Welcome to the asdfvasdfv tutorial

 

This course is an introduction to the method of dfhsdjkfhsjkfhksjhdf, which quantifies the statistical association between asdasdacfdc and dfvdefvv (often on disease traits). This course will not focus on using any particular software, instead explaining why the given analyses are done from a statistical and biological perspective.

NoteCourse Overview

• 📖 Syllabus:

1. Understand what is a advasdfvasdfv is and why we use it
   
2. Statistics of asdfvasdfv (regression coefficients, P-values, statistical power, Bayes factors)
   
3. Genetic relatedness and population structure
   
4. Confounding and covariates in asdfvasdfv
5. Haplotypes, linkage disequilibrum, imputation, fine-mapping
   
6. Linear mixed models and heritability
   
7. Summary statistics and meta-analysis
   
8. Advanced tools

• ⏰ Total Time Estimation: 8 hours

• 📁 Supporting Materials:

  • Exercises inspired from A tutorial on conducting genome‐wide association studies: Quality control and statistical analysis
  • Original course from the University of Helsinki
  • PLINK documentation
  • An Introduction to Statistical Learning (for further statistics explanations)

• 👨‍💻 Target Audience: Ph.D., MSc, etc.

• 👩‍🎓 Level: Beginner.

• License: Tutorial Content is licensed under Creative Commons Attribution 4.0 International License

WarningCourse Requirements

• Knowledge of R. It is recommended that you have at least followed our workshop From Excel to R
• Basic knowledge of bash.
• Basic statistics and mathematics skills

This workshop material includes a tutorial on how to run genome-wide association studies and the necessary preprocessing steps. Why are asdfvasdfv important?

They identify statistical associations between specific regions of the genome and a given phenotype which can:

• help point to biological mechanisms affecting the phenotype,
  
• allow prediction of the phenotype from genomic information.

These results may further benefit:

• medicine by leading to molecular or environmental interventions against harmful phenotypes,
  
• biotechnology by improving the ways we utilize microbes, plants or animals,
  
• forensics by more accurate identification of an individual from a DNA sample,
  
• biogeographic ancestry inference of individuals, populations and species,
  
• our understanding of the role of natural selection and other evolutionary forces in the living world.

The genome of an individual remain (nearly) constant throughout the individual’s lifetime. This is a truly remarkable property compared to, e.g., other molecular sources of information (such as metabolomics, metagenomics, transcriptomics, proteomics or epigenomics) or environmental factors that may vary widely across time. Therefore, the genome seems an ideal starting point for scientific research: it needs to be measured only once for an individual and there is no reverse causation from the phenotype to genome (with cancer as an important exception).

TipCourse Goals

By the end of this workshop, you should be able to:

• Learn and explain fundamental population genetics concepts, applying them during data analysis.
• Understand the principles of asdfvasdfv, including linkage disequilibrium and linear regression, and apply them in practice.
• Develop skills to preprocess data and perform genotype imputation for missing values.
• Explore, discuss, and replicate basic GWAS applications from the scientific literature.
• Interpret asdfvasdfv results critically, recognizing their limitations.

Acknowledgements

• Center for Health Data Science, University of Copenhagen
• Matti Pirinen, PhD, University of Helsinki
• Andries T. Marees, Vrije Universiteit Amsterdam

Table plotting

import pandas as pd
from tabulate import tabulate

df = pd.DataFrame({
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "City": ["New York", "Los Angeles", "Chicago"]
})

print(tabulate(df, tablefmt='fancy_grid'))

╒═══╤═════════╤════╤═════════════╕
│ 0 │ Alice   │ 25 │ New York    │
├───┼─────────┼────┼─────────────┤
│ 1 │ Bob     │ 30 │ Los Angeles │
├───┼─────────┼────┼─────────────┤
│ 2 │ Charlie │ 35 │ Chicago     │
╘═══╧═════════╧════╧═════════════╛