McNulty_et_al_APOL1_BlackFSGS_SampleInfo.csv
Clinical and demographic information for analysis samples 
N=30: 16 high-risk, 14 low-risk
Columns: 
ID: Sample ID matches transcriptomic datasets 
RNAseq_batch: Batch from RNAseq analysis (Note: batch effect was adjusted in McNulty_et_al_APOL1_BlackFSGS_NormalizedGeneExpression.csv) 
high_risk: Indicator of APOL1 high risk status, "0" = 0,1 risk alleles (low-risk), "1" = 2 risk alleles (high-risk)
haplotype: APOL1 risk variant composition, G1= rs73885319 and rs60910145, G2= rs71785313, G0 = wild-type G1/G2 alleles
sex: Male/Female, 
age_cat: Categorical age 
eGFR_at_biopsy: Estimated glomerular filtration rate at the time of biopsy
UPCR_at_biopsy: Urine protein to creatinine ratio at the time of biopsy

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McNulty_et_al_APOL1_BlackFSGS_GeneCounts.csv
Gene count matrix including all genes with non-zero rows 
Nsamples=30 (columns), Ngenes=51,228 (rows)

Total RNA from sample biopsies were prepared using the Clontech SMARTSeq v4 kit. Samples underwent sequencing using Illumina HiSeq 2500, resulting in 150bp unstranded, paired-end reads. Fastq files underwent quality control filtering and trimming using fastQC, fastQScreen, and Picard Tools. Trimmed reads were aligned to the human genome (GRCh37) with STAR 2.6.0a. Gene expression counts were quantified using StringTie v2.1.4. 

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McNulty_et_al_APOL1_BlackFSGS_NormalizedGeneExpression.csv
Normalized gene expression of genes used in our transcriptomic analyses. 
Nsamples=30 (columns), Ngenes=15,703 (rows)

Following the above methods for gene counts, genes were filtered to protein-coding genes with at least ten normalized counts in 14 samples.  Variance stabilizing transformation was applied to counts in DESeq. The transformed reads were quantile normalized and corrected for batch effects with Combat. 

Note: This data is ready for linear modeling, and the expression of a single gene can be compared between samples. However, this data was not normalized by gene length; thus, expression levels cannot be compared between genes.