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Heredity May 2024When a population is isolated and composed of few individuals, genetic drift is the paramount evolutionary force and results in the loss of genetic diversity. Inbreeding...
When a population is isolated and composed of few individuals, genetic drift is the paramount evolutionary force and results in the loss of genetic diversity. Inbreeding might also occur, resulting in genomic regions that are identical by descent, manifesting as runs of homozygosity (ROHs) and the expression of recessive traits. Likewise, the genes underlying traits of interest can be revealed by comparing fixed SNPs and divergent haplotypes between affected and unaffected individuals. Populations of white-tailed deer (Odocoileus virginianus) on islands of Saint Pierre and Miquelon (SPM, France) have high incidences of leucism and malocclusions, both considered genetic defects; on the Florida Keys islands (USA) deer exhibit smaller body sizes, a polygenic trait. Here we aimed to reconstruct island demography and identify the genes associated with these traits in a pseudo case-control design. The two island populations showed reduced levels of genomic diversity and a build-up of deleterious mutations compared to mainland deer; there was also significant genome-wide divergence in Key deer. Key deer showed higher inbreeding levels, but not longer ROHs, consistent with long-term isolation. We identified multiple trait-related genes in ROHs including LAMTOR2 which has links to pigmentation changes, and NPVF which is linked to craniofacial abnormalities. Our mixed approach of linking ROHs, fixed SNPs and haplotypes matched a high number (~50) of a-priori body size candidate genes in Key deer. This suite of biomarkers and candidate genes should prove useful for population monitoring, noting all three phenotypes show patterns consistent with a complex trait and non-Mendelian inheritance.
PubMed: 38802598
DOI: 10.1038/s41437-024-00685-2 -
Journal of Alzheimer's Disease : JAD 2024Polygenic risk scores (PRS) are linear combinations of genetic markers weighted by effect size that are commonly used to predict disease risk. For complex heritable...
BACKGROUND
Polygenic risk scores (PRS) are linear combinations of genetic markers weighted by effect size that are commonly used to predict disease risk. For complex heritable diseases such as late-onset Alzheimer's disease (LOAD), PRS models fail to capture much of the heritability. Additionally, PRS models are highly dependent on the population structure of the data on which effect sizes are assessed and have poor generalizability to new data.
OBJECTIVE
The goal of this study is to construct a paragenic risk score that, in addition to single genetic marker data used in PRS, incorporates epistatic interaction features and machine learning methods to predict risk for LOAD.
METHODS
We construct a new state-of-the-art genetic model for risk of Alzheimer's disease. Our approach innovates over PRS models in two ways: First, by directly incorporating epistatic interactions between SNP loci using an evolutionary algorithm guided by shared pathway information; and second, by estimating risk via an ensemble of non-linear machine learning models rather than a single linear model. We compare the paragenic model to several PRS models from the literature trained on the same dataset.
RESULTS
The paragenic model is significantly more accurate than the PRS models under 10-fold cross-validation, obtaining an AUC of 83% and near-clinically significant matched sensitivity/specificity of 75%. It remains significantly more accurate when evaluated on an independent holdout dataset and maintains accuracy within APOE genotype strata.
CONCLUSIONS
Paragenic models show potential for improving disease risk prediction for complex heritable diseases such as LOAD over PRS models.
Topics: Humans; Alzheimer Disease; Machine Learning; Multifactorial Inheritance; Epistasis, Genetic; Genetic Predisposition to Disease; Female; Male; Polymorphism, Single Nucleotide; Aged; Genome-Wide Association Study; Apolipoproteins E; Models, Genetic; Genetic Risk Score
PubMed: 38788065
DOI: 10.3233/JAD-230236 -
ELife May 2024Rich data from large biobanks, coupled with increasingly accessible association statistics from genome-wide association studies (GWAS), provide great opportunities to...
Rich data from large biobanks, coupled with increasingly accessible association statistics from genome-wide association studies (GWAS), provide great opportunities to dissect the complex relationships among human traits and diseases. We introduce BADGERS, a powerful method to perform polygenic score-based biobank-wide association scans. Compared to traditional approaches, BADGERS uses GWAS summary statistics as input and does not require multiple traits to be measured in the same cohort. We applied BADGERS to two independent datasets for late-onset Alzheimer's disease (AD; n=61,212). Among 1738 traits in the UK biobank, we identified 48 significant associations for AD. Family history, high cholesterol, and numerous traits related to intelligence and education showed strong and independent associations with AD. Furthermore, we identified 41 significant associations for a variety of AD endophenotypes. While family history and high cholesterol were strongly associated with AD subgroups and pathologies, only intelligence and education-related traits predicted pre-clinical cognitive phenotypes. These results provide novel insights into the distinct biological processes underlying various risk factors for AD.
Topics: Alzheimer Disease; Humans; Risk Factors; Endophenotypes; Genome-Wide Association Study; Male; Biological Specimen Banks; Female; United Kingdom; Aged; Genetic Predisposition to Disease; Multifactorial Inheritance; Aged, 80 and over
PubMed: 38787369
DOI: 10.7554/eLife.91360 -
Journal of the Association For Research... May 2024Age-related hearing loss is the most common form of permanent hearing loss that is associated with various health traits, including Alzheimer's disease, cognitive...
PURPOSE
Age-related hearing loss is the most common form of permanent hearing loss that is associated with various health traits, including Alzheimer's disease, cognitive decline, and depression. The present study aims to identify genetic comorbidities of age-related hearing loss. Past genome-wide association studies identified multiple genomic loci involved in common adult-onset health traits. Polygenic risk scores (PRS) could summarize the polygenic inheritance and quantify the genetic susceptibility of complex traits independent of trait expression. The present study conducted a PRS-based association analysis of age-related hearing difficulty in the UK Biobank sample (N = 425,240), followed by a replication analysis using hearing thresholds (HTs) and distortion-product otoacoustic emissions (DPOAEs) in 242 young adults with self-reported normal hearing. We hypothesized that young adults with genetic comorbidities associated with age-related hearing difficulty would exhibit subclinical decline in HTs and DPOAEs in both ears.
METHODS
A total of 111,243 participants reported age-related hearing difficulty in the UK Biobank sample (> 40 years). The PRS models were derived from the polygenic risk score catalog to obtain 2627 PRS predictors across the health spectrum. HTs (0.25-16 kHz) and DPOAEs (1-16 kHz, L1/L2 = 65/55 dB SPL, F2/F1 = 1.22) were measured on 242 young adults. Saliva-derived DNA samples were subjected to low-pass whole genome sequencing, followed by genome-wide imputation and PRS calculation. The logistic regression analyses were performed to identify PRS predictors of age-related hearing difficulty in the UK Biobank cohort. The linear mixed model analyses were performed to identify PRS predictors of HTs and DPOAEs.
RESULTS
The PRS-based association analysis identified 977 PRS predictors across the health spectrum associated with age-related hearing difficulty. Hearing difficulty and hearing aid use PRS predictors revealed the strongest association with the age-related hearing difficulty phenotype. Youth with a higher genetic predisposition to hearing difficulty revealed a subclinical elevation in HTs and a decline in DPOAEs in both ears. PRS predictors associated with age-related hearing difficulty were enriched for mental health, lifestyle, metabolic, sleep, reproductive, digestive, respiratory, hematopoietic, and immune traits. Fifty PRS predictors belonging to various trait categories were replicated for HTs and DPOAEs in both ears.
CONCLUSION
The study identified genetic comorbidities associated with age-related hearing loss across the health spectrum. Youth with a high genetic predisposition to age-related hearing difficulty and other related complex traits could exhibit sub-clinical decline in HTs and DPOAEs decades before clinically meaningful age-related hearing loss is observed. We posit that effective communication of genetic risk, promoting a healthy lifestyle, and reducing exposure to environmental risk factors at younger ages could help prevent or delay the onset of age-related hearing difficulty at older ages.
PubMed: 38782831
DOI: 10.1007/s10162-024-00947-0 -
Familial Cancer May 2024Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related death in the Western world. The number of diagnosed cases and the mortality rate...
The best linear unbiased prediction (BLUP) method as a tool to estimate the lifetime risk of pancreatic ductal adenocarcinoma in high-risk individuals with no known pathogenic germline variants.
Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related death in the Western world. The number of diagnosed cases and the mortality rate are almost equal as the majority of patients present with advanced disease at diagnosis. Between 4 and 10% of pancreatic cancer cases have an apparent hereditary background, known as hereditary pancreatic cancer (HPC) and familial pancreatic cancer (FPC), when the genetic basis is unknown. Surveillance of high-risk individuals (HRI) from these families by imaging aims to detect PDAC at an early stage to improve prognosis. However, the genetic basis is unknown in the majority of HRIs, with only around 10-13% of families carrying known pathogenic germline mutations. The aim of this study was to assess an individual's genetic cancer risk based on sex and personal and family history of cancer. The Best Linear Unbiased Prediction (BLUP) methodology was used to estimate an individual's predicted risk of developing cancer during their lifetime. The model uses different demographic factors in order to estimate heritability. A reliable estimation of heritability for pancreatic cancer of 0.27 on the liability scale, and 0.07 at the observed data scale as obtained, which is different from zero, indicating a polygenic inheritance pattern of PDAC. BLUP was able to correctly discriminate PDAC cases from healthy individuals and those with other cancer types. Thus, providing an additional tool to assess PDAC risk HRI with an assumed genetic predisposition in the absence of known pathogenic germline mutations.
PubMed: 38780705
DOI: 10.1007/s10689-024-00397-w -
Arteriosclerosis, Thrombosis, and... Jul 2024Heterozygous familial hypercholesterolemia (FH) is among the most common genetic conditions worldwide that affects ≈ 1 in 300 individuals. FH is characterized by...
BACKGROUND
Heterozygous familial hypercholesterolemia (FH) is among the most common genetic conditions worldwide that affects ≈ 1 in 300 individuals. FH is characterized by increased levels of low-density lipoprotein cholesterol (LDL-C) and increased risk of coronary artery disease (CAD), but there is a wide spectrum of severity within the FH population. This variability in expression is incompletely explained by known risk factors. We hypothesized that genome-wide genetic influences, as represented by polygenic risk scores (PRSs) for cardiometabolic traits, would influence the phenotypic severity of FH.
METHODS
We studied individuals with clinically diagnosed FH (n=1123) from the FH Canada National Registry, as well as individuals with genetically identified FH from the UK Biobank (n=723). For all individuals, we used genome-wide gene array data to calculate PRSs for CAD, LDL-C, lipoprotein(a), and other cardiometabolic traits. We compared the distribution of PRSs in individuals with clinically diagnosed FH, genetically diagnosed FH, and non-FH controls and examined the association of the PRSs with the risk of atherosclerotic cardiovascular disease.
RESULTS
Individuals with clinically diagnosed FH had higher levels of LDL-C, and the incidence of atherosclerotic cardiovascular disease was higher in individuals with clinically diagnosed compared with genetically identified FH. Individuals with clinically diagnosed FH displayed enrichment for higher PRSs for CAD, LDL-C, and lipoprotein(a) but not for other cardiometabolic risk factors. The CAD PRS was associated with a risk of atherosclerotic cardiovascular disease among individuals with an FH-causing genetic variant.
CONCLUSIONS
Genetic background, as expressed by genome-wide PRSs for CAD, LDL-C, and lipoprotein(a), influences the phenotypic severity of FH, expanding our understanding of the determinants that contribute to the variable expressivity of FH. A PRS for CAD may aid in risk prediction among individuals with FH.
Topics: Humans; Hyperlipoproteinemia Type II; Female; Male; Middle Aged; Multifactorial Inheritance; Cholesterol, LDL; Phenotype; Registries; Genetic Predisposition to Disease; Coronary Artery Disease; Risk Assessment; Genome-Wide Association Study; Lipoprotein(a); Adult; Aged; Canada; United Kingdom; Severity of Illness Index; Risk Factors; Case-Control Studies; Biomarkers; Incidence
PubMed: 38779854
DOI: 10.1161/ATVBAHA.123.320287 -
Human Genomics May 2024Given the high prevalence of BPH among elderly men, pinpointing those at elevated risk can aid in early intervention and effective management. This study aimed to...
BACKGROUND
Given the high prevalence of BPH among elderly men, pinpointing those at elevated risk can aid in early intervention and effective management. This study aimed to explore that polygenic risk score (PRS) is effective in predicting benign prostatic hyperplasia (BPH) incidence, prognosis and risk of operation in Han Chinese.
METHODS
A retrospective cohort study included 12,474 male participants (6,237 with BPH and 6,237 non-BPH controls) from the Taiwan Precision Medicine Initiative (TPMI). Genotyping was performed using the Affymetrix Genome-Wide TWB 2.0 SNP Array. PRS was calculated using PGS001865, comprising 1,712 single nucleotide polymorphisms. Logistic regression models assessed the association between PRS and BPH incidence, adjusting for age and prostate-specific antigen (PSA) levels. The study also examined the relationship between PSA, prostate volume, and response to 5-α-reductase inhibitor (5ARI) treatment, as well as the association between PRS and the risk of TURP.
RESULTS
Individuals in the highest PRS quartile (Q4) had a significantly higher risk of BPH compared to the lowest quartile (Q1) (OR = 1.51, 95% CI = 1.274-1.783, p < 0.0001), after adjusting for PSA level. The Q4 group exhibited larger prostate volumes and a smaller volume reduction after 5ARI treatment. The Q1 group had a lower cumulative TURP probability at 3, 5, and 10 years compared to the Q4 group. PRS Q4 was an independent risk factor for TURP.
CONCLUSIONS
In this Han Chinese cohort, higher PRS was associated with an increased susceptibility to BPH, larger prostate volumes, poorer response to 5ARI treatment, and a higher risk of TURP. Larger prospective studies with longer follow-up are warranted to further validate these findings.
Topics: Humans; Male; Prostatic Hyperplasia; Aged; Middle Aged; Polymorphism, Single Nucleotide; Genetic Predisposition to Disease; Retrospective Studies; Multifactorial Inheritance; Asian People; Risk Factors; 5-alpha Reductase Inhibitors; Prostate-Specific Antigen; Taiwan; Prognosis; Prostate; Genetic Risk Score; East Asian People
PubMed: 38778357
DOI: 10.1186/s40246-024-00619-3 -
Scientific Reports May 2024In recent years, the utility of polygenic risk scores (PRS) in forecasting disease susceptibility from genome-wide association studies (GWAS) results has been widely...
In recent years, the utility of polygenic risk scores (PRS) in forecasting disease susceptibility from genome-wide association studies (GWAS) results has been widely recognised. Yet, these models face limitations due to overfitting and the potential overestimation of effect sizes in correlated variants. To surmount these obstacles, we devised the Stacked Neural Network Polygenic Risk Score (SNPRS). This novel approach synthesises outputs from multiple neural network models, each calibrated using genetic variants chosen based on diverse p-value thresholds. By doing so, SNPRS captures a broader array of genetic variants, enabling a more nuanced interpretation of the combined effects of these variants. We assessed the efficacy of SNPRS using the UK Biobank data, focusing on the genetic risks associated with breast and prostate cancers, as well as quantitative traits like height and BMI. We also extended our analysis to the Korea Genome and Epidemiology Study (KoGES) dataset. Impressively, our results indicate that SNPRS surpasses traditional PRS models and an isolated deep neural network in terms of accuracy, highlighting its promise in refining the efficacy and relevance of PRS in genetic studies.
Topics: Humans; Multifactorial Inheritance; Genome-Wide Association Study; Neural Networks, Computer; Genetic Predisposition to Disease; Polymorphism, Single Nucleotide; Female; Male; Prostatic Neoplasms; Breast Neoplasms; Risk Factors; Genetic Risk Score
PubMed: 38773257
DOI: 10.1038/s41598-024-62513-1 -
Scientific Reports May 2024Predisposing factors underlying familial aggregation of non-syndromic gliomas are still to be uncovered. Whole-exome sequencing was performed in four Finnish families...
Predisposing factors underlying familial aggregation of non-syndromic gliomas are still to be uncovered. Whole-exome sequencing was performed in four Finnish families with brain tumors to identify rare predisposing variants. A total of 417 detected exome variants and 102 previously reported glioma-related variants were further genotyped in 19 Finnish families with brain tumors using targeted sequencing. Rare damaging variants in GALNT13, MYO10 and AR were identified. Two families carried either c.553C>T (R185C) or c.1214T>A (L405Q) on GALNT13. Variant c.553C>T is located on the substrate-binding site of GALNT13. AR c.2180G>T (R727L), which is located on a ligand-binding domain of AR, was detected in two families, one of which also carried a GALNT13 variant. MYO10 c.4448A>G (N1483S) was detected in two families and c.1511C>T (A504V) variant was detected in one family. Both variants are located on functional domains related to MYO10 activity in filopodia formation. In addition, affected cases in six families carried a known glioma risk variant rs55705857 in CCDC26 and low-risk glioma variants. These novel findings indicate polygenic inheritance of familial glioma in Finland and increase our understanding of the genetic contribution to familial glioma susceptibility.
Topics: Humans; Finland; Glioma; Female; Male; N-Acetylgalactosaminyltransferases; Genetic Predisposition to Disease; Pedigree; Polypeptide N-acetylgalactosaminyltransferase; Germ-Line Mutation; Adult; Middle Aged; Brain Neoplasms; Exome Sequencing
PubMed: 38773237
DOI: 10.1038/s41598-024-62296-5 -
Briefings in Bioinformatics Mar 2024Polygenetic Risk Scores are used to evaluate an individual's vulnerability to developing specific diseases or conditions based on their genetic composition, by taking... (Review)
Review
Polygenetic Risk Scores are used to evaluate an individual's vulnerability to developing specific diseases or conditions based on their genetic composition, by taking into account numerous genetic variations. This article provides an overview of the concept of Polygenic Risk Scores (PRS). We elucidate the historical advancements of PRS, their advantages and shortcomings in comparison with other predictive methods, and discuss their conceptual limitations in light of the complexity of biological systems. Furthermore, we provide a survey of published tools for computing PRS and associated resources. The various tools and software packages are categorized based on their technical utility for users or prospective developers. Understanding the array of available tools and their limitations is crucial for accurately assessing and predicting disease risks, facilitating early interventions, and guiding personalized healthcare decisions. Additionally, we also identify potential new avenues for future bioinformatic analyzes and advancements related to PRS.
Topics: Humans; Multifactorial Inheritance; Genetic Predisposition to Disease; Software; Computational Biology; Genome-Wide Association Study; Risk Factors; Risk Assessment; Genetic Risk Score
PubMed: 38770718
DOI: 10.1093/bib/bbae240