Human Genomic Data Research Articles

Genome privacy: challenges, technical approaches to mitigate risk, and ethical considerations in the United States

Accessing and integrating human genomic data with phenotypes are important for biomedical research. Making genomic data accessible for research purposes, however, must be handled carefully to avoid leakage of sensitive individual information to unauthorized parties and improper use of data. In this article, we focus on data sharing within the scope of data accessibility for research. Current common practices to gain biomedical data access are strictly rule based, without a clear and quantitative measurement of the risk of privacy breaches. In addition, several types of studies require privacy-preserving linkage of genotype and phenotype information across different locations (e.g., genotypes stored in a sequencing facility and phenotypes stored in an electronic health record) to accelerate discoveries. The computer science community has developed a spectrum of techniques for data privacy and confidentiality protection, many of which have yet to be tested on real-world problems. In this article, we discuss clinical, technical, and ethical aspects of genome data privacy and confidentiality in the United States, as well as potential solutions for privacy-preserving genotype-phenotype linkage in biomedical research.

Identifying outlier loci in admixed and in continuous populations using ancestral population differentiation statistics.

Finding genetic signatures of local adaptation is of great interest for many population genetic studies. Common approaches to sorting selective loci from their genomic background focus on the extreme values of the fixation index, FST , across loci. However, the computation of the fixation index becomes challenging when the population is genetically continuous, when predefining subpopulations is a difficult task, and in the presence of admixed individuals in the sample. In this study, we present a new method to identify loci under selection based on an extension of the FST statistic to samples with admixed individuals. In our approach, FST values are computed from the ancestry coefficients obtained with ancestry estimation programs. More specifically, we used factor models to estimate FST , and we compared our neutrality tests with those derived from a principal component analysis approach. The performances of the tests were illustrated using simulated data and by re-analysing genomic data from European lines of the plant species Arabidopsis thaliana and human genomic data from the population reference sample, POPRES.

An overview of human genetic privacy.

The study of human genomics is becoming a Big Data science, owing to recent biotechnological advances leading to availability of millions of personal genome sequences, which can be combined with biometric measurements from mobile apps and fitness trackers, and of human behavior data monitored from mobile devices and social media. With increasing research opportunities for integrative genomic studies through data sharing, genetic privacy emerges as a legitimate yet challenging concern that needs to be carefully addressed, not only for individuals but also for their families. In this paper, we present potential genetic privacy risks and relevant ethics and regulations for sharing and protecting human genomics data. We also describe the techniques for protecting human genetic privacy from three broad perspectives: controlled access, differential privacy, and cryptographic solutions.

Parallel or convergent evolution in human population genomic data revealed by genotype networks.

BackgroundGenotype networks are representations of genetic variation data that are complementary to phylogenetic trees. A genotype network is a graph whose nodes are genotypes (DNA sequences) with the same broadly defined phenotype. Two nodes are connected if they differ in some minimal way, e.g., in a single nucleotide.ResultsWe analyze human genome variation data from the 1,000 genomes project, and construct haploid genotype (haplotype) networks for 12,235 protein coding genes. The structure of these networks varies widely among genes, indicating different patterns of variation despite a shared evolutionary history. We focus on those genes whose genotype networks show many cycles, which can indicate homoplasy, i.e., parallel or convergent evolution, on the sequence level.ConclusionFor 42 genes, the observed number of cycles is so large that it cannot be explained by either chance homoplasy or recombination. When analyzing possible explanations, we discovered evidence for positive selection in 21 of these genes and, in addition, a potential role for constrained variation and purifying selection. Balancing selection plays at most a small role. The 42 genes with excess cycles are enriched in functions related to immunity and response to pathogens. Genotype networks are representations of genetic variation data that can help understand unusual patterns of genomic variation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12862-016-0722-0) contains supplementary material, which is available to authorized users.

Abstract 3858: Feasibility and efficacy of a precision treatment approach for triple-negative breast cancer in mouse models

Abstract 15-20% of human breast tumors are triple negative breast cancer (TNBC), an aggressive and deadly subtype of breast cancer that currently lack targeted therapies, leaving chemotherapy as the only systemic treatment option. There is a pressing need for a better understanding of disease mechanisms of TNBC, and for the development of new treatment options. While activating mutations of PIK3CA are frequently found in ER-positive and Her2-amplified breast cancer, inactivation of lipid phosphatases is more frequent in TNBC. Analysis of large human genomics data in TCGA reveals that heterozygous-loss of INPP4B, a lipid phosphates in the PI3K signaling pathway, is enriched in TNBC subtype, and strongly correlates with loss of ER expression. Guided by human genomics information, we have crossed INPP4B phosphatase deletion mice into TNBC mouse model to determine whether INPP4B-loss cooperates with loss-of-function of p53 and/or Brca1 to promote tumorigenesis in vivo. Our results show dose-dependent increase in tumor development frequency in K14cre; Brca1flox/flox; p53flox/flox mice carrying INPP4B Phosphatase KO allele, HET allele compared to INPP4B WT allele. Importantly, these tumors resemble human TNBC in their pathology, histological patterns and gene expression patterns, providing a valuable platform to test their responsiveness to various therapeutic drugs, including PI3K-inhibitors. Our goal is to generate data that form the basis for a phase I/II clinical study that lead to substantially improved treatment strategies for basal-like breast cancer. Toward this goal, we have banked the endogenous tumors in a way allowing transplantation in nude mice, and have performed randomized drug treatment studies. We found that tumors that are heterozygous or deletion for INPP4B are more sensitive to PI3K-inhibition, suggesting these tumors are more dependent on Pi3K activation to thrive. Although tumors show partial response early on, they inevitably relapsed, begging for an understanding of innate and selected drug-resistant mechanisms. To this end, we have completed large-scale RNAseq and whole exome sequencing (WES), and have performed in-depth analyses on the mouse genomics data to identify genetic alternations including chromosomal number variations (CNVs), mutations and gene fusions. Focusing on gene-fusions, we found that by targeting these fusions, we can achieve better treatment outcomes and in some cases, even complete tumor remission. Significantly TCGA data analyses revealed the presence of similar fusions in human TNBC patients and we are in the process of investigating whether human cells carrying these fusions respond equally well compared to their mouse counterparts. Our approach of precision-medicine guided treatment optimization has lead to substantially improved treatment outcomes. Citation Format: Hui Liu, Charles Murphy, Florian Karreth, Kangkang Yang, Gerburg Wulf, Lewis C. Cantley. Feasibility and efficacy of a precision treatment approach for triple-negative breast cancer in mouse models. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3858.

Rare Variants in Cardiomyopathy Genes Associated With Stress-Induced Cardiomyopathy.

Stress-induced cardiomyopathy (SIC) is a poorly understood condition associated with periods of emotional and physical stress. The clinical approaches for management of SIC are supportive and reactive to patient symptoms. To utilize next-generation exome sequencing to define genetic variation associated with, and potentially responsible for, this disease. We performed exome sequencing of 7 white female patients with SIC. Filtering of the identified variants was performed to limit our investigation to those sequences that passed quality control criteria, were rare or novel, were determined algorithmically to have high impact on the associated protein, and were within regions of high species conservation. All variants were verified by using Sanger sequencing. Exome-sequencing analysis revealed that each patient carried predicted deleterious variants affecting known cardiomyopathy genes. In each case, the identified variant was either not previously found in public human genome data or was previously annotated in a database of clinical variants associated with cardiac dysfunction. Patients with SIC harbor deleterious mutations in established cardiomyopathy genes at a level higher than healthy controls. We hypothesize that patients at highest risk for SIC likely live in a compensated state of cardiac dysfunction that manifests clinically only after the myocardium is stressed. In short, we propose that SIC is another example of an occult cardiomyopathy with a distinct physiological trigger and suggest that alternative clinical approaches to these patients may be warranted. CADD, Combined Annotation Dependent DepletionFPKM, fragments per kilobase pair of exon per million fragments mappedNHLBI GO ESP, National Heart, Lung, and Blood Institute Grand Opportunity Exome Sequencing ProjectPCR, polymerase chain reactionSIC, stress-induced cardiomyopathy.

Why individual thermo sensation and pain perception varies? Clue of disruptive mutations in TRPVs from 2504 human genome data

ABSTRACTEvery individual varies in character and so do their sensory functions and perceptions. The molecular mechanism and the molecular candidates involved in these processes are assumed to be similar if not same. So far several molecular factors have been identified which are fairly conserved across the phylogenetic tree and are involved in these complex sensory functions. Among all, members belonging to Transient Receptor Potential (TRP) channels have been widely characterized for their involvement in thermo-sensation. These include TRPV1 to TRPV4 channels which reveal complex thermo-gating behavior in response to changes in temperature. The molecular evolution of these channels is highly correlative with the thermal response of different species. However, recent 2504 human genome data suggest that these thermo-sensitive TRPV channels are highly variable and carry possible deleterious mutations in human population. These unexpected findings may explain the individual differences in terms of complex sensory functions.

Identification of the BRD1 interaction network and its impact on mental disorder risk.

BackgroundThe bromodomain containing 1 (BRD1) gene has been implicated with transcriptional regulation, brain development, and susceptibility to schizophrenia and bipolar disorder. To advance the understanding of BRD1 and its role in mental disorders, we characterized the protein and chromatin interactions of the BRD1 isoforms, BRD1-S and BRD1-L.MethodsStable human cell lines expressing epitope tagged BRD1-S and BRD1-L were generated and used as discovery systems for identifying protein and chromatin interactions. Protein-protein interactions were identified using co-immunoprecipitation followed by mass spectrometry and chromatin interactions were identified using chromatin immunoprecipitation followed by next generation sequencing. Gene expression profiles and differentially expressed genes were identified after upregulating and downregulating BRD1 expression using microarrays. The presented functional molecular data were integrated with human genomic and transcriptomic data using available GWAS, exome-sequencing datasets as well as spatiotemporal transcriptomic datasets from the human brain.ResultsWe present several novel protein interactions of BRD1, including isoform-specific interactions as well as proteins previously implicated with mental disorders. By BRD1-S and BRD1-L chromatin immunoprecipitation followed by next generation sequencing we identified binding to promoter regions of 1540 and 823 genes, respectively, and showed correlation between BRD1-S and BRD1-L binding and regulation of gene expression. The identified BRD1 interaction network was found to be predominantly co-expressed with BRD1 mRNA in the human brain and enriched for pathways involved in gene expression and brain function. By interrogation of large datasets from genome-wide association studies, we further demonstrate that the BRD1 interaction network is enriched for schizophrenia risk.ConclusionOur results show that BRD1 interacts with chromatin remodeling proteins, e.g. PBRM1, as well as histone modifiers, e.g. MYST2 and SUV420H1. We find that BRD1 primarily binds in close proximity to transcription start sites and regulates expression of numerous genes, many of which are involved with brain development and susceptibility to mental disorders. Our findings indicate that BRD1 acts as a regulatory hub in a comprehensive schizophrenia risk network which plays a role in many brain regions throughout life, implicating e.g. striatum, hippocampus, and amygdala at mid-fetal stages.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0308-x) contains supplementary material, which is available to authorized users.

Abstract B25: A novel in vivo gain-of-function screen for metastasis drivers in non-small cell lung cancers

Abstract Metastatic lung cancer is the leading cause of cancer related mortality in the world. Insights on specific genetic causes are limited and novel approaches are necessary to elucidate specific genes that drive lung cancer progression and metastasis. Extensive genomic and transcriptomic profiling of human lung cancers by consortia like ‘The Cancer Genome Atlas’ (TCGA) and the ‘International Cancer Genome Consortium’ (ICGC) has revealed a complex genetic landscape marked by abundant aberrations. In addition to acquiring mutations or amplification of well documented drivers like Kras, p53 or EGFR, lung cancers also demonstrate an enrichment of hundreds of low frequency gene aberrations which are a mix of key driver mutations and nascent passenger alterations caused by progressive genomic instability that could have definite role in cancer progression and metastasis. Our goal was to establish a robust screening platform to selectively identify these functionally critical “driver” genes in lung cancers. To identify a prioritized list of potential candidates we performed a multi-level cross-species comparison of our published high confidence transcriptome data from genetically-engineered mouse models of lung cancers and human lung cancer genomic data from TCGA, focusing on elevated gene expression and/or gene amplification. We identified an enriched list of 225 putative driver genes in lung cancers which were used to construct a lentiviral based cDNA expression library with unique molecular barcoding of individual cDNAs. Using a non-metastatic syngeneic mouse lung cancer model we generated individually transduced stable over-expressing lines for each of the 225 genes. These candidate lines were then entered into parallel in vitro and in vivo screens. In vitro testing includes cell invasion assays in a 96-well format, with positive hits defined as those scoring above 3x standard deviations relative to the control cells expressing mCherry. Positive hits were revalidated using standard 24-well invasion assays and also studied for growth phenotypes in 3D cultures. To selectively identify genes with functional role in metastasis, we performed a unique in vivo positive selection screen to test individual and combinatorial effects of lung cancer driver aberrations. For this we used pools of 20 cDNA expressing lines transplanted into syngeneic mice and observed for primary tumor growth and occurrence of metastases in lungs and other organs. Metastatic lesions and primary tumors were collected and genomic DNA from these was used to identify the unique individual barcodes by NGS barcode enrichment analysis. Metastatic drivers were identified by the relative enrichment of the unique barcode sequences for all the respective pool cDNAs in the metastatic tissue. We have identified both known (e.g. MYC) and several novel (e.g. THRA, TMEM106B, GNAS) potential oncogenic and metastatic drivers from the pooled screen which are currently being revalidated for their individual in vivo metastatic potencies. We are performing gain-of-function/loss-of-function studies for the identified driver genes to determine the pathways which they regulate. We are also analyzing the clinical relevance of these genes based upon their expression pattern and prognostic utility across TCGA and other public datasets, along with in-house patient samples and tissue microarrays of resected and biopsy specimens. Our unique high through-put approach will significantly advance the field of cancer target discovery by identifying new drug targets and biomarkers, essential for novel effective treatment options for lung cancer patients. Citation Format: Samrat T. Kundu, Caitlin Grzeskowiak, Chad J. Creighton, Kenneth L. Scott, Don L. Gibbons. A novel in vivo gain-of-function screen for metastasis drivers in non-small cell lung cancers. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Metastasis; 2015 Nov 30-Dec 3; Austin, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(7 Suppl):Abstract nr B25.

DNAdigest and Repositive: Connecting the World of Genomic Data.

There is no unified place where genomics researchers can search through all available raw genomic data in a way similar to OMIM for genes or Uniprot for proteins. With the recent increase in the amount of genomic data that is being produced and the ever-growing promises of precision medicine, this is becoming more and more of a problem. DNAdigest is a charity working to promote efficient sharing of human genomic data to improve the outcome of genomic research and diagnostics for the benefit of patients. Repositive, a social enterprise spin-out of DNAdigest, is building an online platform that indexes genomic data stored in repositories and thus enables researchers to search for and access a range of human genomic data sources through a single, easy-to-use interface, free of charge.

On the importance of being structured: instantaneous coalescence rates and human evolution--lessons for ancestral population size inference?

Most species are structured and influenced by processes that either increased or reduced gene flow between populations. However, most population genetic inference methods assume panmixia and reconstruct a history characterized by population size changes. This is potentially problematic as population structure can generate spurious signals of population size change through time. Moreover, when the model assumed for demographic inference is misspecified, genomic data will likely increase the precision of misleading if not meaningless parameters. For instance, if data were generated under an n-island model (characterized by the number of islands and migrants exchanged) inference based on a model of population size change would produce precise estimates of a bottleneck that would be meaningless. In addition, archaeological or climatic events around the bottleneck's timing might provide a reasonable but potentially misleading scenario. In a context of model uncertainty (panmixia versus structure) genomic data may thus not necessarily lead to improved statistical inference. We consider two haploid genomes and develop a theory that explains why any demographic model with structure will necessarily be interpreted as a series of changes in population size by inference methods ignoring structure. We formalize a parameter, the inverse instantaneous coalescence rate, and show that it is equivalent to a population size only in panmictic models, and is mostly misleading for structured models. We argue that this issue affects all population genetics methods ignoring population structure which may thus infer population size changes that never took place. We apply our approach to human genomic data.

The role of vitamin D in reducing gastrointestinal disease risk and assessment of individual dietary intake needs: Focus on genetic and genomic technologies.

With the endogenous formation of vitamin D being significantly curtailed because of public awareness of skin cancer dangers, attention is turning to dietary sources. Cumulative evidence has implicated vitamin D deficiency in increasing susceptibility to various gastrointestinal disorders, including colorectal cancer, inflammatory bowel diseases, diverticulitis, and irritable bowel syndrome. There is also reason to suggest adjunct vitamin D therapy for such diseases. Although there is justification for increasing vitamin D intake overall, optimal intakes will vary among individuals. Genomic technologies have revealed several hundreds of genes associated with vitamin D actions. The nature of these genes emphasizes the potentially negative implications of modulating vitamin D intakes in the absence of complementary human genetic and genomic data, including information on the gut microbiome. However, we are not yet in a position to apply this information. Genomic data (transcriptomics, metabolomics, proteomics, and metagenomics) could provide evidence that vitamin D sufficiency has been achieved. We suggest that there is an increasingly strong case for considering the more widespread use of vitamin D fortified foods and/or dietary supplements to benefit gastrointestinal health. However, intake levels might beneficially be informed by personalized genetic and genomic information, for optimal disease prevention and maintenance of remission.

Increasing Nucleosome Occupancy Is Correlated with an Increasing Mutation Rate so Long as DNA Repair Machinery Is Intact.

Deciphering the multitude of epigenomic and genomic factors that influence the mutation rate is an area of great interest in modern biology. Recently, chromatin has been shown to play a part in this process. To elucidate this relationship further, we integrated our own ultra-deep sequenced human nucleosomal DNA data set with a host of published human genomic and cancer genomic data sets. Our results revealed, that differences in nucleosome occupancy are associated with changes in base-specific mutation rates. Increasing nucleosome occupancy is associated with an increasing transition to transversion ratio and an increased germline mutation rate within the human genome. Additionally, cancer single nucleotide variants and microindels are enriched within nucleosomes and both the coding and non-coding cancer mutation rate increases with increasing nucleosome occupancy. There is an enrichment of cancer indels at the theoretical start (74 bp) and end (115 bp) of linker DNA between two nucleosomes. We then hypothesized that increasing nucleosome occupancy decreases access to DNA by DNA repair machinery and could account for the increasing mutation rate. Such a relationship should not exist in DNA repair knockouts, and we thus repeated our analysis in DNA repair machinery knockouts to test our hypothesis. Indeed, our results revealed no correlation between increasing nucleosome occupancy and increasing mutation rate in DNA repair knockouts. Our findings emphasize the linkage of the genome and epigenome through the nucleosome whose properties can affect genome evolution and genetic aberrations such as cancer.

A Complete and Accurate Ab Initio Repeat Finding Algorithm.

It has become clear that repetitive sequences have played multiple roles in eukaryotic genome evolution including increasing genetic diversity through mutation, changes in gene expression and facilitating generation of novel genes. However, identification of repetitive elements can be difficult in the ab initio manner. Currently, some classical ab initio tools of finding repeats have already presented and compared. The completeness and accuracy of detecting repeats of them are little pool. To this end, we proposed a new ab initio repeat finding tool, named HashRepeatFinder, which is based on hash index and word counting. Furthermore, we assessed the performances of HashRepeatFinder with other two famous tools, such as RepeatScout and Repeatfinder, in human genome data hg19. The results indicated the following three conclusions: (1) The completeness of HashRepeatFinder is the best one among these three compared tools in almost all chromosomes, especially in chr9 (8 times of RepeatScout, 10 times of Repeatfinder); (2) in terms of detecting large repeats, HashRepeatFinder also performed best in all chromosomes, especially in chr3 (24 times of RepeatScout and 250 times of Repeatfinder) and chr19 (12 times of RepeatScout and 60 times of Repeatfinder); (3) in terms of accuracy, HashRepeatFinder can merge the abundant repeats with high accuracy.

Abstract 2179: Gene expression models for predicting doxorubicin response in canine osteosarcoma

Abstract Purpose: The use of gene expression models for predicting individual response to cancer therapy is a promising area of research for improving outcome. Osteosarcoma is the most common primary bone cancer in dogs, with an average 1 year survival after treatment. The co-expression extrapolation (COXEN) method has been established as a gene expression method for accurately predicting drug sensitivity in human cancer. Our purpose is to explore the ability of COXEN for the intra- or interspecies extrapolation of cancer cell line data for prediction of chemosensitivity in canine osteosarcoma patients. Methods: Gene expression and drug sensitivity data to doxorubicin (DOX) for the human NCI60 cell line panel were publicly available. Corresponding data was obtained and generated for the canine ACC29 cell line panel and canine osteosarcoma tumors via cell viability assays, hospital record search, and microarray gene expression analysis. Differentially expressed genes (DEGs) were identified in reference sets by Significance Analysis of Microarrays or t-test analysis between sensitive and resistant samples to DOX. Probe matching between species was done based on highest target sequence homology or by collapsing probes to gene level by selecting probes with maximum variance between samples. Hierarchical clustering was done using the CIMminer program. DEGs are filtered for genes sharing strong co-expression between reference and co-expression datasets, followed by prediction model building using the Misclassification Penalized Posterior (MiPP) algorithm. Results: The NCI60 and ACC29 panels are similarly sensitive to DOX with log GI50 molar ranges of -8.3 to -5.7 and -8.2 to -5.9, respectively. Samples from ACC29 and COS16 clustered according to DOX sensitivity 67 and 100% correctly when DEGs from NCI60 were used. NCI60-trained models using DEGs co-expressed with the ACC29 were 83% accurate in predicting sensitivity to DOX in ACC29 (p = 0.019, binomial). NCI60-trained models using DEGs co-expressed with COS49 were 67% accurate in predicting response to DOX in COS49 (p = 0.049, binomial, p = 0.045, Log Rank test). Models with NCI60 DEGs but co-expressed and built on COS16 were 73% accurate in COS33 (p = 0.026, binomial, p = 0.001, Log Rank test). ACC29-trained models using DEGs co-expressed with COS49 were 73% accurate in COS49 (p = 0.008, binomial). Models with ACC29 or ACCosteo DEGs but co-expressed and built on COS16 were each 70% accurate in COS33 (p = 0.047, binomial, p = 0.071 and 0.071, Log Rank test). Conclusions: Both human and canine COXEN models were able to accurately predict response of canine osteosarcoma patients to DOX. The best performing model involved identifying DEGs from human cell lines, co-expressing and building the model with dog tumors, suggesting that genomic strategies in canine oncology could benefit from the wealth of available human genomic data, and could provide validation for personalized medicine in human cancer. Citation Format: Jared S. Fowles, Kristen C. Brown, Ann M. Hess, Dawn L. Duval, Daniel L. Gustafson. Gene expression models for predicting doxorubicin response in canine osteosarcoma. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2179. doi:10.1158/1538-7445.AM2015-2179

Abstract 4870: GenePool: A cloud-based technology for rapidly data mining large-scale, patient-derived cancer genomic cohorts including The Cancer Genome Atlas

Abstract GenePool is a cloud-based system for the secure storage, management, analysis, visualization and interpretation of large-scale human genomics data. The GenePool platform enables rapid interrogation of massive amounts of data generated by current laboratory methods and was designed to meet the needs of scientists engaged in cancer biomarker discovery and characterization. The software solution provides users with an intuitive interface, an easy way to store and select genomics datasets according to sample-associated metadata for analysis, and an automated system for performing routine, well-characterized genomics workflows. Results are presented with multiple options for annotation, sorting and data export, with a visualization tool that facilitates browsing of genomic data for biomarker identification. We will demonstrate how GenePool removes the data download, management, and computing burdens faced by researchers interested in working with large amounts of patient-derived cancer genomics exemplified by The Cancer Genome Atlas. To date, TCGA has completed molecular characterization of more than 9,000 patient samples from a target of 17,000, and will have released an estimated 2.5 petabytes of data at project completion. We will present case studies using the >25 cancer cohorts worth of RNA-Seq, miRNA-Seq, Exome Sequencing, Protein Expression, Copy Number, and DNA Methylation data (provided to the community through the TCGA Data Portal) and made readily available for sample selection and analysis within GenePool. As validation of the GenePool platform, we will present results obtained within minutes that validate knowledge reported in the literature over the course of decades. We will then present novel findings for less understood cancer indications. Note: This abstract was not presented at the meeting. Citation Format: Sandeep Sanga, Antoaneta Vladimirova, Richard D. Goold, Tod M. Klingler. GenePool: A cloud-based technology for rapidly data mining large-scale, patient-derived cancer genomic cohorts including The Cancer Genome Atlas. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4870. doi:10.1158/1538-7445.AM2015-4870

Finding Pathogenic Nucleic Acid Sequences in Next Generation Sequencing Data.

Viruses and bacteria are established as one of the main causes of human diseases from hepatitis to cancer. Recently, the presence of such pathogens has been extensively studied using human whole genome and transcriptome sequencing data. However, detecting and studying pathogens via next generation sequencing data is a challenging task in terms of time and computational resources. In this protocol we give instructions for a simple and quick method to find pathogenic DNA or RNA and detect possible integration of the pathogen genome into the host genome.

Using populations of human and microbial genomes for organism detection in metagenomes

Identifying causative disease agents in human patients from shotgun metagenomic sequencing (SMS) presents a powerful tool to apply when other targeted diagnostics fail. Numerous technical challenges remain, however, before SMS can move beyond the role of research tool. Accurately separating the known and unknown organism content remains difficult, particularly when SMS is applied as a last resort. The true amount of human DNA that remains in a sample after screening against the human reference genome and filtering nonbiological components left from library preparation has previously been underreported. In this study, we create the most comprehensive collection of microbial and reference-free human genetic variation available in a database optimized for efficient metagenomic search by extracting sequences from GenBank and the 1000 Genomes Project. The results reveal new human sequences found in individual Human Microbiome Project (HMP) samples. Individual samples contain up to 95% human sequence, and 4% of the individual HMP samples contain 10% or more human reads. Left unidentified, human reads can complicate and slow down further analysis and lead to inaccurately labeled microbial taxa and ultimately lead to privacy concerns as more human genome data is collected.

Deterministic identification of specific individuals from GWAS results.

Genome-wide association studies (GWASs) are commonly applied on human genomic data to understand the causal gene combinations statistically connected to certain diseases. Patients involved in these GWASs could be re-identified when the studies release statistical information on a large number of single-nucleotide polymorphisms. Subsequent work, however, found that such privacy attacks are theoretically possible but unsuccessful and unconvincing in real settings. We derive the first practical privacy attack that can successfully identify specific individuals from limited published associations from the Wellcome Trust Case Control Consortium (WTCCC) dataset. For GWAS results computed over 25 randomly selected loci, our algorithm always pinpoints at least one patient from the WTCCC dataset. Moreover, the number of re-identified patients grows rapidly with the number of published genotypes. Finally, we discuss prevention methods to disable the attack, thus providing a solution for enhancing patient privacy. Proofs of the theorems and additional experimental results are available in the support online documents. The attack algorithm codes are publicly available at https://sites.google.com/site/zhangzhenjie/GWAS_attack.zip. The genomic dataset used in the experiments is available at http://www.wtccc.org.uk/ on request.

How should the legal framework for the protection of human genomic data be formulated?—Implications from the revision processes of the Act on the Protection of Personal Information (PPI Act)

How should the legal framework for the protection of human genomic data be formulated?—Implications from the revision processes of the Act on the Protection of Personal Information (PPI Act)