HLA imputation in an admixed population: An assessment of the 1000 Genomes data as a training set

Nunes, Kelly; Zheng, Xiuwen; Torres, Margareth; Moraes, M. E.; Piovezan, Bruno Z.; Pontes, Gerlândia N.; Kimura, Lilian; Carnavalli, Juliana Emilia Prior; Mingroni-Netto, Regina Célia; Meyer, Diogo

doi:10.1016/j.humimm.2015.11.004

Cited by 24 publications

(18 citation statements)

References 22 publications

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“…DNA samples were extracted from peripheral blood and genotyped with the SNP array Axiom Genome-Wide Human Origins (~600,000 SNPs) according to the manufacturer's standards (Affymetrix/Thermo-Fisher Scientific). We analyzed DNA samples from 541 individuals ( S1 Table ) from the Ribeira River Valley, 365 of them having already been genotyped in a previous study [ 22 ] and the remaining 176 samples of this study. The research was approved by the Ethics Committee, Instituto de Ciências Biomédicas, Universidade de São Paulo (111/CEP, Feb. 14 th 2001), and an informed consent was obtained from all its participants or their legal guardians.…”

Section: Methodsmentioning

confidence: 99%

Inbreeding estimates in human populations: Applying new approaches to an admixed Brazilian isolate

et al. 2018

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The analysis of genomic data (~400,000 autosomal SNPs) enabled the reliable estimation of inbreeding levels in a sample of 541 individuals sampled from a highly admixed Brazilian population isolate (an African-derived quilombo in the State of São Paulo). To achieve this, different methods were applied to the joint information of two sets of markers (one complete and another excluding loci in patent linkage disequilibrium). This strategy allowed the detection and exclusion of markers that biased the estimation of the average population inbreeding coefficient (Wright’s fixation index FIS), which value was eventually estimated as around 1% using any of the methods we applied. Quilombo demographic inferences were made by analyzing the structure of runs of homozygosity (ROH), which were adapted to cope with a highly admixed population with a complex foundation history. Our results suggest that the amount of ROH <2Mb of admixed populations should be somehow proportional to the genetic contribution from each parental population.

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Section: Methodsmentioning

confidence: 99%

Inbreeding estimates in human populations: Applying new approaches to an admixed Brazilian isolate

et al. 2018

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“…When interest is in identifying novel variants (Klitz et al 2012 ), deep sequencing associated with mapping methods that account for variation will be required. In addition, imputation accuracy depends on the availability of reference panels with shared ancestry to the target population, representing an important challenge for studies of highly admixed populations with ancestral components which are relatively poorly studied (Levin et al 2014 ; Nunes et al 2016 ).…”

Section: Hla Variation In the Age Of Genome Sequencingmentioning

confidence: 99%

A genomic perspective on HLA evolution

et al. 2017

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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.

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“…Exploration of HLA alleles, haplotypes, supertypes and lineages as susceptibility markers has not been given much importance in GWAS. It is practically impossible to run association studies for all HLA alleles defined by DNA sequencing at the highest resolution ( n > 16,000 as of March 2017), but algorithms have been developed to predict four‐digit HLA alleles from HLA tag SNP data (Karnes et al., ), which has been used successfully (Neville et al., ), and also work in admixed populations (Nunes et al., ). This approach is useful, but there are many other levels of functional MHC specificities as discussed before.…”

Section: Statistical Analysis Of the Xhla Region Gwas Datamentioning

confidence: 99%

What has GWAS done for HLA and disease associations?

Kennedy

Özbek

Dorak

2017

Int J Immunogenetics

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The major histocompatibility complex (MHC) is located in chromosome 6p21 and contains crucial regulators of immune response, including human leucocyte antigen (HLA) genes, alongside other genes with nonimmunological roles. More recently, a repertoire of noncoding RNA genes, including expressed pseudogenes, has also been identified. The MHC is the most gene dense and most polymorphic part of the human genome. The region exhibits haplotype-specific linkage disequilibrium patterns, contains the strongest cis- and trans-eQTLs/meQTLs in the genome and is known as a hot spot for disease associations. Another layer of complexity is provided to the region by the extreme structural variation and copy number variations. While the HLA-B gene has the highest number of alleles, the HLA-DR/DQ subregion is structurally most variable and shows the highest number of disease associations. Reliance on a single reference sequence has complicated the design, execution and analysis of GWAS for the MHC region and not infrequently, the MHC region has even been excluded from the analysis of GWAS data. Here, we contrast features of the MHC region with the rest of the genome and highlight its complexities, including its functional polymorphisms beyond those determined by single nucleotide polymorphisms or single amino acid residues. One of the several issues with customary GWAS analysis is that it does not address this additional layer of polymorphisms unique to the MHC region. We highlight alternative approaches that may assist with the analysis of GWAS data from the MHC region and unravel associations with all functional polymorphisms beyond single SNPs. We suggest that despite already showing the highest number of disease associations, the true extent of the involvement of the MHC region in disease genetics may not have been uncovered.

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HLA imputation in an admixed population: An assessment of the 1000 Genomes data as a training set

Cited by 24 publications

References 22 publications

Inbreeding estimates in human populations: Applying new approaches to an admixed Brazilian isolate

Inbreeding estimates in human populations: Applying new approaches to an admixed Brazilian isolate

A genomic perspective on HLA evolution

What has GWAS done for HLA and disease associations?

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