The publication of the human genome, more than a decade ago, alongside the development of high-throughput technologies for DNA sequencing, marked the dawn of a new era in genetics. Large genomic projects have been initiated to decipher the mysteries hidden within the human genetic code. With the rapidly ever-growing amount of genetic information, and the importance of understanding what it all means, there is a need to generate an interdisciplinary hub that will connect researchers, both experimentalists and bioinformaticians, along with physicians and community representatives in order to develop a common genomic language. This should lead to an accessible, readable and interpretive human genome with a short list of personal actionable items. We will then be able to declare that we are moving ever closer to the point at which one's own genome will affect one's personal life at a scope beyond our current comprehension.

With many molecular markers in many species, research efforts in quantitative genetics have focused on dissecting these traits and understanding the importance of factors such as correlated response due to hitchhiking or pleiotropy. Here, in an examination of long-term selection experiments in mice, the evidence strongly supports the primary importance of hitchhiking on the coat colour loci brown and dilute in mice selected for high weight gain. First, the amount of observed change in coat colour allele frequency could not be explained by genetic drift alone, implying that selection was of high importance. Second, the allele frequency changes included reversals in the direction change, but there were still positive correlations in the early generations with differences in weight gain between the phenotypes. Third, the correlation between the change in allele frequencies and phenotypic difference in weight gain declined over time, consistent with the decay expected from linkage associations. Fourth, the changes at both loci in a short-term selection experiment for low weight gain were in the opposite direction than the changes in the contemporaneous related population selected for high weight gain.

Gene expression profiling using microarrays and xenograft transplants of human cancer cell lines are both popular tools to investigate human cancer. However, the undefined degree of cross hybridization between the mouse and human genomes hinders the use of microarrays to characterize gene expression of both the host and the cancer cell within the xenograft. Since an increasingly recognized aspect of cancer is the host response (or cancer–stroma interaction), we describe here a bioinformatic manipulation of the Affymetrix profiling that allows interrogation of the gene expression of both the mouse host and the human tumour. Evidence of microenvironmental regulation of epithelial mesenchymal transition of the tumour component in vivo is resolved against a background of mesenchymal gene expression. This tool could allow deeper insight to the mechanism of action of anti-cancer drugs, as typically novel drug efficacy is being tested in xenograft systems.

Compared with single markers, polygenic scores that evaluate the joint effects of multiple trait-associated variants are more effective in explaining the variance of traits and risk of diseases. In total, 182 CHDWB (Emory-Georgia Tech Center for Health Discovery and Well Being study) adults were genotyped to investigate the common variant contributions to three traits (height, BMI, serum triglycerides) and three diseases (coronary artery disease (CAD), type 2 diabetes (T2D) and asthma). Association was contrasted between weighted and simple allelic sum polygenic scores with quantitative traits, and with the Framingham risk scores for CAD and T2D. Although the cohort size is two or three orders of magnitude smaller than typical discovery cohorts, we were able to detect significant associations and to explain up to 5% of the traits by the genetic risk scores, despite a strong influence of outliers. An unexpected finding was that CAD-associated single nucleotide polymorphisms (SNPs) explain a significant amount of the variation for total serum cholesterol. Forward step-wise sequential addition of SNPs into the regression model showed that the top-ranked SNPs explain a large proportion of variance, whereas inclusion of gender and ethnicity also affect the performance of polygenic scores.

Intronic microsatellites repeats were implicated in the pathogenic mechanisms of several diseases. SLC26A4 gene, involved in the genetic susceptibility of autoimmune thyroid disease (AITD), harbours large non-coding introns. Using the tandem repeat finder (TRF) Software, two new polymorphic microsatellite markers, rs59736472 and rs57250751, located at introns 10 and 20, respectively, were identified. A case-control design including 308 patients affected with AITD (146 GD, 90 HT and 72 PIM) and 212 unmatched healthy controls were performed for each marker (rs59736472, D7S2459 and rs57250751). Furthermore, we used PHASE 2.0 version to reconstruct haplotypes, Kolmogorov–Smirnov (KS) and the Clump analysis program for multivariate analysis. The fluorescent genotyping revealed three alleles (106,112 and 115 bp) for rs57250751 and 12 alleles for both D7S2459 and rs59736472 ranging from 134 to 156 bp and from 144 to 168 bp, respectively. The case-control analysis confirmed the positive association of D7S2459 with Hashimoto thyroiditis (HT) disease previously reported. Moreover, a significant association was found only with rs59736472 and HT disease. Haplotype-specific analysis showed that the 140-148-115 haplotype may increase the risk of HT (χ2=9·8, 1 df, P=0·0017, OR=2·07, IC [1·27–3·36]). Consequently, considering the number of repetitions of both D7S2459 and rs59736472, we found 15 alleles ranging from 45 to 59 repetitions. The case-control analysis showed a significant association of the 55 repetition with HT disease (χ2=6·32, 1 df, pc=0·012, OR=1·74, IC [1·1–2·76]). In conclusion, we suggest the association of longer alleles of intron 10 of SLC26A4 gene with HT disease.