- Jul 2018
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On 2014 Oct 06, Raha Pazoki commented:
None
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On 2014 Oct 06, Raha Pazoki commented:
Addendum to previous comment "Tissue-specific online eQTL databases"
In the previous analysis, I compared heart-specific eQTLs from the GTEx consortium with eQTLs from the study by Koopman and co-workers. In that analysis, I considered SNP-gene pairs that appear with exactly the same names in the 2 databases. However, SNPs in high linkage disequilibrium (LD) and genes with various annotations may exist between these databases. It would be interesting to put a little more effort and search for such SNPs and genes to come-up with additional SNPs that consistently change cardiac expression of specific genes.
Raha Pazoki (twitter:@rahap)
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On 2014 Sep 20, Raha Pazoki commented:
Tissue-specific online eQTL databases
Online eQTL databases provide exciting opportunities for the researchers in the field of genomics. Tissue-specific eQTL databases are especially important to provide information about functional mechanisms related to diseases that have tissue specific characteristics. Examples are heart-specific eQTL databases presented in the GTEx online eQTL database (GTEx Consortium., 2013) or the human heart eQTL database from a study by Koopman and co-workers (Koopmann TT, 2014). For researchers in the field of cardiovascular genetics, such databases provide exciting candidate genes for further follow-up in human disease populations to predict disease or further investigation in experimental studies to identify novel mechanisms. Such databases also provide an opportunity for comparison across eQTL studies to highlight the most promising eQTLs with consistent effects in tissue-specific manner. In the example above, comparison of the eQTLs identified in the human heart, left ventricle tissue from the GTEx eQTL database and the human heart eQTLs identified in the study by Koopman and co-workers reveals that 18 SNP-gene pairs have been reported in both eQTL studies by passing stringent statistical significance thresholds. These pairs include (rs1006771 -DDTL), (rs1030421 -ADHFE1), (rs11603384 -TRPT1), (rs11880207 -ZNF266), (rs12609437 -MARCH2), (rs1319763 -NT5C3L), (rs17518363 -SUSD4), (rs2054365 -QRSL1), (rs241443 -TAP2), (rs371671 -MRI1), (rs3814231 -CASP7), (rs4970777 -GSTM3), (rs4985407 -EXOSC6), (rs6488713 -C12orf60), (rs651601 -RPS16), (rs8066107 -RPH3AL), (rs863214 -DHFR), and (rs8850 -MRPS10). However, only the effect of rs241443 on the expression level of TAP2 gene shows the same direction in both studies. The magnitude of the effect of rs241443 on TAP2 is greater in the GTEx database (Beta= -0.64, P value= 1.1 ×10<sup>-9</sup> ) than in the study of Koopman and co-workers (Beta=-0.34, P value= 4.11 ×10<sup>-9</sup> ). Obviously, more research work is necessary to identify more tissue-specific eQTLs and consequently consistent and promising functional loci.
This comment, imported by Hypothesis from PubMed Commons, is licensed under CC BY.
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- Feb 2018
-
www.nature.com www.nature.com
-
On 2014 Sep 20, Raha Pazoki commented:
Tissue-specific online eQTL databases
Online eQTL databases provide exciting opportunities for the researchers in the field of genomics. Tissue-specific eQTL databases are especially important to provide information about functional mechanisms related to diseases that have tissue specific characteristics. Examples are heart-specific eQTL databases presented in the GTEx online eQTL database (GTEx Consortium., 2013) or the human heart eQTL database from a study by Koopman and co-workers (Koopmann TT, 2014). For researchers in the field of cardiovascular genetics, such databases provide exciting candidate genes for further follow-up in human disease populations to predict disease or further investigation in experimental studies to identify novel mechanisms. Such databases also provide an opportunity for comparison across eQTL studies to highlight the most promising eQTLs with consistent effects in tissue-specific manner. In the example above, comparison of the eQTLs identified in the human heart, left ventricle tissue from the GTEx eQTL database and the human heart eQTLs identified in the study by Koopman and co-workers reveals that 18 SNP-gene pairs have been reported in both eQTL studies by passing stringent statistical significance thresholds. These pairs include (rs1006771 -DDTL), (rs1030421 -ADHFE1), (rs11603384 -TRPT1), (rs11880207 -ZNF266), (rs12609437 -MARCH2), (rs1319763 -NT5C3L), (rs17518363 -SUSD4), (rs2054365 -QRSL1), (rs241443 -TAP2), (rs371671 -MRI1), (rs3814231 -CASP7), (rs4970777 -GSTM3), (rs4985407 -EXOSC6), (rs6488713 -C12orf60), (rs651601 -RPS16), (rs8066107 -RPH3AL), (rs863214 -DHFR), and (rs8850 -MRPS10). However, only the effect of rs241443 on the expression level of TAP2 gene shows the same direction in both studies. The magnitude of the effect of rs241443 on TAP2 is greater in the GTEx database (Beta= -0.64, P value= 1.1 ×10<sup>-9</sup> ) than in the study of Koopman and co-workers (Beta=-0.34, P value= 4.11 ×10<sup>-9</sup> ). Obviously, more research work is necessary to identify more tissue-specific eQTLs and consequently consistent and promising functional loci.
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On 2014 Oct 06, Raha Pazoki commented:
None
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