Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

Genomic Variation and Gene Expression

In course

BIO-373: Genetics and genomics

The theoretical part of this course covers classical genetics and contemporary genomics. Because bioinformatics has become important for genomic research, the course also includes practical applications to genomic analyses using Python, including group pro ...

Description

This lecture covers whole genome association studies, mapping eQTLs, and the impact of genomic variation on gene expression in Drosophila melanogaster. It discusses cis and trans-eQTLs, tfQTLs, and xQTLs, providing insights into the molecular mechanisms underlying GWAS variants.

Instructors (2)

Bart Deplancke

Jacques Fellay

Jacques Fellay is a medical scientist with expertise in infectious diseases and human genomics. He obtained his MD from the University of Lausanne in 2002 and his PhD from University of Utrecht. After a clinical training in infectious diseases in Switzerland and a 4-years postdoctoral fellowship at Duke University, he joined the EPFL in April 2011 with an SNF Professorship. On top of his EPFL affiliation, Jacques is also Head of Precision Medicine at the University Hospital (CHUV) in Lausanne, Group Leader at the Swiss Institute of Bioinformatics, and Co-director of the Health2030 Genome Center at Campus Biotech in Geneva.

Official source

Related concepts (21)

Quantitative trait locus

A quantitative trait locus (QTL) is a locus (section of DNA) that correlates with variation of a quantitative trait in the phenotype of a population of organisms. QTLs are mapped by identifying which molecular markers (such as SNPs or AFLPs) correlate with an observed trait. This is often an early step in identifying the actual genes that cause the trait variation. A quantitative trait locus (QTL) is a region of DNA which is associated with a particular phenotypic trait, which varies in degree and which can be attributed to polygenic effects, i.

Genome-wide association study

In genomics, a genome-wide association study (GWA study, or GWAS), is an observational study of a genome-wide set of genetic variants in different individuals to see if any variant is associated with a trait. GWA studies typically focus on associations between single-nucleotide polymorphisms (SNPs) and traits like major human diseases, but can equally be applied to any other genetic variants and any other organisms. When applied to human data, GWA studies compare the DNA of participants having varying phenotypes for a particular trait or disease.

Gene expression

Gene expression is the process by which information from a gene is used in the synthesis of a functional gene product that enables it to produce end products, proteins or non-coding RNA, and ultimately affect a phenotype. These products are often proteins, but in non-protein-coding genes such as transfer RNA (tRNA) and small nuclear RNA (snRNA), the product is a functional non-coding RNA.

Complex traits

Complex traits, also known as quantitative traits, are traits that do not behave according to simple Mendelian inheritance laws. More specifically, their inheritance cannot be explained by the genetic segregation of a single gene. Such traits show a continuous range of variation and are influenced by both environmental and genetic factors. Compared to strictly Mendelian traits, complex traits are far more common, and because they can be hugely polygenic, they are studied using statistical techniques such as quantitative genetics and quantitative trait loci (QTL) mapping rather than classical genetics methods.

Gene expression profiling

In the field of molecular biology, gene expression profiling is the measurement of the activity (the expression) of thousands of genes at once, to create a global picture of cellular function. These profiles can, for example, distinguish between cells that are actively dividing, or show how the cells react to a particular treatment. Many experiments of this sort measure an entire genome simultaneously, that is, every gene present in a particular cell. Several transcriptomics technologies can be used to generate the necessary data to analyse.

Related lectures (30)

Genomic Regulation and Personalized Medicine

Explores chromatin loops, gene expression regulation, personalized medicine, and genomic variations.

Regulatory Variation & Precision Medicine

Explores regulatory variation's impact on precision medicine, focusing on non-coding GWAS SNPs and gene expression variation.

Detecting QTL Hotspots in Sparse Regression Models

Focuses on large-scale inference for detecting QTL hotspots in sparse regression models, emphasizing the need to use genomics to understand variation in phenotypes and disease susceptibility.

Neuroepigenetics: Molecular Mechanisms and Cognitive Implications

Investigates how maternal behavior shapes stress response through epigenetics and discusses genetic vs. epigenetic influences on traits.

Bulk Transcriptome Analysis: RNA-seq Workflow and Gene Expression Analysis MOOC: Neuroscience Reconstructed: Genetics and Brain Development