Tagging large CNV blocks in wheat boosts digitalization of germplasm resources by ultra-low-coverage sequencing.

BACKGROUND: The massive structural variations and frequent introgression highly contribute to the genetic diversity of wheat, while the huge and complex genome of polyploid wheat hinders efficient genotyping of abundant varieties towards accurate identification, management, and exploitation of germplasm resources. RESULTS: We develop a novel workflow that identifies 1240 high-quality large copy number variation blocks (CNVb) in wheat at the pan-genome level, demonstrating that CNVb can serve as an ideal DNA fingerprinting marker for discriminating massive varieties, with the accuracy validated by PCR assay. We then construct a digitalized genotyping CNVb map across 1599 global wheat accessions. Key CNVb markers are linked with trait-associated introgressions, such as the 1RS·1BL translocation and 2NvS translocation, and the beneficial alleles, such as the end-use quality allele Glu-D1d (Dx5 + Dy10) and the semi-dwarf r-e-z allele. Furthermore, we demonstrate that these tagged CNVb markers promote a stable and cost-effective strategy for evaluating wheat germplasm resources with ultra-low-coverage sequencing data, competing with SNP array for applications such as evaluating new varieties, efficient management of collections in gene banks, and describing wheat germplasm resources in a digitalized manner. We also develop a user-friendly interactive platform, WheatCNVb ( http://wheat.cau.edu.cn/WheatCNVb/ ), for exploring the CNVb profiles over ever-increasing wheat accessions, and also propose a QR-code-like representation of individual digital CNVb fingerprint. This platform also allows uploading new CNVb profiles for comparison with stored varieties. CONCLUSIONS: The CNVb-based approach provides a low-cost and high-throughput genotyping strategy for enabling digitalized wheat germplasm management and modern breeding with precise and practical decision-making.

Population Genomics Reveals the Underlying Structure of the Small Pelagic European Sardine and Suggests Low Connectivity within Macaronesia.

The European sardine (Sardina pilchardus, Walbaum 1792) is indisputably a commercially important species. Previous studies using uneven sampling or a limited number of makers have presented sometimes conflicting evidence of the genetic structure of S. pilchardus populations. Here, we show that whole genome data from 108 individuals from 16 sampling areas across 5000 km of the species' distribution range (from the Eastern Mediterranean to the archipelago of Azores) support at least three genetic clusters. One includes individuals from Azores and Madeira, with evidence of substructure separating these two archipelagos in the Atlantic. Another cluster broadly corresponds to the center of the distribution, including the sampling sites around Iberia, separated by the Almeria-Oran front from the third cluster that includes all of the Mediterranean samples, except those from the Alboran Sea. Individuals from the Canary Islands appear to belong to the Mediterranean cluster. This suggests at least two important geographical barriers to gene flow, even though these do not seem complete, with many individuals from around Iberia and the Mediterranean showing some patterns compatible with admixture with other genetic clusters. Genomic regions corresponding to the top outliers of genetic differentiation are located in areas of low recombination indicative that genetic architecture also has a role in shaping population structure. These regions include genes related to otolith formation, a calcium carbonate structure in the inner ear previously used to distinguish S. pilchardus populations. Our results provide a baseline for further characterization of physical and genetic barriers that divide European sardine populations, and information for transnational stock management of this highly exploited species towards sustainable fisheries.

Patchwork: Alignment-Based Retrieval and Concatenation of Phylogenetic Markers from Genomic Data.

Low-coverage whole-genome sequencing (also known as "genome skimming") is becoming an increasingly affordable approach to large-scale phylogenetic analyses. While already routinely used to recover organellar genomes, genome skimming is rather rarely utilized for recovering single-copy nuclear markers. One reason might be that only few tools exist to work with this data type within a phylogenomic context, especially to deal with fragmented genome assemblies. We here present a new software tool called Patchwork for mining phylogenetic markers from highly fragmented short-read assemblies as well as directly from sequence reads. Patchwork is an alignment-based tool that utilizes the sequence aligner DIAMOND and is written in the programming language Julia. Homologous regions are obtained via a sequence similarity search, followed by a "hit stitching" phase, in which adjacent or overlapping regions are merged into a single unit. The novel sliding window algorithm trims away any noncoding regions from the resulting sequence. We demonstrate the utility of Patchwork by recovering near-universal single-copy orthologs within a benchmarking study, and we additionally assess the performance of Patchwork in comparison with other programs. We find that Patchwork allows for accurate retrieval of (putatively) single-copy genes from genome skimming data sets at different sequencing depths with high computational speed, outperforming existing software targeting similar tasks. Patchwork is released under the GNU General Public License version 3. Installation instructions, additional documentation, and the source code itself are all available via GitHub at https://github.com/fethalen/Patchwork.

Y and Mitochondrial Chromosomes in the Heterogeneous Stock Rat Population.

Genome-wide association studies typically evaluate the autosomes and sometimes the X Chromosome, but seldom consider the Y or mitochondrial Chromosomes. We genotyped the Y and mitochondrial chromosomes in heterogeneous stock rats (Rattus norvegicus), which were created in 1984 by intercrossing eight inbred strains and have subsequently been maintained as an outbred population for 100 generations. As the Y and mitochondrial Chromosomes do not recombine, we determined which founder had contributed these chromosomes for each rat, and then performed association analysis for all complex traits (n=12,055; intersection of 12,116 phenotyped and 15,042 haplotyped rats). We found the eight founders had 8 distinct Y and 4 distinct mitochondrial Chromosomes, however only two of each were observed in our modern heterogeneous stock rat population (Generations 81-97). Despite the unusually large sample size, the p-value distribution did not deviate from expectations; there were no significant associations for behavioral, physiological, metabolome, or microbiome traits after correcting for multiple comparisons. However, both Y and mitochondrial Chromosomes were strongly associated with expression of a few genes located on those chromosomes, which provided a positive control. Our results suggest that within modern heterogeneous stock rats there are no Y and mitochondrial Chromosomes differences that strongly influence behavioral or physiological traits. These results do not address other ancestral Y and mitochondrial Chromosomes that do not appear in modern heterogeneous stock rats, nor do they address effects that may exist in other rat populations, or in other species.

CNETML: maximum likelihood inference of phylogeny from copy number profiles of multiple samples.

Phylogenetic trees based on copy number profiles from multiple samples of a patient are helpful to understand cancer evolution. Here, we develop a new maximum likelihood method, CNETML, to infer phylogenies from such data. CNETML is the first program to jointly infer the tree topology, node ages, and mutation rates from total copy numbers of longitudinal samples. Our extensive simulations suggest CNETML performs well on copy numbers relative to ploidy and under slight violation of model assumptions. The application of CNETML to real data generates results consistent with previous discoveries and provides novel early copy number events for further investigation.

Low-coverage sequencing in a deep intercross of the Virginia body weight lines provides insight to the polygenic genetic architecture of growth: novel loci revealed by increased power and improved genome-coverage.

Genetic dissection of highly polygenic traits is a challenge, in part due to the power necessary to confidently identify loci with minor effects. Experimental crosses are valuable resources for mapping such traits. Traditionally, genome-wide analyses of experimental crosses have targeted major loci using data from a single generation (often the F2) with individuals from later generations being generated for replication and fine-mapping. Here, we aim to confidently identify minor-effect loci contributing to the highly polygenic basis of the long-term, bi-directional selection responses for 56-d body weight in the Virginia body weight chicken lines. To achieve this, a strategy was developed to make use of data from all generations (F2-F18) of the advanced intercross line, developed by crossing the low and high selected lines after 40 generations of selection. A cost-efficient low-coverage sequencing based approach was used to obtain high-confidence genotypes in 1Mb bins across 99.3% of the chicken genome for >3,300 intercross individuals. In total, 12 genome-wide significant, and 30 additional suggestive QTL reaching a 10% FDR threshold, were mapped for 56-d body weight. Only 2 of these QTL reached genome-wide significance in earlier analyses of the F2 generation. The minor-effect QTL mapped here were generally due to an overall increase in power by integrating data across generations, with contributions from increased genome-coverage and improved marker information content. The 12 significant QTL explain >37% of the difference between the parental lines, three times more than 2 previously reported significant QTL. The 42 significant and suggestive QTL together explain >80%. Making integrated use of all available samples from multiple generations in experimental crosses are economically feasible using the low-cost, sequencing-based genotyping strategies outlined here. Our empirical results illustrate the value of this strategy for mapping novel minor-effect loci contributing to complex traits to provide a more confident, comprehensive view of the individual loci that form the genetic basis of the highly polygenic, long-term selection responses for 56-d body weight in the Virginia body weight chicken lines.

Genomic variation across Chinook salmon populations reveals effects of a duplication on migration alleles and supports fine scale structure.

The distribution of ecotypic variation in natural populations is influenced by neutral and adaptive evolutionary forces that are challenging to disentangle. This study provides a high-resolution portrait of genomic variation in Chinook salmon (Oncorhynchus tshawytscha) with emphasis on a region of major effect for ecotypic variation in migration timing. With a filtered data set of ~13 million single nucleotide polymorphisms (SNPs) from low-coverage whole genome resequencing of 53 populations (3566 barcoded individuals), we contrasted patterns of genomic structure within and among major lineages and examined the extent of a selective sweep at a major effect region underlying migration timing (GREB1L/ROCK1). Neutral variation provided support for fine-scale structure of populations, while allele frequency variation in GREB1L/ROCK1 was highly correlated with mean return timing for early and late migrating populations within each of the lineages (r2  = .58-.95; p < .001). However, the extent of selection within the genomic region controlling migration timing was much narrower in one lineage (interior stream-type) compared to the other two major lineages, which corresponded to the breadth of phenotypic variation in migration timing observed among lineages. Evidence of a duplicated block within GREB1L/ROCK1 may be responsible for reduced recombination in this portion of the genome and contributes to phenotypic variation within and across lineages. Lastly, SNP positions across GREB1L/ROCK1 were assessed for their utility in discriminating migration timing among lineages, and we recommend multiple markers nearest the duplication to provide highest accuracy in conservation applications such as those that aim to protect early migrating Chinook salmon. These results highlight the need to investigate variation throughout the genome and the effects of structural variants on ecologically relevant phenotypic variation in natural species.

Identification of RP1 as the genetic cause of retinitis pigmentosa in a multi-generational pedigree using Extremely Low-Coverage Whole Genome Sequencing (XLC-WGS).

MOTIVATION: Next-generation sequencing (NGS) technologies are decisive for discovering disease-causing variants, although their cost limits their utility in a clinical setting. A cost-mitigating alternative is an extremely low coverage whole-genome sequencing (XLC-WGS). We investigated its use to identify causal variants within a multi-generational pedigree of individuals with retinitis pigmentosa (RP). Causing progressive vision loss, RP is a group of genetically heterogeneous eye disorders with approximately 60 known causal genes. RESULTS: We performed XLC-WGS in seventeen members of this pedigree, including three individuals with a confirmed diagnosis of RP. Sequencing data were processed using Illumina's DRAGEN pipeline and filtered using Illumina's genotype quality score metric (GQX). The resulting variants were analyzed using Expert Variant Interpreter (eVai) from enGenome as a prioritization tool. A nonsense known mutation (c.1625C > G; p.Ser542*) in exon 4 of the RP1 gene emerged as the most likely causal variant. We identified two homozygous carriers of this variant among the three sequenced RP cases and three heterozygous individuals with sufficient coverage of the RP1 locus. Our data show the utility of combining pedigree information with XLC-WGS as a cost-effective approach to identify disease-causing variants.

Low-coverage reduced representation sequencing reveals subtle within-island genetic structure in Aldabra giant tortoises.

Aldabrachelys gigantea (Aldabra giant tortoise) is one of only two giant tortoise species left in the world and survives as a single wild population of over 100,000 individuals on Aldabra Atoll, Seychelles. Despite this large current population size, the species faces an uncertain future because of its extremely restricted distribution range and high vulnerability to the projected consequences of climate change. Captive-bred A. gigantea are increasingly used in rewilding programs across the region, where they are introduced to replace extinct giant tortoises in an attempt to functionally resurrect degraded island ecosystems. However, there has been little consideration of the current levels of genetic variation and differentiation within and among the islands on Aldabra. As previous microsatellite studies were inconclusive, we combined low-coverage and double-digest restriction-associated DNA (ddRAD) sequencing to analyze samples from 33 tortoises (11 from each main island). Using 5426 variant sites within the tortoise genome, we detected patterns of within-island population structure, but no differentiation between the islands. These unexpected results highlight the importance of using genome-wide genetic markers to capture higher-resolution genetic structure to inform future management plans, even in a seemingly panmictic population. We show that low-coverage ddRAD sequencing provides an affordable alternative approach to conservation genomic projects of non-model species with large genomes.

Assessment of the performance of different imputation methods for low-coverage sequencing in Holstein cattle.

Low-coverage sequencing (LCS) followed by imputation has been proposed as a cost-effective genotyping approach for obtaining genotypes of whole-genome variants. Imputation performance is essential for the effectiveness of this approach. Several imputation methods have been proposed and successfully applied in genomic studies in human and other species. However, there are few reports on the performance of these methods in livestock. Here, we evaluated a variety of imputation methods, including Beagle v4.1, GeneImp v1.3, GLIMPSE v1.1.0, QUILT v1.0.0, Reveel, and STITCH v1.6.5, with varying sequencing depth, sample size, and reference panel size using LCS data of Holstein cattle. We found that all of these methods, except Reveel, performed well in most cases with an imputation accuracy over 0.9; on the whole, GLIMPSE, QUILT, and STITCH performed better than the other methods. For species with no reference panel available, STITCH followed by Beagle would be an optimal strategy, whereas for species with reference panel available, QUILT would be the method of choice. Overall, this study illustrated the promising potential of LCS for genomic analysis in livestock.

Atlantic herring (Clupea harengus) population structure in the Northeast Atlantic Ocean.

The Atlantic herring Clupea harengus L has a vast geographical distribution and a complex population structure with a few very large migratory units and many small local populations. Each population has its own spawning ground and/or time, thereby maintaining their genetic integrity. Several herring populations migrate between common feeding grounds and over-wintering areas resulting in frequent mixing of populations. Thus, many herring fisheries are based on mixed populations of different demographic status. In order to avoid over-exploitation of weak populations and to conserve biodiversity, understanding the population structure and population mixing is important for maintaining biologically sustainable herring fisheries. The aim of this study was to investigate the genetic population structure of herring in the Faroese and surrounding waters, and to develop genetic markers for distinguishing between four herring management units (often called stocks), namely the Norwegian spring-spawning herring (NSSH), Icelandic summer-spawning herring (ISSH), North Sea autumn-spawning herring (NSAH), and Faroese autumn-spawning herring (FASH). Herring from the four stocks were sequenced at low coverage, and single nucleotide polymorphisms (SNPs) were called and used for population structure analysis and individual assignment. An ancestry-informative SNP panel with 118 SNPs was developed and tested on 240 individuals. The results showed that all four stocks appeared to be genetically differentiated populations, but at lower levels of differentiation between FASH and ISSH than the other two populations. Overall assignment rate with the SNP panel was 80.7%, and agreement between the genetic and traditional visual assignment was 75.5%. The NSAH and NSSH samples had the highest assignment rate (100% and 98.3%, respectively) and highest agreement between traditional and genetic assignment methods (96.6% and 94.9%, respectively). The FASH and ISSH samples had substantially lower assignment rates (72.9% and 51.7%, respectively) and agreement between traditional and genetic methods (39.5% and 48.4%, respectively).

Molecular genetic analysis and growth hormone response in patients with syndromic short stature.

BACKGROUND: Syndromic short stature is a genetic and phenotypic heterogeneous disorder with multiple causes. This study aims to identify genetic causes in patients with syndromic short stature of unknown cause and evaluate the efficacy of the growth hormone response. METHODS: Trio-whole-exome sequencing was applied to identify pathogenic gene mutations in seven patents with short stature, multiple malformations, and/or intellectual disability. Whole-genome low-coverage sequencing was also performed to identify copy number variants in three patients with concurrent intellectual disability. Recombinant human growth hormone was administered to improve height in patients with an identified cause of syndromic short stature. RESULTS: Of the seven patients, three pathogenic/likely pathogenic gene mutations, including one FGFR3 mutation (c.1620C>A p.N540K), one novel GNAS mutation (c.2288C>T p.A763V), and one novel TRPS1 mutation (c.2527_c.2528dupTA p.S843fsX72), were identified in three patients. No copy number variants were identified in the three patients with concurrent intellectual disability. The proband with an FGFR3 mutation, a female 4 and 3/12 years of age, was diagnosed with hypochondroplasia. Long-acting growth hormone improved her height from 85.8 cm [- 5.05 standard deviation (SD)] to 100.4 cm (- 4.02 SD), and her increased height SD score (SDS) was 1.03 after 25 months of treatment. The proband with a GNAS mutation, a female 12 and 9/12 years of age, was diagnosed with pseudohypoparathyroidism Ia. After 14 months of treatment with short-acting growth hormone, her height improved from 139.3 cm (- 2.69 SD) to 145.0 cm (- 2.36 SD), and her increased height SDS was 0.33. CONCLUSIONS: Trio-whole-exome sequencing was an important approach to confirm genetic disorders in patients with syndromic short stature of unknown etiology. Short-term growth hormone was effective in improving height in patients with hypochondroplasia and pseudohypoparathyroidism Ia.

Accelerated deciphering of the genetic architecture of agricultural economic traits in pigs using a low-coverage whole-genome sequencing strategy.

BACKGROUND: Uncovering the genetic architecture of economic traits in pigs is important for agricultural breeding. However, high-density haplotype reference panels are unavailable in most agricultural species, limiting accurate genotype imputation in large populations. Moreover, the infinitesimal model of quantitative traits implies that weak association signals tend to be spread across most of the genome, further complicating the genetic analysis. Hence, there is a need to develop new methods for sequencing large cohorts without large reference panels. RESULTS: We describe a Tn5-based highly accurate, cost- and time-efficient, low-coverage sequencing method to obtain 11.3 million whole-genome single-nucleotide polymorphisms in 2,869 Duroc boars at a mean depth of 0.73×. On the basis of these single-nucleotide polymorphisms, a genome-wide association study was performed, resulting in 14 quantitative trait loci (QTLs) for 7 of 21 important agricultural traits in pigs. These QTLs harbour genes, such as ABCD4 for total teat number and HMGA1 for back fat thickness, and provided a starting point for further investigation. The inheritance models of the different traits varied greatly. Most follow the minor-polygene model, but this can be attributed to different reasons, such as the shaping of genetic architecture by artificial selection for this population and sufficiently interconnected minor gene regulatory networks. CONCLUSIONS: Genome-wide association study results for 21 important agricultural traits identified 14 QTLs/genes and showed their genetic architectures, providing guidance for genetic improvement harnessing genomic features. The Tn5-based low-coverage sequencing method can be applied to large-scale genome studies for any species without a good reference panel and can be used for agricultural breeding.

Ultra-low coverage whole genome sequencing of ccfDNA in multiple myeloma: A tool for laboratory routine?

Multiple myeloma (MM), is a heterogeneous disease in which chromosomal abnormalities are important for prognostic risk stratification. Cytogenetic profiling with FISH on plasma cells from bone marrow samples (BM-PCs) is the current gold standard, but variable infiltration of plasma cells or failed aspiration can hamper this process. Ultra-low coverage sequencing (ULCS) of circulating cell-free DNA (ccfDNA) may offer a minimally invasive alternative for the work-up of these cases. We compared ULCS, aCGH and FISH on selected BM-PCs in a routine setting with ULCS of ccfDNA for the detection of somatic copy number aberrations (CNAs) in MM. METHODS: Purified CD138+ BM-PCs of 23 MM patients at initiation of their treatment were subjected to aCGH, FISH and ULCS. Paired samples of peripheral blood-ccfDNA obtained at diagnosis were analyzed by ULCS and compared to the results found in BM-PCs. RESULTS: Using ULCS of ccfDNA, cytogenetic markers were identified in 18 out of 23 patients; five cases could not be analyzed due to low (≤3%) tumor fraction (TF). High similarity between CNA profiles of BM-PCs and ccfDNA was found. Moreover, 78% of the ccfDNA profiles resulted in the same risk classification as the routine FISH and/or BM-PCs ULCS and aCGH. Chromothripsis was detected in five patients; these had the highest TF values (range 7.1% to 42%) in our series and their profiles showed other high-risk anomalies. CONCLUSION: This proof-of-principle study indicates that ULCS of ccfDNA can reveal CNAs in MM and should be explored further as a cost-efficient alternative, especially in cases where BM-PC purification fails.

Successful birth after preimplantation genetic testing for a couple with two different reciprocal translocations and review of the literature.

BACKGROUND: Preimplantation genetic testing for chromosomal structural rearrangements (PGT-SR) is widely applied in couples with single reciprocal translocation to increase the chance for a healthy live birth. However, limited knowledge is known on the data of PGT-SR when both parents have a reciprocal translocation. Here, we for the first time present a rare instance of PGT-SR for a non-consanguineous couple in which both parents carried an independent balanced reciprocal translocation and show how relevant genetic counseling data can be generated. METHODS: The precise translocation breakpoints were identified by whole genome low-coverage sequencing (WGLCS) and Sanger sequencing. Next-generation sequencing (NGS) combining with breakpoint-specific polymerase chain reaction (PCR) was used to define 24-chromosome and the carrier status of the euploid embryos. RESULTS: Surprisingly, 2 out of 3 day-5 blastocysts were found to be balanced for maternal reciprocal translocation while being normal for paternal translocation and thus transferable. The transferable embryo rate was significantly higher than that which would be expected theoretically. Transfer of one balanced embryo resulted in the birth of a healthy boy. CONCLUSION(S): Our data of PGT-SR together with a systematic review of the literature should help in providing couples carrying two different reciprocal translocations undergoing PGT-SR with more appropriate genetic counseling.

Low-coverage sequencing cost-effectively detects known and novel variation in underrepresented populations.

Genetic studies in underrepresented populations identify disproportionate numbers of novel associations. However, most genetic studies use genotyping arrays and sequenced reference panels that best capture variation most common in European ancestry populations. To compare data generation strategies best suited for underrepresented populations, we sequenced the whole genomes of 91 individuals to high coverage as part of the Neuropsychiatric Genetics of African Population-Psychosis (NeuroGAP-Psychosis) study with participants from Ethiopia, Kenya, South Africa, and Uganda. We used a downsampling approach to evaluate the quality of two cost-effective data generation strategies, GWAS arrays versus low-coverage sequencing, by calculating the concordance of imputed variants from these technologies with those from deep whole-genome sequencing data. We show that low-coverage sequencing at a depth of ≥4× captures variants of all frequencies more accurately than all commonly used GWAS arrays investigated and at a comparable cost. Lower depths of sequencing (0.5-1×) performed comparably to commonly used low-density GWAS arrays. Low-coverage sequencing is also sensitive to novel variation; 4× sequencing detects 45% of singletons and 95% of common variants identified in high-coverage African whole genomes. Low-coverage sequencing approaches surmount the problems induced by the ascertainment of common genotyping arrays, effectively identify novel variation particularly in underrepresented populations, and present opportunities to enhance variant discovery at a cost similar to traditional approaches.

Recalibration of mapping quality scores in Illumina short-read alignments improves SNP detection results in low-coverage sequencing data.

BACKGROUND: Low-coverage sequencing is a cost-effective way to obtain reads spanning an entire genome. However, read depth at each locus is low, making sequencing error difficult to separate from actual variation. Prior to variant calling, sequencer reads are aligned to a reference genome, with alignments stored in Sequence Alignment/Map (SAM) files. Each alignment has a mapping quality (MAPQ) score indicating the probability a read is incorrectly aligned. This study investigated the recalibration of probability estimates used to compute MAPQ scores for improving variant calling performance in single-sample, low-coverage settings. MATERIALS AND METHODS: Simulated tomato, hot pepper and rice genomes were implanted with known variants. From these, simulated paired-end reads were generated at low coverage and aligned to the original reference genomes. Features extracted from the SAM formatted alignment files for tomato were used to train machine learning models to detect incorrectly aligned reads and output estimates of the probability of misalignment for each read in all three data sets. MAPQ scores were then re-computed from these estimates. Next, the SAM files were updated with new MAPQ scores. Finally, Variant calling was performed on the original and recalibrated alignments and the results compared. RESULTS: Incorrectly aligned reads comprised only 0.16% of the reads in the training set. This severe class imbalance required special consideration for model training. The F1 score for detecting misaligned reads ranged from 0.76 to 0.82. The best performing model was used to compute new MAPQ scores. Single Nucleotide Polymorphism (SNP) detection was improved after mapping score recalibration. In rice, recall for called SNPs increased by 5.2%, while for tomato and pepper it increased by 3.1% and 1.5%, respectively. For all three data sets the precision of SNP calls ranged from 0.91 to 0.95, and was largely unchanged both before and after mapping score recalibration. CONCLUSION: Recalibrating MAPQ scores delivers modest improvements in single-sample variant calling results. Some variant callers operate on multiple samples simultaneously. They exploit every sample's reads to compensate for the low read-depth of individual samples. This improves polymorphism detection and genotype inference. It may be that small improvements in single-sample settings translate to larger gains in a multi-sample experiment. A study to investigate this is ongoing.

Skim-Sequencing Based Genotyping Reveals Genetic Divergence of the Wild and Domesticated Population of Black Tiger Shrimp (Penaeus monodon) in the Indo-Pacific Region.

The domestication of a wild-caught aquatic animal is an evolutionary process, which results in genetic discrimination at the genomic level in response to strong artificial selection. Although black tiger shrimp (Penaeus monodon) is one of the most commercially important aquaculture species, a systematic assessment of genetic divergence and structure of wild-caught and domesticated broodstock populations of the species is yet to be documented. Therefore, we used skim sequencing (SkimSeq) based genotyping approach to investigate the genetic structure of 50 broodstock individuals of P. monodon species, collected from five sampling sites (n = 10 in each site) across their distribution in Indo-Pacific regions. The wild-caught P. monodon broodstock population were collected from Malaysia (MS) and Japan (MJ), while domesticated broodstock populations were collected from Madagascar (MMD), Hawaii, HI, USA (MMO), and Thailand (MT). After various filtering process, a total of 194,259 single nucleotide polymorphism (SNP) loci were identified, in which 4983 SNP loci were identified as putatively adaptive by the pcadapt approach. In both datasets, pairwise FST estimates high genetic divergence between wild and domesticated broodstock populations. Consistently, different spatial clustering analyses in both datasets categorized divergent genetic structure into two clusters: (1) wild-caught populations (MS and MJ), and (2) domesticated populations (MMD, MMO and MT). Among 4983 putatively adaptive SNP loci, only 50 loci were observed to be in the coding region. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses suggested that non-synonymous mutated genes might be associated with the energy production, metabolic functions, respiration regulation and developmental rates, which likely act to promote adaptation to the strong artificial selection during the domestication process. This study has demonstrated the applicability of SkimSeq in a highly duplicated genome of P. monodon specifically, across a range of genetic backgrounds and geographical distributions, and would be useful for future genetic improvement program of this species in aquaculture.

Whole Genome Low-Coverage Sequencing Concurrently Detecting Copy Number Variations and Their Underlying Complex Chromosomal Rearrangements by Systematic Breakpoint Mapping in Intellectual Deficiency/Developmental Delay Patients.

Simple copy number variations (CNVs) detected by chromosomal microarray (CMA) can result from complex structural changes. Therefore, it is necessary to characterize potential structural changes that cause pathogenic CNVs. We applied whole-genome low-coverage sequencing (WGLCS) to concurrently detect pathogenic CNVs and their associated chromosomal rearrangements in 15 patients. All the patients had an average of 2-3 pathogenic CNVs involving 1-2 chromosomes. WGLCS identified all the 34 pathogenic CNVs found by microarray. By identifying chimeric read pairs, WGLCS mapped 70 breakpoints in these patients, of which 47 were finely mapped at the nucleotide level and confirmed by subsequent PCR amplification and Sanger sequencing of the junction fragments. In 15 patients, structural rearrangements were defined at molecular level in 13 patients. In 13 patients, WGLCS reveal no additional results in two patients. In another 11 patients, WGLCS revealed new breakpoints or finely mapped the genes disrupted by breakpoints or 1-6 bp microhomology and/or short insertion (4-70 bp) in the breakpoints junctions. However, structural changes in the other two patients still remained unclear after WGLCS was performed. The structural alteration identified in the 13 patients could be divided into the following categories: (1) interstitial inverted duplication with concomitant terminal deletion (inv dup del) (P1,P4,P9,P11); (2) the product of pericentric inversion (P5); (3) ring chromosome (P8); (4) interstitial duplication and/or triplication (P6, P7); and (5) +der(22)t(11;22) (P2,P15); (6) complex structural rearrangements (P3,P12,P14). WGLCS displayed the ability to discover CNVs and define breakpoints and its disrupted genes and its surrounding sequences in one experiment at base-pair-resolution, which help us to learn more about the mechanisms of formation of observed genomic rearrangements, and in which DNA replicative/repair mechanism might contribute to the formation of complex rearrangements in 11 patients. Clear karyotype at molecular level could help provide an accurate evaluation of recurrent risk and guide prenatal diagnosis or reproductive planning.

Next-Generation Sequencing Data Analysis on Pool-Seq and Low-Coverage Retinoblastoma Data.

Next-generation sequencing (NGS) is related to massively parallel or deep deoxyribonucleic acid (DNA) sequencing technology which has revolutionized genomic researches in recent years. Although the cost of generating NGS data was decreased compared to the one at the time of emerging this technology, its cost might still be somewhat a problem. Hence, new strategies as pool-seq and low-coverage NGS data have been developed to overcome the cost problem. Despite decreasing cost, it is important to elucidate whether they are efficient in NGS studies. We applied a bioinformatics pipeline on pool-seq and low-coverage retinoblastoma data retrieved from only tumor data. Retinoblastoma is an eye malignancy in childhood that is initiated by RB1 mutation or MYCN amplification and can lead to the loss of vision of eye(s), and even sometimes life. We applied our pipeline on both retinoblastoma disease data and two other particular data to testify the validity and also for comparison purposes in the aspect of performance. High-confidence variant calls from Genome in a Bottle Consortium were used for fulfilling these purposes. We observed that our pipeline successfully called higher number of variants than a standard pipeline for all these three different data. Besides, the recall and F-score values were quite better in our pipeline as being noteworthy. We further presented our results on disease data in the aspects of the variants, variant types and disease-related genes. This study provides a guideline for performing NGS data analysis pipeline on pool-seq and low-coverage sequencing data in conjunction. To get more conclusive outcomes of these two strategies, we recommend using cancer data having higher mutation rates and larger pools.