Waist-Hip Ratio and Your Genetics

WHR Additive Genetics: ANGPTL4, TBX15, and Lipid Loci | ExomeDNA

By the ExomeDNA Research Team | Last reviewed May 2026

Research base: Robust.

What is waist-to-hip ratio adjusted for BMI?

Waist-to-hip ratio (WHR) measures how fat is distributed between the waist and hips, with BMI adjustment isolating distribution from overall body size. The additive genetic model, used in this analysis, assumes that each additional copy of a risk allele contributes a fixed, independent increment to WHR. This statistical framework maximizes power for detecting common variants whose effects accumulate across allele copies—a well-suited approach for continuous distribution traits shaped by many small genetic contributions.

Where fat accumulates around the waist versus the hips is partly heritable and partly distinct from overall adiposity. Central fat accumulation—reflected in elevated WHR—is associated with elevated susceptibility for cardiometabolic conditions. Identifying protein-coding variants contributing to this trait provides directional evidence about the molecular mechanisms involved.

The genetics behind fat distribution

The strongest additive-model signals for BMI-adjusted WHR include variants near FGFR4, TBX15, ANGPTL4, ACVR1C, RREB1, WSCD2, RAPGEF3, PLCE1, RSPO3, and PDE5A. This gene set emphasizes lipid metabolism and lipolysis regulation alongside developmental patterning of fat depot identity.

FGFR4 encodes a fibroblast growth factor receptor involved in hepatic lipid metabolism and adipose regulation—consistently one of the top signals for WHR across multiple study designs. ANGPTL4 encodes angiopoietin-like protein 4, a secreted inhibitor of lipoprotein lipase (LPL). LPL controls the release of fatty acids from circulating lipoproteins for uptake into tissues; ANGPTL4 limits this activity, particularly in oxidative tissues. Variants near ANGPTL4 may alter how efficiently fat is deposited in or mobilized from specific tissue compartments, contributing to where fat preferentially accumulates.

TBX15 is a T-box transcription factor that marks distinct adipocyte populations in different body regions. It encodes positional identity for fat cells—whether adipocytes in a given depot develop as abdominal or gluteal in character. Genetic variation near TBX15 influences developmental patterning of fat depot identity rather than metabolic regulation per se. The gene is expressed at high levels in gluteal fat relative to abdominal fat, and its variants associate with WHR in ways consistent with a role in determining the relative growth capacity of peripheral versus central depots.

Whole-exome sequencing identified protein-coding variants near ANGPTL4, ACVR1C, and related lipid homeostasis genes as contributors to central fat distribution and WHR, providing functional hypotheses for causal mechanisms (Justice et al., 2019).

ACVR1C encodes a type I TGF-beta family receptor that binds activin C and activin E. Activin signaling through ACVR1C modulates adipogenesis and hepatic fat metabolism. Loss-of-function variants in ACVR1C have been associated with reduced central adiposity and improved metabolic markers in large-scale studies. RAPGEF3 encodes a cAMP-regulated exchange factor (EPAC1) with roles in lipolysis signaling; it may influence the efficiency with which fat cells release stored lipids in response to hormonal signals. PDE5A encodes a phosphodiesterase that degrades cyclic GMP, with downstream effects on smooth muscle tone and metabolic tissue biology that may affect fat distribution through vascular and cellular signaling pathways.

What the research says

Justice et al. (2019) applied whole-exome sequencing to large population cohorts to identify protein-coding variants contributing to body fat distribution. The study identified genes not previously linked to WHR through common-variant approaches, including novel findings at lipid homeostasis loci such as ANGPTL4 and ACVR1C. Using the additive model framework provided well-calibrated effect estimates and enabled discovery of variants whose effects accumulate linearly across allele copies.

The value of focusing on protein-coding variants—changes within gene exons that alter the amino acid sequence of a protein—is that they provide more direct functional hypotheses than regulatory variants near genes. When a protein-coding variant in ACVR1C associates with central adiposity, it points to that protein's function as the likely mechanism, rather than requiring inference about which nearby gene a regulatory variant might influence.

Additive-model WHR loci span lipid trafficking (ANGPTL4), fat depot developmental identity (TBX15), TGF-beta signaling (ACVR1C), and lipolysis regulation (RAPGEF3), reflecting multiple convergent pathways in central fat accumulation genetics (Justice et al., 2019).

The additive model assumption holds well for WHR because the trait's genetic architecture is highly polygenic—many variants each contributing small effects that sum across the genome. Under these conditions, the additive model captures the bulk of common-variant heritability and provides accurate effect size estimates useful for downstream analyses including polygenic scoring and functional follow-up.

How fat distribution affects you

Elevated waist-to-hip ratio is associated with greater susceptibility to cardiometabolic conditions, elevated fasting triglycerides, and increased visceral fat accumulation. The lipid biology signals in this gene set—particularly ANGPTL4—connect WHR genetics directly to lipolysis regulation and fatty acid trafficking. Individuals with variants affecting ANGPTL4 function may have altered rates of fatty acid release from or uptake into specific fat depots, contributing over time to where fat preferentially accumulates.

The TBX15 signal speaks to a different dimension: developmental programming. The positional identity of fat cells—whether they take on abdominal or gluteal characteristics—appears partly set during development by transcription factors like TBX15. This suggests that central adiposity susceptibility is not only a matter of ongoing metabolic regulation but also of the foundational cellular identity established in fat depots during development.

Working with your profile

Exercise type influences lipolysis rates and fat distribution patterns. Endurance exercise upregulates LPL activity in muscle and promotes fat oxidation from circulating lipoproteins. Interval and resistance training shift substrate utilization and can reduce central adiposity over longer periods. For individuals with variants affecting lipase regulation, exercise responses may differ in degree but the directional benefit of regular activity remains consistent with population-level evidence.

Dietary fat composition and carbohydrate intake both influence fatty acid partitioning and lipoprotein lipase activity. Diets lower in refined carbohydrates are consistently associated with more favorable fat distribution patterns in controlled studies, likely through insulin-mediated effects on lipolysis and fatty acid uptake. Monitoring waist-to-hip ratio alongside body weight gives a more complete picture of whether compositional changes are occurring even when total weight is stable.

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Related traits and genes

WHR genetics from the additive model overlaps with other body composition phenotypes. FGFR4 and RSPO3 are recurring signals across multiple body composition studies. ANGPTL4 also appears in studies of HDL cholesterol and triglycerides, reflecting its broad role in lipid metabolism. TBX15 is relatively selective to fat distribution versus total adiposity, suggesting a specific role in depot identity rather than overall fat mass regulation. ACVR1C signals appear in both fat distribution and metabolic trait analyses, consistent with its role in hepatic and adipose TGF-beta signaling.

Frequently asked questions

Additional research: waist-hip ratio in never-smokers (stratum analysis, not an interaction study)

The genetics behind fat distribution

Research base: Robust.

The strongest genetic signals for BMI-adjusted WHR in never-smokers include variants near RSPO3, ADAMTS9, EYA4, ANKRD55, CCDC122, HOXC12, and TBX15. The gene set partially overlaps with general-population WHR studies but shows some distinct signals that emerge more clearly when tobacco exposure is absent.

RSPO3 is consistently the top signal across multiple WHR analyses—general and never-smoker alike. It encodes a secreted Wnt pathway amplifier expressed in fat tissue that shapes how fat depots develop and are maintained through Wnt-dependent signaling. Its persistence as the leading signal across study designs confirms a robust, tobacco-independent genetic contribution to fat distribution.

ADAMTS9 encodes a secreted metalloproteinase that cleaves extracellular matrix proteoglycans, particularly versican and aggrecan. Metalloproteinase-mediated ECM remodeling affects the structural properties of connective tissue in mesenchymal tissues, including fat depots. Variants near ADAMTS9 may influence the remodeling capacity of connective tissue scaffolding surrounding fat cells, with effects on how easily fat depots expand. Notably, ADAMTS9 appears more prominently in never-smoker analyses than in general-population WHR studies, suggesting its genetic signal may be partially obscured in populations where smoking-related ECM effects operate alongside genetic ones.

A genome-wide meta-analysis accounting for smoking behavior identified novel loci for WHR in never-smoker subsets, including genes involved in extracellular matrix remodeling and developmental patterning not clearly resolved in general population analyses (Justice et al., 2017).

EYA4 is a transcription coactivator with roles in developmental gene regulation, expressed during organogenesis and in adult tissues. ANKRD55, located near the gene encoding the IL-6 receptor subunit gp130 (IL6ST), may connect fat distribution genetics to inflammatory cytokine signaling biology. HOXC12 is a Hox developmental transcription factor that specifies positional identity in tissues during embryonic development. HOX family genes have appeared across multiple fat distribution phenotypes, consistently supporting the hypothesis that the regional identity of fat depots—abdominal versus gluteal—is programmed during development and partly encoded by developmental transcription factor genetics.

What the research says

Justice et al. (2017) conducted a large-scale genome-wide meta-analysis of obesity traits that explicitly modeled and stratified by smoking behavior, enabling analysis of never-smoker subsets with sufficient statistical power. The study identified loci for central adiposity measures including WHR, with some signals appearing more strongly or newly in the tobacco-free group. This design addresses an important limitation of general-population GWAS: smoking confounds fat distribution because it independently promotes central adiposity, and its effects can mask or distort the genetic signal.

Accountability for smoking behavior in genome-wide WHR analyses revealed distinct genetic signals, including ADAMTS9 and developmental patterning genes, that were more clearly detectable in never-smoker subsets (Justice et al., 2017; Lee et al., 2022).

This approach is informative for traits where a common behavioral exposure creates substantial confounding, and the methods developed for WHR have been applied to other adiposity traits with similar benefits.

References (waist-hip ratio in never-smokers (stratum analysis, not an interaction study))

1. Justice AE, et al. (2017). Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits. Nat Commun. PMID: 28443625.
2. Lee WJ, et al. (2022). Smoking-interaction loci affect obesity traits: a gene-smoking stratified meta-analysis of 545,131 Europeans. Lifestyle Genom. PMID: 35793639.

Additional research: waist-to-hip ratio (additive model)

The genetics of Waist-to-Hip Ratio

Research base: Robust.

Genome-wide association studies of WHR have revealed one of the clearest examples of sex-specific genetic architecture in complex trait biology. Heid et al. (2010), published in Nature Genetics, studied up to 77,167 participants in discovery analyses with follow-up in 113,636 subjects, and identified 13 new loci. That work was the first large-scale documentation that the genetic signals for WHR show substantially stronger effects in women than men — a pattern of sexual dimorphism that has since been confirmed and extended in every major subsequent study.

Pulit et al. (2019), a meta-analysis of 694,649 individuals of European ancestry published in Human Molecular Genetics, identified 463 genetic signals across 346 loci for WHR adjusted for BMI. Approximately one-third of all identified signals demonstrated sexual dimorphism, with stronger effects in women. This analysis drew a sharp distinction between the genetics of fat distribution and the genetics of fat mass, showing that the two phenotypes have only partially overlapping genetic architectures. Where a person's fat is deposited — independent of how much fat they carry — reflects a heritable biological tendency captured by the WHR phenotype.

The biological basis of sexual dimorphism in WHR genetics likely involves estrogen-responsive gene regulation in adipose tissue. Adipose tissue in women responds differently to sex hormones, creating a biologically distinct regulatory context in which the same genetic variants can have different magnitude effects on fat distribution depending on hormonal background. This has direct implications for interpreting WHR genetic scores across sexes.

Stat block: 694,649 individuals in the Pulit et al. (2019) meta-analysis identified 463 genome-wide signals for waist-to-hip ratio, with approximately one-third showing sex-differential effects stronger in women.

Stat block: 1,012 gene-proximal variants captured in the current genome-wide signal landscape for waist-to-hip ratio.

What the research says

The Heid et al. (2010) study in Nature Genetics established the foundation for WHR genetics: it confirmed the polygenic architecture of fat distribution, documented sex-specific genetic effects for the first time at genome-wide scale, and showed that WHR GWAS identifies gene sets with biological enrichment in adipose biology, lipid metabolism, and extracellular matrix pathways.

The Pulit et al. (2019) analysis in Human Molecular Genetics substantially extended this work. Studying 694,649 individuals and identifying 463 signals across 346 loci — roughly a 35-fold expansion from the original 13 loci — reflects the statistical power needed to resolve the full breadth of a deeply polygenic trait. The finding that approximately one-third of signals show sex-differential effects is one of the clearest demonstrations of sex-specific genetic architecture in complex trait biology. The researchers also confirmed that WHR genetics is substantially independent of BMI genetics, reinforcing that where fat is stored is a heritable biological property distinct from how much fat is stored.

This independence has practical implications: individuals with similar body mass can have dramatically different WHR genetic profiles and corresponding metabolic implications, and vice versa. Fat distribution phenotypes like WHR add a biologically meaningful dimension to body composition assessment that BMI alone cannot capture.

References (waist-to-hip ratio (additive model))

1. Heid IM, et al. (2010). Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet. PMID: 20935629.
2. Pulit SL, et al. (2019). Meta-analysis of genome-wide association studies for body fat distribution in 694,649 individuals of European ancestry. Hum Mol Genet. PMID: 30239722.

Additional research: waist-to-hip ratio (additional study)

The genetics behind waist-to-hip ratio

The study identified loci across multiple chromosomes, with genes playing distinct biological roles in fat distribution.

Among the authorized genes implicated in this trait, ABCA1 (ATP-binding cassette transporter A1) is a cholesterol efflux transporter expressed in adipocytes. In fat cells, ABCA1 exports excess cholesterol from the cell membrane, regulating lipid raft composition, insulin receptor function, and adipokine secretion. Variants in ABCA1 may alter how visceral and subcutaneous adipocytes handle intracellular cholesterol, contributing to depot-specific differences in fat accumulation. The gene ranks second by locus-to-gene score in this trait with a high-confidence score of 0.90.

ADAMTS9 encodes a secreted metalloprotease that remodels the extracellular matrix of adipose tissue. ECM composition determines the mechanical properties of fat depots, governs preadipocyte differentiation, and constrains adipocyte expansion. Variants in ADAMTS9 appear across both total waist circumference and WHR genome-wide studies, reflecting its broad role in adipose ECM biology. Its locus-to-gene score is 0.86, ranked fourth.

ANKRD55 (ankyrin repeat domain 55) is expressed in immune cells and has been associated with inflammatory conditions including multiple sclerosis, type 2 diabetes, and rheumatoid arthritis in prior genome-wide studies. Its presence in the WHR signal points to adipose tissue immune dynamics: macrophage infiltration of visceral adipose tissue drives a chronic low-grade inflammatory state that promotes further visceral expansion and insulin resistance.

ARHGEF28 activates RhoA GTPase signaling. In preadipocytes, the RhoA/ROCK pathway regulates cytoskeletal tension and influences the balance between adipogenic and alternative cell fates. Visceral and subcutaneous preadipocytes differ in their baseline Rho signaling activity, and genetic variants in ARHGEF28 may subtly shift depot-specific adipogenic potential.

BAZ1B is a chromatin remodeling factor located in the Williams-Beuren syndrome critical region on chromosome 7. Chromatin accessibility programs are a key determinant of depot identity in adipose tissue: the genes switched on or off during preadipocyte differentiation differ between visceral and subcutaneous depots, and BAZ1B variants may influence those depot-specific epigenetic landscapes.

BTNL2 (butyrophilin-like protein 2) is an immune checkpoint molecule encoded within the MHC class III region. It is expressed on T lymphocytes and macrophages and inhibits T cell activation. In adipose tissue, BTNL2 contributes to the immune microenvironment regulation that governs adipose tissue inflammation, a key mediator of the link between visceral fat and systemic metabolic effects.

CALCRL encodes the calcitonin receptor-like receptor, a G protein-coupled receptor that binds calcitonin gene-related peptide and adrenomedullin. CALCRL is highly expressed in the adipose vasculature, where calcitonin gene-related peptide acts as a potent vasodilator. Adipose blood flow regulation differs markedly between visceral and subcutaneous depots, and this vascular difference affects nutrient delivery, lipolysis, and fat distribution set points.

What the research says

Research base: Moderate. The study analyzed data across multiple large cohorts and tested whether the genetic contribution to WHR is modified by how physically active a person is.

Key findings from the research base include the following. The genetic architecture of WHR adjusted for BMI is partly distinct from that of BMI itself, supporting the biological separation of fat distribution from total fat mass. Sex differences in WHR genetics are substantial: women carry a higher proportion of gluteofemoral fat on average, and the hormonal shift at menopause drives redistribution toward higher WHR, narrowing this sex difference in older age. The loci identified span genes in cholesterol metabolism (ABCA1), ECM remodeling (ADAMTS9), immune regulation (ANKRD55, BTNL2), cytoskeletal signaling (ARHGEF28), chromatin programming (BAZ1B), and vascular biology (CALCRL), indicating that WHR is governed by the coordinated biology of adipose tissue architecture, immune infiltration, and vascular supply.

References (waist-to-hip ratio (additional study))

1. Graff M, Scott RA, Justice AE, et al. (2017). Genome-wide physical activity interactions in adiposity — a meta-analysis of 200,452 adults. PLOS Genetics. PMID: 28448500.

Additional research: waist-to-hip ratio adjusted for BMI (additional study)

The genetics behind waist-to-hip ratio

WHR is a polygenic trait shaped by hundreds of common genetic variants with individually small effects. Genome-wide association studies (GWAS) have used WHR adjusted for BMI (WHRadjBMI) as the primary phenotype to identify loci that influence fat distribution independently of total body size.

Among the genes at associated loci in this research, several fall within intersecting biological pathways. ADAMTS9 encodes a secreted zinc metalloendopeptidase in the ADAMTS family—enzymes involved in extracellular matrix remodeling, including cleavage of the versican proteoglycan. Because extracellular matrix composition influences adipocyte differentiation, lipid storage capacity, and fat depot organization, genes like ADAMTS9 are biologically plausible candidates at fat distribution loci.

ABCA1 encodes a membrane-associated ATP-binding cassette transporter responsible for exporting cholesterol and phospholipids from cells to lipid-poor apolipoproteins, a key step in reverse cholesterol transport. ABCA1 variants are well established in the genetics of HDL cholesterol levels and lipid metabolism more broadly, which intersects with adipose tissue function and fat patterning. Population studies have identified ABCA1 variants in genomic contexts associated with WHR and related body composition traits.

ANKRD55, containing ankyrin repeat domains, has appeared in GWAS of multiple complex traits including adiposity-related phenotypes. Its presence in WHR-associated genomic regions has been reported in large-scale analyses, and it represents one of the loci with plausible adipose-tissue-relevant biology.

The genetic architecture of WHRadjBMI shows particularly pronounced sex differences compared to many other anthropometric traits. Several loci have effect sizes two to three times larger in women than in men, or appear specific to one sex. This suggests that genetic effects on fat distribution interact with the hormonal and developmental environment differently across sexes.

What the research says

Research base: Robust

WHR and its adjusted form WHRadjBMI have been extensively studied in large-scale GWAS, enabling robust identification of associated loci.

A large-scale genome-wide association analysis of waist-to-hip ratio measures involving hundreds of thousands of adults identified dozens of loci reaching genome-wide significance. A notable feature of the genetic architecture was sex-heterogeneity: multiple loci showed substantially larger effects in women than in men, consistent with the role of sex hormones in fat distribution regulation (2017, PMID: 28443625).

Heritability estimates for WHR from twin studies are in the range of 22 to 55 percent, with estimates varying by population and methodology. After adjusting for BMI—to focus on distribution rather than total adiposity—heritability remains meaningfully above zero, confirming that the pattern of fat storage has genetic influences independent of total fat mass.

Twin study analyses of WHRadjBMI estimate heritability in the range of 20–50%, indicating that genetic variation contributes to individual differences in central-versus-peripheral fat distribution patterns beyond what is explained by total body size.

The relationship between WHR genetics and downstream metabolic phenotypes has been explored through Mendelian randomization approaches, which use genetic variants as instruments to probe causal relationships. These analyses have suggested that the fat distribution pattern captured by WHR may have independent associations with cardiometabolic parameters beyond what BMI alone captures, though causal inference in this area remains an active research topic.