Pieter B. T. Neerincx

Whole-genome sequence variation, population structure and demographic history of the Dutch population

Whole-genome sequencing enables complete characterization of genetic variation, but geographic clustering of rare alleles demands many diverse populations be studied. Here we describe the Genome of the Netherlands (GoNL) Project, in which we sequenced the whole genomes of 250 Dutch parent-offspring families and constructed a haplotype map of 20.4 million single-nucleotide variants and 1.2 million insertions and deletions. The intermediate coverage (∼13×) and trio design enabled extensive characterization of structural variation, including midsize events (30–500 bp) previously poorly catalogued and de novo mutations. We demonstrate that the quality of the haplotypes boosts imputation accuracy in independent samples, especially for lower frequency alleles. Population genetic analyses demonstrate fine-scale structure across the country and support multiple ancient migrations, consistent with historical changes in sea level and flooding. The GoNL Project illustrates how single-population whole-genome sequencing can provide detailed characterization of genetic variation and may guide the design of future population studies.

The Genome of the Netherlands: design, and project goals

Within the Netherlands a national network of biobanks has been established (Biobanking and Biomolecular Research Infrastructure-Netherlands (BBMRI-NL)) as a national node of the European BBMRI. One of the aims of BBMRI-NL is to enrich biobanks with different types of molecular and phenotype data. Here, we describe the Genome of the Netherlands (GoNL), one of the projects within BBMRI-NL. GoNL is a whole-genome-sequencing project in a representative sample consisting of 250 trio-families from all provinces in the Netherlands, which aims to characterize DNA sequence variation in the Dutch population. The parent–offspring trios include adult individuals ranging in age from 19 to 87 years (mean=53 years; SD=16 years) from birth cohorts 1910–1994. Sequencing was done on blood-derived DNA from uncultured cells and accomplished coverage was 14–15x. The family-based design represents a unique resource to assess the frequency of regional variants, accurately reconstruct haplotypes by family-based phasing, characterize short indels and complex structural variants, and establish the rate of de novo mutational events. GoNL will also serve as a reference panel for imputation in the available genome-wide association studies in Dutch and other cohorts to refine association signals and uncover population-specific variants. GoNL will create a catalog of human genetic variation in this sample that is uniquely characterized with respect to micro-geographic location and a wide range of phenotypes. The resource will be made available to the research and medical community to guide the interpretation of sequencing projects. The present paper summarizes the global characteristics of the project.

Computational pan-genomics: status, promises and challenges

Abstract Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains.Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains.

A high-quality human reference panel reveals the complexity and distribution of genomic structural variants

Structural variation (SV) represents a major source of differences between individual human genomes and has been linked to disease phenotypes. However, the majority of studies provide neither a global view of the full spectrum of these variants nor integrate them into reference panels of genetic variation. Here, we analyse whole genome sequencing data of 769 individuals from 250 Dutch families, and provide a haplotype-resolved map of 1.9 million genome variants across 9 different variant classes, including novel forms of complex indels, and retrotransposition-mediated insertions of mobile elements and processed RNAs. A large proportion are previously under reported variants sized between 21 and 100u2009bp. We detect 4 megabases of novel sequence, encoding 11 new transcripts. Finally, we show 191 known, trait-associated SNPs to be in strong linkage disequilibrium with SVs and demonstrate that our panel facilitates accurate imputation of SVs in unrelated individuals.

Rapid Targeted Genomics in Critically Ill Newborns

Rapid genomic diagnostics in critically ill infants results in a diagnostic yield of 30% and impacts clinical decision-making. BACKGROUND: Rapid diagnostic whole-genome sequencing has been explored in critically ill newborns, hoping to improve their clinical care and replace time-consuming and/or invasive diagnostic testing. A previous retrospective study in a research setting showed promising results with diagnoses in 57%, but patients were highly selected for known and likely Mendelian disorders. The aim of our prospective study was to assess the speed and yield of rapid targeted genomic diagnostics for clinical application. METHODS: We included 23 critically ill children younger than 12 months in ICUs over a period of 2 years. A quick diagnosis could not be made after routine clinical evaluation and diagnostics. Targeted analysis of 3426 known disease genes was performed by using whole-genome sequencing data. We measured diagnostic yield, turnaround times, and clinical consequences. RESULTS: A genetic diagnosis was obtained in 7 patients (30%), with a median turnaround time of 12 days (ranging from 5 to 23 days). We identified compound heterozygous mutations in the EPG5 gene (Vici syndrome), the RMND1 gene (combined oxidative phosphorylation deficiency-11), and the EIF2B5 gene (vanishing white matter), and homozygous mutations in the KLHL41 gene (nemaline myopathy), the GFER gene (progressive mitochondrial myopathy), and the GLB1 gene (GM1-gangliosidosis). In addition, a 1p36.33p36.32 microdeletion was detected in a child with cardiomyopathy. CONCLUSIONS: Rapid targeted genomics combined with copy number variant detection adds important value in the neonatal and pediatric intensive care setting. It led to a fast diagnosis in 30% of critically ill children for whom the routine clinical workup was unsuccessful.

A framework for the detection of de novo mutations in family-based sequencing data

Germline mutation detection from human DNA sequence data is challenging due to the rarity of such events relative to the intrinsic error rates of sequencing technologies and the uneven coverage across the genome. We developed PhaseByTransmission (PBT) to identify de novo single nucleotide variants and short insertions and deletions (indels) from sequence data collected in parent-offspring trios. We compute the joint probability of the data given the genotype likelihoods in the individual family members, the known familial relationships and a prior probability for the mutation rate. Candidate de novo mutations (DNMs) are reported along with their posterior probability, providing a systematic way to prioritize them for validation. Our tool is integrated in the Genome Analysis Toolkit and can be used together with the ReadBackedPhasing module to infer the parental origin of DNMs based on phase-informative reads. Using simulated data, we show that PBT outperforms existing tools, especially in low coverage data and on the X chromosome. We further show that PBT displays high validation rates on empirical parent-offspring sequencing data for whole-exome data from 104 trios and X-chromosome data from 249 parent-offspring families. Finally, we demonstrate an association between father’s age at conception and the number of DNMs in female offspring’s X chromosome, consistent with previous literature reports.

Running-wheel activity delays mitochondrial respiratory flux decline in aging mouse muscle via a post-transcriptional mechanism

Loss of mitochondrial respiratory flux is a hallmark of skeletal muscle aging, contributing to a progressive decline of muscle strength. Endurance exercise alleviates the decrease in respiratory flux, both in humans and in rodents. Here, we dissect the underlying mechanism of mitochondrial flux decline by integrated analysis of the molecular network.

Germline de novo mutation clusters arise during oocyte aging in genomic regions with high double-strand-break incidence

Clustering of mutations has been observed in cancer genomes as well as for germline de novo mutations (DNMs). We identified 1,796 clustered DNMs (cDNMs) within whole-genome-sequencing data from 1,291 parent–offspring trios to investigate their patterns and infer a mutational mechanism. We found that the number of clusters on the maternal allele was positively correlated with maternal age and that these clusters consisted of more individual mutations with larger intermutational distances than those of paternal clusters. More than 50% of maternal clusters were located on chromosomes 8, 9 and 16, in previously identified regions with accelerated maternal mutation rates. Maternal clusters in these regions showed a distinct mutation signature characterized by C>G transversions. Finally, we found that maternal clusters were associated with processes involving double-strand-breaks (DSBs), such as meiotic gene conversions and de novo deletion events. This result suggested accumulation of DSB-induced mutations throughout oocyte aging as the mechanism underlying the formation of maternal mutation clusters.Analysis of whole-genome sequencing data from 1,291 parent–offspring trios identifies patterns of clustered de novo mutations. Clusters increase in number with maternal age and are associated with DNA double-strand-break processes.