The primary objective of this study was to demonstrate the utility and validity of a novel, data-driven approach for generating a list of pain gene candidates. Such a list could facilitate the discovery of pain genes. We first validated our approach by demonstrating a statistically significant sensitivity and specificity prioritization of known pain-related genes contained in the Pain Gene Database (PGD). In addition, further genotyping of a human cohort revealed a significant association between variants of the newly discovered pain gene candidates ABLIM3 and NCALD with measures of pain sensitivity in an independent human cohort.
A major emphasis of this study was to document the utility of the principal approach and highlight its future potential. The ever growing amount of publically available molecular and clinical data should allow for expanding and refining this approach to generate more comprehensive and specific lists. For example, as more data becomes available, it may be possible to link gene expression of diseases to specific types of pain, such as neuropathic pain. Similarly, the outlined approach can be expanded to include proteomic data sets, which should provide additional insight into signaling pathways relevant to the processing of pain. Finally, the pain associated with a specific disease can be construed differently. For example, disease-specific pain ratings could be retrieved from databases of large health care organizations 
There are a few limitations in our approach and study. First, among the 47 candidate pain genes significantly correlating with the DSPI, only two are referenced in the PGD. While the PGD is a valuable resource of curated information and likely represents the best available reference, it is not yet a globally accepted master repository containing all pain genes, especially those resulting from human studies. The database is constrained by the fact that it only catalogs genes revealed by studies examining nociception in mechanistic – but not disease-related – models in knock-out mice. It should also be noted that gene expression data for diseases and matched controls were only available for 121 diseases. As a result only 130 of the 300 genes listed in the PGD could be explored in the current study.
The presented paradigm did not capture genes such as KCNS1
, each of which has been implicated in the processing of pain 
. This may partially be due to the fact that the current algorithm favored the discovery of genes exhibiting gradual gene expression change across different diseases. Additionally, our approach relied on gene expression changes in diseased tissue, which may not always capture important changes in secondary tissues relevant for the processing of pain, such as neuronal tissues or blood vessels. Additionally, some of these genes, such as KCNS1
, are thought to be important in specific types of pain like neuropathic pain, but might not participate genetically in determining pain of other etiologies represented in the 121 diseases. There is considerable potential for more refined approaches in the near future to resolve some of these limitations, as there are a constantly growing number of publicly available repositories containing molecular and phenotypic data sets.
is a newly characterized protein-coding gene belonging to the actin binding LIM protein family, which is composed of 3 members (ABLIM1-3
) and shows a high degree of conservation throughout evolution in vertebrates. ABLIM3
is expressed in various tissues, most prominently in muscle and neuronal tissue 
. While relatively little is known about the biological function of the ABLIM
protein family, conservation of key structural features suggests comparable biological function as linkers between the actin cytoskeleton, cell signaling pathways and transcription events 
. For example, the ortholog of ABLIM1
in C. elegans
(UNC-115) has been implicated in axonal guidance during outgrowth through interaction with Receptor for Activated C Kinase (RACK-1
Presently, a potential functional role for ABLIM3
in the perception or processing of pain is not apparent. ABLIM3
could potentially affect nociceptive signaling by regulating synaptic strength through actin rearrangement and modulation of synaptic spine density 
. Neuroplasticity has been shown to play a role in pathological pain and to happen both at the molecular and cellular levels 
. However, the association of ABLIM3
with pain is a novel finding that is based on a data-driven approach but is not anchored in our current understanding of pain biology. While this approach may offer the advantage of making unexpected and important discoveries, it requires establishing the biological relevance of such discoveries in subsequent experimental steps. The SNP rs4512126 (5q32) is located in the second and largest intron of ABLIM3
. This variant was found in weak linkage disequilibrium (>0.6) with five other SNPs and in perfect linkage disequilibrium with rs4546368 located in the same intronic region of ABLIM3
. All were non-coding SNPs. Similar to ABLIM3
has never been reported to be associated with pain. However, several polymorphisms in the 3′ UTR have been associated with mRNA instability and diabetic nephropathy 
. Individuals carrying the A/A allele possessed a higher cold pain threshold. Nevertheless, we acknowledge that our discovered association between NCALD
and pain cold was modest, demonstrated in only a single cohort, and barely above the Bonferroni corrected threshold. The parallel approach using quantile normalized phenotypical pain measures did not sustain the association for NCALD
). Further genotyping in alternative cohorts and deep sequencing of these regions would be needed to reveal a potentially causal SNP.
Interestingly, only males with homozygous ABLIM3
T/T showed a significant association with cold pressor pain sensitivity in our study. The sex-specific association of a gene variant with the cold pressor pain threshold is not surprising. Genetic polymorphisms associated with pain in humans and animals have identified a striking number of sexual dimorphisms with either male- or female-specific genetic effects, or a significant difference between the sexes 
We present a novel paradigm linking publically available molecular data to clinically relevant phenotypic data for generating a list of candidate genes relevant to the processing of pain. Algorithms for accessing and integrating such data to examine disease-relevant mechanisms are of growing interest as publically available data sets grow at an ever-increasing rate. The outlined approach can complement existing research efforts by assisting the formulation of data-driven hypotheses, and may serve as a template to discover genetic components of other clinically important phenotypes.