rs13107325

This is a variant in the SLC39A8 gene that changes a alanine to an threonine.

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GWAS Catalog Trait Associations (357)

Genome-wide significant associations (p < 5×10⁻⁸) from the NHGRI-EBI GWAS Catalog.

cortical thickness

van der Meer D et al. “The genetic architecture of human cortical folding.” Science Advances 7(51):eabj9446 (2021)

Allele T

OR 28.20

p 6.0e-175

N 33,748

Large GWAS

European

Shadrin AA et al. “Vertex-wise multivariate genome-wide association study identifies 780 unique genetic loci associated with cortical morphology.” Neuroimage 244:118603 (2021)

Allele T

OR —

p 1.0e-17

N 35,657

Large GWAS

European

Zhukovsky P et al. “Genetic influences on brain and cognitive health and their interactions with cardiovascular conditions and depression.” Nature Communications 15(1):5207 (2024)

Allele T

OR —

p 5.0e-8

N 34,571

Large GWAS

European

Smith SM et al. “An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank.” Nature Neuroscience 24(5):737-745 (2021)

Allele T

OR 0.11

p 1.0e-8

N 21,282

Major Consortium StudyLarge GWAS

European

level of syndecan-1 in blood

Loya H et al. “A scalable variational inference approach for increased mixed-model association power.” Nature Genetics 57(2):461-468 (2025)

Allele T

OR 0.28

p 5.0e-165

N 47,745

Large GWAS

European

liver fat measurement, liver disease biomarker

Parisinos CA et al. “Genome-wide and Mendelian randomisation studies of liver MRI yield insights into the pathogenesis of steatohepatitis.” Journal of Hepatology 73(2):241-251 (2020)

Allele T

OR 0.54

p 1.0e-133

N 14,440

Large GWAS

European

level of SLIT and NTRK-like protein 2 in blood serum

Loya H et al. “A scalable variational inference approach for increased mixed-model association power.” Nature Genetics 57(2):461-468 (2025)

Allele T

OR 0.22

p 6.0e-127

N 47,745

Large GWAS

European

hematocrit

Loya H et al. “A scalable variational inference approach for increased mixed-model association power.” Nature Genetics 57(2):461-468 (2025)

Allele T

OR 0.07

p 7.0e-103

N 394,642

Large GWAS

European

Jee YH et al. “Genome-wide association studies in a large Korean cohort identify quantitative trait loci for 36 traits and illuminate their genetic architectures.” Nature Communications 16(1):4935 (2025)

Allele T

OR 0.05

p 1.0e-31

N 928,679

Large GWAS

multi-ancestry

Verma A et al. “Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program.” Science (new York, N.y.) 385(6706):eadj1182 (2024)

Allele T

OR 0.05

p 9.0e-17

N 584,645

Major Consortium StudyLarge GWAS

multi-ancestry

Chen MH et al. “Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations.” Cell 182(5):1198-1213.e14 (2020)

Allele T

OR 0.05

p 2.0e-40

N 562,259

Large GWAS

European

Sakaue S et al. “A cross-population atlas of genetic associations for 220 human phenotypes.” Nature Genetics 53(10):1415-1424 (2021)

Allele T

OR 0.04

p 4.0e-24

N 503,490

Large GWAS

multi-ancestry

Vuckovic D et al. “The Polygenic and Monogenic Basis of Blood Traits and Diseases.” Cell 182(5):1214-1231.e11 (2020)

Allele T

OR 0.05

p 2.0e-31

N 408,112

Large GWAS

European

level of carboxypeptidase Q in blood

Loya H et al. “A scalable variational inference approach for increased mixed-model association power.” Nature Genetics 57(2):461-468 (2025)

Allele T

OR 0.21

p 1.0e-102

N 47,745

Large GWAS

European

diastolic blood pressure

Keaton JM et al. “Genome-wide analysis in over 1 million individuals of European ancestry yields improved polygenic risk scores for blood pressure traits.” Nature Genetics 56(5):778-791 (2024)

Allele T

OR 0.61

p 5.0e-100

N 1,028,980

Large GWAS

multi-ancestry

Koskeridis F et al. “Multi-trait association analysis reveals shared genetic loci between Alzheimer's disease and cardiovascular traits.” Nature Communications 15(1):9827 (2024)

Allele T

OR 0.06

p 3.0e-81

N 1,212,859

Large GWAS

European

Allele T

OR 0.05

p 9.0e-31

N 928,679

Large GWAS

multi-ancestry

Verma A et al. “Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program.” Science (new York, N.y.) 385(6706):eadj1182 (2024)

Allele T

OR 0.05

p 8.0e-23

N 609,477

Major Consortium StudyLarge GWAS

multi-ancestry

Plotnikov D et al. “High Blood Pressure and Intraocular Pressure: A Mendelian Randomization Study.” Investigative Ophthalmology & Visual Science 63(6):29 (2022)

Allele T

OR 0.64

p 6.0e-54

N 526,001

Large GWAS

European

Sakaue S et al. “A cross-population atlas of genetic associations for 220 human phenotypes.” Nature Genetics 53(10):1415-1424 (2021)

Allele T

OR 0.04

p 1.0e-21

N 485,677

Large GWAS

multi-ancestry

Giri A et al. “Trans-ethnic association study of blood pressure determinants in over 750,000 individuals.” Nature Genetics 51(1):51-62 (2019)

Allele T

OR 0.32

p 8.0e-12

N 459,777

Large GWAS

multi-ancestry

Loya H et al. “A scalable variational inference approach for increased mixed-model association power.” Nature Genetics 57(2):461-468 (2025)

Allele T

OR 0.04

p 4.0e-23

N 394,642

Large GWAS

European

Gurholt TP et al. “Large-scale brainstem neuroimaging and genetic analyses provide new insights into the neuronal mechanisms of hypertension.” Hgg Advances 6(1):100392 (2025)

Allele T

OR 14.72

p 5.0e-49

N 322,141

Large GWAS

European

Ehret GB et al. “The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals.” Nature Genetics 48(10):1171-1184 (2016)

Allele T

OR 0.60

p 2.0e-14

N 201,529

Large GWAS

European

Yang ML et al. “Sex-specific genetic architecture of blood pressure.” Nature Medicine 30(3):818-828 (2024)

Allele T

OR 0.06

p 9.0e-21

N 174,664

Large GWAS

multi-ancestry

Wain LV et al. “Novel Blood Pressure Locus and Gene Discovery Using Genome-Wide Association Study and Expression Data Sets From Blood and the Kidney.” Hypertension (dallas, Tex. : 1979) 70(3):e4-e19 (2017)

Allele T

OR 0.69

p 1.0e-13

N 150,134

Large GWAS

multi-ancestry

Hoffmann TJ et al. “Genome-wide association analyses using electronic health records identify new loci influencing blood pressure variation.” Nature Genetics 49(1):54-64 (2017)

Allele T

OR 0.48

p 8.0e-11

N 99,785

Large GWAS

multi-ancestry

Ehret GB et al. “Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk.” Nature 478(7367):103-9 (2011)

Allele T

OR 0.68

p 2.0e-17

N 69,395

Large GWAS

European

Kulminski AM et al. “Strong impact of natural-selection-free heterogeneity in genetics of age-related phenotypes.” Aging 10(3):492-514 (2018)

Allele T

OR 0.91

p 9.0e-10

N 33,431

Large GWAS

European

HDL cholesterol change measurement

Zoodsma M et al. “A genetic map of human metabolism across the allele frequency spectrum.” Nature Genetics 57(10):2445-2455 (2025)

Allele T

OR 0.07

p 3.0e-86

N 450,015

Large GWAS

multi-ancestry

cholesteryl esters in HDL measurement

Zoodsma M et al. “A genetic map of human metabolism across the allele frequency spectrum.” Nature Genetics 57(10):2445-2455 (2025)

Allele T

OR 0.07

p 6.0e-86

N 450,015

Large GWAS

multi-ancestry

apolipoprotein A 1 measurement

Richardson TG et al. “Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: A multivariable Mendelian randomisation analysis.” Plos Medicine 17(3):e1003062 (2020)

Allele C

OR 0.07

p 5.0e-82

N 393,193

Large GWAS

European

Zoodsma M et al. “A genetic map of human metabolism across the allele frequency spectrum.” Nature Genetics 57(10):2445-2455 (2025)

Allele C

OR 0.06

p 4.0e-73

N 450,015

Large GWAS

multi-ancestry

Loya H et al. “A scalable variational inference approach for increased mixed-model association power.” Nature Genetics 57(2):461-468 (2025)

Allele C

OR 0.06

p 2.0e-79

N 394,642

Large GWAS

European

Richardson TG et al. “Characterising metabolomic signatures of lipid-modifying therapies through drug target mendelian randomisation.” Plos Biology 20(2):e3001547 (2022)

Allele C

OR 0.07

p 7.0e-22

N 115,082

Large GWAS

European

Davyson E et al. “Metabolomic Investigation of Major Depressive Disorder Identifies a Potentially Causal Association With Polyunsaturated Fatty Acids.” Biological Psychiatry 94(8):630-639 (2023)

Allele C

OR 0.07

p 2.0e-16

N 88,329

Large GWAS

European

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ClinVar annotation

Benign★★★☆

5 submitters5 publications

Inflammatory bowel disease 1 (IBD1)

View on ClinVar →

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Research that mentions this SNP (4)

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Association of the LINGO2-related SNP rs10968576 with body mass in a cohort of elderly Swedes

AssociationN=949Mathias Rask-Andersen et al.(2015)· Molecular Genetics and Genomics

Association study of 35 GWAS-identified body mass SNPs in 949 elderly Swedish participants (mean age 70-75 years). Significant association found between rs10968576 (LINGO2, intron 4) and BMI with a larger effect size (β = 0.69 kg/m²) than reported in younger populations, suggesting age-specific genetic effects on body mass in the elderly.

Traits studied:Body Mass IndexBody adiposityObesityOverweight

PubMed DOI Metascience Observatory

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Common obesity risk alleles in childhood attention‐deficit/hyperactivity disorder

AssociationN=4,415Özgür Albayrak et al.(2013)· American Journal of Medical Genetics Part B: Neuropsychiatric Genetics

This study examined whether 32 obesity-associated genetic risk alleles are associated with childhood ADHD in a German GWAS sample (495 cases, 1,300 controls) and a meta-analysis (2,064 trios, 896 cases, 2,455 controls). The obesity risk allele G at rs206936 in NUDT3 was associated with increased ADHD risk (OR=1.39, P=3.4×10⁻⁴), and rs6497416 in GPRC5B showed association with ADHD in the meta-analysis (P=7.2×10⁻⁴). Several obesity-related SNPs were associated with ADHD endophenotypes including inattention and hyperactivity/impulsivity.

Traits studied:Attention-deficit/hyperactivity disorder (ADHD)Body mass index (BMI)Hyperactivity/impulsivityInattentionObesity

PubMed DOI Metascience Observatory

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Associations of polymorphisms in the genes of FGFR2, FGF1, and RBFOX2 with breast cancer risk by estrogen/progesterone receptor status

AssociationN=2,416Yu‐Ling Cen et al.(2013)· Molecular Carcinogenesis

A hospital-based case-control study in rural and urban India (1,204 cases; 1,212 controls) examined genetic and lifestyle risk factors for breast cancer. Four SNPs in FGFR2 (rs1219648, rs2420946, rs2981575, rs2981582) showed positive associations with breast cancer (ORs 1.32-1.47). Additional SNPs in obesity and metabolic genes (rs374748 in FBN2, rs2922763 in HNF4G, rs2116830 in KCNMA1, rs11121832 in MTHFR, rs16886165 in MAP3K1, rs11594610 in TCF7L2, rs2274459 in MLN) were associated with increased breast cancer risk. Waist-to-hip ratio ≥0.95 showed strong association (OR 3.78; 95% CI 2.92-4.89), and women living first 20 years in rural areas showed protective effect (OR 0.77).

Traits studied:Breast cancerBreast cancer riskER+/PR+ breast cancerER/PR negative breast cancerTriple negative breast cancer

PubMed DOI Metascience Observatory

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Association studies of novel obesity-related gene variants with quantitative metabolic phenotypes in a population-based sample of 6,039 Danish individuals

AssociationN=6,039Burgdorf KS et al.(2012)· Diabetologia

This association study investigates 18 BMI-associated and 14 WHR-associated gene variants identified by prior GWAS in 6,039 Danish individuals from the Inter99 cohort. The study found that QPCTL rs2287019 C allele was associated with increased insulinogenic index (7.4%, p=4.0×10⁻⁷) and disposition index (5.6%, p=6.4×10⁻⁵), while LRP1B rs2890652 C allele was associated with insulin resistance (3.3% increase in HOMA-IR, p=0.0011). For WHR variants, LYPLAL1/SLC30A10 rs4846567 G allele carriers showed improved insulin sensitivity (5.2% lower HOMA-IR in women, p=0.00086), whereas VEGFA rs6905288 A allele carriers showed insulin resistance in women (3.7% increase in HOMA-IR, p=0.00036).

Traits studied:BIGTT-AIR (Beta Cell Function)Body Mass Index (BMI)Disposition IndexFasting Plasma GlucoseFasting Serum InsulinHOMA-IR (Insulin Resistance)Insulinogenic IndexMatsuda Index (Insulin Sensitivity)Waist-Hip Ratio (WHR)

PubMed DOI Metascience Observatory

About SLC39A8

This gene encodes a member of the SLC39 family of solute-carrier genes, which show structural characteristics of zinc transporters. The encoded protein is glycosylated and found in the plasma membrane and mitochondria, and functions in the cellular import of zinc at the onset of inflammation. It is also thought to be the primary transporter of the toxic cation cadmium, which is found in cigarette smoke. Multiple transcript variants encoding different isoforms have been found for this gene. Additional alternatively spliced transcript variants of this gene have been described, but their full-length nature is not known. [provided by RefSeq, Oct 2008]

rs13107325

GWAS Catalog Trait Associations (357)

cortical thickness

level of syndecan-1 in blood

liver fat measurement, liver disease biomarker

level of SLIT and NTRK-like protein 2 in blood serum

hematocrit

level of carboxypeptidase Q in blood

diastolic blood pressure

HDL cholesterol change measurement

cholesteryl esters in HDL measurement

apolipoprotein A 1 measurement

ClinVar annotation

Research that mentions this SNP (4)

About SLC39A8

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