Machine Learning Application in Genomic, Exercise, and Vital Datasets

Abstracts PURPOSE Machine learning (ML) refers to newly developed computer algorithms that are improved through iterative experiences. ML applications are expected to assist humans in analyzing large amounts of data. This review has outlined the application of ML in analyzing variable vital data such as walking steps, exercise intensity, heart rate, sleeping hours, sleep quality, resting heart rate, blood pressure, and calorie consumption in a day. Vital data consist of different variables that are closely related to genomic or exercise data. The prediction of healthy traits from a vital dataset has become a necessity in personalized medicine. METHODS Considerations and repeated tasks in supervised, semi-supervised, and unsupervised ML methods are presented. ML methods such as artificial neural networks, Bayesian networks, support vector machines, and decision trees have been widely used in biomedical studies to develop predictive models. Through vital data, these models can help in effective and accurate decision-making for a healthier life. PURPOSE Models based on genomic, exercise, and vital datasets provide a healthy lifestyle through regular exercise. We have provided guidelines to help in the selection of these ML methods and their practical application for variable vital data analysis. CONCLUSIONS Our guidelines could serve as a foundation for implementing both participatory medicine and data-driven exercise science

Association of metabolic and genetic heterogeneity in head and neck squamous cell carcinoma with prognostic implications: integration of FDG PET and genomic analysis

Purpose The linkage between the genetic and phenotypic heterogeneity of the tumor has not been thoroughly evaluated. Herein, we investigated how the genetic and metabolic heterogeneity features of the tumor are associated with each other in head and neck squamous cell carcinoma (HNSC). We further assessed the prognostic significance of those features. Methods The mutant-allele tumor heterogeneity (MATH) score (n = 508), a genetic heterogeneity feature, and tumor glycolysis feature (GlycoS) (n = 503) were obtained from the HNSC dataset in the cancer genome atlas (TCGA). We identified matching patients (n = 33) who underwent 18F-fluorodeoxyglucose positron emission tomography (FDG PET) from the cancer imaging archive (TCIA) and obtained the following information from the primary tumor: metabolic, metabolic-volumetric, and metabolic heterogeneity features. The association between the genetic and metabolic features and their prognostic values were assessed. Results Tumor metabolic heterogeneity and metabolic-volumetric features showed a mild degree of association with MATH (n = 25, ρ = 0.4~0.5, P < 0.05 for all features). The patients with higher FDG PET features and MATH died sooner. Combination of MATH and tumor metabolic heterogeneity features showed a better stratification of prognosis than MATH. Also, higher MATH and GlycoS were associated with significantly worse overall survival (n = 499, P = 0.002 and 0.0001 for MATH and GlycoS, respectively). Furthermore, both MATH and GlycoS independently predicted overall survival after adjusting for clinicopathologic features and the other (P = 0.015 and 0.006, respectively). Conclusion Both tumor metabolic heterogeneity and metabolic-volumetric features assessed by FDG PET showed a mild degree of association with genetic heterogeneity in HNSC. Both metabolic and genetic heterogeneity features were predictive of survival and there was an additive prognostic value when the metabolic and genetic heterogeneity features were combined. Also, MATH and GlycoS were independent prognostic factors in HNSC; they can be used for precise prognostication once validated.This study was supported by the National Research Foundation of Korea (NRF) (NRF-2017R1D1A1B03035556, and NRF-2019M2D2A1A01058210), and the Ministry of Health and Welfare Korea (HI18C0886, and HI19C0339)

Depression and suicide risk prediction models using blood-derived multi-omics data

More than 300 million people worldwide experience depression; annually, ~800,000 people die by suicide. Unfortunately, conventional interview-based diagnosis is insufficient to accurately predict a psychiatric status. We developed machine learning models to predict depression and suicide risk using blood methylome and transcriptome data from 56 suicide attempters (SAs), 39 patients with major depressive disorder (MDD), and 87 healthy controls. Our random forest classifiers showed accuracies of 92.6% in distinguishing SAs from MDD patients, 87.3% in distinguishing MDD patients from controls, and 86.7% in distinguishing SAs from controls. We also developed regression models for predicting psychiatric scales with R2 values of 0.961 and 0.943 for Hamilton Rating Scale for Depression???17 and Scale for Suicide Ideation, respectively. Multi-omics data were used to construct psychiatric status prediction models for improved mental health treatment

Identification and expression of equine MER-derived miRNAs

MicroRNAs (miRNAs) are single-stranded, small RNAs (21-23 nucleotides) that function in gene silencing and translational inhibition via the RNA interference mechanism. Most miRNAs originate from host genomic regions, such as intergenic regions, introns, exons, and transposable elements (TEs). Here, we focused on the palindromic structure of medium reiteration frequencies (MERs), which are similar to precursor miRNAs. Five MER consensus sequences (MER5A1, MER53, MER81, MER91C, and MER117) were matched with paralogous transcripts predicted to be precursor miRNAs in the horse genome (equCab2) and located in either intergenic regions or introns. The MER5A1, MER53, and MER91C sequences obtained from RepeatMasker were matched with the eca-miR-544b, eca-miR-1302, and eca-miR-652 precursor sequences derived from Ensembl transcript database, respectively. Each precursor form was anticipated to yield two mature forms, and we confirmed miRNA expression in six different tissues (cerebrum, cerebellum, lung, spleen, adrenal gland, and duodenum) of one thoroughbred horse. MER5A1-derived miRNAs generally showed significantly higher expression in the lung than in other tissues. MER91C-derived miRNA-5p also showed significantly higher expression in the duodenum than in other tissues (cerebellum, lung, spleen, and adrenal gland). The MER117-overlapped expressed sequence tag generated polycistronic miRNAs, which showed higher expression in the duodenum than other tissues. These data indicate that horse MER transposons encode miRNAs that are expressed in several tissues and are thought to have biological functions

Genome-wide analysis of DNA methylation patterns in horse

Background: DNA methylation is an epigenetic regulatory mechanism that plays an essential role in mediating biological processes and determining phenotypic plasticity in organisms. Although the horse reference genome and whole transcriptome data are publically available the global DNA methylation data are yet to be known. Results: We report the first genome-wide DNA methylation characteristics data from skeletal muscle, heart, lung, and cerebrum tissues of thoroughbred (TH) and Jeju (JH) horses, an indigenous Korea breed, respectively by methyl-DNA immunoprecipitation sequencing. The analysis of the DNA methylation patterns indicated that the average methylation density was the lowest in the promoter region, while the density in the coding DNA sequence region was the highest. Among repeat elements, a relatively high density of methylation was observed in long interspersed nuclear elements compared to short interspersed nuclear elements or long terminal repeat elements. We also successfully identified differential methylated regions through a comparative analysis of corresponding tissues from TH and JH, indicating that the gene body regions showed a high methylation density. Conclusions: We provide report the first DNA methylation landscape and differentially methylated genomic regions (DMRs) of thoroughbred and Jeju horses, providing comprehensive DMRs maps of the DNA methylome. These data are invaluable resource to better understanding of epigenetics in the horse providing information for the further biological function analyses.open1

APOE Promoter Polymorphism-219T/G is an Effect Modifier of the Influence of APOE ε4 on Alzheimer's Disease Risk in a Multiracial Sample

Variants in the APOE gene region may explain ethnic differences in the association of Alzheimer's disease (AD) with ε4. Ethnic differences in allele frequencies for three APOE region SNPs (single nucleotide polymorphisms) were identified and tested for association in 19,398 East Asians (EastA), including Koreans and Japanese, 15,836 European ancestry (EuroA) individuals, and 4985 African Americans, and with brain imaging measures of cortical atrophy in sub-samples of Koreans and EuroAs. Among ε4/ε4 individuals, AD risk increased substantially in a dose-dependent manner with the number of APOE promoter SNP rs405509 T alleles in EastAs (TT: OR (odds ratio) = 27.02, p = 8.80 × 10-94; GT: OR = 15.87, p = 2.62 × 10-9) and EuroAs (TT: OR = 18.13, p = 2.69 × 10-108; GT: OR = 12.63, p = 3.44 × 10-64), and rs405509-T homozygotes had a younger onset and more severe cortical atrophy than those with G-allele. Functional experiments using APOE promoter fragments demonstrated that TT lowered APOE expression in human brain and serum. The modifying effect of rs405509 genotype explained much of the ethnic variability in the AD/ε4 association, and increasing APOE expression might lower AD risk among ε4 homozygotes

Genome-Wide Analysis of DNA Methylation before- and after Exercise in the Thoroughbred Horse with MeDIP-Seq

Athletic performance is an important criteria used for the selection of superior horses. However, little is known about exercise-related epigenetic processes in the horse. DNA methylation is a key mechanism for regulating gene expression in response to environmental changes. We carried out comparative genomic analysis of genome-wide DNA methylation profiles in the blood samples of two different thoroughbred horses before and after exercise by methylated-DNA immunoprecipitation sequencing (MeDIP-Seq). Differentially methylated regions (DMRs) in the pre- and post-exercise blood samples of superior and inferior horses were identified. Exercise altered the methylation patterns. After 30 min of exercise, 596 genes were hypomethylated and 715 genes were hypermethylated in the superior horse, whereas in the inferior horse, 868 genes were hypomethylated and 794 genes were hypermethylated. These genes were analyzed based on gene ontology (GO) annotations and the exercise-related pathway patterns in the two horses were compared. After exercise, gene regions related to cell division and adhesion were hypermethylated in the superior horse, whereas regions related to cell signaling and transport were hypermethylated in the inferior horse. Analysis of the distribution of methylated CpG islands confirmed the hypomethylation in the gene-body methylation regions after exercise. The methylation patterns of transposable elements also changed after exercise. Long interspersed nuclear elements (LINEs) showed abundance of DMRs. Collectively, our results serve as a basis to study exercise-based reprogramming of epigenetic traitsclose

New Drug Development and Clinical Trial Design by Applying Genomic Information Management

Depending on the patients&rsquo; genotype, the same drug may have different efficacies or side effects. With the cost of genomic analysis decreasing and reliability of analysis methods improving, vast amount of genomic information has been made available. Several studies in pharmacology have been based on genomic information to select the optimal drug, determine the dose, predict efficacy, and prevent side effects. This paper reviews the tissue specificity and genomic information of cancer. If the tissue specificity of cancer is low, cancer is induced in various organs based on a single gene mutation. Basket trials can be performed for carcinomas with low tissue specificity, confirming the efficacy of one drug for a single gene mutation in various carcinomas. Conversely, if the tissue specificity of cancer is high, cancer is induced in only one organ based on a single gene mutation. An umbrella trial can be performed for carcinomas with a high tissue specificity. Some drugs are effective for patients with a specific genotype. A companion diagnostic strategy that prescribes a specific drug for patients selected with a specific genotype is also reviewed. Genomic information is used in pharmacometrics to identify the relationship among pharmacokinetics, pharmacodynamics, and biomarkers of disease treatment effects. Utilizing genomic information, sophisticated clinical trials can be designed that will be better suited to the patients of specific genotypes. Genomic information also provides prospects for innovative drug development. Through proper genomic information management, factors relating to drug response and effects can be determined by selecting the appropriate data for analysis and by understanding the structure of the data. Selecting pre-processing and appropriate machine-learning libraries for use as machine-learning input features is also necessary. Professional curation of the output result is also required. Personalized medicine can be realized using a genome-based customized clinical trial design

Identification and Expression Analyses of Equine Endogenous Retroviruses in Horses

Endogenous retroviruses (ERVs) have been integrated into vertebrate genomes and have momentously affected host organisms. Horses (Equus caballus) have been domesticated and selected for elite racing ability over centuries. ERVs played an important role in the evolutionary diversification of the horse genome. In the present study, we identified six equine ERV families (EqERVs-E1, I1, M2, P1, S1, and Y4), their full-length viral open reading frames (ORFs), and elucidated their phylogenetic relationships. The divergence time of EqERV families assuming an evolutionary rate of 0.2%/Myr indicated that EqERV-S3 (75.4 million years ago; mya) on chromosome 10 is an old EqERV family and EqERV-P5 (1.2 Mya) on chromosome 12 is a young member. During the evolutionary diversification of horses, the EqERV-I family diverged 1.7 Mya to 38.7 Mya. Reverse transcription quantitative real-time PCR (RT-qPCR) amplification of EqERV pol genes showed greater expression in the cerebellum of the Jeju horse than the Thoroughbred horse. These results could contribute further dynamic studies for horse genome in relation to EqERV gene functio

Identification and Expression Analyses of Equine Endogenous Retroviruses in Horses

Endogenous retroviruses (ERVs) have been integrated into vertebrate genomes and have momentously affected host organisms. Horses (Equus caballus) have been domesticated and selected for elite racing ability over centuries. ERVs played an important role in the evolutionary diversification of the horse genome. In the present study, we identified six equine ERV families (EqERVs-E1, I1, M2, P1, S1, and Y4), their full-length viral open reading frames (ORFs), and elucidated their phylogenetic relationships. The divergence time of EqERV families assuming an evolutionary rate of 0.2%/Myr indicated that EqERV-S3 (75.4 million years ago; mya) on chromosome 10 is an old EqERV family and EqERV-P5 (1.2 Mya) on chromosome 12 is a young member. During the evolutionary diversification of horses, the EqERV-I family diverged 1.7 Mya to 38.7 Mya. Reverse transcription quantitative real-time PCR (RT-qPCR) amplification of EqERV pol genes showed greater expression in the cerebellum of the Jeju horse than the Thoroughbred horse. These results could contribute further dynamic studies for horse genome in relation to EqERV gene functio