and environmental effects. As such, compensating for these non-genetic effects would increase the power to detect cis-eQTL effects. To mitigate the effects of non-genetic sources of variability, we first log2 transformed the data and centered and scaled each tag, and subsequently applied PCA on the sample correlation matrix. We then used the first PCs as covariates, and re-did the non-parametric cis-eQTL mapping on the residual expression data (using the procedure described by \cite{6}).

\subsection{Validation of genotype-dependent alternative polyadenylation in RNA-seq datasets}
The genomic coordinates of the 3$'$-UTR, obtained from Refseq Genes, were split into two separate regions (distal and proximal 3$'$-UTRs) according to the position of the DeepSAGE tags with opposite directions, the position of LongSAGE tags from CGAP, and the position of reported and predicted polyadenylation sites from polyA\_DB database. To calculate the coverage in proximal and distal regions in RNA-seq datasets, we created a coverage histogram from each .bam align2 file using coverageBed tool from BEDTools package (version 2.17.0) \cite{39}. Subsequently, a custom Python script was used to convert the histogram in number of nucleotides mapped per region, normalized by the length of the region. The ratio between the number of counts in the proximal region and the distal region was then calculated.

\begin{table}[h]
\centering
\caption{Number of detected cis-eQTLs in transcript-wise analysis of three harmonized RNA NGS datasets.}
\label{table_6}
\begin{tabular}{lcc}
\hline
 & \multicolumn{2}{c}{Number of unique genes with cis-eQTLs} \\
Dataset & Without principal component correction & With principal component correction \\
\hline
Montgomery et al. (paired-end RNA-seq) & 94 & 145 \\
Pickrell et al. (single-end RNA-seq) & 199 & 438 \\
NTR-NESDA transcript-wise (DeepSAGE) & 292 & 579 \\
Meta-analysis & 651 & 1,207 \\
\hline
\end{tabular}
\end{table}

\subsection{qPCR validation of alternative polyadenylation}
Expression of short and long variants of \textit{HPS1} and \textit{IRF5} was quantified by qRT-PCR, which was performed on a subset of RNA samples used for the DeepSAGE sequencing. cDNA was synthesized from 400 ng of total RNA using BioScript MMLV.

\begin{table}[h]
\centering
\caption{Trait-associated SNPs detected in the sequencing-based transcript-wise meta-analysis, but not detected in array-based eQTL dataset of 1,469 peripheral blood samples.}
\label{table_7}
\begin{tabular}{lllll}
\hline
SNP name & Chr. & Transcript position (midpoint) & Cis-regulated gene & Associated trait \\
\hline
rs1052501 & 3 & 41963564 & ULK4 & Multiple myeloma \\
rs347685 & 3 & 141782879 & TFDP2 & Chronic kidney disease \\
rs4580814 & 5 & 1081324 & SLC12A7 & Hematological and biochemical traits \\
rs4947339 & 6 & 28911984 & C6orf100 & Platelet aggregation \\
rs2517532 & 6 & 31024818 & HCG22 & Hypothyroidism \\
rs2844665 & 6 & 31024818 & HCG22 & Stevens-Johnson syndrome and toxic epidermal necrolysis (SJS-TEN) \\
rs6457327 & 6 & 31024818 & HCG22 & Follicular lymphoma \\
rs3130501 & 6 & 31324124 & HLA-B & Stevens-Johnson syndrome and toxic epidermal necrolysis (SJS-TEN) \\
rs2858870 & 6 & 32434437 & HLA-DRB9 & Nodular sclerosis Hodgkin lymphoma \\
rs3129889 & 6 & 32434437 & HLA-DRB9 & Multiple sclerosis \\
rs3135388 & 6 & 32434437 & HLA-DRB9 & Multiple sclerosis \\
rs477515 & 6 & 32434437 & HLA-DRB9 & Inflammatory bowel disease \\
rs9271100 & 6 & 32524134 & HLA-DRB6 & Systemic lupus erythematosus \\
rs9273349 & 6 & 32632106 & HLA-DQB1 & Asthma \\
rs3807989 & 7 & 116183034 & CAV1 & PR interval \\
rs12680655 & 8 & 135604552 & ZFAT & Height \\
rs4929923 & 11 & 8642408 & TRIM66 & Menarche (age at onset) \\
rs12785878 & 11 & 71161461 & RP11-660L16.2 & Vitamin D insufficiency \\
rs12580100 & 12 & 56436876 & RPS26 & Psoriasis \\
rs4924410 & 15 & 40329664 & SRP14 & Ewing sarcoma \\
rs7364180 & 22 & 42184613 & MEI1 & Alzheimer’s disease biomarkers \\
\hline
\end{tabular}
\end{table}