Cindy Boer

184 | Chapter 4.1 Statistical independence To define independent signals for each osteoarthritis phenotype, we used clumping function in PLINK 1.9[82] with the following parameters: (a) significance threshold for index variants: p-value>1x10 -07 , (b) LD threshold for clumping: 0.10, and (c) physical distance threshold for clumping: 1Mb (2Mb window around the index variant). LD cal- culations were based on the full UK Biobank imputed set. To test that the index var- iants defined by clumping were statistically independent, we performed an approxi- mate stepwise model-selection procedure, as implemented by COJO in GCTA[83, 84]. A signal in a region was defined as independent if its P-value of association in the step- wise regression was less than the adjusted genome-wide significant threshold (p-val- ue<1.3x10 -08 ). To define independent signals across the 11 osteoarthritis phenotypes, we performed reciprocal approximate conditional analyses, as implemented by COJO in GCTA[83, 84], of each independent variant of one osteoarthritis phenotype condi- tioned on each independent variant of the other osteoarthritis phenotypes within 1-Mb region. Two signals were considered dependent if the p-value for either signal conditioned on the other was either ≥1x10 -07 , or attenuated by at least two orders of magnitude. Among dependent variants, the one with the lowest P-value was classified as independent. Using an approximate conditional and joint multiple-SNP analysis, as implemented by COJO in GCTA, we investigated the statistical independence between index signals per osteoarthritis phenotype and previously reported osteoarthritis var- iants within a 1-Mb region. The index variant was classified as a new association if it had a conditional p-value≤1x10 -07 or the P-value difference between conditional and unconditional analysis increased by more than two orders of magnitude. Index variants were classified as known if they have previously been reported or the association signal disappeared after conditioning on the variant of a previously reported locus. Genetic signals across osteoarthritis phenotypes Results from all independent lead SNVs (n=100) across all osteoarthritis phenotypes were extracted from the full meta-analysis results. All OR were calculated on the mi- nor allele (allele frequency < 50%), and SNVs with a MAF<1% were excluded (n=6). For all the remaining SNVs (n=94) the OR for each osteoarthritis phenotype GWAS was plotted in a heatmap, together with the corresponding association p-value ( Figure 2 ). SNVs with MAF<1% were plotted in a separate heatmap ( Figure 2 ). All figures were plotted using R and adjusted for publication quality using Adobe illustrator. We also cre- ated three classification groups: 0=Weight-bearing joints only (hip and/or knee, knee, hip, total joint replacement, total knee replacement, total hip replacement and spine), 1=Both, 2=Non-weight bearing joints only (hand, finger, thumb). Osteoarthritis at any