BBS Faculty Member - Joel Hirschhorn

Joel Hirschhorn

Department of Genetics

Boston Children's Hospital
Genetics & Endocrinology, CLS 16028
3 Blackfan Circle
Boston, MA 02115
Tel: 617-919-2129
Fax: 617-730-0244
Visit my lab page here.

There are clear resemblances between parents and children for height, weight, and many other traits and diseases; these familial resemblances are influenced strongly by inherited genetic factors. Most common diseases (such as diabetes, obesity, or asthma) and quantitative traits (such as height and weight) are polygenic, meaning they are influenced by the likely additive effects of multiple genetic factors. Our laboratory's long-term goal is to use human genetics to identify the genetic factors contributing to human height and weight, as well as other polygenic traits and diseases, and to translate these genetic results into biological insights. 

We study body mass index and other anthropometric measures of obesity because these are heritable and readily measured polygenic risk factors for a number of important diseases, including diabetes, cancer and heart disease. We study height (stature) because of its relevance to human growth and development, and also because it is a classic polygenic trait through which we and others have learned a lot about the genetic architecture of human polygenic traits. To study height and obesity, our lab helps lead the Genetic Investigation of ANthropometric Traits (GIANT) consortium, which uses genome-wide association studies and other methods to discover common genetic variants that influence anthropometric traits like height and weight.  We use the results of these large-scale genetic studies for computational analyses to generate and test biological hypothesis about the regulation of human height and weight. To accomplish this goal, we also develop and implement new computational tools to help translate genetic association into actionable biological hypotheses. 

Although our main focus is on obesity and height, the lab also has projects related to other diseases (such as diabetic kidney disease) and quantitative traits (timing of puberty and metabolite levels). We continue to work in the intersection between population genetics and the genetic analysis of polygenic traits, including studying different signatures of selection. We also generate metabolite profiling data to inform our studies of obesity, and develop and implement tools to use untargeted metabolite profiling to identify predictive and causal biomarkers for disease. 

We are continuing to expand our genetic studies of height and weight (sample sizes currently in the millions), and are focusing on integrating genome-wide association and sequencing data with non-genetic data (RNA sequencing, epigenetics, and metabolite profiling) to generate new hypotheses about causal cell types and biological pathways for height, weight, and other human diseases and traits. We continue to do experimental work using in vitro and in vivo models to test these hypotheses. We also are combining polygenic risk scores, exome or genome sequencing, and clinical phenotypes for insights into disease biology and prediction, and to improve clinical diagnosis of children with endocrine disorders. Our lab collaborates closely with investigators at the Broad Institute in many of these areas.

Last Update: 6/13/2019


For a complete listing of publications click here.



Lohmueller KE, Pearce CL, Pike M, Lander ES, Hirschhorn JN. Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease. Nature Genet. 2003; 33:177-82. 

Bersaglieri T, Sabeti PC, Patterson N, Vanderploeg T, Schaffner SF, Drake JA, Rhodes M, Reich DE, Hirschhorn JN. Genetic signatures of strong recent positive selection at the lactase gene. Am. J. Hum. Genet. 2004; 74:1111-20. 

Campbell CD, Ogburn EL, Lunetta KL, Lyon HN, Freedman ML, Groop LC, Altshuler D, Ardlie KG, Hirschhorn JN. Demonstrating stratification in a European-American population. Nature Genet. 2005; 37:868-72. 

Lango Allen H+, Estrada K+, Lettre G+, Berndt S+, Weedon MN+, Rivadeneira F+, … (many additional authors),  Abecasis GR*, Stefansson K*, Frayling TM*, Hirschhorn JN*. Hundreds of variants influence height and cluster in genomic loci and biological pathways. Nature 2010; 467:832-8. PMC2955183 

Turchin MC, Chiang CWK, Palmer CD, Sankararaman S, Reich D, GIANT Consortium, Hirschhorn JN. Evidence of widespread selection on standing variation in Europe at height-associated SNPs. Nature Genet. 2012; 44:1015-9. PMC3480734 

Wang SR, Carmichael H, Andrew SF, Miller TC, Moon JE, Derr MA, Hwa V, Hirschhorn JN, Dauber A. Large-scale pooled next-generation sequencing of 1077 genes to identify genetic causes of short stature. J Clin Endocrinol Metab. 2013; 98:E1428-37. PMC3733853

Abreu AP*, Dauber A*, Macedo DB, Noel SD, Brito VN, Gill JC, Cukier P, Thompson IR, Navarro VM, Gagliardi PC, Rodrigues T, Kochi C, Longui CA, Beckers D, de Zegher F, Montenegro LR, Mendonca BB, Carroll RS, Hirschhorn JN, Latronico AC+, Kaiser UB+. Central precocious puberty caused by mutations in the imprinted gene MKRN3. N Engl J Med. 2013; 368:2467-75. PMC3808195

Locke AE+, Kahali B+, Berndt SI+, Justice AE+, Pers TH+, …  (many additional authors), North KE*, Ingelsson E*, Hirschhorn JN*, Loos RJF*, Speliotes EK*. Genetic studies of body mass index yield new insights for obesity biology. Nature 2015;518:197–206. PMC4382211. 

Pers TH, Karjalainen JM, Chan Y, Westra, H-J, Wood AR, Yang J, Lui JC, Vedantam S, Gustafsson S, Esko T, Frayling T, Speliotes EK, GIANT consortium, Boehnke M, Raychaudhuri S, Fehrmann RSN, Hirschhorn JN*, Franke L*. Biological interpretation of genome-wide association studies using predicted gene function. Nature Comm. 2015; 6:5890. PMC4420238 

Marouli E+, Graff M+, Medina-Gomez C+, Lo KS+, Wood AR+, Kjaer TR+, Fine RS+, Lu Y+, …(many additional authors), Oxvig C*, Kutalik Z*, Rivadeneira F*, Loos RJ*, Frayling TM*, Hirschhorn JN*, Deloukas P*, Lettre G*. Rare and low-frequency coding variants alter human adult height.
Nature 2017;542:186-190. 

Fine RS, Pers TH, Amariuta T, Raychaudhuri S, Hirschhorn JN. Benchmarker: an unbiased, association-data-driven strategy to evaluate gene prioritization algorithms. Am J Hum Genet 2019 (in press).

Hsu YH, Churchhouse C, Pers TH, Mercader JM, Metspalu A, Fischer K, Fortney K, Morgen EK, Gonzalez C, Gonzalez ME, Esko T, Hirschhorn JN. PAIRUP-MS: Pathway analysis and imputation to relate unknowns in profiles from mass spectrometry-based metabolite data. PLoS Comput Biol. 2019;15:e1006734.  

+,* contributed equally

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of Harvard College