Large-scale whole-genome sequencing of the Icelandic population. Nat Genet. 2015;47:435-444. [PMID: 25807286 DOI: 10.1038/ng.3247]

Inference Under a Wright-Fisher Model Using an Accurate Beta Approximation

The large amount and high quality of genomic data available today enable, in principle, accurate inference of evolutionary histories of observed populations. The Wright-Fisher model is one of the most widely used models for this purpose. It describes the stochastic behavior in time of allele frequencies and the influence of evolutionary pressures, such as mutation and selection. Despite its simple mathematical formulation, exact results for the distribution of allele frequency (DAF) as a function of time are not available in closed analytical form. Existing approximations build on the computationally intensive diffusion limit or rely on matching moments of the DAF. One of the moment-based approximations relies on the beta distribution, which can accurately describe the DAF when the allele frequency is not close to the boundaries (0 and 1). Nonetheless, under a Wright-Fisher model, the probability of being on the boundary can be positive, corresponding to the allele being either lost or fixed. Here we introduce the beta with spikes, an extension of the beta approximation that explicitly models the loss and fixation probabilities as two spikes at the boundaries. We show that the addition of spikes greatly improves the quality of the approximation. We additionally illustrate, using both simulated and real data, how the beta with spikes can be used for inference of divergence times between populations with comparable performance to an existing state-of-the-art method.

Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes

Cancer is often driven by the accumulation of genetic alterations, including single nucleotide variants, small insertions or deletions, gene fusions, copy-number variations, and large chromosomal rearrangements. Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data and catalog somatic mutations in both common and rare cancer types. So far, the somatic mutation landscapes and signatures of >10 major cancer types have been reported; however, pinpointing driver mutations and cancer genes from millions of available cancer somatic mutations remains a monumental challenge. To tackle this important task, many methods and computational tools have been developed during the past several years and, thus, a review of its advances is urgently needed. Here, we first summarize the main features of these methods and tools for whole-exome, whole-genome and whole-transcriptome sequencing data. Then, we discuss major challenges like tumor intra-heterogeneity, tumor sample saturation and functionality of synonymous mutations in cancer, all of which may result in false-positive discoveries. Finally, we highlight new directions in studying regulatory roles of noncoding somatic mutations and quantitatively measuring circulating tumor DNA in cancer. This review may help investigators find an appropriate tool for detecting potential driver or actionable mutations in rapidly emerging precision cancer medicine.

Rare variant discovery by deep whole-genome sequencing of 1,070 Japanese individuals

The Tohoku Medical Megabank Organization reports the whole-genome sequences of 1,070 healthy Japanese individuals and construction of a Japanese population reference panel (1KJPN). Here we identify through this high-coverage sequencing (32.4 × on average), 21.2 million, including 12 million novel, single-nucleotide variants (SNVs) at an estimated false discovery rate of <1.0%. This detailed analysis detected signatures for purifying selection on regulatory elements as well as coding regions. We also catalogue structural variants, including 3.4 million insertions and deletions, and 25,923 genic copy-number variants. The 1KJPN was effective for imputing genotypes of the Japanese population genome wide. These data demonstrate the value of high-coverage sequencing for constructing population-specific variant panels, which covers 99.0% SNVs of minor allele frequency ≥0.1%, and its value for identifying causal rare variants of complex human disease phenotypes in genetic association studies.

The Tohoku Medical Megabank Organization establishes a biobank with detailed patient health care and genome information. Here the authors analyse whole-genome sequences of 1,070 Japanese individuals, allowing them to catalogue 21 million single-nucleotide variants including 12 million novel ones.

Common and rare variants associated with kidney stones and biochemical traits

Kidney stone disease is a complex disorder with a strong genetic component. We conducted a genome-wide association study of 28.3 million sequence variants detected through whole-genome sequencing of 2,636 Icelanders that were imputed into 5,419 kidney stone cases, including 2,172 cases with a history of recurrent kidney stones, and 279,870 controls. We identify sequence variants associating with kidney stones at ALPL (rs1256328[T], odds ratio (OR)=1.21, P=5.8 × 10⁻¹⁰) and a suggestive association at CASR (rs7627468[A], OR=1.16, P=2.0 × 10⁻⁸). Focusing our analysis on coding sequence variants in 63 genes with preferential kidney expression we identify two rare missense variants SLC34A1 p.Tyr489Cys (OR=2.38, P=2.8 × 10⁻⁵) and TRPV5 p.Leu530Arg (OR=3.62, P=4.1 × 10⁻⁵) associating with recurrent kidney stones. We also observe associations of the identified kidney stone variants with biochemical traits in a large population set, indicating potential biological mechanism.

Kidney stone formation is influenced by genetic factors and recurrent stone formation places a significant burden on health care systems. Here Oddsson et al. perform a large-scale genome-wide association study and uncover new genetic variants associated with kidney stone susceptibility and associated biochemical traits.

The frequent evolutionary birth and death of functional promoters in mouse and human

Promoters are central to the regulation of gene expression. Changes in gene regulation are thought to underlie much of the adaptive diversification between species and phenotypic variation within populations. In contrast to earlier work emphasizing the importance of enhancer evolution and subtle sequence changes at promoters, we show that dramatic changes such as the complete gain and loss (collectively, turnover) of functional promoters are common. Using quantitative measures of transcription initiation in both humans and mice across 52 matched tissues, we discriminate promoter sequence gains from losses and resolve the lineage of changes. We also identify expression divergence and functional turnover between orthologous promoters, finding only the latter is associated with local sequence changes. Promoter turnover has occurred at the majority (>56%) of protein-coding genes since humans and mice diverged. Tissue-restricted promoters are the most evolutionarily volatile where retrotransposition is an important, but not the sole, source of innovation. There is considerable heterogeneity of turnover rates between promoters in different tissues, but the consistency of these in both lineages suggests that the same biological systems are similarly inclined to transcriptional rewiring. The genes affected by promoter turnover show evidence of adaptive evolution. In mice, promoters are primarily lost through deletion of the promoter containing sequence, whereas in humans, many promoters appear to be gradually decaying with weak transcriptional output and relaxed selective constraint. Our results suggest that promoter gain and loss is an important process in the evolutionary rewiring of gene regulation and may be a significant source of phenotypic diversification.

Loss-of-function variants in ATM confer risk of gastric cancer

Gastric cancer is a serious health problem worldwide, with particularly high prevalence in eastern Asia. Genome-wide association studies (GWAS) in Asian populations have identified several loci that associate with gastric cancer risk. Here we report a GWAS of gastric cancer in a European population, using information on 2,500 population-based gastric cancer cases and 205,652 controls. We found a new gastric cancer association with loss-of-function mutations in ATM (gene test, P = 8.0 × 10(-12); odds ratio (OR) = 4.74). The combination of the loss-of-function variants p.Gln852*, p.Ser644* and p.Tyr103* (combined minor allele frequency (MAF) = 0.3%) also associates with pancreatic and prostate cancers (OR = 3.81 and 2.18, respectively) and gives an indication of risk of breast and colorectal cancers (OR = 1.82 and 1.97, respectively). Cancers in those carrying loss-of-function ATM mutations are diagnosed at a significantly earlier age than in non-carriers. Our results confirm an association between gastric cancer in Europeans and three loci previously reported in Asians, MUC1, PRKAA1 and PSCA, refine the association signal at PRKAA1 and support a pathogenic role for the tandem repeat identified in MUC1.

Variants in ELL2 influencing immunoglobulin levels associate with multiple myeloma

Multiple myeloma (MM) is characterized by an uninhibited, clonal growth of plasma cells. While first-degree relatives of patients with MM show an increased risk of MM, the genetic basis of inherited MM susceptibility is incompletely understood. Here we report a genome-wide association study in the Nordic region identifying a novel MM risk locus at ELL2 (rs56219066T; odds ratio (OR)=1.25; P=9.6 × 10(-10)). This gene encodes a stoichiometrically limiting component of the super-elongation complex that drives secretory-specific immunoglobulin mRNA production and transcriptional regulation in plasma cells. We find that the MM risk allele harbours a Thr298Ala missense variant in an ELL2 domain required for transcription elongation. Consistent with a hypomorphic effect, we find that the MM risk allele also associates with reduced levels of immunoglobulin A (IgA) and G (IgG) in healthy subjects (P=8.6 × 10(-9) and P=6.4 × 10(-3), respectively) and, potentially, with an increased risk of bacterial meningitis (OR=1.30; P=0.0024).

PopIns: population-scale detection of novel sequence insertions

MOTIVATION

The detection of genomic structural variation (SV) has advanced tremendously in recent years due to progress in high-throughput sequencing technologies. Novel sequence insertions, insertions without similarity to a human reference genome, have received less attention than other types of SVs due to the computational challenges in their detection from short read sequencing data, which inherently involves de novo assembly. De novo assembly is not only computationally challenging, but also requires high-quality data. Although the reads from a single individual may not always meet this requirement, using reads from multiple individuals can increase power to detect novel insertions.

RESULTS

We have developed the program PopIns, which can discover and characterize non-reference insertions of 100 bp or longer on a population scale. In this article, we describe the approach we implemented in PopIns. It takes as input a reads-to-reference alignment, assembles unaligned reads using a standard assembly tool, merges the contigs of different individuals into high-confidence sequences, anchors the merged sequences into the reference genome, and finally genotypes all individuals for the discovered insertions. Our tests on simulated data indicate that the merging step greatly improves the quality and reliability of predicted insertions and that PopIns shows significantly better recall and precision than the recent tool MindTheGap. Preliminary results on a dataset of 305 Icelanders demonstrate the practicality of the new approach.

AVAILABILITY AND IMPLEMENTATION

The source code of PopIns is available from http://github.com/bkehr/popins

CONTACT

birte.kehr@decode.is

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Sequence variants from whole genome sequencing a large group of Icelanders

We have accumulated considerable data on the genetic makeup of the Icelandic population by sequencing the whole genomes of 2,636 Icelanders to depth of at least 10X and by chip genotyping 101,584 more. The sequencing was done with Illumina technology. The median sequencing depth was 20X and 909 individuals were sequenced to a depth of at least 30X. We found 20 million single nucleotide polymorphisms (SNPs) and 1.5 million insertions/deletions (indels) that passed stringent quality control. Almost all the common SNPs (derived allele frequency (DAF) over 2%) that we identified in Iceland have been observed by either dbSNP (build 137) or the Exome Sequencing Project (ESP) while only 60 and 20% of rare (DAF<0.5%) SNPs and indels in coding regions, the most heavily studied parts of the genome, have been observed in the public databases. Features of our variant data, such as the transition/transversion ratio and the length distribution of indels, are similar to published reports.