PQG Seminar Series

The goal of the PQG Seminar Series is to encourage the exchanging of ideas and promote interaction, collaboration, and research in quantitative genomics.  It seeks to further the development and application of quantitative methods, especially for high dimensional data, as well as focus on the training of quantitative genomic scientists.

2021/2022 Seminar Organizers: Sasha Gusev and Hailiang Huang

Please direct any logistical questions to Amanda King

Upcoming Seminar


PQG seminar meetings for the semester will be held by Zoom.  The link to each meeting will be posted along with the talk information.

Tuesday, May 3, 2022
1:00-2:00 PM
Join Zoom meeting:
https://harvard.zoom.us/j/98730593343?pwd=UVVjRTU2UkVDZzJpWUxvY0tlRDV3UT09

Haky Im

Assistant Professor of Medicine and Human Genetics
University of Chicago

Polygenic Transcriptome Risk Scores Translate Polygenic Evidence Across Ancestries and Species

Polygenic risk scores (PRS) are promising to translate the results of genome-wide association studies (GWAS) into clinical practice. However, extrapolation to ancestries beyond the training populations fails to produce similar performance, which is likely to exacerbate existing health disparities. I will show how  PTRS (polygenic transcriptome risk scores) in combination with PRS can improve portability across populations. After discussing the utility and limitations of this approach, I will show how the framework can be pushed further and start translating PRS across species. I will show preliminary results on building the PrediXcan framework in rats and translating PTRS from humans to rats.

 

2020-2021 Dates


September 21, 2021 - Kyle Gaulton, UC San Diego

Kyle Gaulton

Assistant Professor, Department of Pediatrics
UC San Diego

Interpreting complex disease genetics using single cell epigenomics

Genetic risk variants for complex disease are primarily non-coding, and single cell epigenomics provides new opportunities to dissect the cell type-specific cis regulatory function of risk variant activity.  In our recent work we have combined genetic association mapping and single cell epigenomics to annotate mechanisms of complex disease risk, several of which are described below. First, we performed single nuclear ATAC-seq (snATAC-seq) in 15.3k cells from primary pancreatic islets, which revealed 228,873 cell type candidate cis-regulatory elements (cCREs) in endocrine and other cell types. Within endocrine cell types we further identified epigenomic heterogeneity representing hormone producing and signaling responsive cell states and defined state-specific cis-regulatory programs within endocrine cell types. Genetic variants associated with type 2 diabetes (T2D) were enriched in both beta cell states whereas fasting glucose-associated variants were enriched only in beta cells from the hormone producing state.  We annotated risk variants at 380 known T2D signals in cell type cCREs and that were predicted to alter cCRE activity using machine learning and linked to target genes with single cell co-accessibility.  At the KCNQ1 locus, causal T2D risk variant rs231361 was predicted to affect a beta cell state-specific cCRE co-accessible with the INS promoter over 500kb distal, which we validated using genome editing in stem cell-derived beta cells. Second, we performed a genetic association study of type 1 diabetes (T1D) in 520,580 samples and fine-mapping of 136 known and novel T1D signals, which we combined with cell type-specific cis-regulatory programs defined using snATAC-seq in 131,554 cells from pancreas and peripheral blood.  T1D risk variants were enriched in cCREs active in T cells and beta cells as well as other cell types not previously implicated in T1D risk such as acinar and ductal cells of the exocrine pancreas. T1D variants at multiple loci mapped in exocrine-specific cCREs that were linked to genes with exocrine-specific expression. For example, at the CFTR locus T1D risk variant rs7795896 mapped in a ductal cCRE which regulated CFTR expression in ductal cells.  Third, we performed snATAC-seq in 66,843 nuclei from 10 peripheral blood mononuclear cell (PBMC) samples, and mapped chromatin quantitative trait loci (caQTLs) for immune cell types and sub-types. In total we identified 6,248 immune cell type caQTLs, including caQTLs with cell type-specific effects as well as with opposed effects on different cell types which are masked from bulk assays. We fine-mapped loci for 16 complex immune traits and diseases and identified immune cell type caQTLs at 517 candidate causal variants, many of which had cell type-specific effects. For example, at the BACH2 locus associated with T1D and other diseases, fine-mapped variant rs72928038 was a caQTL in naïve CD4+ T cells. In total, combining genetics and single cell epigenomics identifies cell types, cell states, genes and variants involved in complex disease risk.

October 5, 2021 - Eimear Kenny, Icahn School of Medicine at Mount Sinai

Eimear Kenny

Director, Institute for Genomic Health
Icahn School of Medicine at Mount Sinai

Race, identity, and genetic ancestry as variables in science and medicine

November 16, 2021 - Oliver Stegle, EMBL Heidelberg

Oliver Stegle

Professor of Computational Genomics and Systems Genetics
EMBL Heidelberg

December 7, 2021 - Olivier Delaneau, University of Lausanne

Olivier Delaneau

Professor, Systems and Population Genetics Group, Department of Computational Biology
University of Lausanne

Efficient imputation of low-coverage whole genome sequencing data

Low-coverage whole-genome sequencing (LC-WGS) followed by imputation has been proposed as a cost-effective genotyping approach for disease and population genetics studies. In this seminar, we describe GLIMPSE, an efficient method for phasing and imputation of LC-WGS datasets from large reference panels. We compare its performance to existing methods and show the potential of LC-WGS versus SNP arrays across multiple sequencing coverages, human populations and reference panels. As a proof of concept, we also show that 1× coverage enables effective gene expression association studies. Overall, we illustrate the benefits of low coverage sequencing followed by imputation for future genomic studies.

February 8, 2022 - Melissa Gymrek, UC San Diego

Melissa Gymrek

Assistant Professor, Medicine
UC San Diego

Polymorphic short tandem repeats make widespread contributions to complex traits

Recent studies have made substantial progress in identifying genetic variants associated with disease and molecular phenotypes in humans. However, these studies have primarily focused on single nucleotide polymorphisms (SNPs), ignoring more complex variants that have been shown to play important functional roles. Here, I focus on short tandem repeats (STRs), one of the most polymorphic and abundant classes of genetic variation. First I will briefly discuss challenges and solutions for analysis of STR-phenotype associations in large cohorts. Then, I will describe how we used population-wide catalogs of STR variation to identify hundreds of polymorphic STRs associated with complex traits. Finally, I will discuss specific STRs predicted to be causal drivers of some of the strongest known GWAS signals for blood traits and biomarkers in the UK Biobank.

March 1, 2022 - Priya Moorjani, UC Berkeley

Priya Moorjani

Assistant Professor, Dept. of Molecular and Cell Biology
UC Berkeley
An evolutionary perspective on the human mutation rate
Germline mutations are the source of all heritable variation. Understanding the rate and mechanisms by which mutations occur is of paramount importance for studies of human genetics (to interpret heritable disease prevalence) and evolutionary biology (to date evolutionary events). Over the past decade, there has been a flood of data in genomics––within pedigrees, among populations and across species––that is fundamentally revising our understanding of the process of mutagenesis. In my talk, I will first briefly summarize the key findings from these different datasets and then discuss recent findings investigating differences in mutation rate and spectrum (i.e., proportions of different mutation types) across human populations. To investigate inter-population differences, we developed a framework to compare polymorphisms that arose in different time windows in the past while controlling for the effects of selection and biased gene conversion. Applying this approach to Europeans, Africans and East Asians from 1000 Genomes Project, we uncovered three key signals across populations, including one that differentiates non-Africans and Africans even for ancient polymorphisms that predate out-of-Africa migration. This signal is likely due to mutational differences between the ancestors of modern humans and archaic hominins. By relating the observed variations in polymorphisms to the parental age effects on de novo mutations, we show that plausible estimates of reproduction ages cannot explain the joint patterns observed for different mutation types, impling that changes at the molecular level such as genetic modifiers and varying environments have had a non-negligible impact in shaping the human mutation landscape. The composite nature of mutation rate underscores the challenges of using it as the molecular clock for dating evolutionary events even for recent timescales.

April 12, 2022 - Alex Marson, UCSF

Alex Marson

Professor, Director of the Gladstone-UCSF Institute of Genomic Immunology
UCSF

May 3, 2022 - Haky Im, University of Chicago

Haky Im

Assistant Professor of Medicine and Human Genetics
University of Chicago

Polygenic Transcriptome Risk Scores Translate Polygenic Evidence Across Ancestries and Species

Polygenic risk scores (PRS) are promising to translate the results of genome-wide association studies (GWAS) into clinical practice. However, extrapolation to ancestries beyond the training populations fails to produce similar performance, which is likely to exacerbate existing health disparities. I will show how  PTRS (polygenic transcriptome risk scores) in combination with PRS can improve portability across populations. After discussing the utility and limitations of this approach, I will show how the framework can be pushed further and start translating PRS across species. I will show preliminary results on building the PrediXcan framework in rats and translating PTRS from humans to rats.


Seminar Archive