DNA isolation protocol effects on nuclear DNA analysis by microarrays, droplet digital PCR, and whole genome sequencing, and on mitochondrial DNA copy number estimation.

Potential bias introduced during DNA isolation is inadequately explored, although it could have significant impact on downstream analysis. To investigate this in human brain, we isolated DNA from cerebellum and frontal cortex using spin columns under different conditions, and salting-out. We first analysed DNA using array CGH, which revealed a striking wave pattern suggesting primarily GC-rich cerebellar losses, even against matched frontal cortex DNA, with a similar pattern on a SNP array. The aCGH changes varied with the isolation protocol. Droplet digital PCR of two genes also showed protocol-dependent losses. Whole genome sequencing showed GC-dependent variation in coverage with spin column isolation from cerebellum. We also extracted and sequenced DNA from substantia nigra using salting-out and phenol / chloroform. The mtDNA copy number, assessed by reads mapping to the mitochondrial genome, was higher in substantia nigra when using phenol / chloroform. We thus provide evidence for significant method-dependent bias in DNA isolation from human brain, as reported in rat tissues. This may contribute to array "waves", and could affect copy number determination, particularly if mosaicism is being sought, and sequencing coverage. Variations in isolation protocol may also affect apparent mtDNA abundance

The ‘bright’ side of social capital: how ’bridging’ makes Italian provinces more innovative

Social capital has remained relatively underexplored in innovation literature. Existing studies have failed to reach a consensus on its impact on local innovative performance: some empirical analyses emphasize a positive effect, others speak about a ‘dark side’ of social capital. This paper aims to fill this gap by shedding new light on the differential role of ‘bonding’ and ‘bridging’ social capital. The quantitative analysis of the innovative performance of the Italian provinces shows that social capital is an important predictor of innovative performance after controlling for ‘traditional’ knowledge inputs (R&D investments and human capital endowment) and other characteristics of the local economy. However, only ‘bridging’ social capital – based on weak ties – can be identified as the key driver of the process of innovation while ‘bonding’ social capital is shown to be negative for innovation. Instrumental variable analysis makes it possible to identify clear causal links between bridging (positive) and bonding (negative) social capital and innovation