TRUE RESEARCH: CATALYST FOR CHANGE AND PROGRESS

Development is akin to progress. Societies which have the dream to progress focus on conducting and implementing researches which are responsive to development. Research and development are the inseparable partners to nation-building. It is believed and safer to say that the progress of a nation does not happen by sheer chance. It is the sustaining motivation and concentration on what to research plus the conscientious use of available and right resources that determine progress. Once research results are generated, concerned agencies need to assess which ones should be utilized or commercialized to propel growth and development. Conducting true research is oftentimes costly both financial and otherwise, but the results might also be overwhelmingly satisfying. Developing and instilling a culture of research to the citizenry is a good national agenda to every country. Since research is educative, education has to be research-oriented. People’s clamor for a better society may be answered partly or wholly through the conduct of true research. Indeed, if research will be taken sincerely and honestly, good or excellent outputs may be produced that could fuel development to proceed. It is a big challenge to all nations especially the developing and underdeveloped economies, to use their limited capital in pursuing a priority-based research agenda. Wise use of meager resources through genuine research is a real exponent of developmental change

Designing and implementing sample and data collection for an international genetics study: the Type 1 Diabetes Genetics Consortium (T1DGC)

Background and Purpose The Type 1 Diabetes Genetics Consortium (T1DGC) is an international project whose primary aims are to: (a) discover genes that modify type 1 diabetes risk; and (b) expand upon the existing genetic resources for type 1 diabetes research. The initial goal was to collect 2500 affected sibling pair (ASP) families worldwide

Confirmation of novel type 1 diabetes risk loci in families.

AIMS/HYPOTHESIS: Over 50 regions of the genome have been associated with type 1 diabetes risk, mainly using large case/control collections. In a recent genome-wide association (GWA) study, 18 novel susceptibility loci were identified and replicated, including replication evidence from 2,319 families. Here, we, the Type 1 Diabetes Genetics Consortium (T1DGC), aimed to exclude the possibility that any of the 18 loci were false-positives due to population stratification by significantly increasing the statistical power of our family study. METHODS: We genotyped the most disease-predicting single-nucleotide polymorphisms at the 18 susceptibility loci in 3,108 families and used existing genotype data for 2,319 families from the original study, providing 7,013 parent-child trios for analysis. We tested for association using the transmission disequilibrium test. RESULTS: Seventeen of the 18 susceptibility loci reached nominal levels of significance (p < 0.05) in the expanded family collection, with 14q24.1 just falling short (p = 0.055). When we allowed for multiple testing, ten of the 17 nominally significant loci reached the required level of significance (p < 2.8 × 10(-3)). All susceptibility loci had consistent direction of effects with the original study. CONCLUSIONS/INTERPRETATION: The results for the novel GWA study-identified loci are genuine and not due to population stratification. The next step, namely correlation of the most disease-associated genotypes with phenotypes, such as RNA and protein expression analyses for the candidate genes within or near each of the susceptibility regions, can now proceed

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of diseas

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease

The Type I Diabetes Genetics Consortium ‘Rapid Response’ family-based candidate gene study: strategy, genes selection, and main outcome

Candidate gene studies have long been the principal method for identification of susceptibility genes for type I diabetes (T1D), resulting in the discovery of HLA, INS, PTPN22, CTLA4, and IL2RA. However, many of the initial studies that relied on this strategy were largely underpowered, because of the limitations in genomic information and genotyping technology, as well as the limited size of available cohorts. The Type I Diabetes Genetic Consortium (T1DGC) has established resources to reevaluate earlier reported genes associated with T1D, using its collection of 2298 Caucasian affected sib-pair families (with 11 159 individuals). A total of 382 single-nucleotide polymorphisms (SNPs) located in 21 T1D candidate genes were selected for this study and genotyped in duplicate on two platforms, Illumina and Sequenom. The genes were chosen based on published literature as having been either ‘confirmed’ (replicated) or not (candidates). This study showed several important features of genetic association studies. First, it showed the major impact of small rates of genotyping errors on association statistics. Second, it confirmed associations at INS, PTPN22, IL2RA, IFIH1 (earlier confirmed genes), and CTLA4 (earlier confirmed, with distinct SNPs) loci. Third, it did not find evidence for an association with T1D at SUMO4, despite confirmed association in Asian populations, suggesting the potential for population-specific gene effects. Fourth, at PTPN22, there was evidence for a novel contribution to T1D risk, independent of the replicated effect of the R620W variant. Fifth, among the candidate genes selected for replication, the association of TCF7-P19T with T1D was newly replicated in this study. In summary, this study was able to replicate some genetic effects, reject others, and provide suggestions of association with several of the other candidate genes in stratified analyses (age at onset, HLA status, population of origin). These results have generated additional interesting functional hypotheses that will require further replication in independent cohorts