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Downstream genomic data analysis
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A1. Functional annotation in risk loci
Danielle Posthuma, June 2021 Virtual Workshop
Functional annotation in risk loci.
A2. Gene-set analysis
Danielle Posthuma, June 2021 Virtual Workshop
Looking for convergence of gene functions 鈥 gene-set analysis.
A3. Pathway analyses using MAGMA
Christiaan de Leeuw, June 2021 Virtual Workshop
Pathway analysis using MAGMA.
B1. Introduction to genetic relatedness
Katrina Grasby, June 2022 Virtual Workshop
In this video various terms that describe genetic relatedness are introduced. The role of recombination in genetic variation and linkage disequilibrium is described. The concepts of identity-by-state and identity-by-descent are compared.
C1. From correlation coefficients to variance components: Part 1 - Introduction to models for estimating heritability
Baptiste Couvy-Duchesne, June 2022 Virtual Workshop
Models to estimate heritability (twin or SNP h2) are typically linear models that can be seen as extensions of the simple linear model between two variables, from which one estimates a correlation. Here, we start from the simplest model and progressively complexify it.
C2. From correlation coefficients to variance components: Part 2 - Twin and SNP heritability from measured genomes
Baptiste Couvy-Duchesne, June 2022 Virtual Workshop
This section covers Twin and SNP heritability, h2 from whole genome sequencing, non-additive genetic effects and GWAS approaches. To better compare and understand the different approaches and models we position them in the statistical framework of linear models.听
C3. From correlation coefficients to variance components: Part 3 - Polygenic risk scores, longitudinal models, and SEM
Baptiste Couvy-Duchesne, June 2022 Virtual Workshop
We continue the exploration of the statistical landscape, including polygenic risk scores (calculation and evaluation), longitudinal models, to conclude on how Statistical Equation Modeling (SEM) can also be decomposed as a set of linear models.听
D1. Heritability of individual level data - Welcome message
Lo茂c Yengo, June 2021 Virtual Workshop
This is a very short video giving an overview of the lecture about the estimation of additive genetic (co-)variance using individual-level genomic data.
D2. Heritability of individual level data - Introduction (part I)
Lo茂c Yengo, June 2022 Virtual Workshop
This video briefly introduces the concepts of heritability and genetic correlation, and illustrates what these concepts could be used for.
D3. Heritability of individual level data - Concepts and tools (part 2)
Lo茂c Yengo, June 2021 Virtual Workshop
This video introduces how to measure genetic relatedness between individuals using genomic data and how to use these measures to estimate the heritability of a traits (or a disease).
D4. Heritability of individual level data - Methods (part 3)
Lo茂c Yengo, June 2021 Virtual Workshop
This video introduces two estimators of the SNP-based heritability: (1) the Haseman-Elston regression, which is a method of moment, and (2) the Restricted Maximum Likelihood (REML) methods, which is a likelihood-based method.
D5. Heritability of individual level data - Interpretation (part 4)
Lo茂c Yengo, June 2021 Virtual Workshop
This video discusses how to interpret estimates of SNP-based heritability, what can bias those estimates and what implication these estimates have for Genome-Wide Association Studies.
D6. Heritability of individual level data - Overview of research topics (part 5)
Lo茂c Yengo, June 2021 Virtual Workshop
This video presents examples of active research related to the estimation of heritability from SNP data. It addresses issues related to computational efficiency, estimation of the contribution of non-additive genetic effects or how mate choice may impact the interpretation of these estimates.
D7. Practical: Compute GRMs and estimate SNP heritability with GREML
Lo茂c Yengo, June 2021 Virtual Workshop
This practical is based on using the software package GCTA to estimate the heritability of a trait for which causal SNP effects depend on allele frequency and linkage disequilibrium patterns (Lecture Part 4).
E1. Estimating SNP Heritability with LD score regression
Hilary Finucane, June 2021 Virtual Workshop
How LD Score regression can be used to distinguish confounding from polygenicity and estimate SNP heritability.
E2. Practical: Estimating SNP Heritability with LD score regression
Lo茂c Yengo, June 2021 Virtual Workshop
This practical is on using the software package LDSC, which implements the Linkage Disequilibrium Regression method to estimate the heritability of a trait (or a disease) using summary statistics from a Genome-Wide Association Study.
F2. Practical: Polygenic Risk Scores
Baptiste Couvy-Duchesne, June 2022 Virtual Workshop
This practical focuses on calculating a polygenic risk score on a toy dataset (using PRSice) and evaluating its prediction accuracy in a sample of related individuals. The practical involves using several software and packages (OpenMx, R, GCTA) and includes key data management steps (merge, wide-long data formatting).
G1 GCTA (GREML) & M-GCTA - Part 1: Estimating Maternal Genetic Effects on Offspring
David Evans, June 2022 Virtual Workshop
Introduction to the G-REML method and GCTA software package.
G2. GCTA (GREML) & M-GCTA - Part 2: GCTA- Genetic Relationship Matrix
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How to calculate a genetic relationship matrix (GRM)
G3. GCTA (GREML) & M-GCTA - Part 3: Estimate Variance Components
David Evans, June 2022 Virtual Workshop
This video introduces the variance components model underlying the GCTA software package.
G4. GCTA (GREML) & M-GCTA - Part 4: The M-GCTA model
David Evans, June 2022 Virtual Workshop
This video introduces the M-GCTA model for estimating maternal genetic variance components.
G5. GCTA (GREML) & M-GCTA - Part 5: Deriving the Phenotypic Variance
David Evans, June 2022 Virtual Workshop
This video shows how the phenotypic variance is parameterized under the M-GCTA model.
G6. GCTA (GREML) & M-GCTA - Part 6: Deriving the Phenotypic Covariance
David Evans, June 2022 Virtual Workshop
This video shows how the phenotypic covariance is parameterized under the M-GCTA model.
H1. Estimating Parental Effects using Polygenic Scores Part I: Model Introduction
Jared V. Balbona, June 2022 Virtual Workshop
SEM-PGS is a model that uses genetic and phenotypic data to estimate parental effects (both genetic and environmental) as well as assortative mating. Here, we cover the underlying logic of SEM-PGS and explain how it is able to obtain these estimates.
H2. Estimating Parental Effects using Polygenic Scores Part II: Model Extensions
Jared V. Balbona, June 2022 Virtual Workshop
In this second video, we cover how SEM-PGS can be used to study assortative mating, and discuss several potential model extensions that can be used to address different types of questions.
I1. Genomic SEM Introduction
Andrew Grotzinger, June 2021 Virtual Workshop
This video provides a broad overview of the Genomic Structural Equation Modeling (Genomic SEM). The video is particularly focused on background information (e.g., motivations for developing the method) and results produced from empirical applications to GWAS summary data.听
I2. Lavaan syntax and SEM introduction
Andrew Grotzinger, June 2021 Virtual Workshop
In this video the basic of structural equation modeling (SEM) are introduced. In addition, the video illustrates how SEMs are specified using Lavaan syntax, which is what Genomic SEM uses for model estimation.听
I3. Explaining how S and V are estimated and what they are
Michel Nivard, June 2021 Virtual Workshop
This video explains what goes into a genomic structural model and gives a refresher on LD Score regression.
I4. The Genomic SEM wiki - walkthrough - Part 1
Michel Nivard, June 2021 Virtual Workshop
A walkthrough working on the examples of the Genomic SEM wiki: munge; ldsc; usermodel functions.
I5. The Genomic SEM wiki - walkthrough - Part 2
Andrew Grotzinger, June 2021 Virtual Workshop
This video introduces how to use the sumstats function and multivariate GWAS functions (userGWAS; commonfactorGWAS) in Genomic SEM. These functions are often used to estimate the effect of a SNP on a latent factor and to produce the QSNP heterogeneity metric. However, this suite of functions can more generally be used to examine the effect of individual SNPs within a multivariate system of genetically overlapping traits.听
I6. GWAS-by-subtraction
Michel Nivard, June 2021 Virtual Workshop
GWAS-by-subtraction using Genomic SEM
J1. Introduction to Mendelian randomization - Part 1
David M. Evans, June 2021 Virtual Workshop
Introduction to Mendelian randomization, the problems with using traditional observational studies to investigate causality, and Randomized controlled trials as the gold standard for causal research.
J2. Introduction to Mendelian randomization - Part 2
David M. Evans, June 2021 Virtual Workshop
How does Mendelian randomization work?
J2. Practical: Mendelian Randomization 2
David M. Evans, June 2021 Virtual Workshop
Practical: Mendelian Randomization 2
J3. Introduction to Mendelian randomization - Part 3
David M. Evans, June 2021 Virtual Workshop
Calculating causal effect estimates via Mendelian randomization
J4. Introduction to Mendelian randomization - Part 4
David M. Evans, June 2021 Virtual Workshop
An example using Mendelian randomization
J5. Introduction to Mendelian randomization - Part 5
David M. Evans, June 2021 Virtual Workshop
Limitations to Mendelian randomization
J6. Introduction to Mendelian randomization - Part 6
David M. Evans, June 2021 Virtual Workshop
Introduction to the MR Base website
J7. Practical: Mendelian Randomization 1
David M. Evans, June 2021 Virtual Workshop
Practical: Mendelian Randomization 1
K1. Sensitivity analyses in Mendelian randomization studies - Part 1
David M. Evans, June 2021 Virtual Workshop
Inverse variance weighted MR analysis. The importance of 鈥渟trand鈥 when conducting two sample MR studies across cohorts
K2. Sensitivity analyses in Mendelian randomization studies - Part 2
David M. Evans, June 2021 Virtual Workshop
Horizontal pleiotropy in Mendelian randomization studies, heterogeneity testing and multivariable Mendelian randomization
K3. Sensitivity analyses in Mendelian randomization studies - Part 3
David M. Evans, June 2021 Virtual Workshop
MR-Egger regression
K4. Sensitivity analyses in Mendelian randomization studies - Part 4
David M. Evans, June 2021 Virtual Workshop
The MR median estimator
K5. Sensitivity analyses in Mendelian randomization studies - Part 5
David M. Evans, June 2021 Virtual Workshop
The MR modal based estimator
K6. Sensitivity analyses in Mendelian randomization studies - Part 6
David M. Evans, June 2021 Virtual Workshop
Reverse causal instruments and Steiger filtering