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- A genome-wide scan for common alleles affecting risk for autismPublication . Anney, R.; Klei, L.; Pinto, D.; Regan, R.; Conroy, J.; Magalhaes, T.R.; Correia, C.; Abrahams, B.S.; Sykes, N.; Pagnamenta, A.T.; Almeida, J.; Bacchelli, E.; Bailey, A.J.; Baird, G.; Battaglia, A.; Berney, T.; Bolshakova, N.; Bölte, S.; Bolton, P.F.; Bourgeron, T.; Brennan, S.; Brian, J.; Carson, A.R.; Casallo, G.; Casey, J.; Chu, S.H.; Cochrane, L.; Corsello, C.; Crawford, E.L.; Crossett, A.; Dawson, G.; de Jonge, M.; Delorme, R.; Drmic, I.; Duketis, E.; Duque, F.; Estes, A.; Farrar, P.; Fernandez, B.A.; Folstein, S.E.; Fombonne, E.; Freitag, C.M.; Gilbert, J.; Gillberg, C.; Glessner, J.T.; Goldberg, J.; Green, J.; Guter, S.J.; Hakonarson, H.; Heron, E.A.; Hill, M.; Holt, R.; Howe, J.L.; Hughes, G.; Hus, V.; Igliozzi, R.; Kim, C.; Klauck, S.M.; Kolevzon, A.; Korvatska, O.; Kustanovich, V.; Lajonchere, C.M.; Lamb, J.A.; Laskawiec, M.; Leboyer, M.; Le Couteur, A.; Leventhal, B.L.; Lionel, A.C.; Liu, X.Q.; Lord, C.; Lotspeich, L.; Lund, S.C.; Maestrini, E.; Mahoney, W.; Mantoulan, C.; Marshall, C.R.; McConachie, H.; McDougle, C.J.; McGrath, J.; McMahon, W.M.; Melhem, N.M.; Merikangas, A.; Migita, O.; Minshew, N.J.; Mirza, G.K.; Munson, J.; Nelson, S.F.; Noakes, C.; Noor, A.; Nygren, G.; Oliveira, G.; Papanikolaou, K.; Parr, J.R.; Parrini, B.; Paton, T.; Pickles, A.; Piven, J.; Posey, D.J.; Poustka, A.; Poustka, F.; Prasad, A.; Ragoussis, J.; Renshaw, K.; Rickaby, J.; Roberts, W.; Roeder, K.; Roge, B.; Rutter, M.L.; Bierut, L.J.; Rice, J.P.; Salt, J.; Sansom, K.; Sato, D.; Segurado, R.; Senman, L.; Shah, N.; Sheffield, V.C.; Soorya, L.; Sousa, I.; Stoppioni, V.; Strawbridge, C.; Tancredi, R.; Tansey, K.; Thiruvahindrapduram, B.; Thompson, A.P.; Thomson, S.; Tryfon, A.; Tsiantis, J.; Van Engeland, H.; Vincent, J.B.; Volkmar, F.; Wallace, S.; Wang, K.; Wang, Z.; Wassink, T.H.; Wing, K.; Wittemeyer, K.; Wood, S.; Yaspan, B.L.; Zurawiecki, D.; Zwaigenbaum, L.; Betancur, C.; Buxbaum, J.D.; Cantor, R.M.; Cook, E.H.; Coon, H.; Cuccaro, M.L.; Gallagher, L.; Geschwind, D.H.; Gill, M.; Haines, J.L.; Miller, J.; Monaco, A.P.; Nurnberger Jr, J.I.; Paterson, A.D.; Pericak-Vance, M.A.; Schellenberg, G.D.; Scherer, S.W.; Sutcliffe, J.S.; Szatmari, P.; Vicente, A.M.; Vieland, V.J.; Wijsman, E.M.; Devlin, B.; Ennis, S.; Hallmayer, J.Although autism spectrum disorders (ASDs) have a substantial genetic basis, most of the known genetic risk has been traced to rare variants, principally copy number variants (CNVs). To identify common risk variation, the Autism Genome Project (AGP) Consortium genotyped 1558 rigorously defined ASD families for 1 million single-nucleotide polymorphisms (SNPs) and analyzed these SNP genotypes for association with ASD. In one of four primary association analyses, the association signal for marker rs4141463, located within MACROD2, crossed the genome-wide association significance threshold of P < 5 × 10(-8). When a smaller replication sample was analyzed, the risk allele at rs4141463 was again over-transmitted; yet, consistent with the winner's curse, its effect size in the replication sample was much smaller; and, for the combined samples, the association signal barely fell below the P < 5 × 10(-8) threshold. Exploratory analyses of phenotypic subtypes yielded no significant associations after correction for multiple testing. They did, however, yield strong signals within several genes, KIAA0564, PLD5, POU6F2, ST8SIA2 and TAF1C.
- A study protocol to evaluate the relationship between outdoor air pollution and pregnancy outcomesPublication . Ribeiro, M.C.; Pereira, M.J.; Soares, A.; Branquinho, C.; Augusto, S.; Llop, E.; Fonseca, S.; Nave, J.G.; Tavares, A.B.; Dias, Carlos Matias; Silva, A.; Selemane, I.; De Toro, J.; Santos, M.J.; Santos, F.Background - The present study protocol is designed to assess the relationship between outdoor air pollution and low birth weight and preterm births outcomes performing a semi-ecological analysis. Semi-ecological design studies are widely used to assess effects of air pollution in humans. In this type of analysis, health outcomes and covariates are measured in individuals and exposure assignments are usually based on air quality monitor stations. Therefore, estimating individual exposures are one of the major challenges when investigating these relationships with a semi-ecologic design. Methods/Design Semi-ecologic study consisting of a retrospective cohort study with ecologic assignment of exposure is applied. Health outcomes and covariates are collected at Primary Health Care Center. Data from pregnant registry, clinical record and specific questionnaire administered orally to the mothers of children born in period 2007-2010 in Portuguese Alentejo Litoral region, are collected by the research team. Outdoor air pollution data are collected with a lichen diversity biomonitoring program, and individual pregnancy exposures are assessed with spatial geostatistical simulation, which provides the basis for uncertainty analysis of individual exposures. Awareness of outdoor air pollution uncertainty will improve validity of individual exposures assignments for further statistical analysis with multivariate regression models. Discussion Exposure misclassification is an issue of concern in semi-ecological design. In this study, personal exposures are assigned to each pregnant using geocoded addresses data. A stochastic simulation method is applied to lichen diversity values index measured at biomonitoring survey locations, in order to assess spatial uncertainty of lichen diversity value index at each geocoded address. These methods assume a model for spatial autocorrelation of exposure and provide a distribution of exposures in each study location. We believe that variability of simulated exposure values at geocoded addresses will improve knowledge on variability of exposures, improving therefore validity of individual exposures to input in posterior statistical analysis.