Daniel Q. Naiman

New genes involved in cancer identified by retroviral tagging

Retroviral insertional mutagenesis in BXH2 and AKXD mice induces a high incidence of myeloid leukemia and B- and T-cell lymphoma, respectively. The retroviral integration sites (RISs) in these tumors thus provide powerful genetic tags for the discovery of genes involved in cancer. Here we report the first large-scale use of retroviral tagging for cancer gene discovery in the post-genome era. Using high throughput inverse PCR, we cloned and analyzed the sequences of 884 RISs from a tumor panel composed primarily of B-cell lymphomas. We then compared these sequences, and another 415 RIS sequences previously cloned from BXH2 myeloid leukemias and from a few AKXD lymphomas, against the recently assembled mouse genome sequence. These studies identified 152 loci that are targets of retroviral integration in more than one tumor (common retroviral integration sites, CISs) and therefore likely to encode a cancer gene. Thirty-six CISs encode genes that are known or predicted to be genes involved in human cancer or their homologs, whereas others encode candidate genes that have not yet been examined for a role in human cancer. Our studies demonstrate the power of retroviral tagging for cancer gene discovery in the post-genome era and indicate a largely unrecognized complexity in mouse and presumably human cancer.

Polysubstance abuse-vulnerability genes: genome scans for association, using 1,004 subjects and 1,494 single-nucleotide polymorphisms.

Strong genetic contributions to drug abuse vulnerability are well documented, but few chromosomal locations for human drug-abuse vulnerability alleles have been confirmed. We now identify chromosomal markers whose alleles distinguish drug abusers from control individuals in each of two samples, on the basis of pooled-sample microarray and association analyses. Reproducibly positive chromosomal regions defined by these markers in conjunction with previous results were especially unlikely to have been identified by chance. Positive markers identify the alcohol dehydrogenase (ADH) locus, flank the brain-derived neurotropic factor (BDNF) locus, and mark seven other regions previously linked to vulnerability to nicotine or alcohol abuse. These data support polygenic contributions of common allelic variants to polysubstance abuse vulnerability.

A survey of ovary-, testis-, and soma-biased gene expression in Drosophila melanogaster adults

BackgroundSexual dimorphism results in the formation of two types of individuals with specialized reproductive roles and is most evident in the germ cells and gonads.ResultsWe have undertaken a global analysis of transcription between the sexes using a 31,464 element FlyGEM microarray to determine what fraction of the genome shows sex-biased expression, what tissues express these genes, the predicted functions of these genes, and where these genes map onto the genome. Females and males (both with and without gonads), dissected testis and ovary, females and males with genetically ablated germlines, and sex-transformed flies were sampled.ConclusionsUsing any of a number of criteria, we find extensive sex-biased expression in adults. The majority of cases of sex differential gene expression are attributable to the germ cells. There is also a large class of genes with soma-biased expression. There is little germline-biased expression indicating that nearly all genes with germline expression also show sex-bias. Monte Carlo simulations show that some genes with sex-biased expression are non-randomly distributed in the genome.

Number sense across the lifespan as revealed by a massive Internet-based sample

It has been difficult to determine how cognitive systems change over the grand time scale of an entire life, as few cognitive systems are well enough understood; observable in infants, adolescents, and adults; and simple enough to measure to empower comparisons across vastly different ages. Here we address this challenge with data from more than 10,000 participants ranging from 11 to 85 years of age and investigate the precision of basic numerical intuitions and their relation to students’ performance in school mathematics across the lifespan. We all share a foundational number sense that has been observed in adults, infants, and nonhuman animals, and that, in humans, is generated by neurons in the intraparietal sulcus. Individual differences in the precision of this evolutionarily ancient number sense may impact school mathematics performance in children; however, we know little of its role beyond childhood. Here we find that population trends suggest that the precision of one’s number sense improves throughout the school-age years, peaking quite late at ∼30 y. Despite this gradual developmental improvement, we find very large individual differences in number sense precision among people of the same age, and these differences relate to school mathematical performance throughout adolescence and the adult years. The large individual differences and prolonged development of number sense, paired with its consistent and specific link to mathematics ability across the age span, hold promise for the impact of educational interventions that target the number sense.

Classifying Gene Expression Profiles from Pairwise mRNA Comparisons

We present a new approach to molecular classification based on mRNA comparisons. Our method, referred to as the top-scoring pair(s) (TSP) classifier, is motivated by current technical and practical limitations in using gene expression microarray data for class prediction, for example to detect disease, identify tumors or predict treatment response. Accurate statistical inference from such data is difficult due to the small number of observations, typically tens, relative to the large number of genes, typically thousands. Moreover, conventional methods from machine learning lead to decisions which are usually very difficult to interpret in simple or biologically meaningful terms. In contrast, the TSP classifier provides decision rules which i) involve very few genes and only relative expression values (e.g., comparing the mRNA counts within a single pair of genes); ii) are both accurate and transparent; and iii) provide specific hypotheses for follow-up studies. In particular, the TSP classifier achieves prediction rates with standard cancer data that are as high as those of previous studies which use considerably more genes and complex procedures. Finally, the TSP classifier is parameter-free, thus avoiding the type of over-fitting and inflated estimates of performance that result when all aspects of learning a predictor are not properly cross-validated.

Daily intake of bisphenol A and potential sources of exposure: 2005–2006 National Health and Nutrition Examination Survey

Nationally representative data on urinary levels of bisphenol A (BPA) and its metabolites in the United States from the 2005–2006 National Health and Nutrition Examination Survey (NHANES) were used to estimate daily BPA intakes. In addition, NHANES data on potential sources of BPA exposure and personal characteristics were explored for their association with urinary BPA levels. On the basis of 2005–2006 NHANES urinary BPA data and assumptions described in this paper, median daily intake for the overall population is approximately 34 ng/kg-day. Median daily BPA intakes for men are statistically significantly higher than for women; there is a significant decrease in daily BPA intake with increasing age. Gender- and age-specific median intakes differ from the overall population by less than a factor of 2. Although estimates of daily BPA intake have decreased compared with those from the 2003–2004 NHANES, it is premature to draw conclusions regarding trends at this time, as there is no indication that BPA use declined from 2003 to 2006. On the basis of an assessment of urinary BPA and questionnaire data from the 2005–2006 NHANES, consumption of soda, school lunches, and meals prepared outside the home — but not bottled water or canned tuna — was statistically significantly associated with higher urinary BPA.

Bisphenol A (BPA) daily intakes in the United States: Estimates from the 2003–2004 NHANES urinary BPA data

Investigations into human exposure to bisphenol A (BPA) have, for the most part, assessed intake based on food consumption estimates combined with measurements or estimates of BPA in foods. In this study, nationally representative data on urinary levels of BPA in the United States (US) from the 2003–2004 National Health and Nutrition Examination Survey (NHANES) were used to estimate daily intake of BPA, assuming steady-state excretion. Distributions of intakes for the US population were determined for (i) all NHANES participants with urinary BPA data; (ii) participants by the following age groups: 6–11 years, 12–19 years, 20–39 years, 40–59 years, and 60+ years; and (iii) participants by gender. On the basis of the NHANES urinary BPA data and the assumptions described in this paper, daily BPA intakes for male participants are statistically significantly higher than for female participants, and there are statistically significant differences in daily BPA intakes according to age groups, with the oldest group having the lowest estimated intakes. Median intake was approximately three orders of magnitude below health-based guidance values of 50 μg/kg-day.

Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data

MOTIVATION DNA microarray data analysis has been used previously to identify marker genes which discriminate cancer from normal samples. However, due to the limited sample size of each study, there are few common markers among different studies of the same cancer. With the rapid accumulation of microarray data, it is of great interest to integrate inter-study microarray data to increase sample size, which could lead to the discovery of more reliable markers. RESULTS We present a novel, simple method of integrating different microarray datasets to identify marker genes and apply the method to prostate cancer datasets. In this study, by applying a new statistical method, referred to as the top-scoring pair (TSP) classifier, we have identified a pair of robust marker genes (HPN and STAT6) by integrating microarray datasets from three different prostate cancer studies. Cross-platform validation shows that the TSP classifier built from the marker gene pair, which simply compares relative expression values, achieves high accuracy, sensitivity and specificity on independent datasets generated using various array platforms. Our findings suggest a new model for the discovery of marker genes from accumulated microarray data and demonstrate how the great wealth of microarray data can be exploited to increase the power of statistical analysis. CONTACT leixu@jhu.edu.

Molecular Genetics of Addiction and Related Heritable Phenotypes

Genome‐wide association (GWA) can elucidate molecular genetic bases for human individual differences in complex phenotypes that include vulnerability to addiction. Here, we review (a) evidence that supports polygenic models with (at least) modest heterogeneity for the genetic architectures of addiction and several related phenotypes; (b) technical and ethical aspects of importance for understanding GWA data, including genotyping in individual samples versus DNA pools, analytic approaches, power estimation, and ethical issues in genotyping individuals with illegal behaviors; (c) the samples and the data that shape our current understanding of the molecular genetics of individual differences in vulnerability to substance dependence and related phenotypes; (d) overlaps between GWA data sets for dependence on different substances; and (e) overlaps between GWA data for addictions versus other heritable, brain‐based phenotypes that include bipolar disorder, cognitive ability, frontal lobe brain volume, the ability to successfully quit smoking, neuroticism, and Alzheimers disease. These convergent results identify potential targets for drugs that might modify addictions and play roles in these other phenotypes. They add to evidence that individual differences in the quality and quantity of brain connections make pleiotropic contributions to individual differences in vulnerability to addictions and to related brain disorders and phenotypes. A “connectivity constellation” of brain phenotypes and disorders appears to receive substantial pathogenic contributions from individual differences in a constellation of genes whose variants provide individual differences in the specification of brain connectivities during development and in adulthood. Heritable brain differences that underlie addiction vulnerability thus lie squarely in the midst of the repertoire of heritable brain differences that underlie vulnerability to other common brain disorders and phenotypes.

Substance abuse vulnerability loci: converging genome scanning data

Classical genetic studies suggest strong complex genetic contributions to a predisposition to abuse multiple addictive substances. Until recently, there were no reproducible genome scanning data identifying chromosomal positions likely to contain allelic variants that predispose the carrier to illegal substance addiction. Nominal results of linkage-based genome scanning studies for ethanol and nicotine addictions failed to display much agreement. Our recent data from association-based genome scans for illegal addictions, and reanalyses of previous results now provide a substantial body of converging results. The 15 reproducible chromosomal loci identified here are good candidates to harbor allelic variants that alter human substance abuse vulnerabilities. We discuss several approaches to identifying the specific gene variants that underlie these convergent association and linkage observations, and the impact that these convergent observations should have on understanding important human addictive disorders.