Here are some personal notes/resources for new lab comers. This is all just written from personal experience (nothing official, no official code or anything, you can let me know if you have any questions, I'll be happy to help if I can! email- cinliu@berkeley.edu)

Understanding Background info

If you are confused about what our lab does (I was slightly confused for quite a while) I find it quite helpful to read the grant proposal page

- Go to ‘research activities and funding’ tab: https://profiles.ucsd.edu/chi-hua.chen#toc-id2
- click on the link to the most recent grant proposal; at the time of this page's making, this is the link:
  - https://grantome.com/grant/NIH/R01-MH118281-03
- If the link above fails - at the time of writing this page, this was the abstract for the grant proposal on grantome.com (I've also included a screenshot below):

Genetic architecture of the human brain and neuropsychiatric disorders

Chen, Chi-Hua

University of California, San Diego, La Jolla, CA, United States

Our goal is to determine gene-brain-behavior relationships, including those that underlie neuropsychiatric disorders. These disorders are common and increasing in incidence worldwide. However, there are significant clinical gaps in current treatment and objective diagnosis. Drug development trial failure rates are disproportionately high for brain disorders due to largely unknown differences in genetic bases between model species? and human?s brains, which prevent translational success. A major challenge hindering progress in neuropsychiatric medicine is our limited understanding of the genetics underlying the complexity of human brain structure and function. Our first objective in the current study is to uncover genetic variants associated with brain imaging phenotypes (Aim 1). Our second objective is to test the genetic effects of regulatory and phylogenically annotated genomic elements on the human brain (Aim 2). Our third objective is to determine the impact of neuropsychiatric genetic risks and their environmental interactions on the brain (Aim 3). Previously we produced the first genetic atlas of the human cortex based on magnetic resonance imaging (MRI) data of twins using fuzzy clustering. This work not only confirmed that human brain phenotypes are heritable traits but also demonstrated a clear region-specific genetic pattern, which facilitates identification of genetic variants associated with brain subdivisions. Our recent work demonstrated the value of using this atlas to determine the segments of the genome that are enriched for genetic effects influencing brain structure. In this proposal, we will leverage a ten-fold enlarged sample with both MRI and single nucleotide polymorphism (SNP) data, and advanced genetic and imaging methods to significantly expand the scope of our work. The larger sample increases power for discovering SNPs associated with individual brain structures and will enable us to examine genetic heterogeneity by age, sex, and genetic ancestries. Characterizing subgroups is critical for precision medicine approaches and to increase statistical power using genetically more homogeneous groups (Aim 1). We will also characterize pleiotropy and regulatory epistasis effects on the brain by multimodal imaging (structural, diffusion and functional imaging). This will provide insight into shared and distinct genetic influences among different brain regions. We hypothesize that variations in the highly expanded human cortex are associated with regulatory genetic effects (Aim 2). Third, building on improved genetic knowledge of the brain, we will determine its genetic relationship with neuropsychiatric disorders. We will estimate effects of psychiatric and neurological genetic risks and environmental exposures on deviations of MRI phenotypes from normal neurodevelopmental and aging trajectories (Aim 3). The current project has strong potential to significantly increase our understanding of genetic basis of the human brain, and to determine genetic and environmental factors that drive brain deviations from typical trajectories across lifespan, with a long-term goal to ultimately improve diagnostics, early intervention and therapeutic development for neuropsychiatric disorders.

Public Health Relevance

Many devastating human illnesses, such as neuropsychiatric disorders are heritable, but the connection between genes, the brain and brain disorders is not well understood. Through application of genetically-based cortical atlases and state-of-the-art statistical approaches in a large sample with brain imaging and genotyping data, we will increase understanding of the genetic basis of the human brain and neuropsychiatric disorders, which could lead to new insight into disease mechanisms. Understanding genetic and environmental influences on neurodevelopmental and aging trajectories could provide early detection and specify targets for trajectory-altering therapeutics and prevention.

Start Lab Basic Training

Below is a brief overview of how I got started in the lab. If you would like to try and recreate my first project you can follow the instructions below (although most likely you will have already been assigned something else as your first project - since there's always something new to work on in the lab - in that case recreation of this project is unnecessary, instructions for R set up might be useful though) :

Go to the computing page first, the home page has resources for basic set up on R. Follow instructions on how to setup R
- Computation: https://sites.google.com/view/lab-learning-computating/home
  - if you have no previous experience with programming (when I came into the lab I had taken one course in Java and one course in R), I'd recommend you watch the free R coursora course: https://www.coursera.org/learn/r-programming
  - OR if a course is too intimidating you can begin with shorter videos, we have a couple good ones here:

Then go to bio genetics page, it will give you a basic introduction on what to read and get you started on understanding the genetics background.
- Human Genetics: https://sites.google.com/view/lab-learning/home (might be locked out from the links because you need permission from the chchenlab@gmail.com account)

These are some protocol site we build for this lab (I'd say feel free to just save the sites somewhere, keep them handy as a learning resource):

Computation: https://sites.google.com/view/lab-learning-computating/home
Neuroimaging: https://sites.google.com/view/lab-learning-neuroimaging/home
Quantitative Biology: https://sites.google.com/view/lab-learning-quantitative-biol/home
Human Genetics: https://sites.google.com/view/lab-learning/home

(sites not working because it is under construction, need permission from the chchenlab@gmail.com account to fix)

Recommended courses for absolute new beginners:

Computer Science classes

Python

If you are at beginner level I'd recommend 2 classes to take. They might be a bit challenging and long to go through, but definitely worthwhile to take as the skills you will learn from them will be highly applicable to many areas. Both classes have their resources (videos, lecture power point, readings..etc) open to the public (at least the time I am writing this they are open to all of public), so you can follow along even without enrolling in anything.

Data8 - Foundation of Data Science :

Course site: http://data8.org/

- Data8 is a class from UC Berkeley's Division of Data Science. It is designed for undergrad students who are new to data science and statistics. For more details, see: https://data.berkeley.edu/education/courses/data-8
  - Website is organized by semesters. Normally you can find links to lecture material under calendar. For example, the 2021 Spring semester's page is here: http://data8.org/sp21/ (You should be able to see all the lectures linked in the calendar)
  - Course textbook: https://inferentialthinking.com/chapters/intro.html

CS61A - Structure and Interpretation of Computer Programs

course site: https://cs61a.org/

- CS61A is an intro computer programming course from UC Berkeley https://www2.eecs.berkeley.edu/Courses/CS61A/

This is a course designed for beginners (it is recommended you have some previous knowledge in calculus or programming, but not required). This course goes more in depth into computer programming, so not necessarily as applicable to our lab as Data8. However, if you would like to have a solid foundation in computing that can be appliable in almost any field later in life, this is definitely worth taking.

- Syllabus: https://cs61a.org/articles/about/
- One can follow along the course without enrolling, all the lectures and recourses are open to the public (see course site: https://cs61a.org/).
  - Archive of all the previous 61A classes: https://inst.eecs.berkeley.edu/~cs61a/archives.html
- Although it is quite a challenging course it does provide many useful resources that is applicable for our lab such as:
  - Unix terminal intro tutorial: https://inst.eecs.berkeley.edu/~cs61a/sp19/articles/unix.html
  - CS61A Textbook: http://composingprograms.com/
  - Sequoia's textbook: https://sequoiatree.github.io/
- UC Online (you can formally enroll in it UC system-wide and get credits): https://search.uconline.edu/catalog/view/43916?title=COMPSCI+61A+The+Structure+and+Interpretation+of+Computer+Programs
- Professor John DeNero's youtube (go to 'Playlist' you will see organized lectures): https://www.youtube.com/c/JohnDeNero

I'd recommend going to the archive page and choosing a semester to follow along with: https://inst.eecs.berkeley.edu/~cs61a/archives.html

Other Notes

Not sure how helpful this might be but, here are some of my hand written notes for understanding very simple concepts at the beginning:

Need a reminder of just the basics?

Genetics 101 | National Geographic

Genetics 101 (Part 1 of 5): What are genes?

Most are specific recipe for making specific proteins

Keywords:

Segments of DNA = genes
Chromosomes : bundles of DNA, we humans have 23 of them
SNP: single nucleotide polymorphism - it is a specific letter/section of the DNA we are talking about here. Like a specific address of a specific point for your DNA. More specific than gene. When a gene mutates it can be because of a change in one SNP within that gene.

StatQuest: A gentle introduction to RNA-seq https://www.youtube.com/watch?v=tlf6wYJrwKY