Background & Aims Risk for colorectal cancer (CRC) can be greatly reduced through screening. To aid in the development of screening strategies, we refined models designed to determine risk of CRC by ...incorporating information from common genetic susceptibility loci. Methods By using data collected from more than 12,000 participants in 6 studies performed from 1990 through 2011 in the United States and Germany, we developed risk determination models based on sex, age, family history, genetic risk score (number of risk alleles carried at 27 validated common CRC susceptibility loci), and history of endoscopic examinations. The model was validated using data collected from approximately 1800 participants in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, conducted from 1993 through 2001 in the United States. Results We identified a CRC genetic risk score that independently predicted which patients in the training set would develop CRC. Compared with determination of risk based only on family history, adding the genetic risk score increased the discriminatory accuracy from 0.51 to 0.59 ( P = .0028) for men and from 0.52 to 0.56 ( P = .14) for women. We calculated age- and sex-specific 10-year CRC absolute risk estimates based on the number of risk alleles, family history, and history of endoscopic examinations. A model that included a genetic risk score better determined the recommended starting age for screening in subjects with and without family histories of CRC. The starting age for high-risk men (family history of CRC and genetic risk score, 90%) was 42 years, and for low-risk men (no family history of CRC and genetic risk score, 10%) was 52 years. For men with no family history and a high genetic risk score (90%), the starting age would be 47 years; this is an intermediate value that is 5 years earlier than it would be for men with a genetic risk score of 10%. Similar trends were observed in women. Conclusions By incorporating information on CRC risk alleles, we created a model to determine the risk for CRC more accurately. This model might be used to develop screening and prevention strategies.
Background. The etiology of childhood diarrhea is frequently unknown. Methods. We sought Aeromonas, Campylobacter, Escherichia coli O157:H7, Pleisiomonas shigelloides, Salmonella, Shigella, Vibrio, ...and Yersinia (by culture), adenoviruses, astroviruses, noroviruses, rotavirus, and Shiga toxin-producing E. coli (STEC; by enzyme immunoassay), Clostridium difficile (by cytotoxicity), parasites (by microscopy), and enteroaggregative E. coli (EAEC; by polymerase chain reaction PCR analysis) in the stools of 254 children with diarrhea presenting to a pediatric emergency facility. Age- and geographic-matched community controls without diarrhea (n = 452) were similarly studied, except bacterial cultures of the stool were limited only to cases. Results. Twenty-nine (11.4%) case stools contained 13 Salmonella, 10 STEC (6 O157:H7 and 4 non-O157:H7 serotypes), 5 Campylobacter, and 2 Shigella. PCR-defined EAEC were present more often in case (3.2%) specimens than in control (0.9%) specimens (adjusted odds ratio OR, 3.9; 95% confidence interval CI, 1.1–13.7), and their adherence phenotypes were variable. Rotavirus, astrovirus, and adenovirus were more common among cases than controls, but both groups contained noroviruses and C. difficile at similar rates. PCR evidence of hypervirulent C. difficile was found in case and control stools; parasites were much more common in control specimens. Conclusions. EAEC are associated with childhood diarrhea in Seattle, but the optimal way to identify these agents warrants determination. Children without diarrhea harbor diarrheagenic pathogens, including hypervirulent C. difficile. Our data support the importance of taking into account host susceptibility, microbial density, and organism virulence traits in future case-control studies, not merely categorizing candidate pathogens as being present or absent.
The data-intensive fields of genomics and machine learning (ML) are in an early stage of convergence. Genomics researchers increasingly seek to harness the power of ML methods to extract knowledge ...from their data; conversely, ML scientists recognize that genomics offers a wealth of large, complex, and well-annotated datasets that can be used as a substrate for developing biologically relevant algorithms and applications. The National Human Genome Research Institute (NHGRI) inquired with researchers working in these two fields to identify common challenges and receive recommendations to better support genomic research efforts using ML approaches. Those included increasing the amount and variety of training datasets by integrating genomic with multiomics, context-specific (e.g., by cell type), and social determinants of health datasets; reducing the inherent biases of training datasets; prioritizing transparency and interpretability of ML methods; and developing privacy-preserving technologies for research participants’ data.
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In this perspective, Sen at al. discuss the evolving convergence of machine learning and genomics research. They discuss the data and algorithmic resources that will be needed to catalyze genomic machine learning, highlight the ethical challenges that will need careful consideration, and offer ideas for developing a future workforce that is able to traverse both fields with ease.
ABSTRACT
Cancer risk is determined by a complex interplay of genetic and environmental factors. Genome‐wide association studies (GWAS) have identified hundreds of common (minor allele frequency MAF > ...0.05) and less common (0.01 < MAF < 0.05) genetic variants associated with cancer. The marginal effects of most of these variants have been small (odds ratios: 1.1–1.4). There remain unanswered questions on how best to incorporate the joint effects of genes and environment, including gene‐environment (G × E) interactions, into epidemiologic studies of cancer. To help address these questions, and to better inform research priorities and allocation of resources, the National Cancer Institute sponsored a “Gene‐Environment Think Tank” on January 10–11, 2012. The objective of the Think Tank was to facilitate discussions on (1) the state of the science, (2) the goals of G × E interaction studies in cancer epidemiology, and (3) opportunities for developing novel study designs and analysis tools. This report summarizes the Think Tank discussion, with a focus on contemporary approaches to the analysis of G × E interactions. Selecting the appropriate methods requires first identifying the relevant scientific question and rationale, with an important distinction made between analyses aiming to characterize the joint effects of putative or established genetic and environmental factors and analyses aiming to discover novel risk factors or novel interaction effects. Other discussion items include measurement error, statistical power, significance, and replication. Additional designs, exposure assessments, and analytical approaches need to be considered as we move from the current small number of success stories to a fuller understanding of the interplay of genetic and environmental factors.
Recent studies have highlighted the imperatives of including diverse and under-represented individuals in human genomics research and the striking gaps in attaining that inclusion. With its ...multidecade experience in supporting research and policy efforts in human genomics, the National Human Genome Research Institute is committed to establishing foundational approaches to study the role of genomic variation in health and disease that include diverse populations. Large-scale efforts to understand biology and health have yielded key scientific findings, lessons and recommendations on how to increase diversity in genomic research studies and the genomic research workforce. Increased attention to diversity will increase the accuracy, utility and acceptability of using genomic information for clinical care.
Objective
An inversion polymorphism of approximately 900kb on chromosome 17q21, which includes the microtubule‐associated protein tau (MAPT) gene defines two haplotype clades, H1 and H2. Several ...small case–control studies have observed a marginally significant excess of the H1/H1 diplotype among patients with Parkinson's disease (PD), and one reported refining the association to a region spanning exons 1 to 4 of MAPT. We sought to replicate these findings.
Methods
We genotyped 1,762 PD patients and 2,010 control subjects for a single nucleotide polymorphism (SNP) that differentiates the H1 and H2 clades. We also analyzed four SNPs that define subhaplotypes within H1 previously reported to associate with PD or other neurodegenerative disorders.
Results
After adjusting for age, sex, and site, we observed a robust association between the H1/H1 diplotype and PD risk (odds ratio for H1/H1 vs H1/H2 and H2/H2, 1.46; 95% confidence interval, 1.25–1.69; p = 8 × 10−7). The effect was evident in both familial and sporadic subgroups, men and women, and early‐ and late‐onset disease. Within H1/H1 individuals, there was no significant difference between cases and control subjects in the overall frequency distribution of H1 subhaplotypes.
Interpretation
Our data provide strong evidence that the H1 clade, which contains MAPT and several other genes, is a risk factor for PD. However, attributing this finding to variants within a specific region of MAPT is premature. Thorough fine‐mapping of the H1 clade in large numbers of individuals is now needed to identify the underlying functional variant(s) that alter susceptibility for PD. Ann Neurol 2007
Starting with the launch of the Human Genome Project three decades ago, and continuing after its completion in 2003, genomics has progressively come to have a central and catalytic role in basic and ...translational research. In addition, studies increasingly demonstrate how genomic information can be effectively used in clinical care. In the future, the anticipated advances in technology development, biological insights, and clinical applications (among others) will lead to more widespread integration of genomics into almost all areas of biomedical research, the adoption of genomics into mainstream medical and public-health practices, and an increasing relevance of genomics for everyday life. On behalf of the research community, the National Human Genome Research Institute recently completed a multi-year process of strategic engagement to identify future research priorities and opportunities in human genomics, with an emphasis on health applications. Here we describe the highest-priority elements envisioned for the cutting-edge of human genomics going forward-that is, at 'The Forefront of Genomics'.
Objective Chorioamnionitis can cause severe complications for the infant; therefore, characterization of the risk of recurrence and identification of the factors that modify it are clinically ...relevant to pregnant women and their providers. Study Design The risk of recurrence was examined in a retrospective population-based cohort study with the use of birth certificate and delivery hospitalization discharge data from Washington State for the years 1989–2008. Results Women who had chorioamnionitis in their first deliveries were 3.43 times as likely to have chorioamnionitis in their second deliveries as were women who did not have chorioamnionitis in their first deliveries (95% confidence interval CI, 2.67–4.42; P < .001). Smoking status modified this association (smokers: odds ratio, 1.38 95% CI, 0.62–3.08; nonsmokers: odds ratio, 3.80 95% CI, 2.88–5.00). Conclusion These data provide strong evidence for the occurrence of repeat chorioamnionitis; the association is strongest in women who do not smoke during pregnancy.
Recently, many new approaches, study designs, and statistical and analytical methods have emerged for studying gene-environment interactions (G×Es) in large-scale studies of human populations. There ...are opportunities in this field, particularly with respect to the incorporation of -omics and next-generation sequencing data and continual improvement in measures of environmental exposures implicated in complex disease outcomes. In a workshop called "Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases," held October 17-18, 2014, by the National Institute of Environmental Health Sciences and the National Cancer Institute in conjunction with the annual American Society of Human Genetics meeting, participants explored new approaches and tools that have been developed in recent years for G×E discovery. This paper highlights current and critical issues and themes in G×E research that need additional consideration, including the improved data analytical methods, environmental exposure assessment, and incorporation of functional data and annotations.
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