Genome‐wide Interaction and Stratified Study with Smoking Identifies Association of APAF1 and MIXL1/LIN9 with Alzheimer’s Disease
Authors: Dacey Ryan , Han Xudong , Moore Mackenzie R , Chung Jaeyoon , Durape Shruti , Rosenthaler Max , Uretsky Madeline , Abdolmohammadi Bobak , Lee Annie J. , Brickman Adam M. , Hohman Timothy J. , Cuccaro Michael L. , Bennett David A. , Crane Paul K. , Kamboh M. Ilyas , Kukull Walter A. , Au Rhoda , Haines Jonathan L. , Pericak‐Vance Margaret A. , Schellenberg Gerard D. , Mayeux Richard , Lunetta Kathryn L. , Farrer Lindsay A. , Mez Jesse 
Published: January 2025

Background

Alzheimer’s disease (AD) has both genetic and environmental risk factors. Gene‐environment interaction may help explain some missing heritability. There is strong evidence for cigarette smoking as a risk factor for AD. To identify genetic‐smoking‐related associations with AD, we conducted a genome‐wide association study (GWAS) assessing a SNP‐smoking interaction and stratified analysis by smoking status.

Method

Lifetime smoking data were available and analyzed among 22,030 non‐Hispanic White (NHW; 8,232 cases; 13,798 controls) and 3,126 African American (AFA; 921 cases; 2,205 controls) participants from the AD Genetic Consortium and the Framingham Heart Study. “Ever smoking” status was considered as a dichotomous exposure, defined by current smoking status or past history of smoking. Across 35 datasets, we conducted GWAS with two approaches: inclusion of a SNP‐by‐smoking interaction term and stratification by smoking status (12,080 smokers, 13,428 non‐smokers). MAGEE was used to estimate SNP‐by‐smoking interaction effects and SAIGE was used to estimate SNP effects in stratified analysis. Age, sex, and principal components for population structure were included as covariates. METAL was used for inverse‐variance weighted meta‐analysis across datasets to estimate within‐ and cross‐ancestry effects.

Result

The stratified analysis identified a genome‐wide significant association among smokers in APAF1 on chromosome 12 (top SNP: rs12368451; smokers: MAF = 0.44, p = 2.2 × 10‐8, OR = 1.20; non‐smokers: MAF = 0.44, p = 0.97, OR = 1.00). Effects were present in both ancestry groups (NHW: MAF = 0.45, p = 6.1 × 10‐6, OR = 1.16; AFA: MAF = 0.35, p = 6.6 × 10‐5, OR = 1.46). APAF1 has been linked to gene‐smoking interaction for non‐AD related outcomes. A neighboring gene, ANKS1B, is highly expressed in the brain, interacts with amyloid‐b precursor protein, and has shown GWAS signals for smoking initiation and cognitive ability. We also identified a genome‐wide significant SNP‐by‐smoking interaction in the MIXL1/LIN9 region on chromosome 1 (top SNP: rs1091961, MAF = 0.35, p = 4.9 × 10‐8, βSNP*smoking = 0.24; smokers: OR = 1.12, p = 0.0006; non‐smokers: OR = 0.89, p = 0.0001). Within LIN9, several SNPs in linkage disequilibrium with rs1091961 have shown sub‐genome wide association with nicotine dependence.

Conclusion

In this gene‐smoking interaction and smoking‐stratified GWAS of AD, we identified two promising loci. These findings highlight the strength of utilizing cross‐ancestry datasets and considering both genetic and environmental factors together towards a personalized medicine approach to AD.