An overview of the process, progress, and outcomes of a National Center for Accelerated Innovation: The Boston Biomedical Innovation Center Experience.

Implementation of clinically useful research discoveries in the academic environment is challenged by limited funding for early phase proof-of-concept studies and inadequate expertise in product development and commercialization. To address these limitations, the National Institutes of Health (NIH) established the National Centers for Accelerated Innovations (NCAI) program in 2013. Three centers competed successfully for awards through this mechanism. Here, we present the experience of one such center, the Boston Biomedical Innovation Center (B-BIC), and demonstrate its remarkable success at the translation of innovations to clinical application and commercialization, as well as skills development and education.

Opportunities and challenges for transcriptome-wide association studies.

Transcriptome-wide association studies (TWAS) integrate genome-wide association studies (GWAS) and gene expression datasets to identify gene-trait associations. In this Perspective, we explore properties of TWAS as a potential approach to prioritize causal genes at GWAS loci, by using simulations and case studies of literature-curated candidate causal genes for schizophrenia, low-density-lipoprotein cholesterol and Crohn's disease. We explore risk loci where TWAS accurately prioritizes the likely causal gene as well as loci where TWAS prioritizes multiple genes, some likely to be non-causal, owing to sharing of expression quantitative trait loci (eQTL). TWAS is especially prone to spurious prioritization with expression data from non-trait-related tissues or cell types, owing to substantial cross-cell-type variation in expression levels and eQTL strengths. Nonetheless, TWAS prioritizes candidate causal genes more accurately than simple baselines. We suggest best practices for causal-gene prioritization with TWAS and discuss future opportunities for improvement. Our results showcase the strengths and limitations of using eQTL datasets to determine causal genes at GWAS loci.

Bayesian-based noninvasive prenatal diagnosis of single-gene disorders.

In the last decade, noninvasive prenatal diagnosis (NIPD) has emerged as an effective procedure for early detection of inherited diseases during pregnancy. This technique is based on using cell-free DNA (cfDNA) and fetal cfDNA (cffDNA) in maternal blood, and hence, has minimal risk for the mother and fetus compared with invasive techniques. NIPD is currently used for identifying chromosomal abnormalities (in some instances) and for single-gene disorders (SGDs) of paternal origin. However, for SGDs of maternal origin, sensitivity poses a challenge that limits the testing to one genetic disorder at a time. Here, we present a Bayesian method for the NIPD of monogenic diseases that is independent of the mode of inheritance and parental origin. Furthermore, we show that accounting for differences in the length distribution of fetal- and maternal-derived cfDNA fragments results in increased accuracy. Our model is the first to predict inherited insertions-deletions (indels). The method described can serve as a general framework for the NIPD of SGDs; this will facilitate easy integration of further improvements. One such improvement that is presented in the current study is a machine learning model that corrects errors based on patterns found in previously processed data. Overall, we show that next-generation sequencing (NGS) can be used for the NIPD of a wide range of monogenic diseases, simultaneously. We believe that our study will lead to the achievement of a comprehensive NIPD for monogenic diseases.

Inferring Relevant Cell Types for Complex Traits by Using Single-Cell Gene Expression.

Previous studies have prioritized trait-relevant cell types by looking for an enrichment of genome-wide association study (GWAS) signal within functional regions. However, these studies are limited in cell resolution by the lack of functional annotations from difficult-to-characterize or rare cell populations. Measurement of single-cell gene expression has become a popular method for characterizing novel cell types, and yet limited work has linked single-cell RNA sequencing (RNA-seq) to phenotypes of interest. To address this deficiency, we present RolyPoly, a regression-based polygenic model that can prioritize trait-relevant cell types and genes from GWAS summary statistics and gene expression data. RolyPoly is designed to use expression data from either bulk tissue or single-cell RNA-seq. In this study, we demonstrated RolyPoly's accuracy through simulation and validated previously known tissue-trait associations. We discovered a significant association between microglia and late-onset Alzheimer disease and an association between schizophrenia and oligodendrocytes and replicating fetal cortical cells. Additionally, RolyPoly computes a trait-relevance score for each gene to reflect the importance of expression specific to a cell type. We found that differentially expressed genes in the prefrontal cortex of individuals with Alzheimer disease were significantly enriched with genes ranked highly by RolyPoly gene scores. Overall, our method represents a powerful framework for understanding the effect of common variants on cell types contributing to complex traits.

Building Workforce Capacity Abroad While Strengthening Global Health Programs at Home: Participation of Seven Harvard-Affiliated Institutions in a Health Professional Training Initiative in Rwanda.

A consortium of 22 U.S. academic institutions is currently participating in the Rwanda Human Resources for Health Program (HRH Program). Led by the Rwandan Ministry of Health and funded by both the U.S. Government and the Global Fund to Fight AIDS, Tuberculosis and Malaria, the primary goal of this seven-year initiative is to help Rwanda train the number of health professionals necessary to reach the country's health workforce targets. Since 2012, the participating U.S. academic institutions have deployed faculty from a variety of health-related disciplines and clinical specialties to Rwanda. In this Article, the authors describe how U.S. academic institutions (focusing on the seven Harvard-affiliated institutions participating in the HRH Program-Harvard Medical School, Brigham and Women's Hospital, Harvard School of Dental Medicine, Boston Children's Hospital, Beth Israel Deaconess Medical Center, Massachusetts General Hospital, and Massachusetts Eye and Ear Infirmary) have also benefited: (1) by providing opportunities to their faculty and trainees to engage in global health activities; (2) by establishing long-term, academic partnerships and collaborations with Rwandan academic institutions; and (3) by building the administrative and mentorship capacity to support global health initiatives beyond the HRH Program. In doing this, the authors describe the seven Harvard-affiliated institutions' contributions to the HRH Program, summarize the benefits accrued by these institutions as a result of their participation in the program, describe the challenges they encountered in implementing the program, and outline potential solutions to these challenges that may inform similar future health professional training initiatives.

Detection of human adaptation during the past 2000 years.

Detection of recent natural selection is a challenging problem in population genetics. Here we introduce the singleton density score (SDS), a method to infer very recent changes in allele frequencies from contemporary genome sequences. Applied to data from the UK10K Project, SDS reflects allele frequency changes in the ancestors of modern Britons during the past ~2000 to 3000 years. We see strong signals of selection at lactase and the major histocompatibility complex, and in favor of blond hair and blue eyes. For polygenic adaptation, we find that recent selection for increased height has driven allele frequency shifts across most of the genome. Moreover, we identify shifts associated with other complex traits, suggesting that polygenic adaptation has played a pervasive role in shaping genotypic and phenotypic variation in modern humans.

Pooled ChIP-Seq Links Variation in Transcription Factor Binding to Complex Disease Risk.

Cis-regulatory elements such as transcription factor (TF) binding sites can be identified genome-wide, but it remains far more challenging to pinpoint genetic variants affecting TF binding. Here, we introduce a pooling-based approach to mapping quantitative trait loci (QTLs) for molecular-level traits. Applying this to five TFs and a histone modification, we mapped thousands of cis-acting QTLs, with over 25-fold lower cost compared to standard QTL mapping. We found that single genetic variants frequently affect binding of multiple TFs, and CTCF can recruit all five TFs to its binding sites. These QTLs often affect local chromatin and transcription but can also influence long-range chromosomal contacts, demonstrating a role for natural genetic variation in chromosomal architecture. Thousands of these QTLs have been implicated in genome-wide association studies, providing candidate molecular mechanisms for many disease risk loci and suggesting that TF binding variation may underlie a large fraction of human phenotypic variation.

RNA splicing is a primary link between genetic variation and disease.

Noncoding variants play a central role in the genetics of complex traits, but we still lack a full understanding of the molecular pathways through which they act. We quantified the contribution of cis-acting genetic effects at all major stages of gene regulation from chromatin to proteins, in Yoruba lymphoblastoid cell lines (LCLs). About ~65% of expression quantitative trait loci (eQTLs) have primary effects on chromatin, whereas the remaining eQTLs are enriched in transcribed regions. Using a novel method, we also detected 2893 splicing QTLs, most of which have little or no effect on gene-level expression. These splicing QTLs are major contributors to complex traits, roughly on a par with variants that affect gene expression levels. Our study provides a comprehensive view of the mechanisms linking genetic variation to variation in human gene regulation.

NF2/Merlin mediates contact-dependent inhibition of EGFR mobility and internalization via cortical actomyosin.

The proliferation of normal cells is inhibited at confluence, but the molecular basis of this phenomenon, known as contact-dependent inhibition of proliferation, is unclear. We previously identified the neurofibromatosis type 2 (NF2) tumor suppressor Merlin as a critical mediator of contact-dependent inhibition of proliferation and specifically found that Merlin inhibits the internalization of, and signaling from, the epidermal growth factor receptor (EGFR) in response to cell contact. Merlin is closely related to the membrane-cytoskeleton linking proteins Ezrin, Radixin, and Moesin, and localization of Merlin to the cortical cytoskeleton is required for contact-dependent regulation of EGFR. We show that Merlin and Ezrin are essential components of a mechanism whereby mechanical forces associated with the establishment of cell-cell junctions are transduced across the cell cortex via the cortical actomyosin cytoskeleton to control the lateral mobility and activity of EGFR, providing novel insight into how cells inhibit mitogenic signaling in response to cell contact.

Deep sequencing analysis of viral infection and evolution allows rapid and detailed characterization of viral mutant spectrum.

MOTIVATION: The study of RNA virus populations is a challenging task. Each population of RNA virus is composed of a collection of different, yet related genomes often referred to as mutant spectra or quasispecies. Virologists using deep sequencing technologies face major obstacles when studying virus population dynamics, both experimentally and in natural settings due to the relatively high error rates of these technologies and the lack of high performance pipelines. In order to overcome these hurdles we developed a computational pipeline, termed ViVan (Viral Variance Analysis). ViVan is a complete pipeline facilitating the identification, characterization and comparison of sequence variance in deep sequenced virus populations. RESULTS: Applying ViVan on deep sequenced data obtained from samples that were previously characterized by more classical approaches, we uncovered novel and potentially crucial aspects of virus populations. With our experimental work, we illustrate how ViVan can be used for studies ranging from the more practical, detection of resistant mutations and effects of antiviral treatments, to the more theoretical temporal characterization of the population in evolutionary studies. AVAILABILITY AND IMPLEMENTATION: Freely available on the web at http://www.vivanbioinfo.org CONTACT: : nshomron@post.tau.ac.il SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Measuring missing heritability: inferring the contribution of common variants.

Genome-wide association studies (GWASs), also called common variant association studies (CVASs), have uncovered thousands of genetic variants associated with hundreds of diseases. However, the variants that reach statistical significance typically explain only a small fraction of the heritability. One explanation for the "missing heritability" is that there are many additional disease-associated common variants whose effects are too small to detect with current sample sizes. It therefore is useful to have methods to quantify the heritability due to common variation, without having to identify all causal variants. Recent studies applied restricted maximum likelihood (REML) estimation to case-control studies for diseases. Here, we show that REML considerably underestimates the fraction of heritability due to common variation in this setting. The degree of underestimation increases with the rarity of disease, the heritability of the disease, and the size of the sample. Instead, we develop a general framework for heritability estimation, called phenotype correlation-genotype correlation (PCGC) regression, which generalizes the well-known Haseman-Elston regression method. We show that PCGC regression yields unbiased estimates. Applying PCGC regression to six diseases, we estimate the proportion of the phenotypic variance due to common variants to range from 25% to 56% and the proportion of heritability due to common variants from 41% to 68% (mean 60%). These results suggest that common variants may explain at least half the heritability for many diseases. PCGC regression also is readily applicable to other settings, including analyzing extreme-phenotype studies and adjusting for covariates such as sex, age, and population structure.

Effective genetic-risk prediction using mixed models.

For predicting genetic risk, we propose a statistical approach that is specifically adapted to dealing with the challenges imposed by disease phenotypes and case-control sampling. Our approach (termed Genetic Risk Scores Inference [GeRSI]), combines the power of fixed-effects models (which estimate and aggregate the effects of single SNPs) and random-effects models (which rely primarily on whole-genome similarities between individuals) within the framework of the widely used liability-threshold model. We demonstrate in extensive simulation that GeRSI produces predictions that are consistently superior to current state-of-the-art approaches. When applying GeRSI to seven phenotypes from the Wellcome Trust Case Control Consortium (WTCCC) study, we confirm that the use of random effects is most beneficial for diseases that are known to be highly polygenic: hypertension (HT) and bipolar disorder (BD). For HT, there are no significant associations in the WTCCC data. The fixed-effects model yields an area under the ROC curve (AUC) of 54%, whereas GeRSI improves it to 59%. For BD, using GeRSI improves the AUC from 55% to 62%. For individuals ranked at the top 10% of BD risk predictions, using GeRSI substantially increases the BD relative risk from 1.4 to 2.5.

Membrane protein dynamics and functional implications in mammalian cells.

The organization of the plasma membrane is both highly complex and highly dynamic. One manifestation of this dynamic complexity is the lateral mobility of proteins within the plane of the membrane, which is often an important determinant of intermolecular protein-binding interactions, downstream signal transduction, and local membrane mechanics. The mode of membrane protein mobility can range from random Brownian motion to immobility and from confined or restricted motion to actively directed motion. Several methods can be used to distinguish among the various modes of protein mobility, including fluorescence recovery after photobleaching, single-particle tracking, fluorescence correlation spectroscopy, and variations of these techniques. Here, we present both a brief overview of these methods and examples of their use to elucidate the dynamics of membrane proteins in mammalian cells-first in erythrocytes, then in erythroblasts and other cells in the hematopoietic lineage, and finally in non-hematopoietic cells. This multisystem analysis shows that the cytoskeleton frequently governs modes of membrane protein motion by stably anchoring the proteins through direct-binding interactions, by restricting protein diffusion through steric interactions, or by facilitating directed protein motion. Together, these studies have begun to delineate mechanisms by which membrane protein dynamics influence signaling sequelae and membrane mechanical properties, which, in turn, govern cell function.

Using state machines to model the Ion Torrent sequencing process and to improve read error rates.

MOTIVATION: The importance of fast and affordable DNA sequencing methods for current day life sciences, medicine and biotechnology is hard to overstate. A major player is Ion Torrent, a pyrosequencing-like technology which produces flowgrams--sequences of incorporation values--which are converted into nucleotide sequences by a base-calling algorithm. Because of its exploitation of ubiquitous semiconductor technology and innovation in chemistry, Ion Torrent has been gaining popularity since its debut in 2011. Despite the advantages, however, Ion Torrent read accuracy remains a significant concern. RESULTS: We present FlowgramFixer, a new algorithm for converting flowgrams into reads. Our key observation is that the incorporation signals of neighboring flows, even after normalization and phase correction, carry considerable mutual information and are important in making the correct base-call. We therefore propose that base-calling of flowgrams should be done on a read-wide level, rather than one flow at a time. We show that this can be done in linear-time by combining a state machine with a Viterbi algorithm to find the nucleotide sequence that maximizes the likelihood of the observed flowgram. FlowgramFixer is applicable to any flowgram-based sequencing platform. We demonstrate FlowgramFixer's superior performance on Ion Torrent Escherichia coli data, with a 4.8% improvement in the number of high-quality mapped reads and a 7.1% improvement in the number of uniquely mappable reads. AVAILABILITY: Binaries and source code of FlowgramFixer are freely available at: http://www.cs.tau.ac.il/~davidgo5/flowgramfixer.html.

Statistical modeling of coverage in high-throughput data.

In high-throughput sequencing experiments, the number of reads mapping to a genomic region, also known as the "coverage" or "coverage depth," is often used as a proxy for the abundance of the underlying genomic region in the sample. The abundance, in turn, can be used for many purposes including calling SNPs, estimating the allele frequency in a pool of individuals, identifying copy number variations, and identifying differentially expressed shRNAs in shRNA-seq experiments.In this chapter we describe the fundamentals of statistical modeling of coverage depth and discuss the problems of estimation and inference in the relevant experimental scenarios.

Involvement of IGF-1R regulation by miR-515-5p modifies breast cancer risk among BRCA1 carriers.

Several lines of evidence indicate that sequence alterations within microRNA (miRNA)-binding sites can modify the binding to its target gene resulting in altered expression patterns. We hypothesized that a single nucleotide polymorphism (SNP) located in the miR-515-5p binding site of igf-1r gene may alter IGF-1R regulation, with consequent effects on breast cancer risk in BRCA1 mutation carriers. Computational prediction revealed that the rs28674628 SNP in the igf-1r 3' UTR is located within a predicted binding site for miR-515-5p. The effect of this SNP on breast cancer risk was evaluated by genotyping 115 Jewish Ashkenazi carriers of the 185delAG mutation in the BRCA1 gene using the Sequenom platform followed by Kaplan-Meier analysis. Additional data set of 378 Jewish BRCA1 carriers was analyzed to validate our results. MiRNA transfection, Western blot analysis, luciferase reporter assay, real time PCR, and immunohistochemistry were performed to assess direct regulation of igf-1r by miR-515-5p. We show direct regulation of IGF-1R by miR-515-5p. We identified that disrupting miR-515-5p and igf-1r 3' UTR binding by SNP may cause elevated IGF-1R protein levels. Interestingly, miR-515-5p is downregulated in tumor tissue compared to its non-neoplastic surrounding tissue while IGF-1R levels are elevated. This igf-1r SNP was found to be significantly associated with age at diagnosis of breast cancer in Jewish Ashkenazi BRCA1 mutation carriers. These findings support the hypothesis that a SNP located in igf-1r gene may alter miRNA regulation of IGF-1R, with a putative effect on BRCA1 penetrance and breast cancer risk.

Inactivation of retinoblastoma protein does not overcome the requirement for human cytomegalovirus UL97 in lamina disruption and nuclear egress.

Human cytomegalovirus (HCMV) encodes one conventional protein kinase, UL97. During infection, UL97 phosphorylates the retinoblastoma tumor suppressor protein (pRb) on sites ordinarily phosphorylated by cyclin-dependent kinases (CDK), inactivating the ability of pRb to repress host genes required for cell cycle progression to S phase. UL97 is important for viral DNA synthesis in quiescent cells, but this function can be replaced by human papillomavirus type 16 E7, which targets pRb for degradation. However, viruses in which E7 replaces UL97 are still defective for virus production. UL97 is also required for efficient nuclear egress of viral nucleocapsids, which is associated with disruption of the nuclear lamina during infection, and phosphorylation of lamin A/C on serine 22, which antagonizes lamin polymerization. We investigated whether inactivation of pRb might overcome the requirement of UL97 for these roles, as pRb inactivation induces CDK1, and CDK1 phosphorylates lamin A/C on serine 22. We found that lamin A/C serine 22 phosphorylation during HCMV infection correlated with expression of UL97 and was considerably delayed in UL97-null mutants, even when E7 was expressed. E7 failed to restore gaps in the nuclear lamina seen in wild-type but not UL97-null virus infections. In electron microscopy analyses, a UL97-null virus expressing E7 was as impaired as a UL97-null mutant in cytoplasmic accumulation of viral nucleocapsids. Our results demonstrate that pRb inactivation is insufficient to restore efficient viral nuclear egress of HCMV in the absence of UL97 and instead argue further for a direct role of UL97 in this stage of the infectious cycle.

Identifying personal genomes by surname inference.

Sharing sequencing data sets without identifiers has become a common practice in genomics. Here, we report that surnames can be recovered from personal genomes by profiling short tandem repeats on the Y chromosome (Y-STRs) and querying recreational genetic genealogy databases. We show that a combination of a surname with other types of metadata, such as age and state, can be used to triangulate the identity of the target. A key feature of this technique is that it entirely relies on free, publicly accessible Internet resources. We quantitatively analyze the probability of identification for U.S. males. We further demonstrate the feasibility of this technique by tracing back with high probability the identities of multiple participants in public sequencing projects.