Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci

We undertook a meta-analysis of six Crohn's disease genome-wide association studies (GWAS) comprising 6,333 affected individuals (cases) and 15,056 controls and followed up the top association signals in 15,694 cases, 14,026 controls and 414 parent-offspring trios. We identified 30 new susceptibility loci meeting genome-wide significance (P < 5 × 10 ? ? ). A series of in silico analyses highlighted particular genes within these loci and, together with manual curation, implicated functionally interesting candidate genes including SMAD3, ERAP2, IL10, IL2RA, TYK2, FUT2, DNMT3A, DENND1B, BACH2 and TAGAP. Combined with previously confirmed loci, these results identify 71 distinct loci with genome-wide significant evidence for association with Crohn's disease.

Meta-analysis identifies 29 additional ulcerative colitis risk loci, increasing the number of confirmed associations to 47

Genome-wide association studies and candidate gene studies in ulcerative colitis have identified 18 susceptibility loci. We conducted a meta-analysis of six ulcerative colitis genome-wide association study datasets, comprising 6,687 cases and 19,718 controls, and followed up the top association signals in 9,628 cases and 12,917 controls. We identified 29 additional risk loci (P < 5 × 10(-8)), increasing the number of ulcerative colitis-associated loci to 47. After annotating associated regions using GRAIL, expression quantitative trait loci data and correlations with non-synonymous SNPs, we identified many candidate genes that provide potentially important insights into disease pathogenesis, including IL1R2, IL8RA-IL8RB, IL7R, IL12B, DAP, PRDM1, JAK2, IRF5, GNA12 and LSP1. The total number of confirmed inflammatory bowel disease risk loci is now 99, including a minimum of 28 shared association signals between Crohn's disease and ulcerative colitis.

The quiet eye and tasks demands: Do tougher shots need a quieter eye?

The Quiet Eye (QE; Vickers 1996) has been shown to underpin successful performance, differentiating both expertise (inter-individual) and proficiency (intra-individual), with experts and successful attempts characterised by longer QE durations. The QE is proposed to reflect the time needed to organise and fine tune the parameters of movement (e.g. force and direction). In order to examine this prediction and build upon previous research we experimentally manipulated the difficulty of a golf putting task; we hypothesised that if the QE is related to motor programming then a more difficult task should be associated with longer QE durations. 33 golfers (M age= 21.16, SD= 3.98) with an average handicap of 6.5 (SD= 6.02) performed putts in 4 conditions of increasing difficulty. We manipulated the length of the golf putt (short-4ft, long-8ft) and the contact point of the putter head (large-1.7cm, small-0.5cm,) giving increasingly difficult putting conditions of short-large [1], short-small [2], long-large [3] and long-small [4]. We measured performance (radial error from hole in cm) and QE (in ms) for 10 putts in each condition. A repeated measures ANOVA was performed on the performance and QE data. The performance data suggest that we were successful in increasing the difficulty of the task (F (3,93) = 26.46. p = .000), with the best performance in condition [1] (8.57cm), followed by [2] (9.10cm) followed by [3] (16.11cm) and finally the worst performance was in condition [4] (23.40cm). The QE data suggest that, in keeping with our hypothesis, the QE was lengthened as task difficulty increased (F (3,87) = 11.91, p = .043). The QE was shortest in condition [1] (1787.85ms) and increased to condition [2] (1939.78ms) and condition [3] (2076.51ms), with the longest QE in condition [4] (2164.08ms). More detailed analysis of the QE reveal that it was the proportion of the QE that occurred before movement initiation (pre-QE) which increased with shot difficulty, rather than the proportion that occurred during the swing (Online-QE; see Vine et al., 2013). Results support the notion that more complex tasks are associated with a longer QE duration, specifically participants appear to spend longer fixating the target prior to movement. This likely reflects the time needed to process visual information gathered in a pre-performance routine, to inhibit external distraction, and to pre-programme the increasingly difficult parameters of the movement. Vickers, J.N. (1996). Visual control when aiming at a far target. Journal of Experimental Psychology: Human Perception and Performance, 22, 342-354. Vine, S.J. et al. (2013). Quiet eye and choking: Online control breaks down at the point of performance failure. Medicine and Science in Sports and Exercise, 45, 1988-1994.