Street typology and bicyclist safety : a systems approach

Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning; and, (S. M. in Transportation)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 98-100).Cycling is an attractive transportation mode but has not attained a large mode share in the United States, in part because it is correctly perceived as dangerous. Much literature on cyclist safety and the built environment has focused on bicycle facilities such as bike lanes, while fewer studies have addressed route choice, specifically side streets versus arterials. The cities of Berkeley, CA and Cambridge, MA have pursued opposite strategies, Berkeley creating “bicycle boulevards” out of residential side streets while Cambridge has added bike lanes to major arterial roads. I hypothesize that side streets are safer than arterials regardless of which street has been treated for cyclists, both in terms of collision rate and fraction of collisions that are severe. For each city, I use cyclist count data with police-reported bicycle-motor vehicle collision data to compute relative collision rates for pairs of parallel streets that are alternate routes to reach the same destinations. In a detailed analysis of Berkeley I find that bicycle boulevards have categorically lower collision rates than arterials, with no difference in severity. I demonstrate, with very limited data, how to apply the same methodology to Cambridge and find results somewhat suggestive that side streets have lower collision rates than arterials. I highlight shortcomings in current data collection practices that make this research difficult.by Eric Vallabh Minikel.S.M.in TransportationM.C.P

Autism Spectrum Disorder Genetics: Diverse Genes with Diverse Clinical Outcomes

The last several years have seen unprecedented advances in deciphering the genetic etiology of autism spectrum disorders (ASDs). Heritability studies have repeatedly affirmed a contribution of genetic factors to the overall disease risk. Technical breakthroughs have enabled the search for these genetic factors via genome-wide surveys of a spectrum of potential sequence variations, from common single-nucleotide polymorphisms to essentially private chromosomal abnormalities. Studies of copy-number variation have identified significant roles for both recurrent and nonrecurrent large dosage imbalances, although they have rarely revealed the individual genes responsible. More recently, discoveries of rare point mutations and characterization of balanced chromosomal abnormalities have pinpointed individual ASD genes of relatively strong effect, including both loci with strong a priori biological relevance and those that would have otherwise been unsuspected as high-priority biological targets. Evidence has also emerged for association with many common variants, each adding a small individual contribution to ASD risk. These findings collectively provide compelling empirical data that the genetic basis of ASD is highly heterogeneous, with hundreds of genes capable of conferring varying degrees of risk, depending on their nature and the predisposing genetic alteration. Moreover, many genes that have been implicated in ASD also appear to be risk factors for related neurodevelopmental disorders, as well as for a spectrum of psychiatric phenotypes. While some ASD genes have evident functional significance, like synaptic proteins such as the SHANKs, neuroligins, and neurexins, as well as fragile x mental retardation–associated proteins, ASD genes have also been discovered that do not present a clear mechanism of specific neurodevelopmental dysfunction, such as regulators of chromatin modification and global gene expression. In their sum, the progress from genetic studies to date has been remarkable and increasingly rapid, but the interactive impact of strong-effect genetic lesions coupled with weak effect common polymorphisms has not yet led to a unified understanding of ASD pathogenesis or explained its highly variable clinical expression. With an increasingly firm genetic foundation, the coming years will hopefully see equally rapid advances in elucidating the functional consequences of ASD genes and their interactions with environmental/experiential factors, supporting the development of rational interventions

Prion protein lowering is a disease-modifying therapy across prion disease stages, strains and endpoints

Lowering of prion protein (PrP) expression in the brain is a genetically validated therapeutic hypothesis in prion disease. We recently showed that antisense oligonucleotide (ASO)-mediated PrP suppression extends survival and delays disease onset in intracerebrally prion-infected mice in both prophylactic and delayed dosing paradigms. Here, we examine the efficacy of this therapeutic approach across diverse paradigms, varying the dose and dosing regimen, prion strain, treatment timepoint, and examining symptomatic, survival, and biomarker readouts. We recapitulate our previous findings with additional PrP-targeting ASOs, and demonstrate therapeutic benefit against four additional prion strains. We demonstrate that \u3c25% PrP suppression is sufficient to extend survival and delay symptoms in a prophylactic paradigm. Rise in both neuroinflammation and neuronal injury markers can be reversed by a single dose of PrP-lowering ASO administered after the detection of pathological change. Chronic ASO-mediated suppression of PrP beginning at any time up to early signs of neuropathology confers benefit similar to constitutive heterozygous PrP knockout. Remarkably, even after emergence of frank symptoms including weight loss, a single treatment prolongs survival by months in a subset of animals. These results support ASO-mediated PrP lowering, and PrP-lowering therapeutics in general, as a promising path forward against prion disease

Evaluating drug targets through human loss-of-function genetic variation

Naturally occurring human genetic variants that are predicted to inactivate protein-coding genes provide an in vivo model of human gene inactivation that complements knockout studies in cells and model organisms. Here we report three key findings regarding the assessment of candidate drug targets using human loss-of-function variants. First, even essential genes, in which loss-of-function variants are not tolerated, can be highly successful as targets of inhibitory drugs. Second, in most genes, loss-of-function variants are sufficiently rare that genotype-based ascertainment of homozygous or compound heterozygous 'knockout' humans will await sample sizes that are approximately 1,000 times those presently available, unless recruitment focuses on consanguineous individuals. Third, automated variant annotation and filtering are powerful, but manual curation remains crucial for removing artefacts, and is a prerequisite for recall-by-genotype efforts. Our results provide a roadmap for human knockout studies and should guide the interpretation of loss-of-function variants in drug development.Peer reviewe

The effect of LRRK2 loss-of-function variants in humans

Analysis of large genomic datasets, including gnomAD, reveals that partial LRRK2 loss of function is not strongly associated with diseases, serving as an example of how human genetics can be leveraged for target validation in drug discovery. Human genetic variants predicted to cause loss-of-function of protein-coding genes (pLoF variants) provide natural in vivo models of human gene inactivation and can be valuable indicators of gene function and the potential toxicity of therapeutic inhibitors targeting these genes(1,2). Gain-of-kinase-function variants in LRRK2 are known to significantly increase the risk of Parkinson's disease(3,4), suggesting that inhibition of LRRK2 kinase activity is a promising therapeutic strategy. While preclinical studies in model organisms have raised some on-target toxicity concerns(5-8), the biological consequences of LRRK2 inhibition have not been well characterized in humans. Here, we systematically analyze pLoF variants in LRRK2 observed across 141,456 individuals sequenced in the Genome Aggregation Database (gnomAD)(9), 49,960 exome-sequenced individuals from the UK Biobank and over 4 million participants in the 23andMe genotyped dataset. After stringent variant curation, we identify 1,455 individuals with high-confidence pLoF variants in LRRK2. Experimental validation of three variants, combined with previous work(10), confirmed reduced protein levels in 82.5% of our cohort. We show that heterozygous pLoF variants in LRRK2 reduce LRRK2 protein levels but that these are not strongly associated with any specific phenotype or disease state. Our results demonstrate the value of large-scale genomic databases and phenotyping of human loss-of-function carriers for target validation in drug discovery.Peer reviewe

Analysis of protein-coding genetic variation in 60,706 humans

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. We describe the aggregation and analysis of high-quality exome (protein-coding region) sequence data for 60,706 individuals of diverse ethnicities generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of truncating variants with 72% having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human “knockout” variants in protein-coding genes

Analysis of protein-coding genetic variation in 60,706 humans

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human 'knockout' variants in protein-coding genes.Peer reviewe

HudsonAlpha/clinvar: Sep2017 clinvar data used in analysis

This repo provides tools to convert ClinVar data into a tab-delimited flat file, and also provides that resulting tab-delimited flat file

ClinVar data parsing

This software repository provides a pipeline for converting raw ClinVar data files into analysis-friendly tab-delimited tables, and also provides these tables for the most recent ClinVar release. Separate tables are generated for genome builds GRCh37 and GRCh38 as well as for mono-allelic variants and complex multi-allelic variants. Additionally, the tables are augmented with allele frequencies from the ExAC and gnomAD datasets as these are often consulted when analyzing ClinVar variants. Overall, this work provides ClinVar data in a format that is easier to work with and can be directly loaded into a variety of popular analysis tools such as R, python pandas, and SQL databases