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Made available in DSpace on 2022-04-29T08:32:46Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-01-01 National Institutes of Health Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Meeting the challenges brought by the COVID-19 pandemic requires an interdisciplinary approach. In this context, integrating knowledge of immune function with an understanding of how genetic variation influences the nature of immunity is a key challenge. Immunogenetics can help explain the heterogeneity of susceptibility and protection to the viral infection and disease progression. Here, we review the knowledge developed so far, discussing fundamental genes for triggering the innate and adaptive immune responses associated with a viral infection, especially with the SARS-CoV-2 mechanisms. We emphasize the role of the HLA and KIR genes, discussing what has been uncovered about their role in COVID-19 and addressing methodological challenges of studying these genes. Finally, we comment on questions that arise when studying admixed populations, highlighting the case of Brazil. We argue that the interplay between immunology and an understanding of genetic associations can provide an important contribution to our knowledge of COVID-19. Universidade de São Paulo Departamento de Genética e Biologia Evolutiva University of California UCSF Weill Institute for Neurosciences Department of Neurology Universidade Federal do Paraná Departamento de Genética Universidade Estadual Paulista Faculdade de Medicina de Botucatu Departamento de Patologia Universidade Estadual Paulista Faculdade de Medicina de Botucatu Departamento de Patologia National Institutes of Health: 1R01NS102153 CNPq: 308783/2019-0 National Institutes of Health: R01 GM075091

BACKGROUND When the mother's DNA profile is not available for paternity testing, there is a smaller probability that a locus will exclude an alleged father. This study aims to evaluate the risk of potential false paternity inclusions in motherless cases. STUDY DESIGN AND METHODS More than 20 000 duos were generated by removing the maternal genotypes from exclusion trios. After recalculating paternity in these duos, any found inclusions would be false. RESULTS The use of an appropriate number of loci, mutation model, and mutation rates to analyze motherless paternity cases was robust against false inclusions. A single potential false inclusion was observed in a case wherein kinship plays a role. This result highlights the importance of testing the mother when available and of obtaining information on family circumstances for the proper handling of cases involving related individuals. CONCLUSION The guidelines we used here were sufficient to avoid false inclusions in a data set of more than 20 000 motherless cases.

The plethora of RNA-seq data which have been generated in the recent years constitutes an attractive resource to investigate HLA variation and its relationship with normal and disease phenotypes, such as cancer. However, next generation sequencing (NGS) brings new challenges to HLA analysis because of the mapping bias introduced by aligning short reads originated from polymorphic genes to a single reference genome. Here we describe HLApers, a pipeline which adapts widely used tools for analysis of standard RNA-seq data to infer HLA genotypes and estimate expression. By generating reliable expression estimates for each HLA allele that an individual carries, HLApers allows a better understanding of the relationship between HLA alleles and phenotypes manifested by an individual.

Several decades of research have convincingly shown that classical human leukocyte antigen (HLA) loci bear signatures of natural selection. Despite this conclusion, many questions remain regarding the type of selective regime acting on these loci, the time frame at which selection acts, and the functional connections between genetic variability and natural selection. In this review, we argue that genomic datasets, in particular those generated by next-generation sequencing (NGS) at the population scale, are transforming our understanding of HLA evolution. We show that genomewide data can be used to perform robust and powerful tests for selection, capable of identifying both positive and balancing selection at HLA genes. Importantly, these tests have shown that natural selection can be identified at both recent and ancient timescales. We discuss how findings from genomewide association studies impact the evolutionary study of HLA genes, and how genomic data can be used to survey adaptive change involving interaction at multiple loci. We discuss the methodological developments which are necessary to correctly interpret genomic analyses involving the HLA region. These developments include adapting the NGS analysis framework so as to deal with the highly polymorphic HLA data, as well as developing tools and theory to search for signatures of selection, quantify differentiation, and measure admixture within the HLA region. Finally, we show that high throughput analysis of molecular phenotypes for HLA genes—namely transcription levels—is now a feasible approach and can add another dimension to the study of genetic variation.

The plethora of RNA-seq data which have been generated in the recent years constitutes an attractive resource to investigate HLA variation and its relationship with normal and disease phenotypes, such as cancer. However, next generation sequencing (NGS) brings new challenges to HLA analysis because of the mapping bias introduced by aligning short reads originated from polymorphic genes to a single reference genome. Here we describe HLApers, a pipeline which adapts widely used tools for analysis of standard RNA-seq data to infer HLA genotypes and estimate expression. By generating reliable expression estimates for each HLA allele that an individual carries, HLApers allows a better understanding of the relationship between HLA alleles and phenotypes manifested by an individual.

Next-generation sequencing (NGS) technologies have become the standard for data generation in studies of population genomics, as the 1000 Genomes Project (1000G). However, these techniques are known to be problematic when applied to highly polymorphic genomic regions, such as the human leukocyte antigen (HLA) genes. Because accurate genotype calls and allele frequency estimations are crucial to population genomics analyses, it is important to assess the reliability of NGS data. Here, we evaluate the reliability of genotype calls and allele frequency estimates of the single-nucleotide polymorphisms (SNPs) reported by 1000G (phase I) at five HLA genes (HLA-A, -B, -C, -DRB1, and -DQB1). We take advantage of the availability of HLA Sanger sequencing of 930 of the 1092 1000G samples and use this as a gold standard to benchmark the 1000G data. We document that 18.6% of SNP genotype calls in HLA genes are incorrect and that allele frequencies are estimated with an error greater than ±0.1 at approximately 25% of the SNPs in HLA genes. We found a bias toward overestimation of reference allele frequency for the 1000G data, indicating mapping bias is an important cause of error in frequency estimation in this dataset. We provide a list of sites that have poor allele frequency estimates and discuss the outcomes of including those sites in different kinds of analyses. Because the HLA region is the most polymorphic in the human genome, our results provide insights into the challenges of using of NGS data at other genomic regions of high diversity.

We have reported the allele frequencies of 15 STR loci, including the original 13 CODIS core loci, in over 100,000 Brazilian individuals. A new CODIS core loci has been proposed, but the recently established Brazilian Integrated Network of DNA Databases made a decision in 2010 to postpone the implementation of this new set of loci due to the lack of allele frequency data for the Brazilian population. We aimed to report allele frequencies of 20 loci, estimated from 96,400 Brazilian individuals undergoing paternity testing during 2011-2013. The percentage of missing data was less than 0.6% for all loci, except for CSF1PO (3.15%) and D7S820 (2.5%). The dropout rates estimated by the MicroDrop software were 0.013 for CSF1PO, 0.000037 for D7S820 and less than 0.000001 for other loci. Small missing data percentages and dropout rates reflect the high quality of the data.

The HLA (Human Leukocyte Antigens) genes are well-documented targets of balancing selection, and variation at these loci is associated with many disease phenotypes. Variation in expression levels also influences disease susceptibility and resistance, but little information exists about the regulation and population-level patterns of expression. This results from the difficulty in mapping short reads originated from these highly polymorphic loci, and in accounting for the existence of several paralogues. We developed a computational pipeline to accurately estimate expression for HLA genes based on RNA-seq, improving both locus-level and allele-level estimates. First, reads are aligned to all known HLA sequences in order to infer HLA genotypes, then quantification of expression is carried out using a personalized index. We use simulations to show that expression estimates obtained in this way are not biased due to divergence from the reference genome. We applied our pipeline to the GEUVADIS dataset, and compared the quantifications to those obtained with reference transcriptome. Although the personalized pipeline recovers more reads, we found that using the reference transcriptome produces estimates similar to the personalized pipeline (r ≥ 0.87) with the exception of HLA-DQA1. We describe the impact of the HLA-personalized approach on downstream analyses for nine classical HLA loci (HLA-A, HLA-C, HLA-B, HLA-DRA, HLA-DRB1, HLA-DQA1, HLA-DQB1, HLA-DPA1, HLA-DPB1). Although the influence of the HLA-personalized approach is modest for eQTL mapping, the p-values and the causality of the eQTLs obtained are better than when the reference transcriptome is used. We investigate how the eQTLs we identified explain variation in expression among lineages of HLA alleles. Finally, we discuss possible causes underlying differences between expression estimates obtained using RNA-seq, antibody-based approaches and qPCR., Author summary The level at which a gene is expressed can have important influence on the phenotype of an organism, including its predisposition to develop diseases. One way to estimate gene expression is by quantifying the abundance of RNA. RNA-seq has become the method of choice to provide such estimates at the genomewide scale. However, the application of RNA-seq to HLA genes —key players in the immune adaptive response— has remained a rarely explored approach. This is due to the problem of mapping bias, which causes deficient read alignment at genes which are very polymorphic and different from the reference genome. This has motivated approaches that replace the single reference genome with personalized sequences, comprised of the individual’s specific HLA genotype. Here we explore the use of computational frameworks to obtain reliable expression levels for HLA genes from RNA-seq datasets. We present a pipeline in which the quantification of HLA expression is carried out using methods which account for HLA diversity, avoiding the biases of standard approaches. We then evaluate the impact of this form of quantifying HLA expression on downstream analyses. The pipeline also allows us to integrate information on eQTLs with expression levels at the HLA allele-level, which can help disentangle different contributions to disease phenotypes and help understand the regulatory architecture at the HLA region.

The presence of TP53 gene mutations in breast cancer has been associated with worse prognosis. These mutations interfere with the ability of the p53 protein, a transcription factor, to regulate the expression of target genes. Unlike the wild-type protein, which is rapidly degraded in cells, mutated forms have increased half-life and accumulate in tumor cells. Immunohistochemistry (IHC) is widely used in Brazil in the determination of breast cancer patients' prognosis. However, this technique is not able to detect many altered forms of the p53 protein (false-negative results) and readily detects the accumulation of wild-type p53 (false-positive results) that is associated with non-tumoral processes. For these reasons, we have set out to compare the efficiency of IHC with a molecular technique that detects gene variations at the DNA level in the evaluation of Brazilian patients with sporadic breast cancer. We have used denaturing gradient gel electrophoresis (DGGE) to study the TP53 status in 45 tumors, finding 26 allelic variants, most of them located in exon 4. Comparing the two techniques, IHC showed a false-negative rate of 64% and a false-positive rate of 50%. These results confirm the inability of IHC to correctly detect TP53 status, reason because it should not be routinely used to establish prognosis of breast cancer patients in Brazilian Pathology Laboratories. We recommend the utilization of a screening method, such as DGGE, followed by sequencing of altered exonic fragments to correctly detect TP53 gene variants and establish the prognosis of breast cancer patients.

Large forensic databases provide an opportunity to compare observed empirical rates of genotype matching with those expected under forensic genetic models. A number of researchers have taken advantage of this opportunity to validate some forensic genetic approaches, particularly to ensure that estimated rates of genotype matching between unrelated individuals are indeed slight overestimates of those observed. However, these studies have also revealed systematic error trends in genotype probability estimates. In this analysis, we investigate these error trends and show how they result from inappropriate implementation of the Balding–Nichols model in the context of database-wide matching. Specifically, we show that in addition to accounting for increased allelic matching between individuals with recent shared ancestry, studies must account for relatively decreased allelic matching between individuals with more ancient shared ancestry.

Large forensic databases provide an opportunity to compare observed empirical rates of genotype matching with those expected under forensic genetic models. A number of researchers have taken advantage of this opportunity to validate some forensic genetic approaches, particularly to ensure that estimated rates of genotype matching between unrelated individuals are indeed slight overestimates of those observed. However, these studies have also revealed systematic error trends in genotype probability estimates. In this analysis, we investigate these error trends and show how they result from inappropriate implementation of the Balding-Nichols model in the context of database-wide matching. Specifically, we show that in addition to accounting for increased allelic matching between individuals with recent shared ancestry, studies must account for relatively decreased allelic matching between individuals with more ancient shared ancestry.

DNA isolation is the initial step of most genetic studies, and ideally it should use a reliable and non-invasive method. Buccal samples are adequate for such purposes, being painless, easy to collect and a very reliable DNA source. FTA ® Elute Cards are relatively new on the market and are designed for rapid blood DNA extraction, in which DNA is solubilized in water, instead of attached to the paper matrix. In this study, we sought to test if FTA ® Elute Cards are suitable for buccal DNA extraction. Furthermore, several time and temperature conditions were analyzed in order to determine the best concentration/time ratio. Twenty-five different conditions were tested in quadruplicate and extracted DNA was quantified by real-time PCR.

The Brazilian population is one of the most heterogeneous populations of the world, formed mainly by an admixture of European, African and Native American populations. Brazil is the fifth largest country in the world (8,511,960 km(2)), being divided into five geographical regions. This study provides population genetic data of up to 137,161 unrelated individuals representing the entire Brazilian territory. Allelic frequencies and other population data analysis are reported for the 15 autosomal STR loci included in the PowerPlex®16 kit (Promega Corporation, Madison, WI, USA). In order to guarantee that individuals were not related, we have considered only F1 data from couples undergoing paternity testing. The number of individuals genotyped for each locus was: CSF1PO (113,526); D3S1358 (135,133); D5S818 (135,181); D7S820 (137,136); D8S1179 (134,211); D13S317 (137,161); D16S539 (136,942); D18S51 (136,739); D21S11 (130,014); FGA (135,839); Penta D (110,333); Penta E (128,055); TH01 (112,695); TPOX (123,102); vWA (127,415). Allele sizes ranged from 1 to 48.2. Statistic parameters (PD, PIC and Ho; considering values ≥0.75) suggest that markers D13S317, D16S539, D18S51, D21S11, D7S820, D8S1179, Penta D, Penta E, TH01, FGA and vWA were more informative for genetic identification purposes in the Brazilian population.