scholarly journals Monogenic forms of DSD: An update

2021 ◽  
Author(s):  
Kenneth McElreavey ◽  
Anu Bashamboo
Keyword(s):  
Genetic Factors ◽  
Association Studies ◽  
Genetic Diagnosis ◽  
Genetic Association Studies ◽  
Genetic Modifiers ◽  
Genomic Variants ◽  
Sequencing Technologies ◽  
Wide Range ◽  
And Function ◽  
Reproductive Phenotype

DSD encompasses a wide range of pathologies that impact gonad formation, development and function in both 46,XX and 46,XY individuals. The majority of these conditions are considered to be monogenic, although the expression of the phenotype may be influenced by genetic modifiers. Although considered monogenic, establishing the genetic etiology in DSD has been difficult compared to other congenital disorders for a number of reasons including the absence of family cases for classical genetic association studies and the lack of evolutionary conservation of key genetic factors involved in gonad formation. In recent years, the widespread use of genomic sequencing technologies has resulted in multiple genes being identified and proposed as novel monogenic causes of 46,XX and/or 46,XY DSD. In this review, we will focus on the main genomic findings of recent years, which consists of new candidate genes or loci for DSD as well as new reproductive phenotypes associated with genes that are well established to cause DSD. For each gene or loci, we summarise the data that is currently available in favor of or against a role for these genes in DSD or the contribution of genomic variants within well-established genes to a new reproductive phenotype. Based on this analysis we propose a series of recommendations that should aid the interpretation of genomic data and ultimately help to improve the accuracy and yield genetic diagnosis of DSD.

scholarly journals Patternize: An R Package For Quantifying Color Pattern Variation

2017 ◽  
Author(s):  
Steven M. Van Belleghem ◽  
Riccardo Papa ◽  
Humberto Ortiz-Zuazaga ◽  
Frederik Hendrickx ◽  
Chris Jiggins ◽  
...  
Keyword(s):  
Association Studies ◽  
Genetic Association Studies ◽  
Image Data ◽  
Principal Component ◽  
R Package ◽  
Color Pattern ◽  
Color Patterns ◽  
Wide Range ◽  
Potential Applications ◽  
Population Comparisons

The use of image data to quantify, study and compare variation in the colors and patterns of organisms requires the alignment of images to establish homology, followed by color-based segmentation of images. Here we describe an R package for image alignment and segmentation that has applications to quantify color patterns in a wide range of organisms. patternize is an R package that quantifies variation in color patterns obtained from image data. patternize first defines homology between pattern positions across specimens either through manually placed homologous landmarks or automated image registration. Pattern identification is performed by categorizing the distribution of colors using an RGB threshold, k-means clustering or watershed transformation. We demonstrate that patternize can be used for quantification of the color patterns in a variety of organisms by analyzing image data for butterflies, guppies, spiders and salamanders. Image data can be compared between sets of specimens, visualized as heatmaps and analyzed using principal component analysis (PCA). patternize has potential applications for fine scale quantification of color pattern phenotypes in population comparisons, genetic association studies and investigating the basis of color pattern variation across a wide range of organisms.

scholarly journals A systematic review and meta-analysis of host genetic factors associated with influenza severity

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Nina Van Goethem ◽  
Célestin Danwang ◽  
Nathalie Bossuyt ◽  
Herman Van Oyen ◽  
Nancy H. C. Roosens ◽  
...  
Keyword(s):  
Systematic Review ◽  
Genetic Factors ◽  
Association Studies ◽  
Influenza Infection ◽  
Meta Analysis ◽  
Genetic Association Studies ◽  
Genome Wide Association Studies ◽  
Severe Influenza ◽  
Host Genetic ◽  
Host Genetic Factors

Abstract Background The severity of influenza disease can range from mild symptoms to severe respiratory failure and can partly be explained by host genetic factors that predisposes the host to severe influenza. Here, we aimed to summarize the current state of evidence that host genetic variants play a role in the susceptibility to severe influenza infection by conducting a systematic review and performing a meta-analysis for all markers with at least three or more data entries. Results A total of 34 primary human genetic association studies were identified that investigated a total of 20 different genes. The only significant pooled ORs were retrieved for the rs12252 polymorphism: an overall OR of 1.52 (95% CI [1.06–2.17]) for the rs12252-C allele compared to the rs12252-T allele. A stratified analysis by ethnicity revealed opposite effects in different populations. Conclusion With exception for the rs12252 polymorphism, we could not identify specific genetic polymorphisms to be associated with severe influenza infection in a pooled meta-analysis. This advocates for the use of large, hypothesis-free, genome-wide association studies that account for the polygenic nature and the interactions with other host, pathogen and environmental factors.

Genetic association studies in β-hemoglobinopathies

Hematology ◽  
2013 ◽  
Vol 2013 (1) ◽  
pp. 354-361 ◽  
Author(s):  
Swee Lay Thein
Keyword(s):  
Association Studies ◽  
Globin Gene ◽  
Genetic Association Studies ◽  
Fetal Hemoglobin ◽  
Clinical Severity ◽  
Genetic Modifiers ◽  
Genetic Studies ◽  
Globin Gene Regulation ◽  
Innate Ability

Abstract Characterization of the molecular basis of the β-thalassemias and sickle cell disease (SCD) clearly showed that individuals with the same β-globin genotypes can have extremely diverse clinical severity. Two key modifiers, an innate ability to produce fetal hemoglobin and coinheritance of α-thalassemia, both derived from family and population studies, affect the pathophysiology of both disorders at the primary level. In the past 2 decades, scientific research had applied genetic approaches to identify additional genetic modifiers. The review summarizes recent genetic studies and key genetic modifiers identified and traces the story of fetal hemoglobin genetics, which has led to an emerging network of globin gene regulation. The discoveries have provided insights on new targets for therapeutic intervention and raise possibilities of developing fetal hemoglobin predictive diagnostics for predicting disease severity in the newborn and for integration into prenatal diagnosis to better inform genetic counseling.

scholarly journals Genetic Diversity of Toll-Like Receptors and Immunity toM. lepraeInfection

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Bryan E. Hart ◽  
Richard I. Tapping
Keyword(s):  
Association Studies ◽  
Genetic Association Studies ◽  
Adaptive Immune System ◽  
Mycobacterial Infection ◽  
Innate Immune ◽  
Genetic Associations ◽  
Toll Like Receptors ◽  
Adaptive Immune ◽  
Polymorphic Variants ◽  
And Function

Genetic association studies of leprosy cohorts across the world have identified numerous polymorphisms which alter susceptibility and outcome to infection withMycobacterium leprae. As expected, many of the polymorphisms reside within genes that encode components of the innate and adaptive immune system. Despite the preponderance of these studies, our understanding of the mechanisms that underlie these genetic associations remains sparse. Toll-like receptors (TLRs) have emerged as an essential family of innate immune pattern recognition receptors which play a pivotal role in host defense against microbes, including pathogenic strains of mycobacteria. This paper will highlight studies which have uncovered the association of specific TLR gene polymorphisms with leprosy or tuberculosis: two important diseases resulting from mycobacterial infection. This analysis will focus on the potential influence these polymorphic variants have on TLR expression and function and how altered TLR recognition or signaling may contribute to successful antimycobacterial immunity.

Genetics of Depression

2017 ◽  
Author(s):  
Douglas F. Levinson ◽  
Walter E. Nichols
Keyword(s):  
Genetic Factors ◽  
Association Studies ◽  
Complex Trait ◽  
Current Evidence ◽  
Genome Wide Association Studies ◽  
Adverse Life Events ◽  
Major Depressive ◽  
Genome Wide ◽  
Increase Risk ◽  
And Function

Major depressive disorder (MDD) is a common and heterogeneous complex trait. Twin heritability is 35%–40%, perhaps higher in severe/recurrent cases. Adverse life events (particularly during childhood) increase risk. Current evidence suggests some overlap in genetic factors among MDD, bipolar disorder, and schizophrenia. Large genome-wide association studies (GWAS) are now proving successful. Polygenic effects of common SNPs are substantial. Findings implicate genes with effects on synaptic development and function, including two obesity-associated genes (NEGR1 and OLFM4), but not previous “candidate genes.” It can now be expected that larger GWAS samples will produce additional associations that shed new light on MDD genetics.

scholarly journals Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics

2018 ◽  
Vol 35 (14) ◽  
pp. 2495-2497 ◽  
Author(s):  
Gregory McInnes ◽  
Yosuke Tanigawa ◽  
Chris DeBoever ◽  
Adam Lavertu ◽  
Julia Eve Olivieri ◽  
...  
Keyword(s):  
Association Studies ◽  
Genetic Association Studies ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Uk Biobank ◽  
Patient Privacy ◽  
Web Based ◽  
Genome Wide ◽  
Wide Range ◽  
The Uk

Abstract Summary Large biobanks linking phenotype to genotype have led to an explosion of genetic association studies across a wide range of phenotypes. Sharing the knowledge generated by these resources with the scientific community remains a challenge due to patient privacy and the vast amount of data. Here, we present Global Biobank Engine (GBE), a web-based tool that enables exploration of the relationship between genotype and phenotype in biobank cohorts, such as the UK Biobank. GBE supports browsing for results from genome-wide association studies, phenome-wide association studies, gene-based tests and genetic correlation between phenotypes. We envision GBE as a platform that facilitates the dissemination of summary statistics from biobanks to the scientific and clinical communities. Availability and implementation GBE currently hosts data from the UK Biobank and can be found freely available at biobankengine.stanford.edu.

scholarly journals Understanding the genetic determinant of severity in viral diseases: a case of SARS-Cov-2 infection

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Babayemi Olawale Oladejo ◽  
Covenant Femi Adeboboye ◽  
Tinuola Tokunbo Adebolu
Keyword(s):  
Genetic Factors ◽  
Viral Infections ◽  
Genetic Diagnosis ◽  
Young Patients ◽  
Disease Genes ◽  
Main Body ◽  
Severe Condition ◽  
Increased Risk ◽  
Co Morbidity ◽  
Wide Range

Abstract Background Numerous research studies have identified specific human gene variants that affect enhanced susceptibility to viral infections. More recently is the current pandemic where the SARS-CoV-2 infection has shown a high degree of person-to-person clinical variability. A wide range of disease severity occurs in the patients’ experiences, from asymptomatic cases, mild infections to serious life threatening conditions requiring admission into the intensive care unit (ICU). Main body of the abstract Although, it is generally reported that age and co-morbidities contribute significantly to the variations in the clinical outcome of the scourge of COVID-19, a hypothetical question of the possibility of genetic involvement in the susceptibility and severity of the disease arose when some unique severe outcomes were seen among young patients with no co-morbidity. The role human genetics play in clinical response to the viral infections is scarcely understood; however, several ongoing researches all around the world are currently focusing on possible genetic factors. This review reports the possible genetic factors that have been widely studied in defining the severity of viral infections using SARS-CoV-2 as a case study. These involve the possible involvements of ACE2, HLA, and TLR genes such as TLR7 and TLR3 in the presentation of a more severe condition. Short conclusion Understanding these variations could help to inform efforts to identify people at increased risk of infection outbreaks through genetic diagnosis of infections by locating disease genes or mutations that predispose patients to severe infection. This will also suggest specific targets for therapy and prophylaxis.

scholarly journals Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics

2018 ◽  
Author(s):  
Gregory McInnes ◽  
Yosuke Tanigawa ◽  
Chris DeBoever ◽  
Adam Lavertu ◽  
Julia Eve Olivieri ◽  
...  
Keyword(s):  
Association Studies ◽  
Genetic Association Studies ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Uk Biobank ◽  
Patient Privacy ◽  
Web Based ◽  
Genome Wide ◽  
Wide Range ◽  
The Uk

Large biobanks linking phenotype to genotype have led to an explosion of genetic association studies across a wide range of phenotypes. Sharing the knowledge generated by these resources with the scientific community remains a challenge due to patient privacy and the vast amount of data. Here we present Global Biobank Engine (GBE), a web-based tool that enables the exploration of the relationship between genotype and phenotype in large biobank cohorts, such as the UK Biobank. GBE supports browsing for results from genome-wide association studies, phenome-wide association studies, gene-based tests, and genetic correlation between phenotypes. We envision GBE as a platform that facilitates the dissemination of summary statistics from biobanks to the scientific and clinical communities. GBE currently hosts data from the UK Biobank and can be found freely available at biobankengine.stanford.edu.

scholarly journals Playing Musical Chairs in Big Data to Reveal Variables’ Associations

2016 ◽  
Author(s):  
Hugues Aschard ◽  
Bjarni Vilhjalmsson ◽  
Chirag Patel ◽  
David Skurnik ◽  
Jimmy Yu ◽  
...  
Keyword(s):  
Big Data ◽  
Statistical Power ◽  
Genetic Factors ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Simulated Data ◽  
Correlated Variables ◽  
Large Sets ◽  
Multiple Phenotypes ◽  
Direct Support

Testing for associations in big data faces the problem of multiple comparisons, with true signals buried inside the noise of all associations queried. This is particularly true in genetic association studies where a substantial proportion of the variation of human phenotypes is driven by numerous genetic variants of small effect. The current strategy to improve power to identify these weak associations consists of applying standard marginal statistical approaches and increasing study sample sizes. While successful, this approach does not leverage the environmental and genetic factors shared between the multiple phenotypes collected in contemporary cohorts. Here we develop a method that improves the power of detecting associations when a large number of correlated variables have been measured on the same samples. Our analyses over real and simulated data provide direct support that large sets of correlated variables can be leveraged to achieve dramatic increases in statistical power equivalent to a two or even three folds increase in sample size.

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