Genetic and environmental pathways to complex diseases

Background  

Pathogenesis of complex diseases involves the integration of genetic and environmental factors over time, making it particularly difficult to tease apart relationships between phenotype, genotype, and environmental factors using traditional experimental approaches.     

Mapping rare and common causal alleles for complex human diseases

Advances in genotyping and sequencing technologies have revolutionized the genetics of complex disease by locating rare and common variants that influence an individual's risk for diseases, such as diabetes, cancers, and psychiatric disorders. However, to capitalize on these data for prevention and therapies requires the identification of causal alleles and a mechanistic understanding for how these variants contribute to the disease. After discussing the strategies currently used to map variants for complex diseases, this Primer explores how variants may be prioritized for follow-up functional studies and the challenges and approaches for assessing the contributions of rare and common variants to disease phenotypes.     

Cholesterol and matrisome pathways dysregulated in astrocytes and microglia

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MRLocus: Identifying causal genes mediating a trait through Bayesian estimation of allelic heterogeneity

Expression quantitative trait loci (eQTL) studies are used to understand the regulatory function of non-coding genome-wide association study (GWAS) risk loci, but colocalization alone does not demonstrate a causal relationship of gene expression affecting a trait. Evidence for mediation, that perturbation of gene expression in a given tissue or developmental context will induce a change in the downstream GWAS trait, can be provided by two-sample Mendelian Randomization (MR). Here, we introduce a new statistical method, MRLocus, for Bayesian estimation of the gene-to-trait effect from eQTL and GWAS summary data for loci with evidence of allelic heterogeneity, that is, containing multiple causal variants. MRLocus makes use of a colocalization step applied to each nearly-LD-independent eQTL, followed by an MR analysis step across eQTLs. Additionally, our method involves estimation of the extent of allelic heterogeneity through a dispersion parameter, indicating variable mediation effects from each individual eQTL on the downstream trait. Our method is evaluated against other state-of-the-art methods for estimation of the gene-to-trait mediation effect, using an existing simulation framework. In simulation, MRLocus often has the highest accuracy among competing methods, and in each case provides more accurate estimation of uncertainty as assessed through interval coverage. MRLocus is then applied to five candidate causal genes for mediation of particular GWAS traits, where gene-to-trait effects are concordant with those previously reported. We find that MRLocus’s estimation of the causal effect across eQTLs within a locus provides useful information for determining how perturbation of gene expression or individual regulatory elements will affect downstream traits. The MRLocus method is implemented as an R package available at https://mikelove.github.io/mrlocus.     

Enabling technologies for manipulating multiple genes on complex pathways

Many complex biochemical pathways in plants have now been manipulated genetically, usually by suppression or over-expression of single genes. Further exploitation of the potential for plant genetic manipulation, both as a research tool and as a vehicle for plant biotechnology, will require the co-ordinate manipulation of multiple genes on a pathway. This goal is currently very difficult to achieve. A number of approaches have been taken to combine or `pyramid' transgenes in one plant and have met with varying degrees of success. These approaches include sexual crossing, re-transformation, co-transformation and the use of linked transgenes. Novel, alternative `enabling' technologies are also being developed that aim to use single transgenes to manipulate the expression of multiple genes. A chimeric transgene with linked partial gene sequences placed under the control of a single promoter can be used to co-ordinately suppress numerous plant endogenous genes. Constructs modelled on viral polyproteins can be used to simultaneously introduce multiple protein-coding genes into plant cells. In the course of our work on the lignin biosynthetic pathway, we have tested both conventional and novel methods for achieving co-ordinate suppression or over-expression of up to three plant lignin genes. In this article we review the literature concerning the manipulation of multiple genes in plants. We also report on our own experiences and results using different methods to perform directed manipulation of lignin biosynthesis in tobacco.     

多基因遗传病基因研究的策略和方法

基因在决定个体表型方面起着决定性的作用。虽然单基因疾病的致病基因的克隆工作取得了显著的进展,但对于多基因疾病来说,仍然存在许多问题,同时也是巨大的挑战。本文综述了多基因疾病的遗传特点和多基因疾病易感基因识别、分离和克隆的一般步骤和存在的问题,介绍了人类基因组计划在此过程中的作用和单核苷酸多态性的应用前景,提出 了最有可能克隆出多基因疾病易感基因的策略和方法。     

Identifying significant genes and functionally enriched pathways in familial hypercholesterolemia using integrated gene co-expression network analysis

Familial hypercholesterolemia (FH) is a monogenic lipid disorder which promotes atherosclerosis and cardiovascular diseases. Owing to the lack of sufficient published information, this study aims to identify the potential genetic biomarkers for FH by studying the global gene expression profile of blood cells. The microarray expression data of FH patients and controls was analyzed by different computational biology methods like differential expression analysis, protein network mapping, hub gene identification, functional enrichment of biological pathways, and immune cell restriction analysis. Our results showed the dysregulated expression of 115 genes connected to lipid homeostasis, immune responses, cell adhesion molecules, canonical Wnt signaling, mucin type O-glycan biosynthesis pathways in FH patients. The findings from expanded protein interaction network construction with known FH genes and subsequent Gene Ontology (GO) annotations have also supported the above findings, in addition to identifying the involvement of dysregulated thyroid hormone and ErbB signaling pathways in FH patients. The genes like CSNK1A1, JAK3, PLCG2, RALA, and ZEB2 were found to be enriched under all GO annotation categories. The subsequent phenotype ontology results have revealed JAK3I, PLCG2, and ZEB2 as key hub genes contributing to the inflammation underlying cardiovascular and immune response related phenotypes. Immune cell restriction findings show that above three genes are highly expressed by T-follicular helper CD4⁺ T cells, naïve B cells, and monocytes, respectively. These findings not only provide a theoretical basis to understand the role of immune dysregulations underlying the atherosclerosis among FH patients but may also pave the way to develop genomic medicine for cardiovascular diseases.     

Mapping genes of complex diseases in genetic isolates of Dagestan

Original results of the analysis of genetic linkage between some genomic markers and two complex clinical phenotypes, schizophrenia and mental retardation, in pedigrees from Dagestan genetic isolates are described. Interpopulation differences in the epidemiology of the complex phenotypes were studied and in their genetic linkage was demonstrated. These differences are evidently related to the genetic structure of the isolates determined by their genetic history. The MR epidemiological index characterizing the lifetime morbid risk of schizophrenia varies in the Dagestan isolates studied from 0 to 4.95%, which is almost five times higher than the average worldwide population rate, 1%. Comparative genetic mapping permitted determination of the most probable genetic linkages and associations of loci from chromosomal regions 17p11.1-12, 3q13.3, and a locus from 22q with schizophrenia and locus 12q23 with mental retardation. There is evidence that this approach is effective for detailed study of the relationship between the genetic (allele and locus) and clinical heterogeneity of complex diseases, which favors successful identification of the genes determining them. The study of linkage disequilibrium (LD) in genetic isolates of Daghestan populations (which have a common genetic background) may be an effective methodological approach for revealing the numerous contradictory results of mapping of the same genes of complex disease performed by different researchers in different regions of the world.     

Investigating multiple dysregulated pathways in rheumatoid arthritis based on pathway interaction network

The traditional methods of identifying biomarkers in rheumatoid arthritis (RA) have focussed on the differentially expressed pathways or individual pathways, which however, neglect the interactions between pathways. To better understand the pathogenesis of RA, we aimed to identify dysregulated pathway sets using a pathway interaction network (PIN), which considered interactions among pathways. Firstly, RA-related gene expression profile data, protein–protein interactions (PPI) data and pathway data were taken up from the corresponding databases. Secondly, principal component analysis method was used to calculate the pathway activity of each of the pathway, and then a seed pathway was identified using data gleaned from the pathway activity. A PIN was then constructed based on the gene expression profile, pathway data, and PPI information. Finally, the dysregulated pathways were extracted from the PIN based on the seed pathway using the method of support vector machines and an area under the curve (AUC) index. The PIN comprised of a total of 854 pathways and 1064 pathway interactions. The greatest change in the activity score between RA and control samples was observed in the pathway of epigenetic regulation of gene expression, which was extracted and regarded as the seed pathway. Starting with this seed pathway, one maximum pathway set containing 10 dysregulated pathways was extracted from the PIN, having an AUC of 0.8249, and the result indicated that this pathway set could distinguish RA from the controls. These 10 dysregulated pathways might be potential biomarkers for RA diagnosis and treatment in the future.     

Mutations in structural genes of complex I associated with neurological diseases

This paper summarizes observations on the genetic and biochemical basis of hereditary defects of complex I (NADH-ubiquinone oxidoreductase) of the respiratory chain in human neurological patients. Two different types of functional defects of the complex are described. In one type mutations in the NDUFS1 and NDUFS4 nuclear structural genes of the complex were identified in two unrelated families. Both NDUFS1 and NDUFS4 neurological disorders were transmitted by autosomic recessive inheritance. The two mutations resulted in different impact on cellular metabolism. The NDUFS4 mutation, giving a more severe, fatal pathological pattern, resulted in a defective assembly of the complex and complete suppression of the enzymatic activity. The NDUFS1 mutation, with less severe progressive pathology, caused only partial inhibition of the complex but enhanced production of oxygen free radicals. In the second type of deficiencies extensive mutational analysis did not reveal pathogenic mutations in complex I genes but a decline in the level and activity of complex I, III, and IV were found, apparently associated with alteration in the cardiolipin membrane distribution.     

Integrative analysis for finding genes and networks involved in diabetes and other complex diseases

We have developed an integrative analysis method combining genetic interactions, identified using type 1 diabetes genome scan data, and a high-confidence human protein interaction network. Resulting networks were ranked by the significance of the enrichment of proteins from interacting regions. We identified a number of new protein network modules and novel candidate genes/proteins for type 1 diabetes. We propose this type of integrative analysis as a general method for the elucidation of genes and networks involved in diabetes and other complex diseases.     

Identifying protein-coding genes in genomic sequences

The vast majority of the biology of a newly sequenced genome is inferred from the set of encoded proteins. Predicting this set is therefore invariably the first step after the completion of the genome DNA sequence. Here we review the main computational pipelines used to generate the human reference protein-coding gene sets.     

Identifying causal variants by fine mapping across multiple studies

Increasingly large Genome-Wide Association Studies (GWAS) have yielded numerous variants associated with many complex traits, motivating the development of “fine mapping” methods to identify which of the associated variants are causal. Additionally, GWAS of the same trait for different populations are increasingly available, raising the possibility of refining fine mapping results further by leveraging different linkage disequilibrium (LD) structures across studies. Here, we introduce multiple study causal variants identification in associated regions (MsCAVIAR), a method that extends the popular CAVIAR fine mapping framework to a multiple study setting using a random effects model. MsCAVIAR only requires summary statistics and LD as input, accounts for uncertainty in association statistics using a multivariate normal model, allows for multiple causal variants at a locus, and explicitly models the possibility of different SNP effect sizes in different populations. We demonstrate the efficacy of MsCAVIAR in both a simulation study and a trans-ethnic, trans-biobank fine mapping analysis of High Density Lipoprotein (HDL).     

Identifying essential genes in Arabidopsis thaliana

Eight years after publication of the Arabidopsis genome sequence and two years before completing the first phase of an international effort to characterize the function of every Arabidopsis gene, plant biologists remain unable to provide a definitive answer to the following basic question: what is the minimal gene set required for normal growth and development? The purpose of this review is to summarize different strategies employed to identify essential genes in Arabidopsis, an important component of the minimal gene set in plants, to present an overview of the datasets and specific genes identified to date, and to discuss the prospects for future saturation of this important class of genes. The long-term goal of this collaborative effort is to facilitate basic research in plant biology and complement ongoing research with other model organisms.