Genetic analyses of diverse populations improves discovery for complex traits

Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry1–3. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific4–10. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations11,12. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States—where minority populations have a disproportionately higher burden of chronic conditions13—the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities. © 2019, The Author(s), under exclusive licence to Springer Nature Limited

Um problema de corte com padrões compartimentados

Neste artigo apresentaremos a aplicação do Problema da Mochila Compartimentada (PMC) no Problema de Corte de Bobinas de Aço (PCBA), que é um problema de corte em duas etapas com restrições especiais de agrupamento dos itens. O PMC consiste em construir compartimentos de capacidades desconhecidas em uma mochila de capacidade conhecida, tendo em vista que os itens de interesse estão agrupados em subconjuntos, de modo que, itens de um agrupamento não podem ser combinados com itens de outro. Para entender melhor o PMC admita que a mochila de um alpinista deve ser composta por um número ideal de compartimentos com itens de quatro categorias (remédios, alimentos, ferramentas, roupas), porém, itens de categorias distintas não podem ser combinados para formar um mesmo compartimento, além do mais, são desconhecidas as capacidades ideais de cada compartimento da mochila.<br>In this paper we will present the application of the Compartmented Knapsack Problem (CKP) in the Cut Problem of Steel Rolls (CPSR), that it is a problem of cut in two stages with restrictions special of grouping of items. The CKP consists of constructing compartments of unknown capacities in a knapsack of known capacity, in view of that items of interest is grouped in subgroups, in mode that, items of a grouping cannot be matched with items of another one. To understand the CKP more good it admits that the knapsack of a alpinist must be composite for an ideal number of compartments with items of four categories (remedies, foods, tools, clothes), however, items of distinct categories cannot be matched to form one same compartment, in addition, is unknown the ideal capacities of each compartment of the knapsack