonical pathways plausibly linked to neuronal function were over-represented. Altered biological processes and functions included: neuromotor damage, malformation of major brain structures, brain growth, neuroprotection, neuronal development and de-differentiation, and cranial sensory neuron development. In conclusion, blood leucocyte epigenetic changes analyzed using AI/ML techniques appeared to accurately predict CP and provided plausible mechanistic information on CP pathogenesis.

scholarly journals Genome-Wide Scanning of Potential Hotspots for Adenosine Methylation: A Potential Path to Neuronal Development

Life ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1185
Author(s):  
Sanjay Kumar ◽  
Lung-Wen Tsai ◽  
Pavan Kumar ◽  
Rajni Dubey ◽  
Deepika Gupta ◽  
...  
Keyword(s):  
Human Genome ◽  
Neuronal Development ◽  
Sliding Window ◽  
Neuron Development ◽  
Detection Techniques ◽  
Coding Regions ◽  
Genome Wide ◽  
Window Technique ◽  
Current Detection

Methylation of adenosines at N6 position (m6A) is the most frequent internal modification in mRNAs of the human genome and attributable to diverse roles in physiological development, and pathophysiological processes. However, studies on the role of m6A in neuronal development are sparse and not well-documented. The m6A detection remains challenging due to its inconsistent pattern and less sensitivity by the current detection techniques. Therefore, we applied a sliding window technique to identify the consensus site (5′-GGACT-3′) n ≥ 2 and annotated all m6A hotspots in the human genome. Over 6.78 × 107 hotspots were identified and 96.4% were found to be located in the non-coding regions, suggesting that methylation occurs before splicing. Several genes, RPS6K, NRP1, NRXN, EGFR, YTHDF2, have been involved in various stages of neuron development and their functioning. However, the contribution of m6A in these genes needs further validation in the experimental model. Thus, the present study elaborates the location of m6A in the human genome and its function in neuron physiology.

scholarly journals DeepCOMBI: Explainable artificial intelligence for the analysis and discovery in genome-wide association studies

2020 ◽  
Author(s):  
Bettina Mieth ◽  
Alexandre Rozier ◽  
Juan Antonio Rodriguez ◽  
Marina M.-C. Höhne ◽  
Nico Görnitz ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Data Science ◽  
Association Studies ◽  
Multiple Hypothesis Testing ◽  
Genome Wide Association ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Explainable Artificial Intelligence

AbstractDeep learning algorithms have revolutionized data science in many fields by greatly improving prediction performances in comparison to conventional approaches. Recently, explainable artificial intelligence (XAI) has emerged as a novel area of research that goes beyond pure prediction improvement. Knowledge embodied in deep learning methodologies is extracted by interpreting their results. We investigate such explanations to explore the genetic architectures of phenotypes in genome-wide association studies. Instead of testing each position in the genome individually, the novel three-step algorithm, called DeepCOMBI, first trains a neural network for the classification of subjects into their respective phenotypes. Second, it explains the classifiers’ decisions by applying layerwise relevance propagation as one example from the pool of XAI techniques. The resulting importance scores are eventually used to determine a subset of most relevant locations for multiple hypothesis testing in the third step. The performance of DeepCOMBI in terms of power and precision is investigated on generated datasets and a 2007 WTCCC study. Verification of the latter is achieved by validating all findings with independent studies published up until 2020. DeepCOMBI is shown to outperform ordinary raw p-value thresholding as well as other baseline methods. Moreover, two novel disease associations (rs10889923 for hypertension and rs4769283 for type 1 diabetes) were identified.

Artificial Intelligence for epigenetics: towards personalized medicine

2020 ◽  
Vol 27 ◽  
Author(s):  
Giulia De Riso ◽  
Sergio Cocozza
Keyword(s):  
Biological Sciences ◽  
Artificial Intelligence ◽  
Personalized Medicine ◽  
Molecular Mechanisms ◽  
Genomic Sequence ◽  
Health Care Interventions ◽  
Genome Wide ◽  
Genetic And Environmental Factors ◽  
Opening Up ◽  
Omic Data

: Epigenetics is a field of biological sciences focused on the study of reversible, heritable changes in gene function not due to modifications of the genomic sequence. These changes are the result of a complex cross-talk between several molecular mechanisms, that is in turn orchestrated by genetic and environmental factors. The epigenetic profile captures the unique regulatory landscape and the exposure to environmental stimuli of an individual. It thus constitutes a valuable reservoir of information for personalized medicine, which is aimed at customizing health-care interventions based on the unique characteristics of each individual. Nowadays, the complex milieu of epigenomic marks can be studied at the genome-wide level thanks to massive, highthroughput technologies. This new experimental approach is opening up new and interesting knowledge perspectives. However, the analysis of these complex omic data requires to face important analytic issues. Artificial Intelligence, and in particular Machine Learning, are emerging as powerful resources to decipher epigenomic data. In this review, we will first describe the most used ML approaches in epigenomics. We then will recapitulate some of the recent applications of ML to epigenomic analysis. Finally, we will provide some examples of how the ML approach to epigenetic data can be useful for personalized medicine.

scholarly journals Genomic prediction for growth using a low-density SNP panel in dromedary camels

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Morteza Bitaraf Sani ◽  
Javad Zare Harofte ◽  
Mohammad Hossein Banabazi ◽  
Saeid Esmaeilkhanian ◽  
Ali Shafei Naderi ◽  
...  
Keyword(s):  
Body Weight ◽  
Birth Weight ◽  
Sample Size ◽  
Genetic Improvement ◽  
Growth Traits ◽  
P Value ◽  
Dromedary Camels ◽  
Genome Wide ◽  
A Genome ◽  
Daily Gain

AbstractFor thousands of years, camels have produced meat, milk, and fiber in harsh desert conditions. For a sustainable development to provide protein resources from desert areas, it is necessary to pay attention to genetic improvement in camel breeding. By using genotyping-by-sequencing (GBS) method we produced over 14,500 genome wide markers to conduct a genome- wide association study (GWAS) for investigating the birth weight, daily gain, and body weight of 96 dromedaries in the Iranian central desert. A total of 99 SNPs were associated with birth weight, daily gain, and body weight (p-value < 0.002). Genomic breeding values (GEBVs) were estimated with the BGLR package using (i) all 14,522 SNPs and (ii) the 99 SNPs by GWAS. Twenty-eight SNPs were associated with birth weight, daily gain, and body weight (p-value < 0.001). Annotation of the genomic region (s) within ± 100 kb of the associated SNPs facilitated prediction of 36 candidate genes. The accuracy of GEBVs was more than 0.65 based on all 14,522 SNPs, but the regression coefficients for birth weight, daily gain, and body weight were 0.39, 0.20, and 0.23, respectively. Because of low sample size, the GEBVs were predicted using the associated SNPs from GWAS. The accuracy of GEBVs based on the 99 associated SNPs was 0.62, 0.82, and 0.57 for birth weight, daily gain, and body weight. This report is the first GWAS using GBS on dromedary camels and identifies markers associated with growth traits that could help to plan breeding program to genetic improvement. Further researches using larger sample size and collaboration of the camel farmers and more profound understanding will permit verification of the associated SNPs identified in this project. The preliminary results of study show that genomic selection could be the appropriate way to genetic improvement of body weight in dromedary camels, which is challenging due to a long generation interval, seasonal reproduction, and lack of records and pedigrees.

The association of artificial intelligence-enabled electrocardiogram-derived age (physiologic age) with atherosclerotic cardiovascular events in the community

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
J Medina-Inojosa ◽  
A Ladejobi ◽  
Z Attia ◽  
M Shelly-Cohen ◽  
B Gersh ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bypass Graft ◽  
P Value ◽  
Funding Source ◽  
Atherosclerotic Cardiovascular Disease ◽  
Age Gap ◽  
Predicted Risk ◽  
Age And Sex

Abstract Background We have demonstrated that artificial intelligence interpretation of ECGs (AI-ECG) can estimate an individual's physiologic age and that the gap between AI-ECG and chronologic age (Age-Gap) is associated with increased mortality. We hypothesized that Age-Gap would predict long-term atherosclerotic cardiovascular disease (ASCVD) and that Age-Gap would refine the ACC/AHA Pooled Cohort Equations' (PCE) predictive abilities. Methods Using the Rochester Epidemiology Project (REP) we evaluated a community-based cohort of consecutive patients seeking primary care between 1998–2000 and followed through March 2016. Inclusion criteria were age 40–79 and complete data to calculate PCE. We excluded those with known ASCVD, AF, HF or an event within 30 days of baseline.A neural network, trained, validated, and tested in an independent cohort of ∼ 500,000 independent patients, using 10-second digital samples of raw, 12 lead ECGs. PCE was categorized as low&lt;5%, intermediate 5–9.9%, high 10–19.9%, and very high≥20%. The primary endpoint was ASCVD and included fatal and non-fatal myocardial infarction and ischemic stroke; the secondary endpoint also included coronary revascularization [Percutaneous Coronary Intervention (PCI) or Coronary Artery Bypass Graft (CABG)], TIA and Cardiovascular mortality. Events were validated in duplicate. Follow-up was truncated at 10 years for PCE analysis. The association between Age-Gap with ASCVD and expanded ASCVD was assessed with cox proportional hazard models that adjusted for chronological age, sex and risk factors. Models were stratified by PCE risk categories to evaluate the effect of PCE predicted risk. Results We included 24,793 patients (54% women, 95% Caucasian) with mean follow up of 12.6±5.1 years. 2,366 (9.5%) developed ASCVD events and 3,401 (13.7%) the expanded ASCVD. Mean chronologic age was 53.6±11.6 years and the AI-ECG age was 54.5±10.9 years, R2=0.7865, p&lt;0.0001. The mean Age-Gap was 0.87±7.38 years. After adjusting for age and sex, those considered older by ECG, compared to their chronologic age had a higher risk for ASCVD when compared to those with &lt;−2 SD age gap (considered younger by ECG). (Figure 1A), with similar results when using the expanded definition of ASCVD (data not shown). Furthermore, Age-Gap enhanced predicted capabilities of the PCE among those with low 10-year predicted risk (&lt;5%): Age and sex adjusted HR 4.73, 95% CI 1.42–15.74, p-value=0.01 and among those with high predicted risk (&gt;20%) age and sex adjusted HR 6.90, 95% CI 1.98–24.08, p-value=0.0006, when comparing those older to younger by ECG respectively (Figure 1B). Conclusion The difference between physiologic AI-ECG age and chronologic age is associated with long-term ASCVD, and enhances current risk calculators (PCE) ability to identify high and low risk individuals. This may help identify individuals who should or should not be treated with newer, expensive risk-reducing therapies. Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Mayo Clinic

scholarly journals Age by Single Nucleotide Polymorphism Interactions on Bronchodilator Response in Asthmatics

2021 ◽  
Vol 11 (1) ◽  
pp. 59
Author(s):  
Kirsten Voorhies ◽  
Joanne E. Sordillo ◽  
Michael McGeachie ◽  
Elizabeth Ampleford ◽  
Alberta L. Wang ◽  
...  
Keyword(s):  
Candidate Genes ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Single Nucleotide ◽  
Significance Level ◽  
Bronchodilator Response ◽  
Genome Wide ◽  
A Genome ◽  
Β2 Agonists

An unaddressed and important issue is the role age plays in modulating response to short acting β2-agonists in individuals with asthma. The objective of this study was to identify whether age modifies genetic associations of single nucleotide polymorphisms (SNPs) with bronchodilator response (BDR) to β2-agonists. Using three cohorts with a total of 892 subjects, we ran a genome wide interaction study (GWIS) for each cohort to examine SNP by age interactions with BDR. A fixed effect meta-analysis was used to combine the results. In order to determine if previously identified BDR SNPs had an age interaction, we also examined 16 polymorphisms in candidate genes from two published genome wide association studies (GWAS) of BDR. There were no significant SNP by age interactions on BDR using the genome wide significance level of 5 × 10−8. Using a suggestive significance level of 5 × 10−6, three interactions, including one for a SNP within PRAG1 (rs4840337), were significant and replicated at the significance level of 0.05. Considering candidate genes from two previous GWAS of BDR, three SNPs (rs10476900 (near ADRB2) [p-value = 0.009], rs10827492 (CREM) [p-value = 0.02], and rs72646209 (NCOA3) [p-value = 0.02]) had a marginally significant interaction with age on BDR (p < 0.05). Our results suggest age may be an important modifier of genetic associations for BDR in asthma.

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