Mesenchymal stem cells or cardiac progenitors for cardiac repair? A comparative study

In the past, clinical trials transplanting bone marrow–derived mononuclear cells reported a limited improvement in cardiac function. Therefore, the search for stem cells leading to more successful stem cell therapies continues. Good candidates are the so-called cardiac stem cells (CSCs). To date, there is no clear evidence to show if these cells are intrinsic stem cells from the heart or mobilized cells from bone marrow. In this study we performed a comparative study between human mesenchymal stem cells (hMSCs), purified c-kit⁺ CSCs, and cardiosphere-derived cells (CDCs). Our results showed that hMSCs can be discriminated from CSCs by their differentiation capacity towards adipocytes and osteocytes and the expression of CD140b. On the other hand, cardiac progenitors display a greater cardiomyogenic differentiation capacity. Despite a different isolation protocol, no distinction could be made between c-kit⁺ CSCs and CDCs, indicating that they probably derive from the same precursor or even are the same cells.     

Transferability of tag SNPs in genetic association studies in multiple populations

A general question for linkage disequilibrium-based association studies is how power to detect an association is compromised when tag SNPs are chosen from data in one population sample and then deployed in another sample. Specifically, it is important to know how well tags picked from the HapMap DNA samples capture the variation in other samples. To address this, we collected dense data uniformly across the four HapMap population samples and eleven other population samples. We picked tag SNPs using genotype data we collected in the HapMap samples and then evaluated the effective coverage of these tags in comparison to the entire set of common variants observed in the other samples. We simulated case-control association studies in the non-HapMap samples under a disease model of modest risk, and we observed little loss in power. These results demonstrate that the HapMap DNA samples can be used to select tags for genome-wide association studies in many samples around the world.     

The transmembrane protein meckelin (MKS3) is mutated in Meckel-Gruber syndrome and the wpk rat

Meckel-Gruber syndrome is a severe autosomal, recessively inherited disorder characterized by bilateral renal cystic dysplasia, developmental defects of the central nervous system (most commonly occipital encephalocele), hepatic ductal dysplasia and cysts and polydactyly. MKS is genetically heterogeneous, with three loci mapped: MKS1, 17q21-24 (ref. 4); MKS2, 11q13 (ref. 5) and MKS3 (ref. 6). We have refined MKS3 mapping to a 12.67-Mb interval (8q21.13-q22.1) that is syntenic to the Wpk locus in rat, which is a model with polycystic kidney disease, agenesis of the corpus callosum and hydrocephalus. Positional cloning of the Wpk gene suggested a MKS3 candidate gene, TMEM67, for which we identified pathogenic mutations for five MKS3-linked consanguineous families. MKS3 is a previously uncharacterized, evolutionarily conserved gene that is expressed at moderate levels in fetal brain, liver and kidney but has widespread, low levels of expression. It encodes a 995-amino acid seven-transmembrane receptor protein of unknown function that we have called meckelin.     

Evaluating and improving power in whole-genome association studies using fixed marker sets

Emerging technologies make it possible for the first time to genotype hundreds of thousands of SNPs simultaneously, enabling whole-genome association studies. Using empirical genotype data from the International HapMap Project, we evaluate the extent to which the sets of SNPs contained on three whole-genome genotyping arrays capture common SNPs across the genome, and we find that the majority of common SNPs are well captured by these products either directly or through linkage disequilibrium. We explore analytical strategies that use HapMap data to improve power of association studies conducted with these fixed sets of markers and show that limited inclusion of specific haplotype tests in association analysis can increase the fraction of common variants captured by 25-100%. Finally, we introduce a Bayesian approach to association analysis by weighting the likelihood of each statistical test to reflect the number of putative causal alleles to which it is correlated.     

Common variants at 12q15 and 12q24 are associated with infant head circumference

To identify genetic variants associated with head circumference in infancy, we performed a meta-analysis of seven genome-wide association studies (GWAS) (N = 10,768 individuals of European ancestry enrolled in pregnancy and/or birth cohorts) and followed up three lead signals in six replication studies (combined N = 19,089). rs7980687 on chromosome 12q24 (P = 8.1 × 10(-9)) and rs1042725 on chromosome 12q15 (P = 2.8 × 10(-10)) were robustly associated with head circumference in infancy. Although these loci have previously been associated with adult height, their effects on infant head circumference were largely independent of height (P = 3.8 × 10(-7) for rs7980687 and P = 1.3 × 10(-7) for rs1042725 after adjustment for infant height). A third signal, rs11655470 on chromosome 17q21, showed suggestive evidence of association with head circumference (P = 3.9 × 10(-6)). SNPs correlated to the 17q21 signal have shown genome-wide association with adult intracranial volume, Parkinson's disease and other neurodegenerative diseases, indicating that a common genetic variant in this region might link early brain growth with neurological disease in later life.     

Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways

Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have increased the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin concentration showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional analysis of these newly discovered loci will further improve our understanding of glycemic control.