Загрузка...

Genome wide association studies (GWAS) part 2: Signal vs noise

This video builds on the basics of GWAS by diving deeper into study design, quality control, and data analysis procedures. It explains how researchers clean genetic data, handle missingness, and control for confounding factors such as population structure. The lecture also covers interpretation of GWAS outputs, including Manhattan plots and significance thresholds.

Видео Genome wide association studies (GWAS) part 2: Signal vs noise канала International Statistical Genetics Workshop
Комментарии отсутствуют
Зарегистрируйтесь или войдите с
Информация о видео
18 мая 2026 г. 5:00:05
00:26:38
International Statistical Genetics Workshop
Правообладателям
Жалоба на материал Недопустимый материал Нарушение авторских прав
Комментарии
Поделиться
Другие видео канала

Formatting GWAS data using sumstatsFormatting GWAS data using sumstats

7 Ways to do a Univariate Twin Model7 Ways to do a Univariate Twin Model

Running GWAS with generalized mixed modelsRunning GWAS with generalized mixed models

Estimating Parental Effects using Polygenic Scores Part I: Model IntroductionEstimating Parental Effects using Polygenic Scores Part I: Model Introduction

Introduction to Mendelian randomization - Part 6Introduction to Mendelian randomization - Part 6

Binary Model Practical Walk throughBinary Model Practical Walk through

HAIL 101: Introduction to HAILHAIL 101: Introduction to HAIL

Multivariate twin models: Practical walk through part 2Multivariate twin models: Practical walk through part 2

The munge functionThe munge function

Twin Models using OpenMx Part 2: Saturated ModelsTwin Models using OpenMx Part 2: Saturated Models

Biometrical GeneticsBiometrical Genetics

From cor. coef. to variance components: Part 3 - Polygenic risk scores, longitudinal models, and SEMFrom cor. coef. to variance components: Part 3 - Polygenic risk scores, longitudinal models, and SEM

GCTA (GREML) & M-GCTA - Part 3: Estimate Variance ComponentsGCTA (GREML) & M-GCTA - Part 3: Estimate Variance Components

Heritability of individual level data - Methods, part 3Heritability of individual level data - Methods, part 3

How do we use meta-analysis in GWAS?How do we use meta-analysis in GWAS?

Polygenic Prediction: Part 3 Conventional methods for PGS predictionPolygenic Prediction: Part 3 Conventional methods for PGS prediction

Twin Models using OpenMx Part 4: Estimating variances vs. pathsTwin Models using OpenMx Part 4: Estimating variances vs. paths

Expression quantitative trait locus (eQTL) mapping: Context, GTEx, and interpretationExpression quantitative trait locus (eQTL) mapping: Context, GTEx, and interpretation

Multivariate Twin Models using OpenMx Part 2: atheoretical and theoretical multivariate modelsMultivariate Twin Models using OpenMx Part 2: atheoretical and theoretical multivariate models

Polygenic Prediction: Part 5 Markov chain Monte Carlo (MCMC) for polygenic predictionPolygenic Prediction: Part 5 Markov chain Monte Carlo (MCMC) for polygenic prediction

Multivariate Twin Models using OpenMx Part 1: ACE models for two phenotypes and genetic correlationMultivariate Twin Models using OpenMx Part 1: ACE models for two phenotypes and genetic correlation

Яндекс.Метрика
© 2008-2026 «Информационно-развлекательный портал города Верхняя Салда»
salda.ws@mail.ru salda_ws Контакты Карта портала Сообщить об ошибке Соглашения Пользовательское соглашение Соглашение о конфиденциальности Согласие на обработку персональных данных Информация для правообладателей Об использовании Cookies
Все заметки Новая заметка Страницу в заметки
Страницу в закладки Мои закладки
На информационно-развлекательном портале SALDA.WS применяются cookie-файлы. Нажимая кнопку Принять, вы подтверждаете свое согласие на их использование.
О CookiesНапомнить позжеПринять