scholarly journals Predicting age from hearing test results with machine learning reveals the genetic and environmental factors underlying accelerated auditory aging

2021 ◽  
Author(s):  
Alan Le Goallec ◽  
Samuel Diai ◽  
Theo Vincent ◽  
Chirag J Patel
Keyword(s):  
Machine Learning ◽  
Environmental Factors ◽  
Genome Wide Association Study ◽  
Mean Squared Error ◽  
World Population ◽  
Test Results ◽  
Age Related ◽  
A Genome ◽  
Hearing Test ◽  
Genetic And Environmental Factors

With the aging of the world population, age-related hearing loss (presbycusis) and other hearing disorders such as tinnitus become more prevalent, leading to reduced quality of life and social isolation. Unveiling the genetic and environmental factors leading to age-related auditory disorders could suggest lifestyle and therapeutic interventions to slow auditory aging. In the following, we built the first machine learning-based hearing age predictor by training models to predict chronological age from hearing test results (root mean squared error=7.10+/-0.07 years; R-Squared=31.4+/-0.8%). We defined hearing age as the prediction outputted by the model on unseen samples, and accelerated auditory aging as the difference between a participant's hearing age and age. We then performed a genome wide association study [GWAS] and found that accelerated hearing aging is 14.1+/-0.4% GWAS-heritable. Specifically, accelerated auditory aging is associated with 662 single nucleotide polymorphisms in 243 genes (e.g OR2B4P, involved in smell perception). Similarly, it is associated with biomarkers (e.g cognitive tests), clinical phenotypes (e.g chest pain), diseases (e.g depression), environmental (e.g smoking, sleep) and socioeconomic (e.g income, education, social support) variables. The hearing age predictor could be used to evaluate the efficiency of emerging rejuvenation therapies on hearing.

scholarly journals Identifying the genetic and non-genetic factors associated with accelerated eye aging by using deep learning to predict age from fundus and optical coherence tomography images

2021 ◽  
Author(s):  
Alan Le Goallec ◽  
Samuel Diai ◽  
Sasha Collin ◽  
Theo Vincent ◽  
Chirag J Patel
Keyword(s):  
Optical Coherence Tomography ◽  
Genome Wide Association Study ◽  
Mean Squared Error ◽  
Optical Coherence ◽  
Nucleotide Polymorphisms ◽  
Age Related ◽  
Genome Wide ◽  
A Genome ◽  
Eye Pigmentation ◽  
The Difference

With age, eyesight declines and the vulnerability to age-related eye diseases such as glaucoma, cataract, macular degeneration and diabetic retinopathy increases. With the aging of the global population, the prevalence of these diseases is projected to increase, leading to reduced quality of life and increased healthcare cost. In the following, we built an eye age predictor by training convolutional neural networks to predict age from 175,000 eye fundus and optical coherence tomography images (R-Squared=83.6+/-0.6%; root mean squared error=3.34+/-0.07 years). We used attention maps to identify the features driving the eye age prediction. We defined accelerated eye aging as the difference between eye age and chronological age and performed a genome wide association study [GWAS] on this phenotype. Accelerated eye aging is 28.2+-1.2% GWAS-heritable, and is associated with 255 single nucleotide polymorphisms in 122 genes (e.g HERC2, associated with eye pigmentation). Similarly, we identified biomarkers (e.g blood pressure), clinical phenotypes (e.g chest pain), diseases (e.g cataract), environmental variables (e.g sleep deprivation) and socioeconomic variables (e.g income) associated with our newly defined phenotype. Our predictor could be used to detect premature eye aging in patients, and to evaluate the effect of emerging rejuvenation therapies on eye health.

scholarly journals A Splice Variant in SLC16A8 Gene Leads to Lactate Transport Deficit in Human iPS Cell-Derived Retinal Pigment Epithelial Cells

Cells ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 179
Author(s):  
Laurence Klipfel ◽  
Marie Cordonnier ◽  
Léa Thiébault ◽  
Emmanuelle Clérin ◽  
Frédéric Blond ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Pigment Epithelium ◽  
Retinal Pigment ◽  
Retinal Pigment Epithelial Cells ◽  
Age Related Macular Degeneration ◽  
Lactate Transport ◽  
Ips Cell ◽  
Risk Alleles ◽  
Age Related ◽  
A Genome

Age-related macular degeneration (AMD) is a blinding disease for which most of the patients remain untreatable. Since the disease affects the macula at the center of the retina, a structure specific to the primate lineage, rodent models to study the pathophysiology of AMD and to develop therapies are very limited. Consequently, our understanding relies mostly on genetic studies highlighting risk alleles at many loci. We are studying the possible implication of a metabolic imbalance associated with risk alleles within the SLC16A8 gene that encodes for a retinal pigment epithelium (RPE)-specific lactate transporter MCT3 and its consequences for vision. As a first approach, we report here the deficit in transepithelial lactate transport of a rare SLC16A8 allele identified during a genome-wide association study. We produced induced pluripotent stem cells (iPSCs) from the unique patient in our cohort that carries two copies of this allele. After in vitro differentiation of the iPSCs into RPE cells and their characterization, we demonstrate that the rare allele results in the retention of intron 2 of the SLC16A8 gene leading to the absence of MCT3 protein. We show using a biochemical assay that these cells have a deficit in transepithelial lactate transport.

GENETIC AND ENVIRONMENTAL FACTORS ASSOCIATED WITH RETICULAR PSEUDODRUSEN IN AGE-RELATED MACULAR DEGENERATION

Retina ◽  
2013 ◽  
Vol 33 (5) ◽  
pp. 998-1004 ◽  
Author(s):  
Nathalie Puche ◽  
Rocio Blanco-Garavito ◽  
Florence Richard ◽  
Nicolas Leveziel ◽  
Jennyfer Zerbib ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Factors Associated ◽  
Reticular Pseudodrusen ◽  
Age Related ◽  
Genetic And Environmental Factors

scholarly journals Hematopoietic mosaic chromosomal alterations and risk for infection among 767,891 individuals without blood cancer

2020 ◽  
Author(s):  
Seyedeh M. Zekavat ◽  
Shu-Hong Lin ◽  
Alexander G. Bick ◽  
Aoxing Liu ◽  
Kaavya Paruchuri ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Genome Wide Association Study ◽  
Chromosomal Alterations ◽  
Cell Counts ◽  
Age Related ◽  
Genome Wide ◽  
Transcriptional Regulatory ◽  
A Genome ◽  
Independent Risk Factors ◽  
Dna Genotyping

Summary ParagraphAge is the dominant risk factor for infectious diseases, but the mechanisms linking the two are incompletely understood1,2. Age-related mosaic chromosomal alterations (mCAs) detected from blood-derived DNA genotyping, are structural somatic variants associated with aberrant leukocyte cell counts, hematological malignancy, and mortality3-11. Whether mCAs represent independent risk factors for infection is unknown. Here we use genome-wide genotyping of blood DNA to show that mCAs predispose to diverse infectious diseases. We analyzed mCAs from 767,891 individuals without hematological cancer at DNA acquisition across four countries. Expanded mCA (cell fraction >10%) prevalence approached 4% by 60 years of age and was associated with diverse incident infections, including sepsis, pneumonia, and coronavirus disease 2019 (COVID-19) hospitalization. A genome-wide association study of expanded mCAs identified 63 significant loci. Germline genetic alleles associated with expanded mCAs were enriched at transcriptional regulatory sites for immune cells. Our results link mCAs with impaired immunity and predisposition to infections. Furthermore, these findings may also have important implications for the ongoing COVID-19 pandemic, particularly in prioritizing individual preventive strategies and evaluating immunization responses.

scholarly journals Predicting Moisture Content During Maize Nixtamalization Using Machine Learning with NIR Spectroscopy

2021 ◽  
Author(s):  
Michael Burns ◽  
Jonathan Renk ◽  
David Eickholt ◽  
Amanda Gilbert ◽  
Travis Hattery ◽  
...  
Keyword(s):  
Machine Learning ◽  
Moisture Content ◽  
High Throughput ◽  
Genome Wide Association Study ◽  
Rank Correlation ◽  
Quantitative Measure ◽  
Support Vector ◽  
Environment Interaction ◽  
Genome Wide ◽  
A Genome

Lack of high throughput phenotyping systems for determining moisture content during the maize nixtamalization cooking process has led to difficulty in breeding for this trait. This study provides a high throughput, quantitative measure of kernel moisture content during nixtamalization based on NIR scanning of uncooked maize kernels. Machine learning was utilized to develop models based on the combination of NIR spectra and moisture content determined from a scaled-down benchtop cook method. A linear support vector machine (SVM) model with a Spearman's rank correlation coefficient of 0.852 between wet lab and predicted values was developed from 100 diverse temperate genotypes grown in replicate across two environments. This model was applied to NIR data from 501 diverse temperate genotypes grown in replicate in five environments. Analysis of variance revealed environment explained the highest percent of the variation (51.5%), followed by genotype (15.6%) and genotype-by-environment interaction (11.2%). A genome-wide association study identified 26 significant loci across five environments that explained between 5.04% and 16.01% (average = 10.41%). However, genome-wide markers explained 10.54% to 45.99% (average = 31.68%) of the variation, indicating the genetic architecture of this trait is likely complex and controlled by many loci of small effect. This study provides a high-throughput method to evaluate moisture content during nixtamalization that is feasible at the scale of a breeding program and provides important information about the factors contributing to variation of this trait for breeders and food companies to make future strategies to improve this important processing trait.

scholarly journals CFH and VIPR2 as susceptibility loci in choroidal thickness and pachychoroid disease central serous chorioretinopathy

2018 ◽  
Vol 115 (24) ◽  
pp. 6261-6266 ◽  
Author(s):  
Yoshikatsu Hosoda ◽  
Munemitsu Yoshikawa ◽  
Masahiro Miyake ◽  
Yasuharu Tabara ◽  
Jeeyun Ahn ◽  
...  
Keyword(s):  
Central Serous Chorioretinopathy ◽  
Choroidal Thickness ◽  
Vision Loss ◽  
Genome Wide Association Study ◽  
Age Related Macular Degeneration ◽  
Common Disease ◽  
Susceptibility Loci ◽  
Case Control Studies ◽  
Age Related ◽  
A Genome

Central serous chorioretinopathy (CSC) is a common disease affecting younger people and may lead to vision loss. CSC shares phenotypic overlap with age-related macular degeneration (AMD). As recent studies have revealed a characteristic increase of choroidal thickness in CSC, we conducted a genome-wide association study on choroidal thickness in 3,418 individuals followed by TaqMan assays in 2,692 subjects, and we identified two susceptibility loci: CFH rs800292, an established AMD susceptibility polymorphism, and VIPR2 rs3793217 (P = 2.05 × 10−10 and 6.75 × 10−8, respectively). Case–control studies using patients with CSC confirmed associations between both polymorphisms and CSC (P = 5.27 × 10−5 and 5.14 × 10−5, respectively). The CFH rs800292 G allele is reportedly a risk allele for AMD, whereas the A allele conferred risk for thicker choroid and CSC development. This study not only shows that susceptibility genes for CSC could be discovered using choroidal thickness as a defining variable but also, deepens the understanding of differences between CSC and AMD pathophysiology.

scholarly journals Genome-wide prediction and prioritization of human aging genes by data fusion: a machine learning approach

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Masoud Arabfard ◽  
Mina Ohadi ◽  
Vahid Rezaei Tabar ◽  
Ahmad Delbari ◽  
Kaveh Kavousi
Keyword(s):  
Machine Learning ◽  
Process Comparison ◽  
Age Related ◽  
Genome Wide ◽  
Human Genes ◽  
Disease Candidate Gene ◽  
A Genome ◽  
Machine Learning Approach ◽  
Wide Scale ◽  
Biological Aspects

Abstract Background Machine learning can effectively nominate novel genes for various research purposes in the laboratory. On a genome-wide scale, we implemented multiple databases and algorithms to predict and prioritize the human aging genes (PPHAGE). Results We fused data from 11 databases, and used Naïve Bayes classifier and positive unlabeled learning (PUL) methods, NB, Spy, and Rocchio-SVM, to rank human genes in respect with their implication in aging. The PUL methods enabled us to identify a list of negative (non-aging) genes to use alongside the seed (known age-related) genes in the ranking process. Comparison of the PUL algorithms revealed that none of the methods for identifying a negative sample were advantageous over other methods, and their simultaneous use in a form of fusion was critical for obtaining optimal results (PPHAGE is publicly available at https://cbb.ut.ac.ir/pphage). Conclusion We predict and prioritize over 3,000 candidate age-related genes in human, based on significant ranking scores. The identified candidate genes are associated with pathways, ontologies, and diseases that are linked to aging, such as cancer and diabetes. Our data offer a platform for future experimental research on the genetic and biological aspects of aging. Additionally, we demonstrate that fusion of PUL methods and data sources can be successfully used for aging and disease candidate gene prioritization.

scholarly journals Gpr54−/− mice show more pronounced defects in spermatogenesis than Kiss1−/− mice and improved spermatogenesis with age when exposed to dietary phytoestrogens

Reproduction ◽  
2011 ◽  
Vol 141 (3) ◽  
pp. 357-366 ◽  
Author(s):  
Hua Mei ◽  
Cara Walters ◽  
Richard Carter ◽  
William H Colledge
Keyword(s):  
Environmental Factors ◽  
Signaling Pathway ◽  
Mutant Mice ◽  
Plasma Testosterone ◽  
Seminiferous Tubules ◽  
Wild Type ◽  
Age Related ◽  
Reproductive Axis ◽  
Genetic And Environmental Factors ◽  
Related Increase

Mice with mutations in the kisspeptin signaling pathway (Kiss1−/− or Gpr54−/−) have low gonadotrophic hormone levels, small testes, and impaired spermatogenesis. Between 2 and 7 months of age, however, the testes of the mutant mice increase in weight and in Gpr54−/− mice, the number of seminiferous tubules containing spermatids/spermatozoa increases from 17 to 78%. In contrast, the Kiss1−/− mice have a less severe defect in spermatogenesis and larger testes than Gpr54−/− mice at both 2 and 7 months of age. The reason for the improved spermatogenesis was investigated. Plasma testosterone and FSH levels did not increase with age in the mutant mice and remained much lower than in wild-type (WT) mice. In contrast, intratesticular testosterone levels were similar between mutant and WT mice. These data indicate that age-related spermatogenesis can be completed under conditions of low plasma testosterone and FSH and that intratesticular testosterone may contribute to this process. In addition, however, when the Gpr54−/− mice were fed a phytoestrogen-free diet, they showed no age-related increase in testes weight or improved spermatogenesis. Thus, both genetic and environmental factors are involved in the improved spermatogenesis in the mutant mice as they age although the mice still remain infertile. These data show that the possible impact of dietary phytoestrogens should be taken into account when studying the phenotype of mutant mice with defects in the reproductive axis.

scholarly journals Genetic and environmental factors strongly influence risk, severity and progression of age-related macular degeneration

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Wenqiu Wang ◽  
Katarzyna Gawlik ◽  
Joe Lopez ◽  
Cindy Wen ◽  
Jie Zhu ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Macular Degeneration ◽  
Smoking Status ◽  
Geographic Atrophy ◽  
Age Related Macular Degeneration ◽  
Prediction Algorithm ◽  
Nucleotide Polymorphisms ◽  
Personalized Care ◽  
Age Related ◽  
Genetic And Environmental Factors

Abstract Age-related macular degeneration (AMD) is characterized by complex interactions between genetic and environmental factors. Here we genotyped the selected 25 single-nucleotide polymorphisms (SNPs) in 983 cases with advanced AMD and 271 cases with intermediate AMD and build an AMD life-risk score model for assessment of progression from intermediate to advanced AMD. We analyzed the performance of the prediction model for geographic atrophy progressors or choroidal neovascularization progressors versus non-progressors based on the 25 SNPs plus body mass index and smoking status. Our results suggest that a class prediction algorithm can be used for the risk assessment of progression from intermediate to late AMD stages. The algorithm could also be potentially applied for therapeutic response, and toward personalized care and precision medicine.

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