Genotype Calls Research Articles

B-236 New real-time PCR test for APOE genotyping in patients with Alzheimer’s disease before anti-amyloid therapy

Abstract Background In 2023, the US Food and Drug Administration approved LEQEMBI, a monoclonal antibody therapy targeting beta-amyloid plaques, for patients with early-stage Alzheimer’s disease (AD). However, anti-amyloid drug trials demonstrated an elevated risk for amyloid-related imaging abnormalities with cerebral edema (ARIA-E) in carriers of the AD risk allele apolipoprotein E epsilon 4 (APOE-ɛ4), especially in ɛ4/ɛ4 homozygous individuals [1-3]. Here, we report on the evaluation of a new real-time polymerase chain reaction (PCR) test for APOE genotyping. Methods The EURORealTime APOE (EUROIMMUN), which enables simultaneous testing for all six APOE genotypes (ɛ2/ɛ2, ɛ2/ɛ3, ɛ2/ɛ4, ɛ3/ɛ3, ɛ3/ɛ4, and ɛ4/ɛ4) by real-time PCR, was performed using genomic DNA (gDNA) isolated from EDTA blood samples. The PCR amplification results were evaluated using the EURORealTime Analysis Software (EUROIMMUN). Samples from healthy blood donors (HBD) were used to assess intra-assay, inter-assay and between-lot precision (n=6) as well as analytical sensitivity (n=21), with each APOE genotype represented by at least one sample. Assay accuracy was analyzed by comparing genotype assignments to the results of the CE-IVD-marked EUROArray APOE (EUROIMMUN) and bidirectional Sanger sequencing using 110 samples (100 AD, 10 HBD). APOE genotype frequencies were compared between patients (100 AD) and controls (250 HBD). Results Precision testing of the EURORealTime APOE resulted in 100% correct genotype calls. Assessment of the analytical sensitivity revealed valid and correct genotype assignment in all 21 samples, confirming 1 ng/µl as the recommended minimum gDNA concentration. The analytical accuracy amounted to 100% based on agreement with the two reference methods in 110/110 cases. Comparison of the APOE genotype distribution showed a higher proportion of ɛ4 allele carriers (i.e., ɛ2/ɛ4, ɛ3/ɛ4, and ɛ4/ɛ4) in AD patients than in HBD (50.0% vs. 25.2%). The prevalence of the ɛ4/ɛ4 genotype in these cohorts was 25.0% for AD patients compared with 2.8% for HBD. Conclusions The well-established high prevalence of the APOE-ɛ4/ɛ4 genotype in AD patients, together with the increased risk of ARIA-E in these homozygous carriers, substantiates the relevance of APOE genotyping prior to anti-amyloid treatment. The EURORealTime APOE test provides sensitive and reliable determination of APOE alleles, thus presenting a suitable tool to improve risk stratification for potential LEQEMBI recipients.

GenoTriplo: A SNP genotype calling method for triploids.

Triploidy is very useful in both aquaculture and some cultivated plants as the induced sterility helps to enhance growth and product quality, as well as acting as a barrier against the contamination of wild populations by escapees. To use genetic information from triploids for academic or breeding purposes, an efficient and robust method to genotype triploids is needed. We developed such a method for genotype calling from SNP arrays, and we implemented it in the R package named GenoTriplo. Our method requires no prior information on cluster positions and remains unaffected by shifted luminescence signals. The method relies on starting the clustering algorithm with an initial higher number of groups than expected from the ploidy level of the samples, followed by merging groups that are too close to each other to be considered as distinct genotypes. Accurate classification of SNPs is achieved through multiple thresholds of quality controls. We compared the performance of GenoTriplo with that of fitPoly, the only published method for triploid SNP genotyping with a free software access. This was assessed by comparing the genotypes generated by both methods for a dataset of 1232 triploid rainbow trout genotyped for 38,033 SNPs. The two methods were consistent for 89% of the genotypes, but for 26% of the SNPs, they exhibited a discrepancy in the number of different genotypes identified. For these SNPs, GenoTriplo had >95% concordance with fitPoly when fitPoly genotyped better. On the contrary, when GenoTriplo genotyped better, fitPoly had less than 50% concordance with GenoTriplo. GenoTriplo was more robust with less genotyping errors. It is also efficient at identifying low-frequency genotypes in the sample set. Finally, we assessed parentage assignment based on GenoTriplo genotyping and observed significant differences in mismatch rates between the best and second-best couples, indicating high confidence in the results. GenoTriplo could also be used to genotype diploids as well as individuals with higher ploidy level by adjusting a few input parameters.

Evaluating the Efficacy of Target Capture Sequencing for Genotyping in Cattle.

(1) Background: Target capture sequencing (TCS) is potentially a cost-effective way to detect single-nucleotide polymorphisms (SNPs) and an alternative to SNP array-based genotyping. (2) Methods: We evaluated the effectiveness and reliability of TCS in cattle breeding scenarios using 48 female and 8 male samples. DNA was extracted from blood samples, targeted for 71,746 SNPs with TWIST probes, and sequenced on an MGI platform. GATK and BCFtools were evaluated for the best genotyping calling tool. The genotypes were compared to existing genotypes from the Versa50K SNP array of the same animals by measuring accuracy as concordance (%) and R2. (3) Results: In this study, 71,553 SNPs and 166 indels were identified. The genotype comparison of 37,130 common SNPs between TCS and SNP arrays yielded high agreement, with a mean concordance of 98%, R2 of 0.98 and Cohen's kappa of 0.97. The concordances of sex prediction, parent verification and validation of five genotype markers of interest important for Wagyu breeding were 100% between TCS and SNP array. The elements of the genomic relationship matrix (GRM) constructed from the SNP array and TCS data demonstrated a correlation coefficient approaching unity (r = 0.9998). (4) Conclusions: Compared to the SNP array, TCS is a comparable, cost-effective and flexible platform for genotyping SNPs, including non-model organisms and underrepresented commercial animal populations.

A blended genome and exome sequencing method captures genetic variation in an unbiased, high-quality, and cost-effective manner.

We deployed the Blended Genome Exome (BGE), a DNA library blending approach that generates low pass whole genome (1-4× mean depth) and deep whole exome (30-40× mean depth) data in a single sequencing run. This technology is cost-effective, empowers most genomic discoveries possible with deep whole genome sequencing, and provides an unbiased method to capture the diversity of common SNP variation across the globe. To evaluate this new technology at scale, we applied BGE to sequence >53,000 samples from the Populations Underrepresented in Mental Illness Associations Studies (PUMAS) Project, which included participants across African, African American, and Latin American populations. We evaluated the accuracy of BGE imputed genotypes against raw genotype calls from the Illumina Global Screening Array. All PUMAS cohorts had concordance ≥95% among SNPs with MAF≥1%, and never fell below ≥90% for SNPs with MAF<1%. Furthermore, concordance rates among local ancestries within two recently admixed cohorts were consistent among SNPs with MAF≥1%, with only minor deviations in SNPs with MAF<1%. We also benchmarked the discovery capacity of BGE to access protein-coding copy number variants (CNVs) against deep whole genome data, finding that deletions and duplications spanning at least 3 exons had a positive predicted value of ~90%. Our results demonstrate BGE scalability and efficacy in capturing SNPs, indels, and CNVs in the human genome at 28% of the cost of deep whole-genome sequencing. BGE is poised to enhance access to genomic testing and empower genomic discoveries, particularly in underrepresented populations.

Variant graph craft (VGC): a comprehensive tool for analyzing genetic variation and identifying disease-causing variants

BackgroundThe variant call format (VCF) file is a structured and comprehensive text file crucial for researchers and clinicians in interpreting and understanding genomic variation data. It contains essential information about variant positions in the genome, along with alleles, genotype calls, and quality scores. Analyzing and visualizing these files, however, poses significant challenges due to the need for diverse resources and robust features for in-depth exploration.ResultsTo address these challenges, we introduce variant graph craft (VGC), a VCF file visualization and analysis tool. VGC offers a wide range of features for exploring genetic variations, including extraction of variant data, intuitive visualization, and graphical representation of samples with genotype information. VGC is designed primarily for the analysis of patient cohorts, but it can also be adapted for use with individual probands or families. It integrates seamlessly with external resources, providing insights into gene function and variant frequencies in sample data. VGC includes gene function and pathway information from Molecular Signatures Database (MSigDB) for GO terms, KEGG, Biocarta, Pathway Interaction Database, and Reactome. Additionally, it dynamically links to gnomAD for variant information and incorporates ClinVar data for pathogenic variant information. VGC supports the Human Genome Assembly Hg37 and Hg38, ensuring compatibility with a wide range of data sets, and accommodates various approaches to exploring genetic variation data. It can be tailored to specific user needs with optional phenotype input data.ConclusionsIn summary, VGC provides a comprehensive set of features tailored to researchers working with genomic variation data. Its intuitive interface, rapid filtering capabilities, and the flexibility to perform queries using custom groups make it an effective tool in identifying variants potentially associated with diseases. VGC operates locally, ensuring data security and privacy by eliminating the need for cloud-based VCF uploads, making it a secure and user-friendly tool. It is freely available at https://github.com/alperuzun/VGC.

A map of canine sequence variation relative to a Greenland wolf outgroup.

For over 15years, canine genetics research relied on a reference assembly from a Boxer breed dog named Tasha (i.e., canFam3.1). Recent advances in long-read sequencing and genome assembly have led to the development of numerous high-quality assemblies from diverse canines. These assemblies represent notable improvements in completeness, contiguity, and the representation of gene promoters and gene models. Although genome graph and pan-genome approaches have promise, most genetic analyses in canines rely upon the mapping of Illumina sequencing reads to a single reference. The Dog10K consortium, and others, have generated deep catalogs of genetic variation through an alignment of Illumina sequencing reads to a reference genome obtained from a German Shepherd Dog named Mischka (i.e., canFam4, UU_Cfam_GSD_1.0). However, alignment to a breed-derived genome may introduce bias in genotype calling across samples. Since the use of an outgroup reference genome may remove this effect, we have reprocessed 1929 samples analyzed by the Dog10K consortium using a Greenland wolf (mCanLor1.2) as the reference. We efficiently performed remapping and variant calling using a GPU-implementation of common analysis tools. The resulting call set removes the variability in genetic differences seen across samples and breed relationships revealed by principal component analysis are not affected by the choice of reference genome. Using this sequence data, we inferred the history of population sizes and found that village dog populations experienced a 9-13 fold reduction in historic effective population size relative to wolves.

O-270 Clinical-grade whole genome sequencing-based haplarithmisis enables all forms of preimplantation genetic testing

Abstract Study question Can a simple, and scalable assay be developed to concurrently perform all forms of preimplantation genetic testing (PGT), effectively addressing the increasing demand? Summary answer Whole genome sequencing-based haplarithmisis offers a single-assay solution for all forms of PGT, enhancing resolution and diagnosis in complex cases. What is known already State-of-the-art preimplantation genetic testing (PGT) methods, including genotyping-by-sequencing (GBS)-based haplotyping, enable PGT for monogenic disorders (PGT-M), structural rearrangements (PGT-SR), and aneuploidies (PGT-A) in a single assay. However, they are labour intensive, only partially cover the genome, require a close relative to determine meiotic and mitotic origin of aneuploidies, and are troublesome for consanguineous families and in complex genomic contexts. Furthermore, the prevailing PGT approach for mitochondrial DNA disorders (PGT-MT) involves a PCR-based method. This underscores the need to develop a universal PGT method that streamlines laboratory protocols and provides genome-wide coverage to address genetic disorders, even in complex genomic loci. Study design, size, duration In a retrospective pilot study, we compared whole genome sequencing (WGS) at 30-40X with GBS (6 embryos, 2 families, 3 indications). We carried out subsampling of WGS data and determined 10X as the optimal diagnostic depth-of-coverage. We clinically validated our approach by comparing WGS to GBS for PGT-M and PGT-SR (31 embryos, 16 families, 22 indications), to shallow sequencing for PGT-SR (10 embryos, 3 families) and to PCR-based PGT for PGT-MT (10 embryos, 2 families). Participants/materials, setting, methods DNA from parents and close relative(s) and whole genome amplified (WGAed) DNA from embryos, underwent WGS with subsequent haplarithmisis. Quality metrics, such as Mendelian inconsistency rates, number of informative SNPs, and haplotype concordance were assessed. Translocation breakpoints were identified utilizing aberrantly aligned paired sequencing reads. Additionally, single-nucleotide variants (SNVs), including mitochondrial DNA SNVs, were detected by allele counting. Finally, segregational origin was determined via a parents-only haplarithmisis approach that uses the embryo as phasing reference. Main results and the role of chance WGS-PGT alleviates technical limitations by decreasing WGA artifacts by 68.4%, increasing breadth of coverage by 4-fold, and decreasing wet-lab turnaround time by 2.5-fold. Owing to its inherent higher resolution and improved genomic coverage, 5 out of 13 – previously inconclusive – embryos with genetic disorders in complex genomic regions could be assigned a diagnosis. Furthermore, we show the ability to directly detect single- and few-base pair genetic variants in any region of interest. Importantly, we show trio-based PGT-A for aneuploidy origin, an approach we term PGT-AO that – using the parents-only genotype calls – can detect the segregational origin of aneuploidies without the need of a close relative as reference for phasing. The segregational origin detected by parents-only haplarithmisis was concordant with the segregational origin determined from haplarithmisis when using a close relative. Unbalanced translocations could be detected indirectly via haplotyping or directly at single base resolution using a structural variant caller. Mitochondrial heteroplasmy level calculation from WGS yielded comparable results to PCR-based methods, both in blastomere and trophectoderm biopsy samples. Limitations, reasons for caution Direct variant detection with an average coverage of 10X may occasionally be inadequate, as certain locations may lack sufficient coverage (&amp;lt;5 reads). Inherently to the limited sample size of PGT-MT families, WGS-PGT needs further validation. The need for invasive trophectoderm biopsies of preimplantation embryos remains the key limitation of WGS-PGT. Wider implications of the findings Our approach enables all forms of PGT in a single assay. Direct variant detection complements haplarithmisis, addressing inconclusive findings and aiding families with a de novo variant or lacking a close relative for phasing. Finally, PGT-AO facilitates research on the origin of aneuploidies in preimplantation embryos and their clinical outcome. Trial registration number not applicable

Imputation of ancient canid genomes reveals inbreeding history over the past 10,000 years.

The multi-millenia long history between dogs and humans has placed them at the forefront of archeological and genomic research. Despite ongoing efforts including the analysis of ancient dog and wolf genomes, many questions remain regarding their geographic and temporal origins, and the microevolutionary processes that led to the diversity of breeds today. Although ancient genomes provide valuable information, their use is hindered by low depth of coverage and post-mortem damage, which inhibits confident genotype calling. In the present study, we assess how genotype imputation of ancient dog and wolf genomes, utilising a large reference panel, can improve the resolution provided by ancient datasets. Imputation accuracy was evaluated by down-sampling high coverage dog and wolf genomes to 0.05-2x coverage and comparing concordance between imputed and high coverage genotypes. We measured the impact of imputation on principal component analyses and runs of homozygosity. Our findings show high (R2>0.9) imputation accuracy for dogs with coverage as low as 0.5x and for wolves as low as 1.0x. We then imputed a dataset of 90 ancient dog and wolf genomes, to assess changes in inbreeding during the last 10,000 years of dog evolution. Ancient dog and wolf populations generally exhibited lower inbreeding levels than present-day individuals. Interestingly, regions with low ROH density maintained across ancient and present-day samples were significantly associated with genes related to olfaction and immune response. Our study indicates that imputing ancient canine genomes is a viable strategy that allows for the use of analytical methods previously limited to high-quality genetic data.

Exploring the impact of sequence context on errors in SNP genotype calling with whole genome sequencing data using AI-based autoencoder approach.

A critical step in the analysis of whole genome sequencing data is variant calling. Despite its importance, variant calling is prone to errors. Our study investigated the association between incorrect single nucleotide polymorphism (SNP) calls and variant quality metrics and nucleotide context. In our study, incorrect SNPs were defined in 20 Holstein-Friesian cows by comparing their SNPs genotypes identified by whole genome sequencing with the IlluminaNovaSeq6000 and the EuroGMD50K genotyping microarray. The dataset was divided into the correct SNP set (666 333 SNPs) and the incorrect SNP set (4 557 SNPs). The training dataset consisted of only the correct SNPs, while the test dataset contained a balanced mix of all the incorrectly and correctly called SNPs. An autoencoder was constructed to identify systematically incorrect SNPs that were marked as outliers by a one-class support vector machine and isolation forest algorithms. The results showed that 59.53% (±0.39%) of the incorrect SNPs had systematic patterns, with the remainder being random errors. The frequent occurrence of the CGC 3-mer was due to mislabelling a call for C. Incorrect T instead of A call was associated with the presence of T in the neighbouring downstream position. These errors may arise due to the fluorescence patterns of nucleotide labelling.

Bsgenova: an accurate, robust, and fast genotype caller for bisulfite-sequencing data

BackgroundBisulfite sequencing (BS-Seq) is a fundamental technique for characterizing DNA methylation profiles. Genotype calling from bisulfite-converted BS-Seq data allows allele-specific methylation analysis and the concurrent exploration of genetic and epigenetic profiles. Despite various methods have been proposed, single nucleotide polymorphisms (SNPs) calling from BS-Seq data, particularly for SNPs on chromosome X and in the presence of contaminative data, poses ongoing challenges.ResultsWe introduce bsgenova, a novel SNP caller tailored for bisulfite sequencing data, employing a Bayesian multinomial model. The performance of bsgenova is assessed by comparing SNPs called from real-world BS-Seq data with those from corresponding whole-genome sequencing (WGS) data across three human cell lines. bsgenova is both sensitive and precise, especially for chromosome X, compared with three existing methods. Moreover, in the presence of low-quality reads, bsgenova outperforms other methods notably. In addition, bsgenova is meticulously implemented, leveraging matrix imputation and multi-process parallelization. Compared to existing methods, bsgenova stands out for its speed and efficiency in memory and disk usage. Furthermore, bsgenova integrates bsextractor, a methylation extractor, enhancing its flexibility and expanding its utility.ConclusionsWe introduce bsgenova for SNP calling from bisulfite-sequencing data. The source code is available at https://github.com/hippo-yf/bsgenova under license GPL-3.0.

PNPLA3 fatty liver allele was fixed in Neanderthals and segregates neutrally in humans

ObjectiveFat deposition is modulated by environmental factors and genetic predisposition. Genome-wide association studies identified PNPLA3 p.I148M (rs738409) as a common variant that increases risk of developing liver steatosis. When and...

Assessing the impact of post-mortem damage and contamination on imputation performance in ancient DNA

Low-coverage imputation is becoming ever more present in ancient DNA (aDNA) studies. Imputation pipelines commonly used for present-day genomes have been shown to yield accurate results when applied to ancient genomes. However, post-mortem damage (PMD), in the form of C-to-T substitutions at the reads termini, and contamination with DNA from closely related species can potentially affect imputation performance in aDNA. In this study, we evaluated imputation performance (i) when using a genotype caller designed for aDNA, ATLAS, compared to bcftools, and (ii) when contamination is present. We evaluated imputation performance with principal component analyses and by calculating imputation error rates. With a particular focus on differently imputed sites, we found that using ATLAS prior to imputation substantially improved imputed genotypes for a very damaged ancient genome (42% PMD). Trimming the ends of the sequencing reads led to similar improvements in imputation accuracy. For the remaining genomes, ATLAS brought limited gains. Finally, to examine the effect of contamination on imputation, we added various amounts of reads from two present-day genomes to a previously downsampled high-coverage ancient genome. We observed that imputation accuracy drastically decreased for contamination rates above 5%. In conclusion, we recommend (i) accounting for PMD by either trimming sequencing reads or using a genotype caller such as ATLAS before imputing highly damaged genomes and (ii) only imputing genomes containing up to 5% of contamination.

Development and validation of a pharmacogenomics reporting workflow based on the illumina global screening array chip.

Background: Microarrays are a well-established and widely adopted technology capable of interrogating hundreds of thousands of loci across the human genome. Combined with imputation to cover common variants not included in the chip design, they offer a cost-effective solution for large-scale genetic studies. Beyond research applications, this technology can be applied for testing pharmacogenomics, nutrigenetics, and complex disease risk prediction. However, establishing clinical reporting workflows requires a thorough evaluation of the assay's performance, which is achieved through validation studies. In this study, we performed pre-clinical validation of a genetic testing workflow based on the Illumina Global Screening Array for 25 pharmacogenomic-related genes. Methods: To evaluate the accuracy of our workflow, we conducted multiple pre-clinical validation studies. Here, we present the results of accuracy and precision assessments, involving a total of 73 cell lines. These assessments encompass reference materials from the Genome-In-A-Bottle (GIAB), the Genetic Testing Reference Material Coordination Program (GeT-RM) projects, as well as additional samples from the 1000 Genomes project (1KGP). We conducted an accuracy assessment of genotype calls for target loci in each indication against established truth sets. Results: In our per-sample analysis, we observed a mean analytical sensitivity of 99.39% and specificity 99.98%. We further assessed the accuracy of star-allele calls by relying on established diplotypes in the GeT-RM catalogue or calls made based on 1KGP genotyping. On average, we detected a diplotype concordance rate of 96.47% across 14 pharmacogenomic-related genes with star allele-calls. Lastly, we evaluated the reproducibility of our findings across replicates and observed 99.48% diplotype and 100% phenotype inter-run concordance. Conclusion: Our comprehensive validation study demonstrates the robustness and reliability of the developed workflow, supporting its readiness for further development for applied testing.

A genotyping array for the globally invasive vector mosquito, Aedes albopictus

BackgroundAlthough whole-genome sequencing (WGS) is the preferred genotyping method for most genomic analyses, limitations are often experienced when studying genomes characterized by a high percentage of repetitive elements, high linkage, and recombination deserts. The Asian tiger mosquito (Aedes albopictus), for example, has a genome comprising up to 72% repetitive elements, and therefore we set out to develop a single-nucleotide polymorphism (SNP) chip to be more cost-effective. Aedes albopictus is an invasive species originating from Southeast Asia that has recently spread around the world and is a vector for many human diseases. Developing an accessible genotyping platform is essential in advancing biological control methods and understanding the population dynamics of this pest species, with significant implications for public health.MethodsWe designed a SNP chip for Ae. albopictus (Aealbo chip) based on approximately 2.7 million SNPs identified using WGS data from 819 worldwide samples. We validated the chip using laboratory single-pair crosses, comparing technical replicates, and comparing genotypes of samples genotyped by WGS and the SNP chip. We then used the chip for a population genomic analysis of 237 samples from 28 sites in the native range to evaluate its usefulness in describing patterns of genomic variation and tracing the origins of invasions.ResultsProbes on the Aealbo chip targeted 175,396 SNPs in coding and non-coding regions across all three chromosomes, with a density of 102 SNPs per 1 Mb window, and at least one SNP in each of the 17,461 protein-coding genes. Overall, 70% of the probes captured the genetic variation. Segregation analysis found that 98% of the SNPs followed expectations of single-copy Mendelian genes. Comparisons with WGS indicated that sites with genotype disagreements were mostly heterozygotes at loci with WGS read depth < 20, while there was near complete agreement with WGS read depths > 20, indicating that the chip more accurately detects heterozygotes than low-coverage WGS. Sample sizes did not affect the accuracy of the SNP chip genotype calls. Ancestry analyses identified four to five genetic clusters in the native range with various levels of admixture.ConclusionsThe Aealbo chip is highly accurate, is concordant with genotypes from WGS with high sequence coverage, and may be more accurate than low-coverage WGS.Graphical

The polymorphisms of candidate pharmacokinetic and pharmacodynamic genes and their pharmacogenetic impacts on the effectiveness of risperidone maintenance therapy among Saudi children with autism.

Antipsychotics, including risperidone (RIS), are frequently indicated for various autism spectrum disorder (ASD) manifestations; however, "actionable" PGx testing in psychiatry regarding antipsychotic dosing and selection has limited applications in routine clinical practice because of the lack of standard guidelines, mostly due to the inconsistency and scarcity of genetic variant data. The current study is aimed at examining the association of RIS effectiveness, according to ABC-CV and CGI indexes, with relevant pharmacokinetics (PK) and pharmacodynamics (PD) genes. Eighty-nine ASD children who received a consistent RIS-based regimen for at least 8weeks were included. The Axiom PharmacoFocus Array technique was employed to generate accurate star allele-predicted phenotypes of 3 PK genes (CYP3A4, CYP3A5, and CYP2D6). Genotype calls for 5 candidate PD receptor genes (DRD1, DRD2, DRD3, HTR2C, and HTR2A) were obtained and reported as wild type, heterozygous, or homozygous for 11 variants. Based on the ABC total score, 42 (47.2%) children were classified as responders, while 47 (52.8%) were classified as nonresponders. Multivariate logistic regression analyses, adjusted for nongenetic factors, suggested nonsignificant impacts of the star allele-predicted phenotypes of all 3 PK genes on improvement in ASD symptoms or CGI scores. However, significant positive or negative associations of certain PD variants involved in dopaminergic and serotonergic pathways were observed with specific ASD core and noncore symptom subdomains. Our significant polymorphism findings, mainly those in DRD2 (rs1800497, rs1799978, and rs2734841), HTR2C (rs3813929), and HTR2A (rs6311), were largely consistent with earlier findings (predictors of RIS effectiveness in adult schizophrenia patients), confirming their validity for identifying ASD children with a greater likelihood of core symptom improvement compared to noncarriers/wild types. Other novel findings of this study, such as significant improvements in DRD3 rs167771 carriers, particularly in ABC total and lethargy/social withdrawal scores, and DRD1 rs1875964 homozygotes and DRD2 rs1079598 wild types in stereotypic behavior, warrant further verification in biochemical and clinical studies to confirm their feasibility for inclusion in a PGx panel. In conclusion, we provide evidence of potential genetic markers involved in clinical response variability to RIS therapy in ASD children. However, replication in prospective samples with greater ethnic diversity and sample sizes is necessary.

A targeted amplicon next-generation sequencing assay for tryptase genotyping to support personalized therapy in mast cell-related disorders.

Tryptase, the most abundant mast cell granule protein, is elevated in severe asthma patients independent of type 2 inflammation status. Higher active β tryptase allele counts are associated with higher levels of peripheral tryptase and lower clinical benefit from anti-IgE therapies. Tryptase is a therapeutic target of interest in severe asthma and chronic spontaneous urticaria. Active and inactive allele counts may enable stratification to assess response to therapies in asthmatic patient subpopulations. Tryptase gene loci TPSAB1 and TPSB2 have high levels of sequence identity, which makes genotyping a challenging task. Here, we report a targeted next-generation sequencing (NGS) assay and downstream bioinformatics analysis for determining polymorphisms at tryptase TPSAB1 and TPSB2 loci. Machine learning modeling using multiple polymorphisms in the tryptase loci was used to improve the accuracy of genotyping calls. The assay was tested and qualified on DNA extracted from whole blood of healthy donors and asthma patients, achieving accuracy of 96%, 96% and 94% for estimation of inactive α and βΙΙΙFS tryptase alleles and α duplication on TPSAB1, respectively. The reported NGS assay is a cost-effective method that is more efficient than Sanger sequencing and provides coverage to evaluate known as well as unreported tryptase polymorphisms.

Integrating External Controls by Regression Calibration for Genome-Wide Association Study.

Genome-wide association studies (GWAS) have successfully revealed many disease-associated genetic variants. For a case-control study, the adequate power of an association test can be achieved with a large sample size, although genotyping large samples is expensive. A cost-effective strategy to boost power is to integrate external control samples with publicly available genotyped data. However, the naive integration of external controls may inflate the type I error rates if ignoring the systematic differences (batch effect) between studies, such as the differences in sequencing platforms, genotype-calling procedures, population stratification, and so forth. To account for the batch effect, we propose an approach by integrating External Controls into the Association Test by Regression Calibration (iECAT-RC) in case-control association studies. Extensive simulation studies show that iECAT-RC not only can control type I error rates but also can boost statistical power in all models. We also apply iECAT-RC to the UK Biobank data for M72 Fibroblastic disorders by considering genotype calling as the batch effect. Four SNPs associated with fibroblastic disorders have been detected by iECAT-RC and the other two comparison methods, iECAT-Score and Internal. However, our method has a higher probability of identifying these significant SNPs in the scenario of an unbalanced case-control association study.

The hazards of genotype imputation when mapping disease susceptibility variants

BackgroundThe cost-free increase in statistical power of using imputation to infer missing genotypes is undoubtedly appealing, but is it hazard-free? This case study of three type-2 diabetes (T2D) loci demonstrates that it is not; it sheds light on why this is so and raises concerns as to the shortcomings of imputation at disease loci, where haplotypes differ between cases and reference panel.ResultsT2D-associated variants were previously identified using targeted sequencing. We removed these significantly associated SNPs and used neighbouring SNPs to infer them by imputation. We compared imputed with observed genotypes, examined the altered pattern of T2D-SNP association, and investigated the cause of imputation errors by studying haplotype structure. Most T2D variants were incorrectly imputed with a low density of scaffold SNPs, but the majority failed to impute even at high density, despite obtaining high certainty scores. Missing and discordant imputation errors, which were observed disproportionately for the risk alleles, produced monomorphic genotype calls or false-negative associations. We show that haplotypes carrying risk alleles are considerably more common in the T2D cases than the reference panel, for all loci.ConclusionsImputation is not a panacea for fine mapping, nor for meta-analysing multiple GWAS based on different arrays and different populations. A total of 80% of the SNPs we have tested are not included in array platforms, explaining why these and other such associated variants may previously have been missed. Regardless of the choice of software and reference haplotypes, imputation drives genotype inference towards the reference panel, introducing errors at disease loci.

MCPtaggR: R package for accurate genotype calling in reduced representation sequencing data by eliminating error-prone markers based on genome comparison.

Reduced representation sequencing (RRS) offers cost-effective, high-throughput genotyping platforms such as genotyping-by-sequencing (GBS). RRS reads are typically mapped onto a reference genome. However, mapping reads harbouring mismatches against the reference can potentially result in mismapping and biased mapping, leading to the detection of error-prone markers that provide incorrect genotype information. We established a genotype-calling pipeline named mappable collinear polymorphic tag genotyping (MCPtagg) to achieve accurate genotyping by eliminating error-prone markers. MCPtagg was designed for the RRS-based genotyping of a population derived from a biparental cross. The MCPtagg pipeline filters out error-prone markers prior to genotype calling based on marker collinearity information obtained by comparing the genome sequences of the parents of a population to be genotyped. A performance evaluation on real GBS data from a rice F2 population confirmed its effectiveness. Furthermore, our performance test using a genome assembly that was obtained by genome sequence polishing on an available genome assembly suggests that our pipeline performs well with converted genomes, rather than necessitating de novo assembly. This demonstrates its flexibility and scalability. The R package, MCPtaggR, was developed to provide functions for the pipeline and is available at https://github.com/tomoyukif/MCPtaggR.

Comprehensive whole-genome analyses of the UK Biobank reveal significant sex differences in both genotype missingness and allele frequency on the X chromosome.

The UK Biobank is the most used dataset for genome-wide association studies (GWAS). GWAS of sex, essentially sex differences in minor allele frequencies (sdMAF), has identified autosomal SNPs with significant sdMAF, including in the UK Biobank, but the X chromosome was excluded. Our recent report identified multiple regions on the X chromosome with significant sdMAF, using short-read sequencing of other datasets. We performed a whole genome sdMAF analysis, with ~410k white British individuals from the UK Biobank, using array genotyped, imputed or exome sequencing data. We observed marked sdMAF on the X chromosome, particularly at the boundaries between the pseudo-autosomal regions (PAR) and the non-PAR (NPR), as well as throughout the NPR, consistent with our earlier report. A small fraction of autosomal SNPs also showed significant sdMAF. Using the centrally imputed data, which relied mostly on low-coverage whole genome sequence, resulted in 2.1% of NPR SNPs with significant sdMAF. The whole exome sequencing also displays sdMAF on the X chromosome, including some NPR SNPs with heterozygous genotype calls in males. Genotyping, sequencing and imputation of X chromosomal SNPs requires further attention to ensure the integrity for downstream association analysis.