Polymorphic Inversions Underlie the Shared Genetic Susceptibility of Obesity-Related Diseases

Characterization of non-coding variants associated with transcription-factor binding through ATAC-seq-defined footprint QTLs in liver

Non-coding variants discovered by genome-wide association studies (GWASs) are enriched in regulatory elements harboring transcription factor (TF) binding motifs, strongly suggesting a connection between disease association and the disruption of cis-regulatory sequences. Occupancy of a TF inside a region of open chromatin can be detected in ATAC-seq where bound TFs block the transposase Tn5, leaving a pattern of relatively depleted Tn5 insertions known as a "footprint." Here, we sought to identify variants associated with TF binding, or "footprint quantitative trait loci" (fpQTLs), in ATAC-seq data generated from 170 human liver samples. We used computational tools to scan the ATAC-seq reads to quantify TF binding likelihood as "footprint scores" at variants derived from whole-genome sequencing generated in the same samples. We tested for association between genotype and footprint score and observed 809 fpQTLs associated with footprint-inferred TF binding (FDR < 5%). Given that Tn5 insertion sites are measured with base-pair resolution, we show that fpQTLs can aid GWAS and QTL fine-mapping by precisely pinpointing TF activity within broad trait-associated loci where the underlying causal variant is unknown. Liver fpQTLs were strongly enriched across ChIP-seq peaks, liver expression QTLs (eQTLs), and liver-related GWAS loci, and their inferred effect on TF binding was concordant with their effect on underlying sequence motifs in 78% of cases. We conclude that fpQTLs can reveal causal GWAS variants, define the role of TF binding-site disruption in complex traits, and provide functional insights into non-coding variants, ultimately informing novel treatments for common diseases.

Investigation of a pathogenic inversion in UNC13D and comprehensive analysis of chromosomal inversions across diverse datasets

Inversions are known contributors to the pathogenesis of genetic diseases. Identifying inversions poses significant challenges, making it one of the most demanding structural variants (SVs) to detect and interpret. Recent advancements in sequencing technologies and the development of publicly available SV datasets have substantially enhanced our capability to explore inversions. However, a cross-comparison in those datasets remains unexplored. In this study, we reported a proband with familial hemophagocytic lymphohistiocytosis type-3 carrying a splicing variant (c.1389+1G>A) in trans with an inversion present in 0.006345% of individuals in gnomAD (v4.0) that disrupts UNC13D. Based on this result, we investigate the features of potentially pathogenic inversions in gnomAD which revealed 98.9% of them are rare and disrupt 5% of protein-coding genes associated with a phenotype in OMIM. We then conducted a comparative analysis of additional public datasets, including DGV, 1KGP, and two recent studies from the Human Genome Structural Variation Consortium which revealed common and dataset-specific inversion characteristics suggesting methodology detection biases. Next, we investigated the genetic features of inversions disrupting the protein-coding genes. Notably, we found that the majority of protein-coding genes in OMIM disrupted by inversions are associated with autosomal recessive phenotypes supporting the hypothesis that inversions in trans with other variants are potential hidden causes of monogenic diseases. This effort aims to fill the gap in our understanding of the molecular characteristics of inversions with low frequency in the population and highlight the importance of identifying them in rare disease studies.

Structural variation, selection, and diversification of the NPIP gene family from the human pangenome

The NPIP (nuclear pore interacting protein) gene family has expanded to high copy number in humans and African apes where it has been subject to an excess of amino acid replacement consistent with positive selection (1). Due to the limitations of short-read sequencing, NPIP human genetic diversity has been poorly understood. Using highly accurate assemblies generated from long-read sequencing as part of the human pangenome, we completely characterize 169 human haplotypes (4,665 NPIP paralogs and alleles). Of the 28 NPIP paralogs, just three (NPIPB2, B11, and B14) are fixed at a single copy, and only a single locus, B2, shows no structural variation. Four NPIP paralogs map to large segmental duplication blocks that mediate polymorphic inversions (355 kbp-1.6 Mbp) corresponding to microdeletions associated with developmental delay and autism. Haplotype-based tests of positive selection and selective sweeps identify two paralogs, B9 and B15, within the top percentile for both tests. Using full-length cDNA data from 101 tissue/cell types, we construct paralog-specific gene models and show that 56% (31/55 most abundant isoforms) have not been previously described in RefSeq. We define six distinct translation start sites and other protein structural features that distinguish paralogs, including a variable number tandem repeat that encodes a beta helix of variable size that emerged ~3.1 million years ago in human evolution. Among the 28 NPIP paralogs, we identify distinct tissue and developmental patterns of expression with only a few maintaining the ancestral testis-enriched expression. A subset of paralogs (NPIPA1, A5, A6-9, B3-5, and B12/B13) show increased brain expression. Our results suggest ongoing positive selection in the human population and rapid diversification of NPIP gene models.

Investigation of a Pathogenic Inversion in UNC13D and Comprehensive Analysis of Chromosomal Inversions Across Diverse Datasets

Inversions are known contributors to the pathogenesis of genetic diseases. Identifying inversions poses significant challenges, making it one of the most demanding structural variants (SVs) to detect and interpret. Recent advancements in sequencing technologies and the development of publicly available SV datasets have substantially enhanced our capability to explore inversions. However, a cross-comparison in those datasets remains unexplored. In this study, we reported a proband with familial hemophagocytic lymphohistiocytosis type-3 carrying c.1389+1G>A in trans with NC_000017.11:75576992_75829587inv disrupting UNC13D , an inversion present in 0.006345% of individuals in gnomAD(v4.0). Based on this result, we investigate the features of potentially pathogenic inversions in public datasets. 98.9% of inversions are rare in gnomAD, and they disrupt 5% of protein-coding genes associated with a phenotype in OMIM. We then conducted a comparative analysis of the datasets, including gnomAD, DGV, and 1KGP, and two recent studies from the Human Genome Structural Variation Consortium revealed common and dataset-specific inversion characteristics suggesting methodology detection biases. Next, we investigated the genetic features of inversions disrupting the protein-coding genes by classifying the intersections between them into three categories. We found that most of the protein-coding genes in OMIM disrupted by inversions are associated with autosomal recessive phenotypes regardless of categories supporting the hypothesis that inversions in trans with other variants are hidden causes of monogenic diseases. This effort aims to fill the gap in our understanding of the molecular characteristics of inversions with low frequency in the population and highlight the importance of identifying them in rare disease studies.

Characterization of non-coding variants associated with transcription factor binding through ATAC-seq-defined footprint QTLs in liver

Non-coding variants discovered by genome-wide association studies (GWAS) are enriched in regulatory elements harboring transcription factor (TF) binding motifs, strongly suggesting a connection between disease association and the disruption of cis-regulatory sequences. Occupancy of a TF inside a region of open chromatin can be detected in ATAC-seq where bound TFs block the transposase Tn5, leaving a pattern of relatively depleted Tn5 insertions known as a "footprint". Here, we sought to identify variants associated with TF-binding, or "footprint quantitative trait loci" (fpQTLs) in ATAC-seq data generated from 170 human liver samples. We used computational tools to scan the ATAC-seq reads to quantify TF binding likelihood as "footprint scores" at variants derived from whole genome sequencing generated in the same samples. We tested for association between genotype and footprint score and observed 693 fpQTLs associated with footprint-inferred TF binding (FDR < 5%). Given that Tn5 insertion sites are measured with base-pair resolution, we show that fpQTLs can aid GWAS and QTL fine-mapping by precisely pinpointing TF activity within broad trait-associated loci where the underlying causal variant is unknown. Liver fpQTLs were strongly enriched across ChIP-seq peaks, liver expression QTLs (eQTLs), and liver-related GWAS loci, and their inferred effect on TF binding was concordant with their effect on underlying sequence motifs in 80% of cases. We conclude that fpQTLs can reveal causal GWAS variants, define the role of TF binding site disruption in disease and provide functional insights into non-coding variants, ultimately informing novel treatments for common diseases.

Asthma-Genomic Advances Toward Risk Prediction

Asthma is a common complex airway disease whose prediction of disease risk and most severe outcomes is crucial in clinical practice for adequate clinical management. This review discusses the latest findings in asthma genomics and current obstacles faced in moving forward to translational medicine. While genome-wide association studies have provided valuable insights into the genetic basis of asthma, there are challenges that must be addressed to improve disease prediction, such as the need for diverse representation, the functional characterization of genetic variants identified, variant selection for genetic testing, and refining prediction models using polygenic risk scores.

Genetic architecture distinguishes tinnitus from hearing loss

Tinnitus is a heritable, highly prevalent auditory disorder treated by multiple medical specialties. Previous GWAS indicated high genetic correlations between tinnitus and hearing loss, with little indication of differentiating signals. We present a GWAS meta-analysis, triple previous sample sizes, and expand to non-European ancestries. GWAS in 596,905 Million Veteran Program subjects identified 39 tinnitus loci, and identified genes related to neuronal synapses and cochlear structural support. Applying state-of-the-art analytic tools, we confirm a large number of shared variants, but also a distinct genetic architecture of tinnitus, with higher polygenicity and large proportion of variants not shared with hearing difficulty. Tissue-expression analysis for tinnitus infers broad enrichment across most brain tissues, in contrast to hearing difficulty. Finally, tinnitus is not only correlated with hearing loss, but also with a spectrum of psychiatric disorders, providing potential new avenues for treatment. This study establishes tinnitus as a distinct disorder separate from hearing difficulties.

Mechanisms and treatment of obesity-related hypertension-Part 1: Mechanisms

The prevalence of obesity has tripled over the past five decades. Obesity, especially visceral obesity, is closely related to hypertension, increasing the risk of primary (essential) hypertension by 65%-75%. Hypertension is a major risk factor for cardiovascular disease, the leading cause of death worldwide, and its prevalence is rapidly increasing following the pandemic rise in obesity. Although the causal relationship between obesity and high blood pressure (BP) is well established, the detailed mechanisms for such association are still under research. For more than 30 years sympathetic nervous system (SNS) and kidney sodium reabsorption activation, secondary to insulin resistance and compensatory hyperinsulinemia, have been considered as primary mediators of elevated BP in obesity. However, experimental and clinical data show that severe insulin resistance and hyperinsulinemia can occur in the absence of elevated BP, challenging the causal relationship between insulin resistance and hyperinsulinemia as the key factor linking obesity to hypertension. The purpose of Part 1 of this review is to summarize the available data on recently emerging mechanisms believed to contribute to obesity-related hypertension through increased sodium reabsorption and volume expansion, such as: physical compression of the kidney by perirenal/intrarenal fat and overactivation of the systemic/renal SNS and the renin-angiotensin-aldosterone system. The role of hyperleptinemia, impaired chemoreceptor and baroreceptor reflexes, and increased perivascular fat is also discussed. Specifically targeting these mechanisms may pave the way for a new therapeutic intervention in the treatment of obesity-related hypertension in the context of 'precision medicine' principles, which will be discussed in Part 2.

Ecology, the pace-of-life, epistatic selection and the maintenance of genetic variation in life-history genes

Evolutionary genetics has long struggled with understanding how functional genes under selection remain polymorphic in natural populations. Taking as a starting point that natural selection is ultimately a manifestation of ecological processes, we spotlight an underemphasized and potentially ubiquitous ecological effect that may have fundamental effects on the maintenance of genetic variation. Negative frequency dependency is a well-established emergent property of density dependence in ecology, because the relative profitability of different modes of exploiting or utilizing limiting resources tends to be inversely proportional to their frequency in a population. We suggest that this may often generate negative frequency-dependent selection (NFDS) on major effect loci that affect rate-dependent physiological processes, such as metabolic rate, that are phenotypically manifested as polymorphism in pace-of-life syndromes. When such a locus under NFDS shows stable intermediate frequency polymorphism, this should generate epistatic selection potentially involving large numbers of loci with more minor effects on life-history (LH) traits. When alternative alleles at such loci show sign epistasis with a major effect locus, this associative NFDS will promote the maintenance of polygenic variation in LH genes. We provide examples of the kind of major effect loci that could be involved and suggest empirical avenues that may better inform us on the importance and reach of this process.

Chromosomal inversion polymorphisms shape human brain morphology

The impact of chromosomal inversions on human brain morphology remains underexplored. We studied 35 common inversions classified from genotypes of 33,018 adults with European ancestry. The inversions at 2p22.3, 16p11.2, and 17q21.31 reach genome-wide significance, followed by 8p23.1 and 6p21.33, in their association with cortical and subcortical morphology. The 17q21.31, 8p23.1, and 16p11.2 regions comprise the LRRC37, OR7E, and NPIP duplicated gene families. We find the 17q21.31 MAPT inversion region, known for harboring neurological risk, to be the most salient locus among common variants for shaping and patterning the cortex. Overall, we observe the inverted orientations decreasing brain size, with the exception that the 2p22.3 inversion is associated with increased subcortical volume and the 8p23.1 inversion is associated with increased motor cortex. These significant inversions are in the genomic hotspots of neuropsychiatric loci. Our findings are generalizable to 3,472 children and demonstrate inversions as essential genetic variation to understand human brain phenotypes.

A survey of current methods to detect and genotype inversions

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. This article is protected by copyright. All rights reserved.

Genomic architecture and functional effects of potential human inversion supergenes

Supergenes are involved in adaptation in multiple organisms, but they are little known in humans. Genomic inversions are the most common mechanism of supergene generation and maintenance. Here, we review the information about two large inversions that are the best examples of potential human supergenes. In addition, we do an integrative analysis of the newest data to understand better their functional effects and underlying genetic changes. We have found that the highly divergent haplotypes of the 17q21.31 inversion of approximately 1.5 Mb have multiple phenotypic associations, with consistent effects in brain-related traits, red and white blood cells, lung function, male and female characteristics and disease risk. By combining gene expression and nucleotide variation data, we also analysed the molecular differences between haplotypes, including gene duplications, amino acid substitutions and regulatory changes, and identify CRHR1, KANLS1 and MAPT as good candidates to be responsible for these phenotypes. The situation is more complex for the 8p23.1 inversion, where there is no clear genetic differentiation. However, the inversion is associated with several related phenotypes and gene expression differences that could be linked to haplotypes specific of one orientation. Our work, therefore, contributes to the characterization of both exceptional variants and illustrates the important role of inversions. This article is part of the theme issue 'Genomic architecture of supergenes: causes and evolutionary consequences'.

The early-life exposome modulates the effect of polymorphic inversions on DNA methylation

Polymorphic genomic inversions are chromosomal variants with intrinsic variability that play important roles in evolution, environmental adaptation, and complex traits. We investigated the DNA methylation patterns of three common human inversions, at 8p23.1, 16p11.2, and 17q21.31 in 1,009 blood samples from children from the Human Early Life Exposome (HELIX) project and in 39 prenatal heart tissue samples. We found inversion-state specific methylation patterns within and nearby flanking each inversion region in both datasets. Additionally, numerous inversion-exposure interactions on methylation levels were identified from early-life exposome data comprising 64 exposures. For instance, children homozygous at inv-8p23.1 and higher meat intake were more susceptible to TDH hypermethylation (P = 3.8 × 10⁻²²); being the inversion, exposure, and gene known risk factors for adult obesity. Inv-8p23.1 associated hypermethylation of GATA4 was also detected across numerous exposures. Our data suggests that the pleiotropic influence of inversions during development and lifetime could be substantially mediated by allele-specific methylation patterns which can be modulated by the exposome.

Analysis of the relationship between presence of common DNA sequence inversions and DNA methylation patterns suggests a role for environmental exposures (such as food intake) in mediating inversion state-specific methylation patterns.

Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure

Single nucleotide polymorphisms (SNPs) are the most abundant type of genomic variation and the most accessible to genotype in large cohorts. However, they individually explain a small proportion of phenotypic differences between individuals. Ancestry, collective SNP effects, structural variants, somatic mutations or even differences in historic recombination can potentially explain a high percentage of genomic divergence. These genetic differences can be infrequent or laborious to characterize; however, many of them leave distinctive marks on the SNPs across the genome allowing their study in large population samples. Consequently, several methods have been developed over the last decade to detect and analyze different genomic structures using SNP arrays, to complement genome-wide association studies and determine the contribution of these structures to explain the phenotypic differences between individuals. We present an up-to-date collection of available bioinformatics tools that can be used to extract relevant genomic information from SNP array data including population structure and ancestry; polygenic risk scores; identity-by-descent fragments; linkage disequilibrium; heritability and structural variants such as inversions, copy number variants, genetic mosaicisms and recombination histories. From a systematic review of recently published applications of the methods, we describe the main characteristics of R packages, command-line tools and desktop applications, both free and commercial, to help make the most of a large amount of publicly available SNP data.

GCAT|Panel, a comprehensive structural variant haplotype map of the Iberian population from high-coverage whole-genome sequencing

The combined analysis of haplotype panels with phenotype clinical cohorts is a common approach to explore the genetic architecture of human diseases. However, genetic studies are mainly based on single nucleotide variants (SNVs) and small insertions and deletions (indels). Here, we contribute to fill this gap by generating a dense haplotype map focused on the identification, characterization, and phasing of structural variants (SVs). By integrating multiple variant identification methods and Logistic Regression Models (LRMs), we present a catalogue of 35 431 441 variants, including 89 178 SVs (≥50 bp), 30 325 064 SNVs and 5 017 199 indels, across 785 Illumina high coverage (30x) whole-genomes from the Iberian GCAT Cohort, containing a median of 3.52M SNVs, 606 336 indels and 6393 SVs per individual. The haplotype panel is able to impute up to 14 360 728 SNVs/indels and 23 179 SVs, showing a 2.7-fold increase for SVs compared with available genetic variation panels. The value of this panel for SVs analysis is shown through an imputed rare Alu element located in a new locus associated with Mononeuritis of lower limb, a rare neuromuscular disease. This study represents the first deep characterization of genetic variation within the Iberian population and the first operational haplotype panel to systematically include the SVs into genome-wide genetic studies.