Dr. Eric Boerwinkle
Dr. Eric Boerwinkle

The architecture of the genome – its variation, its genes and the elements that control them – can define traits that affect our bodies and our health – even the levels of so-called “good cholesterol” (high density lipoprotein cholesterol or HDL-C), according to a report that appears online in the journal Nature Genetics.

In this study, researchers from a consortium that included Baylor College of Medicine and the Medial School, looked at the genome sequences of 962 people who were part of the Cohorts for Heart and Aging Research in Genetic Epidemiology (CHARGE) Consortium to determine the genomic determinants of a trait with many components – levels of high density lipoprotein C (HDL-C), the so-called good cholesterol. High levels of HDL-C are believed to protect against heart disease while low levels leave people at risk.

“This is the first time that DNA sequence differences from a large sample of individuals have been analyzed in this way. The results of this research show that parts of the genome with no known function are influencing differences at risk to disease,” said Dr. Eric Boerwinkle, associate director of the Baylor College of Medicine Human Genome Sequencing Center and director of the Brown Foundation Institute for Molecular Medicine’s Center for Human Genetics. All of the sequencing in the project took place at the BCM Genome Sequencing Center.

“This study is a precursor to the application of whole-genome studies of healthy people as a part of medical practice,” said Dr. Richard Gibbs, director of the BCM Human Genome Sequencing Center and a senior author of the report. “Currently, the Whole Genome Laboratory at Baylor is helping to identify the problem in people with known genetic disease. Eventually, we will be at a point where we can sequence everyone, inform those who have known genome problems, and maintain the rest for future reference – both for them and for future studies.

“In this study, we have large-scale genome production integrated with phenotypic data (information about disease, symptoms, etc.) that presages the use of large-scale genomes in clinical practice,” Gibbs said.

“Some people have thought that analyzing whole genomes is an intractable problem, and this work shows that whole genome sequence variation can be analyzed and related to risk of disease,” Boerwinkle said. “This work represents a first in a series of high-profile papers/discoveries originating out of the collaboration among genome researchers at UTHealth and BCM.”

Other centers that took part included the University of Washington, Seattle; the National Heart, Lung and Blood Institute Framingham Heart Study in Massachusetts; the NHLBI; Kansas State University in Manhattan; the University of North Carolina in Chapel Hill; the Group Health Research Institute in Seattle; and Boston University School of Public Health.

The consortium found 25 million genetic variants, which they analyzed across regions and with regard to function. They found that common variants explain 61.8 percent of the variance in HDL-C levels and rare variants explain 7.8 percent. Common variants are variations in a gene that are shared by a significant part of the population. Rare variants occur in only a handful of people.

Much of the analytical work in the study was done by first authors Drs. Alanna Morrison and Xiaoming Liu, of UTHealth; Arend Voorman of the University of Washington at Seattle; and Dr. Andrew Johnson, of the National Heart, Lung and Blood Institute.

“The exciting aspect of this research that has not been done before is that we were able to comprehensively access the parts of the genome that lie outside gene regions and determine if genetic variation in those regions influence risk to disease,” Morrison said.

When they looked for genes known to affect levels of high density lipoprotein cholesterol (HDL-C), they found individuals who had abnormally low levels of the lipoprotein, which is believed to be protective against heart disease.

Looking at the genomic landscape through the prism of whole-genome sequencing, the researchers found that genetic variation contributed more to HDL-C levels than had been understood from previous studies known as genome-wide association studies. For example, examining a gene known as CETP showed that variations in regulatory elements were likely to contribute to its role establishing levels of HDL-C.

The authors wrote: “By using whole-genome sequencing instead of genome-wide association (GWAS) or candidate-gene studies, we are able to obtain an unbiased glimpse of the relative contributions of rare and common variation to the heritability of a model trait. The results indicate that the majority (that is, 61.8%) of the heritability of HDL-C levels can be attributable to common variation. Given the results of GWAS and targeted resequencing, these common variants likely represent true polygenic variations with small effects, which are of limited diagnostic use but may be important in identifying the biological pathways involved.”

Boerwinkle pointed out that the work reflects the great strength of Texas Medical Center collaborators.

“This work is the result of a close partnership between established groups at The University of Texas Health Science Center at Houston and Baylor College of Medicine, and further establishes Houston’s preeminent role in the exploding fields of medical sequencing,” he said.

“UTHealth had a wonderful collection of valuable samples, which we could sequence,” Gibbs said. “This collaboration shows the sequencing and analytical power of the Genome Center and the value of the new analytical approaches that UT and Boerwinkle could bring to the research.”

Others who took part in this work included Jin Yu, Donna Muzny and Dr. Fuli Yu, all of BCM; Alexander Li of UTHealth; Dr. Kenneth Rice, and Joshua Bis of University of Washington; Chengsong Zhu of Kansas State: Dr. Gerardo Heiss,  and Dr. Bruce  Psaty, of the University of North Carolina; Dr. Christopher  O’Donnell,  and Dr.  Adrienne Cupples, of NHLBI.

-Rob Cahill, Public Affairs, Media Relations