Minimal observed impact of HLA genotype on hospitalization and severity of SARS-CoV-2 infection

HLA is a critical component of the viral antigen presentation pathway. We investigated the relationship between severity of SARS-CoV-2 disease and HLA type in 3,235 individuals with confirmed SARS-CoV-2 infection. We found only the DPB1 locus to be associated with the binary outcome of whether an individual developed any COVID-19 symptoms. The number of peptides predicted to bind to an HLA allele had no significant relationship with disease severity both when stratifying individuals by ancestry or age and in a pooled analysis. Age, BMI, asthma status, and autoimmune disorder status were predictive of severity across multiple age and individual ancestry stratificiations. Overall, at the population level, we found HLA type is significantly less predictive of COVID-19 disease severity than certain demographic factors and clinical comorbidities.

Inference of recent admixture using genotype data

The inference of biogeographic ancestry (BGA) has become a focus of forensic genetics. Mis-inference of BGA can have profound unwanted consequences for investigations and society. We show that recent admixture can lead to misclassification and erroneous inference of ancestry proportions, using state of the art analysis tools with (i) simulations, (ii) 1000 genomes project data, and (iii) two individuals analyzed using the ForenSeq DNA Signature Prep Kit. Subsequently, we extend existing tools for estimation of individual ancestry (IA) by allowing for different IA in both parents, leading to estimates of parental individual ancestry (PIA), and a statistical test for recent admixture. Estimation of PIA outperforms IA in most scenarios of recent admixture. Furthermore, additional information about parental ancestry can be acquired with PIA that may guide casework.

New perspectives on multilocus ancestry informativeness

We present an axiomatic approach for multilocus informativeness measures for determining the amount of information that a set of polymorphic genetic markers provides about individual ancestry. We then reveal several surprising properties of a decision-theoretic based measure that is consistent with the set of proposed criteria for multilocus informativeness. In particular, these properties highlight the interplay between information originating from population priors and the information extractable from the population genetic variants. This analysis then reveals a certain deficiency of mutual information based multilocus informativeness measures when such population priors are incorporated. Finally, we analyse and quantify the inevitable inherent decrease in informativeness due to learning from finite population samples.

EVALUACIÓN DEL NÚMERO MÍNIMO DE MARCADORES PARA ESTIMAR ANCESTRÍA INDIVIDUAL EN UNA MUESTRA DE LA POBLACIÓN ARGENTINA / Evaluation of the minimum number of markers for individual ancestry estimation in an Argentinean population sample

<p>La estimación de ancestría individual posee gran relevancia en el estudio de la composición poblacional en regiones como Sudamérica, que han atravesado intensos procesos de mestizaje, lo que también tiene implicancia en ciencias de la salud. Debido a esto, es importante conocer los factores que influyen en la confiabilidad de los resultados obtenidos. En este trabajo se evalúa el número mínimo de marcadores informativos de ancestría (AIMs) a partir del cual las estimaciones resultarían aceptables. Se toma como ejemplo el cálculo en individuos provenientes de una muestra poblacional de diferentes regiones de Argentina. Considerando un modelo de tres componentes (nativo americano, euroasiático y subsahariano), se calculó la ancestría de 441 individuos utilizando 10, 20, 30 y 50 AIMs. Los resultados indican que el número de marcadores influye sobre la estimación de ancestría y su precisión aumenta al incrementarse la cantidad de AIMs. Al comparar con las estimaciones obtenidas en un trabajo previo a partir de 99 AIMs, se observó que para el componente minoritario (en este caso subsahariano) se obtiene una buena correlación utilizando al menos 30 marcadores. Se concluye que es necesario considerar en los estudios de ancestría individual el número de marcadores, su capacidad informativa y las características de la población bajo estudio.</p><p><br /><strong>Abstract</strong></p><p><br />Estimation of individual ancestry has great relevance when studying population composition in regions like South America, where intensive admixture processes have occurred, being also important in biomedical sciences. For that reason, it is important to assess the factors that may affect the reliability of results. In this work, we investigate the minimum number of ancestry informative markers (AIMs) for obtaining acceptable estimations of ancestry. As an example, we take individuals from a population sample of different Argentinean regions. Considering a three component model (Native American, Eurasian and Sub-Saharan), we calculated ancestry of 441 individuals using 10, 20, 30 and 50 AIMs. The results indicate that the number of markers affects ancestry estimation and its accuracy increases with AIMs number. When compared to previous estimations obtained from 99 AIMs, the result shows that at least 30 markers are needed to achieve good correlation values for the minority component (Sub-Saharan in this case). For individual ancestry studies, we suggest to take into account not only the number of markers, but also its informativeness and the background of the studied population.</p>

Efficient analysis of large datasets and sex bias with ADMIXTURE

Background: A number of large genomic datasets are being generated for studies of human ancestry and diseases. The ADMIXTURE program is commonly used to infer individual ancestry from genomic data. Results: We describe two improvements to the ADMIXTURE software. The first enables ADMIXTURE to infer ancestry for a new set of individuals using cluster allele frequencies from a reference set of individuals. Using data from the 1000 Genomes Project, we show that this allows ADMIXTURE to infer ancestry for 10,920 individuals in a few hours (a 5x speedup). This mode also allows ADMIXTURE to correctly estimate individual ancestry and allele frequencies from a set of related individuals. The second modification allows ADMIXTURE to correctly handle X-chromosome (and other haploid) data from both males and females. We demonstrate increased power to detect sex-biased admixture in African-American individuals from the 1000 Genomes project using this extension. Conclusions: These modifications make ADMIXTURE more efficient and versatile, allowing users to extract more information from large genomic datasets.