MONDAY, Dec. 8 (HealthDay News) -- An international research team that screened the genes of more than 40,000 people has identified 11 more regions that govern levels of blood fats such as LDL cholesterol and triglycerides.

"These locations point us to previously unsuspected players in the metabolism of cholesterol in humans," said Dr. Sekar Kathiresan, director of preventive cardiology at Massachusetts General Hospital, and lead author of a report in the Dec. 7 online issue of Nature Genetics. "Now that we have these additional genes that play a role in humans, we have to figure out how they do it by doing additional studies."

The report brings the total number of genes associated with control of blood fat levels to 30. One of those 30 is a gene designated HMGCR, which is the target of the widely used statins.

"By further understanding how these new genes play their roles, some of the genes could become targets themselves and help reduce cholesterol levels and heart disease," Kathiresan said.

The researchers, based at 37 institutions in the United States and around the world, started with genetic information on participants in the Framingham Heart Study, which first established the role of LDL cholesterol in cardiovascular disease in 1961. They added genetic samples from another 20,600 participants in five different studies.

"Of the three billion letters in the human genome, 2.5 million spots differ between people," Kathiresan said. "We tried to correlate blood levels of LDL cholesterol, HDL cholesterol and triglycerides."

Some of the 11 newly identified genes are known to have rare mutations that cause serious cholesterol disorders or conditions such as type 2 diabetes. This study shows for the first time that common variations in the genes are associated with differing lipid levels in individuals.

Putting together the information from the different studies was a challenge, said Goncalo Abecasis, an associate professor of biostatistics at the University of Michigan, and a member of the research team. He and his colleagues at the university's Center for Statistical Genetics helped develop the computer software that made the analysis possible.

While all the studies looked at the same genes, they did not always look for the same variants of those genes, Abecasis said. "So, we had to ask, are they really telling us something consistent? The method we used was that, we can find different little stretches of DNA that are shared between people and find relationships between those stretches."