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Gene-Focused Meta-analysis IDs New Height-Related Loci

By a GenomeWeb staff reporter

NEW YORK (GenomeWeb News) – In a paper appearing online today in the American Journal of Human Genetics, a large international research team outlined the meta-analysis approach that it used to detect dozens of known and previously unidentified loci linked to human height.

The researchers brought together so-called "gene-centric" genotype information and height data for more than 100,000 individuals from nearly 50 past studies. Their results implicate 64 loci in human height, including several relatively rare variants not detected through past genome-wide analyses.

"The increased power to identify variants of small effect, afforded by large sample size and the dense genetic coverage including low-frequency SNPs within loci of interest, has resulted in the identification of association between previously unreported genes and height," co-corresponding author Brendan Keating, a genetics researcher with the Children's Hospital of Philadelphia, and co-authors wrote.

Past research suggests height is somewhat influenced by an individual's environment and exposures. But most variation in the trait appears to have a genetic basis — with many genes each making a subtle contribution to an individual's overall stature. Previous studies have implicated more than 180 loci in height, Keating and his colleagues explained, but a lot remains unknown about the genetics underlying the trait.

In an effort to identify new height-related variants within gene coding regions of the genome, Keating and his co-workers relied on data for 114,223 individuals from six ethnic backgrounds who had been genotyped through 47 studies using the Illumina ITMAT-BroadCare array.

Though it was designed to target genetic variants involved in cardiovascular, metabolic, and inflammation-related processes, the team explained, the IBC array provides information on 49,320 SNPs at some 2,000 gene coding loci, making it a promising tool for finding both common and fairly rare genetic changes within genes.

Individual height and other data were available for participants from 25 of the 47 cohorts, the researchers noted, while summary level data was available for the remaining 22.

The team first focused on data for 53,394 individuals of European descent for whom they had individual level data. They then replicated findings from this group in another 37,052 European individuals for whom summary level data was available before broadening their analysis to include individuals from five non-European populations.

Overall, the researchers found 64 loci tied to height, including 33 identified through a past meta-analysis of European individuals. Of the significantly associated SNPs detected, 22 variants in eight genes were quite rare, they noted, turning up in less than five percent of the population.

Within the European ancestry group, for example, the researchers found associations between height and rare SNPs in the genes IL11 and SMAD3 that would have been missed using strategies based on common SNPs alone.

The team did not find loci with array-wide significance in African American, South Asian, East Asian, Native American, and Hispanic individuals tested, they noted, possibly because the study was not powered to detect such associations.

Nevertheless, their meta-analysis of all six populations indicated that most of the loci that were linked to height in the European population had similar directional effects in the other populations.

More work is needed to explore the functional consequences of such changes. Even so, the team noted, while some of the variants detected have no obvious connections to height, many others affect genes belonging to potentially pertinent pathways, including energy metabolism, collagen formation, and growth hormone pathways.

Moreover, the researchers explained, the genetics of height may serve as a model for other complex human traits that are influenced by multiple genetic loci, suggesting a similar strategy may be useful for understanding other human phenotypes and conditions.

"Overall, we show that dense coverage of genes for uncommon SNPs, coupled with large-scale meta-analysis, can successfully identify additional variants associated with a common complex trait," they wrote.

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