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Bone & Joint Research
Vol. 5, Issue 5 | Pages 169 - 174
1 May 2016
Wang Y Chu M Rong J Xing B Zhu L Zhao Y Zhuang X Jiang L

Objectives. Previous genome-wide association studies (GWAS) have reported significant association of the single nucleotide polymorphism (SNP) rs8044769 in the fat mass and obesity-associated gene (FTO) with osteoarthritis (OA) risk in European populations. However, these findings have not been confirmed in Chinese populations. Methods. We systematically genotyped rs8044769 and evaluated the association between the genetic variants and OA risk in a case-controlled study including 196 OA cases and 442 controls in a northern Chinese population. Genotyping was performed using the Sequenom MassARRAY iPLEX platform. Results. We found that the variant T allele of rs8044769 showed no significant association of OA risk (p = 0.791), or association with body mass index (BMI) (pmeta = 0.786) in an additive genetic model. However, we detected a significant interaction between rs8044769 genotypes and BMI on OA risk (p = 0.037), as well as a borderline interaction between rs8044769 genotypes and age on OA risk (p = 0.062). Conclusions. Our findings indicate that rs8044769 in the FTO gene may not modify individual susceptibility to OA or increased BMI in the Chinese population. Further studies are warranted to validate and extend our findings. Cite this article: Prof L. Jiang. No association of the single nucleotide polymorphism rs8044769 in the fat mass and obesity-associated gene with knee osteoarthritis risk and body mass index: A population-based study in China. Bone Joint Res 2016;5:169–174. DOI: 10.1302/2046-3758.55.2000589


Bone & Joint Open
Vol. 2, Issue 6 | Pages 414 - 421
1 Jun 2021
Kim SK Nguyen C Avins AL Abrams GD

Aims

The aim of this study was to screen the entire genome for genetic markers associated with risk for anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) injury.

Methods

Genome-wide association (GWA) analyses were performed using data from the Kaiser Permanente Research Board (KPRB) and the UK Biobank. ACL and PCL injury cases were identified based on electronic health records from KPRB and the UK Biobank. GWA analyses from both cohorts were tested for ACL and PCL injury using a logistic regression model adjusting for sex, height, weight, age at enrolment, and race/ethnicity using allele counts for single nucleotide polymorphisms (SNPs). The data from the two GWA studies were combined in a meta-analysis. Candidate genes previously reported to show an association with ACL injury in athletes were also tested for association from the meta-analysis data from the KPRB and the UK Biobank GWA studies.