Comparison of whole‐genome (13X) and capture (87X) resequencing methods for SNP and genotype callings

The number of polymorphisms identified with next‐generation sequencing approaches depends directly on the sequencing depth and therefore on the experimental cost. Although higher levels of depth ensure more sensitive and more specific SNP calls, economic constraints limit the increase of depth for whole‐genome resequencing (WGS). For this reason, capture resequencing is used for studies focusing on only some specific regions of the genome. However, several biases in capture resequencing are known to have a negative impact on the sensitivity of SNP detection. Within this framework, the aim of this study was to compare the accuracy of WGS and capture resequencing on SNP detection and genotype calling, which differ in terms of both sequencing depth and biases. Indeed, we have evaluated the SNP calling and genotyping accuracy in a WGS dataset (13X) and in a capture resequencing dataset (87X) performed on 11 individuals. The percentage of SNPs not identified due to a sevenfold sequencing depth decrease was estimated at 7.8% using a down‐sampling procedure on the capture sequencing dataset. A comparison of the 87X capture sequencing dataset with the WGS dataset revealed that capture‐related biases were leading with the loss of 5.2% of SNPs detected with WGS. Nevertheless, when considering the SNPs detected by both approaches, capture sequencing appears to achieve far better SNP genotyping, with about 4.4% of the WGS genotypes that can be considered as erroneous and even 10% focusing on heterozygous genotypes. In conclusion, WGS and capture deep sequencing can be considered equivalent strategies for SNP detection, as the rate of SNPs not identified because of a low sequencing depth in the former is quite similar to SNPs missed because of method biases of the latter. On the other hand, capture deep sequencing clearly appears more adapted for studies requiring great accuracy in genotyping.     

Whole‐genome sequencing approaches for conservation biology: Advantages,limitations and practical recommendations

Whole‐genome resequencing (WGR) is a powerful method for addressing fundamental evolutionary biology questions that have not been fully resolved using traditional methods. WGR includes four approaches: the sequencing of individuals to a high depth of coverage with either unresolved or resolved haplotypes, the sequencing of population genomes to a high depth by mixing equimolar amounts of unlabelled‐individual DNA (Pool‐seq) and the sequencing of multiple individuals from a population to a low depth (lcWGR). These techniques require the availability of a reference genome. This, along with the still high cost of shotgun sequencing and the large demand for computing resources and storage, has limited their implementation in nonmodel species with scarce genomic resources and in fields such as conservation biology. Our goal here is to describe the various WGR methods, their pros and cons and potential applications in conservation biology. WGR offers an unprecedented marker density and surveys a wide diversity of genetic variations not limited to single nucleotide polymorphisms (e.g., structural variants and mutations in regulatory elements), increasing their power for the detection of signatures of selection and local adaptation as well as for the identification of the genetic basis of phenotypic traits and diseases. Currently, though, no single WGR approach fulfils all requirements of conservation genetics, and each method has its own limitations and sources of potential bias. We discuss proposed ways to minimize such biases. We envision a not distant future where the analysis of whole genomes becomes a routine task in many nonmodel species and fields including conservation biology.     

Variant detection sensitivity and biases in whole genome and exome sequencing

Background

Less than two percent of the human genome is protein coding, yet that small fraction harbours the majority of known disease causing mutations. Despite rapidly falling whole genome sequencing (WGS) costs, much research and increasingly the clinical use of sequence data is likely to remain focused on the protein coding exome. We set out to quantify and understand how WGS compares with the targeted capture and sequencing of the exome (exome-seq), for the specific purpose of identifying single nucleotide polymorphisms (SNPs) in exome targeted regions.

Results

We have compared polymorphism detection sensitivity and systematic biases using a set of tissue samples that have been subject to both deep exome and whole genome sequencing. The scoring of detection sensitivity was based on sequence down sampling and reference to a set of gold-standard SNP calls for each sample. Despite evidence of incremental improvements in exome capture technology over time, whole genome sequencing has greater uniformity of sequence read coverage and reduced biases in the detection of non-reference alleles than exome-seq. Exome-seq achieves 95% SNP detection sensitivity at a mean on-target depth of 40 reads, whereas WGS only requires a mean of 14 reads. Known disease causing mutations are not biased towards easy or hard to sequence areas of the genome for either exome-seq or WGS.

Conclusions

From an economic perspective, WGS is at parity with exome-seq for variant detection in the targeted coding regions. WGS offers benefits in uniformity of read coverage and more balanced allele ratio calls, both of which can in most cases be offset by deeper exome-seq, with the caveat that some exome-seq targets will never achieve sufficient mapped read depth for variant detection due to technical difficulties or probe failures. As WGS is intrinsically richer data that can provide insight into polymorphisms outside coding regions and reveal genomic rearrangements, it is likely to progressively replace exome-seq for many applications.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-247) contains supplementary material, which is available to authorized users.     

Haplotype‐based genotyping‐by‐sequencing in oat genome research

In a de novo genotyping‐by‐sequencing (GBS) analysis of short, 64‐base tag‐level haplotypes in 4657 accessions of cultivated oat, we discovered 164741 tag‐level (TL) genetic variants containing 241224 SNPs. From this, the marker density of an oat consensus map was increased by the addition of more than 70000 loci. The mapped TL genotypes of a 635‐line diversity panel were used to infer chromosome‐level (CL) haplotype maps. These maps revealed differences in the number and size of haplotype blocks, as well as differences in haplotype diversity between chromosomes and subsets of the diversity panel. We then explored potential benefits of SNP vs. TL vs. CL GBS variants for mapping, high‐resolution genome analysis and genomic selection in oats. A combined genome‐wide association study (GWAS) of heading date from multiple locations using both TL haplotypes and individual SNP markers identified 184 significant associations. A comparative GWAS using TL haplotypes, CL haplotype blocks and their combinations demonstrated the superiority of using TL haplotype markers. Using a principal component‐based genome‐wide scan, genomic regions containing signatures of selection were identified. These regions may contain genes that are responsible for the local adaptation of oats to Northern American conditions. Genomic selection for heading date using TL haplotypes or SNP markers gave comparable and promising prediction accuracies of up to r = 0.74. Genomic selection carried out in an independent calibration and test population for heading date gave promising prediction accuracies that ranged between r = 0.42 and 0.67. In conclusion, TL haplotype GBS‐derived markers facilitate genome analysis and genomic selection in oat.     

Whole genome sequences are required to fully resolve the linkage disequilibrium structure of human populations

Background

An understanding of linkage disequilibrium (LD) structures in the human genome underpins much of medical genetics and provides a basis for disease gene mapping and investigating biological mechanisms such as recombination and selection. Whole genome sequencing (WGS) provides the opportunity to determine LD structures at maximal resolution.

Results

We compare LD maps constructed from WGS data with LD maps produced from the array-based HapMap dataset, for representative European and African populations. WGS provides up to 5.7-fold greater SNP density than array-based data and achieves much greater resolution of LD structure, allowing for identification of up to 2.8-fold more regions of intense recombination. The absence of ascertainment bias in variant genotyping improves the population representativeness of the WGS maps, and highlights the extent of uncaptured variation using array genotyping methodologies. The complete capture of LD patterns using WGS allows for higher genome-wide association study (GWAS) power compared to array-based GWAS, with WGS also allowing for the analysis of rare variation. The impact of marker ascertainment issues in arrays has been greatest for Sub-Saharan African populations where larger sample sizes and substantially higher marker densities are required to fully resolve the LD structure.

Conclusions

WGS provides the best possible resource for LD mapping due to the maximal marker density and lack of ascertainment bias. WGS LD maps provide a rich resource for medical and population genetics studies. The increasing availability of WGS data for large populations will allow for improved research utilising LD, such as GWAS and recombination biology studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1854-0) contains supplementary material, which is available to authorized users.     

Reconstructing the plant mitochondrial genome for marker discovery: a case study using Pinus

Whole‐genome‐shotgun (WGS) sequencing of total genomic DNA was used to recover ~1 Mbp of novel mitochondrial (mtDNA) sequence from Pinus sylvestris (L.) and three members of the closely related Pinus mugo species complex. DNA was extracted from megagametophyte tissue from six mother trees from locations across Europe, and 100‐bp paired‐end sequencing was performed on the Illumina HiSeq platform. Candidate mtDNA sequences were identified by their size and coverage characteristics, and by comparison with published plant mitochondrial genomes. Novel variants were identified, and primers targeting these loci were trialled on a set of 28 individuals from across Europe. In total, 31 SNP loci were successfully resequenced, characterizing 15 unique haplotypes. This approach offers a cost‐effective means of developing marker resources for mitochondrial genomes in other plant species where reference sequences are unavailable.     

High‐throughput genomics in sorghum: from whole‐genome resequencing to a SNP screening array

With its small, diploid and completely sequenced genome, sorghum (Sorghum bicolor L. Moench) is highly amenable to genomics‐based breeding approaches. Here, we describe the development and testing of a robust single‐nucleotide polymorphism (SNP) array platform that enables polymorphism screening for genome‐wide and trait‐linked polymorphisms in genetically diverse S. bicolor populations. Whole‐genome sequences with 6× to 12× coverage from five genetically diverse S. bicolor genotypes, including three sweet sorghums and two grain sorghums, were aligned to the sorghum reference genome. From over 1 million high‐quality SNPs, we selected 2124 Infinium Type II SNPs that were informative in all six source genomes, gave an optimal Assay Design Tool (ADT) score, had allele frequencies of 50% in the six genotypes and were evenly spaced throughout the S. bicolor genome. Furthermore, by phenotype‐based pool sequencing, we selected an additional 876 SNPs with a phenotypic association to early‐stage chilling tolerance, a key trait for European sorghum breeding. The 3000 attempted bead types were used to populate half of a dual‐species Illumina iSelect SNP array. The array was tested using 564 Sorghum spp. genotypes, including offspring from four unrelated recombinant inbred line (RIL) and F₂ populations and a genetic diversity collection. A high call rate of over 80% enabled validation of 2620 robust and polymorphic sorghum SNPs, underlining the efficiency of the array development scheme for whole‐genome SNP selection and screening, with diverse applications including genetic mapping, genome‐wide association studies and genomic selection.     

High-performance single-chip exon capture allows accurate whole exome sequencing using the Illumina Genome Analyzer

Here we present an adaptation of NimbleGen 2.1M-probe array sequence capture for whole exome sequencing using the Illumina Genome Analyzer (GA) platform. The protocol involves two-stage library construction. The specificity of exome enrichment was approximately 80% with 95.6% even coverage of the 34 Mb target region at an average sequencing depth of 33-fold. Comparison of our results with whole genome shot-gun resequencing results showed that the exome SNP calls gave only 0.97% false positive and 6.27% false negative variants. Our protocol is also well suited for use with whole genome amplified DNA. The results presented here indicate that there is a promising future for large-scale population genomics and medical studies using a whole exome sequencing approach.     

Comparing strategies for selection of low-density SNPs for imputation-mediated genomic prediction in U. S. Holsteins

SNP chips are commonly used for genotyping animals in genomic selection but strategies for selecting low-density (LD) SNPs for imputation-mediated genomic selection have not been addressed adequately. The main purpose of the present study was to compare the performance of eight LD (6K) SNP panels, each selected by a different strategy exploiting a combination of three major factors: evenly-spaced SNPs, increased minor allele frequencies, and SNP-trait associations either for single traits independently or for all the three traits jointly. The imputation accuracies from 6K to 80K SNP genotypes were between 96.2 and 98.2%. Genomic prediction accuracies obtained using imputed 80K genotypes were between 0.817 and 0.821 for daughter pregnancy rate, between 0.838 and 0.844 for fat yield, and between 0.850 and 0.863 for milk yield. The two SNP panels optimized on the three major factors had the highest genomic prediction accuracy (0.821–0.863), and these accuracies were very close to those obtained using observed 80K genotypes (0.825–0.868). Further exploration of the underlying relationships showed that genomic prediction accuracies did not respond linearly to imputation accuracies, but were significantly affected by genotype (imputation) errors of SNPs in association with the traits to be predicted. SNPs optimal for map coverage and MAF were favorable for obtaining accurate imputation of genotypes whereas trait-associated SNPs improved genomic prediction accuracies. Thus, optimal LD SNP panels were the ones that combined both strengths. The present results have practical implications on the design of LD SNP chips for imputation-enabled genomic prediction.     

Whole‐genome resequencing‐based QTL‐seq identified candidate genes and molecular markers for fresh seed dormancy in groundnut

The subspecies fastigiata of cultivated groundnut lost fresh seed dormancy (FSD) during domestication and human‐made selection. Groundnut varieties lacking FSD experience precocious seed germination during harvest imposing severe losses. Development of easy‐to‐use genetic markers enables early‐generation selection in different molecular breeding approaches. In this context, one recombinant inbred lines (RIL) population (ICGV 00350 × ICGV 97045) segregating for FSD was used for deploying QTL‐seq approach for identification of key genomic regions and candidate genes. Whole‐genome sequencing (WGS) data (87.93 Gbp) were generated and analysed for the dormant parent (ICGV 97045) and two DNA pools (dormant and nondormant). After analysis of resequenced data from the pooled samples with dormant parent (reference genome), we calculated delta‐SNP index and identified a total of 10,759 genomewide high‐confidence SNPs. Two candidate genomic regions spanning 2.4 Mb and 0.74 Mb on the B05 and A09 pseudomolecules, respectively, were identified controlling FSD. Two candidate genes—RING‐H2 finger protein and zeaxanthin epoxidase—were identified in these two regions, which significantly express during seed development and control abscisic acid (ABA) accumulation. QTL‐seq study presented here laid out development of a marker, GMFSD1, which was validated on a diverse panel and could be used in molecular breeding to improve dormancy in groundnut.     

Genome‐wide genetic variation discovery in Chinese Taihu pig breeds using next generation sequencing

The Chinese Taihu pig breeds are an invaluable component of the world's pig genetic resources, and they are the most prolific breeds of swine in the world. In this study, the genomes of 252 pigs of the six indigenous breeds in the Taihu Lake region were sequenced using the genotyping by genome reducing and sequencing approach. A total of 950 million good reads were obtained using an Illumina Hiseq2000 at an average depth of 13× (for SNP calling) and an average coverage of 2.3%. In total, 122 632 indels, 31 444 insertions, 44 056 deletions and 455 CNVs (copy number variants) were identified in the genomes of the pigs. Approximately 2.3% of these genetic markers were mapped to gene exon regions, and 25% were in QTL regions related to economically important traits. The KEGG pathway or GO enrichment analyses revealed that genetic variants assumed to be large‐effect mutations were significantly overrepresented in 22 SNP, 56 indel, 26 insertion, 28 deletion and three CNV gene sets. A total of 343 breed‐specific SNPs were also identified in the six Chinese indigenous pigs. The findings from this study can contribute to future investigations of the genetic diversity, population structure, positive selection signals and molecular evolutionary history of these pigs at the genome level and can serve as a valuable reference for improving the breeding and cultivation of these pigs.     

Performance comparison of exome DNA sequencing technologies

Whole exome sequencing by high-throughput sequencing of target-enriched genomic DNA (exome-seq) has become common in basic and translational research as a means of interrogating the interpretable part of the human genome at relatively low cost. We present a comparison of three major commercial exome sequencing platforms from Agilent, Illumina and Nimblegen applied to the same human blood sample. Our results suggest that the Nimblegen platform, which is the only one to use high-density overlapping baits, covers fewer genomic regions than the other platforms but requires the least amount of sequencing to sensitively detect small variants. Agilent and Illumina are able to detect a greater total number of variants with additional sequencing. Illumina captures untranslated regions, which are not targeted by the Nimblegen and Agilent platforms. We also compare exome sequencing and whole genome sequencing (WGS) of the same sample, demonstrating that exome sequencing can detect additional small variants missed by WGS.     

Estimation of linkage disequilibrium and interspecific gene flow in Ficedula flycatchers by a newly developed 50k single‐nucleotide polymorphism array

With the access to draft genome sequence assemblies and whole‐genome resequencing data from population samples, molecular ecology studies will be able to take truly genome‐wide approaches. This now applies to an avian model system in ecological and evolutionary research: Old World flycatchers of the genus Ficedula, for which we recently obtained a 1.1 Gb collared flycatcher genome assembly and identified 13 million single‐nucleotide polymorphism (SNP)s in population resequencing of this species and its sister species, pied flycatcher. Here, we developed a custom 50K Illumina iSelect flycatcher SNP array with markers covering 30 autosomes and the Z chromosome. Using a number of selection criteria for inclusion in the array, both genotyping success rate and polymorphism information content (mean marker heterozygosity = 0.41) were high. We used the array to assess linkage disequilibrium (LD) and hybridization in flycatchers. Linkage disequilibrium declined quickly to the background level at an average distance of 17 kb, but the extent of LD varied markedly within the genome and was more than 10‐fold higher in ‘genomic islands’ of differentiation than in the rest of the genome. Genetic ancestry analysis identified 33 F₁ hybrids but no later‐generation hybrids from sympatric populations of collared flycatchers and pied flycatchers, contradicting earlier reports of backcrosses identified from much fewer number of markers. With an estimated divergence time as recently as <1 Ma, this suggests strong selection against F₁ hybrids and unusually rapid evolution of reproductive incompatibility in an avian system.     

New resources for genetic studies in Populus nigra: genome‐wide SNP discovery and development of a 12k Infinium array

Whole genome resequencing of 51 Populus nigra (L.) individuals from across Western Europe was performed using Illumina platforms. A total number of 1 878 727 SNPs distributed along the P. nigra reference sequence were identified. The SNP calling accuracy was validated with Sanger sequencing. SNPs were selected within 14 previously identified QTL regions, 2916 expressional candidate genes related to rust resistance, wood properties, water‐use efficiency and bud phenology and 1732 genes randomly spread across the genome. Over 10 000 SNPs were selected for the construction of a 12k Infinium Bead‐Chip array dedicated to association mapping. The SNP genotyping assay was performed with 888 P. nigra individuals. The genotyping success rate was 91%. Our high success rate was due to the discovery panel design and the stringent parameters applied for SNP calling and selection. In the same set of P. nigra genotypes, linkage disequilibrium throughout the genome decayed on average within 5–7 kb to half of its maximum value. As an application test, ADMIXTURE analysis was performed with a selection of 600 SNPs spread throughout the genome and 706 individuals collected along 12 river basins. The admixture pattern was consistent with genetic diversity revealed by neutral markers and the geographical distribution of the populations. These newly developed SNP resources and genotyping array provide a valuable tool for population genetic studies and identification of QTLs through natural‐population based genetic association studies in P. nigra.     

Whole‐genome resequencing uncovers molecular signatures of natural and sexual selection in wild bighorn sheep

The identification of genes influencing fitness is central to our understanding of the genetic basis of adaptation and how it shapes phenotypic variation in wild populations. Here, we used whole‐genome resequencing of wild Rocky Mountain bighorn sheep (Ovis canadensis) to >50‐fold coverage to identify 2.8 million single nucleotide polymorphisms (SNPs) and genomic regions bearing signatures of directional selection (i.e. selective sweeps). A comparison of SNP diversity between the X chromosome and the autosomes indicated that bighorn males had a dramatically reduced long‐term effective population size compared to females. This probably reflects a long history of intense sexual selection mediated by male–male competition for mates. Selective sweep scans based on heterozygosity and nucleotide diversity revealed evidence for a selective sweep shared across multiple populations at RXFP2, a gene that strongly affects horn size in domestic ungulates. The massive horns carried by bighorn rams appear to have evolved in part via strong positive selection at RXFP2. We identified evidence for selection within individual populations at genes affecting early body growth and cellular response to hypoxia; however, these must be interpreted more cautiously as genetic drift is strong within local populations and may have caused false positives. These results represent a rare example of strong genomic signatures of selection identified at genes with known function in wild populations of a nonmodel species. Our results also showcase the value of reference genome assemblies from agricultural or model species for studies of the genomic basis of adaptation in closely related wild taxa.     

Linkage disequilibrium among commonly genotyped SNP variants detected from bull sequence,

Genomic prediction utilizing causal variants could increase selection accuracy above that achieved with SNPs genotyped by currently available arrays used for genomic selection. A number of variants detected from sequencing influential sires are likely to be causal, but noticeable improvements in prediction accuracy using imputed sequence variant genotypes have not been reported. Improvement in accuracy of predicted breeding values may be limited by the accuracy of imputed sequence variants. Using genotypes of SNPs on a high‐density array and non‐synonymous SNPs detected in sequence from influential sires of a multibreed population, results of this examination suggest that linkage disequilibrium between non‐synonymous and array SNPs may be insufficient for accurate imputation from the array to sequence. In contrast to 75% of array SNPs being strongly correlated to another SNP on the array, less than 25% of the non‐synonymous SNPs were strongly correlated to an array SNP. When correlations between non‐synonymous and array SNPs were strong, distances between the SNPs were greater than separation that might be expected based on linkage disequilibrium decay. Consistently near‐perfect whole‐genome linkage disequilibrium between the full array and each non‐synonymous SNP within the sequenced bulls suggests that whole‐genome approaches to infer sequence variants might be more accurate than imputation based on local haplotypes. Opportunity for strong linkage disequilibrium between sequence and array SNPs may be limited by discrepancies in allele frequency distributions, so investigating alternate genotyping approaches and panels providing greater chances of frequency‐matched SNPs strongly correlated to sequence variants is also warranted. Genotypes used for this study are available from https://www.animalgenome.org/repository/pub/ ;USDA2017.0519/.     

基于全基因组测序的法医单核苷酸多态性系谱推断研究

目的 通过全基因组测序（whole genome sequencing，WGS）获得高密度单核苷酸多态性（single nucleotide polymorphism，SNP）分型数据，评估分型准确性，研究建立WGS数据用于法医SNP系谱推断的方法。方法 通过华大MGISEQ-200RS测序平台对样本进行深度为30×的WGS，从测序数据中提取Wegene GSA芯片中的645 199个常染色体SNP位点，质控过滤后运用IBS/IBD算法计算预测亲缘关系，并对样本的族群来源进行分析。结果 从测序数据中提取的SNP分型与Wegene GSA芯片分型的一致率大于99.62%。测序获得的SNP数据使用IBS算法可预测1~4级亲缘关系，4级亲缘预测置信区间准确性达100%，使用IBD算法可预测1~7级亲缘关系，7级亲缘预测为有亲缘关系的准确性达100%，通过高深度WGS数据获取的SNP系谱推断能力与芯片预测结果无显著差异。同时，WGS数据用于族群推断与调查结果一致。结论 WGS技术可应用于法医SNP系谱推断，为案件侦破提供线索。     

Whole-genome sequencing in French Canadians from Quebec

Genome-wide association studies (GWAS) have had a tremendous success in the identification of common DNA sequence variants associated with complex human diseases and traits. However, because of their design, GWAS are largely inappropriate to characterize the role of rare and low-frequency DNA variants on human phenotypic variation. Rarer genetic variation is geographically more restricted, supporting the need for local whole-genome sequencing (WGS) efforts to study these variants in specific populations. Here, we present the first large-scale low-pass WGS of the French-Canadian population. Specifically, we sequenced at ~5.6× coverage the whole genome of 1970 French Canadians recruited by the Montreal Heart Institute Biobank and identified 29 million bi-allelic variants (31 % novel), including 19 million variants with a minor allele frequency (MAF) <0.5 %. Genotypes from the WGS data are highly concordant with genotypes obtained by exome array on the same individuals (99.8 %), even when restricting this analysis to rare variants (MAF <0.5, 99.9 %) or heterozygous sites (98.9 %). To further validate our data set, we showed that we can effectively use it to replicate several genetic associations with myocardial infarction risk and blood lipid levels. Furthermore, we analyze the utility of our WGS data set to generate a French-Canadian-specific imputation reference panel and to infer population structure in the Province of Quebec. Our results illustrate the value of low-pass WGS to study the genetics of human diseases in the founder French-Canadian population.