Genetics of Obesity: Loos Lab

Genetic variation accounts for a large portion of interindividual differences in body weight. Twin and family studies estimate heritability of body mass index (BMI) at roughly 40–70 percent depending on age and population. Both rare, highly penetrant mutations and the combined action of many common variants shape risk. Monogenic causes such as pathogenic variants in MC4R, POMC, LEPR and PCSK1 produce severe, early onset obesity in children, while polygenic risk scores built from hundreds to thousands of common single nucleotide variants explain a portion of BMI variance across populations. The scientific challenge is to bridge signals from genome studies to causal genes, mechanisms in adipose tissue and brain, and therapies that alter energy balance.

Mission and programmatic priorities

The Loos Lab focuses on identifying genes that cause or modulate obesity to reveal biological pathways that control body weight. The Genetics of Obesity and Related Traits Program targets three priorities: discovery of genetic loci across diverse populations, functional annotation that links variants to gene regulation and cell types, and translational translation into targets for prevention and precision therapy. Emphasis is placed on reproducible, large sample analyses and on following up genetic hits with laboratory experiments that test mechanism and therapeutic candidacy.

Cohorts, sampling and phenotyping

Large, deeply phenotyped cohorts supply statistical power and biological context. Key cohorts used include UK Biobank (about 500,000 adults aged 40–69 recruited 2006–2010), the Million Veteran Program, and national biobanks in Europe and Asia. Clinical cohorts of children with severe early onset obesity and bariatric surgery registries provide enriched samples for rare variant discovery. Phenotyping extends beyond weight and BMI to waist circumference, body composition by DXA, metabolic labs (fasting glucose, insulin, lipids), resting energy expenditure, and behavior measures such as food intake questionnaires and appetite scales. Standardized protocols and repeat measures increase signal reliability and support longitudinal modeling of weight trajectories.

Genomic strategies: overview of methods

Analytic approaches combine large-scale genome wide association analyses, targeted sequencing, and experimental genomics. Common variant analyses leverage dense genotype arrays with imputation to reference panels such as the Haplotype Reference Consortium and TOPMed. Whole exome and whole genome sequencing detect rare coding and regulatory variants not captured on arrays. Functional genomics uses expression quantitative trait locus mapping, single cell RNA sequencing, chromatin accessibility assays and high throughput reporter assays to link variants to genes and cell types. Statistical workflows employ mixed models, principal component correction for ancestry, and rigorous replication to control false positives.

Population scale discovery and experimental follow up

Genome wide association studies remain the primary discovery tool for common variant loci. Increasing sample sizes into the millions has raised the count of independent BMI associated signals into the hundreds, highlighting neuronal pathways involved in appetite regulation and adipocyte biology. Rare variant discovery through sequencing identifies loss of function alleles in genes such as MC4R, which are enriched in severe early onset cases and show clear functional consequences on receptor signaling. Functional follow up combines CRISPR editing in human cell models, electrophysiology in hypothalamic neurons, and rodent knockout or knockin models to confirm physiological effects on food intake and energy expenditure.

Below is a concise synthesis of notable gene discoveries, evidence types and translational relevance to illustrate how findings link to mechanism and therapy.

Gene	Variant class	Effect on phenotype	Evidence type	Clinical implication
MC4R	Heterozygous loss of function	Increased BMI, early onset obesity; penetrance varies	Human genetics, functional assays, rodent models	Target of melanocortin pathway drugs; variants in 2–5% of severe childhood cases
POMC	Biallelic loss of function	Severe hyperphagia and obesity from infancy	Rare variant sequencing, rescue in animals	Approved therapy with melanocortin receptor agonist for specific deficiency
LEPR	Biallelic or rare severe	Hyperphagia, endocrine dysregulation	Clinical cohorts, signaling studies	Candidate for receptor pathway modulation
FTO locus	Common noncoding variants	Small increments in BMI per allele	GWAS, eQTL, enhancer studies	Points to transcriptional regulation of adipocyte thermogenesis
MC3R	Rare variants	Modest effects on weight regulation	Sequencing in case cohorts	Potential modulatory target in energy balance

Analytical pipelines and causal prioritization

Robust pipelines begin with genotype quality control, imputation and population structure adjustment. Association testing uses linear mixed models such as BOLT-LMM or REGENIE to handle relatedness and case mix. Meta analysis tools aggregate across cohorts while conditional analyses define independent signals. Fine mapping produces credible sets of candidate causal variants using probabilistic models such as SuSiE or CAVIAR, and functional priors refine likelihoods. Colocalization with eQTL and chromatin interaction maps narrows candidate genes. Integration with single cell atlases of human hypothalamus and adipose tissue reveals cell type specific expression patterns for prioritized genes.

Functional validation and systems level integration

Validated causal genes undergo perturbation in cultured human adipocytes or hypothalamic neurons derived from induced pluripotent stem cells. Phenotypes assessed include lipogenesis, lipolysis, mitochondrial function and neuronal firing. In vivo models evaluate appetite, thermogenesis and glucose homeostasis. Multi-omics layers including transcriptomics, proteomics and metabolomics are integrated through network modeling to place genes into pathways controlling appetite, energy storage and nutrient sensing. This systems approach helps move from variant to pathway to potential pharmacology.

Translation, ethics and collaboration

Genetic discoveries inform precision medicine strategies ranging from targeted pharmacologic agents to risk stratification for prevention. The approval of a melanocortin receptor agonist for specific genetic deficiencies exemplifies direct translation. Ethical considerations include consent for genetic testing, equitable access to interventions, and careful communication about genetic risk versus modifiable environmental factors. The program prioritizes data sharing through controlled access repositories and collaborative consortia to accelerate replication and inclusion of underrepresented populations.

Ongoing efforts and trajectory

Current work expands sequencing in diverse ancestries, refines polygenic prediction in clinical settings, and advances high throughput functional screens to evaluate candidate variants at scale. The objective remains to link genetic signals to actionable biology that reduces obesity burden through tailored prevention and therapeutic approaches.