Adverse Cardiovascular Outcomes and Antihypertensive Treatment: A Genome-Wide Interaction Meta-Analysis in the International Consortium for Antihypertensive Pharmacogenomics Studies.

We sought to identify genome-wide variants influencing antihypertensive drug response and adverse cardiovascular outcomes, utilizing data from four randomized controlled trials in the International Consortium for Antihypertensive Pharmacogenomics Studies (ICAPS). Genome-wide antihypertensive drug-single nucleotide polymorphism (SNP) interaction tests for four drug classes (ß-blockers, n = 9,195; calcium channel blockers (CCBs), n = 10,511; thiazide/thiazide-like diuretics, n = 3,516; ACE-inhibitors/ARBs, n = 2,559) and cardiovascular outcomes (incident myocardial infarction, stroke, or death) were analyzed among patients with hypertension of European ancestry. Top SNPs from the meta-analyses were tested for replication of cardiovascular outcomes in an independent Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) study (n = 21,267), blood pressure (BP) response in independent ICAPS studies (n = 1,552), and ethnic validation in African Americans from the Genetics of Hypertension Associated Treatment study (GenHAT; n = 5,115). One signal reached genome-wide significance in the ß-blocker-SNP interaction analysis (rs139945292, Interaction P = 1.56 × 10-8 ). rs139945292 was validated through BP response to ß-blockers, with the T-allele associated with less BP reduction (systolic BP response P = 6 × 10-4 , Beta = 3.09, diastolic BP response P = 5 × 10-3 , Beta = 1.53). The T-allele was also associated with increased adverse cardiovascular risk within the ß-blocker treated patients' subgroup (P = 2.35 × 10-4 , odds ratio = 1.57, 95% confidence interval = 1.23-1.99). The locus showed nominal replication in CHARGE, and consistent directional trends in ß-blocker treated African Americans. rs139945292 is an expression quantitative trait locus for the 50 kb upstream gene NTM (neurotrimin). No SNPs attained genome-wide significance for any other drugs classes. Top SNPs were located near CALB1 (CCB), FLJ367777 (ACE-inhibitor), and CES5AP1 (thiazide). The NTM region is associated with increased risk for adverse cardiovascular outcomes and less BP reduction in ß-blocker treated patients. Further investigation into this region is warranted.

High-throughput toxicogenomic screening of chemicals in the environment using metabolically competent hepatic cell cultures.

The ToxCast in vitro screening program has provided concentration-response bioactivity data across more than a thousand assay endpoints for thousands of chemicals found in our environment and commerce. However, most ToxCast screening assays have evaluated individual biological targets in cancer cell lines lacking integrated physiological functionality (such as receptor signaling, metabolism). We evaluated differentiated HepaRGTM cells, a human liver-derived cell model understood to effectively model physiologically relevant hepatic signaling. Expression of 93 gene transcripts was measured by quantitative polymerase chain reaction using Fluidigm 96.96 dynamic arrays in response to 1060 chemicals tested in eight-point concentration-response. A Bayesian framework quantitatively modeled chemical-induced changes in gene expression via six transcription factors including: aryl hydrocarbon receptor, constitutive androstane receptor, pregnane X receptor, farnesoid X receptor, androgen receptor, and peroxisome proliferator-activated receptor alpha. For these chemicals the network model translates transcriptomic data into Bayesian inferences about molecular targets known to activate toxicological adverse outcome pathways. These data also provide new insights into the molecular signaling network of HepaRGTM cell cultures.

The influence of packed cell volume versus plasma proteins on thromboelastographic variables in canine blood.

OBJECTIVE: Determine the correlation between kaolin-activated thromboelastography (TEG) variables (R, K, angle, and maximum amplitude [MA]) and PCV, fibrinogen concentration (FC), and total fibrinogen (TF) in an ex vivo model. ANIMALS: Two healthy adult mixed-breed dogs. PROCEDURES: Citrated whole blood was obtained and separated into packed red cells, platelet rich plasma, and platelet poor plasma (PPP). An aliquot of PPP was heated to denature heat labile proteins (fibrinogen, factor V, factor VIII). Blood components were recombined for analyses of 6 physiological scenarios: anemia with low fibrinogen; anemia with moderate fibrinogen; anemia with normal fibrinogen; anemia with normal saline; normal PCV and normal fibrinogen; and normal PCV and low fibrinogen. A Kruskal-Wallis test, along with linear regressions on pairwise combinations of TEG variables, was used to determine the correlation between TEG variables and PCV, FC, and TF. RESULTS: Maximum amplitude correlated with FC (R2 0.60, P < 0.001) and TF (R2 0.57, P < 0.001) but not PCV (R2 0.003, P = 0.7). Angle and K time were moderately correlated with FC ([angle: R2 0.53, P < 0.001]; [K: R2 0.55, P < 0.001]) and TF ([alpha angle: R2 0.52, P < 0.001]; [K: R2 0.51, P < 0.001]) but not PCV. The R time was weakly correlated with PCV (R2 0.15, P < 0.009) but not FC or TF. CONCLUSIONS AND CLINICAL RELEVANCE: In an ex vivo model, plasma proteins but not PCV impacted TEG variables. This suggests that TEG changes noted with anemia are imparted by changes in available fibrinogen in a fixed microenvironment rather than artifact of anemia.

Potential yields and emission reductions of biojet fuels produced via hydrotreatment of biocrudes produced through direct thermochemical liquefaction.

BACKGROUND: The hydrotreatment of oleochemical/lipid feedstocks is currently the only technology that provides significant volumes (millions of litres per year) of "conventional" biojet/sustainable aviation fuels (SAF). However, if biojet fuels are to be produced in sustainably sourced volumes (billions of litres per year) at a price comparable with fossil jet fuel, biomass-derived "advanced" biojet fuels will be needed. Three direct thermochemical liquefaction technologies, fast pyrolysis, catalytic fast pyrolysis and hydrothermal liquefaction were assessed for their potential to produce "biocrudes" which were subsequently upgraded to drop-in biofuels by either dedicated hydrotreatment or co-processed hydrotreatment. RESULTS: A significant biojet fraction (between 20.8 and 36.6% of total upgraded fuel volume) was produced by all of the processes. When the fractions were assessed against general ASTM D7566 specifications they showed significant compliance, despite a lack of optimization in any of the process steps. When the life cycle analysis GHGenius model was used to assess the carbon intensity of the various products, significant emission reductions (up to 74%) could be achieved. CONCLUSIONS: It was apparent that the production of biojet fuels based on direct thermochemical liquefaction of biocrudes, followed by hydrotreating, has considerable potential.

Synergistic Chemotherapy Drug Response Is a Genetic Trait in Lymphoblastoid Cell Lines.

Lymphoblastoid cell lines (LCLs) are a highly successful model for evaluating the genetic etiology of cancer drug response, but applications using this model have typically focused on single drugs. Combination therapy is quite common in modern chemotherapy treatment since drugs often work synergistically, and it is an important progression in the use of the LCL model to expand work for drug combinations. In the present work, we demonstrate that synergy occurs and can be quantified in LCLs across a range of clinically important drug combinations. Lymphoblastoid cell lines have been commonly employed in association mapping in cancer pharmacogenomics, but it is so far untested as to whether synergistic effects have a genetic etiology. Here we use cell lines from extended pedigrees to demonstrate that there is a substantial heritable component to synergistic drug response. Additionally, we perform linkage mapping in these pedigrees to identify putative regions linked to this important phenotype. This demonstration supports the premise of expanding the use of the LCL model to perform association mapping for combination therapies.

Altered bile acid profile associates with cognitive impairment in Alzheimer's disease-An emerging role for gut microbiome.

INTRODUCTION: Increasing evidence suggests a role for the gut microbiome in central nervous system disorders and a specific role for the gut-brain axis in neurodegeneration. Bile acids (BAs), products of cholesterol metabolism and clearance, are produced in the liver and are further metabolized by gut bacteria. They have major regulatory and signaling functions and seem dysregulated in Alzheimer's disease (AD). METHODS: Serum levels of 15 primary and secondary BAs and their conjugated forms were measured in 1464 subjects including 370 cognitively normal older adults, 284 with early mild cognitive impairment, 505 with late mild cognitive impairment, and 305 AD cases enrolled in the AD Neuroimaging Initiative. We assessed associations of BA profiles including selected ratios with diagnosis, cognition, and AD-related genetic variants, adjusting for confounders and multiple testing. RESULTS: In AD compared to cognitively normal older adults, we observed significantly lower serum concentrations of a primary BA (cholic acid [CA]) and increased levels of the bacterially produced, secondary BA, deoxycholic acid, and its glycine and taurine conjugated forms. An increased ratio of deoxycholic acid:CA, which reflects 7α-dehydroxylation of CA by gut bacteria, strongly associated with cognitive decline, a finding replicated in serum and brain samples in the Rush Religious Orders and Memory and Aging Project. Several genetic variants in immune response-related genes implicated in AD showed associations with BA profiles. DISCUSSION: We report for the first time an association between altered BA profile, genetic variants implicated in AD, and cognitive changes in disease using a large multicenter study. These findings warrant further investigation of gut dysbiosis and possible role of gut-liver-brain axis in the pathogenesis of AD.

The influence of Neanderthal alleles on cytotoxic response.

Various studies have shown that people of Eurasian origin contain traces of DNA inherited from interbreeding with Neanderthals. Recent studies have demonstrated that these Neanderthal variants influence a range of clinically important traits and diseases. Thus, understanding the genetic factors responsible for the variability in individual response to drug or chemical exposure is a key goal of pharmacogenomics and toxicogenomics, as dose responses are clinically and epidemiologically important traits. It is well established that ethnic and racial differences are important in dose response traits, but to our knowledge the influence of Neanderthal ancestry on response to xenobiotics is unknown. Towards this aim, we examined if Neanderthal ancestry plays a role in cytotoxic response to anti-cancer drugs and toxic environmental chemicals. We identified common Neanderthal variants in lymphoblastoid cell lines (LCLs) derived from the globally diverse 1000 Genomes Project and Caucasian cell lines from the Children's Hospital of Oakland Research Institute. We analyzed the effects of these Neanderthal alleles on cytotoxic response to 29 anti-cancer drugs and 179 environmental chemicals at varying concentrations using genome-wide data. We identified and replicated single nucleotide polymorphisms (SNPs) from these association results, including a SNP in the SNORD-113 cluster. Our results also show that the Neanderthal alleles cumulatively lead to increased sensitivity to both the anti-cancer drugs and the environmental chemicals. Our results demonstrate the influence of Neanderthal ancestry-informative markers on cytotoxic response. These results could be important in identifying biomarkers for personalized medicine or in dissecting the underlying etiology of dose response traits.

Genetic Variants in CPA6 and PRPF31 Are Associated With Variation in Response to Metformin in Individuals With Type 2 Diabetes.

Metformin is the first-line treatment for type 2 diabetes (T2D). Although widely prescribed, the glucose-lowering mechanism for metformin is incompletely understood. Here, we used a genome-wide association approach in a diverse group of individuals with T2D from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial to identify common and rare variants associated with HbA1c response to metformin treatment and followed up these findings in four replication cohorts. Common variants in PRPF31 and CPA6 were associated with worse and better metformin response, respectively (P < 5 × 10-6), and meta-analysis in independent cohorts displayed similar associations with metformin response (P = 1.2 × 10-8 and P = 0.005, respectively). Previous studies have shown that PRPF31(+/-) knockout mice have increased total body fat (P = 1.78 × 10-6) and increased fasted circulating glucose (P = 5.73 × 10-6). Furthermore, rare variants in STAT3 associated with worse metformin response (q <0.1). STAT3 is a ubiquitously expressed pleiotropic transcriptional activator that participates in the regulation of metabolism and feeding behavior. Here, we provide novel evidence for associations of common and rare variants in PRPF31, CPA6, and STAT3 with metformin response that may provide insight into mechanisms important for metformin efficacy in T2D.

Genetic Variants in HSD17B3, SMAD3, and IPO11 Impact Circulating Lipids in Response to Fenofibrate in Individuals With Type 2 Diabetes.

Individuals with type 2 diabetes (T2D) and dyslipidemia are at an increased risk of cardiovascular disease. Fibrates are a class of drugs prescribed to treat dyslipidemia, but variation in response has been observed. To evaluate common and rare genetic variants that impact lipid responses to fenofibrate in statin-treated patients with T2D, we examined lipid changes in response to fenofibrate therapy using a genomewide association study (GWAS). Associations were followed-up using gene expression studies in mice. Common variants in SMAD3 and IPO11 were marginally associated with lipid changes in black subjects (P < 5 × 10-6 ). Rare variant and gene expression changes were assessed using a false discovery rate approach. AKR7A3 and HSD17B13 were associated with lipid changes in white subjects (q < 0.2). Mice fed fenofibrate displayed reductions in Hsd17b13 gene expression (q < 0.1). Associations of variants in SMAD3, IPO11, and HSD17B13, with gene expression changes in mice indicate that transforming growth factor-beta (TGF-ß) and NRF2 signaling pathways may influence fenofibrate effects on dyslipidemia in patients with T2D.

Targeted metabolomics and medication classification data from participants in the ADNI1 cohort.

Alzheimer's disease (AD) is the most common neurodegenerative disease presenting major health and economic challenges that continue to grow. Mechanisms of disease are poorly understood but significant data point to metabolic defects that might contribute to disease pathogenesis. The Alzheimer Disease Metabolomics Consortium (ADMC) in partnership with Alzheimer Disease Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for AD. Using targeted and non- targeted metabolomics and lipidomics platforms we are mapping metabolic pathway and network failures across the trajectory of disease. In this report we present quantitative metabolomics data generated on serum from 199 control, 356 mild cognitive impairment and 175 AD subjects enrolled in ADNI1 using AbsoluteIDQ-p180 platform, along with the pipeline for data preprocessing and medication classification for confound correction. The dataset presented here is the first of eight metabolomics datasets being generated for broad biochemical investigation of the AD metabolome. We expect that these collective metabolomics datasets will provide valuable resources for researchers to identify novel molecular mechanisms contributing to AD pathogenesis and disease phenotypes.

Metabolic network failures in Alzheimer's disease: A biochemical road map.

INTRODUCTION: The Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance. METHODS: Fasting serum samples from the Alzheimer's Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted. RESULTS: Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aß1-42, tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease. DISCUSSION: Metabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery.

A comparison of DMET Plus microarray and genome-wide technologies by assessing population substructure.

OBJECTIVE: The capacity of the Affymetrix drug metabolism enzymes and transporters (DMET) Plus pharmacogenomics genotyping chip to estimate population substructure and cryptic relatedness was evaluated. The results were compared with estimates using genome-wide HapMap data for the same individuals. METHODS: For 301 unrelated individuals, spanning three continental populations and one admixed population, genotypic data were collected using the Affymetrix DMET Plus microarray. Genome-wide data on these individuals were obtained from HapMap release 3. Population substructure was assessed using Eigenstrat and ADMIXTURE software for both platforms. Cryptic relatedness was explored by inbreeding coefficient estimation. Nonparametric tests were used to determine correlations of the analytical results of the two genotyping platforms. RESULTS: Principal components analysis identified population substructure for both datasets, with 15.8 and 16.6% of the total variance explained in the first two principal components for DMET Plus and HapMap data, respectively. ADMIXTURE results correctly identified four subpopulations within each dataset. Nonparametric rank correlations indicated significant associations between analyses with an average ρ=0.7272 (P<10) across the three continental populations and ρ=0.4888 for the admixed population. Concordance correlation coefficients (average ρc=0.9693 across all four subpopulations) strongly indicate concordance between ADMIXTURE results. Inbreeding coefficients were slightly inflated (16 individuals>0.15) using DMET Plus data and no cryptic relatedness was indicated using HapMap data. The inflated inbreeding estimation could be because of the limited number of markers provided by DMET as a random sample of 1832 markers from HapMap also yielded inflated estimates of cryptic relatedness (39 individuals>0.15). Furthermore, use of single nucleotide polymorphisms located in genes involved in metabolism and transport may have different allele frequencies in subpopulations than single nucleotide polymorphisms sampled from the whole genome. CONCLUSION: The DMET Plus pharmacogenomics genotyping chip is effective in quantifying population substructure across the three continental populations and inferring the presence of an admixed population. On the basis of our results, these microarrays offer sufficient depth for covariate adjustment of population substructure in genomic association studies.

Using ToxCast™ Data to Reconstruct Dynamic Cell State Trajectories and Estimate Toxicological Points of Departure.

BACKGROUND: High-content imaging (HCI) allows simultaneous measurement of multiple cellular phenotypic changes and is an important tool for evaluating the biological activity of chemicals. OBJECTIVES: Our goal was to analyze dynamic cellular changes using HCI to identify the "tipping point" at which the cells did not show recovery towards a normal phenotypic state. METHODS: HCI was used to evaluate the effects of 967 chemicals (in concentrations ranging from 0.4 to 200 µM) on HepG2 cells over a 72-hr exposure period. The HCI end points included p53, c-Jun, histone H2A.x, α-tubulin, histone H3, alpha tubulin, mitochondrial membrane potential, mitochondrial mass, cell cycle arrest, nuclear size, and cell number. A computational model was developed to interpret HCI responses as cell-state trajectories. RESULTS: Analysis of cell-state trajectories showed that 336 chemicals produced tipping points and that HepG2 cells were resilient to the effects of 334 chemicals up to the highest concentration (200 µM) and duration (72 hr) tested. Tipping points were identified as concentration-dependent transitions in system recovery, and the corresponding critical concentrations were generally between 5 and 15 times (25th and 75th percentiles, respectively) lower than the concentration that produced any significant effect on HepG2 cells. The remaining 297 chemicals require more data before they can be placed in either of these categories. CONCLUSIONS: These findings show the utility of HCI data for reconstructing cell state trajectories and provide insight into the adaptation and resilience of in vitro cellular systems based on tipping points. Cellular tipping points could be used to define a point of departure for risk-based prioritization of environmental chemicals. CITATION: Shah I, Setzer RW, Jack J, Houck KA, Judson RS, Knudsen TB, Liu J, Martin MT, Reif DM, Richard AM, Thomas RS, Crofton KM, Dix DJ, Kavlock RJ. 2016. Using ToxCast™ data to reconstruct dynamic cell state trajectories and estimate toxicological points of departure. Environ Health Perspect 124:910-919; http://dx.doi.org/10.1289/ehp.1409029.

Patient-centered care for chronic pain in the emergency department: A qualitative study.

Pain is a common problem for which patients seek care in the emergency department, accounting for up to 42% of all ED visits. The purpose of this study was to explore qualitatively the reasons for use of the emergency department (ED) by those frequenting the ED for treatment of chronic pain. The settings for the study were two sites of a large U.S. Midwestern healthcare system. The sample comprised patients who used the ED four or more times in the 3-month time of data collection. From a total of 85 frequent users identified through retrospective chart reviews, a computer generated random sample of patients was selected to explore their reasons for use of ED for treatment of chronic pain. Content analysis was used to identify themes from the interviews. Four themes emerged from the qualitative data analysis: time of day, pain intensity, barriers to and reasons for using the emergency department for care, and lack of individualized plan of care. Reasons patients use the ED for chronic pain are numerous and complex. Leaders of healthcare organizations must address patient-centered care, with specific alternatives to the emergency department such as individualized care plans, and care transition interventions.

Evaluating the role of admixture in cancer therapy via in vitro drug response and multivariate genome-wide associations.

AIM: We investigate the role of ethnicity and admixture in drug response across a broad group of chemotherapeutic drugs. Also, we generate hypotheses on the genetic variants driving differential drug response through multivariate genome-wide association studies. METHODS: Immortalized lymphoblastoid cell lines from 589 individuals (Hispanic or non-Hispanic/Caucasian) were used to investigate dose-response for 28 chemotherapeutic compounds. Univariate and multivariate statistical models were used to elucidate associations between genetic variants and differential drug response as well as the role of ethnicity in drug potency and efficacy. RESULTS & CONCLUSION: For many drugs, the variability in drug response appears to correlate with self-reported race and estimates of genetic ancestry. Additionally, multivariate genome-wide association analyses offered interesting hypotheses governing these differential responses.

Population-based in vitro hazard and concentration-response assessment of chemicals: the 1000 genomes high-throughput screening study.

BACKGROUND: Understanding of human variation in toxicity to environmental chemicals remains limited, so human health risk assessments still largely rely on a generic 10-fold factor (10½ each for toxicokinetics and toxicodynamics) to account for sensitive individuals or subpopulations. OBJECTIVES: We tested a hypothesis that population-wide in vitro cytotoxicity screening can rapidly inform both the magnitude of and molecular causes for interindividual toxicodynamic variability. METHODS: We used 1,086 lymphoblastoid cell lines from the 1000 Genomes Project, representing nine populations from five continents, to assess variation in cytotoxic response to 179 chemicals. Analysis included assessments of population variation and heritability, and genome-wide association mapping, with attention to phenotypic relevance to human exposures. RESULTS: For about half the tested compounds, cytotoxic response in the 1% most "sensitive" individual occurred at concentrations within a factor of 10½ (i.e., approximately 3) of that in the median individual; however, for some compounds, this factor was > 10. Genetic mapping suggested important roles for variation in membrane and transmembrane genes, with a number of chemicals showing association with SNP rs13120371 in the solute carrier SLC7A11, previously implicated in chemoresistance. CONCLUSIONS: This experimental approach fills critical gaps unaddressed by recent large-scale toxicity testing programs, providing quantitative, experimentally based estimates of human toxicodynamic variability, and also testable hypotheses about mechanisms contributing to interindividual variation.

An adaptive permutation approach for genome-wide association study: evaluation and recommendations for use.

BACKGROUND: Permutation testing is a robust and popular approach for significance testing in genomic research, which has the broad advantage of estimating significance non-parametrically, thereby safe guarding against inflated type I error rates. However, the computational efficiency remains a challenging issue that limits its wide application, particularly in genome-wide association studies (GWAS). Because of this, adaptive permutation strategies can be employed to make permutation approaches feasible. While these approaches have been used in practice, there is little research into the statistical properties of these approaches, and little guidance into the proper application of such a strategy for accurate p-value estimation at the GWAS level. METHODS: In this work, we advocate an adaptive permutation procedure that is statistically valid as well as computationally feasible in GWAS. We perform extensive simulation experiments to evaluate the robustness of the approach to violations of modeling assumptions and compare the power of the adaptive approach versus standard approaches. We also evaluate the parameter choices in implementing the adaptive permutation approach to provide guidance on proper implementation in real studies. Additionally, we provide an example of the application of adaptive permutation testing on real data. RESULTS: The results provide sufficient evidence that the adaptive test is robust to violations of modeling assumptions. In addition, even when modeling assumptions are correct, the power achieved by adaptive permutation is identical to the parametric approach over a range of significance thresholds and effect sizes under the alternative. A framework for proper implementation of the adaptive procedure is also generated. CONCLUSIONS: While the adaptive permutation approach presented here is not novel, the current study provides evidence of the validity of the approach, and importantly provides guidance on the proper implementation of such a strategy. Additionally, tools are made available to aid investigators in implementing these approaches.

Single-nucleotide polymorphism data support the general unrelatedness of the males in the agricultural health study.

BACKGROUND: Farming is often a family and multigenerational business. Relatedness among farmers could bias gene-environment interaction analysis. To evaluate the potential relatedness of farmers, we used data from a nested case-control study of prostate cancer conducted in the Agricultural Health Study (AHS), a prospective study of farmers in Iowa and North Carolina. METHODS: We analyzed the genetic data for 25,009 SNPs (single-nucleotide polymorphisms) from 2,220 White participants to test for cryptic relatedness among these farmers. We used two software packages: (i) PLINK, to calculate inbreeding coefficients and identity-by-descent (IBD) statistics and (ii) EIGENSOFT, to perform a principal component analysis on the genetic data. RESULTS: Inbreeding coefficients estimates and IBD statistics show that the subjects are overwhelmingly unrelated, with little potential for cryptic relatedness in these data. CONCLUSIONS: Our analysis rejects the hypothesis that individuals in the case-control study exhibit cryptic relatedness. IMPACT: These findings are important for all subsequent analyses of gene-environment interactions in the AHS.

Genetic heterogeneity beyond CYP2C8*3 does not explain differential sensitivity to paclitaxel-induced neuropathy.

The development of paclitaxel-induced peripheral neuropathy (PIPN) is influenced by drug exposure and patient genetics. The purpose of this analysis was to expand on a previous reported association of CYP2C8*3 and PIPN risk by investigating additional polymorphisms in CYP2C8 and in hundreds of other genes potentially relevant to paclitaxel pharmacokinetics. Clinical data was collected prospectively in an observational registry of newly diagnosed breast cancer patients. Patients treated with paclitaxel-containing regimens were genotyped using the Affymetrix DMET™ Plus chip. Patients who carried the CYP2C8*2, *3, or *4 variant were collapsed into a low-metabolizer CYP2C8 phenotype for association with PIPN. Separately, all SNPs that surpassed quality control were assessed individually and as a composite of genetic ancestry for associations with PIPN. 412 paclitaxel-treated patients and 564 genetic markers were included in the analysis. The risk of PIPN was significantly greater in the CYP2C8 low-metabolizer group (HR = 1.722, p = 0.018); however, the influences of the *2 and *4 SNPs were not independently significant (*2: p = 0.847, *4: p = 0.408). One intronic SNP in ABCG1 (rs492338) surpassed the exploratory significance threshold for an association with PIPN in the Caucasian cohort (p = 0.0008) but not in the non-Caucasian replication group (p = 0.54). Substantial genetic variability was observed within self-reported racial groups but this genetic variability was not associated with risk of grade 2+ PIPN. The pharmacogenetic heterogeneity within a cohort of breast cancer patients is dramatic, though we did not find evidence that this heterogeneity directly influences the risk of PIPN beyond the contribution of CYP2C8*3.

Natural variation in genome architecture among 205 Drosophila melanogaster Genetic Reference Panel lines.

The Drosophila melanogaster Genetic Reference Panel (DGRP) is a community resource of 205 sequenced inbred lines, derived to improve our understanding of the effects of naturally occurring genetic variation on molecular and organismal phenotypes. We used an integrated genotyping strategy to identify 4,853,802 single nucleotide polymorphisms (SNPs) and 1,296,080 non-SNP variants. Our molecular population genomic analyses show higher deletion than insertion mutation rates and stronger purifying selection on deletions. Weaker selection on insertions than deletions is consistent with our observed distribution of genome size determined by flow cytometry, which is skewed toward larger genomes. Insertion/deletion and single nucleotide polymorphisms are positively correlated with each other and with local recombination, suggesting that their nonrandom distributions are due to hitchhiking and background selection. Our cytogenetic analysis identified 16 polymorphic inversions in the DGRP. Common inverted and standard karyotypes are genetically divergent and account for most of the variation in relatedness among the DGRP lines. Intriguingly, variation in genome size and many quantitative traits are significantly associated with inversions. Approximately 50% of the DGRP lines are infected with Wolbachia, and four lines have germline insertions of Wolbachia sequences, but effects of Wolbachia infection on quantitative traits are rarely significant. The DGRP complements ongoing efforts to functionally annotate the Drosophila genome. Indeed, 15% of all D. melanogaster genes segregate for potentially damaged proteins in the DGRP, and genome-wide analyses of quantitative traits identify novel candidate genes. The DGRP lines, sequence data, genotypes, quality scores, phenotypes, and analysis and visualization tools are publicly available.