Hoverfly use for pollination of commercial soft fruits

Poster - No Abstract available

The genetic architecture of type 2 diabetes

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes

Genes contributing to pain sensitivity in the normal population:an exome sequencing study

Sensitivity to pain varies considerably between individuals and is known to be heritable. Increased sensitivity to experimental pain is a risk factor for developing chronic pain, a common and debilitating but poorly understood symptom. To understand mechanisms underlying pain sensitivity and to search for rare gene variants (MAF<5%) influencing pain sensitivity, we explored the genetic variation in individuals' responses to experimental pain. Quantitative sensory testing to heat pain was performed in 2,500 volunteers from TwinsUK (TUK): exome sequencing to a depth of 70× was carried out on DNA from singletons at the high and low ends of the heat pain sensitivity distribution in two separate subsamples. Thus in TUK1, 101 pain-sensitive and 102 pain-insensitive were examined, while in TUK2 there were 114 and 96 individuals respectively. A combination of methods was used to test the association between rare variants and pain sensitivity, and the function of the genes identified was explored using network analysis. Using causal reasoning analysis on the genes with different patterns of SNVs by pain sensitivity status, we observed a significant enrichment of variants in genes of the angiotensin pathway (Bonferroni corrected p = 3.8×10(-4)). This pathway is already implicated in animal models and human studies of pain, supporting the notion that it may provide fruitful new targets in pain management. The approach of sequencing extreme exome variation in normal individuals has provided important insights into gene networks mediating pain sensitivity in humans and will be applicable to other common complex traits

Details of the SNVs identified in TUK1 and TUK2 samples.

<p>The number of SNVs detected is shown according to their functional consequences, for the TUK1 and TUK2 samples.</p

The Angiotensin II regulatory network was identified by causal reasoning from 138 genes associated with pain sensitivity.

<p>Causal reasoning uses directed molecular interactions to work upstream from the genes in this study (green) to identify regulators such as angiotensin II (blue) that have a causally correct regulatory role for a significant number of input genes. Correctness is determined by giving each input gene a direction of effect. Here, we presumed a loss of function (e.g. down regulation in activity) to all of our genes. Angiotensin II has direct causal connections to 12 of the genes from our 138, which can be increased to 30 if one intermediary node is allowed in the network (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003095#pgen.1003095.s001" target="_blank">Figure S1</a>). Distribution of novel rare variants identified according to minor allele frequency in a) TUK1 and b) TUK2 datasets.</p

Quantile–quantile plots for the six different variant burden analysis methods.

<p>Quantile–quantile plots are shown for: (a) AMELIA, (b) CCRaVAT, (c) fixed filter test, minor allele frequency <0.05, (d) Madsen-Browning with polyphen weights, (e) Han and Pan aSumtest, (f) SSU, sum-of-squares test (Han and Pan).</p