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Quantifying the combined heritability of a trait based on a multi-ethnic LD panel with equal distribution of samples among each ancestry group.

• Background
• Data
• Tools
• Process
  • Dependencies
  • Installation
  • Steps to run
• Results
• Team Members

Heritability of a trait is often identified and reported in an ancestry group stratified manner. This limits the ability to estimate and report the combined heritability in a multi-ethnic population. Although there are several methods demonstrated recently with robust ways of calculating heritability with or without individual-level datasets, these methods are limited to ancestry-specific groups. In this project, we are proposing a way to calculate combined heritability using a multi-ethnic reference linkage-disequilibrium (LD) panel with equal proportions of data. We will use current existing tools to simulate and calculate heritability and report it as a framework that can be implemented and explored further. This will lead to the development of a novel approach to estimating the heritability of particular traits in multi-ethnic populations. As a part of Team HeriVar, you will be contributing to the demonstration of methodology, calculation of heritability, and work as a team to promote the method.

With the increasing availability of multi-ethnic whole genome sequence datasets, there is a gaping absence of an approach to estimate the heritability of particular phenotypic trait that accounts for the multi-ethnic genetic architecture. This approach of calculating the combined multi-ethnic heritability has not been pursued previously. This project helps us understand the problems facing this issue in the field of genomics and helps in generating a framework using existing tools to calculate and assess the heritability of a trait in multi-ethnic populations.

• High Coverage 1000g dataset downloaded from http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage/working/20201028_3202_phased/
• GWAS summary statisitcs for NTproBNP (In house) & BP downloaded from Pan-Ukbiobank analysis. (https://pan.ukbb.broadinstitute.org/phenotypes)

• R. ( module load R )
• Python. ( module load Anaconda3 )
• PLINK (https://www.cog-genomics.org/plink/2.0/ or module load PLINK in Cheaha).
• LDAK/SUMHER (https://dougspeed.com/sumher/).
• LDSC (https://github.com/bulik/ldsc).
• LiftOver ( https://genome.ucsc.edu/cgi-bin/hgLiftOver )

• LDSC requires Anaconda3 or Python-2.7 and subpackages like bitarray, nose, pybedtools, scipy, numpy, pandas, bioconda. (will be installed when generating environment).
• SumHer uses Intel MKL Libraries as dependencies. ( module load imkl/2020.1.217-iimpi-2020a )

• LDSC ( Required to be installed by everyone in their home directory to use it )

  • Clone the github of ldsc (git clone https://github.com/bulik/ldsc.git) and cd into the folder
  • Module load Anaconda3 ( module load Anaconda3 )
  • Install dependencies using conda as suggested by github ( conda env create --file environment.yml )
  • Activate ldsc ( source activate ldsc )
  • Test installation by running python scripts shared as path of repo ( ./ldsc.py -h )

• Sumher

  • Download the LDAK Linux executable file by requesting using name and email ( you will get an email from the developer with downloadables if you are a first time user )
  • Unzip the executable file and use it. ( /data/project/ubrite/hackathon2022/staging_area_teams/HeriVar/Tools/ldak5.2.linux - It can be accessible by everyone)
  • It also have executable for MAC users. Note: Please check Dependencies before installing the tools.

• LiftOver

  • Download the file from https://genome.ucsc.edu/cgi-bin/hgLiftOver
  • Download the chain file needed for conversion - we can download it from above link.
  • Run liftOver -h

• Datasets

  • We downloaded 1000g high coverage reference dataset from http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage/working/20201028_3202_phased/.

  • We then extracted individuals files and randomly chose 489 unrelated individuals among each ancestry group.

  • Rationale behind including sample individuals from multiple ancestry groups is by taking equal number of individuals, we can have equal ld pattern distribution among the individuals.

  • Admixed population were excluded from the analysis along with related individuals which to 1956 individuals.

  • We removed variants with less than 1% minor allele frequency and variants with more than 5% missing data.

                             Allele Frequency Distribution among each ancestry and overall.
    

• PCA Analysis

  • We used Plink to calculate principal compnents analysis to test whether we have equal distributions of samples per ancestry group.

                                       PC distributions stratified by Ancestry
    

• Prunning & Thresholding
  • After subsetting to sample of interest, we did prunning and thresholding based on different cutoffs.
  • Plink is used to generate the files needed.
  • We used R2 and window size parameters for analysis.

    • R-squared cutoff of 0.2, 0.4, 0.6, 0.8.

    • Window size of 250kb, 500kb, 1Mb, 10Mb.

                                      Distribution of Variants after P + T
      

• We had ran near 1000 jobs for generating this datasets in Cheaha.

• We decided to exclude High LD regions as recommended by the tools.

• We subsetted the datasets to two categories.

  • Pre HighLD regions removal.
  • Post Hight LD regions removal.

• Refernces panel generation

  • We used the two categories as mentioend above and used two tools to calculated reference LD panels.

  • We used ldsc to generate LD scores for all the categories we have.

                                        LD_scores Distribution for Chromosome 22
    

• For LDAK annotations, We used liftover to convert blk annotations from grch37 to grch38 and working on generting tagging files

  • We had an issue generating LDAK annotations files and decided to pursue analysis after hackathon.

• Phenotypes Processing

  • We have also worked on processing phenotypes based as suggested by the tools.

• Heritability

  • We tried to generate h2 values using LDAK & LDSC but couldnt able to complete because of last minute issues.

• Akhil Pampana | [email protected] | [email protected] | Team Leader
• Nick Sumpter | [email protected] | Team Member
• Yongyu (Frank) Qiang | [email protected] | Team Member