Our understanding of the genetic basis of disease has evolved from descriptions of overall heritability or familiality to the identification of large numbers of risk loci. One can quantify the impact of such loci on disease using a plethora of measures, which can guide future research decisions. However, different measures can attribute varying degrees of importance to a variant. In this Analysis, we consider and contrast the most commonly used measures - specifically, the heritability of disease liability, approximate heritability, sibling recurrence risk, overall genetic variance using a logarithmic relative risk scale, the area under the receiver-operating curve for risk prediction and the population attributable fraction - and give guidelines for their use that should be explicitly considered when assessing the contribution of genetic variants to disease.
Genetic simulation programs are used to model data under specified assumptions to facilitate the understanding and study of complex genetic systems. Standardized data sets generated using genetic simulation are essential for the development and application of novel analytical tools in genetic epidemiology studies. With continuing advances in high-throughput genomic technologies and generation and analysis of larger, more complex data sets, there is a need for updating current approaches in genetic simulation modeling. To provide a forum to address current and emerging challenges in this area, the National Cancer Institute (NCI) sponsored a workshop, entitled "Genetic Simulation Tools for Post-Genome Wide Association Studies of Complex Diseases" at the National Institutes of Health (NIH) in Bethesda, Maryland on March 11-12, 2014. The goals of the workshop were to (1) identify opportunities, challenges, and resource needs for the development and application of genetic simulation models; (2) improve the integration of tools for modeling and analysis of simulated data; and (3) foster collaborations to facilitate development and applications of genetic simulation. During the course of the meeting, the group identified challenges and opportunities for the science of simulation, software and methods development, and collaboration. This paper summarizes key discussions at the meeting, and highlights important challenges and opportunities to advance the field of genetic simulation.
Despite recent progress on estimating the heritability explained by genotyped SNPs (h(2)g), a large gap between h(2)g and estimates of total narrow-sense heritability (h(2)) remains. Explanations for this gap include rare variants or upward bias in family-based estimates of h(2) due to shared environment or epistasis. We estimate h(2) from unrelated individuals in admixed populations by first estimating the heritability explained by local ancestry (h(2)?). We show that h(2)? = 2FSTC?(1 - ?)h(2), where FSTC measures frequency differences between populations at causal loci and ? is the genome-wide ancestry proportion. Our approach is not susceptible to biases caused by epistasis or shared environment. We applied this approach to the analysis of 13 phenotypes in 21,497 African-American individuals from 3 cohorts. For height and body mass index (BMI), we obtained h(2) estimates of 0.55 ± 0.09 and 0.23 ± 0.06, respectively, which are larger than estimates of h(2)g in these and other data but smaller than family-based estimates of h(2).
Evaluation of prostate cancer prognosis after surgery is increasingly relying upon genomic analyses of tumor DNA. We assessed the ability of the biomarker panel Genomic Evaluators of Metastatic Prostate Cancer (GEMCaP) to predict biochemical recurrence in 33 European American and 28 African American prostate cancer cases using genome-wide copy number data from a previous study. "Biomarker positive" was defined as ?20% of the 38 constituent copy number gain/loss GEMCaP loci affected in a given tumor; based on this threshold, the frequency of a positive biomarker was significantly lower in African Americans (n = 2; 7%) than European Americans (n = 11; 33%; P = 0.013). GEMCaP positivity was associated with risk of recurrence [hazard ratio (HR), 5.92; 95% confidence interval (CI), 2.32-15.11; P = 3 × 10(-4)] in the full sample and among European Americans (HR, 3.45; 95% CI, 1.13-10.51; P = 0.032) but was not estimable in African Americans due to the low rate of GEMCaP positivity. Overall, the GEMCaP recurrence positive predictive value (PPV) was 85%; in African Americans, PPV was 100%. When we expanded the definition of loss to include copy-neutral loss of heterozygosity (i.e., loss of one allele with concomitant duplication of the other), recurrence PPV was 83% for European American subjects. Under this definition, 5 African American subjects had a positive GEMCaP test value; 4 went on to develop biochemical recurrence (PPV = 80%). Our results suggest that the GEMCaP biomarker set could be an effective predictor for both European American and African American men diagnosed with localized prostate cancer who may benefit from immediate aggressive therapy after radical prostatectomy.
The extent of recent selection in admixed populations is currently an unresolved question. We scanned the genomes of 29,141 African Americans and failed to find any genome-wide-significant deviations in local ancestry, indicating no evidence of selection influencing ancestry after admixture. A recent analysis of data from 1,890 African Americans reported that there was evidence of selection in African Americans after their ancestors left Africa, both before and after admixture. Selection after admixture was reported on the basis of deviations in local ancestry, and selection before admixture was reported on the basis of allele-frequency differences between African Americans and African populations. The local-ancestry deviations reported by the previous study did not replicate in our very large sample, and we show that such deviations were expected purely by chance, given the number of hypotheses tested. We further show that the previous study's conclusion of selection in African Americans before admixture is also subject to doubt. This is because the FST statistics they used were inflated and because true signals of unusual allele-frequency differences between African Americans and African populations would be best explained by selection that occurred in Africa prior to migration to the Americas.
Observational studies suggest an inverse association between selenium and risk of prostate cancer. However, randomized controlled trials of selenium supplementation have reported conflicting results. Thus, we examined plasma selenium and selenium-related genes in relation to risk of high-grade prostate cancer and prostate cancer recurrence among men initially diagnosed with non-metastatic disease.
Genome-wide association studies (GWAS) have identified 76 variants associated with prostate cancer risk predominantly in populations of European ancestry. To identify additional susceptibility loci for this common cancer, we conducted a meta-analysis of > 10 million SNPs in 43,303 prostate cancer cases and 43,737 controls from studies in populations of European, African, Japanese and Latino ancestry. Twenty-three new susceptibility loci were identified at association P < 5 × 10(-8); 15 variants were identified among men of European ancestry, 7 were identified in multi-ancestry analyses and 1 was associated with early-onset prostate cancer. These 23 variants, in combination with known prostate cancer risk variants, explain 33% of the familial risk for this disease in European-ancestry populations. These findings provide new regions for investigation into the pathogenesis of prostate cancer and demonstrate the usefulness of combining ancestrally diverse populations to discover risk loci for disease.
Birth defects are a leading cause of infant morbidity and mortality worldwide. The vast majority of birth defects are nonsyndromic, and although their etiologies remain mostly unknown, evidence supports the hypothesis that they result from the complex interaction of genetic, epigenetic, environmental, and lifestyle factors. Since our last review published in 2002 describing the basic tools of genetic epidemiology used to study nonsyndromic structural birth defects, many new approaches have become available and have been used with varying success. Through rapid advances in genomic technologies, investigators are now able to investigate large portions of the genome at a fraction of previous costs. With next-generation sequencing, research has progressed from assessing a small percentage of single-nucleotide polymorphisms to assessing the entire human protein-coding repertoire (exome)-an approach that is starting to uncover rare but informative mutations associated with nonsyndromic birth defects. Herein, we report on the current state of the genetic epidemiology of birth defects and comment on future challenges and opportunities. We consider issues of study design, and we discuss common variant approaches, including candidate gene studies and genome-wide association studies. We also discuss the complexities embedded in exploring interactions between genes and the environment. We complete our review by describing new and promising next-generation sequencing technologies and examining how the study of epigenetic mechanisms could become the key to unraveling the complex etiologies of nonsyndromic structural birth defects.
Maternal smoking during pregnancy is one proposed risk factor for gastroschisis, but reported associations have been modest, suggesting that differences in genetic susceptibility might play a role. We included 108 non-Hispanic white and 62 Hispanic families who had infants with gastroschisis, and 1,147 non-Hispanic white and 337 Hispanic families who had liveborn infants with no major structural birth defects (controls) in these analyses. DNA was extracted from buccal cells collected from infants and mothers, and information on periconceptional smoking history was obtained from maternal interviews, as part of the National Birth Defects Prevention Study. We analyzed five polymorphisms in three genes that code for enzymes involved in metabolism of some cigarette smoke constituents (CYP1A1, CYP1A2, and NAT2). Logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) independently for maternal smoking and maternal and infant gene variants, and to assess joint associations of maternal smoking and maternal or infant gene variants with gastroschisis. In analyses adjusted for maternal age at delivery and stratified by maternal race-ethnicity, we identified three suggestive associations among 30 potential associations with sufficient numbers to calculate ORs: CYP1A1*2A for non-Hispanic white mothers who smoked periconceptionally (aOR = 0.38, 95% CI 0.15-0.98), and NAT2*6 for Hispanic non-smoking mothers (aOR = 2.17, 95% CI 1.12-4.19) and their infants (aOR = 2.11, 95% CI 1.00-4.48). This analysis does not support the occurrence of effect modification between periconceptional maternal smoking and most of the xenobiotic metabolizing enzyme gene variants assessed.
Toll-like receptor 4 (TLR4) is one of the best known TLR members expressed on the surface of several leukocytes and tissue cells and has a key function in detecting pathogen and danger-associated molecular patterns. The role of TLR4 in the pathophysiology of several age-related diseases is also well recognized, such as prostate cancer (PCa). TLR4 polymorphisms have been related to PCa risk, but the relationship between TLR4 genotypes and aggressive PCa risk has not been evaluated by any systematic reviews.
Twin studies suggest that heritability of moderate-severe bronchopulmonary dysplasia (BPD) is 53% to 79%, we conducted a genome-wide association study (GWAS) to identify genetic variants associated with the risk for BPD.
Acute myeloid leukemia (AML) is a clinically heterogeneous disease, with a 5-year disease-free survival (DFS) ranging from under 10% to over 70% for distinct groups of patients. At our institution, cytarabine, etoposide and busulfan are used in first or second remission patients treated with a two-step approach to autologous stem cell transplantation (ASCT). In this study, we tested the hypothesis that polymorphisms in the pharmacokinetic and pharmacodynamic pathway genes of these drugs are associated with DFS in AML patients. A total of 1659 variants in 42 genes were analyzed for their association with DFS using a Cox-proportional hazards model. One hundred and fifty-four genetically European patients were used for the primary analysis. An intronic single nucleotide polymorphism (SNP) in ABCC3 (rs4148405) was associated with a significantly shorter DFS (hazard ratios (HR)=3.2, P=5.6 × 10(-6)) in our primary cohort. In addition, a SNP in the GSTM1-GSTM5 locus, rs3754446, was significantly associated with a shorter DFS in all patients (HR=1.8, P=0.001 for 154 European ancestry; HR=1.7, P=0.028 for 125 non-European patients). Thus, for the first time, genetic variants in drug pathway genes are shown to be associated with DFS in AML patients treated with chemotherapy-based autologous ASCT.
The large-scale Collaborative Oncological Gene-environment Study (COGS) presents new findings that further characterize the genetic bases of breast, ovarian and prostate cancers. We summarize and provide insights into this collection of papers from COGS and discuss the implications of the results and future directions for such efforts.
Observational studies suggest an inverse association between vitamin E and risk of prostate cancer, particularly aggressive tumors. However, three large randomized controlled trials have reported conflicting results. Thus, we examined circulating vitamin E and vitamin E-related genes in relation to risk of high-grade prostate cancer and prostate cancer recurrence among men initially diagnosed with clinically organ-confined disease.
Genome-wide association studies (GWAS) have identified 36 loci associated with body mass index (BMI), predominantly in populations of European ancestry. We conducted a meta-analysis to examine the association of >3.2 million SNPs with BMI in 39,144 men and women of African ancestry and followed up the most significant associations in an additional 32,268 individuals of African ancestry. We identified one new locus at 5q33 (GALNT10, rs7708584, P = 3.4 × 10(-11)) and another at 7p15 when we included data from the GIANT consortium (MIR148A-NFE2L3, rs10261878, P = 1.2 × 10(-10)). We also found suggestive evidence of an association at a third locus at 6q16 in the African-ancestry sample (KLHL32, rs974417, P = 6.9 × 10(-8)). Thirty-two of the 36 previously established BMI variants showed directionally consistent effect estimates in our GWAS (binomial P = 9.7 × 10(-7)), five of which reached genome-wide significance. These findings provide strong support for shared BMI loci across populations, as well as for the utility of studying ancestrally diverse populations.
Genome-wide association studies (GWAS) have identified many single nucleotide polymorphisms (SNPs) associated with complex traits. However, the genetic heritability of most of these traits remains unexplained. To help guide future studies, we address the crucial question of whether future GWAS can detect new SNP associations and explain additional heritability given the new availability of larger GWAS SNP arrays, imputation, and reduced genotyping costs. We first describe the pairwise and imputation coverage of all SNPs in the human genome by commercially available GWAS SNP arrays, using the 1000 Genomes Project as a reference. Next, we describe the findings from 6 years of GWAS of 172 chronic diseases, calculating the power to detect each of them while taking array coverage and sample size into account. We then calculate the power to detect these SNP associations under different conditions using improved coverage and/or sample sizes. Finally, we estimate the percentages of SNP associations and heritability previously detected and detectable by future GWAS under each condition. Overall, we estimated that previous GWAS have detected less than one-fifth of all GWAS-detectable SNPs underlying chronic disease. Furthermore, increasing sample size has a much larger impact than increasing coverage on the potential of future GWAS to detect additional SNP-disease associations and heritability.
Prostate cancer (CaP) is the leading cancer among men of African descent in the USA, Caribbean, and Sub-Saharan Africa (SSA). The estimated number of CaP deaths in SSA during 2008 was more than five times that among African Americans and is expected to double in Africa by 2030. We summarize publicly available CaP data and collected data from the men of African descent and Carcinoma of the Prostate (MADCaP) Consortium and the African Caribbean Cancer Consortium (AC3) to evaluate CaP incidence and mortality in men of African descent worldwide. CaP incidence and mortality are highest in men of African descent in the USA and the Caribbean. Tumor stage and grade were highest in SSA. We report a higher proportion of T1 stage prostate tumors in countries with greater percent gross domestic product spent on health care and physicians per 100,000 persons. We also observed that regions with a higher proportion of advanced tumors reported lower mortality rates. This finding suggests that CaP is underdiagnosed and/or underreported in SSA men. Nonetheless, CaP incidence and mortality represent a significant public health problem in men of African descent around the world.
Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17-29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.e.), moderate between schizophrenia and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and ADHD and major depressive disorder (0.32 ± 0.07 s.e.), low between schizophrenia and ASD (0.16 ± 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohns disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.
Recent evidence suggests that inflammation plays a pivotal role in the development of lung cancer. In this study, we used a two-stage approach to investigate associations between genetic variants in inflammation pathways and lung cancer risk based on genome-wide association study (GWAS) data. A total of 7,650 sequence variants from 720 genes relevant to inflammation pathways were identified using keyword and pathway searches from Gene Cards and Gene Ontology databases. In Stage 1, six GWAS datasets from the International Lung Cancer Consortium were pooled (4,441 cases and 5,094 controls of European ancestry), and a hierarchical modeling (HM) approach was used to incorporate prior information for each of the variants into the analysis. The prior matrix was constructed using (1) role of genes in the inflammation and immune pathways; (2) physical properties of the variants including the location of the variants, their conservation scores and amino acid coding; (3) LD with other functional variants and (4) measures of heterogeneity across the studies. HM affected the priority ranking of variants particularly among those having low prior weights, imprecise estimates and/or heterogeneity across studies. In Stage 2, we used an independent NCI lung cancer GWAS study (5,699 cases and 5,818 controls) for in silico replication. We identified one novel variant at the level corrected for multiple comparisons (rs2741354 in EPHX2 at 8q21.1 with p value = 7.4 × 10(-6)), and confirmed the associations between TERT (rs2736100) and the HLA region and lung cancer risk. HM allows for prior knowledge such as from bioinformatic sources to be incorporated into the analysis systematically, and it represents a complementary analytical approach to the conventional GWAS analysis.
A single mutation can alter cellular and global homeostatic mechanisms and give rise to multiple clinical diseases. We hypothesized that these disease mechanisms could be identified using low minor allele frequency (MAF<0.1) non-synonymous SNPs (nsSNPs) associated with "mechanistic phenotypes", comprised of collections of related diagnoses. We studied two mechanistic phenotypes: (1) thrombosis, evaluated in a population of 1,655 African Americans; and (2) four groupings of cancer diagnoses, evaluated in 3,009 white European Americans. We tested associations between nsSNPs represented on GWAS platforms and mechanistic phenotypes ascertained from electronic medical records (EMRs), and sought enrichment in functional ontologies across the top-ranked associations. We used a two-step analytic approach whereby nsSNPs were first sorted by the strength of their association with a phenotype. We tested associations using two reverse genetic models and standard additive and recessive models. In the second step, we employed a hypothesis-free ontological enrichment analysis using the sorted nsSNPs to identify functional mechanisms underlying the diagnoses comprising the mechanistic phenotypes. The thrombosis phenotype was solely associated with ontologies related to blood coagulation (Fishers p?=?0.0001, FDR p?=?0.03), driven by the F5, P2RY12 and F2RL2 genes. For the cancer phenotypes, the reverse genetics models were enriched in DNA repair functions (p?=?2×10-5, FDR p?=?0.03) (POLG/FANCI, SLX4/FANCP, XRCC1, BRCA1, FANCA, CHD1L) while the additive model showed enrichment related to chromatid segregation (p?=?4×10-6, FDR p?=?0.005) (KIF25, PINX1). We were able to replicate nsSNP associations for POLG/FANCI, BRCA1, FANCA and CHD1L in independent data sets. Mechanism-oriented phenotyping using collections of EMR-derived diagnoses can elucidate fundamental disease mechanisms.
Acute lung injury (ALI) mortality is increased among African Americans compared with Americans of European descent, and genetic factors may be involved. A functional T-46C polymorphism (rs2814778) in the promoter region of Duffy antigen/receptor for chemokines (Darc) gene, present almost exclusively in people of African descent, results in isolated erythrocyte DARC deficiency and has been implicated in ALI pathogenesis in preclinical and murine models, possibly because of an increase in circulating Duffy-binding, proinflammatory chemokines like IL-8. We sought to determine the effect of the functional rs2814778 polymorphism, C/C genotype (Duffy null state), on clinical outcomes in African Americans with acute lung injury.
Over the past several years, genome-wide association studies (GWAS) have succeeded in identifying hundreds of genetic markers associated with common diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. To identify obstacles to future progress in genetic epidemiology research and provide recommendations to NIH for overcoming these barriers, the National Cancer Institute sponsored a workshop entitled "Next Generation Analytic Tools for Large-Scale Genetic Epidemiology Studies of Complex Diseases" on September 15-16, 2010. The goal of the workshop was to facilitate discussions on (1) statistical strategies and methods to efficiently identify genetic and environmental factors contributing to the risk of complex disease; and (2) how to develop, apply, and evaluate these strategies for the design, analysis, and interpretation of large-scale complex disease association studies in order to guide NIH in setting the future agenda in this area of research. The workshop was organized as a series of short presentations covering scientific (gene-gene and gene-environment interaction, complex phenotypes, and rare variants and next generation sequencing) and methodological (simulation modeling and computational resources and data management) topic areas. Specific needs to advance the field were identified during each session and are summarized.
Rare variants may help to explain some of the missing heritability of complex diseases. Technological advances in next-generation sequencing give us the opportunity to test this hypothesis. We propose two new methods (one for case-control studies and one for family-based studies) that combine aggregated rare variants and common variants located within a region through principal components analysis and allow for covariate adjustment. We analyzed 200 replicates consisting of 209 case subjects and 488 control subjects and compared the results to weight-based and step-up aggregation methods. The principal components and collapsing method showed an association between the gene FLT1 and the quantitative trait Q1 (P<10-30) in a fraction of the computation time of the other methods. The proposed family-based test has inconclusive results. The two methods provide a fast way to analyze simultaneously rare and common variants at the gene level while adjusting for covariates. However, further evaluation of the statistical efficiency of this approach is warranted.
Despite compelling epidemiological evidence that folic acid supplements reduce the frequency of neural tube defects (NTDs) in newborns, common variant association studies with folate metabolism genes have failed to explain the majority of NTD risk. The contribution of rare alleles as well as genetic interactions within the folate pathway have not been extensively studied in the context of NTDs. Thus, we sequenced the exons in 31 folate-related genes in a 480-member NTD case-control population to identify the full spectrum of allelic variation and determine whether rare alleles or obvious genetic interactions within this pathway affect NTD risk. We constructed a pathway model, predetermined independent of the data, which grouped genes into coherent sets reflecting the distinct metabolic compartments in the folate/one-carbon pathway (purine synthesis, pyrimidine synthesis, and homocysteine recycling to methionine). By integrating multiple variants based on these groupings, we uncovered two provocative, complex genetic risk signatures. Interestingly, these signatures differed by race/ethnicity: a Hispanic risk profile pointed to alterations in purine biosynthesis, whereas that in non-Hispanic whites implicated homocysteine metabolism. In contrast, parallel analyses that focused on individual alleles, or individual genes, as the units by which to assign risk revealed no compelling associations. These results suggest that the ability to layer pathway relationships onto clinical variant data can be uniquely informative for identifying genetic risk as well as for generating mechanistic hypotheses. Furthermore, the identification of ethnic-specific risk signatures for spina bifida resonated with epidemiological data suggesting that the underlying pathogenesis may differ between Hispanic and non-Hispanic groups.
The association between meat consumption and prostate cancer remains unclear, perhaps reflecting heterogeneity in the types of tumors studied and the method of meat preparation--which can impact the production of carcinogens.
Prostate cancer is a common but complex disease, and distinguishing modifiable risk factors such as diet for more aggressive disease is extremely important. Previous work has detected intriguing associations between vegetable, fruit, and grains and more aggressive prostate cancer, although these remain somewhat unclear. Here we further investigate such potential relationships with a case-control study of 982 men (470 more aggressive prostate cancer cases and 512 control subjects). Comparing the highest to lowest quartiles of intake, we found that increasing intakes of leafy vegetables were inversely associated with risk of aggressive prostate cancer [adjusted odds ratio (OR) = 0.66, 95% CI: 0.46, 0.96; P trend = 0.02], as was higher consumption of high carotenoid vegetables (OR = 0.71, 95% CI: 0.48, 1.04; P trend = 0.04). Conversely, increased consumption of high glycemic index foods were positively associated with risk of aggressive disease (OR = 1.64, 95% CI: 1.05, 2.57; P trend = 0.02). These results were driven by a number of specific foods within the food groups. Our findings support the hypothesis that diets high in vegetables and low in high glycemic index foods decrease risk of aggressive prostate cancer.
Detecting genomic alterations that result in more aggressive prostate cancer may improve clinical treatment and our understanding of the biology underlying this common but complex disease. To this end, we undertook a genome-wide copy number alterations (CNAs) study of clinicopathological characteristics of 62 prostate tumors using the Illumina 1M single nucleotide polymorphism array. The highest overall frequencies of CNAs were on chromosomes 8q (gains), 8p (loss and copy-neutral), and 6q (copy-loss). Combined loss and copy-neutral events were associated with increasing disease grade (P = 0.03), stage (P = 0.01), and diagnostic prostate specific antigen (PSA) (P = 0.01). Further evaluation of CNAs using gene ontology identified pathways involved with disease aggressiveness. The "regulation of apoptosis" pathway was associated with stage of disease (P = 0.004), while the "reproductive cellular process" pathway was associated with diagnostic PSA (P = 0.00038). Specific genes within these pathways exhibited strong associations with clinical characteristics; for example, in the apoptosis pathway BNIP3L was associated with increasing prostate tumor stage (P = 0.007). These findings confirm known regions of CNAs in prostate cancer and localize additional regions and possible genes (e.g., BNIP3L, WWOX, and GATM) that may help to clarify the genetic basis of prostate cancer aggressiveness.
Compelling evidence supports a genetic component to prostate cancer susceptibility and aggressiveness. Recent genome-wide association studies have identified more than 30 single-nucleotide polymorphisms associated with prostate cancer susceptibility. It remains unclear, however, whether such genetic variants are associated with disease aggressiveness--one of the most important questions in prostate cancer research today. To help clarify this and substantially expand research in the genetic determinants of prostate cancer aggressiveness, the first National Cancer Institute Prostate Cancer Genetics Workshop assembled researchers to develop plans for a large new research consortium and patient cohort. The workshop reviewed the prior work in this area and addressed the practical issues in planning future studies. With new DNA sequencing technology, the potential application of sequencing information to patient care is emerging. The workshop, therefore, included state-of-the-art presentations by experts on new genotyping technologies, including sequencing and associated bioinformatics issues, which are just beginning to be applied to cancer genetics.
Ewing sarcoma is a malignant bone tumor characterized by a high frequency of somatic EWSR1 translocations. Ewing sarcoma is less common in people of African or African-American ancestry, suggesting a genetic etiology.
Adult height is a classic polygenic trait of high heritability (h(2) approximately 0.8). More than 180 single nucleotide polymorphisms (SNPs), identified mostly in populations of European descent, are associated with height. These variants convey modest effects and explain approximately10% of the variance in height. Discovery efforts in other populations, while limited, have revealed loci for height not previously implicated in individuals of European ancestry. Here, we performed a meta-analysis of genome-wide association (GWA) results for adult height in 20,427 individuals of African ancestry with replication in up to 16,436 African Americans. We found two novel height loci (Xp22-rs12393627, P?=?3.4×10(-12) and 2p14-rs4315565, P?=?1.2×10(-8)). As a group, height associations discovered in European-ancestry samples replicate in individuals of African ancestry (P?=?1.7×10(-4) for overall replication). Fine-mapping of the European height loci in African-ancestry individuals showed an enrichment of SNPs that are associated with expression of nearby genes when compared to the index European height SNPs (P<0.01). Our results highlight the utility of genetic studies in non-European populations to understand the etiology of complex human diseases and traits.
GWAS of prostate cancer have been remarkably successful in revealing common genetic variants and novel biological pathways that are linked with its etiology. A more complete understanding of inherited susceptibility to prostate cancer in the general population will come from continuing such discovery efforts and from testing known risk alleles in diverse racial and ethnic groups. In this large study of prostate cancer in African American men (3,425 prostate cancer cases and 3,290 controls), we tested 49 risk variants located in 28 genomic regions identified through GWAS in men of European and Asian descent, and we replicated associations (at p?0.05) with roughly half of these markers. Through fine-mapping, we identified nearby markers in many regions that better define associations in African Americans. At 8q24, we found 9 variants (p?6×10(-4)) that best capture risk of prostate cancer in African Americans, many of which are more common in men of African than European descent. The markers found to be associated with risk at each locus improved risk modeling in African Americans (per allele OR?=?1.17) over the alleles reported in the original GWAS (OR?=?1.08). In summary, in this detailed analysis of the prostate cancer risk loci reported from GWAS, we have validated and improved upon markers of risk in some regions that better define the association with prostate cancer in African Americans. Our findings with variants at 8q24 also reinforce the importance of this region as a major risk locus for prostate cancer in men of African ancestry.
With the advent of high throughput genomics and high-resolution imaging techniques, there is a growing necessity in biology and medicine for parallel computing, and with the low cost of computing, it is now cost-effective for even small labs or individuals to build their own personal computation cluster.
Genome-wide association studies (GWAS) have successfully detected and replicated associations with numerous diseases, including cancers of the prostate and breast. These findings are helping clarify the genomic basis of such diseases, but appear to explain little of disease heritability. This limitation might reflect the focus of conventional GWAS on a small set of the most statistically significant associations with disease. More information might be obtained by analyzing GWAS using a polygenic model, which allows for the possibility that thousands of genetic variants could impact disease. Furthermore, there may exist common polygenic effects between potentially related phenotypes (e.g., prostate and breast cancer). Here we present and apply a polygenic model to GWAS of prostate and breast cancer. Our results indicate that the polygenic model can explain an increasing--albeit low--amount of heritability for both of these cancers, even when excluding the most statistically significant associations. In addition, nonaggressive prostate cancer and breast cancer appear to share a common polygenic model, potentially reflecting a similar underlying biology. This supports the further development and application of polygenic models to genomic data.
In search of common risk alleles for prostate cancer that could contribute to high rates of the disease in men of African ancestry, we conducted a genome-wide association study, with 1,047,986 SNP markers examined in 3,425 African-Americans with prostate cancer (cases) and 3,290 African-American male controls. We followed up the most significant 17 new associations from stage 1 in 1,844 cases and 3,269 controls of African ancestry. We identified a new risk variant on chromosome 17q21 (rs7210100, odds ratio per allele = 1.51, P = 3.4 × 10(-13)). The frequency of the risk allele is ?5% in men of African descent, whereas it is rare in other populations (<1%). Further studies are needed to investigate the biological contribution of this allele to prostate cancer risk. These findings emphasize the importance of conducting genome-wide association studies in diverse populations.
Recombination, together with mutation, gives rise to genetic variation in populations. Here we leverage the recent mixture of people of African and European ancestry in the Americas to build a genetic map measuring the probability of crossing over at each position in the genome, based on about 2.1 million crossovers in 30,000 unrelated African Americans. At intervals of more than three megabases it is nearly identical to a map built in Europeans. At finer scales it differs significantly, and we identify about 2,500 recombination hotspots that are active in people of West African ancestry but nearly inactive in Europeans. The probability of a crossover at these hotspots is almost fully controlled by the alleles an individual carries at PRDM9 (P?value 10(-245)). We identify a 17-base-pair DNA sequence motif that is enriched in these hotspots, and is an excellent match to the predicted binding target of PRDM9 alleles common in West Africans and rare in Europeans. Sites of this motif are predicted to be risk loci for disease-causing genomic rearrangements in individuals carrying these alleles. More generally, this map provides a resource for research in human genetic variation and evolution.
Expression quantitative trait loci (eQTL) studies have helped identify the genetic determinants of gene expression. Understanding the potential interacting mechanisms underlying such findings, however, is challenging.
The use of a prostate-specific antigen (PSA) test to screen for prostate cancer is controversial because of its modest predictive value and the potential overdiagnosis and over-treatment of the disease. A research article in this issue of Science Translational Medicine describes single-nucleotide polymorphisms (SNPs) in or near six genes that are independently associated with serum PSA concentrations and that help to explain interindividual PSA variation. Three of these SNPs are also associated with prostate biopsy outcomes. These findings are an important step toward incorporating genetic markers into PSA screening, with the ultimate goal of devising personalized PSA tests for use in the clinic.
Genome-wide association studies (GWAS) have identified numerous prostate cancer susceptibility alleles, but these loci have been identified primarily in men of European descent. There is limited information about the role of these loci in men of African descent.
Increasing evidence suggests that prostatic inflammation plays a key role in the development of prostate cancer. It remains controversial whether non-steroidal anti-inflammatory drugs (NSAIDs) reduce the risk of prostate cancer. Here, we investigate how a previously reported inverse association between NSAID use and the risk of aggressive prostate cancer is modulated by variants in several inflammatory genes. We found that NSAIDs may have differential effects on prostate cancer development, depending on ones genetic makeup. Further study of these inflammatory pathways may clarify the mechanisms through which NSAIDs impact prostate cancer risk.
The track record in paying for performance in education is not good; nevertheless, emphasis on accountability and performance has gained momentum in the last 25 years. This emphasis includes systems of merit pay, career ladders, and national board certification. The general failures of these efforts have led some reformers to suggest that teacher pay be directly related to student value-added performance. This suggestion remains controversial but is also the hottest topic in paying for performance in education. Although many similarities exist between education and health care, major differences may make it even harder to install pay-for-performance systems in health than in education. If those systems are to be tried, experiments should begin in a bottom-up fashion at the unit level, rather than being imposed systemwide.
Genome-wide association studies have identified numerous single nucleotide polymorphisms (SNP) associated with the risk of prostate cancer. Our objective was to determine whether these SNPs affect the progression of prostate cancer.
Deciphering the genetic basis of prostate cancer aggressiveness could provide valuable information for the screening and treatment of this common but complex disease. We previously detected linkage between a broad region on chromosome 7q22-35 and Gleason score-a strong predictor of prostate cancer aggressiveness. To further clarify this finding and focus on the potentially causative gene, we undertook a fine-mapping study across the 7q22-35 region.
Recent findings suggest that rare variants play an important role in both monogenic and common diseases. Due to their rarity, however, it remains unclear how to appropriately analyze the association between such variants and disease. A common approach entails combining rare variants together based on a priori information and analyzing them as a single group. Here one must make some assumptions about what to aggregate. Instead, we propose two approaches to empirically determine the most efficient grouping of rare variants. The first considers multiple possible groupings using existing information. The second is an agnostic "step-up" approach that determines an optimal grouping of rare variants analytically and does not rely on prior information. To evaluate these approaches, we undertook a simulation study using sequence data from genes in the one-carbon folate metabolic pathway. Our results show that using prior information to group rare variants is advantageous only when information is quite accurate, but the step-up approach works well across a broad range of plausible scenarios. This agnostic approach allows one to efficiently analyze the association between rare variants and disease while avoiding assumptions required by other approaches for grouping such variants.
Genome-wide association studies (GWAS) provide an important avenue for undertaking an agnostic evaluation of the association between common genetic variants and risk of disease. Recent advances in our understanding of human genetic variation and the technology to measure such variation have made GWAS feasible. Over the past few years a multitude of GWAS have identified and replicated many associated variants. These findings are enriching our knowledge about the genetic basis of disease and leading some to advocate using GWA study results for genetic testing. For many of the GWA study results, however, the underlying mechanisms remain unclear and the findings explain only a limited amount of heritability. These issues may be clarified by more detailed investigations, including analyses of less common variants, sequence-level data, and environmental exposures. Such studies should help clarify the potential value of genetic testing to the publics health.
The Pharmacogenomics Research Network holds a statistical analysis workshop every other year to share novel statistical methods and study designs for pharmacogenomics research, as well as insightful analyses of substantive ongoing studies. The 5th workshop was held 15 April 2009, in Rochester (MN, USA), in conjunction with the general Pharmacogenomics Research Network meeting. This summary of the ten contributed talks highlights a variety of timely topics, including identification of functional variants, how to maximize power using various study designs, and pathway analysis approaches. We also discuss the keynote invited presentation by Terry Speed, which provided an overview of statistical issues with next-generation sequence data with an emphasis on some statistical challenges in mRNA sequence data. Novel applications of Poisson regression models demonstrated innovative, yet practical, approaches to distinguish between biological and technical sources of variation in the counts of mRNA transcript reads. Overall, the workshop emphasized the need for diverse approaches to conducting pharmacogenomics studies, as well as the evolving nature of the field.
Prostatitis and sexually transmitted diseases (STDs) have been positively associated with prostate cancer in previous case-control studies. However, results from recent prospective studies have been inconclusive. METHODOGY/PRINCIPAL FINDINGS: We investigated the association between prostatitis, STDs, and prostate cancer among African American, Asian American, Latino, and White participants of the California Mens Health Study. Our analysis included 68,675 men, who completed a detailed baseline questionnaire in 2002-2003. We identified 1,658 incident prostate cancer cases during the follow-up period to June 30, 2006. Cox proportional hazards models were used to estimate relative risks and 95% confidence intervals. Overall, men having a history of prostatitis had an increased risk of prostate cancer than men with no history (RR = 1.30; 95% CI: 1.10-1.54). Longer duration of prostatitis symptoms was also associated with an increased risk of prostate cancer (P trend = 0.003). In addition, among men screened for prostate cancer (1 or 2 PSA tests), a non-significant positive association was observed between prostatitis and prostate cancer (RR = 1.10; 95% CI: 0.75-1.63). STDs were not associated with overall prostate cancer risk. In racial/ethnic stratified analysis, Latinos reporting any STDs had an increased risk of disease than those with no STDs (RR = 1.43; 95% CI: 1.07-1.91). Interestingly, foreign-born Latinos displayed a larger risk associated with STDs (RR = 1.87; 95% CI: 1.16-3.02) than U.S. born Latinos (RR = 1.15; 95% CI: 0.76-3.02).
Dietary intake of fish and omega-3 polyunsaturated fatty acids (omega-3 PUFAs) may decrease the risk of prostate cancer development and progression to advanced stage disease. This could reflect the anti-inflammatory effects of PUFAs, possibly through mediation of cyclooxygenase (COX), a key enzyme in fatty acid metabolism and inflammation. Despite promising experimental evidence, epidemiological studies have reported somewhat conflicting results regarding the effects of fish/PUFAs on prostate cancer development and progression. The literature suggests that fish, and particularly long-chain omega-3 PUFAs, may have a more pronounced protective effect on biologically aggressive tumors or on their progression, and less on early steps of carcinogenesis. Moreover, the impact of LC omega-3 PUFAs may be modified by variation of the COX-2 gene. Overall, results to date support the hypothesis that long-chain omega-3 PUFAs may impact prostate inflammation and carcinogenesis via the COX-2 enzymatic pathway.
The increasing availability of personal genomic tests has led to discussions about the validity and utility of such tests and the balance of benefits and harms. A multidisciplinary workshop was convened by the National Institutes of Health and the Centers for Disease Control and Prevention to review the scientific foundation for using personal genomics in risk assessment and disease prevention and to develop recommendations for targeted research. The clinical validity and utility of personal genomics is a moving target with rapidly developing discoveries but little translation research to close the gap between discoveries and health impact. Workshop participants made recommendations in five domains: (1) developing and applying scientific standards for assessing personal genomic tests; (2) developing and applying a multidisciplinary research agenda, including observational studies and clinical trials to fill knowledge gaps in clinical validity and utility; (3) enhancing credible knowledge synthesis and information dissemination to clinicians and consumers; (4) linking scientific findings to evidence-based recommendations for use of personal genomics; and (5) assessing how the concept of personal utility can affect health benefits, costs, and risks by developing appropriate metrics for evaluation. To fulfill the promise of personal genomics, a rigorous multidisciplinary research agenda is needed.
The racial/ethnic disparities in prostate cancer rates are well documented, with the highest incidence and mortality rates observed among African-Americans followed by non-Hispanic Whites, Hispanics, and Asian/Pacific Islanders. Whether socioeconomic status (SES) can account for these differences in risk has been investigated in previous studies, but with conflicting results. Furthermore, previous studies have focused primarily on the differences between African-Americans and non-Hispanic Whites, and little is known for Hispanics and Asian/Pacific Islanders.
Dietary intake of long-chain omega-3 (LC n-3) polyunsaturated fatty acids may reduce inflammation and in turn decrease risk of prostate cancer development and progression. This potential effect may be modified by genetic variation in cyclooxygenase-2 (COX-2), a key enzyme in fatty acid metabolism and inflammation.
Recent genetics and genomics studies of prostate cancer have helped to clarify the genetic basis of this common but complex disease. Genome-wide studies have detected numerous variants associated with disease as well as common gene fusions and expression signatures in prostate tumours. On the basis of these results, some advocate gene-based individualized screening for prostate cancer, although such testing might only be worthwhile to distinguish disease aggressiveness. Lessons learned from these studies provide strategies for further deciphering the genetic causes of prostate cancer and other diseases.
Carboxypeptidase 4 (CPA4) is a zinc-dependent metallocarboxypeptidase on chromosome 7q32 in a region linked to prostate cancer aggressiveness. CPA4 is involved in the histone hyperacetylation pathway and may modulate the function of peptides that affect the growth and regulation of prostate epithelial cells. We examined the association between genetic variation in CPA4 and intermediate-to-high risk prostate cancer.
We used conventional and hierarchical logistic regression to examine the association of neural tube defects (NTDs) with intake of 26 nutrients that contribute to the mechanistic pathways of methylation, glycemic control, and oxidative stress, all of which have been implicated in NTD etiology. The hierarchical approach produces more plausible, more stable estimates than the conventional approach, while adjusting for potential confounding by other nutrients.
Promising findings from genetic association studies are commonly presented with two distinct figures: one gives the association study results and the other indicates linkage disequilibrium (LD) between genetic markers in the region(s) of interest. Fully interpreting the results of such studies requires synthesizing the information in these figures, which is generally done in a subjective and unsystematic manner. Here we present a method to formally combine association results and LD and display them in the same figure; we have developed a freely available web-based application that can be used to generate figures to display the combined data. To demonstrate this approach we apply it to fine mapping data from the prostate cancer 8q24 loci. Combining these two sources of information in a single figure allows one to more clearly assess patterns of association, facilitating the interpretation of genome-wide and fine mapping data and improving our ability to localize causal variants.
Prostate cancer is the most frequent and second most lethal cancer in men in the United States. Innate immunity and inflammation may increase the risk of prostate cancer. To determine the role of innate immunity and inflammation in advanced prostate cancer, we investigated the association of 320 single nucleotide polymorphisms, located in 46 genes involved in this pathway, with disease risk using 494 cases with advanced disease and 536 controls from Cleveland, Ohio. Taken together, the whole pathway was associated with advanced prostate cancer risk (P?=?0.02). Two sub-pathways (intracellular antiviral molecules and extracellular pattern recognition) and four genes in these sub-pathways (TLR1, TLR6, OAS1, and OAS2) were nominally associated with advanced prostate cancer risk and harbor several SNPs nominally associated with advanced prostate cancer risk. Our results suggest that the innate immunity and inflammation pathway may play a modest role in the etiology of advanced prostate cancer through multiple small effects.
A recent genome wide association study demonstrated the novel finding that variants in DGKK are associated with hypospadias. Our objectives were to determine whether this finding could be replicated in a more racially/ethnically diverse study population of California births and to provide a more comprehensive investigation of variants.
Genetic fumarylacetoacetate hydrolase (Fah) deficiency is unique in that healthy gene-corrected hepatocytes have a strong growth advantage and can repopulate the diseased liver. Unfortunately, similar positive selection of gene-corrected cells is absent in most inborn errors of liver metabolism and it is difficult to reach the cell replacement index required for therapeutic benefit. Therefore, methods to transiently create a growth advantage for genetically modified hepatocytes in any genetic background would be advantageous. To mimic the selective pressure of Fah deficiency in normal animals, an efficient in vivo small molecule inhibitor of FAH, 4-[(2-carboxyethyl)-hydroxyphosphinyl]-3-oxobutyrate (CEHPOBA) was developed. Microarray analysis demonstrated that pharmacological inhibition of FAH produced highly similar gene expression changes to genetic deficiency. As proof of principle, hepatocytes lacking homogentisic acid dioxygenase (Hgd) and hence resistant to FAH inhibition were transplanted into sex-mismatched wild-type recipients. Time course analyses of 4-6 weeks of CEHPOBA administration after transplantation showed a linear relationship between treatment length and replacement index. Compared to controls, recipients treated with the FAH-inhibitor had 20-100-fold increases in liver repopulation. We conclude that pharmacological inhibition of FAH is a promising approach to in vivo selection of hepatocytes.
New sequencing technologies provide an opportunity for assessing the impact of rare and common variants on complex diseases. Several methods have been developed for evaluating rare variants, many of which use weighted collapsing to combine rare variants. Some approaches require arbitrary frequency thresholds below which to collapse alleles, and most assume that effect sizes for each collapsed variant are either the same or a function of minor allele frequency. Some methods also further assume that all rare variants are deleterious rather than protective. We expect that such assumptions will not hold in general, and as a result performance of these tests will be adversely affected. We propose a hierarchical model, implemented in the new program CHARM, to detect the joint signal from rare and common variants within a genomic region while properly accounting for linkage disequilibrium between variants. Our model explores the scale, rather than the center of the odds ratio distribution, allowing for both causative and protective effects. We use cross-validation to assess the evidence for association in a region. We use model averaging to widen the range of disease models under which we will have good power. To assess this approach, we simulate data under a range of disease models with effects at common and/or rare variants. Overall, our method had more power than other well-known rare variant approaches; it performed well when either only rare, or only common variants were causal, and better than other approaches when both common and rare variants contributed to disease.
Incorporating information about common genetic variants may help improve the design and analysis of clinical trials. For example, if genes impact response to treatment, one can pregenotype potential participants to screen out genetically determined nonresponders and substantially reduce the sample size and duration of a trial. Genetic associations with response to treatment are generally much larger than those observed for development of common diseases, as highlighted here by findings from genome-wide association studies. With the development and decreasing cost of next generation sequencing, more extensive genetic information - including rare variants - is becoming available on individuals treated with drugs and other therapies. We can use this information to evaluate whether rare variants impact treatment response. The sparseness of rare variants, however, raises issues of how the resulting data should be best analyzed. As shown here, simply evaluating the association between each rare variant and treatment response one-at-a-time will require enormous sample sizes. Combining the rare variants together can substantially reduce the required sample sizes, but require a number of assumptions about the similarity among the rare variants effects on treatment response. We have developed an empirical approach for aggregating and analyzing rare variants that limit such assumptions and work well under a range of scenarios. Such analyses provide a valuable opportunity to more fully decipher the genomic basis of response to treatment.
To determine whether accounting for gene-environment (G×E) interactions improves the power to detect associations between rare variants and a disease, we have extended three statistical methods and compared their power under various simulated disease models.
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