Silmara da Costa Pereira Cestari, Marcela da Costa Pereira Cestari, Gabriela Franco Marques, Ivana Lirio, Reinaldo Tovo, Ilana Cruz Silva Labriola
Cutaneous manifestations of COVID-19 patients in a Hospital in São Paulo, Brazil, and global literature review
Anais brasileiros de dermatologia
polygenic prediction complex trait phenotypes become important human genetics especially context precision medicine recently morgante et al introduced mrmash flexible computationally efficient method models multiple phenotypes jointly leverages sharing effects across phenotypes improve prediction accuracy however drawback mrmash requires individuallevel data often publicly available work introduce mrmashrss extension mrmash model requires summary statistics genomewide association studies gwas linkage disequilibrium ld estimates reference panel using summary data achieve twin goal increasing applicability mrmash model data sets publicly available making scalable biobanksize data simulations show mrmashrss competitive often outperforms current stateoftheart methods single multiphenotype polygenic prediction variety scenarios differ pattern effect sharing across phenotypes number phenotypes number causal variants genomic heritability also present real data analysis 16 blood cell phenotypes uk biobank showing mrmashrss achieves higher prediction accuracy competing methods majority traits especially data smaller sample size polygenic prediction refers use individuals genetic information ie genotypes predict traits ie phenotypes often medical relevance known phenotypes related affected genotypes case possible improve accuracy predictions using methods model multiple phenotypes jointly account shared effects mrmash recently developed multiphenotype method learn effects shared shown improve prediction however mrmash requires large data sets genetic phenotypic information collected individual level data often unavailable due privacy concerns difficult work due computational resources needed analyze data size work extends mrmash require summary statistics genomewide association studies instead individuallevel data usually publicly available addition computations using summary statistics depend sample size making newly developed mrmashrss scalable extremely large data sets using simulations real data analysis show method competitive methods polygenic prediction