Quality Of Genotypes Research Articles

Optimizing Strategy for Whole-Genome Genotype Imputation in Scallops

Advancements in sequencing technologies have facilitated low-coverage whole-genome sequencing (lcWGS) for detecting millions of single nucleotide polymorphisms (SNPs) in large populations at low cost. The effectiveness of this approach relies on genotype imputation following lcWGS. While several imputation methods have been applied in human and other terrestrial animals, their performance in aquaculture organisms, particularly mollusks, remains unassessed. Mollusks contribute 70% of mariculture production in China and are characterized by high genetic polymorphism. In this study, we aimed to optimize reference panel construction and evaluate imputation strategies for SNP detection in two scallops, Chlamys farreri and Patinopecten yessoensis, both important bivalve species. High-coverage whole-genome sequencing (hcWGS) data were used to construct haplotype reference panels, and three imputation methods were applied: Beagle5.4, GLIMPSE2, and QUILT. QUILT outperformed other methods, achieving imputation quality scores (IQS) exceeding 0.930 in C. farreri and 0.946 in P. yessoensis, and yielded the highest number of imputed loci in both species. Furthermore, panel construction was optimized by adjusting for genotype quality (GQ) and minor allele frequency (MAF); we determined that a GQ threshold > 20 and an MAF threshold > 0.01 were optimal. We observed that imputation quality improved with increased sequencing depth, plateauing at 0.5×, and varied with sample size depending on the imputation method. A negative correlation was observed between imputation quality and the ratio of non-synonymous to synonymous substitution rates (Ka/Ks) across chromosomal segments, highlighting the influence of selection pressure on imputation efficacy. Collectively, our findings provide an optimized framework for SNP genotyping using lcWGS in scallops, with implications for aquaculture breeding and genetic research.

The Whole Exome Analysis and Clinical Relevance in Patients with Atypical Hemolytic Uremic Syndrome

INTRODUCTION:Atypical hemolytic uremic syndrome (aHUS) is a rare, genetic, progressive, life-threatening form of thrombotic microangiopathy (TMA). A genetic mutation has been found as an explanation for constitutive complement activation in 50-60% of cases. In this abstract, we present whole exome analysis data results and their clinical relevance in 12 patients with aHUS.MATERIALS AND METHOD:We have enrolled 24 patients in this study who were diagnosed as aHUS and followed in our clinic since 2012. For exome sequencing, libraries were prepared using SeqCap EZ Human Exome Library v3.0 kit. Real Time Analysis software version with standard parameters was used for real-time image analysis and base calling. Raw sequencing data were aligned to the human genome hg19 reference (Homo sapiens (hg19) sequence) using CLC Genomic Workbench V10 (Qiagen) with standard parameters in paired-end mode. All variants were filtered according to the following criteria: not in dbSNP144 and also 1000 genome MAF <0.01, homozygous in the affected patients and heterozygous in the carrier parents, Genotype Quality (GQ) >30 and coverage (DP) >20 and, in homozygous region.FINDINGS AND RESULTS:We performed whole exome sequencing in all 24 patients but during raw data analysis 12 patients were found to be eligible for further analysis according to QC value. We didn't find any mutation in one of the patients (case 10) who had presented with diarrhea and had very good response to plasmapheresis only. We detected 32 different complement mutations in 4 different genes including CC3, CFI, CFH and membrane MCP in all other 11 patients. These mutations are summarized in the table.Four of the patients have responded to plasmapheresis and eculizumab was not needed. Of the remaining 20 patients eculizumab was aimed to be used. However, two of them died due to acute respiratory distress and thrombosis prior to eculizumab therapy. Case 9 was one of them and we found 21 different mutation including nine in CC3, five in CFH and 7 in MCF. One of our patients died in her 3rd month of eculizumab therapy (Case 2) and she had also seven mutations mostly in CFH gene. We used eculizumab in 18 of the patients and got complete response for thrombotic microangiopathy (TMA) in all. We had very good response to eculizumab in all these patients; even hemodialysis need disappeared in nine of them during eculizumab therapy. We experienced recurrence of renal failure in one patient (Case11) under eculizumab treatment in two years. She had also two different mutations in CFI gene four of the seven mutations in MCP gene and four of the 15 mutations in CC3 gene. This patient was lost due to multiple organ failure.We decided to stop eculizumab at the ninth month of treatment in two of the patients (Case 1 and Case 3) who had complete hematologic and renal recovery. Case 1 relapsed 16 months later and successfully retreated with eculizumab. She had 15 different mutations as eight mutations in CC3, six mutations in CFH, and 1mutation in MCP gene. Case 3 is still under control for three years after stopping eculizumab treatment. But as an interesting finding, this patient was 80 years old and had the heaviest mutation load with 22 different mutations (6 of the 7 mutations in MCP gene, 11 of the 15 mutation in CC3 gene, 4 of the 8 mutation in CFH gene).AHUS has been diagnosed during pregnancy in two of the patients and they had a successful delivery with healthy babies without any complication including ESRD. We also found 14 different mutations mostly in CFH gene in one of them (Case 8).DISCUSSION AND CONCLUSION:We found at least four different mutations in all patients except one case. Although MCP mutations accounts for only 15% of the aHUS cases we also found at least one MCP mutation in 91% of our cases. We found the rarest mutations in CFI gene. Although there are no clinical predictors of outcome, knowledge of the underlying genetic defect is helpful in predicting prognosis. But the patient who had the most number of mutations also had the best prognosis in our cohort. Therefore it is hard to use mutation data for management and prognosis of the syndrome. But the presence of a mutation is clearly important for the ESRD before transplantation decision and also would encourage patients to continue treatment. [Display omitted] DisclosuresYenerel:Alexion: Honoraria. Caliskan:Alexion: Honoraria. Yildiz:Alexion: Honoraria. Turkmen:Alexion: Honoraria.

Ionizing Radiation Alters the Transition/Transversion Ratio in the Exome of Human Gingiva Fibroblasts.

Little is known about the mutational impact of ionizing radiation (IR) exposure on a genome-wide level in mammalian tissues. Recent advancements in sequencing technology have provided powerful tools to perform exome-wide analyses of genetic variation. This also opened up new avenues for studying and characterizing global genomic IR-induced effects. However, genotypes generated by next generation sequencing (NGS) studies can contain errors, which may significantly impact the power to detect signals in common and rare variant analyses. These genotyping errors are not explicitly detected by the standard Genotype Analysis ToolKit (GATK) and Variant Quality Score Recalibration (VQSR) tool and thus remain a potential source of false-positive variants in whole exome sequencing (WES) datasets. In this context, the transition-transversion ratio (Ti/Tv) is commonly used as an additional quality check. In case of IR experiments, this is problematic when Ti/Tv itself might be influenced by IR treatment. It was the aim of this study to determine a suitable threshold for variant filters for NGS datasets from irradiated cells in order to achieve high data quality using Ti/Tv, while at the same time being able to investigate radiation-specific effects on the Ti/Tv ratio for different radiation doses. By testing a variety of filter settings and comparing the obtained results with publicly available datasets, we observe that a coverage filter setting of depth (DP) 3 and genotype quality (GQ) 20 is sufficient for high quality single nucleotide variants (SNVs) calling in an analysis combining GATK and VSQR and that Ti/Tv values are a consistent and useful indicator for data quality assessment for all tested NGS platforms. Furthermore, we report a reduction in Ti/Tv in IR-induced mutations in primary human gingiva fibroblasts (HGFs), which points to an elevated proportion of transversions among IR-induced SNVs and thus might imply that mismatch repair (MMR) plays a role in the cellular damage response to IR-induced DNA lesions.

Expected Genotype Quality and Diploidized Marker Data from Genotyping-by-Sequencing of Urochloa spp. Tetraploids.

Introduced concept of expected genotype quality (EGQ) and software to calculate it Provided read depth guidelines for GBS in tetraploids Developed software to generate diploidized genotype calls from VCF files Demonstrated value of aligning GBS reads to a mock reference genome for SNP discovery Recommend filtering based on GQ and read depth to prevent genotype bias Although genotyping-by-sequencing (GBS) is a well-established marker technology in diploids, the development of best practices for tetraploid species is a topic of current research. We determined the theoretical relationship between read depth and the phred-scaled probability of genotype misclassification conditioned on the true genotype, which we call expected genotype quality (EGQ). If the GBS method has 0.5% allelic error, then 17 reads are needed to classify simplex tetraploids as heterozygous with 95% accuracy (EGQ = 13) vs. 61 reads to determine allele dosage. We developed an R script to convert tetraploid GBS data in variant call format (VCF) into diploidized genotype calls and applied it to 267 interspecific hybrids of the tetraploid forage grass Urochloa. When reads were aligned to a mock reference genome created from GBS data of the Urochloa brizantha (Hochst. ex A. Rich.) R. D. Webster cultivar Marandu, 25,678 biallelic single nucleotide polymorphism (SNPs) were discovered, compared with ∼3000 SNPs when aligning to the closest true reference genomes, Setaria viridis (L.) P. Beauv. and S. italica (L.) P. Beauv. Cross-validation revealed that missing genotypes were imputed by the random forest method with a median accuracy of 0.85 regardless of heterozygote frequency. Using the Urochloa spp. hybrids, we illustrated how filtering samples based only on genotype quality (GQ) creates genotype bias; a depth threshold based on EGQ is also needed regardless of whether genotypes are called using a diploidized or allele dosage model.

Estimating genotype error rates from high-coverage next-generation sequence data.

Exome and whole-genome sequencing studies are becoming increasingly common, but little is known about the accuracy of the genotype calls made by the commonly used platforms. Here we use replicate high-coverage sequencing of blood and saliva DNA samples from four European-American individuals to estimate lower bounds on the error rates of Complete Genomics and Illumina HiSeq whole-genome and whole-exome sequencing. Error rates for nonreference genotype calls range from 0.1% to 0.6%, depending on the platform and the depth of coverage. Additionally, we found (1) no difference in the error profiles or rates between blood and saliva samples; (2) Complete Genomics sequences had substantially higher error rates than Illumina sequences had; (3) error rates were higher (up to 6%) for rare or unique variants; (4) error rates generally declined with genotype quality (GQ) score, but in a nonlinear fashion for the Illumina data, likely due to loss of specificity of GQ scores greater than 60; and (5) error rates increased with increasing depth of coverage for the Illumina data. These findings, especially (3)–(5), suggest that caution should be taken in interpreting the results of next-generation sequencing-based association studies, and even more so in clinical application of this technology in the absence of validation by other more robust sequencing or genotyping methods.

Abstract 2378: Harmonization of next generation sequencing data within consortia for gene discovery in familial breast cancer

Abstract Massively parallel next generation sequencing data (NGS) has proved to be useful in identification of rare Mendelian disorders. Efforts are on-going to use NGS for discovery of novel rare variants in common disease etiology. Distinguishing rare private mutations from causal variants remains a major challenge in complex disorders. With decreasing cost, investigators are generating NGS data for common cancers. However, power and other considerations suggest that a consortia approach of pooling data is more likely to succeed where individual efforts fail. Combining data within such consortia brings its own level of challenges. We enumerate these challenges and suggest recommendations based on our experience of combining and harmonizing NGS data on 358 breast cancer samples from four centers; (City of Hope n=8, Mayo Clinic n=221, MSKCC n=96 and University of Pennsylvania n=33). Major concerns were ability of individual datasets to discover known and novel variations, depth of coverage across the exome, and quality of variants. Using pre- and post-processed data, we demonstrate an increased power for detecting variants when using a joint-calling method, quality control (QC) and quality assurance pre- and post-merge, as well as the advantages of combined annotations and filtering procedures. To generate a filtered list of high confident calls, we subset the data on Depth per Allele (DP), Genotyping Quality (GQ), Allele balance, Phred likelihood score (PL) as well as using frequency filters on the alternate allele both within the data and comparing against convenience control summaries. We describe general principles and QC measures to be adopted when NGS data is used from diverse sources, specifically applicable to large consortia. Note: This abstract was not presented at the meeting. Citation Format: Joseph Vijai, Steven Hart, Tinu Thomas, Bradley Wubbenhorst, Lucia Guidugli, Kasmintan Schrader, Kara Maxwell, Lauren Jacobs, Danylo Villano, Robert Klein, Steven Lipkin, Susan Neuhausen, Jeffrey Weitzel, David Altshuler, Fergus Couch, Kenneth Offit, Katherine Nathanson. Harmonization of next generation sequencing data within consortia for gene discovery in familial breast cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2378. doi:10.1158/1538-7445.AM2014-2378