{'fraction_non_alphanumeric': 0.030269058295964126, 'fraction_numerical': 0.008221225710014948, 'mean_word_length': 4.9621380846325165, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Linkage-Disequilibrium-BasedBinningMisleadsthe Interpretation of Genome-wide Association Studies

To the Editor: In their report, Christoforou et al. demonstrate the effect that linkage-disequilibrium (LD)-based binning has on the interpretation of genome-wide association studies (GWASs) and conclude that ‘‘ignoring LD can result in the misinterpretation of the GWAS findings and have an impact on subsequent genetic and functional studies.’’ Although this conclusion is true and trivial, we argue that their proposed LD-based binning approach uses the LD information incorrectly and will lead to increased type 1 error (resulting in the misinterpretation of GWAS findings) and will hence have a negative impact on subsequent genetic and functional studies. The LDbased binning approach assigns SNPs to genes or bins by using pairwise LD data calculated from reference data, such as that from the 1000 Genomes Project or HapMap or other user-provided data. It can assign a SNP to more than one gene. After the bins have been defined, standard gene-based approaches, such as taking the minimum SNP p value in a bin after the application of a modified Sidak’s correction, are used. Thus, the essence of this method is to include as ‘‘hits’’ not only those genes in (or around) which extreme p values for SNPs are found but also those genes that include SNPs found to be in significant LD with them. This approach will result in increased correlations among genes because a SNP’s p value can be repeatedly represented in different genes. Christoforou et al. assessed their method of LD binning with respect to (1) gene converge, (2) the interpretation of findings, and (3) pairwise concordance of the findings among three GWASs. We first summarize their results for (1) and (3). On comparing LD binning with positional binning, their Tables 1 and 2 clearly show an increase in the number of post-quality-control-binned SNPs and a decrease in the number of SNPs binned to only one gene, indicating an increased number of SNPs assigned to more than one gene. This automatically increases the correlations among genes in any subsequent pathway analysis. For the genotyped Welcome Trust Case Control Consortium (WTCCC) SNPs in their Table 1, the number of SNPs binned to more than one gene increases from 16% with positional binning to 36% with LD-based binning; similar results are seen for the Norwegian Thematically Organized Psychosis bipolar disorder (BP) GWAS and German BP GWAS data. For imputed genotype data, although the increase is not as large as for actually genotyped data, the absolute percentage is much larger—it increases from 55.5% with positional binning to 63%