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Attention deficit hyperactivity disorder (ADHD) is a complex disorder with heterogeneous clinical presentations that manifest variability in long-term outcomes. The genetic contributions to this clinical heterogeneity, however, are not well understood. Here, we study 14 084 individuals diagnosed with ADHD to identify several genetic factors underlying clinical heterogeneity. One genome-wide significant locus was specifically associated with an autism spectrum disorder (ASD) diagnosis among individuals diagnosed with ADHD and it was not previously associated with ASD nor ADHD, individually. We used a novel approach to compare profiles of polygenic scores for groups of individuals diagnosed with ADHD and uncovered robust evidence that biology is an important factor in on-going clinical debates. Specifically, individuals diagnosed with ASD and ADHD, substance use disorder (SUD) and ADHD, or first diagnosed with ADHD in adulthood had different profiles of polygenic scores for ADHD and multiple other psychiatric, cognitive, and socio-behavioral traits. A polygene overlap between an ASD diagnosis in ADHD and cognitive performance was replicated in an independent, typically developing cohort. Our unique approach uncovered evidence of genetic heterogeneity in a widely studied complex disorder, allowing for timely contributions to the understanding of ADHD etiology and providing a model for similar studies of other disorders.

Attention deficit hyperactivity disorder (ADHD) is a complex disorder with heterogeneous clinical presentations that manifest variability in long-term outcomes. The genetic contributions to this clinical heterogeneity, however, are not well understood. Here, we study 14 084 individuals diagnosed with ADHD to identify several genetic factors underlying clinical heterogeneity. One genome-wide significant locus was specifically associated with an autism spectrum disorder (ASD) diagnosis among individuals diagnosed with ADHD and it was not previously associated with ASD nor ADHD, individually. We used a novel approach to compare profiles of polygenic scores for groups of individuals diagnosed with ADHD and uncovered robust evidence that biology is an important factor in on-going clinical debates. Specifically, individuals diagnosed with ASD and ADHD, substance use disorder (SUD) and ADHD, or first diagnosed with ADHD in adulthood had different profiles of polygenic scores for ADHD and multiple other psychiatric, cognitive, and socio-behavioral traits. A polygene overlap between an ASD diagnosis in ADHD and cognitive performance was replicated in an independent, typically developing cohort. Our unique approach uncovered evidence of genetic heterogeneity in a widely studied complex disorder, allowing for timely contributions to the understanding of ADHD etiology and providing a model for similar studies of other disorders.

Circulating cell-free DNA (cfDNA) in the bloodstream originates from dying cells and is a promising noninvasive biomarker for cell death. Here, we propose an algorithm, CelFiE, to accurately estimate the relative abundances of cell types and tissues contributing to cfDNA from epigenetic cfDNA sequencing. In contrast to previous work, CelFiE accommodates low coverage data, does not require CpG site curation, and estimates contributions from multiple unknown cell types that are not available in external reference data. In simulations, CelFiE accurately estimates known and unknown cell type proportions from low coverage and noisy cfDNA mixtures, including from cell types composing less than 1% of the total mixture. When used in two clinically-relevant situations, CelFiE correctly estimates a large placenta component in pregnant women, and an elevated skeletal muscle component in amyotrophic lateral sclerosis (ALS) patients, consistent with the occurrence of muscle wasting typical in these patients. Together, these results show how CelFiE could be a useful tool for biomarker discovery and monitoring the progression of degenerative disease.

BackgroundMild cognitive impairment is a common yet complex condition that is often underdiagnosed in the early stages. Miro Health is a mobile platform for the self-administration of sensor-based cognitive and behavioral assessments that was developed to measure factors typical of legacy neuropsychological tests in addition to behaviors that are currently left to subjective clinical impression such as eye movement, language, and processing speed.ObjectiveStudies were conducted to measure Miro Health’s concurrent validity, test-retest reliability, and amnestic MCI classification performance.MethodSpearman correlations were calculated to estimate the concurrent validity of Miro Health variables with legacy neuropsychological test variables using data from 160 study participants. Fifty-nine healthy controls were assessed at three time points to evaluate the test-retest reliability of Miro Health scores. Reliability was quantified with the scores’ intraclass correlations. Learning effects were measured as trends. In addition, a machine learning algorithm combined Miro Health variable scores into a Risk Score designed to distinguish 65 healthy controls (HC), 38 MCI participants (21 amnestic MCI (aMCI), and 17 non-amnestic MCI (naMCI)).ResultsSignificant correlations of Miro Health variables with legacy neuropsychological test variables were observed. Longitudinal studies show agreement of subsequent measurements and minimal learning effects. The Risk Score distinguished aMCI from healthy controls with an Area Under the Receiver Operator Curve (AUROC) of 0.97; the naMCI participants and controls were separated with an AUROC of 0.80, and the combined MCI group (aMCI + naMCI) was separated from healthy controls with an AUROC of 0.89.ConclusionMiro Health includes valid and reliable versions of variable scores that are analogous to legacy neuropsychological variable scores and a machine-learning derived risk score that effectively distinguishes HCs and individuals with MCI.

To identify rare variants associated with prostate cancer susceptibility and better characterize the mechanisms and cumulative disease risk associated with common risk variants, we conducted an integrated study of prostate cancer genetic etiology in two cohorts using custom genotyping microarrays, large imputation reference panels, and functional annotation approaches. Specifically, 11,984 men (6,196 prostate cancer cases and 5,788 controls) of European ancestry from Northern California Kaiser Permanente were genotyped and meta-analyzed with 196,269 men of European ancestry (7,917 prostate cancer cases and 188,352 controls) from the UK Biobank. Three novel loci, including two rare variants (European ancestry minor allele frequency < 0.01, at 3p21.31 and 8p12), were significant genome wide in a meta-analysis. Gene-based rare variant tests implicated a known prostate cancer gene (HOXB13), as well as a novel candidate gene (ILDR1), which encodes a receptor highly expressed in prostate tissue and is related to the B7/CD28 family of T-cell immune checkpoint markers. Haplotypic patterns of long-range linkage disequilibrium were observed for rare genetic variants at HOXB13 and other loci, reflecting their evolutionary history. In addition, a polygenic risk score (PRS) of 188 prostate cancer variants was strongly associated with risk (90th vs. 40th–60th percentile OR = 2.62, P = 2.55 × 10−191). Many of the 188 variants exhibited functional signatures of gene expression regulation or transcription factor binding, including a 6-fold difference in log-probability of androgen receptor binding at the variant rs2680708 (17q22). Rare variant and PRS associations, with concomitant functional interpretation of risk mechanisms, can help clarify the full genetic architecture of prostate cancer and other complex traits. Significance: This study maps the biological relationships between diverse risk factors for prostate cancer, integrating different functional datasets to interpret and model genome-wide data from over 200,000 men with and without prostate cancer. See related commentary by Lachance, p. 1637

Large-scale cohorts with combined genetic and phenotypic data, coupled with methodological advances, have produced increasingly accurate genetic predictors of complex human phenotypes called polygenic risk scores (PRSs). In addition to the potential translational impacts of identifying at-risk individuals, PRS are being utilized for a growing list of scientific applications, including causal inference, identifying pleiotropy and genetic correlation, and powerful gene-based and mixed-model association tests. Existing PRS approaches rely on external large-scale genetic cohorts that have also measured the phenotype of interest. They further require matching on ancestry and genotyping platform or imputation quality. In this work, we present a novel reference-free method to produce a PRS that does not rely on an external cohort. We show that naive implementations of reference-free PRS either result in substantial overfitting or prohibitive increases in computational time. We show that our algorithm avoids both of these issues and can produce informative in-sample PRSs over a single cohort without overfitting. We then demonstrate several novel applications of reference-free PRSs, including detection of pleiotropy across 246 metabolic traits and efficient mixed-model association testing.

The potential association between rare germline genetic variants and prostate cancer (PrCa) susceptibility has been understudied due to challenges with assessing rare variation. Furthermore, although common risk variants for PrCa have shown limited individual effect sizes, their cumulative effect may be of similar magnitude as high penetrance mutations. To identify rare variants associated with PrCa susceptibility, and better characterize the mechanisms and cumulative disease risk associated with common risk variants, we analyzed large population-based cohorts, custom genotyping microarrays, and imputation reference panels in an integrative study of PrCa genetic etiology. In particular, 11,649 men (6,196 PrCa cases, 5,453 controls) of European ancestry from the Kaiser Permanente Research Program on Genes, Environment and Health, ProHealth Study, and California Men’s Health Study were genotyped and meta-analyzed with 196,269 European-ancestry male subjects (7,917 PrCa cases, 188,352 controls) from the UK Biobank. Six novel loci were genome-wide significant in our meta-analysis, including two rare variants (minor allele frequency < 0.01, at 3p21.31 and 8p12). Gene-based rare variant tests implicated a previously discovered PrCa gene (HOXB13) as well as a novel candidate (ILDR1) highly expressed in prostate tissue. Haplotypic patterns of long-range linkage disequilibrium were observed for rare genetic variants atHOXB13and other loci, reflecting their evolutionary history. Furthermore, a polygenic risk score (PRS) of 187 known, largely common PrCa variants was strongly associated with risk in non-Hispanic whites (90thvs. 10thdecile OR = 7.66,P= 1.80*10-239). Many of the 187 variants exhibited functional signatures of gene expression regulation or transcription factor binding, including a six-fold difference in log-probability of Androgen Receptor binding at the variant rs2680708 (17q22). Our finding of two novel rare variants associated with PrCa should motivate further consideration of the role of low frequency polymorphisms in PrCa, while the considerable effect of PrCa PRS profiles should prompt discussion of their role in clinical practice.

Circulating cell-free DNA (cfDNA) in the bloodstream originates from dying cells and is a promising non-invasive biomarker for cell death. Here, we develop a method to accurately estimate the relative abundances of cell types contributing to cfDNA. We leverage the distinct DNA methylation profile of each cell type throughout the body. Decomposing the cfDNA mixture is difficult, as fragments from relevant cell types may only be present in a small amount. We propose an algorithm, CelFiE, that estimates cell type proportion from both whole genome cfDNA input and reference data. CelFiE accommodates low coverage data, does not rely on CpG site curation, and estimates contributions from multiple unknown cell types that are not available in reference data. In simulations we show that CelFiE can accurately estimate known and unknown cell type of origin of cfDNA mixtures in low coverage and noisy data. Simulations also demonstrate that we can effectively estimate cfDNA originating from rare cell types composing less than 0.01% of the total cfDNA. To validate CelFiE, we use a positive control: cfDNA extracted from pregnant and non-pregnant women. CelFiE estimates a large placenta component specifically in pregnant women (p = 9.1 × 10−5). Finally, we use CelFiE to decompose cfDNA from ALS patients and age matched controls. We find increased cfDNA concentrations in ALS patients (p = 3.0 × 10−3). Specifically, CelFiE estimates increased skeletal muscle component in the cfDNA of ALS patients (p = 2.6 × 10−3), which is consistent with muscle impairment characterizing ALS. Quantification of skeletal muscle death in ALS is novel, and overall suggests that CelFiE may be a useful tool for biomarker discovery and monitoring of disease progression.

Cells die at different rates as a function of disease state, age, environmental exposure, and behavior [8, 10]. Knowing the rate at which cells die is a fundamental scientific question, with direct translational applicability. A quantifiable indication of cell death could facilitate disease diagnosis and prognosis, prioritize patients for admission into clinical trials, and improve evaluations of treatment efficacy and disease progression [1, 4, 14, 16]. Circulating cell-free DNA (cfDNA) in the bloodstream originates from dying cells and is a promising non-invasive biomarker for cell death.

Biological, technical, and environmental confounders are ubiquitous in the high-dimensional, high-throughput functional genomic measurements being used to understand cellular biology and disease processes, and many approaches have been developed to estimate and correct for unmeasured confounders... High-throughput measurements of molecular phenotypes provide an unprecedented opportunity to model cellular processes and their impact on disease. These highly structured datasets are usually strongly confounded, creating false positives and reducing power. This has motivated many approaches based on principal components analysis (PCA) to estimate and correct for confounders, which have become indispensable elements of association tests between molecular phenotypes and both genetic and nongenetic factors. Here, we show that these correction approaches induce a bias, and that it persists for large sample sizes and replicates out-of-sample. We prove this theoretically for PCA by deriving an analytic, deterministic, and intuitive bias approximation. We assess other methods with realistic simulations, which show that perturbing any of several basic parameters can cause false positive rate (FPR) inflation. Our experiments show the bias depends on covariate and confounder sparsity, effect sizes, and their correlation. Surprisingly, when the covariate and confounder have ρ2≈10%, standard two-step methods all have >10-fold FPR inflation. Our analysis informs best practices for confounder correction in genomic studies, and suggests many false discoveries have been made and replicated in some differential expression analyses.

Common diseases often show sex differences in prevalence, onset, symptomology, treatment, or prognosis. Although studies have been performed to evaluate sex differences at specific SNP associations, this work aims to comprehensively survey a number of complex heritable diseases and anthropometric traits. Potential genetically encoded sex differences we investigated include differential genetic liability thresholds or distributions, gene–sex interaction at autosomal loci, major contribution of the X-chromosome, or gene–environment interactions reflected in genes responsive to androgens or estrogens. Finally, we tested the overlap between sex-differential association with anthropometric traits and disease risk. We utilized complementary approaches of assessing GWAS association enrichment and SNP-based heritability estimation to explore explicit sex differences, as well as enrichment in sex-implicated functional categories. We do not find consistent increased genetic load in the lower-prevalence sex, or a disproportionate role for the X-chromosome in disease risk, despite sex-heterogeneity on the X for several traits. We find that all anthropometric traits show less than complete correlation between the genetic contribution to males and females, and find a convincing example of autosome-wide genome-sex interaction in multiple sclerosis (P = 1 × 10−9). We also find some evidence for hormone-responsive gene enrichment, and striking evidence of the contribution of sex-differential anthropometric associations to common disease risk, implying that general mechanisms of sexual dimorphism determining secondary sex characteristics have shared effects on disease risk.

The genetic control of gene expression is a core component of human physiology. For the past several years, transcriptome-wide association studies have leveraged large datasets of linked genotype and RNA sequencing information to create a powerful gene-based test of association that has been used in dozens of studies. While numerous discoveries have been made, the populations in the training data are overwhelmingly of European descent, and little is known about the generalizability of these models to other populations. Here, we test for cross-population generalizability of gene expression prediction models using a dataset of African American individuals with RNA-Seq data in whole blood. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic simulations of both populations and gene expression, we show that accurate cross-population generalizability of transcriptome prediction only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTLs showed patterns similar to real-world data. Therefore, generating RNA-Seq data in diverse populations is a critical step towards multi-ethnic utility of gene expression prediction., Author summary Advances in RNA sequencing technology have reduced the cost of measuring gene expression at a genome-wide level. However, sequencing enough human RNA samples for adequately-powered disease association studies remains prohibitively costly. To this end, modern transcriptome-wide association analysis tools leverage existing paired genotype-expression datasets by creating models to predict gene expression using genotypes. These predictive models enable researchers to perform cost-effective association tests with gene expression in independently genotyped samples. However, most of these models use European reference data, and the extent to which gene expression prediction models work across populations is not fully resolved. We observe that these models predict gene expression worse than expected in a dataset of African-Americans when derived from European-descent individuals. Using simulations, we show that gene expression predictive model performance depends on both the proportion of genetic variants shared between population-specific prediction models as well as the genetic relatedness between populations. Our findings suggest a need to carefully select reference populations for prediction and point to a pressing need for more genetically diverse genotype-expression datasets.

Large-scale cohorts with combined genetic and phenotypic data, coupled with methodological advances, have produced increasingly accurate genetic predictors of complex human phenotypes called polygenic risk scores (PRS). In addition to the potential translational impacts of identifying at-risk individuals, PRS are being utilized for a growing list of scientific applications including causal inference, identifying pleiotropy and genetic correlation, and powerful gene-based and mixed model association tests. Existing PRS approaches rely on external large-scale genetic cohorts that have also measured the phenotype of interest. They further require matching on ancestry and genotyping platform or imputation quality. In this work we present a novel reference-free method to produce PRS that does not rely on an external cohort. We show that naive implementations of reference-free PRS either result in substantial over-fitting or prohibitive increases in computational time. We show that our algorithm avoids both of these issues, and can produce informative in-sample PRS over any existing cohort without over-fitting. We then demonstrate several novel applications of reference-free PRS including detection of pleiotropy across 246 metabolic traits and efficient mixed-model association testing.

Genetic studies of metabolites have identified thousands of variants many of which are associated with downstream metabolic and obesogenic disorders. However, these studies have relied on univariate analyses, reducing power and limiting context specific understanding. Here we aim to provide an integrated perspective of the genetic basis of metabolites by leveraging the Finnish Metabolic Syndrome In Men (METSIM) cohort, a unique genetic resource which contains metabolic measurements across distinct timepoints as well as detailed information on statin usage. We increase effective sample size by an average of two-fold by applying the Covariates for Multi-phenotype Studies (CMS) approach, identifying 588 significant SNP-metabolite associations, including 248 novel associations. We further show that many of these SNPs are master metabolic regulators, balancing the relative proportion of dozens of metabolite levels. We then identify the first associations to changes in metabolic levels across time as well as evidence of genetic interaction with statin use. Finally, we show an overall decrease in genetic control of metabolic processes with age.

Identification of trans-eQTLs has been limited by a heavy multiple testing burden, read-mapping biases, and hidden confounders. To address these issues, we developed GBAT, a powerful gene-based method that allows robust detection of trans gene regulation. Using simulated and real data, we show that GBAT drastically increases detection of trans-gene regulation over standard trans-eQTL analyses.

The increasing availability of data from scientific projects has spurred progress across a range of disciplines. While access to primary data is often restricted due to privacy or other concerns, s...

BACKGROUND The National Birth Defects Prevention Study (NBDPS) contains a wealth of information on affected and unaffected family triads, and thus provides numerous opportunities to study gene-environment interactions (G×E) in the etiology of birth defect outcomes. Depending on the research objective, several analytic options exist to estimate G×E effects that use varying combinations of individuals drawn from available triads. METHODS In this study, we discuss important considerations in the collection of genetic data and environmental exposures. RESULTS We will also present several population- and family-based approaches that can be applied to data from the NBDPS including case-control, case-only, family-based trio, and maternal versus fetal effects. For each, we describe the data requirements, applicable statistical methods, advantages, and disadvantages. CONCLUSION A range of approaches can be used to evaluate potentially important G×E effects in the NBDPS. Investigators should be aware of the limitations inherent to each approach when choosing a study design and interpreting results.

One-third of type 2 diabetes patients do not respond to metformin. Genetic variants in metformin transporters have been extensively studied as a likely contributor to this high failure rate. Here, we investigate, for the first time, the effect of genetic variants in transcription factors on metformin pharmacokinetics (PK) and response. Overall, 546 patients and healthy volunteers contributed their genome-wide, pharmacokinetic (235 subjects), and HbA1c data (440 patients) for this analysis. Five variants in specificity protein 1 (SP1), a transcription factor that modulates the expression of metformin transporters, were associated with changes in treatment HbA1c (P < 0.01) and metformin secretory clearance (P < 0.05). Population pharmacokinetic modeling further confirmed a 24% reduction in apparent clearance in homozygous carriers of one such variant, rs784888. Genetic variants in other transcription factors, peroxisome proliferator-activated receptor-α and hepatocyte nuclear factor 4-α, were significantly associated with HbA1c change only. Overall, our study highlights the importance of genetic variants in transcription factors as modulators of metformin PK and response.

Peripheral neuropathy is a common dose-limiting toxicity for patients treated with paclitaxel. For most individuals, there are no known risk factors that predispose patients to the adverse event, and pathogenesis for paclitaxel-induced peripheral neuropathy is unknown. Determining whether there is a heritable component to paclitaxel-induced peripheral neuropathy would be valuable in guiding clinical decisions and may provide insight into treatment of and mechanisms for the toxicity. Using genotype and patient information from the paclitaxel arm of CALGB 40101 (Alliance), a phase III clinical trial evaluating adjuvant therapies for breast cancer in women, we estimated the variance in maximum grade and dose at first instance of sensory peripheral neuropathy. Our results suggest that paclitaxel-induced neuropathy has a heritable component, driven in part by genes involved in axon outgrowth. Disruption of axon outgrowth may be one of the mechanisms by which paclitaxel treatment results in sensory peripheral neuropathy in susceptible patients.

Bosentan (Tracleer) is an endothelin receptor antagonist prescribed for the treatment of pulmonary arterial hypertension (PAH). Its use is limited by drug-induced liver injury (DILI). To identify genetic markers of DILI, association analyses were performed on 56 Caucasian PAH patients receiving bosentan. Twelve functional polymorphisms in five genes (ABCB11, ABCC2, CYP2C9, SLCO1B1, and SLCO1B3) implicated in bosentan pharmacokinetics were tested for associations with alanine aminotransferase (ALT), aspartate aminotransferase (AST), and DILI. After adjusting for body mass index, CYP2C9*2 was the only polymorphism associated with ALT, AST, and DILI (β = 2.16, P = 0.024; β = 1.92, P = 0.016; odds ratio 95% CI = 2.29-∞, P = 0.003, respectively). Bosentan metabolism by CYP2C9*2 in vitro was significantly reduced compared with CYP2C9*1 and was comparable to that by CYP2C9*3. These results suggest that CYP2C9*2 is a potential genetic marker for prediction of bosentan-induced liver injury and warrants investigation for the optimization of bosentan treatment.

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.

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.

The gasoline additive MTBE, methyl tert-butyl ether, is a widespread and persistent groundwater contaminant. MTBE undergoes rapid mineralization as the sole carbon and energy source of bacterial strain PM1, isolated from an enrichment culture of compost biofilter material. In this report, we describe the results of microbial community DNA profiling to assess the relative dominance of isolate PM1 and other bacterial strains cultured from the compost enrichment. Three polymerase chain reaction (PCR)-based profiling approaches were evaluated: denaturing gradient gel electrophoresis (DGGE) analysis of 230 bp 16S rDNA fragments; thermal gradient gel electrophoresis (TGGE) analysis of 575 bp 16S rDNA fragments; and non-denaturing polyacrylamide gel electrophoresis of 300-1,500 bp fragments containing 16S/23S ribosomal intergenic transcribed spacer (ITS) regions. Whereas all three DNA profiling approaches indicated that PM1-like bands predominated in mixtures from MTBE-grown enrichments, ITS profiling provided the most abundant and specific sequence data to confirm strain PM1's presence in the enrichment. Moreover, ITS profiling did not produce non-specific PCR products that were observed with T/DGGE. A further advantage of ITS community profiling over other methods requiring restriction digestion (e.g. terminal restriction fragment length polymorphisms) was that it did not require an additional digestion step or the use of automated sequencing equipment. ITS bands, excised from similar locations in profiles of the enrichment and PM1 pure culture, were 99.9% identical across 750 16S rDNA positions and 100% identical across 691 spacer positions. BLAST comparisons of nearly full-length 16S rDNA sequences showed 96% similarity between isolate PM1 and representatives of at least four different genera in the Leptothrix subgroup of the beta-Proteobacteria (Aquabacterium, Leptothrix, Rubrivivax and Ideonella). Maximum likelihood and parsimony analyses of 1,249 nucleotide positions supported isolate PM1's position in a separate lineage within the Leptothrix subgroup.

Purpose: Sensory peripheral neuropathy is a common and sometimes debilitating toxicity associated with paclitaxel therapy. This study aims to identify genetic risk factors for the development of this toxicity. Experimental Design: A prospective pharmacogenetic analysis of patients with primary breast cancer, randomized to the paclitaxel arm of CALGB 40101, was used to identify genetic predictors of the onset and severity of sensory peripheral neuropathy. A genome-wide association study in 855 subjects of European ancestry was conducted and findings were replicated in additional European (n = 154) and African American (n = 117) subjects. Results: A single nucleotide polymorphism in FGD4 was associated with the onset of sensory peripheral neuropathy in the discovery cohort [rs10771973; HR, 1.57; 95% confidence interval (CI), 1.30–1.91; P = 2.6 × 10−6] and in a European (HR, 1.72; 95% CI, 1.06–2.80; P = 0.013) and African American (HR, 1.93; 95% CI, 1.13–3.28; P = 6.7 × 10−3) replication cohort. There is also evidence that markers in additional genes, including EPHA5 (rs7349683) and FZD3 (rs10771973), were associated with the onset or severity of paclitaxel-induced sensory peripheral neuropathy. Conclusions: A genome-wide association study has identified novel genetic markers of paclitaxel-induced sensory peripheral neuropathy, including a common polymorphism in FGD4, a congenital peripheral neuropathy gene. These findings suggest that genetic variation may contribute to variation in development of this toxicity. Validation of these findings may allow for the identification of patients at increased risk of peripheral neuropathy and inform the use of an alternative to paclitaxel and/or the clinical management of this toxicity. Clin Cancer Res; 18(18); 5099–109. ©2012 AACR.

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.

Abstract 2548 Background: AML is a malignancy with an average five-year survival rate of 50% in adults. Great strides have been made in deciphering the genetic heterogeneity of this disease and indentifying subgroups with favorable or unfavorable outcomes based on cytogenetic and molecular factors. We undertook this study to identify patient-specific germline determinants of treatment outcome and survival in AML. Methods: Our cohort was comprised of 314 adult patients with AML in first or subsequent complete remission who underwent consolidation therapy with a busulfan-based autologous stem cell transplant (Bu-ASCT) at UCSF between 1986 and 2009. DNA samples were isolated from patients' peripheral blood mononuclear cell alliquots collected via apheresis following etoposide and high-dose cytarabine consolidation at the time of confirmed complete remission. Genotyping was performed using the Illumina Human OmniExpress Beadchip. To adjust for population substructure, EIGENSTRAT software was used to determine eigenvalues for the SNP correlation matrix and the corresponding eigenvectors were used as covariates. Statistical analyses were conducted with STATA and PLINK software. Association testing with DFS was based on Cox proportional hazards models. We utilized a multiplicative (log-additive) model with a genome wide-alpha set at 10−8. The subset of 168 genetically European patients (cau) was used for the primary analysis. Results: Overall, 9.7% of patients in our cohort had high-risk/relapsed acute promyelocytic leukemia, 12.9% core-binding factor leukemia [inv 16 or t(8;21)], 69.5% cytogenetically-normal leukemia, and 7.9% high-risk disease either by cytogenetic or molecular abnormalities. Disease-free survival in our cohort following Bu-ASCT was 59% at 5 years. As expected, baseline risk was significantly associated with DFS but treatment era was not. We identified several significant SNP clusters in our analysis. A 4-SNP cluster emerged on chr15 with a SNP ranked 2nd and one ranked 8th in the overall analysis. The SNP (rs933813) marked 2nd was upstream of the insulin-like growth factor-1 receptor (IGF1R). After adjustment for baseline risk, this SNP was significantly associated with DFS in cau (p-value= 6.9×10−8) as well as the overall population (p-value= 1.1×10−6). Other clusters are being evaluated. Conclusions: We identified several significant SNPs and SNP clusters that are associated with DFS in adult patients with AML undergoing Bu-ASCT. So far a SNP upstream of IGF1R has emerged as highly significant in our analysis. Ongoing studies are focusing on imputation, fine mapping and functional validation of these results. Disclosures: No relevant conflicts of interest to declare.

An important step in analyzing genetic association study data is deciding whether to adjust for covariates—those variables ancillary to the variants of interest. In particular, when testing for novel associations, should the statistical model also include known genetic or nongenetic covariates that are predictors of the trait (e.g., body mass index when studying type 2 diabetes)? Yes, if the covariates are also correlated with the primary variants but do not mediate their effects, because they may confound the genetic associations. Including them helps control bias and prevent false discoveries (Figure 1a). But the answer is less clear-cut if the covariates are not confounders.

BACKGROUND: First-generation gene signatures that identify breast cancer patients at risk of recurrence are confined to estrogen-positive cases and are driven by genes involved in the cell cycle and proliferation. Previously we induced sets of stromal genes that are prognostic for both estrogen-positive and estrogen-negative samples. Creating risk-management tools that incorporate these stromal signatures, along with existing proliferation-based signatures and established clinicopathological measures such as lymph node status and tumor size, should better identify women at greatest risk for metastasis and death. METHODOLOGY/PRINCIPAL FINDINGS: To investigate the strength and independence of the stromal and proliferation factors in estrogen-positive and estrogen-negative patients we constructed multivariate Cox proportional hazards models along with tree-based partitions of cancer cases for four breast cancer cohorts. Two sets of stromal genes, one consisting of DCN and FBLN1, and the other containing LAMA2, add substantial prognostic value to the proliferation signal and to clinical measures. For estrogen receptor-positive patients, the stromal-decorin set adds prognostic value independent of proliferation for three of the four datasets. For estrogen receptor-negative patients, the stromal-laminin set significantly adds prognostic value in two datasets, and marginally in a third. The stromal sets are most prognostic for the unselected population studies and may depend on the age distribution of the cohorts. CONCLUSION: The addition of stromal genes would measurably improve the performance of proliferation-based first-generation gene signatures, especially for older women. Incorporating indicators of the state of stromal cell types would mark a conceptual shift from epithelial-centric risk assessment to assessment based on the multiple cell types in the cancer-altered tissue.

Background: The transforming growth factor-β (TGF-β) signaling pathway has been reported to play a dual role in prostate carcinogenesis. Initially, TGF-β signaling inhibits tumor growth by its antiproliferative activity, but eventually it promotes tumor development by enhancing tumor cell motility and metastasis. We investigated whether common genetic variation in TGFB1, TGFBR1, and TGFBR2 influences the risk of aggressive prostate cancer and biochemical recurrence. Methods: Forty-four tag SNPs in TGFB1, TGFBR1, and TGFBR2 were examined in a case-control study of aggressive prostate cancer (501 cases/538 controls) and a follow-up study of biochemical recurrence (824 cases). Aggressive disease was defined as having either having a Gleason score ≥7 or TNM stage ≥ T2c, or PSA at diagnosis >10 ng/ml. Biochemical recurrence was defined as post-treatment prostate specific antigen (PSA) levels >0.3 ng/ml for radical prostatectomy patients or 2 ng/ml increase above the nadir for radiotherpay patients. Odds ratios (OR) and 95% Confidence Intervals (CI) were estimated by unconditional logistic regression to evaluate the association between SNPs and aggressive prostate cancer risk. Hazard ratios (HR) and 95% CI were estimated by Cox proportional hazard regression to examine the association between SNPs and biochemical recurrence. Results: For risk of aggressive disease, three SNPs were nominally statistically significant (TGFB1-rs4803455; TGFBR1-rs10512263; TGFBR2-rs2276768) with the most compelling polymorphism (TGFBR1-rs10512263) having an OR=1.76; 95% CI: 1.14-2.43 (P = 0.008) per additional minor allele. For risk of biochemical recurrence, seven SNPs were nominally statistical significant (TGFBR1- rs10739778; TGFBR2- rs12493607, rs2082224, rs1346907, rs1431131, rs1835538, rs11129421) with TGFBR2- rs12493607 associated with a HR=1.40; 95% CI: 1.14-1.71; P=0.001 per additional minor allele. Conclusion: Our study suggests that genetic variation in the TGF-β pathway may be associated with risk of aggressive prostate cancer and biochemical recurrence. Future work will evaluate the cumulative and interactive effects of the associations within this pathway on prostate cancer risk and recurrence. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2832.

Background Two-way hierarchical clustering, with results visualized as heatmaps, has served as the method of choice for exploring structure in large matrices of expression data since the advent of microarrays. While it has delivered important insights, including a typology of breast cancer subtypes, it suffers from instability in the face of gene or sample selection, and an inability to detect small sets that may be dominated by larger sets such as the estrogen-related genes in breast cancer. The rank-based partitioning algorithm introduced in this paper addresses several of these limitations. It delivers results comparable to two-way hierarchical clustering, and much more. Applied systematically across a range of parameter settings, it enumerates all the partition-inducing gene sets in a matrix of expression values. Results Applied to four large breast cancer datasets, this alternative exploratory method detects more than thirty sets of co-regulated genes, many of which are conserved across experiments and across platforms. Many of these sets are readily identified in biological terms, e.g., "estrogen", "erbb2", and 8p11-12, and several are clinically significant as prognostic of either increased survival ("adipose", "stromal"...) or diminished survival ("proliferation", "immune/interferon", "histone",...). Of special interest are the sets that effectively factor "immune response" and "stromal signalling". Conclusion The gene sets induced by the enumeration include many of the sets reported in the literature. In this regard these inventories confirm and consolidate findings from microarray-based work on breast cancer over the last decade. But, the enumerations also identify gene sets that have not been studied as of yet, some of which are prognostic of survival. The sets induced are robust, biologically meaningful, and serve to reveal a finer structure in existing breast cancer microarrays.