Genome-wide association studies (GWAS) have led to a rapid increase in available data on common genetic variants and phenotypes and numerous discoveries of new loci associated with susceptibility to common complex diseases. Integrating the evidence from GWAS and candidate gene studies depends on concerted efforts in data production, online publication, database development, and continuously updated data synthesis. Here the authors summarize current experience and challenges on these fronts, which were discussed at a 2008 multidisciplinary workshop sponsored by the Human Genome Epidemiology Network. Comprehensive field synopses that integrate many reported gene-disease associations have been systematically developed for several fields, including Alzheimer's disease, schizophrenia, bladder cancer, coronary heart disease, preterm birth, and DNA repair genes in various cancers. The authors summarize insights from these field synopses and discuss remaining unresolved issues -- especially in the light of evidence from GWAS, for which they summarize empirical P-value and effect-size data on 223 discovered associations for binary outcomes (142 with P < 10(-7)). They also present a vision of collaboration that builds reliable cumulative evidence for genetic associations with common complex diseases and a transparent, distributed, authoritative knowledge base on genetic variation and human health. As a next step in the evolution of Human Genome Epidemiology reviews, the authors invite investigators to submit field synopses for possible publication in the American Journal of Epidemiology.

The heterogeneity of spontaneous preterm birth (SPTB) requires an interdisciplinary approach to determine potential predictive risk factors of early delivery. The aim of this study was to investigate maternal whole blood gene expression profiles associated with spontaneous preterm birth (SPTB, <37 weeks) in asymptomatic pregnant women. The study population was a matched subgroup of women (51 SPTBs, 114 term delivery controls) who participated in the All Our Babies community based cohort in Calgary (n = 1878). Maternal blood at 17-23 (sampling time point 1, T1) and 27-33 weeks of gestation (T2) were collected. Total RNA was extracted and microarray was performed on 326 samples (165 women). Univariate analyses determined significant clinical factors and differential gene expression associated with SPTB. Thirteen genes were validated using qRT-PCR. Three multivariate logistic models were constructed to identify gene expression at T1 (Model A), T2 (Model B), and gene expression fold change from T1 to T2 (Model C) associated with SPTB. All models were adjusted for clinical factors. Model C can predict SPTB with 65% sensitivity and 88% specificity in asymptomatic women after adjusting for history of abortion and anaemia (occurring before T2). Clinical data enhanced the sensitivity of the Models to predict SPTB. In conclusion, clinical factors and whole blood gene expression are associated with SPTB in asymptomatic women. An effective screening tool for SPTB during pregnancy would enable targeted preventive approaches and personalised antenatal care.

We aimed to establish a comprehensive digital phenotype for postpartum hemorrhage (PPH). Current guidelines rely primarily on estimates of blood loss, which can be inaccurate and biased and ignore complementary information readily available in electronic medical records (EMR). Inaccurate and incomplete phenotyping contributes to ongoing challenges in tracking PPH outcomes, developing more accurate risk assessments, and identifying novel interventions.

Postpartum hemorrhage (PPH) remains a leading cause of preventable maternal mortality in the United States. We sought to develop a novel risk assessment tool and compare its accuracy to tools used in current practice.

The objective of this study was to compare breastfeeding, postpartum mental health, and health service utilization between a group of late preterm (LP) maternal infant pairs and term counterparts. Data was drawn from a prospective community-based cohort in Calgary, Alberta. Bivariate and multivariable analyses were performed. LP infants were more likely to have had a longer median length of stay after birth (P < 0.001) and a higher re-hospitalization rate at 4-months (P < 0.001) compared to term infants. Mothers of LP infants were more likely to report immediate breastfeeding difficulties (P < 0.001) and earlier cessation of breastfeeding at 4-months postpartum (P = 0.008). Multivariable analyses revealed that LP status was an independent risk factor for excessive symptoms of maternal anxiety (OR = 2.07; 95 % CI = 1.08,3.98), but not for depression, stress, or low parenting morale. LP infants and their families are a vulnerable population with unique developmental trajectories. Further longitudinal research is required.

The rapid and continuing progress in gene discovery for complex diseases is fuelling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but they vary widely in completeness of reporting and apparent quality. Transparent reporting of the strengths and weaknesses of these studies is important to facilitate the accumulation of evidence on genetic risk prediction. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by prior reporting guidelines. These recommendations aim to enhance the transparency, quality and completeness of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct or analysis.

The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but they vary widely in completeness of reporting and apparent quality. Transparent reporting of the strengths and weaknesses of these studies is important to facilitate the accumulation of evidence on genetic risk prediction. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by previous reporting guidelines. These recommendations aim to enhance the transparency, quality and completeness of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct or analysis.

The rapid and continuing progress in gene discovery for complex diseases is fuelling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but they vary widely in completeness of reporting and apparent quality. Transparent reporting of the strengths and weaknesses of these studies is important to facilitate the accumulation of evidence on genetic risk prediction. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by prior reporting guidelines. These recommendations aim to enhance the transparency, quality and completeness of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct or analysis.