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Stephens M; et al.
Current routine genotyping methods typically do not provide haplotypeinformation, which is essential for many analyses of fine-scale molecular-genetics data. Haplotypes can be obtained, at considerable cost, experimentally or (partially) through genotyping of additional family members. Alternatively, a statistical method can be used to infer phase and to reconstruct haplotypes. We present a new statistical method, applicable to genotype data at linked loci from a population sample, that improves substantially on current algorithms; often, error rates are reduced by > 50%, relative to its nearest competitor. Furthermore, our algorithm performs well in absolute terms, suggesting that reconstructing haplotypesexperimentally or by genotyping additional family members may be an inefficient use of resources.
Bueno R, et al
Synthetic lethal interactions (SLIs) are robust mechanisms that provide cells with the ability to sustain viability despite having mutations in genes critical to the DNA damage response, a core cellular process. Studies in model organisms such as S. cerevisiae showed that thousands of genes important in maintaining DNA integrity cooperated in a SLI network. Two genes participate in a SLI when mutation in one gene has no effect on the cell, but mutations in both interacting genes are lethal. Furthermore in C. elegans, mutation in a critical gene that is important for development induced a change in expression variability in the synthetic lethal interactor. In cancer, targeting SLIs shows promise in selectively killing cancer cells. For example, targeting PARP1 is an effective treatment for BRCA1/2- breast and ovarian cancers. Although PARP1 is already identified as having a SLI with BRCA1/2-, computationally searching for other genes that cooperate in the SLI network could highlight genes that may have promise for being a cancer-specific drug target. Using RNA sequencing count data for patients with ovarian cancers with BRCA2 mutations and the R software package pathVar, we showed that genes whose expressionchanges to an invariant, stable expression state can identify genes that likely have SLIs with BRCA2. Our results highlight the interactions between the genes with predicted SLIs and protein-coding genes that are functionally important in the DNA damage response. The method of analyzing expression variability to computationally identify genes with SLIs can be applied to query SLIs in other cancers.
Kim JY, et al.
Skeletal muscle degenerates progressively, losing mass (sarcopenia) over time, which leads to reduced physical ability and often results in secondary diseases such as diabetes and obesity. The regulation of gene expression by microRNAs is a key event in muscle development and disease. To understand genome‐wide changes in microRNAs and mRNAs during muscle aging, we sequenced microRNAs and mRNAs from mouse gastrocnemius muscles at two different ages (6 and 24 months). Thirty‐four microRNAs (15 up‐regulated and 19 down‐regulated) were differentially expressed with age, including the microRNAs miR‐206 and ‐434, which were differentially expressed in aged muscle in previous studies. Interestingly, eight microRNAs in a microRNA cluster at the imprinted Dlk1‐Dio3 locus on chromosome 12 were coordinately down‐regulated. In addition, sixteen novel microRNAs were identified. Integrative analysis of microRNA and mRNA expression revealed that microRNAs may contribute to muscle aging through the positive regulation of transcription, metabolic processes, and kinase activity. Many of the age‐related microRNAs have been implicated in human muscular diseases. We suggest that genome‐wide microRNA profiling will expand our knowledge of microRNA function in the muscle aging process.
Arking DE, et al
Sudden cardiac death (SCD) continues to be one of the leading causes of mortality worldwide, with an annual incidence estimated at 250,000-300,000 in the United States and with the vast majority occurring in the setting of coronary disease. We performed a genome-wide associationmeta-analysis in 1,283 SCD cases and >20,000 control individuals of European ancestry from 5 studies, with follow-up genotyping in up to 3,119 SCD cases and 11,146 controls from 11 European ancestry studies, and identify the BAZ2B locus as associated with SCD (P = 1.8×10(-10)). The risk allele, while ancestral, has a frequency of ~1.4%, suggesting strong negative selection and increases risk for SCD by 1.92-fold per allele (95% CI 1.57-2.34). We also tested the role of 49 SNPs previously implicated in modulating electrocardiographic traits (QRS, QT, and RR intervals). Consistent with epidemiological studies showing increased risk of SCD with prolonged QRS/QT intervals, the interval-prolonging alleles are in aggregate associated with increased risk for SCD (P = 0.006).
Voight BF, et al
High plasma HDL cholesterol is associated with reduced risk of myocardial infarction, but whether this association is causal is unclear. Exploiting the fact that genotypes are randomly assigned at meiosis, are independent of non-genetic confounding, and are unmodified by disease processes, mendelian randomisation can be used to test the hypothesis that the association of a plasma biomarker with disease is causal.
We performed two mendelian randomisation analyses. First, we used as an instrument a single nucleotide polymorphism (SNP) in the endothelial lipase gene (LIPG Asn396Ser) and tested this SNP in 20 studies (20,913 myocardial infarction cases, 95,407 controls). Second, we used as an instrument a genetic score consisting of 14 common SNPs that exclusively associate with HDL cholesterol and tested this score in up to 12,482 cases of myocardial infarction and 41,331 controls. As a positive control, we also tested a genetic score of 13 common SNPs exclusively associated with LDL cholesterol.
Carriers of the LIPG 396Ser allele (2·6% frequency) had higher HDL cholesterol (0·14 mmol/L higher, p=8×10(-13)) but similar levels of other lipid and non-lipid risk factors for myocardial infarction compared with non-carriers. This difference in HDL cholesterol is expected to decrease risk of myocardial infarction by 13% (odds ratio [OR] 0·87, 95% CI 0·84-0·91). However, we noted that the 396Ser allele was not associated with risk of myocardial infarction (OR 0·99, 95% CI 0·88-1·11, p=0·85). From observational epidemiology, an increase of 1 SD in HDL cholesterol was associated with reduced risk of myocardial infarction (OR 0·62, 95% CI 0·58-0·66). However, a 1 SD increase in HDL cholesterol due to genetic score was not associated with risk of myocardial infarction (OR 0·93, 95% CI 0·68-1·26, p=0·63). For LDL cholesterol, the estimate from observational epidemiology (a 1 SD increase in LDL cholesterolassociated with OR 1·54, 95% CI 1·45-1·63) was concordant with that from genetic score (OR 2·13, 95% CI 1·69-2·69, p=2×10(-10)).
Some genetic mechanisms that raise plasma HDLcholesterol do not seem to lower risk of myocardial infarction. These data challenge the concept that raising of plasma HDL cholesterol will uniformly translate into reductions in risk of myocardial infarction.
Mahoney JM, et al.
Systemic sclerosis (SSc) is a rare systemic autoimmune disease characterized by skin and organ fibrosis. The pathogenesis of SSc and its progression are poorly understood. The SSc intrinsic gene expression subsets (inflammatory, fibroproliferative, normal-like, and limited) are observed in multiple clinical cohorts of patients with SSc. Analysis of longitudinal skin biopsies suggests that a patient's subset assignment is stable over 6–12 months. Genetically, SSc is multi-factorial with many genetic risk loci for SSc generally and for specific clinical manifestations. Here we identify the genes consistently associated with the intrinsic subsets across three independent cohorts, show the relationship between these genes using a gene-gene interaction network, and place the genetic risk loci in the context of the intrinsic subsets. To identify gene expression modules common to three independent datasets from three different clinical centers, we developed a consensus clustering procedure based on mutual information of partitions, an information theory concept, and performed a meta-analysis of these genome-wide gene expression datasets. We created a gene-gene interaction network of the conserved molecular features across the intrinsic subsets and analyzed their connections with SSc-associated genetic polymorphisms. The network is composed of distinct, but interconnected, components related to interferon activation, M2 macrophages, adaptive immunity, extracellular matrix remodeling, and cell proliferation. The network shows extensive connections between the inflammatory- and fibroproliferative-specific genes. The network also shows connections between these subset-specific genes and 30 SSc-associated polymorphic genes including STAT4, BLK, IRF7, NOTCH4, PLAUR, CSK, IRAK1, and several human leukocyte antigen (HLA) genes. Our analyses suggest that the gene expression changes underlying the SSc subsets may be long-lived, but mechanistically interconnected and related to a patients underlying genetic risk.
This meeting is hosted by the Integrated Training in Microbial Systems (ITiMS) program at the University of Michigan and is sponsored by the Burroughs Wellcome Fund.
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