A population model for genotyping indels from next-generation sequence data

Pathogenic Variants in Cancer Predisposition Genes and Prostate Cancer Risk in Men of African Ancestry

PURPOSE

In studies of men of European ancestry, rare pathogenic variants in DNA repair pathway genes have been shown to be associated with risk of aggressive prostate cancer. The contribution of rare coding variation to prostate cancer risk in men of African ancestry has not been established.

METHODS

We sequenced a panel of 19 DNA repair and cancer predisposition genes in 2,453 African American and 1,151 Ugandan prostate cancer cases and controls. Rare variants were classified as pathogenic or putatively functionally disruptive and examined in association with prostate cancer risk and disease aggressiveness in gene and pathway-level association analyses.

RESULTS

Pathogenic variants were found in 75 out of 2,098 cases (3.6%) and 31 out of 1,481 controls (2.1%) (OR=1.82, 95% CI=1.19 to 2.79, P=0.0044) with the association being stronger for more aggressive disease phenotypes (OR=3.10, 95% CI=1.54 to 6.23, P=0.0022). The highest risks for aggressive disease were observed with pathogenic variants in the ATM, BRCA2, PALB2 and NBN genes, with odds ratios ranging from ~4 to 15 in the combined study sample of African American and Ugandan men. Rare, non-pathogenic, non-synonymous variants did not have a major impact on risk of overall prostate cancer or disease aggressiveness.

CONCLUSIONS

Rare pathogenic variants in DNA repair genes have appreciable effects on risk of aggressive prostate cancer in men of African ancestry. These findings have potential implications for panel testing and risk stratification in this high-risk population.

Genetic Study on Small Insertions and Deletions in Psoriasis Reveals a Role in Complex Human Diseases

Genetic studies based on single-nucleotide polymorphisms have provided valuable insights into the genetic architecture of complex diseases. However, a large fraction of heritability for most of these diseases remains unexplained, and the impact of small insertions and deletions (InDels) has been neglected. We performed a comprehensive screen on the exome sequence data of 1,326 genes using the SOAP-PopIndel method for InDels in 32,043 Chinese Han individuals and identified 29 unreported InDels within 25 susceptibility genes associated with psoriasis. Specifically, we identified 12 common, 9 low-frequency, and 8 rare InDels that explained approximately 1.29% of the heritability of psoriasis. Further analyses identified KIAA0319, RELN, NCAPG, ABO, AADACL2, LMAN1, FLG, HERC5, CCDC66, LEKR1, AFF3, ABCG2, ANXA7, SYTL2,GIPR, METTL1, and FYCO1 as unreported genes for psoriasis. In addition, identified InDels were associated with the following reported genes: IFIH1, ERAP1, ERAP2, LNPEP, UBLCP1, and STAT3; unreported independent associations for exonic InDels were found within GJB2 and ZNF816A. Our study enriched the genetic basis and pathogenesis of psoriasis and highlighted the non-negligible impact of InDels on complex human diseases.

npInv: accurate detection and genotyping of inversions using long read sub-alignment

Background

Detection of genomic inversions remains challenging. Many existing methods primarily target inzversions with a non repetitive breakpoint, leaving inverted repeat (IR) mediated non-allelic homologous recombination (NAHR) inversions largely unexplored.

Result

We present npInv, a novel tool specifically for detecting and genotyping NAHR inversion using long read sub-alignment of long read sequencing data. We benchmark npInv with other tools in both simulation and real data. We use npInv to generate a whole-genome inversion map for NA12878 consisting of 30 NAHR inversions (of which 15 are novel), including all previously known NAHR mediated inversions in NA12878 with flanking IR less than 7kb. Our genotyping accuracy on this dataset was 94%. We used PCR to confirm the presence of two of these novel inversions. We show that there is a near linear relationship between the length of flanking IR and the minimum inversion size, without inverted repeats.

Conclusion

The application of npInv shows high accuracy in both simulation and real data. The results give deeper insight into understanding inversion.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2252-9) contains supplementary material, which is available to authorized users.

Graphtyper enables population-scale genotyping using pangenome graphs

A fundamental requirement for genetic studies is an accurate determination of sequence variation. While human genome sequence diversity is increasingly well characterized, there is a need for efficient ways to use this knowledge in sequence analysis. Here we present Graphtyper, a publicly available novel algorithm and software for discovering and genotyping sequence variants. Graphtyper realigns short-read sequence data to a pangenome, a variation-aware graph structure that encodes sequence variation within a population by representing possible haplotypes as graph paths. Our results show that Graphtyper is fast, highly scalable, and provides sensitive and accurate genotype calls. Graphtyper genotyped 89.4 million sequence variants in the whole genomes of 28,075 Icelanders using less than 100,000 CPU days, including detailed genotyping of six human leukocyte antigen (HLA) genes. We show that Graphtyper is a valuable tool in characterizing sequence variation in both small and population-scale sequencing studies.

Whole-exome sequencing of over 4100 men of African ancestry and prostate cancer risk

Prostate cancer is the most common non-skin cancer in males, with a ∼1.5-2-fold higher incidence in African American men when compared with whites. Epidemiologic evidence supports a large heritable contribution to prostate cancer, with over 100 susceptibility loci identified to date that can explain ∼33% of the familial risk. To explore the contribution of both rare and common variation in coding regions to prostate cancer risk, we sequenced the exomes of 2165 prostate cancer cases and 2034 controls of African ancestry at a mean coverage of 10.1×. We identified 395 220 coding variants down to 0.05% frequency [57% non-synonymous (NS), 42% synonymous and 1% gain or loss of stop codon or splice site variant] in 16 751 genes with the strongest associations observed in SPARCL1 on 4q22.1 (rs13051, Ala49Asp, OR = 0.78, P = 1.8 × 10(-6)) and PTPRR on 12q15 (rs73341069, Val239Ile, OR = 1.62, P = 2.5 × 10(-5)). In gene-level testing, the two most significant genes were C1orf100 (P = 2.2 × 10(-4)) and GORAB (P = 2.3 × 10(-4)). We did not observe exome-wide significant associations (after correcting for multiple hypothesis testing) in single variant or gene-level testing in the overall case-control or case-case analyses of disease aggressiveness. In this first whole-exome sequencing study of prostate cancer, our findings do not provide strong support for the hypothesis that NS coding variants down to 0.5-1.0% frequency have large effects on prostate cancer risk in men of African ancestry. Higher-coverage sequencing efforts in larger samples will be needed to study rarer variants with smaller effect sizes associated with prostate cancer risk.

Development of genetic markers in Eucalyptus species by target enrichment and exome sequencing

The advent of next-generation sequencing has facilitated large-scale discovery, validation and assessment of genetic markers for high density genotyping. The present study was undertaken to identify markers in genes supposedly related to wood property traits in three Eucalyptus species. Ninety four genes involved in xylogenesis were selected for hybridization probe based nuclear genomic DNA target enrichment and exome sequencing. Genomic DNA was isolated from the leaf tissues and used for on-array probe hybridization followed by Illumina sequencing. The raw sequence reads were trimmed and high-quality reads were mapped to the E. grandis reference sequence and the presence of single nucleotide variants (SNVs) and insertions/ deletions (InDels) were identified across the three species. The average read coverage was 216X and a total of 2294 SNVs and 479 InDels were discovered in E. camaldulensis, 2383 SNVs and 518 InDels in E. tereticornis, and 1228 SNVs and 409 InDels in E. grandis. Additionally, SNV calling and InDel detection were conducted in pair-wise comparisons of E. tereticornis vs. E. grandis, E. camaldulensis vs. E. tereticornis and E. camaldulensis vs. E. grandis. This study presents an efficient and high throughput method on development of genetic markers for family– based QTL and association analysis in Eucalyptus.

Design and development of exome capture sequencing for the domestic pig (Sus scrofa)

Background

The domestic pig (Sus scrofa) is both an important livestock species and a model for biomedical research. Exome sequencing has accelerated identification of protein-coding variants underlying phenotypic traits in human and mouse. We aimed to develop and validate a similar resource for the pig.

Results

We developed probe sets to capture pig exonic sequences based upon the current Ensembl pig gene annotation supplemented with mapped expressed sequence tags (ESTs) and demonstrated proof-of-principle capture and sequencing of the pig exome in 96 pigs, encompassing 24 capture experiments. For most of the samples at least 10x sequence coverage was achieved for more than 90% of the target bases. Bioinformatic analysis of the data revealed over 236,000 high confidence predicted SNPs and over 28,000 predicted indels.

Conclusions

We have achieved coverage statistics similar to those seen with commercially available human and mouse exome kits. Exome capture in pigs provides a tool to identify coding region variation associated with production traits, including loss of function mutations which may explain embryonic and neonatal losses, and to improve genomic assemblies in the vicinity of protein coding genes in the pig.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-550) contains supplementary material, which is available to authorized users.

Population genetic analysis of bi-allelic structural variants from low-coverage sequence data with an expectation-maximization algorithm

Background

Population genetics and association studies usually rely on a set of known variable sites that are then genotyped in subsequent samples, because it is easier to genotype than to discover the variation. This is also true for structural variation detected from sequence data. However, the genotypes at known variable sites can only be inferred with uncertainty from low coverage data. Thus, statistical approaches that infer genotype likelihoods, test hypotheses, and estimate population parameters without requiring accurate genotypes are becoming popular. Unfortunately, the current implementations of these methods are intended to analyse only single nucleotide and short indel variation, and they usually assume that the two alleles in a heterozygous individual are sampled with equal probability. This is generally false for structural variants detected with paired ends or split reads. Therefore, the population genetics of structural variants cannot be studied, unless a painstaking and potentially biased genotyping is performed first.

Results

We present svgem, an expectation-maximization implementation to estimate allele and genotype frequencies, calculate genotype posterior probabilities, and test for Hardy-Weinberg equilibrium and for population differences, from the numbers of times the alleles are observed in each individual. Although applicable to single nucleotide variation, it aims at bi-allelic structural variation of any type, observed by either split reads or paired ends, with arbitrarily high allele sampling bias. We test svgem with simulated and real data from the 1000 Genomes Project.

Conclusions

svgem makes it possible to use low-coverage sequencing data to study the population distribution of structural variants without having to know their genotypes. Furthermore, this advance allows the combined analysis of structural and nucleotide variation within the same genotype-free statistical framework, thus preventing biases introduced by genotype imputation.