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Polymorphisms May Predict Pain, Dictate Treatment of Sickle Cell Disease

NEW YORK (GenomeWeb News) – New research is illustrating how diseases that arise from a mutation in a single gene can be modulated by genetic polymorphisms in other parts of the genome.
An international team of researchers identified five polymorphisms in three genes that help moderate fetal hemoglobin level, pain, and symptom severity in two populations with sickle cell disease. The paper, appearing in the Proceedings of the National Academy of Sciences this week, provides new insights into how genetic polymorphisms can influence “monogenic” diseases and the way these diseases are treated.
“Our findings hold potential for increased biological understanding of the clinical variability in [sickle cell disease], with possible implications for future drug development,” senior author Stuart Orkin, a Harvard University hematologist and oncologist, and his colleagues wrote.
Sickle cell disease, sometimes called sickle cell anemia, is a blood disorder caused by a point mutation in a single gene coding for the beta subunit of hemoglobin. This mutation leads to an amino acid substitution that alters red blood cell shape, making it more difficult for red blood cells to move through blood vessels and decreasing the amount of oxygen that the blood can carry.
But despite the monogenic nature of the condition, the severity of sickle cell disease varies greatly from one individual to the next. In some cases it causes severe pain, clinical symptoms, and early death. In other cases the symptoms are less debilitating.
One factor in this variation is the level of fetal hemoglobin, also called HbF, expression. Low HbF expression seems to exacerbate sickle cell disease, leading to more complications, while higher HbF expression decreases sickle cell-related health problems and improves survival. This HbF expression is heritable and is affected by other genes.
For instance, prior studies have shown that SNPs near the BCL11A gene and within the intergenic region between the HBS1L gene and the MYB gene can influence HbF expression in non-anemic populations. And, in a paper published in PNAS earlier this year, Orkin and his colleagues described a BCL11A variant that influences HbF levels in sickle cell patients.
In their latest paper, the researchers genotyped two groups of sickle cell patients from different parts of the world to look for new variants affecting both HbF expression and sickle cell symptoms.
Using Sequenom’s mass spectrometry-based MassArray iPLEX platform, they genotyped more than 1,500 individuals from the African American Cooperative Study of Sickle Cell Disease (CSSCD) and from a study taking place in Brazil.
The researchers confirmed the previously described association between BCL11A polymorphisms and HbF levels and found additional BCL11A variants that influenced HbF expression. They also detected new and previously documented polymorphisms in the intergenic region between HBS1L and MYB and in and around the gamma globin gene HBG2 that influenced HbF levels.
Overall, they predicted that five variations in these three genes/intergenic regions account for more than 20 percent of the variation in fetal hemoglobin in sickle cell patients in the two groups tested.
When they looked at the effect that these variants had on sickle cell related mortality (specifically, pain crisis rate), the researchers found an association between the HbF-related SNPs and pain crisis rate. Although no one SNP had a statistically significant effect on its own, all five of the SNPs that increased HbF expression also added together to decrease the pain rate.
The new biological insights into sickle cell disease, in turn, raise the possibility that these and other genetic polymorphisms may eventually be used to guide sickle cell treatment.
Although they noted that larger, prospective clinical trials — including those looking at treatment response — are necessary before these new SNPs can by used clinically, the authors expressed enthusiasm that “these and other such results could eventually help guide the development of treatment plans tailored for different levels of predicted risk of severe [sickle cell disease]-related complications.”

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