New Insights Into Mitochondrial Dysfunction at Disease Susceptibility Loci in the Development of Type 2 Diabetes

This study investigated the potential genetic mechanisms which underlie adipose tissue mitochondrial dysfunction in Type 2 diabetes (T2D), by systematically identifying nuclear-encoded mitochondrial genes (NEMGs) among the genes regulated by T2D-associated genetic loci. The target genes of these ‘disease loci’ were identified by mapping genetic loci associated with both disease and gene expression levels (expression quantitative trait loci, eQTL) using high resolution genetic maps, with independent estimates co-locating to within a small genetic distance. These co-locating signals were defined as T2D-eQTL and the target genes as T2D cis-genes. In total, 763 cis-genes were associated with T2D-eQTL, of which 50 were NEMGs. Independent gene expression datasets for T2D and insulin resistant cases and controls confirmed that the cis-genes and cis-NEMGs were enriched for differential expression in cases, providing independent validation that genetic maps can identify informative functional genes. Two additional results were consistent with a potential role of T2D-eQTL in regulating the 50 identified cis-NEMGs in the context of T2D risk: (1) the 50 cis-NEMGs showed greater differential expression compared to other NEMGs and (2) other NEMGs showed a trend towards significantly decreased expression if their expression levels correlated more highly with the subset of 50 cis-NEMGs. These 50 cis-NEMGs, which are differentially expressed and associated with mapped T2D disease loci, encode proteins acting within key mitochondrial pathways, including some of current therapeutic interest such as the metabolism of branched-chain amino acids, GABA and biotin.

Inflammatory and infectious upper respiratory diseases associate with 59 genomic loci that link to type 2 inflammation genes

Inflammatory and infectious upper respiratory diseases (IURDs; ICD-10: J30-J39), such as diseases of sinonasal tract, pharynx and larynx, are growing health problems yet their genomic similarity is not known. We analyzed genome-wide association to eight IURDs (61,195 cases) among 260,405 FinnGen participants, meta-analyzing diseases in four groups based on an underlying genetic correlation structure. We aimed to understand which genetic loci contribute to susceptibility to IURDs in general and its subtypes. We detected 59 independent genome-wide significant (GWS) loci, distinguishing impact on sinonasal or pharyngeal diseases, or both. Fine-mapping implicated non-synonymous variants in 16 genes, including 10 linked to immune-related diseases. Phenome-wide analysis implicated asthma and atopic dermatitis at sinonasal disease loci and inflammatory bowel diseases, and other immune-mediated disorders at pharyngeal disease loci. IURDs also genetically correlated with autoimmune diseases such as rheumatoid arthritis, autoimmune hypothyroidism, and psoriasis. Finally, we associated separate gene pathways in sinonasal and pharyngeal diseases that both contribute to type 2 immunological reaction. We show shared heritability among IURDs that extends to several immune-mediated diseases with diverse mechanisms, such as type 2 high inflammation.

Redefining tissue specificity of genetic regulation of gene expression in the presence of allelic heterogeneity

Understanding the mechanisms that underlie genetic regulation of gene expression is crucial to explaining the diversity that governs complex traits. Large scale expression quantitative trait locus (eQTL) studies have been instrumental in identifying genetic variants that influence the expression of target genes. However, a large fraction of disease-associated genetic variants have not been clearly explained by current eQTL data, frustrating attempts to use these data to comprehensively characterize disease loci. One notable observation from recent studies is that cis-eQTL effects are often shared across different cell types and tissues. This would suggest that common genetic variants impacting steady-state, adult gene expression are largely tolerated, shared across tissues, and less relevant to disease. However, allelic heterogeneity and complex patterns of linkage disequilibrium (LD) within each locus may skew the quantification of sharing of genetic effects between tissues, impede our ability to identify causal variants, and hinder the identification of regulatory effects for disease-associated genetic variants. Indeed, recent research suggests that multiple causal variants are often present in many eQTL and complex trait associated loci. Here, we re-analyze tissue-specificity of genetic effects in the presence of LD and allelic heterogeneity, proposing a novel method, CAFEH, that improves the identification of causal regulatory variants across tissues and their relationship to disease loci.

Identification of Aneuploidy in Dogs Screened by a SNP Microarray

Microarray analysis is an efficient approach for screening and identifying cytogenetic imbalances in humans. SNP arrays, in particular, are a powerful way to identify copy number gains and losses representing aneuploidy and aneusomy, but moreover, allow for the direct assessment of individual genotypes in known disease loci. Using these approaches, trisomies, monosomies and mosaicism of whole chromosomes have been identified in human microarray studies. For canines, this approach is not widely used in clinical laboratory diagnostic practice. In our laboratory, we have implemented the use of a propriety SNP array that represents approximately 650,000 loci across the domestic dog genome. During the validation of this microarray prior to clinical use, we identified three cases of aneuploidy after screening 2,053 dogs of various breeds including monosomy X, trisomy X and an apparent, mosaic trisomy of canine chromosome 38 (CFA 38). This study represents the first use of microarrays for copy number evaluation to identify cytogenetic anomalies in canines. As microarray analysis becomes more routine in canine genetic testing, more cases of chromosome aneuploidy are likely to be uncovered.

Phenotypic spectrum and genomics of undiagnosed arthrogryposis multiplex congenita

BackgroundArthrogryposis multiplex congenita (AMC) is characterised by congenital joint contractures in two or more body areas. AMC exhibits wide phenotypic and genetic heterogeneity. Our goals were to improve the genetic diagnosis rates of AMC, to evaluate the added value of whole exome sequencing (WES) compared with targeted exome sequencing (TES) and to identify new genes in 315 unrelated undiagnosed AMC families.MethodsSeveral genomic approaches were used including genetic mapping of disease loci in multiplex or consanguineous families, TES then WES. Sanger sequencing was performed to identify or validate variants.ResultsWe achieved disease gene identification in 52.7% of AMC index patients including nine recently identified genes (CNTNAP1, MAGEL2, ADGRG6, ADCY6, GLDN, LGI4, LMOD3, UNC50 and SCN1A). Moreover, we identified pathogenic variants in ASXL3 and STAC3 expanding the phenotypes associated with these genes. The most frequent cause of AMC was a primary involvement of skeletal muscle (40%) followed by brain (22%). The most frequent mode of inheritance is autosomal recessive (66.3% of patients). In sporadic patients born to non-consanguineous parents (n=60), de novo dominant autosomal or X linked variants were observed in 30 of them (50%).ConclusionNew genes recently identified in AMC represent 21% of causing genes in our cohort. A high proportion of de novo variants were observed indicating that this mechanism plays a prominent part in this developmental disease. Our data showed the added value of WES when compared with TES due to the larger clinical spectrum of some disease genes than initially described and the identification of novel genes.

Regulatory genomic circuitry of human disease loci by integrative epigenomics

Annotating the molecular basis of human disease remains an unsolved challenge, as 93% of disease loci are non-coding and gene-regulatory annotations are highly incomplete1–3. Here we present EpiMap, a compendium comprising 10,000 epigenomic maps across 800 samples, which we used to define chromatin states, high-resolution enhancers, enhancer modules, upstream regulators and downstream target genes. We used this resource to annotate 30,000 genetic loci that were associated with 540 traits4, predicting trait-relevant tissues, putative causal nucleotide variants in enriched tissue enhancers and candidate tissue-specific target genes for each. We partitioned multifactorial traits into tissue-specific contributing factors with distinct functional enrichments and disease comorbidity patterns, and revealed both single-factor monotropic and multifactor pleiotropic loci. Top-scoring loci frequently had multiple predicted driver variants, converging through multiple enhancers with a common target gene, multiple genes in common tissues, or multiple genes and multiple tissues, indicating extensive pleiotropy. Our results demonstrate the importance of dense, rich, high-resolution epigenomic annotations for the investigation of complex traits.

Chemical Genetic Validation of GWAS-derived Disease Loci

Background: Genome-wide association studies (GWAS) have opened the door to unprecedented large-scale identification of susceptibility loci for human diseases and traits. However, it is still a great challenge to validate these loci and elucidate how these sequence variants give rise to the genetic and phenotypic changes. Because many drug targets are genetic disease genes and the general drug mode of action (MoA, agonist or antagonist) is in line with the consequence of target gene mutations (loss-of-function (LOF) or gain-of-function (GOF)), here we propose a chemical genetic method to address the above issues of GWAS. Objective: This study intends to use chemical genetics information to validate GWAS-derived disease loci and interpret their underlying pathogenesis. Method: We conducted a comprehensive comparative analysis on GWAS data and drug/target information (chemical genetics information). Results: We have identified hundreds of GWAS-derived disease loci which are linked to drug target genes and have matched disease traits and drug indications. It is interesting to note that more than 40% genes have been recognized as disorder factors, indicating the potential power of chemical genetic validation. The pathogenesis of these loci was inferred by corresponding drug MoA. Some inferences were supported by prior experimental observations; some were interpreted in terms of microRNA regulation, codon usage bias, and transcriptional regulation, in particular the transcription factorbinding affinity variation induced by disease-causing mutations. Conclusion: In summary, chemical genetics information is useful to validate GWAS-derived disease loci and to interpret their underlying pathogenesis as well, which has important implications not only in medical genetics but also in methodology evaluation of GWAS.

Disease-Causing Mutations and Rearrangements in Long Non-coding RNA Gene Loci

The classic understanding of molecular disease-mechanisms is largely based on protein-centric models. During the past decade however, genetic studies have identified numerous disease-loci in the human genome that do not encode proteins. Such non-coding DNA variants increasingly gain attention in diagnostics and personalized medicine. Of particular interest are long non-coding RNA (lncRNA) genes, which generate transcripts longer than 200 nucleotides that are not translated into proteins. While most of the estimated ~20,000 lncRNAs currently remain of unknown function, a growing number of genetic studies link lncRNA gene aberrations with the development of human diseases, including diabetes, AIDS, inflammatory bowel disease, or cancer. This suggests that the protein-centric view of human diseases does not capture the full complexity of molecular patho-mechanisms, with important consequences for molecular diagnostics and therapy. This review illustrates well-documented lncRNA gene aberrations causatively linked to human diseases and discusses potential lessons for molecular disease models, diagnostics, and therapy.