1.
No evidence for genome-wide interactions on plasma fibrinogen by smoking, alcohol consumption and body mass index: results from meta-analyses of 80,607 subjects.
Baumert, J, Huang, J, McKnight, B, Sabater-Lleal, M, Steri, M, Chu, AY, Trompet, S, Lopez, LM, Fornage, M, Teumer, A, et al
PloS one. 2014;(12):e111156
Abstract
Plasma fibrinogen is an acute phase protein playing an important role in the blood coagulation cascade having strong associations with smoking, alcohol consumption and body mass index (BMI). Genome-wide association studies (GWAS) have identified a variety of gene regions associated with elevated plasma fibrinogen concentrations. However, little is yet known about how associations between environmental factors and fibrinogen might be modified by genetic variation. Therefore, we conducted large-scale meta-analyses of genome-wide interaction studies to identify possible interactions of genetic variants and smoking status, alcohol consumption or BMI on fibrinogen concentration. The present study included 80,607 subjects of European ancestry from 22 studies. Genome-wide interaction analyses were performed separately in each study for about 2.6 million single nucleotide polymorphisms (SNPs) across the 22 autosomal chromosomes. For each SNP and risk factor, we performed a linear regression under an additive genetic model including an interaction term between SNP and risk factor. Interaction estimates were meta-analysed using a fixed-effects model. No genome-wide significant interaction with smoking status, alcohol consumption or BMI was observed in the meta-analyses. The most suggestive interaction was found for smoking and rs10519203, located in the LOC123688 region on chromosome 15, with a p value of 6.2 × 10(-8). This large genome-wide interaction study including 80,607 participants found no strong evidence of interaction between genetic variants and smoking status, alcohol consumption or BMI on fibrinogen concentrations. Further studies are needed to yield deeper insight in the interplay between environmental factors and gene variants on the regulation of fibrinogen concentrations.
2.
Inferring primary tumor sites from mutation spectra: a meta-analysis of histology-specific aberrations in cancer-derived cell lines.
Dietlein, F, Eschner, W
Human molecular genetics. 2014;(6):1527-37
Abstract
Next-generation sequencing technologies have led to profound characterization of mutation spectra for several cancer types. Hence, we sought to systematically compare genomic aberrations between primary tumors and cancer lines. For this, we compiled publically available sequencing data of 1651 genes across 905 cell lines. We used them to characterize 23 distinct primary tumor sites by a novel approach that is based on Bayesian spam-filtering techniques. Thereby, we confirmed the strong overall similarity of alterations between patient samples and cell culture. However, we also identified several suspicious mutations, which had not been associated with their cancer types before. Based on these characterizations, we developed the inferring cancer origins from mutation spectra (ICOMS) tool. On our cell line collection, the algorithm reached a prediction specificity rate of 79%, which strongly variegated between primary cancer sites. On an independent validation cohort of 431 primary tumor samples, we observed a similar accuracy of 71%. Additionally, we found that ICOMS could be employed to deduce further attributes from mutation spectra, including sub-histology and compound sensitivity. Thus, thorough classification of site-specific mutation spectra for cell lines may decipher further genome-phenotype associations in cancer.
3.
Meta-analysis of grain yield QTL identified during agricultural drought in grasses showed consensus.
Swamy, BP, Vikram, P, Dixit, S, Ahmed, HU, Kumar, A
BMC genomics. 2011;:319
Abstract
BACKGROUND In the last few years, efforts have been made to identify large effect QTL for grain yield under drought in rice. However, identification of most precise and consistent QTL across the environments and genetics backgrounds is essential for their successful use in Marker-assisted Selection. In this study, an attempt was made to locate consistent QTL regions associated with yield increase under drought by applying a genome-wide QTL meta-analysis approach. RESULTS The integration of 15 maps resulted in a consensus map with 531 markers and a total map length of 1821 cM. Fifty-three yield QTL reported in 15 studies were projected on a consensus map and meta-analysis was performed. Fourteen meta-QTL were obtained on seven chromosomes. MQTL1.2, MQTL1.3, MQTL1.4, and MQTL12.1 were around 700 kb and corresponded to a reasonably small genetic distance of 1.8 to 5 cM and they are suitable for use in marker-assisted selection (MAS). The meta-QTL for grain yield under drought coincided with at least one of the meta-QTL identified for root and leaf morphology traits under drought in earlier reports. Validation of major-effect QTL on a panel of random drought-tolerant lines revealed the presence of at least one major QTL in each line. DTY12.1 was present in 85% of the lines, followed by DTY4.1 in 79% and DTY1.1 in 64% of the lines. Comparative genomics of meta-QTL with other cereals revealed that the homologous regions of MQTL1.4 and MQTL3.2 had QTL for grain yield under drought in maize, wheat, and barley respectively. The genes in the meta-QTL regions were analyzed by a comparative genomics approach and candidate genes were deduced for grain yield under drought. Three groups of genes such as stress-inducible genes, growth and development-related genes, and sugar transport-related genes were found in clusters in most of the meta-QTL. CONCLUSIONS Meta-QTL with small genetic and physical intervals could be useful in Marker-assisted selection individually and in combinations. Validation and comparative genomics of the major-effect QTL confirmed their consistency within and across the species. The shortlisted candidate genes can be cloned to unravel the molecular mechanism regulating grain yield under drought.