So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Human biochemical genetics

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.

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

The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.

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The UK Biobank resource with deep phenotyping and genomic data

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

Integrative analysis of 111 reference human epigenomes

Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.

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LD score regression distinguishes confounding from polygenicity in genome-wide association studies :

High demands on advanced safety and driving functions, such as active safety and lane departure warnings, increase a vehicle’s dependency on automotive electrical/electronic architectures. Hard real-time requirements and high reliability constraints must be satisfied for the correct functioning of these safety-critical features, which can be achieved by using the AUTOSAR (Automotive Open System Architecture) standard. The AUTOSAR standard was introduced to simplify automotive system design while offering inter-operability, scalability, extensibility, and flexibility. The current version of AUTOSAR does not assist in the replication of tasks for recovering from task failures. Instead, the standard assumes that architecture designers will introduce custom extensions to meet such reliability needs. The introduction of affordable techniques with predictable properties for meeting reliability requirements will prove to be very valuable in future versions of AUTOSAR. In this paper, we propose a new Software-Component (SW-C) allocation algorithm called R-FLOW (Reliable application-FLOWaware SW-C partitioning algorithm) for fail-stop processors to support fault-tolerance with bounded recovery times, and we integrate the R-FLOW algorithm into AUTOSAR. R-FLOW leverages different types of replication schemes to satisfy reliability and timing constraints, while offering a high degree of resource utilization and flexibility. Specifically, R-FLOW classifies real-time periodic tasks into Hard Recovery tasks, Soft Recovery tasks, and Best-Effort Recovery tasks. Hot Standbys are used for recovering from failures of hard recovery tasks, whereas Cold Standbys are utilized for recovering from failures of soft recovery and best-effort recovery tasks. With this goal in mind, we design and implement our proposed architecture within the guidelines of the current AUTOSAR framework. We have built an at-scale prototyping platform, comprising of Freescale HCS12X processing boards, a dualchannel FlexRay bus, and a CAN network. Our proposed architecture is evaluated on this platform using reliability and timeliness metrics in the context of different fault scenarios.

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An AUTOSAR-Compliant Automotive Platform for Meeting Reliability and Timing Constraints

In this paper, we propose a model-based design approach for developing wireless sensor-actuator networks that can map multiple sets of application-level interactions onto a single networking substrate while still enforcing individual requirements. We use a top-down design approach where the functional requirements for each application are graphically modeled using a tool called SysWeaver. Sensor networking applications add unique challenges for model-based design frameworks because the system deployment view is tightly coupled to an installation-specific network topology and link characteristics. Wireless devices can also be mobile and hence may not easily map to standard deployment views. We introduce a SysWeaver plugin called SenseWeaver that is able to capture live toplogy data from an instrumentation deployment and feed the topology and link characteristic information to the system model. A developer can then use SenseWeaver specify the functional requirements of multiple applications, analyze communication and task scheduling requirements based on actual topology data, and automatically generate customized code for each sensor network node.

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A Model-Based Design Approach for Wireless Sensor-Actuator Networks

Multi-core processors are becoming increasingly prevalent, with multiple multi-core solutions being offered for the automotive sector. Recognizing this trend, the AUTomotive Open System ARchitecture (AUTOSAR) standard version 4.0 has introduced support for multi-core embedded real-time operating systems. A key element of the AUTOSAR multi-core specification is the spinlock mechanism for inter-core task synchronization. In this paper, we study this spinlock mechanism from the standpoint of timing predictability. We describe the timing uncertainties introduced by standard test-and-set spinlock mechanisms, and provide a predictable priority-driven solution for inter-core task synchronization. The proposed solution is to arbitrate critical sections using the well-established Multi-processor Priority Ceiling Protocol [3], which is the multiprocessor version of the ceiling protocol for uniprocessors [1, 2] used by AUTOSAR. We also present the associated analysis that can be used in conjunction with the AUTOSAR task model to bound the worst-case waiting times for accessing shared resources. The timing predictability provided by our protocol is an important requirement for automotive applications from both certification and validation standpoints.

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AUTOSAR Extensions for Predictable Task Synchronization in Multi-Core ECUs

More than 44% of all automotive crashes occur in intersections. These incidents in intersections result in more than 8,500 fatalities and approximately 1 million injuries each year in USA [1,18]. It is also established that roundabouts are safer than junctions. According to a study of a sampling of roundabouts in the United States, when compared with the junctions they replaced, roundabouts have 40% fewer vehicle collisions, 80% fewer injuries and 90% fewer serious injuries and fatalities [14]. In earlier work [2,12], we have proposed a family of vehicular network protocols, which use Dedicated Short Range Communications (DSRC) and Wireless Access in Vehicular Environment (WAVE) technologies to coordinate a vehicle’s movement through intersections. We have shown that vehicle-to-vehicle (V2V) communications can be used to avoid collisions at the intersection and also significantly decrease the trip delays introduced by traffic lights and stop signs. In this paper, we investigate the use of our proposed V2V-intersection protocols for autonomous driving at roundabouts. We have extended our hybrid emulator-simulator called AutoSim to implement realistic map and mobility models to study traffic flow at roundabouts and have implemented our V2Vintersection protocols on roundabouts. We quantify the improvement in safety and throughput when our intersection protocols are used to traverse roundabouts.

V2V-Intersection Management at Roundabouts

Population stratification is a well-documented confounder in GWASes, and is often addressed by including principal component (PC) covariates computed from common SNPs (SNP-PCs). In our analyses of summary statistics from 36 GWASes (mean n=88k), including 20 GWASes using 23andMe data that included SNP-PC covariates, we observed a significantly inflated LD score regression (LDSC) intercept for several traits−suggesting that residual stratification remains a concern, even when SNP-PC covariates are included. Here we propose a new method, PC loading regression, to correct for stratification in summary statistics by leveraging SNP loadings for PCs computed in a large reference panel. In addition to SNP-PCs, the method can be applied to haploSNP-PCs, i.e. PCs computed from a larger number of rare haplotype variants that better capture subtle structure. Using simulations based on real genotypes from 54,000 individuals of diverse European ancestry from the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort, we show that PC loading regression effectively corrects for stratification along top PCs. We applied PC loading regression to several traits with inflated LDSC intercepts. Correcting for the top four SNP-PCs in GERA data, we observe a significant reduction in LDSC intercept height summary statistics from the Genetic Investigation of ANthropometric Traits (GIANT) consortium, but not for 23andMe summary statistics, which already included SNP-PC covariates. However, when correcting for additional haploSNP-PCs in 23andMe GWASes, inflation in the LDSC intercept was eliminated for eye color, hair color, and skin color and substantially reduced for height (1.41 to 1.16; n=430k). Correcting for haploSNP-PCs in GIANT height summary statistics eliminated inflation in the LDSC intercept (from 1.35 to 1.00; n=250k), eliminating 27 significant association signals including one at the LCT locus, which is highly differentiated among European populations and widely known to produce spurious signals. Overall, our results suggest that uncorrected population stratification is a concern in GWASes of large sample size and that PC loading regression can correct for this stratification.

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Gaurav Bhatia

Papers

An AUTOSAR-Compliant Automotive Platform for Meeting Reliability and Timing Constraints

A Model-Based Design Approach for Wireless Sensor-Actuator Networks

AUTOSAR Extensions for Predictable Task Synchronization in Multi-Core ECUs

V2V-Intersection Management at Roundabouts

Correcting subtle stratification in summary association statistics