Snowy Owl
Retired in 2010, In Memoriam
Natural variations in genes must be considered in disease studies: Expert
http://www.newkerala.com/one.php?action=fullnews&id=66450
Washington, May 29
A biostatistician in the College of School of Graduate Studies says that it is necessary that scientists, who are studying the human genome to find out genetic causes of diseases, ensure that they are taking into account natural variations in the genes.
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Washington, May 29
A biostatistician in the College of School of Graduate Studies says that it is necessary that scientists, who are studying the human genome to find out genetic causes of diseases, ensure that they are taking into account natural variations in the genes.
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"These differences can create some challenges in analysing data. There is always some difference in ethnic backgrounds across a study population," says Dr. Hongyan Xu.
He says that scientists generally do not take into account the difference in subpopulations, such as while studying a population of blacks from Augusta and blacks from Chicago.
"Some groups of blacks could have different degrees of ancestry from different African groups. Some populations of blacks have different skin tones, which indicate a difference in genetic. That isn't always taken into account," he says.
Dr. Xu says that though some studies account of differences by using control groups who self-report similar ethnicities, the chances of there being wide variations still exist because people are not always completely aware of their ancestry.
He feels that a computer-based statistical tool may be helpful in dealing with this issue.
He has also revealed his team's plans to begin examining an existing data from an ongoing association study of stroke risk in black children who are suffering from sickle cell disease, being led by hematologist/oncologist Dr. Abdullah Kutlar.
With funding from the National Institutes of Health, his team will take a closer look at children already identified as high-risk because of high flow velocity in the brain, as measured by transcranial Doppler tests.
"While Dr. Kutlar is looking for the underlying genetic reasons for the higher stroke risks in this sample of patients, we will be looking for ways to identify the subpopulations in that sample. If population structure isn't taken into account, it could affect the validity of study results," Dr. Xu says.
Dr. Xu says that the research will involve a statistical approach known as coalescent theory, which traces coding sequences of genes in a population sample to a single ancestral copy of a gene.
According to him, that gene would theoretically be copied in the genetics of every member of an identical population.
The researchers points out that two persons having almost identical sets of chromosomes could differ in a very small way - by one structural unit that binds their DNA.
Tracing it back may enable the group to reach a point where the "copied" gene would not be present, he adds, and that would indicate the point where two lineages joined.
Dr. Xu says that genetic differences among the two populations could then be tagged, sub-categorized, and accounted for in study results.
"With the coalescent theory, we focus on the samples rather than the whole population," he says.
"That way, we can generate samples with various levels of population structure with great efficiency using computers, which are important for large-scale genome-wide studies. Understanding the genetic basis for disease is key to prevention, diagnosis and effective treatment. Developing a method that accounts for variations in the genetics of people who are similar but distinct is crucial to better understanding the genetics of health," he adds.
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He says that scientists generally do not take into account the difference in subpopulations, such as while studying a population of blacks from Augusta and blacks from Chicago.
"Some groups of blacks could have different degrees of ancestry from different African groups. Some populations of blacks have different skin tones, which indicate a difference in genetic. That isn't always taken into account," he says.
Dr. Xu says that though some studies account of differences by using control groups who self-report similar ethnicities, the chances of there being wide variations still exist because people are not always completely aware of their ancestry.
He feels that a computer-based statistical tool may be helpful in dealing with this issue.
He has also revealed his team's plans to begin examining an existing data from an ongoing association study of stroke risk in black children who are suffering from sickle cell disease, being led by hematologist/oncologist Dr. Abdullah Kutlar.
With funding from the National Institutes of Health, his team will take a closer look at children already identified as high-risk because of high flow velocity in the brain, as measured by transcranial Doppler tests.
"While Dr. Kutlar is looking for the underlying genetic reasons for the higher stroke risks in this sample of patients, we will be looking for ways to identify the subpopulations in that sample. If population structure isn't taken into account, it could affect the validity of study results," Dr. Xu says.
Dr. Xu says that the research will involve a statistical approach known as coalescent theory, which traces coding sequences of genes in a population sample to a single ancestral copy of a gene.
According to him, that gene would theoretically be copied in the genetics of every member of an identical population.
The researchers points out that two persons having almost identical sets of chromosomes could differ in a very small way - by one structural unit that binds their DNA.
Tracing it back may enable the group to reach a point where the "copied" gene would not be present, he adds, and that would indicate the point where two lineages joined.
Dr. Xu says that genetic differences among the two populations could then be tagged, sub-categorized, and accounted for in study results.
"With the coalescent theory, we focus on the samples rather than the whole population," he says.
"That way, we can generate samples with various levels of population structure with great efficiency using computers, which are important for large-scale genome-wide studies. Understanding the genetic basis for disease is key to prevention, diagnosis and effective treatment. Developing a method that accounts for variations in the genetics of people who are similar but distinct is crucial to better understanding the genetics of health," he adds.
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