ENViz: a Cytoscape App for integrated statistical analysis and visualization of sample-matched data with multiple data types.

ENViz (Enrichment Analysis and Visualization) is a Cytoscape app that performs joint enrichment analysis of two types of sample matched datasets in the context of systematic annotations. Such datasets may be gene expression or any other high-throughput data collected in the same set of samples. The enrichment analysis is done in the context of pathway information, gene ontology or any custom annotation of the data. The results of the analysis consist of significant associations between profiled elements of one of the datasets to the annotation terms (e.g. miR-19 was associated to the cell-cycle process in breast cancer samples). The results of the enrichment analysis are visualized as an interactive Cytoscape network.

Population-genetic properties of differentiated human copy-number polymorphisms.

Copy-number variants (CNVs) can reach appreciable frequencies in the human population, and recent discoveries have shown that several of these copy-number polymorphisms (CNPs) are associated with human diseases, including lupus, psoriasis, Crohn disease, and obesity. Despite new advances, significant biases remain in terms of CNP discovery and genotyping. We developed a method based on single-channel intensity data and benchmarked against copy numbers determined from sequencing read depth to successfully obtain CNP genotypes for 1495 CNPs from 487 human DNA samples of diverse ethnic backgrounds. This microarray contained CNPs in segmental duplication-rich regions and insertions of sequences not represented in the reference genome assembly or on standard SNP microarray platforms. We observe that CNPs in segmental duplications are more likely to be population differentiated than CNPs in unique regions (p = 0.015) and that biallelic CNPs show greater stratification when compared to frequency-matched SNPs (p = 0.0026). Although biallelic CNPs show a strong correlation of copy number with flanking SNP genotypes, the majority of multicopy CNPs do not (40% with r > 0.8). We selected a subset of CNPs for further characterization in 1876 additional samples from 62 populations; this revealed striking population-differentiated structural variants in genes of clinical significance such as OCLN, a tight junction protein involved in hepatitis C viral entry. Our microarray design allows these variants to be rapidly tested for disease association and our results suggest that CNPs (especially those that cannot be imputed from SNP genotypes) might have contributed disproportionately to human diversity and selection.

Characterization of missing human genome sequences and copy-number polymorphic insertions.

The extent of human genomic structural variation suggests that there must be portions of the genome yet to be discovered, annotated and characterized at the sequence level. We present a resource and analysis of 2,363 new insertion sequences corresponding to 720 genomic loci. We found that a substantial fraction of these sequences are either missing, fragmented or misassigned when compared to recent de novo sequence assemblies from short-read next-generation sequence data. We determined that 18-37% of these new insertions are copy-number polymorphic, including loci that show extensive population stratification among Europeans, Asians and Africans. Complete sequencing of 156 of these insertions identified new exons and conserved noncoding sequences not yet represented in the reference genome. We developed a method to accurately genotype these new insertions by mapping next-generation sequencing datasets to the breakpoint, thereby providing a means to characterize copy-number status for regions previously inaccessible to single-nucleotide polymorphism microarrays.

PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements.

Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.

The fine-scale and complex architecture of human copy-number variation.

Despite considerable excitement over the potential functional significance of copy-number variants (CNVs), we still lack knowledge of the fine-scale architecture of the large majority of CNV regions in the human genome. In this study, we used a high-resolution array-based comparative genomic hybridization (aCGH) platform that targeted known CNV regions of the human genome at approximately 1 kb resolution to interrogate the genomic DNAs of 30 individuals from four HapMap populations. Our results revealed that 1020 of 1153 CNV loci (88%) were actually smaller in size than what is recorded in the Database of Genomic Variants based on previously published studies. A reduction in size of more than 50% was observed for 876 CNV regions (76%). We conclude that the total genomic content of currently known common human CNVs is likely smaller than previously thought. In addition, approximately 8% of the CNV regions observed in multiple individuals exhibited genomic architectural complexity in the form of smaller CNVs within larger ones and CNVs with interindividual variation in breakpoints. Future association studies that aim to capture the potential influences of CNVs on disease phenotypes will need to consider how to best ascertain this previously uncharacterized complexity.

High definition cytogenetics and oligonucleotide aCGH analyses of cisplatin-resistant ovarian cancer cells.

Array comparative genomic hybridization (aCGH) is a key platform to assess cancer genomic profiles. Many structural genomic aberrations cannot be detected by aCGH alone. We have applied molecular cytogenetic analyses including spectral karyotyping, multicolor banding, and fluorescence in situ hybridization with aCGH to comprehensively investigate the genomic aberrations associated with cisplatin resistance in A2780 ovarian cancer cells. A2780 is a well-established model of chemotherapeutic resistance with distinct karyotypic abnormalities in the parental and cisplatin-resistant cells. Cytogenetic analysis revealed that two unbalanced translocations, der(8)t(1;8) and der(X)t(X;1), and loss of chromosome 13 were present only in the resistant line. Our aCGH analyses detected imbalances affecting an additional 10.59% of the genome in the cisplatin-resistant cells compared with the parental. DNA copy number changes included deletions at 1p10-p22.1, 8p23.3, and Xq13.1-pter, and a duplication of 8q11.22-q23. Cryptic genomic aberrations associated with concurrent localized changes of specific gene expression included a homozygous deletion of 0.38 Mb at 1p21.3 adjacent to SNX7, and an insertional transposition of 0.85 Mb from 13q12.12 into chromosome 22. This latter rearrangement led to an overexpression of four contiguous genes that flanked one of the breakpoint regions in chromosome 13. Furthermore, 17 genes showed differential expression correlating with genomic gain or loss between the resistant and parent lines, validated by a second expression array platform. These results highlight the integration of comprehensive profiling to determine relationships of genomic aberrations and genes associated with an in vitro drug resistance model in ovarian cancer. This article contains Supplementary Material available at http://www.interscience.wiley.com/jpages/1045-2257/suppmat.

Pathway based analysis of SNPs with relevance to 5-FU therapy: relation to intratumoral mRNA expression and survival.

Genetic factors are thought to play a role in resistance towards chemotherapeutic agents such as 5-fluorouracil (5-FU). Approximately 30 genes are directly or indirectly involved in 5-FU metabolism, and genetic variation in any of these may contribute to anti-tumor response. Polymorphisms in these genes were analyzed in relation to tumoral mRNA levels of 5-FU metabolizing genes, response to chemotherapy and survival. A total of 21 genetic variants were studied in 35 breast cancer patients treated with 5-FluoroUracil, mitomycin (FUMI) and in a similar cohort of 90 doxorubicin treated breast cancer patients. Genotype distributions were compared using 109 healthy controls. No significant association was found between any polymorphisms and response to chemotherapy as measured by shrinkage of tumor. However, carriers of 3 copies of the TYMS 5'UTR repeat had shorter survival than noncarriers (p = .048) in the FUMI treatment group, but not in the doxorubicin treated group. Carriers of 3 copies of the repeat were also more frequently observed in both patients groups than in healthy controls (p = .034). Several highly significant associations were observed between genotypes and expression levels of 5-FU metabolizing genes. A SNP in codon 72 of TP53 was revealed to be a key regulator of 5-FU metabolizing genes such as DHFR and MTHFR, constituting 50% of all significant associations observed after FUMI therapy. These data suggest that 3 copies of the TYMS 5'UTR repeat may give a treatment specific reduced survival in breast cancer patients, and that TP53 may have a direct, allele specific, role in 5-FU mediated response.

A supervised approach for identifying discriminating genotype patterns and its application to breast cancer data.

MOTIVATION: Large-scale association studies, investigating the genetic determinants of a phenotype of interest, are producing increasing amounts of genomic variation data on human cohorts. A fundamental challenge in these studies is the detection of genotypic patterns that discriminate individuals exhibiting the phenotype under study from individuals that do not possess it. The difficulty stems from the large number of single nucleotide polymorphism (SNP) combinations that have to be tested. The discrimination problem becomes even more involved when additional high-throughput data, such as gene expression data, are available for the same cohort. RESULTS: We have developed a graph theoretic approach for identifying discriminating patterns (DPs) for a given phenotype in a genotyped population. The method is based on representing the SNP data as a bipartite graph of individuals and their SNP states, and identifying fully connected subgraphs of this graph that relate individuals enriched for a given phenotypic group. The method can handle additional data types such as expression profiles of the genotyped population. It is reminiscent of biclustering approaches with the crucial difference that its search process is guided by the phenotype under consideration in a supervised manner. We tested our approach in simulations and on real data. In simulations, our method was able to retrieve planted patterns with high success rate. We then applied our approach to a dataset of 72 breast cancer patients with available gene expression profiles, genotyped over 695 SNPs. We detected several DPs that were highly significant with respect to various clinical phenotypes, and investigated the groups of patients and the groups of genes they defined. We found the patient groups to be highly enriched for other phenotypes and to display expression coherency among their profiles. The gene groups displayed functional coherency and involved genes with known role in cancer, providing additional support to their involvement. AVAILABILITY: The program is available upon request.

Differences in vascular bed disease susceptibility reflect differences in gene expression response to atherogenic stimuli.

Atherosclerosis occurs predominantly in arteries and only rarely in veins. The goal of this study was to test whether differences in the molecular responses of venous and arterial endothelial cells (ECs) to atherosclerotic stimuli might contribute to vascular bed differences in susceptibility to atherosclerosis. We compared gene expression profiles of primary cultured ECs from human saphenous vein (SVEC) and coronary artery (CAEC) exposed to atherogenic stimuli. In addition to identifying differentially expressed genes, we applied statistical analysis of gene ontology and pathway annotation terms to identify signaling differences related to cell type and stimulus. Differential gene expression of untreated venous and arterial endothelial cells yielded 285 genes more highly expressed in untreated SVEC (P<0.005 and fold change >1.5). These genes represented various atherosclerosis-related pathways including responses to proliferation, oxidoreductase activity, antiinflammatory responses, cell growth, and hemostasis functions. Moreover, stimulation with oxidized LDL induced dramatically greater gene expression responses in CAEC compared with SVEC, relating to adhesion, proliferation, and apoptosis pathways. In contrast, interleukin 1beta and tumor necrosis factor alpha activated similar gene expression responses in both CAEC and SVEC. The differences in functional response and gene expression were further validated by an in vitro proliferation assay and in vivo immunostaining of alphabeta-crystallin protein. Our results strongly suggest that different inherent gene expression programs in arterial versus venous endothelial cells contribute to differences in atherosclerotic disease susceptibility.

Network analysis of human in-stent restenosis.

BACKGROUND: Recent successes in the treatment of in-stent restenosis (ISR) by drug-eluting stents belie the challenges still faced in certain lesions and patient groups. We analyzed human coronary atheroma in de novo and restenotic disease to identify targets of therapy that might avoid these limitations. METHODS AND RESULTS: We recruited 89 patients who underwent coronary atherectomy for de novo atherosclerosis (n=55) or in-stent restenosis (ISR) of a bare metal stent (n=34). Samples were fixed for histology, and gene expression was assessed with a dual-dye 22,000 oligonucleotide microarray. Histological analysis revealed significantly greater cellularity and significantly fewer inflammatory infiltrates and lipid pools in the ISR group. Gene ontology analysis demonstrated the prominence of cell proliferation programs in ISR and inflammation/immune programs in de novo restenosis. Network analysis, which combines semantic mining of the published literature with the expression signature of ISR, revealed gene expression modules suggested as candidates for selective inhibition of restenotic disease. Two modules are presented in more detail, the procollagen type 1 alpha2 gene and the ADAM17/tumor necrosis factor-alpha converting enzyme gene. We tested our contention that this method is capable of identifying successful targets of therapy by comparing mean significance scores for networks generated from subsets of the published literature containing the terms "sirolimus" or "paclitaxel." In addition, we generated 2 large networks with sirolimus and paclitaxel at their centers. Both analyses revealed higher mean values for sirolimus, suggesting that this agent has a broader suppressive action against ISR than paclitaxel. CONCLUSIONS: Comprehensive histological and gene network analysis of human ISR reveals potential targets for directed abrogation of restenotic disease and recapitulates the results of clinical trials of existing agents.

Robust interlaboratory reproducibility of a gene expression signature measurement consistent with the needs of a new generation of diagnostic tools.

BACKGROUND: The increasing use of DNA microarrays in biomedical research, toxicogenomics, pharmaceutical development, and diagnostics has focused attention on the reproducibility and reliability of microarray measurements. While the reproducibility of microarray gene expression measurements has been the subject of several recent reports, there is still a need for systematic investigation into what factors most contribute to variability of measured expression levels observed among different laboratories and different experimenters. RESULTS: We report the results of an interlaboratory comparison of gene expression array measurements on the same microarray platform, in which the RNA amplification and labeling, hybridization and wash, and slide scanning were each individually varied. Identical input RNA was used for all experiments. While some sources of variation have measurable influence on individual microarray signals, they showed very low influence on sample-to-reference ratios based on averaged triplicate measurements in the two-color experiments. RNA labeling was the largest contributor to interlaboratory variation. CONCLUSION: Despite this variation, measurement of one particular breast cancer gene expression signature in three different laboratories was found to be highly robust, showing a high intralaboratory and interlaboratory reproducibility when using strictly controlled standard operating procedures.

Molecular signatures determining coronary artery and saphenous vein smooth muscle cell phenotypes: distinct responses to stimuli.

OBJECTIVE: Phenotypic differences between vascular smooth muscle cell (VSMC) subtypes lead to diverse pathological processes including atherosclerosis, postangioplasty restenosis and vein graft disease. To better understand the molecular mechanisms underlying functional differences among distinct SMC subtypes, we compared gene expression profiles and functional responses to oxidized low-density lipoprotein (OxLDL) and platelet-derived growth factor (PDGF) between cultured SMCs from human coronary artery (CASM) and saphenous vein (SVSM). METHODS AND RESULTS: OxLDL and PDGF elicited markedly different functional responses and expression profiles between the 2 SMC subtypes. In CASM, OxLDL inhibited cell proliferation and migration and modified gene expression of chemokines (CXCL10, CXCL11 and CXCL12), proinflammatory cytokines (IL-1, IL-6, and IL-18), insulin-like growth factor binding proteins (IGFBPs), and both endothelial and smooth muscle marker genes. In SVSM, OxLDL promoted proliferation partially via IGF1 signaling, activated NF-kappaB and phosphatidylinositol signaling pathways, and upregulated prostaglandin (PG) receptors and synthases. In untreated cells, alpha-chemokines, proinflammatory cytokines, and genes associated with apoptosis, inflammation, and lipid biosynthesis were higher in CASM, whereas some beta-chemokines, metalloproteinase inhibitors, and IGFBPs were higher in SVSM. Interestingly, the basal expression levels of these genes seemed closely related to their responses to OxLDL and PDGF. In summary, our results suggest dramatic differences in gene expression patterns and functional responses to OxLDL and PDGF between venous and arterial SMCs, with venous SMCs having stronger proliferative/migratory responses to stimuli but also higher expression of atheroprotective genes at baseline. CONCLUSIONS: These results reveal molecular signatures that define the distinct phenotypes characteristics of coronary artery and saphenous vein SMC subtypes.

Transcriptional profiling of reporter genes used for molecular imaging of embryonic stem cell transplantation.

Stem cell therapy offers exciting promise for treatment of ischemic heart disease. Recent advances in molecular imaging techniques now allow investigators to monitor cell fate noninvasively and repetitively. Here we examine the effects of a triple-fusion reporter gene on embryonic stem (ES) cell transcriptional profiles. Murine ES cells were stably transfected with a self-inactivating lentiviral vector carrying a triple-fusion (TF) construct consisting of fluorescence, bioluminescence, and positron emission tomography (PET) reporter genes. Fluorescence-activated cell sorting (FACS) analysis allowed isolation of stably transfected populations. Microarray studies comparing gene expression in nontransduced control ES cells vs. stably transduced ES cells expressing triple fusion (ES-TF) revealed some increases in transcriptional variability. Annotation analysis showed that ES-TF cells downregulated cell cycling, cell death, and protein and nucleic acid metabolism genes while upregulating homeostatic and anti-apoptosis genes. Despite these transcriptional changes, expression of the TF reporter gene had no significant effects on ES cell viability, proliferation, and differentiation capability. Importantly, transplantation studies in murine myocardium demonstrated the feasibility of tracking ES-TF cells in living subjects using bioluminescence and PET imaging. Taken together, this is the first study to analyze in detail the effects of reporter genes on molecular imaging of ES cells.

Multilocus analysis of SNP and metabolic data within a given pathway.

BACKGROUND: Complex traits, which are under the influence of multiple and possibly interacting genes, have become a subject of new statistical methodological research. One of the greatest challenges facing human geneticists is the identification and characterization of susceptibility genes for common multifactorial diseases and their association to different quantitative phenotypic traits. RESULTS: Two types of data from the same metabolic pathway were used in the analysis: categorical measurements of 18 SNPs; and quantitative measurements of plasma levels of several steroids and their precursors. Using the combinatorial partitioning method we tested various thresholds for each metabolic trait and each individual SNP locus. One SNP in CYP19, 3UTR, two SNPs in CYP1B1 (R48G and A119S) and one in CYP1A1 (T461N) were significantly differently distributed between the high and low level metabolic groups. The leave one out cross validation method showed that 6 SNPs in concert make 65% correct prediction of phenotype. Further we used pattern recognition, computing the p-value by Monte Carlo simulation to identify sets of SNPs and physiological characteristics such as age and weight that contribute to a given metabolic level. Since the SNPs detected by both methods reside either in the same gene (CYP1B1) or in 3 different genes in immediate vicinity on chromosome 15 (CYP19, CYP11 and CYP1A1) we investigated the possibility that they form intragenic and intergenic haplotypes, which may jointly account for a higher activity in the pathway. We identified such haplotypes associated with metabolic levels. CONCLUSION: The methods reported here may enable to study multiple low-penetrance genetic factors that together determine various quantitative phenotypic traits. Our preliminary data suggest that several genes coding for proteins involved in a common pathway, that happen to be located on common chromosomal areas and may form intragenic haplotypes, together account for a higher activity of the whole pathway.

Analysis of SNP-expression association matrices.

High throughput expression profiling and genotyping technologies provide the means to study the genetic determinants of population variation in gene expression variation. In this paper we present a general statistical framework for the simultaneous analysis of gene expression data and SNP genotype data measured for the same cohort. The framework consists of methods to associate transcripts with SNPs affecting their expression, algorithms to detect subsets of transcripts that share significantly many associations with a subset of SNPs, and methods to visualize the identified relations. We apply our framework to SNP-expression data collected from 50 breast cancer patients. Our results demonstrate an overabundance of transcript-SNP associations in this data, and pinpoint SNPs that are potential master regulators of transcription. We also identify several statistically significant transcript-subsets with common putative regulators that fall into well-defined functional categories.

Pathway analysis of coronary atherosclerosis.

Large-scale gene expression studies provide significant insight into genes differentially regulated in disease processes such as cancer. However, these investigations offer limited understanding of multisystem, multicellular diseases such as atherosclerosis. A systems biology approach that accounts for gene interactions, incorporates nontranscriptionally regulated genes, and integrates prior knowledge offers many advantages. We performed a comprehensive gene level assessment of coronary atherosclerosis using 51 coronary artery segments isolated from the explanted hearts of 22 cardiac transplant patients. After histological grading of vascular segments according to American Heart Association guidelines, isolated RNA was hybridized onto a customized 22-K oligonucleotide microarray, and significance analysis of microarrays and gene ontology analyses were performed to identify significant gene expression profiles. Our studies revealed that loss of differentiated smooth muscle cell gene expression is the primary expression signature of disease progression in atherosclerosis. Furthermore, we provide insight into the severe form of coronary artery disease associated with diabetes, reporting an overabundance of immune and inflammatory signals in diabetics. We present a novel approach to pathway development based on connectivity, determined by language parsing of the published literature, and ranking, determined by the significance of differentially regulated genes in the network. In doing this, we identify highly connected "nexus" genes that are attractive candidates for therapeutic targeting and followup studies. Our use of pathway techniques to study atherosclerosis as an integrated network of gene interactions expands on traditional microarray analysis methods and emphasizes the significant advantages of a systems-based approach to analyzing complex disease.

Novel role for the potent endogenous inotrope apelin in human cardiac dysfunction.

BACKGROUND: Apelin is among the most potent stimulators of cardiac contractility known. However, no physiological or pathological role for apelin-angiotensin receptor-like 1 (APJ) signaling has ever been described. METHODS AND RESULTS: We performed transcriptional profiling using a spotted cDNA microarray with 12 814 unique clones on paired samples of left ventricle obtained before and after placement of a left ventricular assist device in 11 patients. The significance analysis of microarrays and a novel rank consistency score designed to exploit the paired structure of the data confirmed that natriuretic peptides were among the most significantly downregulated genes after offloading. The most significantly upregulated gene was the G-protein-coupled receptor APJ, the specific receptor for apelin. We demonstrate here using immunoassay and immunohistochemical techniques that apelin is localized primarily in the endothelium of the coronary arteries and is found at a higher concentration in cardiac tissue after mechanical offloading. These findings imply an important paracrine signaling pathway in the heart. We additionally extend the clinical significance of this work by reporting for the first time circulating human apelin levels and demonstrating increases in the plasma level of apelin in patients with left ventricular dysfunction. CONCLUSIONS: The apelin-APJ signaling pathway emerges as an important novel mediator of cardiovascular control.

Chemically modified guide RNAs enhance CRISPR-Cas genome editing in human primary cells.

CRISPR-Cas-mediated genome editing relies on guide RNAs that direct site-specific DNA cleavage facilitated by the Cas endonuclease. Here we report that chemical alterations to synthesized single guide RNAs (sgRNAs) enhance genome editing efficiency in human primary T cells and CD34(+) hematopoietic stem and progenitor cells. Co-delivering chemically modified sgRNAs with Cas9 mRNA or protein is an efficient RNA- or ribonucleoprotein (RNP)-based delivery method for the CRISPR-Cas system, without the toxicity associated with DNA delivery. This approach is a simple and effective way to streamline the development of genome editing with the potential to accelerate a wide array of biotechnological and therapeutic applications of the CRISPR-Cas technology.

Identification of endothelial cell genes by combined database mining and microarray analysis.

Vascular endothelial cells maintain the interface between the systemic circulation and soft tissues and mediate critical processes such as inflammation in a vascular bed-selective fashion. To expand our understanding of the genetic pathways that underlie these specific functions, we have focused on the identification of novel genes that are differentially expressed in all endothelial cells, as well as restricted groups of this cell type. Virtual subtraction was conducted employing gene expression data deposited in public databases and 384 genes identified. These genes were spotted on custom microarrays, along with 288 genes identified through subtraction cloning from TGF-beta-stimulated endothelial cells. Arrays were evaluated with RNA samples representing endothelial cells cultured from four vascular sources and five non-endothelial cell types. These studies identified 64 pan-endothelial markers that were differentially expressed with at least a threefold difference (range 3- to 55-fold). In addition, differences in gene expression profiles among endothelial cells from different vascular beds were identified. Validation of these findings was performed by RNA blot expression studies, and a number of the novel genes were shown to be expressed under angiogenic conditions in the developing mouse embryo. The combined tools of database mining and transcriptional profiling thus provide expanded knowledge of endothelial cell gene expression and endothelial cell biology.

Diversity of human copy number variation and multicopy genes.

Copy number variants affect both disease and normal phenotypic variation, but those lying within heavily duplicated, highly identical sequence have been difficult to assay. By analyzing short-read mapping depth for 159 human genomes, we demonstrated accurate estimation of absolute copy number for duplications as small as 1.9 kilobase pairs, ranging from 0 to 48 copies. We identified 4.1 million "singly unique nucleotide" positions informative in distinguishing specific copies and used them to genotype the copy and content of specific paralogs within highly duplicated gene families. These data identify human-specific expansions in genes associated with brain development, reveal extensive population genetic diversity, and detect signatures consistent with gene conversion in the human species. Our approach makes ~1000 genes accessible to genetic studies of disease association.