Coverage and efficiency in current SNP chips

Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms

Background

Understanding the genetic causes for variability in chromatin accessibility can shed light on the molecular mechanisms through which genetic variants may affect complex traits. Thousands of ATAC-seq samples have been collected that hold information about chromatin accessibility across diverse cell types and contexts, but most of these are not paired with genetic information and come from diverse distinct projects and laboratories.

Results

We report here joint genotyping, chromatin accessibility peak calling, and discovery of quantitative trait loci which influence chromatin accessibility (caQTLs), demonstrating the capability of performing caQTL analysis on a large scale in a diverse sample set without pre-existing genotype information. Using 10,293 profiling samples representing 1,454 unique donor individuals across 653 studies from public databases, we catalog 23,381 caQTLs in total. After joint discovery analysis, we cluster samples based on accessible chromatin profiles to identify context-specific caQTLs. We find that caQTLs are strongly enriched for annotations of gene regulatory elements across diverse cell types and tissues and are often strongly linked with genetic variation associated with changes in expression (eQTLs), indicating that caQTLs can mediate genetic effects on gene expression. We demonstrate sharing of causal variants for chromatin accessibility and diverse complex human traits, enabling a more complete picture of the genetic mechanisms underlying complex human phenotypes.

Conclusions

Our work provides a proof of principle for caQTL calling from previously ungenotyped samples, and represents one of the largest, most diverse caQTL resources currently available, informing mechanisms of genetic regulation of gene expression and contribution to disease.

A Pipeline for Phasing and Genotype Imputation on Mixed Human Data (Parents-Offspring Trios and Unrelated Subjects) by Reviewing Current Methods and Software

Genotype imputation has become an essential prerequisite when performing association analysis. It is a computational technique that allows us to infer genetic markers that have not been directly genotyped, thereby increasing statistical power in subsequent association studies, which consequently has a crucial impact on the identification of causal variants. Many features need to be considered when choosing the proper algorithm for imputation, including the target sample on which it is performed, i.e., related individuals, unrelated individuals, or both. Problems could arise when dealing with a target sample made up of mixed data, composed of both related and unrelated individuals, especially since the scientific literature on this topic is not sufficiently clear. To shed light on this issue, we examined existing algorithms and software for performing phasing and imputation on mixed human data from SNP arrays, specifically when related subjects belong to trios. By discussing the advantages and limitations of the current algorithms, we identified LD-based methods as being the most suitable for reconstruction of haplotypes in this specific context, and we proposed a feasible pipeline that can be used for imputing genotypes in both phased and unphased human data.

A paradigm shift in pharmacogenomics: From candidate polymorphisms to comprehensive sequencing

Genetic factors have long been recognized as important determinants of interindividual variability in drug efficacy and toxicity. However, despite the increasing number of established gene-drug associations, candidate polymorphisms can only explain a fraction of the genetically encoded functional variability in drug disposition. Advancements in genetic profiling methods now allow to analyse the landscape of human pharmacogenetic variations comprehensively, which opens new opportunities to identify novel factors that could explain the "missing heritability." Here, we provide an updated overview of the landscape of pharmacogenomic variability based on recent analyses of population-scale sequencing projects. We then summarize the current state-of-the-art how the functional consequences of variants with unknown effects can be quantitatively estimated while discussing challenges and peculiarities that are specific to pharmacogenes. In the last sections, we discuss the importance of considering ethnogeographic diversity to provide equitable benefits of pharmacogenomics and summarize current roadblocks for the implementation of sequencing-based guidance of clinical decision-making. Based on the current state of the field, we conclude that testing is likely to gradually shift from the interrogation of selected candidate polymorphisms to comprehensive sequencing, which allows to consider the full spectrum of pharmacogenomic variations for a true personalization of genomic prescribing.

A comprehensive evaluation of polygenic score and genotype imputation performances of human SNP arrays in diverse populations

Regardless of the overwhelming use of next-generation sequencing technologies, microarray-based genotyping combined with the imputation of untyped variants remains a cost-effective means to interrogate genetic variations across the human genome. This technology is widely used in genome-wide association studies (GWAS) at bio-bank scales, and more recently, in polygenic score (PGS) analysis to predict and stratify disease risk. Over the last decade, human genotyping arrays have undergone a tremendous growth in both number and content making a comprehensive evaluation of their performances became more important. Here, we performed a comprehensive performance assessment for 23 available human genotyping arrays in 6 ancestry groups using diverse public and in-house datasets. The analyses focus on performance estimation of derived imputation (in terms of accuracy and coverage) and PGS (in terms of concordance to PGS estimated from whole-genome sequencing data) in three different traits and diseases. We found that the arrays with a higher number of SNPs are not necessarily the ones with higher imputation performance, but the arrays that are well-optimized for the targeted population could provide very good imputation performance. In addition, PGS estimated by imputed SNP array data is highly correlated to PGS estimated by whole-genome sequencing data in most cases. When optimal arrays are used, the correlations of PGS between two types of data are higher than 0.97, but interestingly, arrays with high density can result in lower PGS performance. Our results suggest the importance of properly selecting a suitable genotyping array for PGS applications. Finally, we developed a web tool that provides interactive analyses of tag SNP contents and imputation performance based on population and genomic regions of interest. This study would act as a practical guide for researchers to design their genotyping arrays-based studies. The tool is available at: https://genome.vinbigdata.org/tools/saa/ .

Iam hiQ-a novel pair of accuracy indices for imputed genotypes

BACKGROUND

Imputation of untyped markers is a standard tool in genome-wide association studies to close the gap between directly genotyped and other known DNA variants. However, high accuracy with which genotypes are imputed is fundamental. Several accuracy measures have been proposed and some are implemented in imputation software, unfortunately diversely across platforms. In the present paper, we introduce Iam hiQ, an independent pair of accuracy measures that can be applied to dosage files, the output of all imputation software. Iam (imputation accuracy measure) quantifies the average amount of individual-specific versus population-specific genotype information in a linear manner. hiQ (heterogeneity in quantities of dosages) addresses the inter-individual heterogeneity between dosages of a marker across the sample at hand.

RESULTS

Applying both measures to a large case-control sample of the International Lung Cancer Consortium (ILCCO), comprising 27,065 individuals, we found meaningful thresholds for Iam and hiQ suitable to classify markers of poor accuracy. We demonstrate how Manhattan-like plots and moving averages of Iam and hiQ can be useful to identify regions enriched with less accurate imputed markers, whereas these regions would by missed when applying the accuracy measure info (implemented in IMPUTE2).

CONCLUSION

We recommend using Iam hiQ additional to other accuracy scores for variant filtering before stepping into the analysis of imputed GWAS data.

Assessment of Imputation Quality: Comparison of Phasing and Imputation Algorithms in Real Data

Despite the widespread use of genotype imputation tools and the availability of different approaches, late developments of currently used programs have not been compared comprehensively. We therefore assessed the performance of 35 combinations of phasing and imputation programs, including versions of SHAPEIT, Eagle, Beagle, minimac, PBWT, and IMPUTE, for genetic imputation of completely missing SNPs with a HRC reference panel regarding quality and speed. We used a data set comprising 1,149 fully sequenced individuals from the German population, subsetting the SNPs to approximate the Illumina Infinium-Omni5 array. Five hundred fifty-three thousand two hundred and thirty-four SNPs across two selected chromosomes were utilized for comparison between imputed and sequenced genotypes. We found that all tested programs with the exception of PBWT impute genotypes with very high accuracy (mean error rate < 0.005). PBTW hardly ever imputes the less frequent allele correctly (mean concordance for genotypes including the minor allele <0.0002). For all programs, imputation accuracy drops for rare alleles with a frequency <0.05. Even though overall concordance is high, concordance drops with genotype probability, indicating that low genotype probabilities are rare. The mean concordance of SNPs with a genotype probability <95% drops below 0.9, at which point disregarding imputed genotypes might prove favorable. For fast and accurate imputation, a combination of Eagle2.4.1 using a reference panel for phasing and Beagle5.1 for imputation performs best. Replacing Beagle5.1 with minimac3, minimac4, Beagle4.1, or IMPUTE4 results in a small gain in accuracy at a high cost of speed.

A comparison of genotyping arrays

Array technology to genotype single-nucleotide variants (SNVs) is widely used in genome-wide association studies (GWAS), clinical diagnostics, and linkage studies. Arrays have undergone a tremendous growth in both number and content over recent years making a comprehensive comparison all the more important. We have compared 28 genotyping arrays on their overall content, genome-wide coverage, imputation quality, presence of known GWAS loci, mtDNA variants and clinically relevant genes (i.e., American College of Medical Genetics (ACMG) actionable genes, pharmacogenetic genes, human leukocyte antigen (HLA) genes and SNV density). Our comparison shows that genome-wide coverage is highly correlated with the number of SNVs on the array but does not correlate with imputation quality, which is the main determinant of GWAS usability. Average imputation quality for all tested arrays was similar for European and African populations, indicating that this is not a good criterion for choosing a genotyping array. Rather, the additional content on the array, such as pharmacogenetics or HLA variants, should be the deciding factor. As the research question of a study will in large part determine which class of genes are of interest, there is not just one perfect array for all different research questions. This study can thus help as a guideline to determine which array best suits a study's requirements.

Cross-Species Application of Illumina iScan Microarrays for Cost-Effective, High-Throughput SNP Discovery

Microarrays can be a cost-effective alternative to high-throughput sequencing for discovering novel single-nucleotide polymorphisms (SNPs). Illumina’s iScan platform dominates the market, but their commercial microarray products are designed for model organisms. Further, the platform outputs data in a proprietary format. This cannot be easily converted to human-readable genotypes or be merged with pre-existing data. To address this, we present and validate a novel pipeline to facilitate data analysis from cross-species application of Illumina microarrays. This facilitates the generation of a compatible VCF from iScan data and the merging of this with a second VCF comprising genotypes derived from other samples and sources. Our pipeline includes a custom script, iScanVCFMerge (presented as a Python package), which we validate using iScan data from three great ape genera. We conclude that cross-species application of microarrays can be a rapid, cost-effective approach for SNP discovery in non-model organisms. Our pipeline surmounts the common challenges of integrating iScan genotypes with pre-existing data.

Genetic testing for men with infertility: techniques and indications

Genetic testing is an integral component in the workup of male infertility as genetic conditions may be responsible for up to 15% of all cases. Currently, three genetic tests are commonly performed and recommended by major urologic associations: karyotype analysis (KA), Y-chromosome microdeletion testing, and CFTR mutation testing. Despite widespread adoption of these tests, an etiology for infertility remains elusive in up to 80% of cases. Recent work has identified intriguing new targets for genetic testing which may soon see clinical relevance. This review will discuss the indications and techniques for currently offered genetic tests and briefly explore ongoing research directions within this field.

Detection of ploidy and chromosomal aberrations in commercial oil palm using high-throughput SNP markers

Karyotyping using high-density genome-wide SNP markers identified various chromosomal aberrations in oil palm (Elaeis guineensis Jacq.) with supporting evidence from the 2C DNA content measurements (determined using FCM) and chromosome counts. Oil palm produces a quarter of the world's total vegetable oil. In line with its global importance, an initiative to sequence the oil palm genome was carried out successfully, producing huge amounts of sequence information, allowing SNP discovery. High-capacity SNP genotyping platforms have been widely used for marker-trait association studies in oil palm. Besides genotyping, a SNP array is also an attractive tool for understanding aberrations in chromosome inheritance. Exploiting this, the present study utilized chromosome-wide SNP allelic distributions to determine the ploidy composition of over 1,000 oil palms from a commercial F₁ family, including 197 derived from twin-embryo seeds. Our method consisted of an inspection of the allelic intensity ratio using SNP markers. For palms with a shifted or abnormal distribution ratio, the SNP allelic frequencies were plotted along the pseudo-chromosomes. This method proved to be efficient in identifying whole genome duplication (triploids) and aneuploidy. We also detected several loss of heterozygosity regions which may indicate small chromosomal deletions and/or inheritance of identical by descent regions from both parents. The SNP analysis was validated by flow cytometry and chromosome counts. The triploids were all derived from twin-embryo seeds. This is the first report on the efficiency and reliability of SNP array data for karyotyping oil palm chromosomes, as an alternative to the conventional cytogenetic technique. Information on the ploidy composition and chromosomal structural variation can help to better understand the genetic makeup of samples and lead to a more robust interpretation of the genomic data in marker-trait association analyses.

Recent advances in clinical diagnosis and treatment of male factor infertility

Infertility is a significant global health issue affecting around 8-12% of couples worldwide with male factor infertility accounting for a substantial proportion of these cases. Despite significant advances within the past few decades, an etiology for male factor infertility cannot be identified in up to 80% of patients and thus, this continues to be an area of active study. This review aims to provide an update on recent advances in the field of male infertility including semen analysis and at-home semen testing, genetics, DNA fragmentation, surgical approaches, and the rise of telemedicine in the era of COVID19.

Genome-Wide Association Study of Sleep Disturbances in Depressive Disorders

Sleep disturbance affects about 75% of depressed individuals and is associated with poorer patient outcomes. The genetics in this field is an emerging area of research. Thus far, only core circadian genes have been examined in this context. We expanded on this by performing a genome-wide association study (GWAS) followed by a preplanned hypothesis-driven analysis with 27 genes associated with the biology of sleep. All participants were diagnosed by their referring physician, completed the Beck Depression Inventory (BDI), and the Udvalg for Kliniske Undersogelser Side Effect Rating Scale at baseline. Our phenotype consisted of replies to 3 questions from these questionnaires. From standard GWAS chip data, imputations were performed. Baseline total BDI scores (n = 364) differed significantly between those with and those without sleep problems. We were unable to find any significant GWAS hits although our top hit was for changes in sleep and an intergenic marker near SNX18 (p = 1.06 × 10^-6). None of the markers in our hypothesis-driven analysis remained significant after applying Bonferroni corrections. Our top finding among these genes was for rs13019460 of Neuronal PAS Domain Protein 2 with changes in sleep (p = 0.0009). Overall, both analyses were unable to detect any significant associations in our modest sample though we did find some interesting preliminary associations worth further exploration.

Genotype-phenotype feasibility studies on khat abuse, traumatic experiences and psychosis in Ethiopia

Studying the relationship between mental illnesses and their environmental and genetic risk factors in low-income countries holds excellent promises. These studies will improve our understanding of how risk factors identified predominantly in high-income countries also apply to other settings and will identify new, sometimes population-specific risk factors. Here we report the successful completion of two intertwined pilot studies on khat abuse, trauma, and psychosis at the Gilgel Gibe Field Research Center in Ethiopia. We found that the Gilgel Gibe Field Research Center offers a unique opportunity to collect well-characterized samples for mental health research and to perform genetic studies that, at this scale, have not been undertaken in Ethiopia yet. We also supported service development, education, and research for strengthening the professional profile of psychiatry at the site.

Genetics of male infertility

Male infertility is a multifactorial pathological condition affecting approximately 7% of the male population. The genetic landscape of male infertility is highly complex as semen and testis histological phenotypes are extremely heterogeneous, and at least 2,000 genes are involved in spermatogenesis. The highest frequency of known genetic factors contributing to male infertility (25%) is in azoospermia, but the number of identified genetic anomalies in other semen and aetiological categories is constantly growing. Genetic screening is relevant for its diagnostic value, clinical decision making, and appropriate genetic counselling. Anomalies in sex chromosomes have major roles in severe spermatogenic impairment. Autosome-linked gene mutations are mainly involved in central hypogonadism, monomorphic teratozoospermia or asthenozoospermia, congenital obstructive azoospermia, and familial cases of quantitative spermatogenic disturbances. Results from whole-genome association studies suggest a marginal role for common variants as causative factors; however, some of these variants can be important for pharmacogenetic purposes. Results of studies on copy number variations (CNVs) demonstrate a considerably higher CNV load in infertile patients than in normozoospermic men, whereas whole-exome analysis has proved to be a highly successful diagnostic tool in familial cases of male infertility. Despite such efforts, the aetiology of infertility remains unknown in about 40% of patients, and the discovery of novel genetic factors in idiopathic infertility is a major challenge for the field of androgenetics. Large, international, and consortium-based whole-exome and whole-genome studies are the most promising approach for the discovery of the missing genetic aetiology of idiopathic male infertility.

Global, pathway and gene coverage of three Illumina arrays with respect to inflammatory and immune-related pathways

Genome-wide association studies have led in the past to the discovery of susceptibility genes for many diseases including cancer and inflammatory conditions. However, a number of these studies did not realise their full potential. A first critical step in developing such large-scale studies is the choice of genotyping array with respect to the study goal. Coverage is the central criterion for array evaluation. We distinguish between estimates of global coverage across the genome, coverage for each chromosome, coverage for selected pathways and the coverage for genes of interest. Here, we focus on inflammatory and immunological pathways and genes relevant for haematopoietic stem cell transplantation. We compared three arrays: the Infinium Global Screening Array-24 v1.0, the Infinium OncoArray-500 K BeadChip and the Infinium PsychArray-24 v1.2 BeadChip. We employed the European population from the 1000 Genomes Project as reference genome. Global coverage was found to range between 12.2 and 14.2% whereas coverage for a selected pathway ranged from 6.2 to 13.2% and gene coverage ranged from 0 to 54.1%. The Global Screening Array outperformed both other arrays in terms of global coverage, for most chromosomes, most considered pathways and most genes. When selecting suitable arrays for a new study, the coverage of pathways or genes of interest should be considered in addition to global coverage. Local coverage should be regarded when discussing association findings inconsistent across studies and can be useful in data analysis and decision making for additional genotyping.

Polygenic risk associated with post-traumatic stress disorder onset and severity

Post-traumatic stress disorder (PTSD) is a psychiatric illness with a highly polygenic architecture without large effect-size common single-nucleotide polymorphisms (SNPs). Thus, to capture a substantial portion of the genetic contribution, effects from many variants need to be aggregated. We investigated various aspects of one such approach that has been successfully applied to many traits, polygenic risk score (PRS) for PTSD. Theoretical analyses indicate the potential prediction ability of PRS. We used the latest summary statistics from the largest published genome-wide association study (GWAS) conducted by Psychiatric Genomics Consortium for PTSD (PGC-PTSD). We found that the PRS constructed for a cohort comprising veterans of recent wars (n = 244) explains a considerable proportion of PTSD onset (Nagelkerke R2 = 4.68%, P = 0.003) and severity (R2 = 4.35%, P = 0.0008) variances. However, the performance on an African ancestry sub-cohort was minimal. A PRS constructed with schizophrenia GWAS also explained a significant fraction of PTSD diagnosis variance (Nagelkerke R2 = 2.96%, P = 0.0175), confirming previously reported genetic correlation between the two psychiatric ailments. Overall, these findings demonstrate the important role polygenic analyses of PTSD will play in risk prediction models as well as in elucidating the biology of the disorder.

Rare Variants Imputation in Admixed Populations: Comparison Across Reference Panels and Bioinformatics Tools

Background

Imputation has become a standard approach in genome-wide association studies (GWAS) to infer in silico untyped markers. Although feasibility for common variants imputation is well established, we aimed to assess rare and ultra-rare variants’ imputation in an admixed Caribbean Hispanic population (CH).

Methods

We evaluated imputation accuracy in CH (N = 1,000), focusing on rare (0.1% ≤ minor allele frequency (MAF) ≤ 1%) and ultra-rare (MAF < 0.1%) variants. We used two reference panels, the Haplotype Reference Consortium (HRC; N = 27,165) and 1000 Genome Project (1000G phase 3; N = 2,504) and multiple phasing (SHAPEIT, Eagle2) and imputation algorithms (IMPUTE2, MACH-Admix). To assess imputation quality, we reported: (a) high-quality variant counts according to imputation tools’ internal indexes (e.g., IMPUTE2 “Info” ≥ 80%). (b) Wilcoxon Signed-Rank Test comparing imputation quality for genotyped variants that were masked and imputed; (c) Cohen’s kappa coefficient to test agreement between imputed and whole-exome sequencing (WES) variants; (d) imputation of G206A mutation in the PSEN1 (ultra-rare in the general population an more frequent in CH) followed by confirmation genotyping. We also tested ancestry proportion (European, African and Native American) against WES-imputation mismatches in a Poisson regression fashion.

Results

SHAPEIT2 retrieved higher percentage of imputed high-quality variants than Eagle2 (rare: 51.02% vs. 48.60%; ultra-rare 0.66% vs. 0.65%, Wilcoxon p-value < 0.001). SHAPEIT-IMPUTE2 employing HRC outperformed 1000G (64.50% vs. 59.17%; 1.69% vs. 0.75% for high-quality rare and ultra-rare variants, respectively, Wilcoxon p-value < 0.001). SHAPEIT-IMPUTE2 outperformed MaCH-Admix. Compared to 1000G, HRC-imputation retrieved a higher number of high-quality rare and ultra-rare variants, despite showing lower agreement between imputed and WES variants (e.g., rare: 98.86% for HRC vs. 99.02% for 1000G). High Kappa (K = 0.99) was observed for both reference panels. Twelve G206A mutation carriers were imputed and all validated by confirmation genotyping. African ancestry was associated with higher imputation errors for uncommon and rare variants (p-value < 1e-05).

Conclusion

Reference panels with larger numbers of haplotypes can improve imputation quality for rare and ultra-rare variants in admixed populations such as CH. Ethnic composition is an important predictor of imputation accuracy, with higher African ancestry associated with poorer imputation accuracy.

A locus on chromosome 5 shows African ancestry-limited association with alloimmunization in sickle cell disease

Red blood cell (RBC) transfusion remains a critical therapeutic intervention in sickle cell disease (SCD); however, the apparent propensity of some patients to regularly develop RBC alloantibodies after transfusion presents a significant challenge to finding compatible blood for so-called alloimmunization responders. Predisposing genetic loci have long been thought to contribute to the responder phenomenon, but to date, no definitive loci have been identified. We undertook a genome-wide association study of alloimmunization responder status in 267 SCD multiple transfusion recipients, using genetic estimates of ancestral admixture to bolster our findings. Analyses revealed single nucleotide polymorphisms (SNPs) on chromosomes 2 and 5 approaching genome-wide significance (minimum P = 2.0 × 10-8 and 8.4 × 10-8, respectively), with local ancestry analysis demonstrating similar levels of admixture in responders and nonresponders at implicated loci. Association at chromosome 5 was nominally replicated in an independent cohort of 130 SCD transfusion recipients, with meta-analysis surpassing genome-wide significance (rs75853687, P meta = 6.6 × 10-9), and this extended to individuals forming multiple (>3) alloantibodies (P meta = 9.4 × 10-5). The associated variant is rare outside of African populations, and orthogonal genome-wide haplotype analyses, contingent on local ancestry, revealed genome-wide significant sharing of a ∼60-kb haplotype of African ancestry at the chromosome 5 locus (Bayes Factor = 4.95). This locus overlaps a putative cis-acting enhancer predicted to regulate transcription of ADRA1B and the lncRNA LINC01847, both members of larger ontologies associated with immune regulation. Our findings provide potential insights to the pathophysiology underlying the development of alloantibodies and implicate non-RBC ancestry-limited loci in the susceptibility to alloimmunization.

Cloud computing for voxel-wise SEM analysis of MRI data

As data collection costs fall and vast quantities of data are collected, data analysis time can become a bottleneck. For massively parallel analyses, cloud computing offers the short-term rental of ample processing power. Recent software innovations have reduced the offort needed to take advantage of cloud computing. To demonstrate, we replicate a voxel-wise examination of the genetic contributions to cortical development by age using evidence from 1,748 MRI scans. Specifically, we employ off-the-shelf Kubernetes software that permits us to re-run our analyses using almost the same computer code as was published in the original article. Large, well funded institutions may continue to maintain their own computing clusters. However, the modest cost of renting and ease of utilizing cloud computing services makes unprecedented compute power available to all researchers, whether or not affliated with a research institution. We expect this to spur innovation in the sophisticated modeling of large datasets.

Cross-ethnicity tagging SNPs for HLA alleles associated with adverse drug reaction

Reduction of adverse drug reaction (ADR) incidence through screening of predisposing human leucocyte antigen (HLA) alleles is a promising approach for many widely used drugs. However, application of these associations has been limited by the cost burden of HLA genotyping. Use of single nucleotide polymorphisms (SNPs) that can approximate ('tag') HLA alleles of interest has been proposed as a cost-effective and simple alternative to conventional genotyping. However, most reported SNP tags have not been validated and there is concern regarding clinical utility of this approach due to tagging inconsistency across different populations. We assess the ability of 67 previously reported and 378 novel tagging SNPs, identified here in 5 HLA reference panels, to tag 15 ADR-associated HLA alleles in a panel of 955 ethnically diverse samples. Tags for 8 HLA alleles of interest were identified with 100% sensitivity and >95% specificity. These SNPs may act as a reliable genotyping approach for the routine screening of patients, without the need to account for patient ethnicity.

Performance of epistasis detection methods in semi-simulated GWAS

BACKGROUND

Part of the missing heritability in Genome Wide Association Studies (GWAS) is expected to be explained by interactions between genetic variants, also called epistasis. Various statistical methods have been developed to detect epistasis in case-control GWAS. These methods face major statistical challenges due to the number of tests required, the complexity of the Linkage Disequilibrium (LD) structure, and the lack of consensus regarding the definition of epistasis. Their limited impact in terms of uncovering new biological knowledge might be explained in part by the limited amount of experimental data available to validate their statistical performances in a realistic GWAS context. In this paper, we introduce a simulation pipeline for generating real scale GWAS data, including epistasis and realistic LD structure. We evaluate five exhaustive bivariate interaction methods, fastepi, GBOOST, SHEsisEpi, DSS, and IndOR. Two hundred thirty four different disease scenarios are considered in extensive simulations. We report the performances of each method in terms of false positive rate control, power, area under the ROC curve (AUC), and computation time using a GPU. Finally we compare the result of each methods on a real GWAS of type 2 diabetes from the Welcome Trust Case Control Consortium.

RESULTS

GBOOST, SHEsisEpi and DSS allow a satisfactory control of the false positive rate. fastepi and IndOR present an increase in false positive rate in presence of LD between causal SNPs, with our definition of epistasis. DSS performs best in terms of power and AUC in most scenarios with no or weak LD between causal SNPs. All methods can exhaustively analyze a GWAS with 6.105 SNPs and 15,000 samples in a couple of hours using a GPU.

CONCLUSION

This study confirms that computation time is no longer a limiting factor for performing an exhaustive search of epistasis in large GWAS. For this task, using DSS on SNP pairs with limited LD seems to be a good strategy to achieve the best statistical performance. A combination approach using both DSS and GBOOST is supported by the simulation results and the analysis of the WTCCC dataset demonstrated that this approach can detect distinct genes in epistasis. Finally, weak epistasis between common variants will be detectable with existing methods when GWAS of a few tens of thousands cases and controls are available.

Efficient genome-wide genotyping strategies and data integration in crop plants

Next-generation sequencing (NGS) has revolutionized plant and animal research by providing powerful genotyping methods. This review describes and discusses the advantages, challenges and, most importantly, solutions to facilitate data processing, the handling of missing data, and cross-platform data integration. Next-generation sequencing technologies provide powerful and flexible genotyping methods to plant breeders and researchers. These methods offer a wide range of applications from genome-wide analysis to routine screening with a high level of accuracy and reproducibility. Furthermore, they provide a straightforward workflow to identify, validate, and screen genetic variants in a short time with a low cost. NGS-based genotyping methods include whole-genome re-sequencing, SNP arrays, and reduced representation sequencing, which are widely applied in crops. The main challenges facing breeders and geneticists today is how to choose an appropriate genotyping method and how to integrate genotyping data sets obtained from various sources. Here, we review and discuss the advantages and challenges of several NGS methods for genome-wide genetic marker development and genotyping in crop plants. We also discuss how imputation methods can be used to both fill in missing data in genotypic data sets and to integrate data sets obtained using different genotyping tools. It is our hope that this synthetic view of genotyping methods will help geneticists and breeders to integrate these NGS-based methods in crop plant breeding and research.

Genome-Wide Association Study on Immunoglobulin G Glycosylation Patterns

Immunoglobulin G (IgG), a glycoprotein secreted by plasma B-cells, plays a major role in the human adaptive immune response and are associated with a wide range of diseases. Glycosylation of the Fc binding region of IgGs, responsible for the antibody’s effector function, is essential for prompting a proper immune response. This study focuses on the general genetic impact on IgG glycosylation as well as corresponding subclass specificities. To identify genetic loci involved in IgG glycosylation, we performed a genome-wide association study (GWAS) on liquid chromatography electrospray mass spectrometry (LC–ESI-MS)—measured IgG glycopeptides of 1,823 individuals in the Cooperative Health Research in the Augsburg Region (KORA F4) study cohort. In addition, we performed GWAS on subclass-specific ratios of IgG glycans to gain power in identifying genetic factors underlying single enzymatic steps in the glycosylation pathways. We replicated our findings in 1,836 individuals from the Leiden Longevity Study (LLS). We were able to show subclass-specific genetic influences on single IgG glycan structures. The replicated results indicate that, in addition to genes encoding for glycosyltransferases (i.e., ST6GAL1, B4GALT1, FUT8, and MGAT3), other genetic loci have strong influences on the IgG glycosylation patterns. A novel locus on chromosome 1, harboring RUNX3, which encodes for a transcription factor of the runt domain-containing family, is associated with decreased galactosylation. Interestingly, members of the RUNX family are cross-regulated, and RUNX3 is involved in both IgA class switching and B-cell maturation as well as T-cell differentiation and apoptosis. Besides the involvement of glycosyltransferases in IgG glycosylation, we suggest that, due to the impact of variants within RUNX3, potentially mechanisms involved in B-cell activation and T-cell differentiation during the immune response as well as cell migration and invasion involve IgG glycosylation.

Assessing runs of Homozygosity: a comparison of SNP Array and whole genome sequence low coverage data

BACKGROUND

Runs of Homozygosity (ROH) are genomic regions where identical haplotypes are inherited from each parent. Since their first detection due to technological advances in the late 1990s, ROHs have been shedding light on human population history and deciphering the genetic basis of monogenic and complex traits and diseases. ROH studies have predominantly exploited SNP array data, but are gradually moving to whole genome sequence (WGS) data as it becomes available. WGS data, covering more genetic variability, can add value to ROH studies, but require additional considerations during analysis.

RESULTS

Using SNP array and low coverage WGS data from 1885 individuals from 20 world populations, our aims were to compare ROH from the two datasets and to establish software conditions to get comparable results, thus providing guidelines for combining disparate datasets in joint ROH analyses. By allowing heterozygous SNPs per window, using the PLINK homozygosity function and non-parametric analysis, we were able to obtain non-significant differences in number ROH, mean ROH size and total sum of ROH between data sets using the different technologies for almost all populations.

CONCLUSIONS

By allowing 3 heterozygous SNPs per ROH when dealing with WGS low coverage data, it is possible to establish meaningful comparisons between data using SNP array and WGS low coverage technologies.

Genome-wide association study of Hirschsprung disease detects a novel low-frequency variant at the RET locus

Hirschsprung disease (HSCR) is a congenital disorder with a population incidence of ~1/5000 live births, defined by an absence of enteric ganglia along variable lengths of the colon. HSCR genome-wide association studies (GWAS) have found common associated variants at RET, SEMA3, and NRG1, but they still fail to explain all of its heritability. To enhance gene discovery, we performed a GWAS of 170 cases identified from the Danish nationwide pathology registry with 4717 controls, based on 6.2 million variants imputed from the haplotype reference consortium panel. We found a novel low-frequency variant (rs144432435), which, when conditioning on the lead RET single-nucleotide polymorphism (SNP), was of genome-wide significance in the discovery analysis. This conditional association signal was replicated in a Swedish HSCR cohort with discovery plus replication meta-analysis conditional odds ratio of 6.6 (P = 7.7 × 10^-10; 322 cases and 4893 controls). The conditional signal was, however, not replicated in two HSCR cohorts from USA and Finland, leading to the hypothesis that rs144432435 tags a rare haplotype present in Denmark and Sweden. Using the genome-wide complex trait analysis method, we estimated the SNP heritability of HSCR to be 88%, close to estimates based on classical family studies. Moreover, by using Lasso (least absolute shrinkage and selection operator) regression we were able to construct a genetic HSCR predictor with a area under the receiver operator characteristics curve of 76% in an independent validation set. In conclusion, we combined the largest collection of sporadic Hirschsprung cases to date (586 cases) to further elucidate HSCR's genetic architecture.

The SNP-set based association study identifies ITGA1 as a susceptibility gene of attention-deficit/hyperactivity disorder in Han Chinese

Genome-wide association studies, which detect the association between single-nucleotide polymorphisms (SNPs) and disease susceptibility, have been extensively applied to study attention-deficit/hyperactivity disorder (ADHD), but genome-wide significant associations have not been found yet. Genetic heterogeneity and insufficient genomic coverage may account for the missing heritability. We performed a two-stage association study for ADHD in the Han Chinese population. In the discovery stage, 1033 ADHD patients and 950 healthy controls were genotyped using both the Affymetrix Genome-Wide Human SNP Array 6.0 and the Illumina Infinium HumanExome BeadChip. The genotyped SNPs were combined to generate a powerful SNP set with better genomic coverage especially for the nonsynonymous variants. In addition to the association of single SNPs, we collected adjacent SNPs as SNP sets, which were determined by either genes or successive sliding windows, to evaluate their synergetic effect. The candidate susceptibility SNPs were further replicated in an independent cohort of 1441 ADHD patients and 1447 healthy controls. No genome-wide significant SNPs or gene-based SNP sets were found to be associated with ADHD. However, two continuous sliding windows located in ITGA1 (P-value=8.33E-7 and P-value=8.43E-7) were genome-wide significant. The quantitative trait analyses also demonstrated their association with ADHD core symptoms and executive functions. The association was further validated by follow-up replications for four selected SNPs: rs1979398 (P-value=2.64E-6), rs16880453 (P-value=3.58E-4), rs1531545 (P-value=7.62E-4) and rs4074793 (P-value=2.03E-4). Our results suggest that genetic variants in ITGA1 may be involved in the etiology of ADHD and the SNP-set based analysis is a promising strategy for the detection of underlying genetic risk factors.

Diversity and inclusion in genomic research: why the uneven progress?

Conducting genomic research in diverse populations has led to numerous advances in our understanding of human history, biology, and health disparities, in addition to discoveries of vital clinical significance. Conducting genomic research in diverse populations is also important in ensuring that the genomic revolution does not exacerbate health disparities by facilitating discoveries that will disproportionately benefit well-represented populations. Despite the general agreement on the need for genomic research in diverse populations in terms of equity and scientific progress, genomic research remains largely focused on populations of European descent. In this article, we describe the rationale for conducting genomic research in diverse populations by reviewing examples of advances facilitated by their inclusion. We also explore some of the factors that perpetuate the disproportionate attention on well-represented populations. Finally, we discuss ongoing efforts to ameliorate this continuing bias. Collaborative and intensive efforts at all levels of research, from the funding of studies to the publication of their findings, will be necessary to ensure that genomic research does not conserve historical inequalities or curtail the contribution that genomics could make to the health of all humanity.

Optimal Design of Low-Density SNP Arrays for Genomic Prediction: Algorithm and Applications

Low-density (LD) single nucleotide polymorphism (SNP) arrays provide a cost-effective solution for genomic prediction and selection, but algorithms and computational tools are needed for the optimal design of LD SNP chips. A multiple-objective, local optimization (MOLO) algorithm was developed for design of optimal LD SNP chips that can be imputed accurately to medium-density (MD) or high-density (HD) SNP genotypes for genomic prediction. The objective function facilitates maximization of non-gap map length and system information for the SNP chip, and the latter is computed either as locus-averaged (LASE) or haplotype-averaged Shannon entropy (HASE) and adjusted for uniformity of the SNP distribution. HASE performed better than LASE with ≤1,000 SNPs, but required considerably more computing time. Nevertheless, the differences diminished when >5,000 SNPs were selected. Optimization was accomplished conditionally on the presence of SNPs that were obligated to each chromosome. The frame location of SNPs on a chip can be either uniform (evenly spaced) or non-uniform. For the latter design, a tunable empirical Beta distribution was used to guide location distribution of frame SNPs such that both ends of each chromosome were enriched with SNPs. The SNP distribution on each chromosome was finalized through the objective function that was locally and empirically maximized. This MOLO algorithm was capable of selecting a set of approximately evenly-spaced and highly-informative SNPs, which in turn led to increased imputation accuracy compared with selection solely of evenly-spaced SNPs. Imputation accuracy increased with LD chip size, and imputation error rate was extremely low for chips with ≥3,000 SNPs. Assuming that genotyping or imputation error occurs at random, imputation error rate can be viewed as the upper limit for genomic prediction error. Our results show that about 25% of imputation error rate was propagated to genomic prediction in an Angus population. The utility of this MOLO algorithm was also demonstrated in a real application, in which a 6K SNP panel was optimized conditional on 5,260 obligatory SNP selected based on SNP-trait association in U.S. Holstein animals. With this MOLO algorithm, both imputation error rate and genomic prediction error rate were minimal.

An 'Omics' Perspective on Cardiomyopathies and Heart Failure

Pathological enlargement of the heart, represented by hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM), occurs in response to many genetic and non-genetic factors. The clinical course of cardiac hypertrophy is remarkably variable, ranging from lifelong absence of symptoms to rapidly declining heart function and sudden cardiac death (SCD). Unbiased omics studies have begun to provide a glimpse into the molecular framework underpinning altered mechanotransduction, mitochondrial energetics, oxidative stress, and extracellular matrix in the heart undergoing physiological and pathological hypertrophy. Omics analyses indicate that post-transcriptional regulation of gene expression plays an overriding role in the normal and diseased heart. Studies to date highlight a need for more effective bioinformatics to better integrate patient omics data with their comprehensive clinical histories.

A novel method of microsatellite genotyping-by-sequencing using individual combinatorial barcoding

This study examines the potential of next-generation sequencing based 'genotyping-by-sequencing' (GBS) of microsatellite loci for rapid and cost-effective genotyping in large-scale population genetic studies. The recovery of individual genotypes from large sequence pools was achieved by PCR-incorporated combinatorial barcoding using universal primers. Three experimental conditions were employed to explore the possibility of using this approach with existing and novel multiplex marker panels and weighted amplicon mixture. The GBS approach was validated against microsatellite data generated by capillary electrophoresis. GBS allows access to the underlying nucleotide sequences that can reveal homoplasy, even in large datasets and facilitates cross laboratory transfer. GBS of microsatellites, using individual combinatorial barcoding, is potentially faster and cheaper than current microsatellite approaches and offers better and more data.

Integration of 'omics' data in aging research: from biomarkers to systems biology

Age is the strongest risk factor for many diseases including neurodegenerative disorders, coronary heart disease, type 2 diabetes and cancer. Due to increasing life expectancy and low birth rates, the incidence of age-related diseases is increasing in industrialized countries. Therefore, understanding the relationship between diseases and aging and facilitating healthy aging are major goals in medical research. In the last decades, the dimension of biological data has drastically increased with high-throughput technologies now measuring thousands of (epi) genetic, expression and metabolic variables. The most common and so far successful approach to the analysis of these data is the so-called reductionist approach. It consists of separately testing each variable for association with the phenotype of interest such as age or age-related disease. However, a large portion of the observed phenotypic variance remains unexplained and a comprehensive understanding of most complex phenotypes is lacking. Systems biology aims to integrate data from different experiments to gain an understanding of the system as a whole rather than focusing on individual factors. It thus allows deeper insights into the mechanisms of complex traits, which are caused by the joint influence of several, interacting changes in the biological system. In this review, we look at the current progress of applying omics technologies to identify biomarkers of aging. We then survey existing systems biology approaches that allow for an integration of different types of data and highlight the need for further developments in this area to improve epidemiologic investigations.

Review: High-performance computing to detect epistasis in genome scale data sets

It is becoming clear that most human diseases have a complex etiology that cannot be explained by single nucleotide polymorphisms (SNPs) or simple additive combinations; the general consensus is that they are caused by combinations of multiple genetic variations. The limited success of some genome-wide association studies is partly a result of this focus on single genetic markers. A more promising approach is to take into account epistasis, by considering the association of multiple SNP interactions with disease. However, as genomic data continues to grow in resolution, and genome and exome sequencing become more established, the number of combinations of variants to consider increases rapidly. Two potential solutions should be considered: the use of high-performance computing, which allows us to consider a larger number of variables, and heuristics to make the solution more tractable, essential in the case of genome sequencing. In this review, we look at different computational methods to analyse epistatic interactions within disease-related genetic data sets created by microarray technology. We also review efforts to use epistatic analysis results to produce biomarkers for diagnostic tests and give our views on future directions in this field in light of advances in sequencing technology and variants in non-coding regions.

Scanning and Filling: Ultra-Dense SNP Genotyping Combining Genotyping-By-Sequencing, SNP Array and Whole-Genome Resequencing Data

Genotyping-by-sequencing (GBS) represents a highly cost-effective high-throughput genotyping approach. By nature, however, GBS is subject to generating sizeable amounts of missing data and these will need to be imputed for many downstream analyses. The extent to which such missing data can be tolerated in calling SNPs has not been explored widely. In this work, we first explore the use of imputation to fill in missing genotypes in GBS datasets. Importantly, we use whole genome resequencing data to assess the accuracy of the imputed data. Using a panel of 301 soybean accessions, we show that over 62,000 SNPs could be called when tolerating up to 80% missing data, a five-fold increase over the number called when tolerating up to 20% missing data. At all levels of missing data examined (between 20% and 80%), the resulting SNP datasets were of uniformly high accuracy (96-98%). We then used imputation to combine complementary SNP datasets derived from GBS and a SNP array (SoySNP50K). We thus produced an enhanced dataset of >100,000 SNPs and the genotypes at the previously untyped loci were again imputed with a high level of accuracy (95%). Of the >4,000,000 SNPs identified through resequencing 23 accessions (among the 301 used in the GBS analysis), 1.4 million tag SNPs were used as a reference to impute this large set of SNPs on the entire panel of 301 accessions. These previously untyped loci could be imputed with around 90% accuracy. Finally, we used the 100K SNP dataset (GBS + SoySNP50K) to perform a GWAS on seed oil content within this collection of soybean accessions. Both the number of significant marker-trait associations and the peak significance levels were improved considerably using this enhanced catalog of SNPs relative to a smaller catalog resulting from GBS alone at ≤20% missing data. Our results demonstrate that imputation can be used to fill in both missing genotypes and untyped loci with very high accuracy and that this leads to more powerful genetic analyses.

Genetics of diabetic nephropathy: a long road of discovery

The global prevalence of diabetic nephropathy is rising in parallel with the increasing incidence of diabetes in most countries. Unfortunately, up to 40 % of persons diagnosed with diabetes may develop kidney complications. Diabetic nephropathy is associated with substantially increased risks of cardiovascular disease and premature mortality. An inherited susceptibility to diabetic nephropathy exists, and progress is being made unravelling the genetic basis for nephropathy thanks to international research collaborations, shared biological resources and new analytical approaches. Multiple epidemiological studies have highlighted the clinical heterogeneity of nephropathy and the need for better phenotyping to help define important subgroups for analysis and increase the power of genetic studies. Collaborative genome-wide association studies for nephropathy have reported unique genes, highlighted novel biological pathways and suggested new disease mechanisms, but progress towards clinically relevant risk prediction models for diabetic nephropathy has been slow. This review summarises the current status, recent developments and ongoing challenges elucidating the genetics of diabetic nephropathy.

Inherited genetic variation in childhood acute lymphoblastic leukemia

Although somatically acquired genomic alterations have long been recognized as the hallmarks of acute lymphoblastic leukemia (ALL), the last decade has shown that inherited genetic variations (germline) are important determinants of interpatient variability in ALL susceptibility, drug response, and toxicities of ALL therapy. In particular, unbiased genome-wide association studies have identified germline variants strongly associated with the predisposition to ALL in children, providing novel insight into the mechanisms of leukemogenesis and evidence for complex interactions between inherited and acquired genetic variations in ALL. Similar genome-wide approaches have also discovered novel germline genetic risk factors that independently influence ALL prognosis and those that strongly modify host susceptibility to adverse effects of antileukemic agents (eg, vincristine, asparaginase, glucocorticoids). There are examples of germline genomic associations that warrant routine clinical use in the treatment of childhood ALL (eg, TPMT and mercaptopurine dosing), but most have not reached this level of actionability. Future studies are needed to integrate both somatic and germline variants to predict risk of relapse and host toxicities, with the eventual goal of implementing genetics-driven precision-medicine approaches in ALL treatment.

Closing the genotype-phenotype gap: emerging technologies for evolutionary genetics in ecological model vertebrate systems

The analysis of genetic and epigenetic mechanisms of the genotype-phenotypic connection has, so far, only been possible in a handful of genetic model systems. Recent technological advances, including next-generation sequencing methods such as RNA-seq, ChIP-seq and RAD-seq, and genome-editing approaches including CRISPR-Cas, now permit to address these fundamental questions of biology also in organisms that have been studied in their natural habitats. We provide an overview of the benefits and drawbacks of these novel techniques and experimental approaches that can now be applied to ecological and evolutionary vertebrate models such as sticklebacks and cichlid fish. We can anticipate that these new methods will increase the understanding of the genetic and epigenetic factors influencing adaptations and phenotypic variation in ecological settings. These new arrows in the methodological quiver of ecologist will drastically increase the understanding of the genetic basis of adaptive traits - leading to a further closing of the genotype-phenotype gap.