scholarly journals Genetics of Complex Disease: From Mouse to Man and Back

1995 ◽  
Vol 9 (3) ◽  
pp. 169-174
Author(s):  
Craig H Warden ◽  
Jerome I Rotter
Keyword(s):  
Complex Traits ◽  
Complex Disease ◽  
Genome Mapping ◽  
Complex Diseases ◽  
Rapid Identification ◽  
Whole Genome ◽  
Donor Strain ◽  
Congenic Strains ◽  
Background Strain ◽  
Combined Application

Identification of genes underlying complex traits has been difficult, but combined application of novel methods and mouse models provides new hope. Rare monogenic syndromes, and candidate gene and biochemical approaches are sometimes useful, but each of these approaches also has limitations. Some problems that prevent identification and isolation of genes underlying complex disease can be avoided by the use of whole genome mapping of mouse crosses or of human families. Mice have many advantages for the study of complex disease, including an extensive genetic map. A generic method has recently been developed and applied for detection of quantitative trait loci (QTLs) using whole genome maps of mouse crosses. Availability of more than 200 congenic strains provides another incentive for studies in mice. Congenic strains provide a rich, but previously unexploited, resource for the rapid identification of genes causing complex diseases. A congenic mouse strain is genetically identical to a background strain, except for a small chromosomal region derived from a donor strain. Thus, comparison of a phenotype in a congenic strain with the phenotype in its background strain allows study of the effects of single genes derived from the donor strain, isolated from the effects of other donor strain genes. Application of all or several techniques to complex disease studies in mice and in humans may lead to the identification and understanding of complex diseases whose etiology is currently unknown.

scholarly journals Evaluating the informativeness of deep learning annotations for human complex diseases

2019 ◽  
Author(s):  
Kushal K. Dey ◽  
Bryce Van de Geijn ◽  
Samuel Sungil Kim ◽  
Farhad Hormozdiari ◽  
David R. Kelley ◽  
...  
Keyword(s):  
Deep Learning ◽  
Complex Traits ◽  
Complex Disease ◽  
Complex Diseases ◽  
Great Promise ◽  
Full Potential ◽  
Learning Models ◽  
Allelic Effect ◽  
Meta Analyses ◽  
Human Complex

AbstractDeep learning models have shown great promise in predicting genome-wide regulatory effects from DNA sequence, but their informativeness for human complex diseases and traits is not fully understood. Here, we evaluate the disease informativeness of allelic-effect annotations (absolute value of the predicted difference between reference and variant alleles) constructed using two previously trained deep learning models, DeepSEA and Basenji. We apply stratified LD score regression (S-LDSC) to 41 independent diseases and complex traits (average N=320K) to evaluate each annotation’s informativeness for disease heritability conditional on a broad set of coding, conserved, regulatory and LD-related annotations from the baseline-LD model and other sources; as a secondary metric, we also evaluate the accuracy of models that incorporate deep learning annotations in predicting disease-associated or fine-mapped SNPs. We aggregated annotations across all tissues (resp. blood cell types or brain tissues) in meta-analyses across all 41 traits (resp. 11 blood-related traits or 8 brain-related traits). These allelic-effect annotations were highly enriched for disease heritability, but produced only limited conditionally significant results – only Basenji-H3K4me3 in meta-analyses across all 41 traits and brain-specific Basenji-H3K4me3 in meta-analyses across 8 brain-related traits. We conclude that deep learning models are yet to achieve their full potential to provide considerable amount of unique information for complex disease, and that the informativeness of deep learning models for disease beyond established functional annotations cannot be inferred from metrics based on their accuracy in predicting regulatory annotations.

scholarly journals Computational Methods for the Discovery of Metabolic Markers of Complex Traits

Metabolites ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 66 ◽  
Author(s):  
Michael Lee ◽  
Ting Hu
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Complex Traits ◽  
Complex Disease ◽  
Physiological State ◽  
Complex Diseases ◽  
Diagnostic Procedures ◽  
Machine Learning Algorithms ◽  
Metabolic Markers ◽  
Metabolic Marker

Metabolomics uses quantitative analyses of metabolites from tissues or bodily fluids to acquire a functional readout of the physiological state. Complex diseases arise from the influence of multiple factors, such as genetics, environment and lifestyle. Since genes, RNAs and proteins converge onto the terminal downstream metabolome, metabolomics datasets offer a rich source of information in a complex and convoluted presentation. Thus, powerful computational methods capable of deciphering the effects of many upstream influences have become increasingly necessary. In this review, the workflow of metabolic marker discovery is outlined from metabolite extraction to model interpretation and validation. Additionally, current metabolomics research in various complex disease areas is examined to identify gaps and trends in the use of several statistical and computational algorithms. Then, we highlight and discuss three advanced machine-learning algorithms, specifically ensemble learning, artificial neural networks, and genetic programming, that are currently less visible, but are budding with high potential for utility in metabolomics research. With an upward trend in the use of highly-accurate, multivariate models in the metabolomics literature, diagnostic biomarker panels of complex diseases are more recently achieving accuracies approaching or exceeding traditional diagnostic procedures. This review aims to provide an overview of computational methods in metabolomics and promote the use of up-to-date machine-learning and computational methods by metabolomics researchers.

scholarly journals Cis-Regulatory Hubs: a Relevant 3D Model to Study the Genetics of Complex Diseases with an Application to Schizophrenia

2021 ◽  
Author(s):  
Loic Mangnier ◽  
Charles Joly-Beauparlant ◽  
Arnaud Droit ◽  
Steve Bilodeau ◽  
Alexandre Bureau
Keyword(s):  
Complex Traits ◽  
3D Model ◽  
Complex Disease ◽  
Complex Diseases ◽  
3D Models ◽  
Gene Promoters ◽  
Disease Etiology ◽  
3 Dimensional ◽  
Regulatory Processes ◽  
Chromatin Interactions

Background: The 3-dimensional (3D) conformation of the chromatin creates complex networks of noncoding regulatory regions (distal elements) and genes with important implications in gene regulation. Despite the importance of the role of noncoding regions in complex traits, little is known about their interplay within regulatory hubs and the implication in multigenic diseases like schizophrenia. Results: Here we show that cis-regulatory hubs (CRHs) in neurons highlight functional interactions between distal elements and promoters, providing a model to explain the epigenetic mechanisms involved in complex diseases. CRHs represent a new 3D model, where several distal elements interact to create a complex network of active genes. Indeed, we found that CRHs represent functional structures, showing higher transcriptional activity. In a disease context, CRHs highlighted strong enrichments in schizophrenia-associated genes, schizophrenia-associated SNPs and schizophrenia heritability compared to equivalent tissue and non-tissue-specific structures. Also, genes, by sharing the same distal elements, converge to common biological processes associated with schizophrenia. Finally, the results showed that in a complex disease etiology, small CRHs by linking fewer distal elements to promoters constitute a more informative structure than larger hubs. Conclusion: CRHs are a new 3D model of the chromatin interactions between gene promoters and their distal elements highlighting causal regulatory processes and providing a better understanding of complex disease etiology such as schizophrenia. Indeed, by providing a finer scale chromosome architecture, we have genetic and statistical evidence that CRHs represent a major advancement in 3D models to study the epigenetic underlying processes involved in complex traits.

scholarly journals Family-based gene-environment interaction using sequence kernel association test (FGE-SKAT) for complex quantitative traits

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chao-Yu Guo ◽  
Reng-Hong Wang ◽  
Hsin-Chou Yang
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Association Test ◽  
Whole Genome Sequence ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Sequence Kernel Association Test ◽  
Gene Environment ◽  
Family Based

AbstractAfter the genome-wide association studies (GWAS) era, whole-genome sequencing is highly engaged in identifying the association of complex traits with rare variations. A score-based variance-component test has been proposed to identify common and rare genetic variants associated with complex traits while quickly adjusting for covariates. Such kernel score statistic allows for familial dependencies and adjusts for random confounding effects. However, the etiology of complex traits may involve the effects of genetic and environmental factors and the complex interactions between genes and the environment. Therefore, in this research, a novel method is proposed to detect gene and gene-environment interactions in a complex family-based association study with various correlated structures. We also developed an R function for the Fast Gene-Environment Sequence Kernel Association Test (FGE-SKAT), which is freely available as supplementary material for easy GWAS implementation to unveil such family-based joint effects. Simulation studies confirmed the validity of the new strategy and the superior statistical power. The FGE-SKAT was applied to the whole genome sequence data provided by Genetic Analysis Workshop 18 (GAW18) and discovered concordant and discordant regions compared to the methods without considering gene by environment interactions.

scholarly journals Bayesian reassessment of the epigenetic architecture of complex traits

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Daniel Trejo Banos ◽  
Daniel L. McCartney ◽  
Marion Patxot ◽  
Lucas Anchieri ◽  
Thomas Battram ◽  
...  
Keyword(s):  
Complex Traits ◽  
Complex Disease ◽  
Target Identification ◽  
Blood Cholesterol ◽  
Methylation Array ◽  
Single Nucleotide ◽  
Epigenetic Effects ◽  
Probability Prediction ◽  
Stimuli Response ◽  
Insight Into

Abstract Linking epigenetic marks to clinical outcomes improves insight into molecular processes, disease prediction, and therapeutic target identification. Here, a statistical approach is presented to infer the epigenetic architecture of complex disease, determine the variation captured by epigenetic effects, and estimate phenotype-epigenetic probe associations jointly. Implicitly adjusting for probe correlations, data structure (cell-count or relatedness), and single-nucleotide polymorphism (SNP) marker effects, improves association estimates and in 9,448 individuals, 75.7% (95% CI 71.70–79.3) of body mass index (BMI) variation and 45.6% (95% CI 37.3–51.9) of cigarette consumption variation was captured by whole blood methylation array data. Pathway-linked probes of blood cholesterol, lipid transport and sterol metabolism for BMI, and xenobiotic stimuli response for smoking, showed >1.5 times larger associations with >95% posterior inclusion probability. Prediction accuracy improved by 28.7% for BMI and 10.2% for smoking over a LASSO model, with age-, and tissue-specificity, implying associations are a phenotypic consequence rather than causal.

scholarly journals Employing whole genome mapping for optimal de novo assembly of bacterial genomes

2014 ◽  
Vol 7 (1) ◽  
pp. 484 ◽  
Author(s):  
Basil Xavier ◽  
Julia Sabirova ◽  
Moons Pieter ◽  
Jean-Pierre Hernalsteens ◽  
Henri de Greve ◽  
...  
Keyword(s):  
De Novo Assembly ◽  
De Novo ◽  
Genome Mapping ◽  
Whole Genome ◽  
Bacterial Genomes ◽  
Whole Genome Mapping

Segregation of genes from donor strain during the production of recombinant congenic strains

1991 ◽  
Vol 25 (3) ◽  
pp. 193-197 ◽  
Author(s):  
L. F. M. Van Zutphen ◽  
M. Den Bieman ◽  
A. Lankhorst ◽  
P. Demant
Keyword(s):  
Donor Strain ◽  
Congenic Strains ◽  
Recombinant Congenic Strains

scholarly journals High Resolution Typing by Whole Genome Mapping Enables Discrimination of LA-MRSA (CC398) Strains and Identification of Transmission Events

PLoS ONE ◽  
2013 ◽  
Vol 8 (6) ◽  
pp. e66493 ◽  
Author(s):  
Thijs Bosch ◽  
Erwin Verkade ◽  
Martijn van Luit ◽  
Bruno Pot ◽  
Paul Vauterin ◽  
...  
Keyword(s):  
High Resolution ◽  
Genome Mapping ◽  
Whole Genome ◽  
Whole Genome Mapping

scholarly journals Whole genome resequencing of a laboratory-adapted Drosophila melanogaster population sample

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2644 ◽  
Author(s):  
William P. Gilks ◽  
Tanya M. Pennell ◽  
Ilona Flis ◽  
Matthew T. Webster ◽  
Edward H. Morrow
Keyword(s):  
Drosophila Melanogaster ◽  
Complex Traits ◽  
Population Sample ◽  
Genomic Variation ◽  
Genotype Data ◽  
Whole Genome ◽  
Unique Haplotype ◽  
Short Read ◽  
Short Read Archive ◽  
Ncbi Short Read Archive

As part of a study into the molecular genetics of sexually dimorphic complex traits, we used next-generation sequencing to obtain data on genomic variation in an outbred laboratory-adapted fruit fly (Drosophila melanogaster) population. We successfully resequenced the whole genome of 220 hemiclonal females that were heterozygous for the same Berkeley reference line genome (BDGP6/dm6), and a unique haplotype from the outbred base population (LHM). The use of a static and known genetic background enabled us to obtain sequences from whole genome phased haplotypes. We used a BWA-Picard-GATK pipeline for mapping sequence reads to the dm6 reference genome assembly, at a median depth of coverage of 31X, and have made the resulting data publicly-available in the NCBI Short Read Archive (Accession number SRP058502). We used Haplotype Caller to discover and genotype 1,726,931 small genomic variants (SNPs and indels, <200bp). Additionally we detected and genotyped 167 large structural variants (1-100Kb in size) using GenomeStrip/2.0. Sequence and genotype data are publicly-available at the corresponding NCBI databases: Short Read Archive, dbSNP and dbVar (BioProject PRJNA282591). We have also released the unfiltered genotype data, and the code and logs for data processing and summary statistics (https://zenodo.org/communities/sussex_drosophila_sequencing/).

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