covid19-hg / covid19_sequencing

As part of the COVID-19 Host Genetics Global initative, this repo serves to corroborate sample scripts for sequencing QC.

covid19_sequencing

As part of the COVID-19 Host Genetics Global initative, this repo serves to corroborate sample scripts for the sequencing analysis. The document that the scripts are based on is titled COVID-19 Host Genetics Initiative: Whole Exome/Genome Sequencing Analysis Plan: link found here

The entire QC plan is using the Hail software developed by the Broad Institute of Harvard and MIT.

Instructions on running the demonstration

1. Clone Git repo git clone https://github.com/mkveerapen/covid19_sequencing.git or download the files found in this repo into your local directory.

2. Install Hail on your local directory by using instructions found here.

3. Running the file

if you are using the Jupyter notebook: Hail_COVID19weswgs.ipynb

jupyter notebook Hail_COVID19weswgs.ipynb

This will open up the file in your default browser for interacting with and exploring Hail code.

if you are using the html file: Hail_COVID19weswgs.html

Double click on the file to open in your default browser.

The demo notebook prepared is meant as a guide which uses a downsampled 1000 genomes dataset (found in the resources/ folder)

Take Note: NOT all lines can be run and are meant to serve as an example or template to building your own code for your own datasets. Further instructions on building your own pipeline and exploring code can be found on the Hail documentation.

If you have questions about set up, debugging, or troubleshooting, please get in contact with the Hail team via the Hail discuss forum.

In addition to the test Jupyter notebook, we have also included an example python and R script in the postSAIGE_QC directory that will allow you to clean up your data post SAIGE analysis. The python script will clean up your data's header and p-values, whereas the R script will receive the python output to plot QQ and Manhattan plots based on a threshold that you have chosen. The package used in the R script is R:qqman. Both scripts were initially written by Juha Karjalainen, PhD and adapted into this directory for the COVID19 project.

The following text is taken from the Google docs

PART 2 : SAMPLE and VARIANT QUALITY CONTROL

2.0 Sample Quality Control

All vcf files can be imported as Hail MatrixTables. This can be achieved using the import_vcf function in Hail. We highly recommend using this input format and the Hail platform for conducting analytics because of ease of use, and portability.

2.0.1 WES Interval QC

This step is an optional step. For sample QC purposes, we would also suggest to filter for intervals where 85% of samples had a mean coverage of 20X, especially when disparate sequencing platforms are used. Intervals that did not pass this interval QC can then be flagged as "fail_interval_qc".

2.0.2 Sex Imputation

We suggest inferring for sex using the Hail function impute_sex. This function should be performed on common biallelic SNPs (AF > 0.05) with a high callrate (callrate > 0.97). Suggested thresholds for this function include the following. We would also recommend plotting the data to observe data is within reasonable limits of thresholds set below:

aaf_threshold: 0.05

female_threshold: 0.5

male_threshold: 0.75.

In order to refine inferred sex, we suggest utilizing each sample's fraction of chromosome Y coverage that are normalized using chromosome 20 coverage, where aneuploidies can be determined in samples that impute female but have normalized chrY fraction > 0.1, as well as samples that impute male but have normalized chrY fraction < 0.1. If sex was imputed missing, sex would then be marked as ambiguous.

2.0.3 Additional Filters

Recommended filters removing samples that are

Mean coverage < 20.0

Ambiguous sex

Aneuploids

Call rate < 97

2.0.4 Relatedness filters

Samples can be filtered to remove one of each pair of related samples using Hail's maximal_independent_set (uses model free relatedness estimation via PC-Relate). We suggest filtering for samples with second-degree relatedness or higher, where one of each pair of samples with a kinship coefficient of > 0.088 can be removed.

2.0.5 Population Ancestry Inference

To increase accuracy of inferring population ancestry, we recommend selecting an approach based on study ascertainment:

If population ascertained is relatively homogenous: We recommend performing PCA projection of the exome data onto the gnomAD population principal components and then to use a random forest classifier trained on gnomAD ancestry labels to assign ancestry to the exome samples. (Konrad to provide loadings and RF for gnomAD without training of model)

If population ascertained is relatively heterogeneous (multi-ancestry): We recommend using a hybrid approach that would first be PCA projection expressed in point a). Secondly, to account for highly admixed samples, we recommend that a from-scratch PCA be performed on the exome dataset using an unsupervised learning/clustering method, e.g. HDBSCAN. Using this hybrid method, any sample that was assigned to a cluster using the from-scratch PCA is given that cluster as their ancestry assignment in order to preserve the substructure observed compared to a projection PCA method. Any sample that was not assigned to a cluster was given the label from the PCA project and random forest classification. Methods outside of the above mentioned are welcome, if the user has good enough reason to choose otherwise.

2.0.6 Outlier Detection

Utilizing the Hail sample_qc method, we suggest removing outliers that deviate from the median and median absolute deviation (MAD) (non-parametric equivalent for mean and standard deviation) for the following metrics. It is also important to note that these outlier detection metrics below would need to be stratified by population ancestry (and sequencing platform) determined from subsection 2.0.5:

n_snp: Number of SNP alternate alleles

r_ti_tv: Transition/transversion ratio

r_insertion_deletion: Insertion/Deletion allele ratio

n_insertion: Number of insertion alternate alleles

n_deletion: Number of deletion alternate alleles

r_het_hom_var: Heterozygous/homozygous call ratio

2.1 Variant Quality Control

Upon completion of the Sample QC described in section 2.0, exomes should then be processed for Variant QC that is further elaborated in this section 3.0. We recommend applying a PASS filter using the Variant Quality Score Recalibration (VQSR) metric.

2.2 Genotype Quality Control

High quality genotypes can be filtered when applying the following thresholds. We would also recommend performing call rate filtering separately for cases and controls: differential missingness is a typical source of false positives:

GQ >= 20

DP >= 10

AB >= 0.25 (for each allele in heterozygous calls)

2.3 Functional Annotation

Variants can be annotated using the Variant Effect Predictor (VEP) annotation as implemented in Hail (annotation_db) using the default parameters for GRCh38 (including LOFTEE). In addition, for downstream gene-based tests, we recommend grouping variants into genes by canonical transcripts in Ensembl Gene ID and/or HGNC symbols with the following annotations:

pLoF: High-confidence LoF variants (LOFTEE), including stop-gained, essential splice, and frameshift variants, filtered according to a set of first principles as described on the Github repo and gnomAD

Missense | Low-confidence(LC): Missense variants are grouped with in-frame insertions and deletions, as well as low-confidence LoF variants (filtered out by LOFTEE). The latter have a frequency spectrum consistent with missense variation, and affect a set of amino acids in a similar fashion (e.g. a frameshift in the final exon).

synonymous: All synonymous variants in the gene (control set). Additional VEP or machine learning method annotations available e.g. ‘splice_region_variant’ or kipoi repository (ref: Julien Gagneur).