Pangenome graphs improve the analysis of structural variants in rare genetic diseases

Long read and preliminary pangenome analyses reveal breed-specific structural variations and novel sequences in Holstein and Jersey cattle

INTRODUCTION

Most SV studies in livestock rely on short-read sequencing, posing challenges in accurately characterizing large genomic variants due to their limited read length.

OBJECTIVES

Our goal is to reveal structural variation and novel sequences specific to Holstein and Jersey cattle breeds using long-read and pan-genome analyses.

METHODS

We sequenced 20 Holsteins and 8 Jersey cattle using PacBio HiFi to 20×, and integrated five read-based and one assembly-based SV caller to determine SVs.

RESULTS

We assembled the 28 genomes averaging 3.25 Gb with a contig N50 of 69.36 Mb and using the ARS-UCD1.2 reference, we acquired Holstein/Jersey SV catalogs with 74,068/54,689 events spanning 202/135 Mb (7.43 %/4.97 % of the genome). SVs were enriched in less conserved, non-coding, and non-regulatory regions. Comparing Holsteins with differing feed efficiency (FE), SVs unique to high FE were linked to energy metabolism and olfactory receptors, while those specific to low FE were associated with material transport. We constructed Holstein/Jersey pangenome graphs with 148,598/105,875 nodes and 208,891/147,990 edges, representing 47,028/37,137 biallelic and multi-allelic events, and 63.75/42.34 Mb of novel sequence. We observed SV count saturation with 20 Holsteins, while adding Jerseys significantly increased the SV count, highlighting breed-specific SV events.

CONCLUSION

Our long-read data and SV catalogs are valuable resources, revealing that the cattle genome is more complex than previously thought.

Expanded methylome and quantitative trait loci detection by long-read profiling of personal DNA

Structural variants (SVs) are omnipresent in human DNA, yet their genotype and methylation statuses are rarely characterized due to previous limitations in genome assembly and detection of modified nucleotides. Also, the extent to which SVs act as methylation quantitative trait loci (SV-mQTLs) is largely unknown. Here, we generated a pangenome graph summarizing SVs in 782 de novo assemblies obtained from Genomic Answers for Kids, capturing 14.6 million CpG dinucleotides that are absent from the CHM13v2 reference (SV-CpGs), thus expanding their number by 43.6%. Using 435 methylomes, we genotyped 4.06 million SV-CpGs, of which 3.93 million (96.8%) are methylated at least once. Nonrepeat sequences contribute 1.59 × 106 novel SV-CpGs, followed by centromeric satellites (6.57 × 105), simple repeats (5.40 × 105), Alu elements (5.07 × 105), satellites (2.17 × 105), LINE-1s (1.83 × 105), and SVA (SINE-VNTR-Alu) elements (1.50 × 105). Centromeric satellites, simple repeats, and SVAs are overrepresented in SV-CpGs versus reference CpGs. Similarly, methylation levels in SV-CpGs are more variable than in reference CpGs. To explore if SVs are potentially causal for functional variation, we measured SV-mQTLs. This revealed over 230,464 methylation bins where the methylation is associated with common SVs within 100 kbp. Finally, we identified 65,659 methylation bins (28.5%) where the leading QTL variant is an SV. In conclusion, we demonstrate that graph pangenomes provide full SV structures, the associated methylation variation, and reveal tens of thousands of SV-mQTLs, underscoring the importance of assembly based analyses of human traits.

The additional diagnostic yield of long-read sequencing in undiagnosed rare diseases

Long-read sequencing (LRS) is a promising technology positioned to study the significant proportion of rare diseases (RDs) that remain undiagnosed as it addresses many of the limitations of short-read sequencing, detecting and clarifying additional disease-associated variants that may be missed by the current standard diagnostic workflow for RDs. Some key areas where additional diagnostic yields may be realized include: (1) detection and resolution of structural variants (SVs); (2) detection and characterization of tandem repeat expansions; (3) coverage of regions of high sequence similarity; (4) variant phasing; (5) the use of de novo genome assemblies for reference-based or graph genome variant detection; and (6) epigenetic and transcriptomic evaluations. Examples from over 50 studies support that the main areas of added diagnostic yield currently lie in SV detection and characterization, repeat expansion assessment, and phasing (with or without DNA methylation information). Several emerging studies applying LRS in cohorts of undiagnosed RDs also demonstrate that LRS can boost diagnostic yields following negative standard-of-care clinical testing and provide an added yield of 7%-17% following negative short-read genome sequencing. With this evidence of improved diagnostic yield, we discuss the incorporation of LRS into the diagnostic care pathway for undiagnosed RDs, including current challenges and considerations, with the ultimate goal of ending the diagnostic odyssey for countless individuals with RDs.

Genome-wide association testing beyond SNPs

Decades of genetic association testing in human cohorts have provided important insights into the genetic architecture and biological underpinnings of complex traits and diseases. However, for certain traits, genome-wide association studies (GWAS) for common SNPs are approaching signal saturation, which underscores the need to explore other types of genetic variation to understand the genetic basis of traits and diseases. Copy number variation (CNV) is an important source of heritability that is well known to functionally affect human traits. Recent technological and computational advances enable the large-scale, genome-wide evaluation of CNVs, with implications for downstream applications such as polygenic risk scoring and drug target identification. Here, we review the current state of CNV-GWAS, discuss current limitations in resource infrastructure that need to be overcome to enable the wider uptake of CNV-GWAS results, highlight emerging opportunities and suggest guidelines and standards for future GWAS for genetic variation beyond SNPs at scale.

Advancing long-read nanopore genome assembly and accurate variant calling for rare disease detection

More than 50% of families with suspected rare monogenic diseases remain unsolved after whole-genome analysis by short-read sequencing (SRS). Long-read sequencing (LRS) could help bridge this diagnostic gap by capturing variants inaccessible to SRS, facilitating long-range mapping and phasing and providing haplotype-resolved methylation profiling. To evaluate LRS's additional diagnostic yield, we sequenced a rare-disease cohort of 98 samples from 41 families, using nanopore sequencing, achieving per sample ∼36× average coverage and 32-kb read N50 from a single flow cell. Our Napu pipeline generated assemblies, phased variants, and methylation calls. LRS covered, on average, coding exons in ∼280 genes and ∼5 known Mendelian disease-associated genes that were not covered by SRS. In comparison to SRS, LRS detected additional rare, functionally annotated variants, including structural variants (SVs) and tandem repeats, and completely phased 87% of protein-coding genes. LRS detected additional de novo variants and could be used to distinguish postzygotic mosaic variants from prezygotic de novos. Diagnostic variants were established by LRS in 11 probands, with diverse underlying genetic causes including de novo and compound heterozygous variants, large-scale SVs, and epigenetic modifications. Our study demonstrates LRS's potential to enhance diagnostic yield for rare monogenic diseases, implying utility in future clinical genomics workflows.

Molecular and clinical Insights into KMT2E-Related O'Donnell-Luria-Rodan syndrome in a novel patient cohort

O'Donnell-Luria-Rodan (ODLURO) syndrome is an autosomal dominant neurodevelopmental disorder mainly characterized by global development delay/intellectual disability, white matter abnormalities, and behavioral manifestations. It is caused by pathogenic variants in the KMT2E gene. Here we report seven new patients with loss-of-function KMT2E variants, six harboring frameshift/nonsense changes, and one with a 7q22.3 microdeletion encompassing the entire gene-locus. We further characterize both the clinical phenotype as well as its associated pathogenic variants' spectrum providing new information on sex-related phenotype distribution, according to the variant groups. We also highlight different epilepsy phenotype-genotype correlation with preferential association of generalized epilepsy and/or developmental and epileptic encephalopathy with missense pathogenic variants and focal epilepsy, childhood absence epilepsy and/or febrile seizures with pathogenic truncating variants and structural rearrangements. By a systematic review of the previously reported series, we also discuss previously unappreciated findings, including progressive macrocephaly, apraxia, and higher risk of bone fractures.

Functional innovation through new genes as a general evolutionary process

In the past decade, our understanding of how new genes originate in diverse organisms has advanced substantially, and more than a dozen molecular mechanisms for generating initial gene structures were identified, in addition to gene duplication. These new genes have been found to integrate into and modify pre-existing gene networks primarily through mutation and selection, revealing new patterns and rules with stable origination rates across various organisms. This progress has challenged the prevailing belief that new proteins evolve from pre-existing genes, as new genes may arise de novo from noncoding DNA sequences in many organisms, with high rates observed in flowering plants. New genes have important roles in phenotypic and functional evolution across diverse biological processes and structures, with detectable fitness effects of sexual conflict genes that can shape species divergence. Such knowledge of new genes can be of translational value in agriculture and medicine.

Long-read genome sequencing and variant reanalysis increase diagnostic yield in neurodevelopmental disorders

Variant detection from long-read genome sequencing (lrGS) has proven to be more accurate and comprehensive than variant detection from short-read genome sequencing (srGS). However, the rate at which lrGS can increase molecular diagnostic yield for rare disease is not yet precisely characterized. We performed lrGS using Pacific Biosciences "HiFi" technology on 96 short-read-negative probands with rare diseases that were suspected to be genetic. We generated hg38-aligned variants and de novo phased genome assemblies, and subsequently annotated, filtered, and curated variants using clinical standards. New disease-relevant or potentially relevant genetic findings were identified in 16/96 (16.7%) probands, nine of which (8/96, ∼9.4%) harbored pathogenic or likely pathogenic variants. Nine probands (∼9.4%) had variants that were accurately called in both srGS and lrGS and represent changes to clinical interpretation, mostly from recently published gene-disease associations. Seven cases included variants that were only correctly interpreted in lrGS, including copy-number variants (CNVs), an inversion, a mobile element insertion, two low-complexity repeat expansions, and a 1 bp deletion. While evidence for each of these variants is, in retrospect, visible in srGS, they were either not called within srGS data, were represented by calls with incorrect sizes or structures, or failed quality control and filtration. Thus, while reanalysis of older srGS data clearly increases diagnostic yield, we find that lrGS allows for substantial additional yield (7/96, 7.3%) beyond srGS. We anticipate that as lrGS analysis improves, and as lrGS data sets grow allowing for better variant-frequency annotation, the additional lrGS-only rare disease yield will grow over time.

A unified framework to analyze transposable element insertion polymorphisms using graph genomes

Transposable elements are ubiquitous mobile DNA sequences generating insertion polymorphisms, contributing to genomic diversity. We present GraffiTE, a flexible pipeline to analyze polymorphic mobile elements insertions. By integrating state-of-the-art structural variant detection algorithms and graph genomes, GraffiTE identifies polymorphic mobile elements from genomic assemblies or long-read sequencing data, and genotypes these variants using short or long read sets. Benchmarking on simulated and real datasets reports high precision and recall rates. GraffiTE is designed to allow non-expert users to perform comprehensive analyses, including in models with limited transposable element knowledge and is compatible with various sequencing technologies. Here, we demonstrate the versatility of GraffiTE by analyzing human, Drosophila melanogaster, maize, and Cannabis sativa pangenome data. These analyses reveal the landscapes of polymorphic mobile elements and their frequency variations across individuals, strains, and cultivars.

De Novo Genome Assemblies From Two Indigenous Americans from Arizona Identify New Polymorphisms in Non-Reference Sequences

There is a collective push to diversify human genetic studies by including underrepresented populations. However, analyzing DNA sequence reads involves the initial step of aligning the reads to the GRCh38/hg38 reference genome which is inadequate for non-European ancestries. In this study, using long-read sequencing technology, we constructed de novo genome assemblies from two indigenous Americans from Arizona (IAZ). Each assembly included ∼17 Mb of DNA sequence not present [nonreference sequence (NRS)] in hg38, which consists mostly of repeat elements. Forty NRSs totaling 240 kb were uniquely anchored to the hg38 primary assembly generating a modified hg38-NRS reference genome. DNA sequence alignment and variant calling were then conducted with whole-genome sequencing (WGS) sequencing data from 387 IAZ using both the hg38 and modified hg38-NRS reference maps. Variant calling with the hg38-NRS map identified ∼50,000 single-nucleotide variants present in at least 5% of the WGS samples which were not detected with the hg38 reference map. We also directly assessed the NRSs positioned within genes. Seventeen NRSs anchored to regions including an identical 187 bp NRS found in both de novo assemblies. The NRS is located in HCN2 79 bp downstream of Exon 3 and contains several putative transcriptional regulatory elements. Genotyping of the HCN2-NRS revealed that the insertion is enriched in IAZ (minor allele frequency = 0.45) compared to other reference populations tested. This study shows that inclusion of population-specific NRSs can dramatically change the variant profile in an underrepresented ethnic groups and thereby lead to the discovery of previously missed common variations.

Advancing long-read nanopore genome assembly and accurate variant calling for rare disease detection

More than 50% of families with suspected rare monogenic diseases remain unsolved after whole genome analysis by short read sequencing (SRS). Long-read sequencing (LRS) could help bridge this diagnostic gap by capturing variants inaccessible to SRS, facilitating long-range mapping and phasing, and providing haplotype-resolved methylation profiling. To evaluate LRS's additional diagnostic yield, we sequenced a rare disease cohort of 98 samples, including 41 probands and some family members, using nanopore sequencing, achieving per sample ∼36x average coverage and 32 kilobase (kb) read N50 from a single flow cell. Our Napu pipeline generated assemblies, phased variants, and methylation calls. LRS covered, on average, coding exons in ∼280 genes and ∼5 known Mendelian disease genes that were not covered by SRS. In comparison to SRS, LRS detected additional rare, functionally annotated variants, including SVs and tandem repeats, and completely phased 87% of protein-coding genes. LRS detected additional de novo variants, and could be used to distinguish postzygotic mosaic variants from prezygotic de novos . Eleven probands were solved, with diverse underlying genetic causes including de novo and compound heterozygous variants, large-scale SVs, and epigenetic modifications. Our study demonstrates LRS's potential to enhance diagnostic yield for rare monogenic diseases, implying utility in future clinical genomics workflows.

Mapping structural variants to rare disease genes using long-read whole genome sequencing and trait-relevant polygenic scores

Recent studies have revealed the pervasive landscape of rare structural variants (rSVs) present in human genomes. rSVs can have extreme effects on the expression of proximal genes and, in a rare disease context, have been implicated in patient cases where no diagnostic single nucleotide variant (SNV) was found. Approaches for integrating rSVs to date have focused on targeted approaches in known Mendelian rare disease genes. This approach is intractable for rare diseases with many causal loci or patients with complex, multi-phenotype syndromes. We hypothesized that integrating trait-relevant polygenic scores (PGS) would provide a substantial reduction in the number of candidate disease genes in which to assess rSV effects. We further implemented a method for ranking PGS genes to define a set of core/key genes where a rSV has the potential to exert relatively larger effects on disease risk. Among a subset of patients enrolled in the Genomic Answers for Kids (GA4K) rare disease program (N=497), we used PacBio HiFi long-read whole genome sequencing (lrWGS) to identify rSVs intersecting genes in trait-relevant PGSs. Illustrating our approach in Autism (N=54 cases), we identified 22, 019 deletions, 2,041 duplications, 87,826 insertions, and 214 inversions overlapping putative core/key PGS genes. Additionally, by integrating genomic constraint annotations from gnomAD, we observed that rare duplications overlapping putative core/key PGS genes were frequently in higher constraint regions compared to controls (P = 1×10-03). This difference was not observed in the lowest-ranked gene set (P = 0.15). Overall, our study provides a framework for the annotation of long-read rSVs from lrWGS data and prioritization of disease-linked genomic regions for downstream functional validation of rSV impacts. To enable reuse by other researchers, we have made SV allele frequencies and gene associations freely available.