Genome-wide association study identifies a new melanoma susceptibility locus at 1q21.3

We performed a genome-wide association study of melanoma in a discovery cohort of 2,168 Australian individuals with melanoma and 4,387 control individuals. In this discovery phase, we confirm several previously characterized melanoma-associated loci at MC1R, ASIP and MTAP-CDKN2A. We selected variants at nine loci for replication in three independent case-control studies (Europe: 2,804 subjects with melanoma, 7,618 control subjects; United States 1: 1,804 subjects with melanoma, 1,026 control subjects; United States 2: 585 subjects with melanoma, 6,500 control subjects). The combined meta-analysis of all case-control studies identified a new susceptibility locus at 1q21.3 (rs7412746, P = 9.0 × 10(-11), OR in combined replication cohorts of 0.89 (95% CI 0.85-0.95)). We also show evidence suggesting that melanoma associates with 1q42.12 (rs3219090, P = 9.3 × 10(-8)). The associated variants at the 1q21.3 locus span a region with ten genes, and plausible candidate genes for melanoma susceptibility include ARNT and SETDB1. Variants at the 1q21.3 locus do not seem to be associated with human pigmentation or measures of nevus density.     

Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer's disease

The Alzheimer Disease Genetics Consortium (ADGC) performed a genome-wide association study of late-onset Alzheimer disease using a three-stage design consisting of a discovery stage (stage 1) and two replication stages (stages 2 and 3). Both joint analysis and meta-analysis approaches were used. We obtained genome-wide significant results at MS4A4A (rs4938933; stages 1 and 2, meta-analysis P (P(M)) = 1.7 × 10(-9), joint analysis P (P(J)) = 1.7 × 10(-9); stages 1, 2 and 3, P(M) = 8.2 × 10(-12)), CD2AP (rs9349407; stages 1, 2 and 3, P(M) = 8.6 × 10(-9)), EPHA1 (rs11767557; stages 1, 2 and 3, P(M) = 6.0 × 10(-10)) and CD33 (rs3865444; stages 1, 2 and 3, P(M) = 1.6 × 10(-9)). We also replicated previous associations at CR1 (rs6701713; P(M) = 4.6 × 10(-10), P(J) = 5.2 × 10(-11)), CLU (rs1532278; P(M) = 8.3 × 10(-8), P(J) = 1.9 × 10(-8)), BIN1 (rs7561528; P(M) = 4.0 × 10(-14), P(J) = 5.2 × 10(-14)) and PICALM (rs561655; P(M) = 7.0 × 10(-11), P(J) = 1.0 × 10(-10)), but not at EXOC3L2, to late-onset Alzheimer's disease susceptibility.     

Genome-wide association study of leaf architecture in the maize nested association mapping population

US maize yield has increased eight-fold in the past 80 years, with half of the gain attributed to selection by breeders. During this time, changes in maize leaf angle and size have altered plant architecture, allowing more efficient light capture as planting density has increased. Through a genome-wide association study (GWAS) of the maize nested association mapping panel, we determined the genetic basis of important leaf architecture traits and identified some of the key genes. Overall, we demonstrate that the genetic architecture of the leaf traits is dominated by small effects, with little epistasis, environmental interaction or pleiotropy. In particular, GWAS results show that variations at the liguleless genes have contributed to more upright leaves. These results demonstrate that the use of GWAS with specially designed mapping populations is effective in uncovering the basis of key agronomic traits.     

Long Memory of Financial Time Series and Hidden Markov Models with Time‐Varying Parameters

Hidden Markov models are often used to model daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time‐varying behavior have not been thoroughly examined. This paper presents an adaptive estimation approach that allows for the parameters of the estimated models to be time varying. It is shown that a two‐state Gaussian hidden Markov model with time‐varying parameters is able to reproduce the long memory of squared daily returns that was previously believed to be the most difficult fact to reproduce with a hidden Markov model. Capturing the time‐varying behavior of the parameters also leads to improved one‐step density forecasts. Finally, it is shown that the forecasting performance of the estimated models can be further improved using local smoothing to forecast the parameter variations. Copyright © 2016 John Wiley & Sons, Ltd.     

Overactive bone morphogenetic protein signaling in heterotopic ossification and Duchenne muscular dystrophy

Bone morphogenetic proteins (BMPs) are important extracellular cytokines that play critical roles in embryogenesis and tissue homeostasis. BMPs signal via transmembrane type I and type II serine/threonine kinase receptors and intracellular Smad effector proteins. BMP signaling is precisely regulated and perturbation of BMP signaling is connected to multiple diseases, including musculoskeletal diseases. In this review, we will summarize the recent progress in elucidation of BMP signal transduction, how overactive BMP signaling is involved in the pathogenesis of heterotopic ossification and Duchenne muscular dystrophy, and discuss possible therapeutic strategies for treatment of these diseases.