Integrating, summarizing and visualizing GWAS-hits and human diversity with DANCE (Disease-ANCEstry networks)

From diagnostic testing to precision medicine: the evolving role of genomics in cardiac channelopathies and cardiomyopathies in children

Pediatric sudden cardiac death (SCD) is the sudden unexpected death of a child or adolescent due to a presumed cardiac etiology. Heritable causes of pediatric SCD are predominantly cardiomyopathies and cardiac ion channelopathies. This review illustrates recent advances in determining the genetic cause of established and emerging channelopathies and cardiomyopathies, and how broader genomic sequencing is uncovering complex interactions between genetic architecture and disease manifestation. We discuss innovative models and experimental platforms for resolving the variant of uncertain significance as both the variants and genes associated with disease continue to evolve. Finally, we highlight the growing problem of incidentally identified variants in cardiovascular disease-causing genes and review innovative methods to determining whether these variants may ultimately result in penetrant disease. Overall, we seek to illustrate both the promise and inherent challenges in bridging the traditional role for genetics in diagnosing cardiomyopathies and channelopathies to one of true risk-predictive precision medicine.

Common BMI and diabetes-related genetic variants: A pilot study among indigenous people in the Brazilian Amazon

This study was carried out to investigate the frequency of genetic variants related to body mass index (BMI) and type 2 diabetes (T2D) and evaluating the potential impact of risk alleles on susceptibility to these disorders in six indigenous peoples from Brazilian Amazon region. The majority of Fst values for pairwise population comparisons among the indigenous groups are low or moderate. The indigenous people show high values of differentiation with Africans, Europeans and Southeast Asians and moderate values with East Asian and American populations, as expected. The allelic frequencies among indigenous indicate that the majority of associations observed with T2D in continental populations can be replicated in native Amazonians. The genetic risk scores calculated for T2D in indigenous are high and similar to those calculated for Americans and East Asians, while the estimates obtained for obesity are low, probably due to the low frequencies of the risk allele of the FTO gene found in our samples. ADRB3-rs4994 and ABCC8-rs1799854 genes showed a significant association with BMI and waist circumference, and the KCNJ11-rs5219 gene with hyperglycemia. These results emphasize the importance of knowing the genetic variability underlying complex genetic diseases in indigenous peoples and the search for particular or rare variants.

AmazonForest: In Silico Metaprediction of Pathogenic Variants

Simple Summary

ClinVar is a valuable platform that stores a large set of relevant genetic associations with complex phenotypes. However, the functional impact of a partial set of such associations remains misinterpreted, due to the presence of variants with uncertain significance or with conflicting pathogenicity interpretations. To fill this gap, we present AmazonForest: a metaprediction model based on Random Forest for pathogenicity prediction. AmazonForest was used to reclassify a set of ∼101,000 variants that were predicted as having high pathogenic probability. AmazonForest is available as a web tool with a simple web interface, and also as an R object for pathogenicity predictions.

Abstract

ClinVar is a web platform that stores ∼789,000 genetic associations with complex diseases. A partial set of these cataloged genetic associations has challenged clinicians and geneticists, often leading to conflicting interpretations or uncertain clinical impact significance. In this study, we addressed the (re)classification of genetic variants by AmazonForest, which is a random-forest-based pathogenicity metaprediction model that works by combining functional impact data from eight prediction tools. We evaluated the performance of representation learning algorithms such as autoencoders to propose a better strategy. All metaprediction models were trained with ClinVar data, and genetic variants were annotated with eight functional impact predictors cataloged with SnpEff/SnpSift. AmazonForest implements the best random forest model with a one hot data-encoding strategy, which shows an Area Under ROC Curve of ≥0.93. AmazonForest was employed for pathogenicity prediction of a set of ∼101,000 genetic variants of uncertain significance or conflict of interpretation. Our findings revealed ∼24,000 variants with high pathogenic probability (RFprob≥0.9). In addition, we show results for Alzheimer’s Disease as a demonstration of its application in clinical interpretation of genetic variants in complex diseases. Lastly, AmazonForest is available as a web tool and R object that can be loaded to perform pathogenicity predictions.

Mitochondria in tumour progression: a network of mtDNA variants in different types of cancer

Background

Mitochondrial participation in tumorigenesis and metastasis has been studied for many years, but several aspects of this mechanism remain unclear, such as the association of mitochondrial DNA (mtDNA) with different cancers. Here, based on two independent datasets, we modelled an mtDNA mutation-cancer network by systematic integrative analysis including 37 cancer types to identify the mitochondrial variants found in common among them.

Results

Our network showed mtDNA associations between gastric cancer and other cancer types, particularly kidney, liver, and prostate cancers, which is suggestive of a potential role of such variants in the metastatic processes among these cancer types. A graph-based interactive web tool was made available at www2.lghm.ufpa.br/mtdna. We also highlighted that most shared variants were in the MT-ND4, MT-ND5 and D-loop, and that some of these variants were nonsynonymous, indicating a special importance of these variants and regions regarding cancer progression, involving genomic and epigenomic alterations.

Conclusions

This study reinforces the importance of studying mtDNA in cancer and offers new perspectives on the potential involvement of different mitochondrial variants in cancer development and metastasis.

Admixture/fine-mapping in Brazilians reveals a West African associated potential regulatory variant (rs114066381) with a strong female-specific effect on body mass and fat mass indexes

BACKGROUND/OBJECTIVES

Admixed populations are a resource to study the global genetic architecture of complex phenotypes, which is critical, considering that non-European populations are severely underrepresented in genomic studies. Here, we study the genetic architecture of BMI in children, young adults, and elderly individuals from the admixed population of Brazil.

SUBJECTS/METHODS

Leveraging admixture in Brazilians, whose chromosomes are mosaics of fragments of Native American, European, and African origins, we used genome-wide data to perform admixture mapping/fine-mapping of body mass index (BMI) in three Brazilian population-based cohorts from Northeast (Salvador), Southeast (Bambuí), and South (Pelotas).

RESULTS

We found significant associations with African-associated alleles in children from Salvador (PALD1 and ZMIZ1 genes), and in young adults from Pelotas (NOD2 and MTUS2 genes). More importantly, in Pelotas, rs114066381, mapped in a potential regulatory region, is significantly associated only in females (p = 2.76e-06). This variant is rare in Europeans but with frequencies of ~3% in West Africa and has a strong female-specific effect (95% CI: 2.32-5.65 kg/m² per each A allele). We confirmed this sex-specific association and replicated its strong effect for an adjusted fat mass index in the same Pelotas cohort, and for BMI in another Brazilian cohort from São Paulo (Southeast Brazil). A meta-analysis confirmed the significant association. Remarkably, we observed that while the frequency of rs114066381-A allele ranges from 0.8 to 2.1% in the studied populations, it attains ~9% among women with morbid obesity from Pelotas, São Paulo, and Bambuí. The effect size of rs114066381 is at least five times higher than the FTO SNPs rs9939609 and rs1558902, already emblematic for their high effects.

CONCLUSIONS

We identified six candidate SNPs associated with BMI. rs114066381 stands out for its high effect that was replicated and its high frequency in women with morbid obesity. We demonstrate how admixed populations are a source of new relevant phenotype-associated genetic variants.

The genetic structure and adaptation of Andean highlanders and Amazonians are influenced by the interplay between geography and culture

Western South America was one of the worldwide cradles of civilization. The well-known Inca Empire was the tip of the iceberg of an evolutionary process that started 11,000 to 14,000 years ago. Genetic data from 18 Peruvian populations reveal the following: 1) The between-population homogenization of the central southern Andes and its differentiation with respect to Amazonian populations of similar latitudes do not extend northward. Instead, longitudinal gene flow between the northern coast of Peru, Andes, and Amazonia accompanied cultural and socioeconomic interactions revealed by archeology. This pattern recapitulates the environmental and cultural differentiation between the fertile north, where altitudes are lower, and the arid south, where the Andes are higher, acting as a genetic barrier between the sharply different environments of the Andes and Amazonia. 2) The genetic homogenization between the populations of the arid Andes is not only due to migrations during the Inca Empire or the subsequent colonial period. It started at least during the earlier expansion of the Wari Empire (600 to 1,000 years before present). 3) This demographic history allowed for cases of positive natural selection in the high and arid Andes vs. the low Amazon tropical forest: in the Andes, a putative enhancer in HAND2-AS1 (heart and neural crest derivatives expressed 2 antisense RNA1, a noncoding gene related to cardiovascular function) and rs269868-C/Ser1067 in DUOX2 (dual oxidase 2, related to thyroid function and innate immunity) genes and, in the Amazon, the gene encoding for the CD45 protein, essential for antigen recognition by T and B lymphocytes in viral-host interaction.

Human ancestry indentification under resource constraints -- what can one chromosome tell us about human biogeographical ancestry?

BACKGROUND

While continental level ancestry is relatively simple using genomic information, distinguishing between individuals from closely associated sub-populations (e.g., from the same continent) is still a difficult challenge.

METHODS

We study the problem of predicting human biogeographical ancestry from genomic data under resource constraints. In particular, we focus on the case where the analysis is constrained to using single nucleotide polymorphisms (SNPs) from just one chromosome. We propose methods to construct such ancestry informative SNP panels using correlation-based and outlier-based methods.

RESULTS

We accessed the performance of the proposed SNP panels derived from just one chromosome, using data from the 1000 Genome Project, Phase 3. For continental-level ancestry classification, we achieved an overall classification rate of 96.75% using 206 single nucleotide polymorphisms (SNPs). For sub-population level ancestry prediction, we achieved an average pairwise binary classification rates as follows: subpopulations in Europe: 76.6% (58 SNPs); Africa: 87.02% (87 SNPs); East Asia: 73.30% (68 SNPs); South Asia: 81.14% (75 SNPs); America: 85.85% (68 SNPs).

CONCLUSION

Our results demonstrate that one single chromosome (in particular, Chromosome 1), if carefully analyzed, could hold enough information for accurate prediction of human biogeographical ancestry. This has significant implications in terms of the computational resources required for analysis of ancestry, and in the applications of such analyses, such as in studies of genetic diseases, forensics, and soft biometrics.