Multi-ethnic GWAS and meta-analysis of sleep quality identify MPP6 as a novel gene that functions in sleep center neurons.

Poor sleep quality can have harmful health consequences. Although many aspects of sleep are heritable, the understandings of genetic factors involved in its physiology remain limited. Here, we performed a genome-wide association study (GWAS) using the Pittsburgh Sleep Quality Index (PSQI) in a multi-ethnic discovery cohort (n = 2868) and found two novel genome-wide loci on chromosomes 2 and 7 associated with global sleep quality. A meta-analysis in 12 independent cohorts (100 000 individuals) replicated the association on chromosome 7 between NPY and MPP6. While NPY is an important sleep gene, we tested for an independent functional role of MPP6. Expression data showed an association of this locus with both NPY and MPP6 mRNA levels in brain tissues. Moreover, knockdown of an orthologue of MPP6 in Drosophila melanogaster sleep center neurons resulted in decreased sleep duration. With convergent evidence, we describe a new locus impacting human variability in sleep quality through known NPY and novel MPP6 sleep genes.

DOT: Gene-set analysis by combining decorrelated association statistics.

Historically, the majority of statistical association methods have been designed assuming availability of SNP-level information. However, modern genetic and sequencing data present new challenges to access and sharing of genotype-phenotype datasets, including cost of management, difficulties in consolidation of records across research groups, etc. These issues make methods based on SNP-level summary statistics particularly appealing. The most common form of combining statistics is a sum of SNP-level squared scores, possibly weighted, as in burden tests for rare variants. The overall significance of the resulting statistic is evaluated using its distribution under the null hypothesis. Here, we demonstrate that this basic approach can be substantially improved by decorrelating scores prior to their addition, resulting in remarkable power gains in situations that are most commonly encountered in practice; namely, under heterogeneity of effect sizes and diversity between pairwise LD. In these situations, the power of the traditional test, based on the added squared scores, quickly reaches a ceiling, as the number of variants increases. Thus, the traditional approach does not benefit from information potentially contained in any additional SNPs, while our decorrelation by orthogonal transformation (DOT) method yields steady gain in power. We present theoretical and computational analyses of both approaches, and reveal causes behind sometimes dramatic difference in their respective powers. We showcase DOT by analyzing breast cancer and cleft lip data, in which our method strengthened levels of previously reported associations and implied the possibility of multiple new alleles that jointly confer disease risk.

Quantifying posterior effect size distribution of susceptibility loci by common summary statistics.

Testing millions of single nucleotide polymorphisms (SNPs) in genetic association studies has become a standard routine for disease gene discovery. In light of recent re-evaluation of statistical practice, it has been suggested that p-values are unfit as summaries of statistical evidence. Despite this criticism, p-values contain information that can be utilized to address the concerns about their flaws. We present a new method for utilizing evidence summarized by p-values for estimating odds ratio (OR) based on its approximate posterior distribution. In our method, only p-values, sample size, and standard deviation for ln(OR) are needed as summaries of data, accompanied by a suitable prior distribution for ln(OR) that can assume any shape. The parameter of interest, ln(OR), is the only parameter with a specified prior distribution, hence our model is a mix of classical and Bayesian approaches. We show that our method retains the main advantages of the Bayesian approach: it yields direct probability statements about hypotheses for OR and is resistant to biases caused by selection of top-scoring SNPs. Our method enjoys greater flexibility than similarly inspired methods in the assumed distribution for the summary statistic and in the form of the prior for the parameter of interest. We illustrate our method by presenting interval estimates of effect size for reported genetic associations with lung cancer. Although we focus on OR, the method is not limited to this particular measure of effect size and can be used broadly for assessing reliability of findings in studies testing multiple predictors.

A functional polymorphism in the ATP-Binding Cassette B1 transporter predicts pharmacologic response to combination of nortriptyline and morphine in neuropathic pain patients.

Many genetic markers have been associated with variations in treatment response to analgesics, but none have been assessed in the context of combination therapies. In this study, the treatment effects of nortriptyline and morphine were tested for an association with genetic markers relevant to pain pathways. Treatment effects were determined for single and combination therapies. A total of 24 functional single nucleotide polymorphisms were tested within the gene loci of mu-opioid receptor (OPRM1) gene locus, ATP-Binding Cassette B1 Transporter (ABCB1), Cytochrome P450 gene family (CYP2C19 and CYP2D6), catecholamine inactivator Catechol-O-Methyl Transferase (COMT), and serotonin receptor 2A (HTR2A). Genotyping was performed in a population of neuropathic pain patients who previously participated in a clinical trial. For monotherapy, neither nortriptyline nor morphine responses were associated with single nucleotide polymorphisms. However, for nortriptyline + morphine combination therapy, the single nucleotide polymorphism rs1045642 within the drug efflux pump ABCB1 transporter significantly predicted analgesic response. The presence of the C allele accounted for 51% of pain variance in this subgroup in response to combination treatment. The T-allele homozygotes demonstrated only 20% improvement in pain scores, whereas the C-allele homozygotes 88%. There was no significant contribution of rs1045642 to the medication side effects under all treatment conditions. The UK Biobank data set was then used to validate this genetic association. Here, patients receiving similar combination therapy (opioid + tricyclic antidepressant) carrying the C allele of rs1045642 displayed 33% fewer body pain sites than patients without that allele, suggesting better pain control. In all, our results show a robust effect of the rs1045642 polymorphism in response to chronic pain treatment with a nortriptyline + morphine combination.

Detecting Weak Signals by Combining Small P-Values in Genetic Association Studies.

We approach the problem of combining top-ranking association statistics or P-values from a new perspective which leads to a remarkably simple and powerful method. Statistical methods, such as the rank truncated product (RTP), have been developed for combining top-ranking associations, and this general strategy proved to be useful in applications for detecting combined effects of multiple disease components. To increase power, these methods aggregate signals across top ranking single nucleotide polymorphisms (SNPs), while adjusting for their total number assessed in a study. Analytic expressions for combined top statistics or P-values tend to be unwieldy, which complicates interpretation and practical implementation and hinders further developments. Here, we propose the augmented rank truncation (ART) method that retains main characteristics of the RTP but is substantially simpler to implement. ART leads to an efficient form of the adaptive algorithm, an approach where the number of top ranking SNPs is varied to optimize power. We illustrate our methods by strengthening previously reported associations of µ-opioid receptor variants with sensitivity to pain.

Genome-wide association reveals contribution of MRAS to painful temporomandibular disorder in males.

Painful temporomandibular disorders (TMDs) are the leading cause of chronic orofacial pain, but its underlying molecular mechanisms remain obscure. Although many environmental factors have been associated with higher risk of developing painful TMD, family and twin studies support a heritable genetic component as well. We performed a genome-wide association study assuming an additive genetic model of TMD in a discovery cohort of 999 cases and 2031 TMD-free controls from the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study. Using logistic models adjusted for sex, age, enrollment site, and race, we identified 3 distinct loci that were significant in combined or sex-segregated analyses. A single-nucleotide polymorphism on chromosome 3 (rs13078961) was significantly associated with TMD in males only (odds ratio = 2.9, 95% confidence interval: 2.02-4.27, P = 2.2 × 10). This association was nominally replicated in a meta-analysis of 7 independent orofacial pain cohorts including 160,194 participants (odds ratio = 1.16, 95% confidence interval: 1.0-1.35, P = 2.3 × 10). Functional analysis in human dorsal root ganglia and blood indicated this variant is an expression quantitative trait locus, with the minor allele associated with decreased expression of the nearby muscle RAS oncogene homolog (MRAS) gene (beta = -0.51, P = 2.43 × 10). Male mice, but not female mice, with a null mutation of Mras displayed persistent mechanical allodynia in a model of inflammatory pain. Genetic and behavioral evidence support a novel mechanism by which genetically determined MRAS expression moderates the resiliency to chronic pain. This effect is male-specific and may contribute to the lower rates of painful TMD in men.

Bayesian prediction intervals for assessing P-value variability in prospective replication studies.

Increased availability of data and accessibility of computational tools in recent years have created an unprecedented upsurge of scientific studies driven by statistical analysis. Limitations inherent to statistics impose constraints on the reliability of conclusions drawn from data, so misuse of statistical methods is a growing concern. Hypothesis and significance testing, and the accompanying P-values are being scrutinized as representing the most widely applied and abused practices. One line of critique is that P-values are inherently unfit to fulfill their ostensible role as measures of credibility for scientific hypotheses. It has also been suggested that while P-values may have their role as summary measures of effect, researchers underappreciate the degree of randomness in the P-value. High variability of P-values would suggest that having obtained a small P-value in one study, one is, ne vertheless, still likely to obtain a much larger P-value in a similarly powered replication study. Thus, "replicability of P-value" is in itself questionable. To characterize P-value variability, one can use prediction intervals whose endpoints reflect the likely spread of P-values that could have been obtained by a replication study. Unfortunately, the intervals currently in use, the frequentist P-intervals, are based on unrealistic implicit assumptions. Namely, P-intervals are constructed with the assumptions that imply substantial chances of encountering large values of effect size in an observational study, which leads to bias. The long-run frequentist probability provided by P-intervals is similar in interpretation to that of the classical confidence intervals, but the endpoints of any particular interval lack interpretation as probabilistic bounds for the possible spread of future P-values that may have been obtained in replication studies. Along with classical frequentist intervals, there exists a Bayesian viewpoint toward interval construction in which the endpoints of an interval have a meaningful probabilistic interpretation. We propose Bayesian intervals for prediction of P-value variability in prospective replication studies. Contingent upon approximate prior knowledge of the effect size distribution, our proposed Bayesian intervals have endpoints that are directly interpretable as probabilistic bounds for replication P-values, and they are resistant to selection bias. We showcase our approach by its application to P-values reported for five psychiatric disorders by the Psychiatric Genomics Consortium group.

The more you test, the more you find: The smallest P-values become increasingly enriched with real findings as more tests are conducted.

The increasing accessibility of data to researchers makes it possible to conduct massive amounts of statistical testing. Rather than follow specific scientific hypotheses with statistical analysis, researchers can now test many possible relationships and let statistics generate hypotheses for them. The field of genetic epidemiology is an illustrative case, where testing of candidate genetic variants for association with an outcome has been replaced by agnostic screening of the entire genome. Poor replication rates of candidate gene studies have improved dramatically with the increase in genomic coverage, due to factors such as adoption of better statistical practices and availability of larger sample sizes. Here, we suggest that another important factor behind the improved replicability of genome-wide scans is an increase in the amount of statistical testing itself. We show that an increase in the number of tested hypotheses increases the proportion of true associations among the variants with the smallest P-values. We develop statistical theory to quantify how the expected proportion of genuine signals (EPGS) among top hits depends on the number of tests. This enrichment of top hits by real findings holds regardless of whether genome-wide statistical significance has been reached in a study. Moreover, if we consider only those "failed" studies that produce no statistically significant results, the same enrichment phenomenon takes place: the proportion of true associations among top hits grows with the number of tests. The enrichment occurs even if the true signals are encountered at the logarithmically decreasing rate with the additional testing.

Epiregulin and EGFR interactions are involved in pain processing.

The EGFR belongs to the well-studied ErbB family of receptor tyrosine kinases. EGFR is activated by numerous endogenous ligands that promote cellular growth, proliferation, and tissue regeneration. In the present study, we have demonstrated a role for EGFR and its natural ligand, epiregulin (EREG), in pain processing. We show that inhibition of EGFR with clinically available compounds strongly reduced nocifensive behavior in mouse models of inflammatory and chronic pain. EREG-mediated activation of EGFR enhanced nociception through a mechanism involving the PI3K/AKT/mTOR pathway and matrix metalloproteinase-9. Moreover, EREG application potentiated capsaicin-induced calcium influx in a subset of sensory neurons. Both the EGFR and EREG genes displayed a genetic association with the development of chronic pain in several clinical cohorts of temporomandibular disorder. Thus, EGFR and EREG may be suitable therapeutic targets for persistent pain conditions.

Genome-Wide Association Analysis of the Sense of Smell in U.S. Older Adults: Identification of Novel Risk Loci in African-Americans and European-Americans.

The human sense of smell decreases with age, and a poor sense of smell are among the most important prodromal symptoms of several neurodegenerative diseases. Recent evidence further suggests a racial difference in the sense of smell among U.S. older adults. However, no genome-wide association study (GWAS) on the sense of smell has been conducted in African-Americans (AAs). We performed the first genome-wide meta-analysis of the sense of smell among 1979 AAs and 6582 European-Americans (EAs) from three U.S. aging cohorts. In the AA population, we identified nine novel regions (KLF4-ACTL7B, RAPGEF2-FSTL5, TCF4-LOC100505474, PCDH10, KIAA1751, MYO5B, MIR320B1-CD2, NR5A2-LINC00862, SALL1-C16orf97) that were associated with the sense of smell (P < 5 × 10-8). Many of these regions have been previously linked to neuropsychiatric (schizophrenia or epilepsy) or neurodegenerative (Parkinson's or Alzheimer's disease) diseases associated with a decreased sense of smell. In the EA population, we identified two novel loci in or near RASGRP1 and ANXA2P3 associated with sense of smell. In conclusion, this study identified several ancestry-specific loci that are associated with the sense of smell in older adults. While these findings need independent confirmation, they may lead to novel insights into the biology of the sense of smell in older adults and its relationships to neuropsychological and neurodegenerative diseases.

Previous GWAS hits in relation to young-onset breast cancer.

PURPOSE: Genome-wide association studies (GWAS) have identified dozens of single-nucleotide polymorphisms (SNPs) associated with breast cancer. Few studies focused on young-onset breast cancer, which exhibits etiologic and tumor-type differences from older-onset disease. Possible confounding by prenatal effects of the maternal genome has also not been considered. METHODS: Using a family-based design for breast cancer before age 50, we assessed the relationship between breast cancer and 77 GWAS-identified breast cancer risk SNPs. We estimated relative risks (RR) for inherited and maternally mediated genetic effects. We also used published RR estimates to calculate genetic risk scores and model joint effects. RESULTS: Seventeen of the candidate SNPs were nominally associated with young-onset breast cancer in our 1296 non-Hispanic white affected families (uncorrected p value <0.05). Top-ranked SNPs included rs3803662-A (TOX3, RR = 1.39; p = 7.0 × 10-6), rs12662670-G (ESR1, RR = 1.56; p = 5.7 × 10-4), rs2981579-A (FGFR2, RR = 1.24; p = 0.002), and rs999737-G (RAD51B, RR = 1.37; p = 0.003). No maternally mediated effects were found. A risk score based on all 77 SNPs indicated that their overall relationship to young-onset breast cancer risk was more than additive (additive-fit p = 2.2 × 10-7) and consistent with a multiplicative joint effect (multiplicative-fit p = 0.27). With the multiplicative formulation, the case sister's genetic risk score exceeded that of her unaffected sister in 59% of families. CONCLUSIONS: The results of this family-based study indicate that no effects of previously identified risk SNPs were explained by prenatal effects of maternal variants. Many of the known breast cancer risk variants were associated with young-onset breast cancer, with evidence that TOX3, ESR1, FGFR2, and RAD51B are important for young-onset disease.

Uncovering Local Trends in Genetic Effects of Multiple Phenotypes via Functional Linear Models.

Recent technological advances equipped researchers with capabilities that go beyond traditional genotyping of loci known to be polymorphic in a general population. Genetic sequences of study participants can now be assessed directly. This capability removed technology-driven bias toward scoring predominantly common polymorphisms and let researchers reveal a wealth of rare and sample-specific variants. Although the relative contributions of rare and common polymorphisms to trait variation are being debated, researchers are faced with the need for new statistical tools for simultaneous evaluation of all variants within a region. Several research groups demonstrated flexibility and good statistical power of the functional linear model approach. In this work we extend previous developments to allow inclusion of multiple traits and adjustment for additional covariates. Our functional approach is unique in that it provides a nuanced depiction of effects and interactions for the variables in the model by representing them as curves varying over a genetic region. We demonstrate flexibility and competitive power of our approach by contrasting its performance with commonly used statistical tools and illustrate its potential for discovery and characterization of genetic architecture of complex traits using sequencing data from the Dallas Heart Study.

A family-based, genome-wide association study of young-onset breast cancer: inherited variants and maternally mediated effects.

Young-onset breast cancer shows certain phenotypic and etiologic differences from older-onset breast cancer and may be influenced by some distinct genetic variants. Few genetic studies of breast cancer have targeted young women and no studies have examined whether maternal variants influence disease in their adult daughters through prenatal effects. We conducted a family-based, genome-wide association study of young-onset breast cancer (age at diagnosis <50 years). A total of 602 188 single-nucleotide polymorphisms (SNPs) were genotyped for 1279 non-Hispanic white cases and their parents or sisters. We used likelihood-based log-linear models to test for transmission asymmetry within families and for maternally mediated genetic effects. Three autosomal SNPs (rs28373882, P=2.8 × 10(-7); rs879162, P=9.2 × 10(-7); rs12606061, P=9.1 × 10(-7)) were associated with risk of young-onset breast cancer at a false-discovery rate below 0.20. None of these loci has been previously linked with young-onset or overall breast cancer risk, and their functional roles are unknown. There was no evidence of maternally mediated, X-linked, or mitochondrial genetic effects, and no notable findings within cancer subcategories defined by menopausal status, estrogen receptor status, or by tumor invasiveness. Further investigations are needed to explore other potential genetic, epigenetic, or epistatic mechanisms and to confirm the association between these three novel loci and young-onset breast cancer.

COMT gene locus: new functional variants.

Catechol-O-methyltransferase (COMT) metabolizes catecholaminergic neurotransmitters. Numerous studies have linked COMT to pivotal brain functions such as mood, cognition, response to stress, and pain. Both nociception and risk of clinical pain have been associated with COMT genetic variants, and this association was shown to be mediated through adrenergic pathways. Here, we show that association studies between COMT polymorphic markers and pain phenotypes in 2 independent cohorts identified a functional marker, rs165774, situated in the 3' untranslated region of a newfound splice variant, (a)-COMT. Sequence comparisons showed that the (a)-COMT transcript is highly conserved in primates, and deep sequencing data demonstrated that (a)-COMT is expressed across several human tissues, including the brain. In silico analyses showed that the (a)-COMT enzyme features a distinct C-terminus structure, capable of stabilizing substrates in its active site. In vitro experiments demonstrated not only that (a)-COMT is catalytically active but also that it displays unique substrate specificity, exhibiting enzymatic activity with dopamine but not epinephrine. They also established that the pain-protective A allele of rs165774 coincides with lower COMT activity, suggesting contribution to decreased pain sensitivity through increased dopaminergic rather than decreased adrenergic tone, characteristic of reference isoforms. Our results provide evidence for an essential role of the (a)-COMT isoform in nociceptive signaling and suggest that genetic variations in (a)-COMT isoforms may contribute to individual variability in pain phenotypes.

Assessing the Probability that a Finding Is Genuine for Large-Scale Genetic Association Studies.

Genetic association studies routinely involve massive numbers of statistical tests accompanied by P-values. Whole genome sequencing technologies increased the potential number of tested variants to tens of millions. The more tests are performed, the smaller P-value is required to be deemed significant. However, a small P-value is not equivalent to small chances of a spurious finding and significance thresholds may fail to serve as efficient filters against false results. While the Bayesian approach can provide a direct assessment of the probability that a finding is spurious, its adoption in association studies has been slow, due in part to the ubiquity of P-values and the automated way they are, as a rule, produced by software packages. Attempts to design simple ways to convert an association P-value into the probability that a finding is spurious have been met with difficulties. The False Positive Report Probability (FPRP) method has gained increasing popularity. However, FPRP is not designed to estimate the probability for a particular finding, because it is defined for an entire region of hypothetical findings with P-values at least as small as the one observed for that finding. Here we propose a method that lets researchers extract probability that a finding is spurious directly from a P-value. Considering the counterpart of that probability, we term this method POFIG: the Probability that a Finding is Genuine. Our approach shares FPRP's simplicity, but gives a valid probability that a finding is spurious given a P-value. In addition to straightforward interpretation, POFIG has desirable statistical properties. The POFIG average across a set of tentative associations provides an estimated proportion of false discoveries in that set. POFIGs are easily combined across studies and are immune to multiple testing and selection bias. We illustrate an application of POFIG method via analysis of GWAS associations with Crohn's disease.

The nicotinic α6 subunit gene determines variability in chronic pain sensitivity via cross-inhibition of P2X2/3 receptors.

Chronic pain is a highly prevalent and poorly managed human health problem. We used microarray-based expression genomics in 25 inbred mouse strains to identify dorsal root ganglion (DRG)-expressed genetic contributors to mechanical allodynia, a prominent symptom of chronic pain. We identified expression levels of Chrna6, which encodes the α6 subunit of the nicotinic acetylcholine receptor (nAChR), as highly associated with allodynia. We confirmed the importance of α6* (α6-containing) nAChRs by analyzing both gain- and loss-of-function mutants. We find that mechanical allodynia associated with neuropathic and inflammatory injuries is significantly altered in α6* mutants, and that α6* but not α4* nicotinic receptors are absolutely required for peripheral and/or spinal nicotine analgesia. Furthermore, we show that Chrna6's role in analgesia is at least partially due to direct interaction and cross-inhibition of α6* nAChRs with P2X2/3 receptors in DRG nociceptors. Finally, we establish the relevance of our results to humans by the observation of genetic association in patients suffering from chronic postsurgical and temporomandibular pain.

Resampling-based multiple comparison procedure with application to point-wise testing with functional data.

In this paper we describe a coherent multiple testing procedure for correlated test statistics such as are encountered in functional linear models. The procedure makes use of two different p-value combination methods: the Fisher combination method and the Sidák correction-based method. P-values for Fisher's and Sidák's test statistics are estimated through resampling to cope with the correlated tests. Building upon these two existing combination methods, we propose the smallest p-value as a new test statistic for each hypothesis. The closure principle is incorporated along with the new test statistic to obtain the overall p-value and appropriately adjust the individual p-values. Furthermore, a shortcut version for the proposed procedure is detailed, so that individual adjustments can be obtained even for a large number of tests. The motivation for developing the procedure comes from a problem of point-wise inference with smooth functional data where tests at neighboring points are related. A simulation study verifies that the methodology performs well in this setting. We illustrate the proposed method with data from a study on the aerial detection of the spectral effect of below ground carbon dioxide leakage on vegetation stress via spectral responses.

Functional analysis of variance for association studies.

While progress has been made in identifying common genetic variants associated with human diseases, for most of common complex diseases, the identified genetic variants only account for a small proportion of heritability. Challenges remain in finding additional unknown genetic variants predisposing to complex diseases. With the advance in next-generation sequencing technologies, sequencing studies have become commonplace in genetic research. The ongoing exome-sequencing and whole-genome-sequencing studies generate a massive amount of sequencing variants and allow researchers to comprehensively investigate their role in human diseases. The discovery of new disease-associated variants can be enhanced by utilizing powerful and computationally efficient statistical methods. In this paper, we propose a functional analysis of variance (FANOVA) method for testing an association of sequence variants in a genomic region with a qualitative trait. The FANOVA has a number of advantages: (1) it tests for a joint effect of gene variants, including both common and rare; (2) it fully utilizes linkage disequilibrium and genetic position information; and (3) allows for either protective or risk-increasing causal variants. Through simulations, we show that FANOVA outperform two popularly used methods - SKAT and a previously proposed method based on functional linear models (FLM), - especially if a sample size of a study is small and/or sequence variants have low to moderate effects. We conduct an empirical study by applying three methods (FANOVA, SKAT and FLM) to sequencing data from Dallas Heart Study. While SKAT and FLM respectively detected ANGPTL 4 and ANGPTL 3 associated with obesity, FANOVA was able to identify both genes associated with obesity.

Integrative framework for identification of key cell identity genes uncovers determinants of ES cell identity and homeostasis.

Identification of genes associated with specific biological phenotypes is a fundamental step toward understanding the molecular basis underlying development and pathogenesis. Although RNAi-based high-throughput screens are routinely used for this task, false discovery and sensitivity remain a challenge. Here we describe a computational framework for systematic integration of published gene expression data to identify genes defining a phenotype of interest. We applied our approach to rank-order all genes based on their likelihood of determining ES cell (ESC) identity. RNAi-mediated loss-of-function experiments on top-ranked genes unearthed many novel determinants of ESC identity, thus validating the derived gene ranks to serve as a rich and valuable resource for those working to uncover novel ESC regulators. Underscoring the value of our gene ranks, functional studies of our top-hit Nucleolin (Ncl), abundant in stem and cancer cells, revealed Ncl's essential role in the maintenance of ESC homeostasis by shielding against differentiation-inducing redox imbalance-induced oxidative stress. Notably, we report a conceptually novel mechanism involving a Nucleolin-dependent Nanog-p53 bistable switch regulating the homeostatic balance between self-renewal and differentiation in ESCs. Our findings connect the dots on a previously unknown regulatory circuitry involving genes associated with traits in both ESCs and cancer and might have profound implications for understanding cell fate decisions in cancer stem cells. The proposed computational framework, by helping to prioritize and preselect candidate genes for tests using complex and expensive genetic screens, provides a powerful yet inexpensive means for identification of key cell identity genes.