Genotype imputation

RobusTAD: reference panel based annotation of nested topologically associating domains

Topologically associating domains (TADs) are fundamental units of 3D genomes and play essential roles in gene regulation. Hi-C data suggests a hierarchical organization of TADs. Accurately annotating nested TADs from Hi-C data remains challenging, both in terms of the precise identification of boundaries and the correct inference of hierarchies. While domain boundary is relatively well conserved across cells, few approaches have taken advantage of this fact. Here, we present RobusTAD to annotate TAD hierarchies. It incorporates additional Hi-C data to refine boundaries annotated from the study sample. RobusTAD outperforms existing tools at boundary and domain annotation across several benchmarking tasks.

Endophenotype-Informed Association Analyses for Liver Fat Accumulation and Metabolic Dysfunction in the Fels Longitudinal Study

The identification of causal genomic regions for liver fat accumulation in the context of metabolic dysfunction remains a challenging goal. This study aimed to identify potential endophenotypes for liver fat content and employ them in bivariate linkage searches for pleiotropic genetic regions where targeted association analysis is more likely to reveal significant variants. Multiple metabolic risk and adiposity distribution traits were assessed using the endophenotype ranking value. The top-ranked endophenotypes were then used in a bivariate linkage analysis, paired with liver fat content. Quantitative trait loci (QTLs) identified as significant or suggestive were targeted for measured genotype association analyses. The highest-ranked endophenotypes for liver fat accumulation were insulin resistance (IR), visceral adipose tissue (VAT), and high-density lipoprotein cholesterol (HDL-C). The univariate linkage analysis for liver fat content identified one significant QTL at chromosome 17p13.2 (Logarithm of odds score (LOD) = 2.90, p = 1.29 × 10-4). The bivariate linkage analysis pairing liver fat with IR and VAT improved the localization of two suggestive QTLs at 13q21.31 (LOD = 2.11, p = 9.03 × 10-4), and 6q21 (LOD = 2.35, p = 5.07 × 10-4), respectively. Targeted association analyses within the -1-LOD score regions of these QTLs revealed 17 marginally significant single nucleotide polymorphisms (SNPs) associated with liver fat content or its combination with the selected endophenotypes. The endophenotype-informed linkage analysis successfully identified regions suitable for the targeted association analysis of liver fat content, either alone or in combination with IR or VAT, leading to the discovery of marginally significant variants with potential for future functional studies.

Aggregating single nucleotide polymorphisms improves filtering for false-positive associations postimputation

Imputation causes bias in P-values in downstream genome-wide association studies. Imputation quality measures such as IMPUTE info are used to discriminate between false and true associations. However, implementing a high threshold often discards true associations, while a low threshold preserves false associations. This poses a challenge, especially for studies genotyped with SNP arrays. In practice, association signals register as spikes of low P-values for SNPs in close proximity owing to linkage disequilibrium, but postimputation filtering is conducted on SNPs independently. We simulated 1536 small case-control studies on the human chromosome 19 both to quantify the introduced bias and to evaluate postimputation filtering. The established IMPUTE info thresholds 0.3 and 0.8 were compared on individual SNPs and aggregated spikes in the formats "best guess genotype" and "dosage." Furthermore, we applied 2 recently published methods, Iam hiQ and MagicalRsq, to assess their effect on filtering. We found differences in false signals and imputation quality between the genotype formats, especially in the midrange between thresholds. In this midrange, 51 and 60% of associated SNPs for best guess and dosage format, respectively, are true associations. For aggregated SNPs, the majority of spikes in the midrange are true associations. We propose a new method, the Midrange Filter, which uses both thresholds and formats to classify spikes instead of SNPs. This method discards up to the same number of false signals as the upper threshold, while preserving all true associations in most simulation settings. The PsyCourse study is included as a real-data application.

PRED-LD: efficient imputation of GWAS summary statistics

BACKGROUND

Genome-wide association studies have identified connections between genetic variations and diseases, but they only examine a small portion of single nucleotide polymorphisms. To enhance genetic findings, researchers suggest imputing genotypes for unmeasured SNPs to improve coverage and statistical power. When this is not possible, summary statistics imputation can be used as an alternative. The available summary statistics imputation tools rely on reference panels, such as the 1000 Genomes Project, to estimate linkage disequilibrium (LD) between variants for accurate imputation. Tools like FAPI and SSIMP use these reference panels in variant call format (VCF) for this purpose, though this process can be time-consuming. A more effective approach for processing reference panels in summary statistics imputation was proposed in RAISS. In this approach, the LD among the variants is precomputed from the reference panel, prior to imputation, thereby reducing computational time.

RESULTS

We present PRED-LD, an imputation method for GWAS summary statistics that aims to enhance the resolution of genetic association analyses. The proposed method uses precomputed linkage disequilibrium statistics from HapMap, Pheno Scanner and TOP-LD to impute summary statistics, given beta coefficients and standard errors. The single-point approach that we describe provides a fast and accurate way to estimate associations for untyped single nucleotide polymorphisms that exhibit high linkage disequilibrium (LD). The proposed method is faster, provides accurate imputation compared to existing tools, and has been implemented in both a web service ( https://compgen.dib.uth.gr/PRED-LD/ ) and a command-line tool ( https://github.com/pbagos/PRED-LD ), making it a useful resource for the research community.

CONCLUSIONS

PRED-LD offers an efficient and accurate method for GWAS summary statistics imputation, providing faster performance, direct result interpretation, and the ability to use multiple reference panels. Also, the online version of PRED-LD simplifies obtaining LD information and performing imputation tasks without downloading reference panels and will be continuously updated to support tools for meta-analysis and fine-mapping in GWAS.

Mind the Gap: A Neural Network Framework for Imputing Genotypes in Non-Model Species

Reduced representation sequencing (RRS) has proven to be a cost-effective solution for sequencing subsets of the genome in non-model species for large-scale studies. However, the targeted nature of RRS approaches commonly introduces large amounts of missing data, leading to reduced statistical power and biased estimates in downstream analyses. Genotype imputation, the statistical inference of missing sites across the genome, is a powerful alternative to overcome the caveats associated with missing sites. Typically, genotype imputation requires the presence of a reference panel of haplotypes, however, this is not always feasible for non-model species. In this issue of Molecular Ecology Resources, Mora-Márquez et al. (2024) develop gtImputation, an unsupervised machine learning imputation tool with an interactive GUI, which leverages information from the underlying data structure itself, without the need for a reference panel. They showcase that their method performs equally well and even surpasses existing haplotype-clustering and unsupervised machine learning algorithms, particularly for sites with low minor allele frequency (MAF) and for data sets with strong underlying population structure. This innovative framework adds to the ongoing efforts to expand the applicability of imputation to non-model species, offering the opportunity to apply varied types of analyses requiring dense sets of markers, while also maintaining lower sequencing costs.

Missing genotype imputation in non-model species using self-organizing maps

Current methodologies of genome-wide single-nucleotide polymorphism (SNP) genotyping produce large amounts of missing data that may affect statistical inference and bias the outcome of experiments. Genotype imputation is routinely used in well-studied species to buffer the impact in downstream analysis, and several algorithms are available to fill in missing genotypes. The lack of reference haplotype panels precludes the use of these methods in genomic studies on non-model organisms. As an alternative, machine learning algorithms are employed to explore the genotype data and to estimate the missing genotypes. Here, we propose an imputation method based on self-organizing maps (SOM), a widely used neural networks formed by spatially distributed neurons that cluster similar inputs into close neurons. The method explores genotype datasets to select SNP loci to build binary vectors from the genotypes, and initializes and trains neural networks for each query missing SNP genotype. The SOM-derived clustering is then used to impute the best genotype. To automate the imputation process, we have implemented gtImputation, an open-source application programmed in Python3 and with a user-friendly GUI to facilitate the whole process. The method performance was validated by comparing its accuracy, precision and sensitivity on several benchmark genotype datasets with other available imputation algorithms. Our approach produced highly accurate and precise genotype imputations even for SNPs with alleles at low frequency and outperformed other algorithms, especially for datasets from mixed populations with unrelated individuals.

High performance imputation of structural and single nucleotide variants using low-coverage whole genome sequencing

BACKGROUND

Whole genome sequencing (WGS), despite its advantages, is yet to replace methods for genotyping single nucleotide variants (SNVs) such as SNP arrays and targeted genotyping assays. Structural variants (SVs) have larger effects on traits than SNVs, but are more challenging to accurately genotype. Using low-coverage WGS with genotype imputation offers a cost-effective strategy to achieve genome-wide variant coverage, but is yet to be tested for SVs.

METHODS

Here, we investigate combined SNV and SV imputation with low-coverage WGS data in Atlantic salmon (Salmo salar). As the reference panel, we used genotypes for high-confidence SVs and SNVs for n = 365 wild individuals sampled from diverse populations. We also generated 15 × WGS data (n = 20 samples) for a commercial population external to the reference panel, and called SVs and SNVs with gold-standard approaches. An imputation method selected for its established performance using low-coverage sequencing data (GLIMPSE) was tested at WGS depths of 1 × , 2 × , 3 × , and 4 × for samples within and external to the reference panel.

RESULTS

SNVs were imputed with high accuracy and recall across all WGS depths, including for samples out-with the reference panel. For SVs, we compared imputation based purely on linkage disequilibrium (LD) with SNVs, to that supplemented with SV genotype likelihoods (GLs) from low-coverage WGS. Including SV GLs increased imputation accuracy, but as a trade-off with recall, requiring 3-4 × depth for best performance. Combining strategies allowed us to capture 84% of the reference panel deletions with 87% accuracy at 1 × depth. We also show that SV length affects imputation performance, with provision of SV GLs greatly enhancing accuracy for the longest SVs in the dataset.

CONCLUSIONS

This study highlights the promise of reference panel imputation using low-coverage WGS, including novel opportunities to enhance the resolution of genome-wide association studies by capturing SVs.

Using genotype imputation to integrate Canola populations for genome-wide association and genomic prediction of blackleg resistance

BACKGROUND

Integrating germplasm populations genotyped by different genotyping platforms via genotype imputation is a way to utilize accumulated genetic resources. In this study, we used 278 canola samples genotyped via whole-genome sequencing (WGS) at 10× coverage to evaluate the imputation accuracy of three imputation approaches. The optimal imputation methods were used to impute and integrate two Canola genotype datasets: a diverse canola collection genotyped by genotyping-by-sequencing via transcriptome (GBS-t) and a double haploid (DH) line collection genotyped with low-coverage WGS (skim-WGS). The genomic predictive ability (GP) and detection power of marker‒trait association (GWAS) of the combined population for blackleg resistance were evaluated.

RESULTS

The empirical imputation accuracy (r2) measured as the squared correlation between observed and imputed genotypes was moderate for Minimac3 when imputing from the GBS-t density to the WGS. The accuracy dramatically improved from 0.64 to 0.82 by removing SNPs with poor Minimac3-reported Rsq (Rsq < 0.2) quality statistics. The r2 for GLIMPSE was higher than that for Beagle when imputing from different low-coverage to full-coverage WGS. We imputed and integrated the diverse canola collection and the DH lines, and the combined population showed similar or slightly greater predictive ability (PA) for blackleg resistance traits than did each of the single populations with ~ 921 K SNPs. Higher marker-trait association (MTA) detection powers were indicated with the combined population; however, similar numbers of MTAs were discovered when each single population was combined in a meta-GWAS.

CONCLUSION

It is feasible to impute and integrate germplasms from different sequencing platforms for downstream analyses. However, genetic heterogeneity across populations could add complexity to the analysis. Increasing the sample size by combining datasets showed slightly greater predictive ability and greater detection power in GWASs in the present study.

Genetic QT score as a predictor of sudden cardiac death in participants with sleep-disordered breathing in the UK Biobank

STUDY OBJECTIVES

The goal of this study was to evaluate the association between a polygenic risk score (PRS) for QT prolongation (QTc-PRS), corrected QT intervals (QTc) and sudden cardiac death (SCD) in participants enrolled in the UK Biobank with and without sleep-disordered breathing (SDB).

METHODS

The QTc-PRS was calculated using allele copy number and previously reported effect estimates for each single nuclear polymorphism. Competing-risk regression models adjusting for age, sex, body mass index, QT prolonging medication, race, and comorbid cardiovascular conditions were used for SCD analyses.

RESULTS

A total of 500,584 participants were evaluated (56.5 ± 8 years, 54% female, 1.4% diagnosed with sleep apnea). A higher QTc-PRS was independently associated with the increased QTc interval duration (P < .0001). The mean QTc for the top QTc-PRS quintile was 15 msec longer than the bottom quintile (P < .001). SDB was found to be an effect modifier in the relationship between QTc-PRS and SCD. The adjusted hazard ratio per 5-unit change in QTc-PRS for SCD was 1.64 (95% confidence interval 1.16-2.31, P = .005) among those with SDB and 1.04 (95% confidence interval 0.95-1.14, P = .44) among those without SDB (P for interaction = .01). Black participants with SDB had significantly elevated adjusted risk of SCD (hazard ratio = 9.6, 95% confidence interval 1.24-74, P = .03).

CONCLUSIONS

In the UK Biobank population, the QTc-PRS was associated with SCD among participants with SDB but not among those without SDB, indicating that SDB is a significant modifier of the genetic risk. Black participants with SDB had a particularly high risk of SCD.

CITATION

Arora A, Zareba W, Woosley RL, et al. Genetic QT score as a predictor of sudden cardiac death in participants with sleep-disordered breathing in the UK Biobank. J Clin Sleep Med. 2025;21(3):549-557.

Integrating phenomic selection using single-kernel near-infrared spectroscopy and genomic selection for corn breeding improvement

KEY MESSAGE

Phenomic selection using intact seeds is a promising tool to improve gain and complement genomic selection in corn breeding. Models that combine genomic and phenomic data maximize the predictive ability. Phenomic selection (PS) is a cost-effective method proposed for predicting complex traits and enhancing genetic gain in breeding programs. The statistical procedures are similar to those utilized in genomic selection (GS) models, but molecular markers data are replaced with phenomic data, such as near-infrared spectroscopy (NIRS). However, the use of NIRS applied to PS typically utilized destructive sampling or collected data after the establishment of selection experiments in the field. Here, we explored the application of PS using nondestructive, single-kernel NIRS in a sweet corn breeding program, focusing on predicting future, unobserved field-based traits of economic importance, including ear and vegetative traits. Three models were employed on a diversity panel: genomic and phenomic best linear unbiased prediction models, which used relationship matrices based on SNP and NIRS data, respectively, and a combined model. The genomic relationship matrices were evaluated with varying numbers of SNPs. Additionally, the PS model trained on the diversity panel was used to select doubled haploid (DH) lines for germination before planting, with predictions validated using observed data. The findings indicate that PS generated good predictive ability (e.g., 0.46 for plant height) and distinguished between high and low germination rates in untested DH lines. Although GS generally outperformed PS, the model combining both information yielded the highest predictive ability, with higher accuracies than GS when low marker densities were used. This study highlights NIRS's potential to achieve genetic gain where GS may not be feasible and to maintain/improve accuracy with SNP-based information while reducing genotyping costs.

Genome-Wide Association Study Pinpoints Novel Candidate Genes Associated with the Gestation Length of the First Parity in French Large White Sows

Gestation length (GL) is a critical indicator of reproductive performance in sows and is closely associated with other reproductive traits, such as total number born (TNB) and number born alive (NBA). Despite its importance, the genetic mechanisms underlying GL and its impact on reproductive traits remain poorly understood. In this study, we investigated the relationship between GL and reproductive traits using 7013 farrowing records and conducted an imputed whole-genome sequence-based genome-wide association study (GWAS) for GL in first-parity sows, involving 3005 French Large White sows. Our findings revealed that the heritability of GL ranged from 0.22 to 0.26. Both excessively short and long GLs were associated with negative impacts on TNB, NBA, and other reproductive traits. A total of 64 SNPs exceeded the significance threshold, leading to the identification of two novel quantitative trait loci (QTLs) on chromosome 5 (QTL-1: 15.29-15.39 Mb and QTL-2: 12.86-12.94 Mb) and three promising candidate genes: TROAP, RFX4, and ADCY6. Gene ontology and KEGG pathway enrichment analyses revealed that these candidate genes are enriched in key biological processes, including ovarian steroidogenesis, the GnRH signaling pathway, and the regulation of cAMP biosynthesis, all of which are crucial for gestation and pregnancy maintenance. These findings improve our understanding of the genetic architecture of GL in sows and offer valuable genetic markers for enhancing reproductive efficiency in breeding programs.

Sequence-based GWAS in 180,000 German Holstein cattle reveals new candidate variants for milk production traits

BACKGROUND

Milk production traits are complex and influenced by many genetic and environmental factors. Although extensive research has been performed for these traits, with many associations unveiled thus far, due to their crucial economic importance, complex genetic architecture, and the fact that causal variants in cattle are still scarce, there is a need for a better understanding of their genetic background. In this study, we aimed to identify new candidate loci associated with milk production traits in German Holstein cattle, the most important dairy breed in Germany and worldwide. For that purpose, 180,217 cattle were imputed to the sequence level and large-scale genome-wide association study (GWAS) followed by fine-mapping and evolutionary and functional annotation were carried out to identify and prioritize new association signals.

RESULTS

Using the imputed sequence data of a large cattle dataset, we identified 50,876 significant variants, confirming many known and identifying previously unreported candidate variants for milk (MY), fat (FY), and protein yield (PY). Genome-wide significant signals were fine-mapped with the Bayesian approach that determines the credible variant sets and generates the probability of causality for each signal. The variants with the highest probabilities of being causal were further classified using external information about the function and evolution, making the prioritization for subsequent validation experiments easier. The top potential causal variants determined with fine-mapping explained a large percentage of genetic variance compared to random ones; 178 variants explained 11.5%, 104 explained 7.7%, and 68 variants explained 3.9% of the variance for MY, FY, and PY, respectively, demonstrating the potential for causality.

CONCLUSIONS

Our findings proved the power of large samples and sequence-based GWAS in detecting new association signals. In order to fully exploit the power of GWAS, one should aim at very large samples combined with whole-genome sequence data. These can also come with both computational and time burdens, as presented in our study. Although milk production traits in cattle are comprehensively investigated, the genetic background of these traits is still not fully understood, with the potential for many new associations to be revealed, as shown. With constantly growing sample sizes, we expect more insights into the genetic architecture of milk production traits in the future.

Benchmarking for genotyping and imputation using degraded DNA for forensic applications across diverse populations

Advancements in sequencing and laboratory technologies have enabled forensic genetic analysis on increasingly low quality and degraded DNA samples. However, existing computational methods applied to genotyping and imputation for generating DNA profiles from degraded DNA have not been tested for forensic applications. Here we simulated sequencing data of varying qualities-coverage, fragment lengths, and deamination patterns-from forty individuals of diverse genetic ancestries. We used this dataset to test the performance of commonly used genotype and imputation methods (SAMtools, GATK, ATLAS, Beagle, and GLIMPSE) on five different SNP panels (MPS-plex, FORCE, two extended kinship panels, and the Human Origins array) that are used for forensic and population genetics applications. For genome mapping and variant calling with degraded DNA, we find use of parameters and methods (such as ATLAS) developed for ancient DNA analysis provides a marked improvement over conventional standards used for next generation sequencing analysis. We find that ATLAS outperforms GATK and SAMtools, achieving over 90 % genotyping accuracy for the four largest SNP panels with coverages greater than 10X. For lower coverages, decreased concordance rates are correlated with increased rates of heterozygosity. Genotype refinement and imputation improve the accuracy at lower coverages by leveraging population reference data. For all five SNP panels, we find that using a population reference panel representative of worldwide populations (e.g., the 1000 Genomes Project) results in increased genotype accuracies across genetic ancestries, compared to ancestry-matched population reference panels. Importantly, we find that the low SNP density of commonly used forensics SNP panels can impact the reliability and performance of genotype refinement and imputation. This highlights a critical trade-off between enhancing privacy by using panels with fewer SNPs and maintaining the effectiveness of genomic tools. We provide benchmarks and recommendations for analyzing degraded DNA from diverse populations with widely used genomic methods in forensic casework.

Comparing genomic studies in animal breeding and human genetics: focus on disease-related traits in livestock - A review

Genomic studies of diseases can be divided into two types: i) analyses that reveal causal genes by focusing on linkage disequilibrium between observed and causal variants and ii) those that simultaneously assess numerous genetic markers to estimate the polygenic effects of a particular genomic region or entire genome. The field of human genetics has emphasized the discovery of causal genes, but these represent only a fraction of the total genetic variance. Therefore, alternative approaches, such as the polygenic risk score, which estimates the genetic risk for a given trait or disease based on all genetic markers (rather than on known causal variants only), have begun to garner attention. In many respects, these human genetic methods are similar to those originally developed for the estimation of breeding values (i.e., total additive genetic effects) in livestock. However, despite these similarities in methods, the fields of human and animal genetics still differ markedly in terms of research objectives, target populations, and other characteristics. For example, livestock populations have continually been selected and inbred throughout their history; consequently, their effective population size has shrunk and preferred genes (such as those influencing disease resistance and production traits) have accumulated in the modern breeding populations. By examining the characteristics of these two fields, particularly from the perspectives of disease and disease resistance, this review aims to improve understanding of the intrinsic differences between genomic studies using human compared with livestock populations.

Technical note: anomaly detection for breed purity analysis in pigs using a single breed genotype panel

Recent advancements in genotyping technologies have revolutionized our ability to estimate breed composition in pigs. The classical compositional regression used for this purpose requires a multibreed panel to detect crossbreeding and its application is limited to breeds in the panel. Some breed entities may not have access to the multibreed panel but may have access to a single breed panel. We present crossbreeding detection methods based on semi-supervised anomaly detection techniques that use a single-breed genotype panel from purebred animals of interest. By utilizing these methods, we identified and assessed breed outliers within large pig genotype panels.

Disparities in ABO blood type determination across diverse ancestries: a systematic review and validation in the All of Us Research Program

OBJECTIVES

ABO blood types have widespread clinical use and robust associations with disease. The purpose of this study is to evaluate the portability and suitability of tag single-nucleotide polymorphisms (tSNPs) used to determine ABO alleles and blood types across diverse populations in published literature.

MATERIALS AND METHODS

Bibliographic databases were searched for studies using tSNPs to determine ABO alleles. We calculated linkage between tSNPs and functional variants across inferred continental ancestry groups from 1000 Genomes. We compared r2 across ancestry and assessed real-world consequences by comparing tSNP-derived blood types to serology in a diverse population from the All of Us Research Program.

RESULTS

Linkage between functional variants and O allele tSNPs was significantly lower in African (median r2 = 0.443) compared to East Asian (r2 = 0.946, P = 1.1 × 10-5) and European (r2 = 0.869, P = .023) populations. In All of Us, discordance between tSNP-derived blood types and serology was high across all SNPs in African ancestry individuals and linkage was strongly correlated with discordance across all ancestries (ρ = -0.90, P = 3.08 × 10-23).

DISCUSSION

Many studies determine ABO blood types using tSNPs. However, tSNPs with low linkage disequilibrium promote misinference of ABO blood types, particularly in diverse populations. We observe common use of inappropriate tSNPs to determine ABO blood type, particularly for O alleles and with some tSNPs mistyping up to 58% of individuals.

CONCLUSION

Our results highlight the lack of transferability of tSNPs across ancestries and potential exacerbation of disparities in genomic research for underrepresented populations. This is especially relevant as more diverse cohorts are made publicly available.

Identification of QTL-allele systems of seed size and oil content for simultaneous genomic improvement in Northeast China soybeans

Northeast China (NEC) is the major production area for soybeans in China, whereas its soybean germplasm has played key roles in world soybean production, especially in the Americas. For plant breeding, genomic selection involves two stages, cross design and progeny selection, with the former determining the latter's potential. In NEC, one of the major breeding purposes is for 100-seed weight (100SW) and seed oil content (SOC). A diverse sample with 361 NEC soybean germplasm accessions was evaluated for their 100SW and SOC in Tieling, Liaoning, China. Both traits exhibited significant phenotypic, genotypic, and G × E variation, with a trait heritability of 82.38% and 86.26%, respectively. A restricted two-stage multi-locus genome-wide association study (RTM-GWAS) with 15,501 SNPLDB (SNP linkage disequilibrium block) markers identified 80 and 92 QTLs with 230 and 299 alleles for 100SW and SOC, respectively. Corresponding to some increase of the two traits, almost all the alleles in the early maturity groups (MG 0 + 00 + 000) were inherited from the late MGs (MG I+II+III), indicating that genetic recombination was the major motivator in addition to a few allele emergence and some allele exclusion fluctuations among early MGs. Using the 95th percentile as indicator, the prediction of recombination potentials showed that 30.43 g 100SW and 27.73% SOC might be achieved, respectively. Three strategies of simultaneous genomic improvement of both traits in designing optimal crosses, namely, 100SW-first, SOC-first, and 100SW-SOC-balance, were proved to be efficient. Thus, the optimal cross design could be extended to multiple traits based on a relatively thorough identification of the QTL-alleles using RTM-GWAS.

A deep learning approach to prediction of blood group antigens from genomic data

BACKGROUND

Deep learning methods are revolutionizing natural science. In this study, we aim to apply such techniques to develop blood type prediction models based on cheap to analyze and easily scalable screening array genotyping platforms.

METHODS

Combining existing blood types from blood banks and imputed screening array genotypes for ~111,000 Danish and 1168 Finnish blood donors, we used deep learning techniques to train and validate blood type prediction models for 36 antigens in 15 blood group systems. To account for missing genotypes a denoising autoencoder initial step was utilized, followed by a convolutional neural network blood type classifier.

RESULTS

Two thirds of the trained blood type prediction models demonstrated an F1-accuracy above 99%. Models for antigens with low or high frequencies like, for example, Cw, low training cohorts like, for example, Cob, or very complicated genetic underpinning like, for example, RhD, proved to be more challenging for high accuracy (>99%) DL modeling. However, in the Danish cohort only 4 out of 36 models (Cob, Cw, D-weak, Kpa) failed to achieve a prediction F1-accuracy above 97%. This high predictive performance was replicated in the Finnish cohort.

DISCUSSION

High accuracy in a variety of blood groups proves viability of deep learning-based blood type prediction using array chip genotypes, even in blood groups with nontrivial genetic underpinnings. These techniques are suitable for aiding in identifying blood donors with rare blood types by greatly narrowing down the potential pool of candidate donors before clinical grade confirmation.

Apolipoprotein B gene expression and regulation in relation to Alzheimer's disease pathophysiology

Apolipoprotein B (APOB), a receptor-binding protein present in cholesterol-rich lipoproteins, has been implicated in Alzheimer's disease (AD). High levels of APOB-containing low-density lipoproteins (LDL) are linked to the pathogenesis of both early-onset familial and late-onset sporadic AD. Rare coding mutations in the APOB gene are associated with familial AD, suggesting a role for APOB-bound lipoproteins in the central nervous system. This research explores APOB gene regulation across the AD spectrum using four cohorts: BRAINEAC (elderly control brains), DBCBB (controls, AD brains), ROSMAP (controls, MCI, AD brains), and ADNI (control, MCI, AD clinical subjects). APOB protein levels, measured via mass spectrometry and ELISA, positively correlated with AD pathology indices and cognition, while APOB mRNA levels showed negative correlations. Brain APOB protein levels are also correlated with cortical Aβ levels. A common coding variant in the APOB gene locus affected its expression but didn't impact AD risk or brain cholesterol concentrations, except for 24-S-hydroxycholesterol. Polymorphisms in the CYP27A1 gene, notably rs4674344, were associated with APOB protein levels. A negative correlation was observed between brain APOB gene expression and AD biomarker levels. CSF APOB correlated with Tau pathology in presymptomatic subjects, while cortical APOB was strongly associated with cortical Aβ deposition in late-stage AD. The study discusses the potential link between blood-brain barrier dysfunction and AD symptoms in relation to APOB neurobiology. Overall, APOB's involvement in lipoprotein metabolism appears to influence AD pathology across different stages of the disease.

SNP Genotype Imputation in Forensics-A Performance Study

BACKGROUND/OBJECTIVES

Emerging forensic genetic applications, such as forensic investigative genetic genealogy (FIGG), advanced DNA phenotyping, and distant kinship inference, increasingly require dense SNP genotype datasets. However, forensic-grade DNA often contains missing genotypes due to its quality and quantity limitations, potentially hindering these applications. Genotype imputation, a method that predicts missing genotypes, is widely used in population and medical genetics, but its utility in forensic genetics has not been thoroughly explored. This study aims to assess the performance of genotype imputation in forensic contexts and determine the conditions under which it can be effectively applied.

METHODS

We employed a simulation-based approach to generate realistic forensic SNP genotype datasets with varying numbers, densities, and qualities of observed genotypes. Genotype imputation was performed using Beagle software, and the performance was evaluated based on the call rate and imputation accuracy across different datasets and imputation settings.

RESULTS

The results demonstrate that genotype imputation can significantly increase the number of SNP genotypes. However, imputation accuracy was dependent on factors such as the quality of the original genotype data and the characteristics of the reference population. Higher SNP density and fewer genotype errors generally resulted in improved imputation accuracy.

CONCLUSIONS

This study highlights the potential of genotype imputation to enhance forensic SNP datasets but underscores the importance of optimizing imputation parameters and understanding the limitations of the original data. These findings will inform the future application of imputation in forensic genetics, supporting its integration into forensic workflows.

Use of Polygenic Risk Score for Prediction of Heart Failure in Cancer Survivors

Background

The risk for heart failure (HF) is increased among cancer survivors, but predicting individual HF risk is difficult. Polygenic risk scores (PRS) for HF prediction summarize the combined effects of multiple genetic variants specific to the individual.

Objectives

The aim of this study was to compare clinical HF prediction models with PRS in both cancer and noncancer populations.

Methods

Cancer and HF diagnoses were identified using International Classification of Diseases-10th Revision codes. HF risk was calculated using the ARIC (Atherosclerosis Risk in Communities) HF score (ARIC-HF). The PRS for HF (PRS-HF) was calculated according to the Global Biobank Meta-analysis Initiative. The predictive performance of the ARIC-HF and PRS-HF was compared using the area under the curve (AUC) in both cancer and noncancer populations.

Results

After excluding 2,644 participants with HF prior to consent, 440,813 participants without cancer (mean age 57 years, 53% women) and 43,720 cancer survivors (mean age 60 years, 65% women) were identified at baseline. Both the ARIC-HF and PRS-HF were significant predictors of incident HF after adjustment for chronic kidney disease, overall health rating, and total cholesterol. The PRS-HF performed poorly in predicting HF among cancer (AUC: 0.552; 95% CI: 0.539-0.564) and noncancer (AUC: 0.561; 95% CI: 0.556-0.566) populations. However, the ARIC-HF predicted incident HF in the noncancer population (AUC: 0.804; 95% CI: 0.800-0.808) and provided acceptable performance among cancer survivors (AUC: 0.748; 95% CI: 0.737-0.758).

Conclusions

The prediction of HF on the basis of conventional risk factors using the ARIC-HF score is superior compared to the PRS, in cancer survivors, and especially among the noncancer population.

Genotype imputation in human genomic studies

Imputation is a method that supplies missing information about genetic variants that could not be directly genotyped with DNA microarrays or low-coverage sequencing. Imputation plays a critical role in genome-wide association studies (GWAS). It leads to a significant increase in the number of studied variants, which improves the resolution of the method and enhances the comparability of data obtained in different cohorts and/or by using different technologies, which is important for conducting meta-analyses. When performing imputation, genotype information from the study sample, in which only part of the genetic variants are known, is complemented using the standard (reference) sample, which has more complete genotype data (most often the results of whole-genome sequencing). Imputation has become an integral part of human genomic research due to the benefits it provides and the increasing availability of imputation tools and reference sample data. This review focuses on imputation in human genomic research. The first section of the review provides a description of technologies for obtaining information about human genotypes and characteristics of these types of data. The second section describes the imputation methodology, lists the stages of its implementation and the corresponding programs, provides a description of the most popular reference panels and methods for assessing the quality of imputation. The review concludes with examples of the use of imputation in genomic studies of samples from Russia. This review shows the importance of imputation, provides information on how to carry it out, and systematizes the results of its application using Russian samples.

The goldmine of GWAS summary statistics: a systematic review of methods and tools

Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. However, the increasing number of GWAS summary statistics and the diversity of software tools available for their analysis can make it challenging for researchers to select the most appropriate tools for their specific needs. This systematic review aims to provide a comprehensive overview of the currently available software tools and databases for GWAS summary statistics analysis. We conducted a comprehensive literature search to identify relevant software tools and databases. We categorized the tools and databases by their functionality, including data management, quality control, single-trait analysis, and multiple-trait analysis. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a total of 305 functioning software tools and databases dedicated to GWAS summary statistics, each with unique strengths and limitations. We provide descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discuss the overall usability and applicability of each tool for different research scenarios. This comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases. By providing a detailed overview of the available tools and databases, we aim to facilitate informed tool selection and maximize the effectiveness of GWAS summary statistics analysis.

Cardiovascular Pharmacogenetics: From Discovery of Genetic Association to Clinical Adoption of Derived Test

Recent breakthroughs in human genetics and in information technologies have markedly expanded our understanding at the molecular level of the response to drugs, i.e., pharmacogenetics (PGx), across therapy areas. This review is restricted to PGx for cardiovascular (CV) drugs. First, we examined the PGx information in the labels approved by regulatory agencies in Europe, Japan, and North America and related recommendations from expert panels. Out of 221 marketed CV drugs, 36 had PGx information in their labels approved by one or more agencies. The level of annotations and recommendations varied markedly between agencies and expert panels. Clopidogrel is the only CV drug with consistent PGx recommendation (i.e., "actionable"). This situation prompted us to dissect the steps from discovery of a PGx association to clinical translation. We found 101 genome-wide association studies that investigated the response to CV drugs or drug classes. These studies reported significant associations for 48 PGx traits mapping to 306 genes. Six of these 306 genes are mentioned in the corresponding PGx labels or recommendations for CV drugs. Genomic analyses also highlighted the wide between-population differences in risk allele frequencies and the individual load of actionable PGx variants. Given the high attrition rate and the long road to clinical translation, additional work is warranted to identify and validate PGx variants for more CV drugs across diverse populations and to demonstrate the utility of PGx testing. To that end, pre-emptive PGx combining genomic profiling with electronic medical records opens unprecedented opportunities to improve healthcare, for CV diseases and beyond. SIGNIFICANCE STATEMENT: Despite spectacular breakthroughs in human molecular genetics and information technologies, consistent evidence supporting PGx testing in the cardiovascular area is limited to a few drugs. Additional work is warranted to discover and validate new PGx markers and demonstrate their utility. Pre-emptive PGx combining genomic profiling with electronic medical records opens unprecedented opportunities to improve healthcare, for CV diseases and beyond.

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.

PTPRS is a novel marker for early Tau pathology and synaptic integrity in Alzheimer's disease

We examined the role of protein tyrosine phosphatase receptor sigma (PTPRS) in the context of Alzheimer's disease and synaptic integrity. Publicly available datasets (BRAINEAC, ROSMAP, ADC1) and a cohort of asymptomatic but "at risk" individuals (PREVENT-AD) were used to explore the relationship between PTPRS and various Alzheimer's disease biomarkers. We identified that PTPRS rs10415488 variant C shows features of neuroprotection against early Tau pathology and synaptic degeneration in Alzheimer's disease. This single nucleotide polymorphism correlated with higher PTPRS transcript abundance and lower p(181)Tau and GAP-43 levels in the CSF. In the brain, PTPRS protein abundance was significantly correlated with the quantity of two markers of synaptic integrity: SNAP25 and SYT-1. We also found the presence of sexual dimorphism for PTPRS, with higher CSF concentrations in males than females. Male carriers for variant C were found to have a 10-month delay in the onset of AD. We thus conclude that PTPRS acts as a neuroprotective receptor in Alzheimer's disease. Its protective effect is most important in males, in whom it postpones the age of onset of the disease.

Genome-Wide Association Study with Three Control Cohorts of Japanese Patients with Esotropia and Exotropia of Comitant Strabismus and Idiopathic Superior Oblique Muscle Palsy

Esotropia and exotropia in the entity of comitant strabismus are multifactorial diseases with both genetic and environmental backgrounds. Idiopathic superior oblique muscle palsy, as the predominant entity of non-comitant (paralytic) strabismus, also has a genetic background, as evidenced by varying degrees of muscle hypoplasia. A genome-wide association study (GWAS) was conducted of 711 Japanese patients with esotropia (n= 253), exotropia (n = 356), and idiopathic superior oblique muscle palsy (n = 102). The genotypes of single nucleotide polymorphisms (SNPs) were determined by Infinium Asian Screening Array. Three control cohorts from the Japanese population were used: two cohorts from BioBank Japan (BBJ) and the Nagahama Cohort. BBJ (180K) was genotyped by a different array, Illumina Infinium OmniExpressExome or HumanOmniExpress, while BBJ (ASA) and the Nagahama Cohort were genotyped by the same Asian array. After quality control of SNPs and individuals, common SNPs between the case cohort and the control cohort were chosen in the condition of genotyping by different arrays, while all SNPs genotyped by the same array were used for SNP imputation. The SNPs imputed with R-square values ≥ 0.3 were used to compare the case cohort of each entity or the combined entity with the control cohort. In comparison with BBJ (180K), the esotropia group and the exotropia group showed CDCA7 and HLA-F, respectively, as candidate genes at a significant level of p < 5 × 10-8, while the idiopathic superior oblique muscle palsy group showed DAB1 as a candidate gene which is involved in neuronal migration. DAB1 was also detected as a candidate in comparison with BBJ (ASA) and the Nagahama Cohort at a weak level of significance of p < 1 × 10-6. In comparison with BBJ (180K), RARB (retinoic acid receptor-β) was detected as a candidate at a significant level of p < 5 × 10-8 in the combined group of esotropia, exotropia, and idiopathic superior oblique muscle palsy. In conclusion, a series of GWASs with three different control cohorts would be an effective method with which to search for candidate genes for multifactorial diseases such as strabismus.

PTPRS is a novel marker for early tau pathology and synaptic integrity in Alzheimer's disease

We examined the role of protein tyrosine phosphatase receptor sigma (PTPRS) in the context of Alzheimer's disease and synaptic integrity. Publicly available datasets (BRAINEAC, ROSMAP, ADC1) and a cohort of asymptomatic but "at risk" individuals (PREVENT-AD) were used to explore the relationship between PTPRS and various Alzheimer's disease biomarkers. We identified that PTPRS rs10415488 variant C shows features of neuroprotection against early tau pathology and synaptic degeneration in Alzheimer's disease. This single nucleotide polymorphism correlated with higher PTPRS transcript abundance and lower P-tau181 and GAP-43 levels in the CSF. In the brain, PTPRS protein abundance was significantly correlated with the quantity of two markers of synaptic integrity: SNAP25 and SYT-1. We also found the presence of sexual dimorphism for PTPRS, with higher CSF concentrations in males than females. Male carriers for variant C were found to have a 10-month delay in the onset of AD. We thus conclude that PTPRS acts as a neuroprotective receptor in Alzheimer's disease. Its protective effect is most important in males, in whom it postpones the age of onset of the disease.

Novel putative causal mutations associated with fat traits in Nellore cattle uncovered by eQTLs located in open chromatin regions

Intramuscular fat (IMF) and backfat thickness (BFT) are critical economic traits impacting meat quality. However, the genetic variants controlling these traits need to be better understood. To advance knowledge in this area, we integrated RNA-seq and single nucleotide polymorphisms (SNPs) identified in genomic and transcriptomic data to generate a linkage disequilibrium filtered panel of 553,581 variants. Expression quantitative trait loci (eQTL) analysis revealed 36,916 cis-eQTLs and 14,408 trans-eQTLs. Association analysis resulted in three eQTLs associated with BFT and 24 with IMF. Functional enrichment analysis of genes regulated by these 27 eQTLs revealed noteworthy pathways that can play a fundamental role in lipid metabolism and fat deposition, such as immune response, cytoskeleton remodeling, iron transport, and phospholipid metabolism. We next used ATAC-Seq assay to identify and overlap eQTL and open chromatin regions. Six eQTLs were in regulatory regions, four in predicted insulators and possible CCCTC-binding factor DNA binding sites, one in an active enhancer region, and the last in a low signal region. Our results provided novel insights into the transcriptional regulation of IMF and BFT, unraveling putative regulatory variants.

MagicalRsq-X: A cross-cohort transferable genotype imputation quality metric

Since genotype imputation was introduced, researchers have been relying on the estimated imputation quality from imputation software to perform post-imputation quality control (QC). However, this quality estimate (denoted as Rsq) performs less well for lower-frequency variants. We recently published MagicalRsq, a machine-learning-based imputation quality calibration, which leverages additional typed markers from the same cohort and outperforms Rsq as a QC metric. In this work, we extended the original MagicalRsq to allow cross-cohort model training and named the new model MagicalRsq-X. We removed the cohort-specific estimated minor allele frequency and included linkage disequilibrium scores and recombination rates as additional features. Leveraging whole-genome sequencing data from TOPMed, specifically participants in the BioMe, JHS, WHI, and MESA studies, we performed comprehensive cross-cohort evaluations for predominantly European and African ancestral individuals based on their inferred global ancestry with the 1000 Genomes and Human Genome Diversity Project data as reference. Our results suggest MagicalRsq-X outperforms Rsq in almost every setting, with 7.3%-14.4% improvement in squared Pearson correlation with true R2, corresponding to 85-218 K variant gains. We further developed a metric to quantify the genetic distances of a target cohort relative to a reference cohort and showed that such metric largely explained the performance of MagicalRsq-X models. Finally, we found MagicalRsq-X saved up to 53 known genome-wide significant variants in one of the largest blood cell trait GWASs that would be missed using the original Rsq for QC. In conclusion, MagicalRsq-X shows superiority for post-imputation QC and benefits genetic studies by distinguishing well and poorly imputed lower-frequency variants.

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.

The Women4Health cohort: a unique cohort to study women-specific mechanisms of cardio-metabolic regulation

Aims

Epidemiological research has shown relevant differences between sexes in clinical manifestations, severity, and progression of cardiovascular and metabolic disorders. To date, the mechanisms underlying these differences remain unknown. Given the rising incidence of such diseases, gender-specific research on established and emerging risk factors, such as dysfunction of glycaemic and/or lipid metabolism, of sex hormones and of gut microbiome, is of paramount importance. The relationships between sex hormones, gut microbiome, and host glycaemic and/or lipid metabolism are largely unknown even in the homoeostasis status. Yet this knowledge gap would be pivotal to pinpoint to key mechanisms that are likely to be disrupted in disease context.

Methods and results

Here we present the Women4Health (W4H) cohort, a unique cohort comprising up to 300 healthy women followed up during a natural menstrual cycle, set up with the primary goal to investigate the combined role of sex hormones and gut microbiota variations in regulating host lipid and glucose metabolism during homoeostasis, using a multi-omics strategy. Additionally, the W4H cohort will take into consideration another ecosystem that is unique to women, the vaginal microbiome, investigating its interaction with gut microbiome and exploring-for the first time-its role in cardiometabolic disorders.

Conclusion

The W4H cohort study lays a foundation for improving current knowledge of women-specific mechanisms in cardiometabolic regulation. It aspires to transform insights on host-microbiota interactions into prevention and therapeutic approaches for personalized health care.

The predictive capacity of polygenic risk scores for disease risk is only moderately influenced by imputation panels tailored to the target population

MOTIVATION

Polygenic risk scores (PRSs) predict individuals' genetic risk of developing complex diseases. They summarize the effect of many variants discovered in genome-wide association studies (GWASs). However, to date, large GWASs exist primarily for the European population and the quality of PRS prediction declines when applied to other ethnicities. Genetic profiling of individuals in the discovery set (on which the GWAS was performed) and target set (on which the PRS is applied) is typically done by SNP arrays that genotype a fraction of common SNPs. Therefore, a key step in GWAS analysis and PRS calculation is imputing untyped SNPs using a panel of fully sequenced individuals. The imputation results depend on the ethnic composition of the imputation panel. Imputing genotypes with a panel of individuals of the same ethnicity as the genotyped individuals typically improves imputation accuracy. However, there has been no systematic investigation into the influence of the ethnic composition of imputation panels on the accuracy of PRS predictions when applied to ethnic groups that differ from the population used in the GWAS.

RESULTS

We estimated the effect of imputation of the target set on prediction accuracy of PRS when the discovery and the target sets come from different ethnic groups. We analyzed binary phenotypes on ethnically distinct sets from the UK Biobank and other resources. We generated ethnically homogenous panels, imputed the target sets, and generated PRSs. Then, we assessed the prediction accuracy obtained from each imputation panel. Our analysis indicates that using an imputation panel matched to the ethnicity of the target population yields only a marginal improvement and only under specific conditions.

AVAILABILITY AND IMPLEMENTATION

The source code used for executing the analyses is this paper is available at https://github.com/Shamir-Lab/PRS-imputation-panels.

Association of genetic variation in COL11A1 with adolescent idiopathic scoliosis

Adolescent idiopathic scoliosis (AIS) is a common and progressive spinal deformity in children that exhibits striking sexual dimorphism, with girls at more than fivefold greater risk of severe disease compared to boys. Despite its medical impact, the molecular mechanisms that drive AIS are largely unknown. We previously defined a female-specific AIS genetic risk locus in an enhancer near the PAX1 gene. Here, we sought to define the roles of PAX1 and newly identified AIS-associated genes in the developmental mechanism of AIS. In a genetic study of 10,519 individuals with AIS and 93,238 unaffected controls, significant association was identified with a variant in COL11A1 encoding collagen (α1) XI (rs3753841; NM_080629.2_c.4004C>T; p.(Pro1335Leu); p=7.07E-11, OR = 1.118). Using CRISPR mutagenesis we generated Pax1 knockout mice (Pax1-/-). In postnatal spines we found that PAX1 and collagen (α1) XI protein both localize within the intervertebral disc-vertebral junction region encompassing the growth plate, with less collagen (α1) XI detected in Pax1-/- spines compared to wild-type. By genetic targeting we found that wild-type Col11a1 expression in costal chondrocytes suppresses expression of Pax1 and of Mmp3, encoding the matrix metalloproteinase 3 enzyme implicated in matrix remodeling. However, the latter suppression was abrogated in the presence of the AIS-associated COL11A1P1335L mutant. Further, we found that either knockdown of the estrogen receptor gene Esr2 or tamoxifen treatment significantly altered Col11a1 and Mmp3 expression in chondrocytes. We propose a new molecular model of AIS pathogenesis wherein genetic variation and estrogen signaling increase disease susceptibility by altering a PAX1-COL11a1-MMP3 signaling axis in spinal chondrocytes.

Construction of a strawberry breeding core collection to capture and exploit genetic variation

BACKGROUND

Genetic diversity is crucial for the success of plant breeding programs and core collections are important resources to capture this diversity. Many core collections have already been constructed by gene banks, whose main goal is to obtain a panel of a limited number of genotypes to simplify management practices and to improve shareability while retaining as much diversity as possible. However, as gene banks have a different composition and goal than plant breeding programs, constructing a core collection for a plant breeding program should consider different aspects.

RESULTS

In this study, we present a novel approach for constructing a core collection by integrating both genomic and pedigree information to maximize the representation of the breeding germplasm in a minimum subset of genotypes while accounting for future genetic variation within a strawberry breeding program. Our stepwise approach starts with selecting the most important crossing parents of advanced selections and genotypes included for specific traits, to represent also future genetic variation. We then use pedigree-genomic-based relationship coefficients combined with the 'accession to nearest entry' criterion to complement the core collection and maximize its representativeness of the current breeding program. Combined pedigree-genomic-based relationship coefficients allow for accurate relationship estimation without the need to genotype every individual in the breeding program.

CONCLUSIONS

This stepwise construction of a core collection in a strawberry breeding program can be applied in other plant breeding programs to construct core collections for various purposes.

Association of genetic variation in COL11A1 with adolescent idiopathic scoliosis

Adolescent idiopathic scoliosis (AIS) is a common and progressive spinal deformity in children that exhibits striking sexual dimorphism, with girls at more than five-fold greater risk of severe disease compared to boys. Despite its medical impact, the molecular mechanisms that drive AIS are largely unknown. We previously defined a female-specific AIS genetic risk locus in an enhancer near the PAX1 gene. Here we sought to define the roles of PAX1 and newly-identified AIS-associated genes in the developmental mechanism of AIS. In a genetic study of 10,519 individuals with AIS and 93,238 unaffected controls, significant association was identified with a variant in COL11A1 encoding collagen (α1) XI (rs3753841; NM_080629.2_c.4004C>T; p.(Pro1335Leu); P=7.07e-11, OR=1.118). Using CRISPR mutagenesis we generated Pax1 knockout mice (Pax1-/-). In postnatal spines we found that PAX1 and collagen (α1) XI protein both localize within the intervertebral disc (IVD)-vertebral junction region encompassing the growth plate, with less collagen (α1) XI detected in Pax1-/- spines compared to wildtype. By genetic targeting we found that wildtype Col11a1 expression in costal chondrocytes suppresses expression of Pax1 and of Mmp3, encoding the matrix metalloproteinase 3 enzyme implicated in matrix remodeling. However, this suppression was abrogated in the presence of the AIS-associated COL11A1P1335L mutant. Further, we found that either knockdown of the estrogen receptor gene Esr2, or tamoxifen treatment, significantly altered Col11a1 and Mmp3 expression in chondrocytes. We propose a new molecular model of AIS pathogenesis wherein genetic variation and estrogen signaling increase disease susceptibility by altering a Pax1-Col11a1-Mmp3 signaling axis in spinal chondrocytes.

Review: Computational analysis of human skeletal remains in ancient DNA and forensic genetics

Degraded DNA is used to answer questions in the fields of ancient DNA (aDNA) and forensic genetics. While aDNA studies typically center around human evolution and past history, and forensic genetics is often more concerned with identifying a specific individual, scientists in both fields face similar challenges. The overlap in source material has prompted periodic discussions and studies on the advantages of collaboration between fields toward mutually beneficial methodological advancements. However, most have been centered around wet laboratory methods (sampling, DNA extraction, library preparation, etc.). In this review, we focus on the computational side of the analytical workflow. We discuss limitations and considerations to consider when working with degraded DNA. We hope this review provides a framework to researchers new to computational workflows for how to think about analyzing highly degraded DNA and prompts an increase of collaboration between the forensic genetics and aDNA fields.

Genetic QT Score and Sleep Apnea as Predictors of Sudden Cardiac Death in the UK Biobank

Introduction

The goal of this study was to evaluate the association between a polygenic risk score (PRS) for QT prolongation (QTc-PRS), QTc intervals and mortality in patients enrolled in the UK Biobank with and without sleep apnea.

Methods

The QTc-PRS was calculated using allele copy number and previously reported effect estimates for each single nuclear polymorphism SNP. Competing-risk regression models adjusting for age, sex, BMI, QT prolonging medication, race, and comorbid cardiovascular conditions were used for sudden cardiac death (SCD) analyses.

Results

500,584 participants were evaluated (56.5 ±8 years, 54% women, 1.4% diagnosed with sleep apnea). A higher QTc-PRS was independently associated with the increased QTc interval duration (p<0.0001). The mean QTc for the top QTc-PRS quintile was 15 msec longer than the bottom quintile (p<0.001). Sleep apnea was found to be an effect modifier in the relationship between QTc-PRS and SCD. The adjusted HR per 5-unit change in QTc-PRS for SCD was 1.64 (95% CI 1.16 - 2.31, p=0.005) among those with sleep apnea and 1.04 (95% CI 0.95 - 1.14, p=0.44) among those without sleep apnea (p for interaction =0.01). Black participants with sleep apnea had significantly elevated adjusted risk of SCD compared to White participants (HR=9.6, 95% CI 1.24 - 74, p=0.03).

Conclusion

In the UK Biobank population, the QTc-PRS was associated with SCD among participants with sleep apnea but not among those without sleep apnea, indicating that sleep apnea is a significant modifier of the genetic risk. Black participants with sleep apnea had a particularly high risk of SCD.

Additive and non-additive genetic variance in juvenile Sitka spruce (Picea sitchensis Bong. Carr)

Many quantitative genetic models assume that all genetic variation is additive because of a lack of data with sufficient structure and quality to determine the relative contribution of additive and non-additive variation. Here the fractions of additive (fa) and non-additive (fd) genetic variation were estimated in Sitka spruce for height, bud burst and pilodyn penetration depth. Approximately 1500 offspring were produced in each of three sib families and clonally replicated across three geographically diverse sites. Genotypes from 1525 offspring from all three families were obtained by RADseq, followed by imputation using 1630 loci segregating in all families and mapped using the newly developed linkage map of Sitka spruce. The analyses employed a new approach for estimating fa and fd, which combined all available genotypic and phenotypic data with spatial modelling for each trait and site. The consensus estimate for fa increased with age for height from 0.58 at 2 years to 0.75 at 11 years, with only small overlap in 95% support intervals (I95). The estimated fa for bud burst was 0.83 (I95=[0.78, 0.90]) and 0.84 (I95=[0.77, 0.92]) for pilodyn depth. Overall, there was no evidence of family heterogeneity for height or bud burst, or site heterogeneity for pilodyn depth, and no evidence of inbreeding depression associated with genomic homozygosity, expected if dominance variance was the major component of non-additive variance. The results offer no support for the development of sublines for crossing within the species. The models give new opportunities to assess more accurately the scale of non-additive variation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11295-023-01627-5.

Record-matching of STR profiles with fragmentary genomic SNP data

In many forensic settings, identity of a DNA sample is sought from poor-quality DNA, for which the typical STR loci tabulated in forensic databases are not possible to reliably genotype. Genome-wide SNPs, however, can potentially be genotyped from such samples via next-generation sequencing, so that queries can in principle compare SNP genotypes from DNA samples of interest to STR genotype profiles that represent proposed matches. We use genetic record-matching to evaluate the possibility of testing SNP profiles obtained from poor-quality DNA samples to identify exact and relatedness matches to STR profiles. Using simulations based on whole-genome sequences, we show that in some settings, similar match accuracies to those seen with full coverage of the genome are obtained by genetic record-matching for SNP data that represent 5-10% genomic coverage. Thus, if even a fraction of random genomic SNPs can be genotyped by next-generation sequencing, then the potential may exist to test the resulting genotype profiles for matches to profiles consisting exclusively of nonoverlapping STR loci. The result has implications in relation to criminal justice, mass disasters, missing-person cases, studies of ancient DNA, and genomic privacy.

Animal board invited review: Practical applications of genomic information in livestock

Access to high-dimensional genomic information in many livestock species is accelerating. This has been greatly aided not only by continual reductions in genotyping costs but also an expansion in the services available that leverage genomic information to create a greater return-on-investment. Genomic information on individual animals has many uses including (1) parentage verification and discovery, (2) traceability, (3) karyotyping, (4) sex determination, (5) reporting and monitoring of mutations conferring major effects or congenital defects, (6) better estimating inbreeding of individuals and coancestry among individuals, (7) mating advice, (8) determining breed composition, (9) enabling precision management, and (10) genomic evaluations; genomic evaluations exploit genome-wide genotype information to improve the accuracy of predicting an animal's (and by extension its progeny's) genetic merit. Genomic data also provide a huge resource for research, albeit the outcome from this research, if successful, should eventually be realised through one of the ten applications already mentioned. The process for generating a genotype all the way from sample procurement to identifying erroneous genotypes is described, as are the steps that should be considered when developing a bespoke genotyping panel for practical application.

Association of PDGFRA polymorphisms with the risk of corneal astigmatism in a Japanese population

Corneal astigmatism is reportedly associated with polymorphisms of the platelet-derived growth factor receptor alpha (PDGFRA) gene region in Asian populations of Chinese, Malay, and Indian ancestry and populations of European ancestry. In this study, we investigated whether these PDGFRA polymorphisms are associated with corneal astigmatism in a Japanese population. We recruited 1,535 cases with corneal astigmatism (mean corneal cylinder power across both eyes: ≤  - 0.75 diopters [D]) and 842 controls (> - 0.75 D) to genotype 13 single-nucleotide polymorphisms (SNPs) in the PDGFRA gene region. We also performed imputation analysis in the region, with 179 imputed SNPs included in the statistical analyses. The PDGFRA SNPs were not significantly associated with the cases with corneal astigmatism ≤  - 0.75 D. However, the odds ratios (ORs) of the minor alleles of SNPs in the upstream region of PDGFRA, including rs7673984, rs4864857, and rs11133315, tended to increase according to the degree of corneal astigmatism, and these SNPs were significantly associated with the cases with corneal astigmatism ≤  - 1.25 D or ≤  - 1.50 D (Pc < 0.05, OR = 1.34-1.39). These results suggest that PDGFRA SNPs play a potential role in the development of greater corneal astigmatism.

Methods for Assessing Population Relationships and History Using Genomic Data

Genetic data contain a record of our evolutionary history. The availability of large-scale datasets of human populations from various geographic areas and timescales, coupled with advances in the computational methods to analyze these data, has transformed our ability to use genetic data to learn about our evolutionary past. Here, we review some of the widely used statistical methods to explore and characterize population relationships and history using genomic data. We describe the intuition behind commonly used approaches, their interpretation, and important limitations. For illustration, we apply some of these techniques to genome-wide autosomal data from 929 individuals representing 53 worldwide populations that are part of the Human Genome Diversity Project. Finally, we discuss the new frontiers in genomic methods to learn about population history. In sum, this review highlights the power (and limitations) of DNA to infer features of human evolutionary history, complementing the knowledge gleaned from other disciplines, such as archaeology, anthropology, and linguistics.

A whole-genome reference panel of 14,393 individuals for East Asian populations accelerates discovery of rare functional variants

Underrepresentation of non-European (EUR) populations hinders growth of global precision medicine. Resources such as imputation reference panels that match the study population are necessary to find low-frequency variants with substantial effects. We created a reference panel consisting of 14,393 whole-genome sequences including more than 11,000 Asian individuals. Genome-wide association studies were conducted using the reference panel and a population-specific genotype array of 72,298 subjects for eight phenotypes. This panel yields improved imputation accuracy of rare and low-frequency variants within East Asian populations compared with the largest reference panel. Thirty-nine previously unidentified associations were found, and more than half of the variants were East Asian specific. We discovered genes with rare protein-altering variants, including LTBP1 for height and GPR75 for body mass index, as well as putative regulatory mechanisms for rare noncoding variants with cell type-specific effects. We suggest that this dataset will add to the potential value of Asian precision medicine.

Assessing the performance of European-derived cardiometabolic polygenic risk scores in South-Asians and their interplay with family history

BACKGROUND & AIMS

We aimed to assess the performance of European-derived polygenic risk scores (PRSs) for common metabolic diseases such as coronary artery disease (CAD), obesity, and type 2 diabetes (T2D) in the South Asian (SAS) individuals in the UK Biobank. Additionally, we studied the interaction between PRS and family history (FH) in the same population.

METHODS

To calculate the PRS, we used a previously published model derived from the EUR population and applied it to the individuals of SAS ancestry from the UKB study. Each PRS was adjusted according to an individual's genotype location in the principal components (PC) space to derive an ancestry adjusted PRS (aPRS). We calculated the percentiles based on aPRS and stratified individuals into three aPRS categories: low, intermediate, and high. Considering the intermediate-aPRS percentile as a reference, we compared the low and high aPRS categories and generated the odds ratio (OR) estimates. Further, we measured the combined role of aPRS and first-degree family history (FH) in the SAS population.

RESULTS

The risk of developing severe obesity for SAS individuals was almost twofold higher for individuals with high aPRS than for those with intermediate aPRS, with an OR of 1.95 (95% CI = 1.71-2.23, P < 0.01). At the same time, the risk of severe obesity was lower in the low-aPRS group (OR = 0.60, CI = 0.53-0.67, P < 0.01). Results in the same direction were found in the EUR data, where the low-PRS group had an OR of 0.53 (95% CI = 0.51-0.56, P < 0.01) and the high-PRS group had an OR of 2.06 (95% CI = 2.00-2.12, P < 0.01). We observed similar results for CAD and T2D. Further, we show that SAS individuals with a familial history of CAD and T2D with high-aPRS are associated with a higher risk of these diseases, implying a greater genetic predisposition.

CONCLUSION

Our findings suggest that CAD, obesity, and T2D GWAS summary statistics generated predominantly from the EUR population can be potentially used to derive aPRS in SAS individuals for risk stratification. With future GWAS recruiting more SAS participants and tailoring the PRSs towards SAS ancestry, the predictive power of PRS is likely to improve further.

Evidence of causality of low body mass index on risk of adolescent idiopathic scoliosis: a Mendelian randomization study

Introduction

Adolescent idiopathic scoliosis (AIS) is a disorder with a three-dimensional spinal deformity and is a common disease affecting 1-5% of adolescents. AIS is also known as a complex disease involved in environmental and genetic factors. A relation between AIS and body mass index (BMI) has been epidemiologically and genetically suggested. However, the causal relationship between AIS and BMI remains to be elucidated.

Material and methods

Mendelian randomization (MR) analysis was performed using summary statistics from genome-wide association studies (GWASs) of AIS (Japanese cohort, 5,327 cases, 73,884 controls; US cohort: 1,468 cases, 20,158 controls) and BMI (Biobank Japan: 173430 individual; meta-analysis of genetic investigation of anthropometric traits and UK Biobank: 806334 individuals; European Children cohort: 39620 individuals; Population Architecture using Genomics and Epidemiology: 49335 individuals). In MR analyses evaluating the effect of BMI on AIS, the association between BMI and AIS summary statistics was evaluated using the inverse-variance weighted (IVW) method, weighted median method, and Egger regression (MR-Egger) methods in Japanese.

Results

Significant causality of genetically decreased BMI on risk of AIS was estimated: IVW method (Estimate (beta) [SE] = -0.56 [0.16], p = 1.8 × 10-3), weighted median method (beta = -0.56 [0.18], p = 8.5 × 10-3) and MR-Egger method (beta = -1.50 [0.43], p = 4.7 × 10-3), respectively. Consistent results were also observed when using the US AIS summary statistic in three MR methods; however, no significant causality was observed when evaluating the effect of AIS on BMI.

Conclusions

Our Mendelian randomization analysis using large studies of AIS and GWAS for BMI summary statistics revealed that genetic variants contributing to low BMI have a causal effect on the onset of AIS. This result was consistent with those of epidemiological studies and would contribute to the early detection of AIS.

Genome-wide DNA methylation association study of recent and cumulative marijuana use in middle aged adults

Marijuana is a widely used psychoactive substance in the US and medical and recreational legalization has risen over the past decade. Despite the growing number of individuals using marijuana, studies investigating the association between epigenetic factors and recent and cumulative marijuana use remain limited. We therefore investigated the association between recent and cumulative marijuana use and DNA methylation levels. Participants from the Coronary Artery Risk Development in Young Adults Study with whole blood collected at examination years (Y) 15 and Y20 were randomly selected to undergo DNA methylation profiling at both timepoints using the Illumina MethylationEPIC BeadChip. Recent use of marijuana was queried at each examination and used to estimate cumulative marijuana use from Y0 to Y15 and Y20. At Y15 (n = 1023), we observed 22 and 31 methylation markers associated (FDR P ≤ 0.05) with recent and cumulative marijuana use and 132 and 16 methylation markers at Y20 (n = 883), respectively. We replicated 8 previously reported methylation markers associated with marijuana use. We further identified 640 cis-meQTLs and 198 DMRs associated with recent and cumulative use at Y15 and Y20. Differentially methylated genes were statistically overrepresented in pathways relating to cellular proliferation, hormone signaling, and infections as well as schizophrenia, bipolar disorder, and substance-related disorders. We identified numerous methylation markers, pathways, and diseases associated with recent and cumulative marijuana use in middle-aged adults, providing additional insight into the association between marijuana use and the epigenome. These results provide novel insights into the role marijuana has on the epigenome and related health conditions.

The Bayesian lens and Bayesian blinkers

I discuss the benefits of looking through the 'Bayesian lens' (seeking a Bayesian interpretation of ostensibly non-Bayesian methods), and the dangers of wearing 'Bayesian blinkers' (eschewing non-Bayesian methods as a matter of philosophical principle). I hope that the ideas may be useful to scientists trying to understand widely used statistical methods (including confidence intervals and [Formula: see text]-values), as well as teachers of statistics and practitioners who wish to avoid the mistake of overemphasizing philosophy at the expense of practical matters. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.

Genome-wide association analyses identified novel susceptibility loci for pulmonary embolism among Han Chinese population

BACKGROUND

A large proportion of pulmonary embolism (PE) heritability remains unexplained, particularly among the East Asian (EAS) population. Our study aims to expand the genetic architecture of PE and reveal more genetic determinants in Han Chinese.

METHODS

We conducted the first genome-wide association study (GWAS) of PE in Han Chinese, then performed the GWAS meta-analysis based on the discovery and replication stages. To validate the effect of the risk allele, qPCR and Western blotting experiments were used to investigate possible changes in gene expression. Mendelian randomization (MR) analysis was employed to implicate pathogenic mechanisms, and a polygenic risk score (PRS) for PE risk prediction was generated.

RESULTS

After meta-analysis of the discovery dataset (622 cases, 8853 controls) and replication dataset (646 cases, 8810 controls), GWAS identified 3 independent loci associated with PE, including the reported loci FGG rs2066865 (p-value = 3.81 × 10^-14), ABO rs582094 (p-value = 1.16 × 10^-10) and newly reported locus FABP2 rs1799883 (p-value = 7.59 × 10^-17). Previously reported 10 variants were successfully replicated in our cohort. Functional experiments confirmed that FABP2-A163G(rs1799883) promoted the transcription and protein expression of FABP2. Meanwhile, MR analysis revealed that high LDL-C and TC levels were associated with an increased risk of PE. Individuals with the top 10% of PRS had over a fivefold increased risk for PE compared to the general population.

CONCLUSIONS

We identified FABP2, related to the transport of long-chain fatty acids, contributing to the risk of PE and provided more evidence for the essential role of metabolic pathways in PE development.