Office of Public Health Genomics

We have developed an online catalog of SNP-trait associations from published genome-wide association studies for use in investigating genomic characteristics of trait/disease-associated SNPs (TASs). Reported TASs were common [median risk allele frequency 36%, interquartile range (IQR) 21%-53%] and were associated with modest effect sizes [median odds ratio (OR) 1.33, IQR 1.20-1.61]. Among 20 genomic annotation sets, reported TASs were significantly overrepresented only in nonsynonymous sites [OR = 3.9 (2.2-7.0), p = 3.5 x 10(-7)] and 5kb-promoter regions [OR = 2.3 (1.5-3.6), p = 3 x 10(-4)] compared to SNPs randomly selected from genotyping arrays. Although 88% of TASs were intronic (45%) or intergenic (43%), TASs were not overrepresented in introns and were significantly depleted in intergenic regions [OR = 0.44 (0.34-0.58), p = 2.0 x 10(-9)]. Only slightly more TASs than expected by chance were predicted to be in regions under positive selection [OR = 1.3 (0.8-2.1), p = 0.2]. This new online resource, together with bioinformatic predictions of the underlying functionality at trait/disease-associated loci, is well-suited to guide future investigations of the role of common variants in complex disease etiology.

BACKGROUND: Despite best current therapy, up to 20% of pediatric patients with acute lymphoblastic leukemia (ALL) have a relapse. Recent genomewide analyses have identified a high frequency of DNA copy-number abnormalities in ALL, but the prognostic implications of these abnormalities have not been defined. METHODS: We studied a cohort of 221 children with high-risk B-cell-progenitor ALL with the use of single-nucleotide-polymorphism microarrays, transcriptional profiling, and resequencing of samples obtained at diagnosis. Children with known very-high-risk ALL subtypes (i.e., BCR-ABL1-positive ALL, hypodiploid ALL, and ALL in infants) were excluded from this cohort. A copy-number abnormality was identified as a predictor of poor outcome, and it was then tested in an independent validation cohort of 258 patients with B-cell-progenitor ALL. RESULTS: More than 50 recurring copy-number abnormalities were identified, most commonly involving genes that encode regulators of B-cell development (in 66.8% of patients in the original cohort); PAX5 was involved in 31.7% and IKZF1 in 28.6% of patients. Using copy-number abnormalities, we identified a predictor of poor outcome that was validated in the independent validation cohort. This predictor was strongly associated with alteration of IKZF1, a gene that encodes the lymphoid transcription factor IKAROS. The gene-expression signature of the group of patients with a poor outcome revealed increased expression of hematopoietic stem-cell genes and reduced expression of B-cell-lineage genes, and it was similar to the signature of BCR-ABL1-positive ALL, another high-risk subtype of ALL with a high frequency of IKZF1 deletion. CONCLUSIONS: Genetic alteration of IKZF1 is associated with a very poor outcome in B-cell-progenitor ALL.

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

BACKGROUND: Circulating sex hormone-binding globulin levels are inversely associated with insulin resistance, but whether these levels can predict the risk of developing type 2 diabetes is uncertain. METHODS: We performed a nested case-control study of postmenopausal women in the Women's Health Study who were not using hormone therapy (359 with newly diagnosed type 2 diabetes and 359 controls). Plasma levels of sex hormone-binding globulin were measured; two polymorphisms of the gene encoding sex hormone-binding globulin, SHBG, that were robustly associated with the protein levels were genotyped and applied in mendelian randomization analyses. We then conducted a replication study in an independent cohort of men from the Physicians' Health Study II (170 with newly diagnosed type 2 diabetes and 170 controls). RESULTS: Among women, higher plasma levels of sex hormone-binding globulin were prospectively associated with a lower risk of type 2 diabetes: multivariable odds ratios were 1.00 for the first (lowest) quartile of plasma levels, 0.16 (95% confidence interval [CI], 0.08 to 0.33) for the second quartile, 0.04 (95% CI, 0.01 to 0.12) for the third quartile, and 0.09 (95% CI, 0.03 to 0.21) for the fourth (highest) quartile (P<0.001 for trend). These prospective associations were replicated among men (odds ratio for the highest quartile of plasma levels vs. the lowest quartile, 0.10; 95% CI, 0.03 to 0.36; P<0.001 for trend). As compared with homozygotes of the respective wild-type allele, carriers of a variant allele of the SHBG single-nucleotide polymorphism (SNP) rs6259 had 10% higher sex hormone-binding globulin levels (P=0.005), and carriers of an rs6257 variant had 10% lower plasma levels (P=0.004); variants of both SNPs were also associated with a risk of type 2 diabetes in directions corresponding to their associated sex hormone-binding globulin levels. In mendelian randomization analyses, the predicted odds ratio of type 2 diabetes per standard-deviation increase in the plasma level of sex hormone-binding globulin was 0.28 (95% CI, 0.13 to 0.58) among women and 0.29 (95% CI, 0.15 to 0.58) among men, a finding that suggests that sex hormone-binding globulin may have a causal role in the risk of type 2 diabetes. CONCLUSIONS: Low circulating levels of sex hormone-binding globulin are a strong predictor of the risk of type 2 diabetes in women and men. The clinical usefulness of both SHBG genotypes and plasma levels in stratification and intervention for the risk of type 2 diabetes warrants further examination.

OBJECTIVE: To use the rs1229984 variant in the alcohol dehydrogenase 1B gene (ADH1B) as an instrument to investigate the causal role of alcohol in cardiovascular disease. DESIGN: Mendelian randomisation meta-analysis of 56 epidemiological studies. PARTICIPANTS: 261 991 individuals of European descent, including 20 259 coronary heart disease cases and 10 164 stroke events. Data were available on ADH1B rs1229984 variant, alcohol phenotypes, and cardiovascular biomarkers. MAIN OUTCOME MEASURES: Odds ratio for coronary heart disease and stroke associated with the ADH1B variant in all individuals and by categories of alcohol consumption. RESULTS: Carriers of the A-allele of ADH1B rs1229984 consumed 17.2% fewer units of alcohol per week (95% confidence interval 15.6% to 18.9%), had a lower prevalence of binge drinking (odds ratio 0.78 (95% CI 0.73 to 0.84)), and had higher abstention (odds ratio 1.27 (1.21 to 1.34)) than non-carriers. Rs1229984 A-allele carriers had lower systolic blood pressure (-0.88 (-1.19 to -0.56) mm Hg), interleukin-6 levels (-5.2% (-7.8 to -2.4%)), waist circumference (-0.3 (-0.6 to -0.1) cm), and body mass index (-0.17 (-0.24 to -0.10) kg/m(2)). Rs1229984 A-allele carriers had lower odds of coronary heart disease (odds ratio 0.90 (0.84 to 0.96)). The protective association of the ADH1B rs1229984 A-allele variant remained the same across all categories of alcohol consumption (P=0.83 for heterogeneity). Although no association of rs1229984 was identified with the combined subtypes of stroke, carriers of the A-allele had lower odds of ischaemic stroke (odds ratio 0.83 (0.72 to 0.95)). CONCLUSIONS: Individuals with a genetic variant associated with non-drinking and lower alcohol consumption had a more favourable cardiovascular profile and a reduced risk of coronary heart disease than those without the genetic variant. This suggests that reduction of alcohol consumption, even for light to moderate drinkers, is beneficial for cardiovascular health.

While exome sequencing is readily amenable to single-nucleotide variant discovery, the sparse and nonuniform nature of the exome capture reaction has hindered exome-based detection and characterization of genic copy number variation. We developed a novel method using singular value decomposition (SVD) normalization to discover rare genic copy number variants (CNVs) as well as genotype copy number polymorphic (CNP) loci with high sensitivity and specificity from exome sequencing data. We estimate the precision of our algorithm using 122 trios (366 exomes) and show that this method can be used to reliably predict (94% overall precision) both de novo and inherited rare CNVs involving three or more consecutive exons. We demonstrate that exome-based genotyping of CNPs strongly correlates with whole-genome data (median r(2) = 0.91), especially for loci with fewer than eight copies, and can estimate the absolute copy number of multi-allelic genes with high accuracy (78% call level). The resulting user-friendly computational pipeline, CoNIFER (copy number inference from exome reads), can reliably be used to discover disruptive genic CNVs missed by standard approaches and should have broad application in human genetic studies of disease.

The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Initiative, established by the National Office of Public Health Genomics at the Centers for Disease Control and Prevention, supports the development and implementation of a rigorous, evidence-based process for evaluating genetic tests and other genomic applications for clinical and public health practice in the United States. An independent, non-federal EGAPP Working Group (EWG), a multidisciplinary expert panel selects topics, oversees the systematic review of evidence, and makes recommendations based on that evidence. This article describes the EGAPP processes and details the specific methods and approaches used by the EWG.

Established guidelines for causal inference in epidemiological studies may be inappropriate for genetic associations. A consensus process was used to develop guidance criteria for assessing cumulative epidemiologic evidence in genetic associations. A proposed semi-quantitative index assigns three levels for the amount of evidence, extent of replication, and protection from bias, and also generates a composite assessment of 'strong', 'moderate' or 'weak' epidemiological credibility. In addition, we discuss how additional input and guidance can be derived from biological data. Future empirical research and consensus development are needed to develop an integrated model for combining epidemiological and biological evidence in the rapidly evolving field of investigation of genetic factors.

More than 800 published genetic association studies have implicated dozens of potential risk loci in Parkinson's disease (PD). To facilitate the interpretation of these findings, we have created a dedicated online resource, PDGene, that comprehensively collects and meta-analyzes all published studies in the field. A systematic literature screen of -27,000 articles yielded 828 eligible articles from which relevant data were extracted. In addition, individual-level data from three publicly available genome-wide association studies (GWAS) were obtained and subjected to genotype imputation and analysis. Overall, we performed meta-analyses on more than seven million polymorphisms originating either from GWAS datasets and/or from smaller scale PD association studies. Meta-analyses on 147 SNPs were supplemented by unpublished GWAS data from up to 16,452 PD cases and 48,810 controls. Eleven loci showed genome-wide significant (P < 5 × 10(-8)) association with disease risk: BST1, CCDC62/HIP1R, DGKQ/GAK, GBA, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, and SYT11/RAB25. In addition, we identified novel evidence for genome-wide significant association with a polymorphism in ITGA8 (rs7077361, OR 0.88, P = 1.3 × 10(-8)). All meta-analysis results are freely available on a dedicated online database (www.pdgene.org), which is cross-linked with a customized track on the UCSC Genome Browser. Our study provides an exhaustive and up-to-date summary of the status of PD genetics research that can be readily scaled to include the results of future large-scale genetics projects, including next-generation sequencing studies.

IMPORTANCE: The causal direction and magnitude of the association between telomere length and incidence of cancer and non-neoplastic diseases is uncertain owing to the susceptibility of observational studies to confounding and reverse causation. OBJECTIVE: To conduct a Mendelian randomization study, using germline genetic variants as instrumental variables, to appraise the causal relevance of telomere length for risk of cancer and non-neoplastic diseases. DATA SOURCES: Genomewide association studies (GWAS) published up to January 15, 2015. STUDY SELECTION: GWAS of noncommunicable diseases that assayed germline genetic variation and did not select cohort or control participants on the basis of preexisting diseases. Of 163 GWAS of noncommunicable diseases identified, summary data from 103 were available. DATA EXTRACTION AND SYNTHESIS: Summary association statistics for single nucleotide polymorphisms (SNPs) that are strongly associated with telomere length in the general population. MAIN OUTCOMES AND MEASURES: Odds ratios (ORs) and 95% confidence intervals (CIs) for disease per standard deviation (SD) higher telomere length due to germline genetic variation. RESULTS: Summary data were available for 35 cancers and 48 non-neoplastic diseases, corresponding to 420 081 cases (median cases, 2526 per disease) and 1 093 105 controls (median, 6789 per disease). Increased telomere length due to germline genetic variation was generally associated with increased risk for site-specific cancers. The strongest associations (ORs [95% CIs] per 1-SD change in genetically increased telomere length) were observed for glioma, 5.27 (3.15-8.81); serous low-malignant-potential ovarian cancer, 4.35 (2.39-7.94); lung adenocarcinoma, 3.19 (2.40-4.22); neuroblastoma, 2.98 (1.92-4.62); bladder cancer, 2.19 (1.32-3.66); melanoma, 1.87 (1.55-2.26); testicular cancer, 1.76 (1.02-3.04); kidney cancer, 1.55 (1.08-2.23); and endometrial cancer, 1.31 (1.07-1.61). Associations were stronger for rarer cancers and at tissue sites with lower rates of stem cell division. There was generally little evidence of association between genetically increased telomere length and risk of psychiatric, autoimmune, inflammatory, diabetic, and other non-neoplastic diseases, except for coronary heart disease (OR, 0.78 [95% CI, 0.67-0.90]), abdominal aortic aneurysm (OR, 0.63 [95% CI, 0.49-0.81]), celiac disease (OR, 0.42 [95% CI, 0.28-0.61]) and interstitial lung disease (OR, 0.09 [95% CI, 0.05-0.15]). CONCLUSIONS AND RELEVANCE: It is likely that longer telomeres increase risk for several cancers but reduce risk for some non-neoplastic diseases, including cardiovascular diseases.

We present a potentially useful alternative approach based on support vector machine (SVM) techniques to classify persons with and without common diseases. We illustrate the method to detect persons with diabetes and pre-diabetes in a cross-sectional representative sample of the U.S. population. We used data from the 1999-2004 National Health and Nutrition Examination Survey (NHANES) to develop and validate SVM models for two classification schemes: Classification Scheme I (diagnosed or undiagnosed diabetes vs. pre-diabetes or no diabetes) and Classification Scheme II (undiagnosed diabetes or pre-diabetes vs. no diabetes). The SVM models were used to select sets of variables that would yield the best classification of individuals into these diabetes categories. For Classification Scheme I, the set of diabetes-related variables with the best classification performance included family history, age, race and ethnicity, weight, height, waist circumference, body mass index (BMI), and hypertension. For Classification Scheme II, two additional variables--sex and physical activity--were included. The discriminative abilities of the SVM models for Classification Schemes I and II, according to the area under the receiver operating characteristic (ROC) curve, were 83.5% and 73.2%, respectively. The web-based tool-Diabetes Classifier was developed to demonstrate a user-friendly application that allows for individual or group assessment with a configurable, user-defined threshold. Support vector machine modeling is a promising classification approach for detecting persons with common diseases such as diabetes and pre-diabetes in the population. This approach should be further explored in other complex diseases using common variables.

OBJECTIVES: Prophylactic vaccines against human papillomavirus (HPV) are expected to be available for public use by 2007 and likely will be targeted to preadolescent children. Parental acceptance of these vaccines will be critical for their success. The objectives of this study were (1) to determine the overall acceptance of HPV vaccines for preadolescent children by parents, (2) to evaluate the influence of written educational information about HPV on parental acceptability of HPV vaccines, and (3) to identify independent predictors associated with HPV vaccine acceptability by parents. METHODS: A randomized intervention study within a cross-sectional survey was conducted. Parental HPV vaccine acceptability was measured under 3 different hypothetical scenarios. A self-administered survey on the knowledge, attitudes, and beliefs about HPV and HPV vaccines was sent to 1600 parents of 8- to 12-year-old children. In addition to a baseline paragraph about HPV that was received by all study participants, a random half of the study participants received a detailed "HPV Information Sheet" outlining the epidemiology and potential clinical sequelae of HPV infection. Independent predictors of parental HPV vaccine acceptability were determined using multivariate linear regression models. RESULTS: Parents who received the HPV information sheet had higher mean scores on the HPV knowledge assessment tool than the control group. However, despite this apparent improvement in knowledge, there was not a statistically significant difference in HPV vaccine acceptability between the 2 groups. CONCLUSIONS: Providing parents with an HPV information sheet did seem to improve knowledge about HPV, but this increased knowledge had little effect on the acceptability of these vaccines by parents for their children. Instead, attitudes and life experiences seemed to be more important factors influencing HPV vaccine acceptability among parents.

An original evidence review examined screening and diagnosis of hereditary nonpolyposis colorectal cancer (HNPCC) and the subsequent outcomes in a population of newly diagnosed cases of colorectal cancer (CRC). This supplementary evidence review focuses on five issues of further interest to the Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group (EWG), as summarized below.

Newborns are now routinely screened for the presence of a number of inherited diseases. In these cases, early diagnosis allows treatment to be instituted before there are irreversible adverse consequences. However, as outlined in this installment in the Genomic Medicine series, clinical benefit can be derived from screening adults as well — not only for the genes associated with classic inherited conditions, such as cystic fibrosis and Tay–Sachs disease, but also for genes that are better considered risk factors for complex diseases, such as factor V Leiden and mutations associated with hereditary hemochromatosis.

OBJECTIVE: Language barriers may lead to medical errors by impeding patient-provider communication. The objective of this study was to determine whether hospitalized pediatric patients whose families have language barriers are more likely to incur serious medical errors than patients whose families do not have language barriers. METHODS: A case-control study was conducted in a large, academic, regional children's hospital in the Pacific Northwest. Case patients (n = 97) included all hospitalizations of patients who were younger than 21 years and had a reported serious medical event from January 1, 1998, to December 31, 2003. Control patients (n = 475) were chosen from hospitalizations without a reported serious medical event and were matched with case patients on age, admitting service, admission to intensive care, and date of admission. The main exposure was a language barrier defined by self- or provider-reported need for an interpreter. Serious medical events were defined as events that led to unintended or potentially adverse outcomes identified by the hospital's quality improvement staff. RESULTS: Fourteen (14.4%) of the case patients and 53 (11.2%) of the control patients were assigned an interpreter during their hospitalization. Overall, we found no increased risk for serious medical events in patients and families who requested an interpreter compared with patients and families who did not request an interpreter (odds ratio: 1.36; 95% confidence interval: 0.73-2.55). Spanish-speaking patients who requested an interpreter comprised 11 (11.3%) of the case patients and 26 (5.5%) of the control patients. This subgroup had a twofold increased risk for serious medical events compared with patients who did not request an interpreter (odds ratio: 2.26; 95% confidence interval: 1.06-4.81). CONCLUSIONS: Spanish-speaking patients whose families have a language barrier seem to have a significantly increased risk for serious medical events during pediatric hospitalization compared with patients whose families do not have a language barrier.

Human well-being could benefit from large-scale data if large-scale noise is minimized

Importance: Polygenic risk scores comprising millions of single-nucleotide polymorphisms (SNPs) could be useful for population-wide coronary heart disease (CHD) screening. Objective: To determine whether a polygenic risk score improves prediction of CHD compared with a guideline-recommended clinical risk equation. Design, Setting, and Participants: A retrospective cohort study of the predictive accuracy of a previously validated polygenic risk score was assessed among 4847 adults of white European ancestry, aged 45 through 79 years, participating in the Atherosclerosis Risk in Communities (ARIC) study and 2390 participating in the Multi-Ethnic Study of Atherosclerosis (MESA) from 1996 through December 31, 2015, the final day of follow-up. The performance of the polygenic risk score was compared with that of the 2013 American College of Cardiology and American Heart Association pooled cohort equations. Exposures: Genetic risk was computed for each participant by summing the product of the weights and allele dosage across 6 630 149 SNPs. Weights were based on an international genome-wide association study. Main Outcomes and Measures: Prediction of 10-year first CHD events (including myocardial infarctions, fatal coronary events, silent infarctions, revascularization procedures, or resuscitated cardiac arrest) assessed using measures of model discrimination, calibration, and net reclassification improvement (NRI). Results: The study population included 4847 adults from the ARIC study (mean [SD] age, 62.9 [5.6] years; 56.4% women) and 2390 adults from the MESA cohort (mean [SD] age, 61.8 [9.6] years; 52.2% women). Incident CHD events occurred in 696 participants (14.4%) and 227 participants (9.5%), respectively, over median follow-up of 15.5 years (interquartile range [IQR], 6.3 years) and 14.2 (IQR, 2.5 years) years. The polygenic risk score was significantly associated with 10-year CHD incidence in ARIC with hazard ratios per SD increment of 1.24 (95% CI, 1.15 to 1.34) and in MESA, 1.38 (95% CI, 1.21 to 1.58). Addition of the polygenic risk score to the pooled cohort equations did not significantly increase the C statistic in either cohort (ARIC, change in C statistic, -0.001; 95% CI, -0.009 to 0.006; MESA, 0.021; 95% CI, -0.0004 to 0.043). At the 10-year risk threshold of 7.5%, the addition of the polygenic risk score to the pooled cohort equations did not provide significant improvement in reclassification in either ARIC (NRI, 0.018, 95% CI, -0.012 to 0.036) or MESA (NRI, 0.001, 95% CI, -0.038 to 0.076). The polygenic risk score did not significantly improve calibration in either cohort. Conclusions and Relevance: In this analysis of 2 cohorts of US adults, the polygenic risk score was associated with incident coronary heart disease events but did not significantly improve discrimination, calibration, or risk reclassification compared with conventional predictors. These findings suggest that a polygenic risk score may not enhance risk prediction in a general, white middle-aged population.

While the authors agree with John Ioannidis that "most research findings are false," here they show that replication of research findings enhances the positive predictive value of research findings being true.

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Abstract Rationale Idiopathic pulmonary fibrosis (IPF) is a complex lung disease characterized by scarring of the lung that is believed to result from an atypical response to injury of the epithelium. Genome-wide association studies have reported signals of association implicating multiple pathways including host defense, telomere maintenance, signaling, and cell–cell adhesion. Objectives To improve our understanding of factors that increase IPF susceptibility by identifying previously unreported genetic associations. Methods We conducted genome-wide analyses across three independent studies and meta-analyzed these results to generate the largest genome-wide association study of IPF to date (2,668 IPF cases and 8,591 controls). We performed replication in two independent studies (1,456 IPF cases and 11,874 controls) and functional analyses (including statistical fine-mapping, investigations into gene expression, and testing for enrichment of IPF susceptibility signals in regulatory regions) to determine putatively causal genes. Polygenic risk scores were used to assess the collective effect of variants not reported as associated with IPF. Measurements and Main Results We identified and replicated three new genome-wide significant (P &amp;lt; 5 × 10−8) signals of association with IPF susceptibility (associated with altered gene expression of KIF15, MAD1L1, and DEPTOR) and confirmed associations at 11 previously reported loci. Polygenic risk score analyses showed that the combined effect of many thousands of as yet unreported IPF susceptibility variants contribute to IPF susceptibility. Conclusions The observation that decreased DEPTOR expression associates with increased susceptibility to IPF supports recent studies demonstrating the importance of mTOR signaling in lung fibrosis. New signals of association implicating KIF15 and MAD1L1 suggest a possible role of mitotic spindle-assembly genes in IPF susceptibility.