scholarly journals The genetic makeup of the electrocardiogram

2019 ◽  
Author(s):  
Niek Verweij ◽  
Jan-Walter Benjamins ◽  
Michael P. Morley ◽  
Yordi van de Vegte ◽  
Alexander Teumer ◽  
...  

AbstractSince its original description in 1893 by Willem van Einthoven, the electrocardiogram (ECG) has been instrumental in the recognition of a wide array of cardiac disorders1,2. Although many electrocardiographic patterns have been well described, the underlying biology is incompletely understood. Genetic associations of particular features of the ECG have been identified by genome wide studies. This snapshot approach only provides fragmented information of the underlying genetic makeup of the ECG. Here, we follow the effecs of individual genetic variants through the complete cardiac cycle the ECG represents. We found that genetic variants have unique morphological signatures not identfied by previous analyses. By exploiting identified abberations of these morphological signatures, we show that novel genetic loci can be identified for cardiac disorders. Our results demonstrate how an integrated approach to analyse high-dimensional data can further our understanding of the ECG, adding to the earlier undertaken snapshot analyses of individual ECG components. We anticipate that our comprehensive resource will fuel in silico explorations of the biological mechanisms underlying cardiac traits and disorders represented on the ECG. For example, known disease causing variants can be used to identify novel morphological ECG signatures, which in turn can be utilized to prioritize genetic variants or genes for functional validation. Furthermore, the ECG plays a major role in the development of drugs, a genetic assessment of the entire ECG can drive such developments.

2016 ◽  
Author(s):  
Mark Barash ◽  
Philipp E. Bayer ◽  
Angela van Daal

AbstractDespite intensive research on genetics of the craniofacial morphology using animal models and human craniofacial syndromes, the genetic variation that underpins normal human facial appearance is still largely elusive. Recent development of novel digital methods for capturing the complexity of craniofacial morphology in conjunction with high-throughput genotyping methods, show great promise for unravelling the genetic basis of such a complex trait.As a part of our efforts on detecting genomic variants affecting normal craniofacial appearance, we have implemented a candidate gene approach by selecting 1,201 single nucleotide polymorphisms (SNPs) and 4,732 tag SNPs in over 170 candidate genes and intergenic regions. We used 3-dimentional (3D) facial scans and direct cranial measurements of 587 volunteers to calculate 104 craniofacial phenotypes. Following genotyping by massively parallel sequencing, genetic associations between 2,332 genetic markers and 104 craniofacial phenotypes were tested.An application of a Bonferroni–corrected genome–wide significance threshold produced significant associations between five craniofacial traits and six SNPs. Specifically, associations of nasal width with rs8035124 (15q26.1), cephalic index with rs16830498 (2q23.3), nasal index with rs37369 (5q13.2), transverse nasal prominence angle with rs59037879 (10p11.23) and rs10512572 (17q24.3), and principal component explaining 73.3% of all the craniofacial phenotypes, with rs37369 (5p13.2) and rs390345 (14q31.3) were observed.Due to over-conservative nature of the Bonferroni correction, we also report all the associations that reached the traditional genome-wide p-value threshold (<5.00E-08) as suggestive. Based on the genome-wide threshold, 8 craniofacial phenotypes demonstrated significant associations with 34 intergenic and extragenic SNPs. The majority of associations are novel, except PAX3 and COL11A1 genes, which were previously reported to affect normal craniofacial variation.This study identified the largest number of genetic variants associated with normal variation of craniofacial morphology to date by using a candidate gene approach, including confirmation of the two previously reported genes. These results enhance our understanding of the genetics that determines normal variation in craniofacial morphology and will be of particular value in medical and forensic fields.Author SummaryThere is a remarkable variety of human facial appearances, almost exclusively the result of genetic differences, as exemplified by the striking resemblance of identical twins. However, the genes and specific genetic variants that affect the size and shape of the cranium and the soft facial tissue features are largely unknown. Numerous studies on animal models and human craniofacial disorders have identified a large number of genes, which may regulate normal craniofacial embryonic development.In this study we implemented a targeted candidate gene approach to select more than 1,200 polymorphisms in over 170 genes that are likely to be involved in craniofacial development and morphology. These markers were genotyped in 587 DNA samples using massively parallel sequencing and analysed for association with 104 traits generated from 3-dimensional facial images and direct craniofacial measurements. Genetic associations (p-values<5.00E-08) were observed between 8 craniofacial traits and 34 single nucleotide polymorphisms (SNPs), including two previously described genes and 26 novel candidate genes and intergenic regions. This comprehensive candidate gene study has uncovered the largest number of novel genetic variants affecting normal facial appearance to date. These results will appreciably extend our understanding of the normal and abnormal embryonic development and impact our ability to predict the appearance of an individual from a DNA sample in forensic criminal investigations and missing person cases.


2020 ◽  
Author(s):  
Pavel P Kuksa ◽  
Chia-Lun Lui ◽  
Wei Fu ◽  
Liming Qu ◽  
Yi Zhao ◽  
...  

Background: Alzheimer's disease (AD) genetic findings span progressively larger genome-wide association studies (GWASs) for various outcomes and populations. These genetic findings are obtained from a single GWAS, joint- or meta- analyses of multiple GWAS datasets. However, no single resource provides harmonized and searchable information on all AD genetic associations obtained from these analyses, nor linking the identified genetic variants and reported genes with other supporting functional genomic evidence. Methods: We created the Alzheimer's Disease Variant Portal (ADVP), which provides unified access to a uniquely extensive collection of high-quality GWAS association results for AD. Records in ADVP are curated from the genome-wide significant and suggestive loci reported in AD genetics literature. ADVP contains curated results from all AD GWAS publications by Alzheimer's Disease Genetics Consortium (ADGC) since 2009 and AD GWAS publications identified from other public catalogs (GWAS catalog). Genetic association information was systematically extracted from these publications, harmonized, and organized into three types of tables. These tables included structured publication, variant, and association categories to ensure consistent representation of all AD genetic findings. All extracted AD genetic associations were further annotated and integrated with NIAGADS Genomics DB in order to provide extensive biological and functional genomics annotations. Results: Currently, ADVP contains 6,990 AD-association records curated from >200 AD GWAS publications corresponding to >900 unique genomic loci and >1,800 unique genetic variants. The ADVP collection contains genetic findings from >80 cohorts and across various populations, including Caucasians, Hispanics, African-Americans, and Asians. Of all the association records, 46% are disease-risk, 13% are related to expression quantitative trait analyses, and 27% are related to AD endophenotypes and neuropathology. ADVP web interface allows accessing AD association records by individual variants, genes, publications, genomic regions of interest, and genome-wide interactive variant views. ADVP is integrated with the NIAGADS Alzheimer's Genomics Database. Researchers can explore additional biological annotations at the genetic variant or gene level and view cross-reference functional genomics evidence provided by other public resources. Conclusions: ADVP is the largest, most up-to-date, and comprehensive literature-derived collection of AD genetic associations. All records have been systematically curated, harmonized, and comprehensively annotated. ADVP is freely accessible at https://advp.niagads.org/.


Author(s):  
Jin-Fang Chai ◽  
Suryaprakash Raichur ◽  
Ing Wei Khor ◽  
Federico Torta ◽  
Wee Siong Chew ◽  
...  

Abstract Metabolites are small intermediate products of cellular metabolism perturbed in a variety of complex disorders. Identifying genetic markers associated with metabolite concentrations could delineate disease-related metabolic pathways in humans. We tested genetic variants for associations with 136 metabolites in 1,954 Chinese from Singapore. At a conservative genome-wide threshold (3.7 x 10-10), we detected 1,899 variant-metabolite associations at 16 genetic loci. Three loci (ABCA7, A4GALT, GSTM2) represented novel associations with metabolites, with the strongest association observed between ABCA7 and d18:1/24:1 dihexosylceramide. Among 13 replicated loci, we identified six new variants independent of previously reported metabolite or lipid signals. We observed variant-metabolite associations at two loci (ABCA7, CHCHD2) that have been linked to neurodegenerative diseases. At SGPP1 and SPTLC3 loci, genetic variants showed preferential selectivity for sphingolipids with d16 (rather than d18) sphingosine backbone, including sphingosine-1-phosphate (S1P). Our results provide new genetic associations for metabolites and highlight the role of metabolites as intermediate modulators in disease metabolic pathways.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Emmanuel Acheampong ◽  
Evans Asamoah Adu ◽  
Christian Obirikorang ◽  
George Amoah ◽  
Osei Owusu Afriyie ◽  
...  

Abstract Background Prostate cancer (PCa) has one of the highest heritability of all major cancers, where the genetic contribution has been documented, and knowledge about the molecular genetics of the disease is increasing. However, the extent and aspects to which genetic variants explain PCa heritability in Africa are limited. Main body In this review, we summarize studies that highlight how identified genetic variants explain differences in PCa incidence and presentation across ethnic groups. We also present the knowledge gaps in PCa genetics in Africa and why Africa represents an untapped potential ground for genetic studies on PCa. A significant number of genome-wide association studies, linkage, and fine-mapping analyses have been conducted globally, and that explains 30–33% of PCa heritability. The African ancestry has a significant mention in PCa incidence and presentation. To date, the candidate gene approach has replicated 23 polymorphisms including dinucleotide and trinucleotide repeats in 16 genes. CYP17-rs743572, CYP3A4-rs2740574, CYP3A5-rs776746, CYP3A43-rs501275, and haplotype blocks, containing these variants, are significantly associated with PCa among some population groups but not others. With the few existing studies, the extent of genetic diversity in Africa suggests that genetic associations of PCa to African ancestry go beyond nucleotide sequence polymorphisms, to a level of environmental adaptation, which may interpret genetic risk profiles. Also, the shreds of evidence suggest that evolutionary history contributes to the high rates of PCa relative to African ancestry, and genetic associations do not always replicate across populations. Conclusion The genetic architecture of PCa in Africa provides important contributions to the global understanding of PCa specifically the African-ancestry hypothesis. There is a need for more prostate cancer consortiums to justify the heritable certainties of PCa among Africans, and emphasis should be placed on the genetic epidemiological model of PCa in Africa.


F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 97 ◽  
Author(s):  
Ilya Y. Zhbannikov ◽  
Konstantin Arbeev ◽  
Anatoliy I. Yashin

There exists a set of web-based tools for integration and exploring information linked to annotated genetic variants. We developed haploR, an R-package for querying such web-based genome annotation tools (currently implementing on HaploReg and RegulomeDB) and gathering information in a format suitable for downstream bioinformatic analyses. This will facilitate post-genome wide association studies streamline analysis for rapid discovery and interpretation of genetic associations.


2018 ◽  
Author(s):  
Moises Exposito-Alonso ◽  
Hernán A. Burbano ◽  
Oliver Bossdorf ◽  
Rasmus Nielsen ◽  
Detlef Weigel ◽  
...  

Through the lens of evolution, climate change is an agent of natural selection that forces populations to change and adapt, or face extinction. Current assessments of the risk of biodiversity associated with climate change1, however, do not typically take into account the genetic makeup of populations and how natural selection impacts it2. We made use of the extensive genome information in Arabidopsis thaliana and measured how rainfall-manipulation affected the fitness of 517 natural lines grown in Spain and Germany. This allowed us to directly infer selection along the genome3. Natural selection was particularly strong in the hot-dry Spanish location, killing 63% of lines and significantly changing the frequency of ~5% of all genome-wide variants. A significant portion of this climate-driven natural selection over variants was predictable from signatures of local adaptation (R2=29-52%), as genetic variants found in geographic areas with climates more similar to the experimental sites were positively selected. Field-validated predictions across the species range indicated that Mediterranean and Western Siberian populations — at the edges of the species’ environmental limits — currently experience the strongest climate-driven selection. With more frequent droughts and rising temperatures in Europe4, we forecast an increase in directional natural selection moving northwards from the southern end, and putting many native A. thaliana populations at evolutionary risk.


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