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Islands have been considered natural laboratories to study evolutionary processes. Ibiza is a small island in Spain whose population stands out from other Spanish populations due to its particular demographic and historical processes. War, famine, and several epidemics have af fected Ibizans, and these phenomena could have left signatures of positive selection in their genomes. Here, we used three different methodologies to detect positive selection: The Popu lation Branch Statistic (PBS), the Integrated Haplotype Score (iHS), and the Cross-Population Extended Haplotype Homozygosity (XP-EHH). We used a sliding windows approach to control for spurious results. The candidate windows for selection were chosen using three different criteria for each test: maximum and mean score within each window, and proportion of high scores in each window. Only the windows being simultaneously on the top of each of the three criteria were selected for annotation and enrichment analyses. The most common traits asso ciated with the SNPs present in the candidate windows were blood function, cardiovascular diseases, body mass measures, lipid metabolism, renal function, and skin diseases. We sug gest some hypotheses to explain the selection signatures related to some of these traits and some recommendations for further studies to overcome the present research's limitations.

Proprietary genetic datasets are valuable for boosting the statistical power of genome-wide association studies (GWASs), but their use can restrict investigators from publicly sharing the resulting summary statistics. Although researchers can resort to sharing down-sampled versions that exclude restricted data, down-sampling reduces power and might change the genetic etiology of the phenotype being studied. These problems are further complicated when using multivariate GWAS methods, such as genomic structural equation modeling (Genomic SEM), that model genetic correlations across multiple traits. Here, we propose a systematic approach to assess the comparability of GWAS summary statistics that include versus exclude restricted data. Illustrating this approach with a multivariate GWAS of an externalizing factor, we assessed the impact of down-sampling on (1) the strength of the genetic signal in univariate GWASs, (2) the factor loadings and model fit in multivariate Genomic SEM, (3) the strength of the genetic signal at the factor level, (4) insights from gene-property analyses, (5) the pattern of genetic correlations with other traits, and (6) polygenic score analyses in independent samples. For the externalizing GWAS, down-sampling resulted in a loss of genetic signal and fewer genome-wide significant loci, while the factor loadings and model fit, gene-property analyses, genetic correlations, and polygenic score analyses are robust. Given the importance of data sharing for the advancement of open science, we recommend that investigators who share down-sampled summary statistics report these analyses as accompanying documentation to support other researchers' use of the summary statistics.