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Sleep Oct 2022Sleep occurs universally and is a biological necessity for human functioning. The consequences of diminished sleep quality impact physical and physiological systems such... (Review)
Review
Sleep occurs universally and is a biological necessity for human functioning. The consequences of diminished sleep quality impact physical and physiological systems such as neurological, cardiovascular, and metabolic processes. In fact, people impacted by common complex diseases experience a wide range of sleep disturbances. It is challenging to uncover the underlying molecular mechanisms responsible for decreased sleep quality in many disease systems owing to the lack of suitable sleep biomarkers. However, the discovery of a genetic component to sleep patterns has opened a new opportunity to examine and understand the involvement of sleep in many disease states. It is now possible to use major genomic resources and technologies to uncover genetic contributions to many common diseases. Large scale prospective studies such as the genome wide association studies (GWAS) have successfully revealed many robust genetic signals associated with sleep-related traits. With the discovery of these genetic variants, a major objective of the community has been to investigate whether sleep-related traits are associated with disease pathogenesis and other health complications. Mendelian Randomization (MR) represents an analytical method that leverages genetic loci as proxy indicators to establish causal effect between sleep traits and disease outcomes. Given such variants are randomly inherited at birth, confounding bias is eliminated with MR analysis, thus demonstrating evidence of causal relationships that can be used for drug development and to prioritize clinical trials. In this review, we outline the results of MR analyses performed to date on sleep traits in relation to a multitude of common complex diseases.
Topics: Genome-Wide Association Study; Humans; Infant, Newborn; Mendelian Randomization Analysis; Multifactorial Inheritance; Prospective Studies; Sleep
PubMed: 35908176
DOI: 10.1093/sleep/zsac180 -
International Journal of Molecular... Mar 2020Brugada syndrome (BrS) is diagnosed by a coved-type ST-segment elevation in the right precordial leads on the electrocardiogram (ECG), and it is associated with an... (Review)
Review
Brugada syndrome (BrS) is diagnosed by a coved-type ST-segment elevation in the right precordial leads on the electrocardiogram (ECG), and it is associated with an increased risk of sudden cardiac death (SCD) compared to the general population. Although BrS is considered a genetic disease, its molecular mechanism remains elusive in about 70-85% of clinically-confirmed cases. Variants occurring in at least 26 different genes have been previously considered causative, although the causative effect of all but the gene has been recently challenged, due to the lack of systematic, evidence-based evaluations, such as a variant's frequency among the general population, family segregation analyses, and functional studies. Also, variants within a particular gene can be associated with an array of different phenotypes, even within the same family, preventing a clear genotype-phenotype correlation. Moreover, an emerging concept is that a single mutation may not be enough to cause the BrS phenotype, due to the increasing number of common variants now thought to be clinically relevant. Thus, not only the complete list of genes causative of the BrS phenotype remains to be determined, but also the interplay between rare and common multiple variants. This is particularly true for some common polymorphisms whose roles have been recently re-evaluated by outstanding works, including considering for the first time ever a polygenic risk score derived from the heterozygous state for both common and rare variants. The more common a certain variant is, the less impact this variant might have on heart function. We are aware that further studies are warranted to validate a polygenic risk score, because there is no mutated gene that connects all, or even a majority, of BrS cases. For the same reason, it is currently impossible to create animal and cell line genetic models that represent all BrS cases, which would enable the expansion of studies of this syndrome. Thus, the best model at this point is the human patient population. Further studies should first aim to uncover genetic variants within individuals, as well as to collect family segregation data to identify potential genetic causes of BrS.
Topics: Animals; Brugada Syndrome; Humans; Ion Channels; Multifactorial Inheritance; Mutation; Sarcomeres
PubMed: 32121523
DOI: 10.3390/ijms21051687 -
PloS One 2022The variable presentations and different phenotypes of sepsis suggest that risk of sepsis comes from many genes each having a small effect. The cumulative effect can be...
BACKGROUND
The variable presentations and different phenotypes of sepsis suggest that risk of sepsis comes from many genes each having a small effect. The cumulative effect can be used to create individual risk profile. The purpose of this study was to create a polygenic risk score and determine the genetic variants associated with sepsis.
METHODS
We sequenced ~14 million single nucleotide polymorphisms with a minimac imputation quality R2>0.3 and minor allele frequency >10-6 in patients with Sepsis-2 or Sepsis-3. Genome-wide association was performed using Firth bias-corrected logistic regression. Semi-parsimonious logistic regression was used to create polygenic risk scores and reduced regression to determine the genetic variants independently associated with sepsis.
FINDINGS
2261 patients had sepsis and 13,068 control patients did not. The polygenic risk scores had good discrimination: c-statistic = 0.752 ± 0.005 for Sepsis-2 and 0.752 ± 0.007 for Sepsis-3. We found 772 genetic variants associated with Sepsis-2 and 442 with Sepsis-3, p<0.01. After multivariate adjustment, 100 variants on 85 genes were associated with Sepsis-2 and 69 variants in 54 genes with Sepsis-3. Twenty-five variants were present in both the Sepsis-2 and Sepsis-3 groups out of 32 genes that were present in both groups. The other 7 genes had different variants present. Most variants had small effect sizes.
CONCLUSIONS
Sepsis-2 and Sepsis-3 have both separate and shared genetic variants. Most genetic variants have small effects sizes, but cumulatively, the polygenic risk scores have good discrimination.
Topics: Gene Frequency; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Multifactorial Inheritance; Phenotype; Polymorphism, Single Nucleotide; Sepsis
PubMed: 35275946
DOI: 10.1371/journal.pone.0265052 -
Journal of the American College of... Jul 2019
Topics: Humans; Hypercholesterolemia; Hyperlipoproteinemia Type II; Multifactorial Inheritance
PubMed: 31345426
DOI: 10.1016/j.jacc.2019.06.006 -
Annual Review of Biomedical Data Science Aug 2022Polygenic risk scores (PRS) estimate an individual's genetic likelihood of complex traits and diseases by aggregating information across multiple genetic variants... (Review)
Review
Polygenic risk scores (PRS) estimate an individual's genetic likelihood of complex traits and diseases by aggregating information across multiple genetic variants identified from genome-wide association studies. PRS can predict a broad spectrum of diseases and have therefore been widely used in research settings. Some work has investigated their potential applications as biomarkers in preventative medicine, but significant work is still needed to definitively establish and communicate absolute risk to patients for genetic and modifiable risk factors across demographic groups. However, the biggest limitation of PRS currently is that they show poor generalizability across diverse ancestries and cohorts. Major efforts are underway through methodological development and data generation initiatives to improve their generalizability. This review aims to comprehensively discuss current progress on the development of PRS, the factors that affect their generalizability, and promising areas for improving their accuracy, portability, and implementation.
Topics: Biomarkers; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Multifactorial Inheritance; Risk Factors
PubMed: 35576555
DOI: 10.1146/annurev-biodatasci-111721-074830 -
Journal of the American Academy of... Jun 2021Understanding the genetic architecture of psychiatric disorders is paramount to linking psychopathologies to their genetic underpinnings. In turn, this knowledge can...
Understanding the genetic architecture of psychiatric disorders is paramount to linking psychopathologies to their genetic underpinnings. In turn, this knowledge can inform strategies for identifying high-risk individuals, early intervention, and development of personalized treatment approaches. Over the past 2 decades, owing to lowering per capita costs and relative ease of analysis, a plethora of studies have used single nucleotide polymorphism genotyping and genome-wide association studies (GWASs) to unravel common and rare risk loci underlying psychiatric disorders and their endophenotypes. In contrast to the single allele focus of classical Mendelian inheritance, mental illnesses are often polygenic in nature with multiple common genetic variants, each contributing a small, but meaningful added risk. By interrogating the entire genome, GWASs have allowed the functional assessment of promising candidate genes in in vivo as well as in vitro models of psychiatric disease. Further, these findings have spawned the approach of calculating polygenic risk scores, a promising strategy for inferring genetic susceptibility to the development of psychopathology by taking into account the polygenic structure of psychiatric disorders.
Topics: Child; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Mental Disorders; Multifactorial Inheritance; Polymorphism, Single Nucleotide
PubMed: 33385509
DOI: 10.1016/j.jaac.2020.12.031 -
Cell Metabolism Mar 2017Except in rare cases, obesity tends to be a consequence of both an unhealthy lifestyle and a genetic susceptibility to gain weight. With more than 200 common genetic... (Review)
Review
Except in rare cases, obesity tends to be a consequence of both an unhealthy lifestyle and a genetic susceptibility to gain weight. With more than 200 common genetic variants identified, there is a growing interest in developing personalized preventive and treatment strategies to predict an individual's obesity risk. We review the literature on the prediction of obesity and show that models based on the established genetic variants have poorer predictive ability than traditional predictors, such as family history of obesity and childhood obesity. Current findings suggest that opportunities for precision medicine in common obesity may be limited.
Topics: Genetic Predisposition to Disease; Genetic Variation; Humans; Life Style; Multifactorial Inheritance; Obesity; Risk Factors
PubMed: 28273476
DOI: 10.1016/j.cmet.2017.02.013 -
European Journal of Human Genetics :... Mar 2018Germline variants in the APC gene cause familial adenomatous polyposis. Inherited variants in MutYH, POLE, POLD1, NTHL1, and MSH3 genes and somatic APC mosaicism have...
Germline variants in the APC gene cause familial adenomatous polyposis. Inherited variants in MutYH, POLE, POLD1, NTHL1, and MSH3 genes and somatic APC mosaicism have been reported as alternative causes of polyposis. However, ~30-50% of cases of polyposis remain genetically unsolved. Thus, the aim of this study was to investigate the genetic causes of unexplained adenomatous polyposis. Eight sporadic cases with >20 adenomatous polyps by 35 years of age or >50 adenomatous polyps by 55 years of age, and no causative germline variants in APC and/or MutYH, were enrolled from a cohort of 56 subjects with adenomatous colorectal polyposis. APC gene mosaicism was investigated on DNA from colonic adenomas by Sanger sequencing or Whole Exome Sequencing (WES). Mosaicism extension to other tissues (peripheral blood, saliva, hair follicles) was evaluated using Sanger sequencing and/or digital PCR. APC second hit was investigated in adenomas from mosaic patients. WES was performed on DNA from peripheral blood to identify additional polyposis candidate variants. We identified APC mosaicism in 50% of patients. In three cases mosaicism was restricted to the colon, while in one it also extended to the duodenum and saliva. One patient without APC mosaicism, carrying an APC in-frame deletion of uncertain significance, was found to harbor rare germline variants in OGG1, POLQ, and EXO1 genes. In conclusion, our restrictive selection criteria improved the detection of mosaic APC patients. In addition, we showed for the first time that an oligogenic inheritance of rare variants might have a cooperative role in sporadic colorectal polyposis onset.
Topics: Adenomatous Polyposis Coli; Adult; DNA Glycosylases; DNA Repair Enzymes; DNA-Directed DNA Polymerase; Exodeoxyribonucleases; Female; Genes, APC; Humans; Male; Middle Aged; Mosaicism; Multifactorial Inheritance; DNA Polymerase theta
PubMed: 29367705
DOI: 10.1038/s41431-017-0086-y -
Trends in Genetics : TIG Oct 2018Accurate prediction of complex traits requires using a large number of DNA variants. Advances in statistical and machine learning methodology enable the identification... (Review)
Review
Accurate prediction of complex traits requires using a large number of DNA variants. Advances in statistical and machine learning methodology enable the identification of complex patterns in high-dimensional settings. However, training these highly parameterized methods requires very large data sets. Until recently, such data sets were not available. But the situation is changing rapidly as very large biomedical data sets comprising individual genotype-phenotype data for hundreds of thousands of individuals become available in public and private domains. We argue that the convergence of advances in methodology and the advent of Big Genomic Data will enable unprecedented improvements in complex-trait prediction; we review theory and evidence supporting our claim and discuss challenges and opportunities that Big Data will bring to complex-trait prediction.
Topics: Big Data; Genome-Wide Association Study; Genomics; Genotype; Humans; Models, Genetic; Multifactorial Inheritance; Polymorphism, Single Nucleotide; Quantitative Trait Loci
PubMed: 30139641
DOI: 10.1016/j.tig.2018.07.004 -
Journal of Molecular and Cellular... Jan 2019Morphology underlies subdivision of the primary/heritable sarcomeric cardiomyopathies (CMs) into hypertrophic (HCM) and dilated (DCM). Next-generation DNA-sequencing... (Review)
Review
Morphology underlies subdivision of the primary/heritable sarcomeric cardiomyopathies (CMs) into hypertrophic (HCM) and dilated (DCM). Next-generation DNA-sequencing (NGS) has identified important disease-variants, improving CM diagnosis, management, genetic screening, and prognosis. Although monogenic (Mendelian) analyses directly point at downstream studies, they disregard coexisting genomic variations and gene-by-gene interactions molding detailed CM-phenotypes. In-place of polygenic models, in accounting for observed defective genotype-phenotype correlations, fuzzy concepts having gradations of significance and unsharp domain-boundaries are invoked, including pleiotropy, genetic-heterogeneity, incomplete penetrance, and variable expressivity. HCM and DCM undoubtedly entail cooperativity of unidentified/elusive causative genomic-variants. Modern genomics can exploit comprehensive electronic/digital health records, facilitating consideration of multifactorial variant-models. Genome-wide association studies entailing high-fidelity solid-state catheterization, multimodal-imaging, molecular cardiology, systems biology and bioinformatics, will decipher accurate genotype-phenotype correlations and identify novel therapeutic-targets, fostering personalized medicine/cardiology. This review surveys successes and challenges of genetic/genomic approaches to CMs, and their impact on current and future clinical care.
Topics: Biological Variation, Population; Biomechanical Phenomena; Cardiomyopathies; Humans; Multifactorial Inheritance; Sarcomeres; Translational Research, Biomedical
PubMed: 30423317
DOI: 10.1016/j.yjmcc.2018.10.024