Genotype imputation for genome-wide association studies

Genetic Architecture of Idiopathic Inflammatory Myopathies From Meta-Analyses

OBJECTIVE

Idiopathic inflammatory myopathies (IIMs, myositis) are rare systemic autoimmune disorders that lead to muscle inflammation, weakness, and extramuscular manifestations, with a strong genetic component influencing disease development and progression. Previous genome-wide association studies identified loci associated with IIMs. In this study, we imputed data from two prior genome-wide myositis studies and analyzed the largest myositis data set to date to identify novel risk loci and susceptibility genes associated with IIMs and its clinical subtypes.

METHODS

We performed association analyses on 14,903 individuals (3,206 patients and 11,697 controls) with genotypes and imputed data from the Trans-Omics for Precision Medicine reference panel. Fine-mapping and expression quantitative trait locus colocalization analyses in myositis-relevant tissues indicated potential causal variants. Functional annotation and network analyses using the random walk with restart (RWR) algorithm explored underlying genetic networks and drug repurposing opportunities.

RESULTS

Our analyses identified novel risk loci and susceptibility genes, such as FCRLA, NFKB1, IRF4, DCAKD, and ATXN2 in overall IIMs; NEMP2 in polymyositis; ACBC11 in dermatomyositis; and PSD3 in myositis with anti-histidyl-transfer RNA synthetase autoantibodies (anti-Jo-1). We also characterized effects of HLA region variants and the role of C4. Colocalization analyses suggested putative causal variants in DCAKD in skin and muscle, HCP5 in lung, and IRF4 in Epstein-Barr virus (EBV)-transformed lymphocytes, lung, and whole blood. RWR further prioritized additional candidate genes, including APP, CD74, CIITA, NR1H4, and TXNIP, for future investigation.

CONCLUSION

Our study uncovers novel genetic regions contributing to IIMs, advancing our understanding of myositis pathogenesis and offering new insights for future research.

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.

Genome-wide association study for lung cancer in 6531 African Americans reveals new susceptibility loci

Despite lung cancer affecting all races and ethnicities, disparities are observed in incidence and mortality rates among different ethnic groups in the United States. Non-Hispanic African Americans had a high incidence rate of lung cancer at 55.8 per 100 000 people, as well as the highest death rate at 37.2 per 100 000 people from 2016 to 2020. While previous genome-wide association studies (GWAS) have identified over 45 susceptibility risk loci that influence lung cancer development, few GWAS have investigated the etiology of lung cancer in African Americans. To address this gap in knowledge, we conducted GWAS of lung cancer focused on studying African Americans, comprising 2267 lung cancer cases and 4264 controls. We identified three loci associated with lung cancer, one with lung adenocarcinoma, and four with lung squamous cell carcinoma in this population at the genomic-wide significance level. Among them, three novel loci were identified near VWF at 12p13.31 for overall lung cancer and GACAT3 at 2p24.3 and LMAN1L at 15q24.1 for lung squamous cell carcinoma. In addition, we confirmed previously reported risk loci with known or new lead variants near CHRNA5 at 15q25.1 and CYP2A6 at 19q13.2 associated with lung cancer and TRIP13 at 5p15.33 and ERC1 at 12p13.33 associated with lung squamous cell carcinoma. Further multi-step functional analyses shed light on biological mechanisms underlying these associations of lung cancer in this population. Our study highlights the importance of ancestry-specific studies for the potential alleviation of lung cancer burden in African Americans.

X-chromosome association study reveals genetic susceptibility loci of hypospadias in southern Chinese population

PURPOSE

X-chromosome variants contribute significantly to hypospadias risk but have not been fully elucidated in the Chinese population. Here we aim to assess how X-chromosome variants contribute to hypospadias susceptibility in the Chinese population.

METHODS

We recruited 1,073 boys with hypospadias and 5,150 controls in a southern Chinese population. Single-variant and gene/pathway-based association analyses were conducted for the distal and proximal hypospadias. Haplotype analysis was performed on top susceptibility genes. Additionally, we performed a multi-ancestral comparison between the East Asian and European populations.

RESULTS

We performed an X-chromosome-wide association study on 987 patients and 4,761 controls that met quality control standards. We confirmed DGKK variants as multi-ancestral susceptibility loci for distal hypospadias (lead SNP: rs5961181, P = 1.82 × 10- 7), but not for the proximal subtype. Distinct association signals were identified for distal hypospadias (DGKK-CCNB3-AKAP4, PNPLA4, AR-OPHN1, TAF7L, IL1RAPL1) and proximal hypospadias (SMIM10L2A, PRR32 and Xq28 gene cluster). Pathway analysis revealed that distal hypospadias is associated with male gamete generation, epithelial cell polarity, and lipid/sterol metabolism, while proximal hypospadias is linked to amino acid metabolism and gastrulation. Except for DGKK, all candidate genes showed population-favored associations compared to European studies. Haplotype analysis of DGKK, PNPLA4, OPHN1 and IL1RAPL1 showed increased risk for specific risk haplotypes (OR ranged from 4.35 to 6.25).

CONCLUSION

Our findings highlight the importance of X chromosome variants in hypospadias etiology and reveal subtype- and population-specific genetic architecture. Our results improve the understanding of genetic susceptibility for hypospadias risk and provide insights into risk prediction and personalized prevention strategies in hypospadias management.

Using LDpred2 to adapt polygenic risk score techniques for methylation score creation

OBJECTIVE

This study sought to determine if the R package LDpred2, designed for polygenic risk score creation for genome-wide association studies using summary statistics, could be adapted for deriving DNA methylation scores from methylome-wide association studies. Recognizing that linkage disequilibrium, used as prior in LDpred2, does not apply to methylation, we explored co-methylated regions and topologically associating domains as alternative structural priors for correlation between methylation sites. A genomic sliding-window approach was also tested. The performance of the LDpred2-based models was evaluated on methylation data from schizophrenia and control samples (N = 1,227).

RESULTS

LDpred2 models employing topologically associating domains and sliding window clusters as priors performed similarly to existing methods, explaining approximately 3.6% of schizophrenia phenotypic variance. The co-methylated regions model underperformed due to insufficient clustering of probes. The similarity in performance between the model using topologically associating domains and a null model consisting of random clusters suggests that the structural information provided by these domains enhances performance only marginally. In conclusion, while LDpred2 can be adapted for methylation data, it does not substantially enhance methylation score performance over existing methods, and the choice of structural prior may not be a critical factor.

SURFBAT: a surrogate family based association test building on large imputation reference panels

Genotype-phenotype association tests are typically adjusted for population stratification using principal components that are estimated genome-wide. This lacks resolution when analyzing populations with fine structure and/or individuals with fine levels of admixture. This can affect power and precision, and is a particularly relevant consideration when control individuals are recruited using geographic selection criteria. Such is the case in France where we have recently created reference panels of individuals anchored to different geographic regions. To make correct comparisons against case groups, who would likely be gathered from large urban areas, new methods are needed. We present SURFBAT (a surrogate family based association test), which performs an approximation of the transmission-disequilibrium test. Our method hinges on the application of genotype imputation algorithms to match similar haplotypes between the case and control groups. This permits us to approximate local ancestry informed posterior probabilities of un-transmitted parental alleles of each case individual. This is achieved by assuming haplotypes from the imputation panel are well-matched for ancestry with the case individuals. When the first haplotype of an individual from the imputation panel matches that of a case individual, it is assumed that the second haplotype of the same reference individual can be used as a locally ancestry matched control haplotype and to approximately impute un-transmitted parental alleles. SURFBAT provides an association test that is inherently robust to fine-scale population stratification and opens up the possibility of efficiently using large imputation reference panels as control groups for association testing. In contrast to other methods for association testing that incorporate local-ancestry inference, SURFBAT does not require a set of ancestry groups to be defined, nor for local ancestry to be explicitly estimated. We demonstrate the interest of our tool on simulated datasets, as well as on a real-data example for a group of case individuals affected by Brugada syndrome.

Genome-wide association meta-analyses of drug-resistant epilepsy

BACKGROUND

Epilepsy is one of the most common neurological disorders, affecting over 50 million people worldwide. One-third of people with epilepsy do not respond to currently available anti-seizure medications, constituting one of the most important problems in epilepsy. Little is known about the molecular pathology of drug resistance in epilepsy, in particular, possible underlying genetic factors are largely unknown.

METHODS

We performed a genome-wide association study (GWAS) in two epilepsy cohorts of European ancestry, comparing drug-resistant (N = 4208) to drug-responsive individuals (N = 2618) followed by meta-analyses across the studies. Next, we performed subanalyses split into two broad subtypes: acquired or non-acquired focal and genetic generalized epilepsy.

FINDINGS

Our drug-resistant versus drug-responsive epilepsy GWAS meta-analysis showed no significant loci when combining all epilepsy types. Sub-analyses on individuals with focal epilepsy (FE) identified a significant locus on chromosome 1q42.11-q42.12 (lead SNP: rs35915186, P = 1·51 × 10-8, OR[C] = 0·74). This locus was not associated with any epilepsy subtype in the latest epilepsy GWAS (lowest uncorrected P = 0·009 for FE vs. healthy controls), and drug resistance in FE was not genetically correlated with susceptibility to FE itself. Seven genome-wide significant SNPs within this locus, encompassing the genes CNIH4, WDR26, and CNIH3, were identified to protect against drug-resistant FE. Further transcriptome-wide association studies (TWAS) imply significantly higher expression levels of CNIH3 and WDR26 in drug-resistant FE than in drug-responsive FE. CNIH3 is implicated in AMPA receptor assembly and function, while WDR26 haploinsufficiency is linked to intellectual disability and seizures. These findings suggest that CNIH3 and WDR26 may play a role in mediating drug response in focal epilepsy.

INTERPRETATION

We identified a contribution of common genetic variation to drug-resistant focal epilepsy. These findings provide insights into possible mechanisms underlying drug response variability in epilepsy, offering potential targets for personalised treatment approaches.

FUNDING

This work is part of the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 279062 (EpiPGX) and the Centers for Common Disease Genomics (CCDG) program, funded by the National Human Genome Research Institute (NHGRI) and the National Heart, Lung, and Blood Institute (NHLBI).

Large-scale multi-omics analyses in Hispanic/Latino populations identify genes for cardiometabolic traits

Here, we present a multi-omics study of type 2 diabetes and quantitative blood lipid and lipoprotein traits conducted to date in Hispanic/Latino populations (nmax = 63,184). We conduct a meta-analysis of 16 type 2 diabetes and 19 lipid trait GWAS, identifying 20 genome-wide significant loci for type 2 diabetes, including one novel locus and novel signals at two known loci, based on fine-mapping. We also identify sixty-one genome-wide significant loci across the lipid/lipoprotein traits, including nine novel loci, and novel signals at 19 known loci through fine-mapping. Next, we analyze genetically regulated expression, perform Mendelian randomization, and analyze association with transcriptomic and proteomic measure using multi-omics data from a Hispanic/Latino population. Using this approach, we identify genes linked to type 2 diabetes and lipid/lipoprotein traits, including TMEM205 and NEDD9 for HDL cholesterol, TREH for triglycerides, and ANXA4 for type 2 diabetes.

Evaluation of imputation performance of multiple reference panels in a Pakistani population

Genotype imputation is crucial for genome-wide association studies (GWASs), but reference panels and existing benchmarking studies prioritize European individuals. Consequently, it is unclear which publicly available reference panel should be used for Pakistani individuals, and whether ancestry composition or sample size of the panel matters more for imputation accuracy. Our study compared different reference panels to impute genotype data in 1,814 Pakistani individuals, finding the best performance balancing accuracy and coverage with meta-imputation with TOPMed and the expanded 1000 Genomes (ex1KG) reference. Imputation accuracy of ex1KG outperformed TOPMed for common variants despite its 30-fold smaller sample size, supporting efforts to create future panels with diverse populations.

Dimension Reduction Using Local Principal Components for Regression-Based Multi-SNP Analysis in 1000 Genomes and the Canadian Longitudinal Study on Aging (CLSA)

For genetic association analysis based on multiple SNP regression of genotypes obtained by dense DNA sequencing or array data imputation, multi-collinearity can be a severe issue causing failure to fit the regression model. In this study, we propose a method of Dimension Reduction using Local Principal Components (DRLPC) which aims to resolve multi-collinearity by removing SNPs under the assumption that the remaining SNPs can capture the effect of a removed SNP due to high linear dependency. This approach to dimension reduction is expected to improve the power of regression-based statistical tests. We apply DRLPC to chromosome 22 SNPs of two data sets, the 1000 Genomes Project (phase 3) and the Canadian Longitudinal Study on Aging (CLSA), and calculate variance inflation factors (VIF) in various SNP-sets before and after implementing DRLPC as a metric of collinearity. Notably, DRLPC addresses multi-collinearity by excluding variables with a VIF exceeding a predetermined threshold (VIF = 20), thereby improving applicability for subsequent regression analyses. The number of variables in a final set for regression analysis is reduced to around 20% on average for larger-sized genes, whereas for smaller ones, the proportion is around 48%; suggesting that DRLPC is particularly effective for larger genes. We also compare the power of several multi-SNP statistics constructed for gene-specific analysis to evaluate power gains achieved by DRLPC. In simulation studies based on 100 genes with ≤ 500 SNPs per gene, DRLPC increases the power of the multiple regression Wald test from 60% to around 80%.

Cell-free DNA from clinical testing as a resource of population genetic analysis

As a noninvasive biomarker, cell-free DNA (cfDNA) has achieved remarkable success in clinical applications. Notably, cfDNA is essentially DNA, and conducting whole-genome sequencing (WGS) can yield a wealth of genetic information. These invaluable data should not be confined to one-time use; instead, they should be leveraged for more comprehensive population genetic analysis, including genetic variation spectrum, population structure and genetic selection, and genome-wide association studies (GWASs), among others. Such research findings can, in turn, facilitate clinical practice, enabling more advanced and accurate disease predictions. This review explores the advantages, challenges, and current research areas of cfDNA in population genetics. We hope that this review can serve as a new chapter in the repurposing of cfDNA sequence data generated from clinical testing in population genetics.

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.

weIMPUTE: a user-friendly web-based genotype imputation platform

Background

Genotype imputation is a critical preprocessing step in genome-wide association studies (GWAS), enhancing statistical power for detecting associated single nucleotide polymorphisms (SNPs) by increasing marker size.

Results

In response to the needs of researchers seeking user-friendly graphical tools for imputation without requiring informatics or computer expertise, we have developed weIMPUTE, a web-based imputation graphical user interface (GUI). Unlike existing genotype imputation software, weIMPUTE supports multiple imputation software, including SHAPEIT, Eagle, Minimac4, Beagle, and IMPUTE2, while encompassing the entire workflow, from quality control to data format conversion. This comprehensive platform enables both novices and experienced users to readily perform imputation tasks. For reference genotype data owners, weIMPUTE can be installed on a server or workstation, facilitating web-based imputation services without data sharing.

Conclusion

weIMPUTE represents a versatile imputation solution for researchers across various fields, offering the flexibility to create personalized imputation servers on different operating systems.

Genetic Analysis and Predictive Modeling of COVID-19 Severity in a Hospital-Based Patient Cohort

The COVID-19 pandemic has had a devastating impact, with more than 7 million deaths worldwide. Advanced age and comorbidities partially explain severe cases of the disease, but genetic factors also play a significant role. Genome-wide association studies (GWASs) have been instrumental in identifying loci associated with SARS-CoV-2 infection. Here, we report the results from a >820 K variant GWAS in a COVID-19 patient cohort from the hospitals associated with IIS Biobizkaia. We compared intensive care unit (ICU)-hospitalized patients with non-ICU-hospitalized patients. The GWAS was complemented with an integrated phenotype and genetic modeling analysis using HLA genotypes, a previously identified COVID-19 polygenic risk score (PRS) and clinical data. We identified four variants associated with COVID-19 severity with genome-wide significance (rs58027632 in KIF19; rs736962 in HTRA1; rs77927946 in DMBT1; and rs115020813 in LINC01283). In addition, we designed a multivariate predictive model including HLA, PRS and clinical data which displayed an area under the curve (AUC) value of 0.79. Our results combining human genetic information with clinical data may help to improve risk assessment for the development of a severe outcome of COVID-19.

FLT1 and other candidate fetal haemoglobin modifying loci in sickle cell disease in African ancestries

Known fetal haemoglobin (HbF)-modulating loci explain 10-24% variation of HbF level in Africans with Sickle Cell Disease (SCD), compared to 50% among Europeans. Here, we report fourteen candidate loci from a genome-wide association study (GWAS) of HbF level in patients with SCD from Cameroon, Tanzania, and the United States of America. We present results of cell-based experiments for FLT1 candidate, demonstrating expression in early haematopoiesis and a possible involvement in hypoxia associated HbF induction. Our study employed genotyping arrays that capture a broad range of African and non-African genetic variation and replicated known loci (BCL11A and HBS1L-MYB). We estimated the heritability of HbF level in SCD at 94%, higher than estimated in unselected Europeans, and suggesting a robust capture of HbF-associated loci by these arrays. Our approach, which involved genotype imputation against six reference haplotype panels and association analysis with each of the panels, proved superior over selecting a best-performing panel, evidenced by a substantial proportion of panel-specific (up to 18%) and a low proportion of shared (28%) imputed variants across the panels.

Plant sperm cell sequencing for genome phasing and determination of meiotic crossover points

Haplotype phasing represents a pivotal procedure in genome analysis, entailing the identification of specific genetic variant combinations on each chromosome. Achieving chromosome-level genome phasing constitutes a considerable challenge, particularly in organisms with large and complex genomes. To address this challenge, we have developed a robust, gamete cell-based phasing pipeline, including wet-laboratory processes for plant sperm cell isolation, short-read sequencing and a bioinformatics workflow to generate chromosome-level phasing. The bioinformatics workflow is applicable for both plant and other sperm cells, for example, those of mammals. Our pipeline ensures high-quality single-nucleotide polymorphism (SNP) calling for each sperm cell and the subsequent construction of a high-density genetic map. The genetic map facilitates accurate chromosome-level genome phasing, enables crossover event detection and could be used to correct potential assembly errors. Our bioinformatics pipeline runs on a Linux system and most of its steps can be executed in parallel, expediting the analysis process. The entire workflow can be performed over the course of 1 d. We provide a practical example from our previous research using this protocol and provide the whole bioinformatics pipeline as a Docker image to ensure its easy adaptability to other studies.

A T2T-CHM13 recombination map and globally diverse haplotype reference panel improves phasing and imputation

The T2T-CHM13 complete human reference genome contains ~200 Mb of newly resolved sequence, improving read mapping and variant calling compared to GRCh38. However, the benefits of using complete reference genomes in other contexts are unclear. Here, we present a reference T2T-CHM13 recombination map and phased haplotype panel derived from 3202 samples from the 1000 Genomes Project (1KGP). Using published long-read based assemblies as a reference-neutral ground truth, we compared our T2T-CHM13 1KGP panel to the previously released GRCh38 1KGP phased callset. We find that alignment to T2T-CHM13 resulted in 38% fewer assembly-discordant genotypes and 16% fewer switch errors. The largest gains in panel accuracy are observed on chromosome X and in the regions flanking disease-causing CNVs. Simons Genome Diversity Project samples were more accurately imputed when using the T2T-CHM13 panel. Our study demonstrates that use of a T2T-native phased haplotype panel improves statistical phasing and imputation for samples from diverse human populations.

Population-specific genetic-risk scores enable improved prediction of mortality within 28 days of sepsis onset: a retrospective Taiwanese cohort study

BACKGROUND

Sepsis is characterized by organ dysfunction as a response to infection and is one of the leading causes of mortality and loss of health. The heterogeneous nature of sepsis, along with ethnic differences in susceptibility, challenges a thorough understanding of its etiology. This study aimed to propose prediction models by leveraging genetic-risk scores and clinical variables that can assist in risk stratification of patients.

METHODS

A total of 1,403 patients from Taiwan, diagnosed with sepsis, were utilized. Genome-wide survival analysis was conducted, with death within 28 days from sepsis onset, as the primary event to report significantly associated SNPs. A polygenic risk score (PRS-sepsis) was constructed via clumping and thresholding method which was added to clinical-only models to generate better performing prognostic models for identifying high-risk patients. Kaplan-Meier analysis was conducted using PRS-sepsis.

RESULTS

A total of five single-nucleotide-polymorphisms (SNPs) reached genome-wide significance (p < 5e-8), and 86 SNPs reached suggestive significance (p < 1e-5). The prognostic model using PRS-sepsis showed significantly improved performance with c-index [confidence interval (CI)] of 0.79 [0.62-0.96] and area under receiver operating characteristic curve (AUROC) [CI] of 0.78 [0.75-0.80], in comparison to clinical-only prognostic models (c-index [CI] = 0.63 [0.45- 0.81], AUROC [CI] = 0.61 [0.58-0.64]). The ethnic specificity was established for our proposed models by comparing it with models generated using significant SNPs from prior European studies (c-index [CI] = 0.63 [0.42-0.85], AUROC [CI] = 0.60 [0.58-0.63]). Kaplan-Meier plots showed that patient groups with higher PRSs have inferior survival probability compared to those with lower PRSs.

CONCLUSIONS

This study proposed genetic-risk models specific for Taiwanese populations that outperformed clinical-only models. Also it established a strong racial-effect on the underlying genetics of sepsis-related mortality. The model can potentially be used in real clinical setting for deciding precise treatment courses for patients at high-risk thereby reducing the possibility of worse outcomes.

Sequencing whole genomes of the West Javanese population in Indonesia reveals novel variants and improves imputation accuracy

Existing genotype imputation reference panels are mainly derived from European populations, limiting their accuracy in non-European populations. To improve imputation accuracy for Indonesians, the world's fourth most populous country, we combined Whole Genome Sequencing (WGS) data from 227 West Javanese individuals with East Asian data from the 1,000 Genomes Project. This created three reference panels: EAS 1KGP3 (EASp), Indonesian (INDp), and a combined panel (EASp + INDp). We also used ten West-Javanese samples with WGS and SNP-typing data for benchmarking. We identified 1.8 million novel single nucleotide variants (SNVs) in the West Javanese population, which, while similar to the East Asians, are distinct from the Central Indonesian Flores population. Adding INDp to the EASp reference panel improved imputation accuracy (R2) from 0.85 to 0.90, and concordance from 87.88% to 91.13%. These findings underscore the importance of including West-Javanese genetic data in reference panels, advocating for broader WGS of diverse Indonesian populations to enhance genomic studies.

A cost-effective, high-throughput, highly accurate genotyping method for outbred populations

Affordable sequencing and genotyping methods are essential for large-scale genome-wide association studies. While genotyping microarrays and reference panels for imputation are available for human subjects, nonhuman model systems often lack such options. Our lab previously demonstrated an efficient and cost-effective method to genotype heterogeneous stock rats using double-digest genotyping by sequencing. However, low-coverage whole-genome sequencing offers an alternative method that has several advantages. Here, we describe a cost-effective, high-throughput, high-accuracy genotyping method for N/NIH heterogeneous stock rats that can use a combination of sequencing data previously generated by double-digest genotyping by sequencing and more recently generated by low-coverage whole-genome sequencing data. Using double-digest genotyping-by-sequencing data from 5,745 heterogeneous stock rats (mean 0.21× coverage) and low-coverage whole-genome sequencing data from 8,760 heterogeneous stock rats (mean 0.27× coverage), we can impute 7.32 million biallelic single-nucleotide polymorphisms with a concordance rate > 99.76% compared to high-coverage (mean 33.26× coverage) whole-genome sequencing data for a subset of the same individuals. Our results demonstrate the feasibility of using sequencing data from double-digest genotyping by sequencing or low-coverage whole-genome sequencing for accurate genotyping and demonstrate techniques that may also be useful for other genetic studies in nonhuman subjects.

Genetics of C-Peptide and Age at Diagnosis in Type 1 Diabetes

ARTICLE HIGHLIGHTS

Identified genetic loci for C-peptide and type 1 diabetes (T1D) age at diagnosis (AAD) explain only a small proportion of their variation. We aimed to identify additional genetic loci associated with C-peptide and AAD. Some HLA allele/haplotypes associated with T1D also contributed to variability of C-peptide and AAD, whereas outside the HLA region, T1D loci were mostly not associated with C-peptide or AAD. Genetic variation within CTSH can affect AAD. There is still residual heritability of C-peptide and AAD outside of HLA that could benefit from larger meta-genome-wide association studies.

Multi-ancestry genome-wide association analyses: a comparison of meta- and mega-analyses in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study

BACKGROUND

There is increasing need for effective incorporation of high-dimensional genetics data from individuals with varied ancestry in genome-wide association (GWAS) analyses. Classically, multi-ancestry GWAS analyses are performed using statistical meta-analysis to combine results conducted within homogeneous ancestry groups. The emergence of cosmopolitan reference panels makes collective preprocessing of GWAS data possible, but impact on downstream GWAS results in a mega-analysis framework merits investigation. We utilized GWAS data from the multi-national Hyperglycemia and Adverse Pregnancy Outcome Study to investigate differences in GWAS findings using a homogeneous ancestry meta-analysis versus a heterogeneous ancestry mega-analysis pipeline. Maternal fasting and 1-hr glucose and metabolomics measured during a 2-hr 75-gram oral glucose tolerance test during early third trimester pregnancy were evaluated as phenotypes.

RESULTS

For the homogeneous ancestry meta-analysis pipeline, variant data were prepared by identifying sets of individuals with similar ancestry and imputing to ancestry-specific reference panels. GWAS was conducted within each ancestry group and results were combined using random-effects meta-analysis. For the heterogeneous ancestry mega-analysis pipeline, data for all individuals were collectively imputed to the Trans-Omics for Precision Medicine (TOPMed) cosmopolitan reference panel, and GWAS was conducted using a unified mega-analysis. The meta-analysis pipeline identified genome-wide significant associations for 15 variants in a region close to GCK on chromosome 7 with maternal fasting glucose and no significant findings for 1-hr glucose. Associations in this same region were identified using the mega-analysis pipeline, along with a well-documented association at MTNR1B on chromosome 11 with both fasting and 1-hr maternal glucose. For metabolomics analyses, the number of significant findings in the heterogeneous ancestry mega-analysis far exceeded those from the homogeneous ancestry meta-analysis and confirmed many previously documented associations, but genomic inflation factors were much more variable.

CONCLUSIONS

For multi-ancestry GWAS, heterogeneous ancestry mega-analysis generates a rich set of variants for analysis using a cosmopolitan reference panel and results in vastly more significant, biologically credible and previously documented associations than a homogeneous ancestry meta-analysis approach. Genomic inflation factors do indicate that findings from the mega-analysis pipeline may merit cautious interpretation and further follow-up.

Development of a method for the imputation of the multi-allelic serotonin-transporter-linked polymorphic region (5-HTTLPR) in the Japanese population

Serotonin-transporter-linked polymorphic region (5-HTTLPR), a variable number of tandem repeats in the promoter region of serotonin transporter gene, is classified into short (S) and long (L) alleles. Initial case-control association studies claiming the risks of the S allele in depression and anxiety were not completely supported by recent studies. However, most studies, especially those on East Asian populations, have overlooked the complexity of 5-HTTLPR, which involves multiple different alleles with distinct functional properties. To address this issue, distinguishing multiple 5-HTTLPR alleles is essential. Here, using the 5-HTTLPR genotypes previously determined by exhaustive Sanger sequencing of approximately 1,500 Japanese subjects and their comprehensive SNP data, we constructed a method for 5-HTTLPR genotype imputation. We identified 28 tag SNPs for the imputation of four major 5-HTTLPR alleles, which collectively account for 97.6% of 5-HTTLPR alleles in the Japanese population. Our imputation method, achieved an accuracy of 0.872 in cross-validation, will contribute to association analysis of 5-HTTLPR in the Japanese subjects.

Population-specific reference panel improves imputation quality for genome-wide association studies conducted on the Japanese population

To improve imputation quality for genome-wide association studies (GWAS) conducted on the Japanese population, we developed and evaluated four Japanese population-specific reference panels. These panels were constructed through the augmentation of the 1000 Genomes Project (1KG) panel using Japanese whole genome sequencing (WGS) data, with sample sizes ranging from 1 K to 7 K individuals enrolled through the Biobank Japan (BBJ) project, and sequencing depths ranging from 3× to 30×. Among these panels, an augmented reference panel comprising 7472 WGS samples of mixed depth (1KG+7K) exhibit the greatest improvement in imputation quality relative to the Trans-Omics for Precision Medicine (TOPMed) reference panel. Notably, we observe these improvements primarily for rare variants with a minor allele frequency (MAF) <5%. To demonstrate the benefits of improved imputation quality in association analyses of complex traits, we conducted GWAS for serum uric acid and total cholesterol levels following imputation up to the 1KG+7K panel. The analysis reveals several loci reaching genome-wide significance (P < 5 × 10-8) in the 1KG+7K imputation output yet remaining undetected when the same sample set is imputed up to the TOPMed reference panel. In summary, the 1KG+7K panel demonstrates significant advantages in the discovery of trait-associated loci, particularly those influenced by low-frequency association signals.

Genetic associations of cardiovascular risk genes in European patients with coronary artery spasm

BACKGROUND

Coronary artery spasm (CAS) is a frequent finding in patients presenting with angina pectoris. Although the pathogenesis of CAS is incompletely understood, previous studies suggested a genetic contribution. Our study aimed to elucidate genetic variants in a cohort of European patients with angina and unobstructed coronary arteries who underwent acetylcholine (ACh) provocation testing.

METHODS

A candidate association analysis of 208 genes previously associated with cardiovascular conditions was performed using genotyped and imputed variants in patients grouped in epicardial (focal, diffuse) CAS (n = 119) and microvascular CAS (n = 87). Patients with a negative ACh test result (n = 45) served as controls.

RESULTS

We found no association below the genome-wide significance threshold of p < 5 × 10-8, thus not confirming variants in ALDH2, NOS3, and ROCK2 previously reported in CAS patients of Asian ancestry. However, the analysis identified suggestive associations (p < 10-05) for the groups of focal epicardial CAS (CDH13) and diffuse epicardial CAS (HDAC9, EDN1). Downstream analysis of the potential EDN1 risk locus showed that CAS patients have significantly increased plasma endothelin-1 levels (ET-1) compared to controls. An EDN1 haplotype comprising rs9349379 and rs2070698 was significantly associated to ET-1 levels (p = 0.01).

CONCLUSIONS

In summary, we suggest EDN1 as potential genetic risk loci for patients with diffuse epicardial CAS, and European ancestry. Plasma ET-1 levels may serve as a potential cardiac marker.

Machine learning model identifies genetic predictors of cisplatin-induced ototoxicity in CERS6 and TLR4

BACKGROUND

Cisplatin-induced ototoxicity remains a significant concern in pediatric cancer treatment due to its permanent impact on quality of life. Previously, genetic association analyses have been performed to detect genetic variants associated with this adverse reaction.

METHODS

In this study, a combination of interpretable neural networks and Generative Adversarial Networks (GANs) was employed to identify genetic markers associated with cisplatin-induced ototoxicity. The applied method, BRI-Net, incorporates biological domain knowledge to define the network structure and employs adversarial training to learn an unbiased representation of the data, which is robust to known confounders. Leveraging genomic data from a cohort of 362 cisplatin-treated pediatric cancer patients recruited by the CPNDS (Canadian Pharmacogenomics Network for Drug Safety), this model revealed two statistically significant single nucleotide polymorphisms to be associated with cisplatin-induced ototoxicity.

RESULTS

Two markers within the CERS6 (rs13022792, p-value: 3 × 10-4) and TLR4 (rs10759932, p-value: 7 × 10-4) genes were associated with this cisplatin-induced adverse reaction. CERS6, a ceramide synthase, contributes to elevated ceramide levels, a known initiator of apoptotic signals in mouse models of inner ear hair cells. TLR4, a pattern-recognition protein, initiates inflammation in response to cisplatin, and reduced TLR4 expression has been shown in murine hair cells to confer protection from ototoxicity.

CONCLUSION

Overall, these findings provide a foundation for understanding the genetic landscape of cisplatin-induced ototoxicity, with implications for improving patient care and treatment outcomes.

Empirical versus estimated accuracy of imputation: optimising filtering thresholds for sequence imputation

BACKGROUND

Genotype imputation is a cost-effective method for obtaining sequence genotypes for downstream analyses such as genome-wide association studies (GWAS). However, low imputation accuracy can increase the risk of false positives, so it is important to pre-filter data or at least assess the potential limitations due to imputation accuracy. In this study, we benchmarked three different imputation programs (Beagle 5.2, Minimac4 and IMPUTE5) and compared the empirical accuracy of imputation with the software estimated accuracy of imputation (Rsqsoft). We also tested the accuracy of imputation in cattle for autosomal and X chromosomes, SNP and INDEL, when imputing from either low-density or high-density genotypes.

RESULTS

The accuracy of imputing sequence variants from real high-density genotypes was higher than from low-density genotypes. In our software benchmark, all programs performed well with only minor differences in accuracy. While there was a close relationship between empirical imputation accuracy and the imputation Rsqsoft, this differed considerably for Minimac4 compared to Beagle 5.2 and IMPUTE5. We found that the Rsqsoft threshold for removing poorly imputed variants must be customised according to the software and this should be accounted for when merging data from multiple studies, such as in meta-GWAS studies. We also found that imposing an Rsqsoft filter has a positive impact on genomic regions with poor imputation accuracy due to large segmental duplications that are susceptible to error-prone alignment. Overall, our results showed that on average the imputation accuracy for INDEL was approximately 6% lower than SNP for all software programs. Importantly, the imputation accuracy for the non-PAR (non-Pseudo-Autosomal Region) of the X chromosome was comparable to autosomal imputation accuracy, while for the PAR it was substantially lower, particularly when starting from low-density genotypes.

CONCLUSIONS

This study provides an empirically derived approach to apply customised software-specific Rsqsoft thresholds for downstream analyses of imputed variants, such as needed for a meta-GWAS. The very poor empirical imputation accuracy for variants on the PAR when starting from low density genotypes demonstrates that this region should be imputed starting from a higher density of real genotypes.

Genetic Risk Factors in Isolated Dystonia Escape Genome-Wide Association Studies

BACKGROUND

Despite considerable heritability, previous smaller genome-wide association studies (GWASs) have not identified any robust genetic risk factors for isolated dystonia.

OBJECTIVE

The objective of this study was to perform a large-scale GWAS in a well-characterized, multicenter sample of >6000 individuals to identify genetic risk factors for isolated dystonia.

METHODS

Array-based GWASs were performed on autosomes for 4303 dystonia participants and 2362 healthy control subjects of European ancestry with subgroup analysis based on age at onset, affected body regions, and a newly developed clinical score. Another 736 individuals were used for validation.

RESULTS

This GWAS identified no common genome-wide significant loci that could be replicated despite sufficient power to detect meaningful effects. Power analyses imply that the effects of individual variants are likely very small.

CONCLUSIONS

Moderate single-nucleotide polymorphism-based heritability indicates that common variants do not contribute to isolated dystonia in this cohort. Sequence-based GWASs (eg, by whole-genome sequencing) might help to better understand the genetic basis. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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.

Meta-analysis of genome-wide association studies of stable warfarin dose in patients of African ancestry

ABSTRACT

Warfarin dose requirements are highly variable because of clinical and genetic factors. Although genetic variants influencing warfarin dose have been identified in European and East Asian populations, more work is needed to identify African-specific genetic variants to help optimize warfarin dosing. We performed genome-wide association studies (GWASs) in 4 African cohorts from Uganda, South Africa, and Zimbabwe, totaling 989 warfarin-treated participants who reached stable dose and had international normalized ratios within therapeutic ranges. We also included 2 African American cohorts recruited by the International Warfarin Pharmacogenetics Consortium (n = 316) and the University of Alabama at Birmingham (n = 199). After the GWAS, we performed standard error-weighted meta-analyses and then conducted stepwise conditional analyses to account for known loci in chromosomes 10 and 16. The genome-wide significance threshold was set at P < 5 × 10-8. The meta-analysis, comprising 1504 participants, identified 242 significant SNPs across 3 genomic loci, with 99.6% of these located within known loci on chromosomes 10 (top SNP: rs58800757, P = 4.27 × 10-13) and 16 (top SNP: rs9925964, P = 9.97 × 10-16). Adjustment for the VKORC1 SNP -1639G>A revealed an additional locus on chromosome 2 (top SNPs rs116057875/rs115254730/rs115240773, P = 3.64 × 10-8), implicating the MALL gene, that could indirectly influence warfarin response through interactions with caveolin-1. In conclusion, we reaffirmed the importance of CYP2C9 and VKORC1 in influencing warfarin dose requirements, and identified a new locus (MALL), that still requires direct evidence of biological plausibility.

A genome-wide association study of neonatal metabolites

Genetic factors significantly influence the concentration of metabolites in adults. Nevertheless, the genetic influence on neonatal metabolites remains uncertain. To bridge this gap, we employed genotype imputation techniques on large-scale low-pass genome data obtained from non-invasive prenatal testing. Subsequently, we conducted association studies on a total of 75 metabolic components in neonates. The study identified 19 previously reported associations and 11 novel associations between single-nucleotide polymorphisms and metabolic components. These associations were initially found in the discovery cohort (8,744 participants) and subsequently confirmed in a replication cohort (19,041 participants). The average heritability of metabolic components was estimated to be 76.2%, with a range of 69%-78.8%. These findings offer valuable insights into the genetic architecture of neonatal metabolism.

Genetic analyses of 104 phenotypes in 20,900 Chinese pregnant women reveal pregnancy-specific discoveries

Monitoring biochemical phenotypes during pregnancy is vital for maternal and fetal health, allowing early detection and management of pregnancy-related conditions to ensure safety for both. Here, we conducted a genetic analysis of 104 pregnancy phenotypes in 20,900 Chinese women. The genome-wide association study (GWAS) identified a total of 410 trait-locus associations, with 71.71% reported previously. Among the 116 novel hits for 45 phenotypes, 83 were successfully replicated. Among them, 31 were defined as potentially pregnancy-specific associations, including creatine and HELLPAR and neutrophils and ESR1, with subsequent analysis revealing enrichments in estrogen-related pathways and female reproductive tissues. The partitioning heritability underscored the significant roles of fetal blood, embryoid bodies, and female reproductive organs in pregnancy hematology and birth outcomes. Pathway analysis confirmed the intricate interplay of hormone and immune regulation, metabolism, and cell cycle during pregnancy. This study contributes to the understanding of genetic influences on pregnancy phenotypes and their implications for maternal health.

Early human migration determines the risk of being attacked by wolves: ethnic gene diversity on the development of systemic lupus erythematosus

The prevalence of systemic lupus erythematosus (SLE) varies significantly based on ethnicity rather than geographic distribution; thus, the prevalence is higher in Asian, Hispanic, and Black African populations than in European populations. The risk of developing lupus nephritis (LN) is the highest among Asian populations. Therefore, we hypothesize that human genetic diversity between races has occurred through the early human migration and human genetic adaptation to various environments, with a particular focus on pathogens. Additionally, we compile the currently available evidence on the ethnic gene diversity of SLE and how it relates to disease severity. The human leukocyte antigen (HLA) locus is well established as associated with susceptibility to SLE; specific allele distributions have been observed across diverse populations. Notably, specific amino acid residues within these HLA loci demonstrate significant associations with SLE risk. The non-HLA genetic loci associated with SLE risk also varies across diverse ancestries, implicating distinct immunological pathways, such as the type-I interferon and janus kinase-signal transducers and activators of transcription (JAK-STAT) pathways in Asians, the type-II interferon signaling pathway in Europeans, and B cell activation pathway in Africans. Furthermore, assessing individual genetic susceptibility using genetic risk scores (GRS) for SLE helps to reveal the diverse prevalence, age of onset, and clinical phenotypes across different ethnicities. A higher GRS increases the risk of LN and the severity of SLE. Therefore, understanding ethnic gene diversity is crucial for elucidating disease mechanisms and SLE severity, which could enable the development of novel drugs specific to each race.

Two-stage strategy using denoising autoencoders for robust reference-free genotype imputation with missing input genotypes

Widely used genotype imputation methods are based on the Li and Stephens model, which assumes that new haplotypes can be represented by modifying existing haplotypes in a reference panel through mutations and recombinations. These methods use genotypes from SNP arrays as inputs to estimate haplotypes that align with the input genotypes by analyzing recombination patterns within a reference panel, and then infer unobserved variants. While these methods require reference panels in an identifiable form, their public use is limited due to privacy and consent concerns. One strategy to overcome these limitations is to use de-identified haplotype information, such as summary statistics or model parameters. Advances in deep learning (DL) offer the potential to develop imputation methods that use haplotype information in a reference-free manner by handling it as model parameters, while maintaining comparable imputation accuracy to methods based on the Li and Stephens model. Here, we provide a brief introduction to DL-based reference-free genotype imputation methods, including RNN-IMP, developed by our research group. We then evaluate the performance of RNN-IMP against widely-used Li and Stephens model-based imputation methods in terms of accuracy (R2), using the 1000 Genomes Project Phase 3 dataset and corresponding simulated Omni2.5 SNP genotype data. Although RNN-IMP is sensitive to missing values in input genotypes, we propose a two-stage imputation strategy: missing genotypes are first imputed using denoising autoencoders; RNN-IMP then processes these imputed genotypes. This approach restores the imputation accuracy that is degraded by missing values, enhancing the practical use of RNN-IMP.

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.

Genetic and genomic analysis of reproduction traits in holstein cattle using SNP chip data and imputed sequence level genotypes

BACKGROUND

Reproductive performance plays an important role in animal welfare, health and profitability in animal husbandry and breeding. It is well established that there is a negative correlation between performance and reproduction in dairy cattle. This relationship is being increasingly considered in breeding programs. By elucidating the genetic architecture of underlying reproduction traits, it will be possible to make a more detailed contribution to this. Our study followed two approaches to elucidate this area; in a first part, variance components were estimated for 14 different calving and fertility traits, and then genome-wide association studies were performed for 13 reproduction traits on imputed sequence-level genotypes with subsequent enrichment analyses.

RESULTS

Variance components analyses showed a low to moderate heritability (h2) for the traits analysed, ranging from 0.014 for endometritis up to 0.271 for stillbirth, indicating variable degrees of variation within the reproduction traits. For genome-wide association studies, we were able to detect genome-wide significant association signals for nine out of 13 analysed traits after Bonferroni correction on chromosome 6, 18 and the X chromosome. In total, we detected over 2700 associated SNPs encircling more than 90 different genes using the imputed whole-genome sequence data. Functional associations were reviewed so far known and potential candidate regions in the proximity of reproduction events were hypothesised.

CONCLUSION

Our results confirm previous findings of other authors in a comprehensive cohort including 13 different traits at the same time. Additionally, we identified new candidate genes involved in dairy cattle reproduction and made initial suggestions regarding their potential impact, with special regard to the X chromosome as a putative information source for further research. This work can make a contribution to reveal the genetic architecture of reproduction traits in context of trait specific interactions.

Commonly used genomic arrays may lose information due to imperfect coverage of discovered variants for autism spectrum disorder

BACKGROUND

Common genetic variation has been shown to account for a large proportion of ASD heritability. Polygenic scores generated for autism spectrum disorder (ASD-PGS) using the most recent discovery data, however, explain less variance than expected, despite reporting significant associations with ASD and other ASD-related traits. Here, we investigate the extent to which information loss on the target study genome-wide microarray weakens the predictive power of the ASD-PGS.

METHODS

We studied genotype data from three cohorts of individuals with high familial liability for ASD: The Early Autism Risk Longitudinal Investigation (EARLI), Markers of Autism Risk in Babies-Learning Early Signs (MARBLES), and the Infant Brain Imaging Study (IBIS), and one population-based sample, Study to Explore Early Development Phase I (SEED I). Individuals were genotyped on different microarrays ranging from 1 to 5 million sites. Coverage of the top 88 genome-wide suggestive variants implicated in the discovery was evaluated in all four studies before quality control (QC), after QC, and after imputation. We then created a novel method to assess coverage on the resulting ASD-PGS by correlating a PGS informed by a comprehensive list of variants to a PGS informed with only the available variants.

RESULTS

Prior to imputations, None of the four cohorts directly or indirectly covered all 88 variants among the measured genotype data. After imputation, the two cohorts genotyped on 5-million arrays reached full coverage. Analysis of our novel metric showed generally high genome-wide coverage across all four studies, but a greater number of SNPs informing the ASD-PGS did not result in improved coverage according to our metric.

LIMITATIONS

The studies we analyzed contained modest sample sizes. Our analyses included microarrays with more than 1-million sites, so smaller arrays such as Global Diversity and the PsychArray were not included. Our PGS metric for ASD is only generalizable to samples of European ancestries, though the coverage metric can be computed for traits that have sufficiently large-sized discovery findings in other ancestries.

CONCLUSIONS

We show that commonly used genotyping microarrays have incomplete coverage for common ASD variants, and imputation cannot always recover lost information. Our novel metric provides an intuitive approach to reporting information loss in PGS and an alternative to reporting the total number of SNPs included in the PGS. While applied only to ASD here, this metric can easily be used with other traits.

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.

Accurate genotype imputation from low-coverage whole-genome sequencing data of rainbow trout

With the rapid and significant cost reduction of next-generation sequencing, low-coverage whole-genome sequencing (lcWGS), followed by genotype imputation, is becoming a cost-effective alternative to single-nucleotide polymorphism (SNP)-array genotyping. The objectives of this study were 2-fold: (1) construct a haplotype reference panel for genotype imputation from lcWGS data in rainbow trout (Oncorhynchus mykiss); and (2) evaluate the concordance between imputed genotypes and SNP-array genotypes in 2 breeding populations. Medium-coverage (12×) whole-genome sequences were obtained from a total of 410 fish representing 5 breeding populations with various spawning dates. The short-read sequences were mapped to the rainbow trout reference genome, and genetic variants were identified using GATK. After data filtering, 20,434,612 biallelic SNPs were retained. The reference panel was phased with SHAPEIT5 and was used as a reference to impute genotypes from lcWGS data employing GLIMPSE2. A total of 90 fish from the Troutlodge November breeding population were sequenced with an average coverage of 1.3×, and these fish were also genotyped with the Axiom 57K rainbow trout SNP array. The concordance between array-based genotypes and imputed genotypes was 99.1%. After downsampling the coverage to 0.5×, 0.2×, and 0.1×, the concordance between array-based genotypes and imputed genotypes was 98.7, 97.8, and 96.7%, respectively. In the USDA odd-year breeding population, the concordance between array-based genotypes and imputed genotypes was 97.8% for 109 fish downsampled to 0.5× coverage. Therefore, the reference haplotype panel reported in this study can be used to accurately impute genotypes from lcWGS data in rainbow trout breeding populations.

An overview of recent technological developments in bovine genomics

Cattle are regarded as highly valuable animals because of their milk, beef, dung, fur, and ability to draft. The scientific community has tried a number of strategies to improve the genetic makeup of bovine germplasm. To ensure higher returns for the dairy and beef industries, researchers face their greatest challenge in improving commercially important traits. One of the biggest developments in the last few decades in the creation of instruments for cattle genetic improvement is the discovery of the genome. Breeding livestock is being revolutionized by genomic selection made possible by the availability of medium- and high-density single nucleotide polymorphism (SNP) arrays coupled with sophisticated statistical techniques. It is becoming easier to access high-dimensional genomic data in cattle. Continuously declining genotyping costs and an increase in services that use genomic data to increase return on investment have both made a significant contribution to this. The field of genomics has come a long way thanks to groundbreaking discoveries such as radiation-hybrid mapping, in situ hybridization, synteny analysis, somatic cell genetics, cytogenetic maps, molecular markers, association studies for quantitative trait loci, high-throughput SNP genotyping, whole-genome shotgun sequencing to whole-genome mapping, and genome editing. These advancements have had a significant positive impact on the field of cattle genomics. This manuscript aimed to review recent advances in genomic technologies for cattle breeding and future prospects in this field.

Genetic influence on scar vascularity after burn injury in individuals of European ancestry: A prospective cohort study

After burn injury there is considerable variation in scar outcome, partially due to genetic factors. Scar vascularity is one characteristic that varies between individuals, and this study aimed to identify genetic variants contributing to different scar vascularity outcomes. An exome-wide array association study and gene pathway analysis was performed on a prospective cohort of 665 patients of European ancestry treated for burn injury, using their scar vascularity (SV) sub-score, part of the modified Vancouver Scar Scale (mVSS), as an outcome measure. DNA was genotyped using the Infinium HumanCoreExome-24 BeadChip, imputed to the Haplotype Reference Consortium panel. Associations between genetic variants (single nucleotide polymorphisms) and SV were estimated using an additive genetic model adjusting for sex, age, % total body surface area and number of surgical procedures, utilising linear and multinomial logistic regression. No individual genetic variants achieved the cut-off threshold for significance. Gene sets were also analysed using the Functional Mapping and Annotation (FUMA) platform, in which biological processes indirectly related to angiogenesis were significantly represented. This study suggests that SNPs in genes associated with angiogenesis may influence SV, but further studies with larger datasets are essential to validate these findings.

Genomic patterns of selection in morphometric traits across diverse Indian cattle breeds

This study seeks a comprehensive exploration of genome-wide selective processes impacting morphometric traits across diverse cattle breeds, utilizing an array of statistical methods. Morphometric traits, encompassing both qualitative and quantitative variables, play a pivotal role in characterizing and selecting livestock breeds based on their external appearance, size, and physical attributes. While qualitative traits, such as color, horn structure, and coat type, contribute to adaptive features and breed identification, quantitative traits like body weight and conformation measurements bear a closer correlation with production characteristics. This study employs advanced genotyping technologies, including the Illumina BovineSNP50 Bead Chip and next-generation sequencing methods like Reduced Representation sequencing, to identify genomic signatures associated with these traits. We applied four intra-population methods to find evidence of selection, such as Tajima's D, CLR, iHS, and ROH. We found a total of 40 genes under the selection signature, that were associated with morphometric traits in five cattle breeds (Kankrej, Tharparkar, Nelore, Sahiwal, and Gir). Crucial genes such as ADIPDQ, DPP6, INSIG1, SLC35D2 in Kankrej, LPL, ATP6V1B2, CDC14B in Tharparkar, HPSE2, PLAG1 in Nelore, PCSK1, PRKD1 in Sahiwal, and GNAQ, HPCAL1 in Gir were identified in our study. This approach provides valuable insights into the genetic basis of variations in body weight and conformation traits, facilitating informed selection processes and offering a deeper understanding of the evolutionary and domestication processes in diverse cattle breeds.

Haplotype analysis identifies functional elements in monoclonal gammopathy of unknown significance

Genome-wide association studies (GWASs) based on common single nucleotide polymorphisms (SNPs) have identified several loci associated with the risk of monoclonal gammopathy of unknown significance (MGUS), a precursor condition for multiple myeloma (MM). We hypothesized that analyzing haplotypes might be more useful than analyzing individual SNPs, as it could identify functional chromosomal units that collectively contribute to MGUS risk. To test this hypothesis, we used data from our previous GWAS on 992 MGUS cases and 2910 controls from three European populations. We identified 23 haplotypes that were associated with the risk of MGUS at the genome-wide significance level (p < 5 × 10-8) and showed consistent results among all three populations. In 10 genomic regions, strong promoter, enhancer and regulatory element-related histone marks and their connections to target genes as well as genome segmentation data supported the importance of these regions in MGUS susceptibility. Several associated haplotypes affected pathways important for MM cell survival such as ubiquitin-proteasome system (RNF186, OTUD3), PI3K/AKT/mTOR (HINT3), innate immunity (SEC14L1, ZBP1), cell death regulation (BID) and NOTCH signaling (RBPJ). These pathways are important current therapeutic targets for MM, which may highlight the advantage of the haplotype approach homing to functional units.

Human genetics and epigenetics of alcohol use disorder

Alcohol use disorder (AUD) is a prominent contributor to global morbidity and mortality. Its complex etiology involves genetics, epigenetics, and environmental factors. We review progress in understanding the genetics and epigenetics of AUD, summarizing the key findings. Advancements in technology over the decades have elevated research from early candidate gene studies to present-day genome-wide scans, unveiling numerous genetic and epigenetic risk factors for AUD. The latest GWAS on more than one million participants identified more than 100 genetic variants, and the largest epigenome-wide association studies (EWAS) in blood and brain samples have revealed tissue-specific epigenetic changes. Downstream analyses revealed enriched pathways, genetic correlations with other traits, transcriptome-wide association in brain tissues, and drug-gene interactions for AUD. We also discuss limitations and future directions, including increasing the power of GWAS and EWAS studies as well as expanding the diversity of populations included in these analyses. Larger samples, novel technologies, and analytic approaches are essential; these include whole-genome sequencing, multiomics, single-cell sequencing, spatial transcriptomics, deep-learning prediction of variant function, and integrated methods for disease risk prediction.

Extended genome-wide association study employing the African genome resources panel identifies novel susceptibility loci for Alzheimer's disease in individuals of African ancestry

INTRODUCTION

Despite a two-fold risk, individuals of African ancestry have been underrepresented in Alzheimer's disease (AD) genomics efforts.

METHODS

Genome-wide association studies (GWAS) of 2,903 AD cases and 6,265 controls of African ancestry. Within-dataset results were meta-analyzed, followed by functional genomics analyses.

RESULTS

A novel AD-risk locus was identified in MPDZ on chromosome (chr) 9p23 (rs141610415, MAF = 0.002, p = 3.68×10-9). Two additional novel common and nine rare loci were identified with suggestive associations (P < 9×10-7). Comparison of association and linkage disequilibrium (LD) patterns between datasets with higher and lower degrees of African ancestry showed differential association patterns at chr12q23.2 (ASCL1), suggesting that this association is modulated by regional origin of local African ancestry.

DISCUSSION

These analyses identified novel AD-associated loci in individuals of African ancestry and suggest that degree of African ancestry modulates some associations. Increased sample sets covering as much African genetic diversity as possible will be critical to identify additional loci and deconvolute local genetic ancestry effects.

HIGHLIGHTS

Genetic ancestry significantly impacts risk of Alzheimer's Disease (AD). Although individuals of African ancestry are twice as likely to develop AD, they are vastly underrepresented in AD genomics studies. The Alzheimer's Disease Genetics Consortium has previously identified 16 common and rare genetic loci associated with AD in African American individuals. The current analyses significantly expand this effort by increasing the sample size and extending ancestral diversity by including populations from continental Africa. Single variant meta-analysis identified a novel genome-wide significant AD-risk locus in individuals of African ancestry at the MPDZ gene, and 11 additional novel loci with suggestive genome-wide significance at p < 9×10-7. Comparison of African American datasets with samples of higher degree of African ancestry demonstrated differing patterns of association and linkage disequilibrium at one of these loci, suggesting that degree and/or geographic origin of African ancestry modulates the effect at this locus. These findings illustrate the importance of increasing number and ancestral diversity of African ancestry samples in AD genomics studies to fully disentangle the genetic architecture underlying AD, and yield more effective ancestry-informed genetic screening tools and therapeutic interventions.

A Cost-effective, High-throughput, Highly Accurate Genotyping Method for Outbred Populations

Affordable sequencing and genotyping methods are essential for large scale genome-wide association studies. While genotyping microarrays and reference panels for imputation are available for human subjects, non-human model systems often lack such options. Our lab previously demonstrated an efficient and cost-effective method to genotype heterogeneous stock rats using double-digest genotyping-by-sequencing. However, low-coverage whole-genome sequencing offers an alternative method that has several advantages. Here, we describe a cost-effective, high-throughput, high-accuracy genotyping method for N/NIH heterogeneous stock rats that can use a combination of sequencing data previously generated by double-digest genotyping-by-sequencing and more recently generated by low-coverage whole-genome-sequencing data. Using double-digest genotyping-by-sequencing data from 5,745 heterogeneous stock rats (mean 0.21x coverage) and low-coverage whole-genome-sequencing data from 8,760 heterogeneous stock rats (mean 0.27x coverage), we can impute 7.32 million bi-allelic single-nucleotide polymorphisms with a concordance rate >99.76% compared to high-coverage (mean 33.26x coverage) whole-genome sequencing data for a subset of the same individuals. Our results demonstrate the feasibility of using sequencing data from double-digest genotyping-by-sequencing or low-coverage whole-genome-sequencing for accurate genotyping, and demonstrate techniques that may also be useful for other genetic studies in non-human subjects.

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.

Boosting genome-wide association power and genomic prediction accuracy for date palm fruit traits with advanced statistics

The date palm is economically vital in the Middle East and North Africa, providing essential fibres, vitamins, and carbohydrates. Understanding the genetic architecture of its traits remains complex due to the tree's perennial nature and long generation times. This study aims to address these complexities by employing advanced genome-wide association (GWAS) and genomic prediction models using previously published data involving fruit acid content, sugar content, dimension, and colour traits. The multivariate GWAS model identified seven QTL, including five novel associations, that shed light on the genetic control of these traits. Furthermore, the research evaluates different genomic prediction models that considered genotype by environment and genotype by trait interactions. While colour- traits demonstrate strong predictive power, other traits display moderate accuracies across different models and scenarios aligned with the expectations when using small reference populations. When designing the cross-validation to predict new individuals, the accuracy of the best multi-trait model was significantly higher than all single-trait models for dimension traits, but not for the remaining traits, which showed similar performances. However, the cross-validation strategy that masked random phenotypic records (i.e., mimicking the unbalanced phenotypic records) showed significantly higher accuracy for all traits except acid contents. The findings underscore the importance of understanding genetic architecture for informed breeding strategies. The research emphasises the need for larger population sizes and multivariate models to enhance gene tagging power and predictive accuracy to advance date palm breeding programs. These findings support more targeted breeding in date palm, improving productivity and resilience to various environments.

Shared and Distinct Genomics of Chronic Thromboembolic Pulmonary Hypertension and Pulmonary Embolism

Rationale: Chronic thromboembolic pulmonary hypertension involves the formation and nonresolution of thrombus, dysregulated inflammation, angiogenesis, and the development of a small-vessel vasculopathy. Objectives: We aimed to establish the genetic basis of chronic thromboembolic pulmonary hypertension to gain insight into its pathophysiological contributors. Methods: We conducted a genome-wide association study on 1,907 European cases and 10,363 European control subjects. We coanalyzed our results with existing results from genome-wide association studies on deep vein thrombosis, pulmonary embolism, and idiopathic pulmonary arterial hypertension. Measurements and Main Results: Our primary association study revealed genetic associations at the ABO, FGG, F11, MYH7B, and HLA-DRA loci. Through our coanalysis, we demonstrate further associations with chronic thromboembolic pulmonary hypertension at the F2, TSPAN15, SLC44A2, and F5 loci but find no statistically significant associations shared with idiopathic pulmonary arterial hypertension. Conclusions: Chronic thromboembolic pulmonary hypertension is a partially heritable polygenic disease, with related though distinct genetic associations with pulmonary embolism and deep vein thrombosis.

Sequencing whole genomes of the West Javanese population in Indonesia reveals novel variants and improves imputation accuracy

Existing genotype imputation reference panels are mainly derived from European populations, limiting their accuracy in non-European populations. To improve imputation accuracy for Indonesians, the world's fourth most populous country, we combined Whole Genome Sequencing (WGS) data from 227 West Javanese individuals with East Asian data from the 1000 Genomes Project. This created three reference panels: EAS 1KGP3 (EASp), Indonesian (INDp), and a combined panel (EASp+INDp). We also used ten West-Javanese samples with WGS and SNP-typing data for benchmarking. We identified 1.8 million novel single nucleotide variants (SNVs) in the West Javanese population, which, while similar to the East Asians, are distinct from the Central Indonesian Flores population. Adding INDp to the EASp reference panel improved imputation accuracy (R2) from 0.85 to 0.90, and concordance from 87.88% to 91.13%. These findings underscore the importance of including Indonesian genetic data in reference panels, advocating for broader WGS of diverse Indonesian populations to enhance genomic studies.