| 1. |
|
|
| 2. |
- Kooner, Jaspal S, et al.
(författare)
-
Genome-wide association study in individuals of South Asian ancestry identifies six new type 2 diabetes susceptibility loci.
- 2011
-
Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 43:10
-
Tidskriftsartikel (refereegranskat)abstract
- We carried out a genome-wide association study of type-2 diabetes (T2D) in individuals of South Asian ancestry. Our discovery set included 5,561 individuals with T2D (cases) and 14,458 controls drawn from studies in London, Pakistan and Singapore. We identified 20 independent SNPs associated with T2D at P < 10(-4) for testing in a replication sample of 13,170 cases and 25,398 controls, also all of South Asian ancestry. In the combined analysis, we identified common genetic variants at six loci (GRB14, ST6GAL1, VPS26A, HMG20A, AP3S2 and HNF4A) newly associated with T2D (P = 4.1 × 10(-8) to P = 1.9 × 10(-11)). SNPs at GRB14 were also associated with insulin sensitivity (P = 5.0 × 10(-4)), and SNPs at ST6GAL1 and HNF4A were also associated with pancreatic beta-cell function (P = 0.02 and P = 0.001, respectively). Our findings provide additional insight into mechanisms underlying T2D and show the potential for new discovery from genetic association studies in South Asians, a population with increased susceptibility to T2D.
|
|
| 3. |
- Lazar, Vladimir, et al.
(författare)
-
Integrated molecular portrait of non-small cell lung cancers
- 2013
-
Ingår i: BMC Medical Genomics. - : Springer Science and Business Media LLC. - 1755-8794. ; 6:1, s. 53-
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Non-small cell lung cancer (NSCLC), a leading cause of cancer deaths, represents a heterogeneous group of neoplasms, mostly comprising squamous cell carcinoma (SCC), adenocarcinoma (AC) and large-cell carcinoma (LCC). The objectives of this study were to utilize integrated genomic data including copy-number alteration, mRNA, microRNA expression and candidate-gene full sequencing data to characterize the molecular distinctions between AC and SCC. Methods: Comparative genomic hybridization followed by mutational analysis, gene expression and miRNA microarray profiling were performed on 123 paired tumor and non-tumor tissue samples from patients with NSCLC. Results: At DNA, mRNA and miRNA levels we could identify molecular markers that discriminated significantly between the various histopathological entities of NSCLC. We identified 34 genomic clusters using aCGH data; several genes exhibited a different profile of aberrations between AC and SCC, including PIK3CA, SOX2, THPO, TP63, PDGFB genes. Gene expression profiling analysis identified SPP1, CTHRC1and GREM1 as potential biomarkers for early diagnosis of the cancer, and SPINK1 and BMP7 to distinguish between AC and SCC in small biopsies or in blood samples. Using integrated genomics approach we found in recurrently altered regions a list of three potential driver genes, MRPS22, NDRG1 and RNF7, which were consistently over-expressed in amplified regions, had wide-spread correlation with an average of similar to 800 genes throughout the genome and highly associated with histological types. Using a network enrichment analysis, the targets of these potential drivers were seen to be involved in DNA replication, cell cycle, mismatch repair, p53 signalling pathway and other lung cancer related signalling pathways, and many immunological pathways. Furthermore, we also identified one potential driver miRNA hsa-miR-944. Conclusions: Integrated molecular characterization of AC and SCC helped identify clinically relevant markers and potential drivers, which are recurrent and stable changes at DNA level that have functional implications at RNA level and have strong association with histological subtypes.
|
|
| 4. |
- Suo, Chen
(författare)
-
Statistical methods for the detection, analyses and integration of biomarkers in the human genome and transcriptome
- 2014
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Most human diseases have been shown to have a genetic basis that is linked to regulation of gene expression at the transcriptional or post-transcriptional level. In the central dogma of biology, deoxyribonucleic acid (DNA) is transcribed to messenger ribonucleic acid (mRNA), and then translated into proteins; dysfunction in any of these processes may contribute to the development of disease. Sources of such potential irregularities include, but not limited to, the following: point mutations in DNA sequences, copy number alterations (CNAs) and abnormal mRNA and microRNAs (miRNAs) expression. MiRNAs are a type of non-coding RNA that inhibit the transcription and/or translation of specific target mRNAs. Current technologies allow the identification of biomarkers and study of the complex interplay between DNA, mRNA, miRNA and phenotypic variation. This thesis aims to tackle the statistical challenges that have arisen with the application of these technologies to investigate various genomic and transcriptomic alterations. In study I, modified least-variant set normalization for miRNA microarray, a new algorithm and software were developed for microRNA array data normalization. The algorithm selects miRNAs with the least array-to-array variation as the reference set for normalization. The selection process was refined by accounting for the considerable differences in variances between probes. Data are provided to show that this algorithm results in better operating characteristics than other methods. In study II, joint estimation of isoform expression and isoform-specific read distribution using multi-sample RNA-Seq data, a joint model and software were developed to estimate isoform-specific read distribution and gene isoform expression, using RNA-sequencing data from multiple samples. Observation of similarities in the shape of the read distributions solves the problem that the non-uniform read intensity pattern is not identifiable from the data provided by one sample. In study III, integrated molecular portrait of non-small cell lung cancers, molecular markers at the DNA, mRNA and miRNA level that can distinguish between different histopathological subtypes of non-small cell lung cancer were identified. Additionally, using integrated genomic data including CNAs and mRNA and miRNA expression data, three potential driver genes were identified in non-small cell lung cancer, namely MRPS22, NDRG1 and RNF7. Furthermore, a potential driver miRNA, hsa-miR-944, was identified. In study IV, integration of somatic mutation, expression and functional data reveals potential driver genes predictive of breast cancer survival. An analytic pipeline to process large-scale whole-genome and transcriptome sequencing data was created, and an integrative approach based on network enrichment analyses to combine information across different types of omics data was proposed to identify putative cancer driver genes. Analysis of 60 patients with breast cancer provided evidence that patients carrying more mutated potential driver genes had poorer survival.
|
|
