scoreInvHap: Inversion genotyping for genome-wide association studies

Ruiz‐Arenas, Carlos; Cáceres, Alejandro; López-Sánchez, Marcos; Tolosana, Ignacio; Pérez-Jurado, Luís A.; González, Juan R.

doi:10.1371/journal.pgen.1008203

Cited by 19 publications

(54 citation statements)

References 28 publications

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“…Here, we show that at least two common polymorphic inversions at 8p23.1 and 16q11.2 offer a genetic substrate to some widely observed co-morbidities of obesity, such as those with diabetes, hypertension, asthma and depression. The analysis of UKB dataset validated the estimated inversion allele frequencies in European populations reported in our recent analyses 15 .…”

Section: Discussionsupporting

confidence: 76%

“…The results in obesity and diabetes provide, to our knowledge, the first example of a mechanism in which cis-regulatory SNPs are separated from their target genes by inversion breakpoints. These findings, set a new framework for future studies which is now accessible to the research community thanks to inversion genotyping tools such as our scoreInvHap method 15 .…”

Section: Discussionmentioning

confidence: 91%

“…Variants with an imputation R2 < 0.3 were discarded. Genotype probabilities were used to call inversions using scoreInvHap 15 which is available at Bioconductor. scoreInvHap computes a similarity score between an individual's alleles and the reference alleles in each chromosomal status.…”

Section: Snp Imputation and Inversion Callingmentioning

confidence: 99%

“…Characteristics of the 21 genomic inversions. The table shows the coordinates, SNP content, size, and inversion frequency obtained from 1000 Genomes as described in Ruiz-Arenas et al 15 , the UKB and European regions (north, center and south) using the regions described in the POPRES dataset (see Methods). The p-value correspond to a trend test to assess north-south linear association (in bold those significant at 5% level).…”

Section: Figure 2 Validation Of Positive Associations Between the Inmentioning

confidence: 99%

“…However, the overall impact of polymorphic inversions on human health remains largely unknown because they are difficult to genotype in large cohorts. We overcame this limitation by recently reporting a subset of 20 inversions that can be genotyped with SNP array data as they are old in origin, low or not recurrent and frequent in the population 15 . We have also included an additional inversion in our catalog, 16p11.2, previously validated and genotyped in diverse populations 11 .…”

Section: Introductionmentioning

confidence: 99%

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Polymorphic inversions underlie the shared genetic susceptibility to prevalent common diseases

González

2019

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The burden of several common diseases including obesity, diabetes, hypertension, asthma, and depression is increasing in most world populations. However, the mechanisms underlying the numerous epidemiological and genetic correlations among these disorders remain largely unknown. We investigated whether common polymorphic inversions underlie the shared genetic influence of these disorders. We performed the largest inversion association analysis to date, including 21 inversions and 25 obesity-related traits, on a total of 408,898 Europeans, and validated the results in 67,299 independent individuals. Seven inversions were associated with multiple diseases while inversions at 8p23.1, 16p11.2 and 11q13.2 were strongly associated with the co-occurrence of obesity with other common diseases. Transcriptome analysis across numerous tissues revealed strong candidate genes of obesity-related traits. Analyses in human pancreatic islets indicated the potential mechanism of inversions in the susceptibility of diabetes by disrupting the cis-regulatory effect of SNPs from their target genes. Our data underscore the role of inversions as major genetic contributors to the joint susceptibility to common complex diseases.

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Section: Discussionsupporting

confidence: 76%

Section: Discussionmentioning

confidence: 91%

Section: Snp Imputation and Inversion Callingmentioning

confidence: 99%

Section: Figure 2 Validation Of Positive Associations Between the Inmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Polymorphic inversions underlie the shared genetic susceptibility to prevalent common diseases

González

2019

Preprint

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A survey of current methods to detect and genotype inversions

2022

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Polymorphic inversions are ubiquitous in humans and they have been linked to both adaptation and disease. Following their discovery in Drosophila more than a century ago, inversions have proved to be more elusive than other structural variants. A wide variety of methods for the detection and genotyping of inversions have recently been developed: multiple techniques based on selective amplification by PCR, short‐ and long‐read sequencing approaches, principal component analysis of small variant haplotypes, template strand sequencing, optical mapping, and various genome assembly methods. Many methods apply complex wet lab protocols or increasingly refined bioinformatic analyses. This review is an attempt to provide a practical summary and comparison of the methods that are in current use, with a focus on metrics such as the maximum size of segmental duplications at inversion breakpoints that each method can tolerate, the size range of inversions that they recover, their throughput, and whether the locations of putative inversions must be known beforehand.

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