QCB Special Seminar | Dr. Vsevolod (Seva) Katritch

Dr. Vsevolod Katritch

Assistant Professor, USC Dept. of Biological Sciences, QCB

Lab Website

GPCR modeling: from structure to function to rational design of new receptors and ligands

Thursday, October 1 @ 2 PM

Zoom Meeting ID: 984 8270 9945 | Passcode: 329869

Abstract: Comprising the largest protein superfamily in human, 800 G-protein Coupled Receptors (GPCRs) play key regulatory roles in most physiological processes and serve as a target for about a third of all therapeutic drugs. Over the last few years, a flow of structural information from crystallography and cryo-EM helped to establish a solid framework for computational modelling inquiry into GPCR functional mechanisms and 3D pharmacology of GPCR ligands. The quantitative understanding of atomistic details of GPCR structure-function is also directly applicable to the rational design of both receptors and ligands with new properties. This talk will describe several new approaches to GPCR computational modelling and design developed in my lab. Using sequence-based, structure-based, and machine-learning approaches we developed the CompoMug software for predicting stabilizing mutations in GPCRs, which has already helped to crystallize more than a dozen receptors.  Discovery of new allosteric co-factors, including highly conserved sodium ion in the center of the 7TM bundle in Class A GPCRs, has opened a venue for rational design of highly potent bitopic ligands with unusual signalling properties. This design approach has yielded novel opioid receptor probes and can be applied to many GPCRs.  Finally, we develop a conceptually new Virtual SYNTon Hierarchical Enumeration Screening approach, V SYNTHES, which enables fast and accurate screening in combinatorial REadily AvailabLe (REAL) chemical space as large as 10 Billion compounds and more. Tested in prospective screening for Cannabinoid receptor ligands, V-SYNTHES yielded 20 novel submicromolar ligands, while showing more than 100-fold improved speed and better hit rates than the traditional virtual ligand screening. The approach is scalable to the rapidly growing combinatorial libraries beyond 1010-1013 compounds, yielding better hits and also streamlining their optimization.  We apply these computational tools to the key receptors in inflammation, sleep and pain modulation pathways, facilitating discovery of novel GPCR ligands with desirable functional profiles as molecular probes and lead candidates.

Hosts: Dr. Charles McKenna and Dr. Remo Rohs

QCB Faculty Candidate Seminar | Dr. Geoffrey Fudenberg

Dr. Geoffrey Fudenberg
Bioinformatics Fellow, UCSF, Gladstone Institute
Research Profile

Genomes in 3D: connecting structure and function

Thursday, March 5, 2 PM, RRI 101

Abstract: How are micron-long chromosomes spatially organized by molecular interactions between proteins at the nanometer scale? Acting as a molecular microscope, genome-wide chromosome conformation capture (Hi-C) reveals that genomes are intricately folded in 3D. Here I describe how biophysical simulations and machine learning approaches enable interpretation of these large-scale genomic datasets. First, I describe converging theoretical and experimental evidence arguing that Cohesin-mediated loop extrusion with CTCF-defined barriers plays a crucial role in interphase. Second, I describe how convolutional neural networks enable accurate predictions of genome folding from DNA sequence alone. Together, these advance our understanding of the proteins driving and the sequences underpinning 3D genome folding.

QCB Faculty Candidate Seminar | Dr. Assaf Amitai

Dr. Assaf Amitai
Post-Doctoral Researcher, MIT
Research Website

Geometry and stochastic dynamics in biological systems

Thursday, February 27, 2 PM, RRI 101

Abstract: The interaction of proteins with chromatin regulates many cellular functions. Most DNA-binding proteins interact both non-specifically and transiently with many chromatin sites, as well as specifically and more stably with cognate binding sites. These interactions and chromatin structure are important in governing protein dynamics. By analyzing the motion of CTCF, a DNA binding protein responsible for chromosomal organization, we inferred that it interacts with a new type of small nuclear domains. These domains, composed of RNA, are central in guiding CTCF to find its cognate binding site. Hence, weak transient interactions govern chromatin organization and dynamics. In the second part of the talk, I will describe recent advances in the development of a universal vaccine for the influenza virus. Using coarse-grained molecular dynamics simulations and a population scale models of the adaptive immune system, we study the immune response to nanoparticles presenting flu proteins at unique geometries and compositions. We show that these nanoparticles can direct the immune response in distinct evolutionary paths, and elicit the creation of antibodies of high breadth - capable of neutralizing multiple flu strains.

QCB Faculty Candidate Seminar | Dr. David Zeevi

Dr. David Zeevi
Independent Fellow, Rockerfeller University, Center for Studies in Physics & Biology
Research Website

Mining the marine microbiome for remediation targets: lessons from the human microbiome

Thursday, February 20, 2 PM, RRI 101

Abstract: Microbial communities can have an immense effect on their environment and are strongly affected by it. Using new methods for metagenomic sequencing analysis, we systematically identified microbial genomic structural variants and found them to be highly prevalent in the gut microbiome and to correlate with disease risk factors (Zeevi et al., Nature 2019). Our results suggest that these variants facilitate adaptation to environmental stress. Exploring genes that are clustered in the same variant, we uncovered potential mechanistic links between microbiome and its host. Inspired by our discovery of potential microbial adaptation to host pressures, I developed a strategy for mining marine microbiome samples for novel bioremediation genes. To this end, we devised a high-throughput evolutionary analysis, and revealed an unexpected insight into the structure of our genetic code (Shenhav and Zeevi, bioRxiv 2019). Our primary analyses uncovered overwhelmingly strong purifying selective pressure across marine microbial life. This selection was highly correlated with nutrient concentrations and has led us to explore robustness in the genetic code, common to nearly all life forms. We show that the structure of the genetic code, along with amino acid choices across all kingdoms of Life, confers robustness to mutations that incorporate additional nitrogen and carbon into protein sequences. By accounting for this nutrient-conservation-driven purifying selection, we will be able to expose a new layer of selection associated with marine pollution.

Quantitative & Computational Biology Faculty Candidate Seminar | Yang Yang

Yang Yang
Ph.D. Candidate, Carnegie Mellon University, Computational Biology
Research Website

Computational Methods for Multi-Species Comparison of 3D Genome Structure and Function

Thursday, January 23, 2 PM, RRI 101

Abstract: Recent development in chromatin interaction mapping technologies have greatly advanced the study of three-dimensional (3D) genome organization, which is closely related to vital genome functions such as DNA replication timing (RT) and transcription. However, the principles underlying 3D genome organization and the detailed patterns on how the 3D genome has changed in mammalian evolution remain largely unclear. In this talk, I will primarily introduce two probabilistic models that I have developed during my Ph.D. research: Phylo-HMGP and Phylo-HMRF, which provide the new generic frameworks for genome-wide comparison of continuous genomic features, including RT and Hi-C data for 3D genome structures. The methods incorporate the temporal dependencies of species in the context of evolution with the spatial dependencies of genomic loci, to identify genome-wide evolutionary patterns of continuous genomic features. Real data applications to the RT data and Hi-C data from multiple primate species demonstrated the effectiveness of the models and offered high resolution characterization of evolutionary patterns of 3D genome structure and function. Together, the methods have the potential to help reveal genomic regions with conserved or species-specific structural and regulatory roles, and provide key insights into nuclear organization and function through cross-species comparisons.

QCB Colloquium | Dr. Serghei Mangul

Dr. Serghei Mangul
Assistant Professor, USC School of Pharmacy
Lab Website

Dumpster diving in RNA-sequencing to study the immune receptor repertoires and microbial communities

Thursday, January 16, 2 PM, RRI 101

Abstract: Assay-based approaches provide a detailed view of the adaptive immune system by profiling T- and B-cell receptor (TCR/BCR) repertoires. However, these methods are costly, time-consuming, and lack the scale of RNA sequencing (RNA-seq). The seminar will introduce bioinformatic methods that mine discarded sequences and produce rich research level data including T and B cell receptor sequences, microbiome, genome-wide germline genotypes, and rDNA and mtDNA copy number.  We have applied our methods to GTEx multi-tissue RNA-Seq data (n=8,555) and Profile OncoPanel cancer sequencing data (n=20,000). We validated the accuracy of our methods and showed their utility through replication of known genetic associations. The presented systematic atlas of immunological sequences data contains one of the largest collections of immune receptor sequencing across a broad range of tissue. Additionally, we investigated the functional mechanisms underlying connections between the immune system, microbiome, and disease.

QCB Colloquium | Dr. Shilpa Kobren

Dr. Shilpa Kobren
Research Fellow in Biomedical Informatics, Harvard Medical School
Research Profile

Uncovering genes with significantly perturbed functionalities in cancer

Thursday, Dec. 5, 2 PM, RRI 101

Abstract: A major challenge in cancer genomics is to identify genes with functional roles in cancer and uncover their mechanisms of action. This is a difficult task as there is substantial mutational heterogeneity across tumors, and only a small subset of the numerous mutations in a given tumor may be functionally relevant for the disease. In my talk, I will introduce our newly developed, unified analytical framework that enables rapid integration of multiple sources of information in order to identify cancer-relevant genes by pinpointing those whose interaction or other functional sites are enriched in somatic mutations across tumors. Our method PertInInt combines knowledge about sites participating in interactions with DNA, RNA, peptides, ions or small molecules with domain, evolutionary conservation and gene-level mutation data. When applied to 10,037 tumor samples across 33 cancer types, PertInInt efficiently uncovers both known and newly predicted cancer genes. Importantly, our analytical integration of data allows PertInInt to simultaneously reveal whether interaction potential or other molecular functionalities are disrupted, thereby enabling valuable insights that may help guide personalized cancer treatments. PertInInt’s analysis demonstrates that somatic mutations are frequently enriched in binding residues and functional domains in cancer genes, and implicates interaction perturbation as a pervasive cancer driving event.

QCB Colloquium | Dr. Pei Wang

Dr. Pei Wang
Professor, Icahn School of Medicine at Mount Sinai, Genetics and Genomic Sciences
Lab Website

Constructing tumor-specific gene regulatory networks based on samples with tumor purity heterogeneity

Monday, Dec. 2, 2 PM, RRI 101

Abstract: Tumor tissue samples often contain an unknown fraction of normal cells. This problem well known as tumor purity heterogeneity (TPH) was recently recognized as a severe issue in omics studies. Specifically, if TPH is ignored when inferring co-expression networks, edges are likely to be estimated among genes with mean shift between normal and tumor cells rather than among gene pairs interacting with each other in tumor cells. To address this issue, we propose TSNet a new method which constructs tumor-cell specific gene/protein co-expression networks based on gene/protein expression profiles of tumor tissues. TSNet treats the observed expression profile as a mixture of expressions from different cell types and explicitly models tumor purity percentage in each tumor sample. The advantage of TSNet over existing methods ignoring TPH is illustrated through extensive simulation examples. We then apply TSNet to estimate tumor specific co-expression networks based on ovarian cancer expression profiles. We identify novel co-expression modules and hub structure specific to tumor cells

QCB Colloquium | Dr. Neda Bagheri

Dr. Neda Bagheri
Adjunct Professor of Chemical and Biological Engineering, Northwestern University
Lab Website

Modeling toward systems medicine: predicting how context impacts cell population dynamics

Thursday, Nov. 21, 2 PM, RRI 101

Abstract: Computational models are essential tools that can be used to simultaneously explain and guide biological intuition. My lab employs machine learning, dynamical systems, and agent-based modeling strategies to help explain biological observations, and to uncover fundamental principles that drive both individual cellular decisions and cell populations. We are interested in the inherent multiscale nature of cells—how “the whole is greater than the sum of its parts”—and in predicting cell population dynamics from the composition of simpler biological modules to advance basic science and medicine.

QCB Colloquium | Dr. Alison Hill

Dr. Alison Hill
Research Fellow, Harvard Univ., Prog. for Evolutionary Dynamics
Research Profile

Countdown to a cure? Mathematical approaches to designing better HIV treatments

Thursday, Nov. 14, 2 PM, RRI 101

Abstract: HIV infection can be effectively treated with combination antiretroviral therapy, but new classes of drugs are needed to permanently cure the infection. In this talk I will discuss our work developing mathematical and computational methods to better understand the mechanisms of HIV persistence and evaluate new methods to cure the disease. Firstly, I will show how models have helped us understand how much the pool of latent virus must be reduced to delay or prevent the viral rebound when drugs are stopped. We explain why existing anti-latency drugs have had negligible benefit, and why we have seen multiple cases of apparent (but false) “cures” of HIV. Secondly, I will discuss how longitudinal studies of viral genetics during antiretroviral therapy can be used to help elucidate the dominant cause of long-term persistence. This includes a new method we have developed to quantify how important the proliferation of latently-infected cells is to driving long-term viral persistence, which also suggests that therapies to target this process could be highly effective. Finally, I will describe a series of studies using new immunotherapy strategies to cure HIV, and our work using mathematical models to uncover the mechanism of action of these interventions. Overall, this work highlights the role that simulation, analysis, and inference using mathematical models can play in informing new potentially-curative treatments for HIV.

QCB Colloquium | Dr. Matthew J. Simpson

Dr. Matthew J. Simpson
Professor, Queensland University of Technology, Science & Engineering, Mathematical Sciences, Applied & Computational Mathematics
Research Website

Mathematical and experimental models of cell invasion with fluorescent cell cycle indicators

Thursday, Oct. 31, 2 PM, RRI 101

Abstract: Fluorescent cell cycle indicators, such as FUCCI, allow us to visualize the cell cycle in individual cells. FUCCI reveals real-time information about cell cycle dynamics in individual cells, and can be used to explore how the cell cycle relates to the location of cells, local cell density, and different microenvironments. In this talk I will describe how FUCCI technology can be incorporated into continuum and discrete models of cell invasion. Using experimental data from two-dimensional cell invasion assays with FUCCI-transduced melanoma cells, we show how mathematical models can be used to predict key features of the experiments. The models we present are also amenable to travelling wave analysis, and some key highlights of this analysis will also be presented and discussed.

QCB Colloquium | Dr. John H. Maddocks

Dr. John H. Maddocks
Professor, EPFL Lausanne, Switzerland, Laboratory for Computation & Visualization in Mathematics & Mechanics
Lab Website

The cgDNA sequence-dependent coarse-grain model of dsDNA: Bridging the scales from Molecular Dynamics to Bioinformatics

Tuesday, Oct. 29, 3:15 PM, MCB 102

Abstract: The cgDNA+ coarse-grain model of DNA (lcvmwww.epfl.ch/research/cgDNA/) can now accurately predict the sequence-dependent statistical mechanics properties, for example shape and stiffness (or equivalently first and second moments of the equilibrium distributions), of double-stranded DNA fragments of arbitrary sequence. At scales of tens of base pairs these predictions can be compared with Molecular Dynamics simulations and they agree very well. However the efficiency of the cgDNA+ model allows genome length scales to be scanned in order to identify mechanically exceptional sequence fragments, including in an epigenetically modified sequence alphabet. 

QCB Colloquium | Dr. Yinglei Lai

Dr. Yinglei Lai
Professor, The George Washington University, Dept. of Statistics
Professional Website

Assessing the discovery reproducibility from a large-scale association analysis

Wednesday, Oct. 23, 3 PM, RRI 421

Abstract: Reproducibility plays essential roles in scientific research.  Magnetic Resonance Imaging (MRI) and genomic/proteomic high-throughput technologies have been widely used in brain and health research.  The Dice Similarity Coefficient (DSC) has been commonly used for assessing the reproducibility of discoveries in a large-scale association analysis.  However, in the current assessment of reproducibility, there is a lack of efficiency in the use of all available samples.  More importantly, there is a lack of consistency with the reported discoveries identified based on all available samples.  We have developed a probabilistic framework to assess discovery reproducibility based on all available samples.  In our results, we demonstrated the usefulness of our approach and its advantages over DSC.  We identified the minimal sample size required to achieve a given reproducibility rate, which provides an informative guidance for planning large-scale association studies.

QCB Colloquium | Dr. Siavash Mirarab

Dr. Siavash Mirarab
Assistant Professor, UCSD, Dept. of Electrical & Computer Engineering
Faculty Profile

Assembly-free and alignment-free sample identification and phylogenetic placement using genome skims

Thursday, Oct. 24, 2 PM, RRI 101

Abstract: The ability to inexpensively describe taxonomic diversity is critical in this era of rapid climate and biodiversity changes. The recent genome-skimming approach extends current barcoding practices beyond short markers by applying low-pass sequencing and recovering whole organelle genomes computationally. This approach discards the nuclear DNA, which constitutes the vast majority of the data. In contrast, we suggest using all unassembled reads. We introduce an assembly-free and alignment-free tool, Skmer, to compute genomic distances between the query and reference genome skims. Skmer is based on a fast computation of Jaccard index and appropriate corrections for lack of coverage. Skmer shows excellent accuracy in estimating distances and identifying the closest match in reference datasets. When paired with our new phylogenetic placement tool, APPLES, it can perform distance-based phylogenetics.

QCB Colloquium | Dr. Jill Gallaher

Dr. Jill Gallaher
Research Scientist, Moffit Cancer Center
Google Scholar Profile

Systemic dynamics of multiple metastases during adaptive therapy

Tuesday, October 15
2 PM
RRI 101

Abstract: Although metastatic disease is thought to be responsible for about 90% of cancer deaths, there has been relatively little improvement in the understanding and treatment of cancer at this advanced stage. Increasingly, data point toward intra- and inter-tumor heterogeneity as a major driver of treatment failure in metastatic cancer. We have recently shown that disseminated disease may be better managed using evolutionary-designed maintenance therapies as opposed to maximum tolerated dose, treat-to-kill strategies. Adaptive therapy is one such evolutionary treatment strategy that exploits sensitive and resistant cell competition; a lower dose is given to a shrinking tumor and a higher dose to a growing tumor. From clinical and pre-clinical data, we are learning how the total tumor burden (for example, PSA in prostate cancer) can be used to control disease using this strategy, but details on how multiple distinct heterogeneous metastatic lesions contribute to systemic measures of burden are not fully understood or well documented. We use an off-lattice agent-based computational model to simulate different treatment schedules of an anti-proliferative drug applied systemically to multiple individual micro-metastases. We assume that there is a tradeoff between fast proliferation and drug resistance, and use the total tumor burden from all metastases to make treatment decisions for adaptive therapy. We simulate how intra- and inter- tumor heterogeneity and seeding dynamics affect the best treatment strategy between a maximum continuous dose or an adaptive therapy schedule. When adaptive therapy is optimal, we investigate how tumor composition and number of metastases change the treatment cycling times and indicate future treatment failure. We examine how using different biomarkers that only measure a subset of the tumor phenotypes versus the total tumor burden affect the dose schedule and overall disease control. With these trends in mind, we aim to identify which which patients are best suited for an adaptive therapy strategy, and for those that qualify, identify metrics to assess ongoing treatment response.

QCB Colloquium | Dr. Kai Tan

Dr. Kai Tan
Associate Professor of Pediatrics, University of Pennsylvania, Perelman School of Medicine
Lab Website

Systematic Analysis of Noncoding Genetic Variants

Thursday, October 3
2 PM
RRI 101

Abstract: The vast majority of genetic variants in the human genome are located in the noncoding region. Functional interpretation of causal noncoding variants has remained a major challenge in human genetics. In this talk, I will present a couple of computational algorithms for predicting causal noncoding variants by integrating large scale omics datasets. I will also present a couple of case studies on identifying and validating germline variants in type 1 diabetes and somatic variants in pediatric cancers. Our work established a much-needed systemic approach to identifying and characterizing noncoding causal variants in human diseases.

QCB Colloquium | Dr. Aaron Smargon

Dr. Aaron Smargon
Postdoctoral Scholar, University of California, San Diego, School of Medicine, Department of Cellular and Molecular Medicine
Research Profile

RNA-targeting CRISPR systems: from metagenomic discovery to transcriptomic engineering

Thursday, September 26
2 PM
RRI 101

Abstract: The deployment of RNA-guided DNA endonuclease CRISPR-Cas technology has led to radical advances in biology. As the functional diversity of CRISPR-Cas and other prokaryotic defense systems is further explored, RNA manipulation has emerged as a powerful new mode of CRISPR-based engineering. In this seminar I chart the most recent progress in the RNA-targeting CRISPR-Cas (RCas) field and illustrate how a continuing evolution in scientific discovery translates into applications for RNA biology and insights into the mysteries, obstacles, and alternative technologies that lie ahead.

QCB Colloquium | Dr. Aafke van den Berg

Dr. Aafke van den Berg
Post-Doctoral Fellow, Massachusetts Institute of Technology (MIT), Institute for Medical Engineering and Science
Lab Website (Dr. Leonid Mirny)

Transcription shapes 3D organization of mammalian chromatin

Thursday, September 12, 2019
2 PM
RRI 101

Abstract: Recent theoretical and experimental studies indicate that the process of  loop extrusion is one of the main mechanisms underlying chromosome organization during interphase. According to the model for loop extrusion, a cohesin complex is loaded onto chromatin and extrudes loops until it dissociates or encounters an obstacle such as CTCF. We aim to understand what genomic elements other than CTCF sites, and what processes on DNA can act as extrusion barriers, thus shaping chromosome organization.

We analyzed data from new experiments that remove CTCF and extend cohesin residence time and found profound changes of chromosome folding near active genes. To explain these changes we propose the moving barrier model where cohesin cannot bypass an elongating PolII. As a result cohesin traces PolII at its low speed in the direction of transcription, while a cohesin approaching PolII in the opposite direction is shoveled back to the end of the gene.

The moving barrier model recapitulates both ChIP-seq patterns of cohesin accumulation and patterns in Hi-C around active genes. Interestingly, the model also provides a long-sought mechanism for dynamic enhancer-PolII tracking during transcription elongation. I will discuss how future in vitro and time course experiments can further test the moving barrier model.

QCB Colloquium | Dr. Hannah Carter

Dr. Hannah Carter
Assistant Professor, UCSD, Health Sciences
Lab Website

MHC genotype shapes the oncogenic landscape

Thursday, September 5
2 PM
RRI 101

Abstract: Significant insights into tumorigenesis have been gained by characterizing the extensive somatic alterations that arise during cancer and uncovering rare inherited mutations that lead to early onset cancer syndromes. However, little is understood about the role of genetic background in ‘sporadic’ adulthood cancers. Mounting evidence suggests that the somatic evolution of a tumor is influenced by inherited polymorphisms. We investigated this phenomenon in the context of the immune system, which is a major source of selective pressure during tumor development. The genomic region encoding the Major Histocompatibility Complex Class (MHC) is one of the most variable regions in the human population. MHC molecules expose peptide fragments on the cell surface, allowing T-Cell elimination of cells contaminated by foreign peptide. Although this system has evolved as a defense against microbial and viral agents, MHC can also trigger elimination of cells harboring mutant peptides (neoantigens) in cancer. Each individual carries multiple MHC alleles that define the set of peptides that can be effectively presented for immune surveillance. We hypothesized that individual variation in MHC could create personal gaps in immune surveillance, generating individual-specific susceptibility for cells to acquire specific oncogenic mutations. To test this hypothesis, we developed residue-centric patient presentation scores for MHC class I and II molecules and applied them to 1,018 recurrent oncogenic mutations in 9,176 cancer patients. This analysis uncovered a clear signature of immune selection acting on tumors with implications for age at diagnosis, driver occurrence in tumors and frequency of driver mutations in cancer cohorts. Thus, the landscape of oncogenic mutations observed in clinically diagnosed tumors is shaped by MHC genotype-restricted immunoediting during tumor formation, and individual MHC genotype provides information about the mutations likely to emerge in tumors that develop later in life.

Host: Dr. Michael Waterman

QCB Colloquium | Dr. Charleston Chiang

Dr. Charleston Chiang
Assistant Professor, USC Keck School of Medicine, Center for Genetic Epidemiology, Preventive Medicine
Lab Website

The impact of demographic history and natural selection on human complex traits: examples from Sardinia and Finland

Thursday, August 29, 2019
2 PM

RRI 101

Abstract: How complex traits change through time is a central question in evolutionary biology and genetics. Two of the major evolutionary forces that shaped the distribution of human complex traits are the demographic and adaptive histories of a population. Therefore, in order for human genetics to provide a compelling context to study complex trait evolution, it is necessary to integrate genetic mapping with a detailed knowledge of population history. A well-known example of demographic impact on complex traits is a population bottleneck followed by long-term isolations. I will use examples from European populations of Sardinia and Finland to illustrate the impact of the demographic history on patterns of genetic variation and human complex traits. By utilizing large-scale whole-genome or whole-exome sequencing datasets, I will describe our findings in delineating the population structure and history of these populations, and how the special population history empowered association studies. Moreover, natural selection through polygenic adaptation is also thought to be an important force in shaping the complex traits of extant populations. Adult height differences across European populations had been thought of as the prime example of polygenic adaptation in humans, until recent papers demonstrated that the differences across Europe might have been over-estimated due to uncorrected biases in genome-wide association studies (GWAS). I will show that by using GWAS summary statistics ascertained from an East Asian population, we continue to see signature consistent with polygenic adaptation at height-associated loci in at least some European populations.

Host:  Andrew Smith