Scientists have long known that height is mostly hereditary, but even the geneticists who set out to study height two decades ago weren’t certain they’d ever be able to find the common genetic factors influencing this trait.
Now, by studying the DNA of 5.4 million people, a team led by those same geneticists has done what they thought years ago would be impossible.
In the largest study of its kind to date, members of the GIANT consortium — including researchers at the Broad Institute of MIT and Harvard, Harvard Medical School, and Boston Children’s Hospital — have identified more than 12,000 genetic variants that influence height.
These variants explain 10 percent to 40 percent of all variation in height, depending on a person’s ancestry. They cluster around parts of the genome involved in skeletal growth.
The team said that because of its unprecedented size, the study has uncovered the vast majority of the genetic variants linked to height and is the capstone of a 20-year effort.
“We feel that this is really a milestone,” said co-senior author Joel Hirschhorn, HMS professor of genetics, the HMS Concordia Professor of Pediatrics at Boston Children’s Hospital, and leader of the GIANT consortium. “We’d been studying height for a while but had only identified a small fraction of common variant heritability.”
“We’re now basically done mapping this heritability to specific genomic regions,” he added, “and that highlights what increasing sample size can tell us about traits controlled by multiple genes.”
Hirschhorn said that the findings, published in Nature on Oct. 12, could one day help physicians identify individuals who aren’t reaching their genetically predicted height and might have a hidden disease or deficiency affecting their growth and health.
The results illustrate the power of genome-wide association studies, or GWAS, to uncover the biological basis and, in larger studies, the heritability of disease.
In 2000, as a pediatric endocrinology fellow at Boston Children’s, Hirschhorn saw many children who had been referred by their pediatricians for unusually short stature.
He would often tell parents their child was growing slowly because of the genes the child inherited.
Though scientists estimated that genes contributed to 80 percent of variation in height, they didn’t know what those genes were.
“After about the 20th patient, I thought, Hey, I’m working in a place that can figure this out,” he said. As a postdoctoral researcher at the Broad Institute, Hirschhorn decided then to study height, though everyone told him height was too polygenic — there were too many genes involved to be able to find them.
Undeterred, Hirschhorn turned to GWAS: studies in which scientists scan whole genomes in a population to identify associations between genetic variants and traits.
Over the next two decades, the GIANT consortium found variants associated with height in increasingly large GWAS. Now, together with 23andMe, the consortium has assembled data from seven times more individuals than were included in previous studies.
Their analysis revealed 12,111 common single nucleotide polymorphisms, or SNPs — places in the genome where a single letter varies — that were associated with height.
Together, the SNPs account for 40 percent of all variation in height for individuals of European ancestry and 10 percent to 20 percent of variation for people of non-European ancestry.
This difference is due to the composition of the GIANT study cohort, which is mostly of European ancestry. This lack of diversity is a known and common problem in genetic studies. In the GIANT study, more than one million participants were of East Asian, Hispanic, African, or South Asian ancestry, a number the team says is higher than other GWAS.
The researchers say that, so far, across the different populations, they have found that the same regions of the genome influence height. They emphasize, however, that including more people of non-European ancestry will be critical to increasing prediction accuracy and could help identify genetic variants specific to certain groups.
These SNPs could help researchers develop better height prediction tools for use in clinics.
Pediatricians currently predict how tall a child will be based on their family history, but these estimates aren’t perfect; they don’t, for example, predict different heights for a pair of siblings. A prediction based on SNPs could be more accurate. If a physician noticed that a child’s height didn’t match a prediction, that could be a clue to test the child for rare, hidden conditions that affect growth, such as celiac disease or hormone deficiencies.
Learning from GWAS
Scientists didn’t know if there was a point at which a GWAS could be “saturated” — when additional data would not provide any new insight. GIANT researchers found that the SNPs they pinpointed ultimately explained more than 90 percent of SNP-based variation, which indicated a point of saturation.
They also discovered that discerning the broad brushstrokes of biological pathways that are relevant to height required fewer samples than finding the precise genomic regions.
“We’ve been able to address this long-standing question in GWAS research using empirical data rather than just theoretical models, which had been used previously,” said the study’s first author, Loïc Yengo, of the University of Queensland in Australia.
Geneticists also wondered if, as a GWAS grew in size, the SNPs it revealed would be spread out across more and more of the genome. GIANT’s findings showed instead that SNPs influencing height clustered within regions covering just over 20 percent of the genome.
In particular, the SNPs were near genes previously associated with skeletal growth disorders. Twenty-five clustered near the ACAN gene, which is mutated in patients with short stature and a condition called skeletal dysplasia. Several SNPs also implicated signaling pathways that impact skeletal growth plates — cartilage near the ends of long bones that expands and hardens into solid bone as a child grows.
The researchers believe this clustering of genetic variants likely applies to other traits and could inform the study of other common conditions, such as high blood pressure or asthma, that are influenced by multiple genes.
Now that they know which genomic regions influence height, the GIANT team can begin tracing how individual variants impact height using fine-mapping methods. Rarer and more complex variants likely account for the heritability not explained by SNPs and will also be a target of future studies, the authors said.
When Hirschhorn was getting started on height genetics research in the mid-2000s, conducting a GWAS with even 1,000 participants took years of effort, he said. So to study 5 million was unfathomable at the time.
“Even the most optimistic among us didn’t think we’d get this big this fast,” Hirschhorn said.
People doubted the utility of GWAS, too, when these studies failed to yield real predictive power. Over the arc of his career, Hirschhorn has watched that change.
“When it became apparent that GWAS would be possible, I used to make sure in every talk I said, There’s no way we’re going to get enough information that this will add to what we can do clinically to predict adult height,” he said.
“But we succeeded beyond our wildest dreams. So now I get the chance to prove myself wrong.”
This work was supported in part by the National Institutes of Health (grants listed below), Wellcome Trust, UK Medical Research Council, Cancer Research UK, Australian Research Council, Australian National Health and Medical Research Council, UK National Institute for Health Research Centres, European Union, European Regional Development Fund, Netherlands Heart Foundation, British Heart Foundation, US Department of Veterans Affairs, American Heart Association, Leducq Foundation, Netherlands Organization for Scientific Research NWO, European Research Council, Swedish Research Council, Novo Nordisk Foundation, Academy of Finland, and German Federal Ministry of Education and Research.