Ancestry estimation and control of population stratification for sequence-based association studies

Identification of biomarkers and potential therapeutic targets for pancreatic cancer by proteomic analysis in two prospective cohorts

Pancreatic cancer (PC) is the deadliest malignancy due to late diagnosis. Aberrant alterations in the blood proteome might serve as biomarkers to facilitate early detection of PC. We designed a nested case-control study of incident PC based on a prospective cohort of 38,295 elderly Chinese participants with ∼5.7 years' follow-up. Forty matched case-control pairs passed the quality controls for the proximity extension assay of 1,463 serum proteins. With a lenient threshold of p < 0.005, we discovered regenerating family member 1A (REG1A), REG1B, tumor necrosis factor (TNF), and phospholipase A2 group IB (PLA2G1B) in association with incident PC, among which the two REG1 proteins were replicated using the UK Biobank Pharma Proteomics Project, with effect sizes increasing steadily as diagnosis time approaches the baseline. Mendelian randomization analysis further supported the potential causal effects of REG1 proteins on PC. Taken together, circulating REG1A and REG1B are promising biomarkers and potential therapeutic targets for the early detection and prevention of PC.

Assessing the Clinical Utility of Published Prostate Cancer Polygenic Risk Scores in a Large Biobank Data Set

BACKGROUND AND OBJECTIVE

Polygenic risk scores (PRSs) have been developed to identify men with the highest risk of prostate cancer. Our aim was to compare the performance of 16 PRSs in identifying men at risk of developing prostate cancer and then to evaluate the performance of the top-performing PRSs in differentiating individuals at risk of aggressive prostate cancer.

METHODS

For this case-control study we downloaded 16 published PRSs from the Polygenic Score Catalog on May 28, 2021 and applied them to Michigan Genomics Initiative (MGI) patients. Cases were matched to the Michigan Urological Surgery Improvement Collaborative (MUSIC) registry to obtain granular clinical and pathological data. MGI prospectively enrolls patients undergoing surgery at the University of Michigan, and MUSIC is a multi-institutional registry that prospectively tracks demographic, treatment, and clinical variables. The predictive performance of each PRS was evaluated using the area under the covariate-adjusted receiver operating characteristic curve (aAUC), and the association between PRS and disease aggressiveness according to prostate biopsy data was measured using logistic regression.

KEY FINDINGS AND LIMITATIONS

We included 18 050 patients in the analysis, of whom 15 310 were control subjects and 2740 were prostate cancer cases. The median age was 66.1 yr (interquartile range 59.9-71.6) for cases and 56.6 yr (interquartile range 42.6-66.7) for control subjects. The PRS performance in predicting the risk of developing prostate cancer according to aAUC ranged from 0.51 (95% confidence interval 0.51-0.53) to 0.67 (95% confidence interval 0.66-0.68). By contrast, there was no association between PRS and disease aggressiveness.

CONCLUSIONS AND CLINICAL IMPLICATIONS

Prostate cancer PRSs have modest real-world performance in identifying patients at higher risk of developing prostate cancer; however, they are limited in distinguishing patients with indolent versus aggressive disease.

PATIENT SUMMARY

Risk scores using data for multiple genes (called polygenic risk scores) can identify men at higher risk of developing prostate cancer. However, these scores need to be refined to be able to identify men with the highest risk for clinically significant prostate cancer.

Single-nucleotide variant calling in single-cell sequencing data with Monopogen

Single-cell omics technologies enable molecular characterization of diverse cell types and states, but how the resulting transcriptional and epigenetic profiles depend on the cell's genetic background remains understudied. We describe Monopogen, a computational tool to detect single-nucleotide variants (SNVs) from single-cell sequencing data. Monopogen leverages linkage disequilibrium from external reference panels to identify germline SNVs and detects putative somatic SNVs using allele cosegregating patterns at the cell population level. It can identify 100 K to 3 M germline SNVs achieving a genotyping accuracy of 95%, together with hundreds of putative somatic SNVs. Monopogen-derived genotypes enable global and local ancestry inference and identification of admixed samples. It identifies variants associated with cardiomyocyte metabolic levels and epigenomic programs. It also improves putative somatic SNV detection that enables clonal lineage tracing in primary human clonal hematopoiesis. Monopogen brings together population genetics, cell lineage tracing and single-cell omics to uncover genetic determinants of cellular processes.

The HLA region in ANCA-associated vasculitis: characterisation of genetic associations in a Scandinavian patient population

OBJECTIVE

The antineutrophil cytoplasmic antibody (ANCA)-associated vasculitides (AAV) are inflammatory disorders with ANCA autoantibodies recognising either proteinase 3 (PR3-AAV) or myeloperoxidase (MPO-AAV). PR3-AAV and MPO-AAV have been associated with distinct loci in the human leucocyte antigen (HLA) region. While the association between MPO-AAV and HLA has been well characterised in East Asian populations where MPO-AAV is more common, studies in populations of European descent are limited. The aim of this study was to thoroughly characterise associations to the HLA region in Scandinavian patients with PR3-AAV as well as MPO-AAV.

METHODS

Genotypes of single-nucleotide polymorphisms (SNPs) located in the HLA region were extracted from a targeted exome-sequencing dataset comprising Scandinavian AAV cases and controls. Classical HLA alleles were called using xHLA. After quality control, association analyses were performed of a joint SNP/classical HLA allele dataset for cases with PR3-AAV (n=411) and MPO-AAV (n=162) versus controls (n=1595). Disease-associated genetic variants were analysed for association with organ involvement, age at diagnosis and relapse, respectively.

RESULTS

PR3-AAV was significantly associated with both HLA-DPB1*04:01 and rs1042335 at the HLA-DPB1 locus, also after stepwise conditional analysis. MPO-AAV was significantly associated with HLA-DRB1*04:04. Neither carriage of HLA-DPB1*04:01 alleles in PR3-AAV nor of HLA-DRB1*04:04 alleles in MPO-AAV were associated with organ involvement, age at diagnosis or relapse.

CONCLUSIONS

The association to the HLA region was distinct in Scandinavian cases with MPO-AAV compared with cases of East Asian descent. In PR3-AAV, the two separate signals of association to the HLD-DPB1 region mediate potentially different functional effects.

Aberrant landscapes of maternal meiotic crossovers contribute to aneuploidies in human embryos

Meiotic recombination is crucial for human genetic diversity and chromosome segregation accuracy. Understanding its variation across individuals and the processes by which it goes awry are long-standing goals in human genetics. Current approaches for inferring recombination landscapes rely either on population genetic patterns of linkage disequilibrium (LD)-capturing a time-averaged view-or on direct detection of crossovers in gametes or multigeneration pedigrees, which limits data set scale and availability. Here, we introduce an approach for inferring sex-specific recombination landscapes using data from preimplantation genetic testing for aneuploidy (PGT-A). This method relies on low-coverage (<0.05×) whole-genome sequencing of in vitro fertilized (IVF) embryo biopsies. To overcome the data sparsity, our method exploits its inherent relatedness structure, knowledge of haplotypes from external population reference panels, and the frequent occurrence of monosomies in embryos, whereby the remaining chromosome is phased by default. Extensive simulations show our method's high accuracy, even at coverages as low as 0.02×. Applying this method to PGT-A data from 18,967 embryos, we mapped 70,660 recombination events with ∼150 kbp resolution, replicating established sex-specific recombination patterns. We observed a reduced total length of the female genetic map in trisomies compared with disomies, as well as chromosome-specific alterations in crossover distributions. Based on haplotype configurations in pericentromeric regions, our data indicate chromosome-specific propensities for different mechanisms of meiotic error. Our results provide a comprehensive view of the role of aberrant meiotic recombination in the origins of human aneuploidies and offer a versatile tool for mapping crossovers in low-coverage sequencing data from multiple siblings.

Whole genome sequencing of 4,787 individuals identifies gene-based rare variants in age-related macular degeneration

Genome-wide association studies have contributed extensively to the discovery of disease-associated common variants. However, the genetic contribution to complex traits is still largely difficult to interpret. We report a genome-wide association study of 2394 cases and 2393 controls for age-related macular degeneration (AMD) via whole-genome sequencing, with 46.9 million genetic variants. Our study reveals significant single-variant association signals at four loci and independent gene-based signals in CFH, C2, C3, and NRTN. Using data from the Exome Aggregation Consortium (ExAC) for a gene-based test, we demonstrate an enrichment of predicted rare loss-of-function variants in CFH, CFI, and an as-yet unreported gene in AMD, ORMDL2. Our method of using a large variant list without individual-level genotypes as an external reference provides a flexible and convenient approach to leverage the publicly available variant datasets to augment the search for rare variant associations, which can explain additional disease risk in AMD.

Identifying risk variants for embryo aneuploidy using ultra-low coverage whole-genome sequencing from preimplantation genetic testing

Aneuploidy frequently arises during human meiosis and is the primary cause of early miscarriage and in vitro fertilization (IVF) failure. Individuals undergoing IVF exhibit significant variability in aneuploidy rates, although the exact genetic causes of the variability in aneuploid egg production remain unclear. Preimplantation genetic testing for aneuploidy (PGT-A) using next-generation sequencing is a standard test for identifying and selecting IVF-derived euploid embryos. The wealth of embryo aneuploidy data and ultra-low coverage whole-genome sequencing (ulc-WGS) data from PGT-A have the potential to discover variants in parental genomes that are associated with aneuploidy risk in their embryos. Using ulc-WGS data from ∼10,000 PGT-A biopsies, we imputed genotype likelihoods of genetic variants in embryo genomes. We then used the imputed variants and embryo aneuploidy calls to perform a genome-wide association study of aneuploidy incidence. Finally, we carried out functional evaluation of the identified candidate gene in a mouse oocyte system. We identified one locus on chromosome 3 that is significantly associated with meiotic aneuploidy risk. One candidate gene, CCDC66, encompassed by this locus, is involved in chromosome segregation during meiosis. Using mouse oocytes, we showed that CCDC66 regulates meiotic progression and chromosome segregation fidelity, especially in older mice. Our work extended the research utility of PGT-A ulc-WGS data by allowing robust association testing and improved the understanding of the genetic contribution to maternal meiotic aneuploidy risk. Importantly, we introduce a generalizable method that has potential to be leveraged for similar association studies that use ulc-WGS data.

Uncovering associations between pre-existing conditions and COVID-19 Severity: A polygenic risk score approach across three large biobanks

OBJECTIVE

To overcome the limitations associated with the collection and curation of COVID-19 outcome data in biobanks, this study proposes the use of polygenic risk scores (PRS) as reliable proxies of COVID-19 severity across three large biobanks: the Michigan Genomics Initiative (MGI), UK Biobank (UKB), and NIH All of Us. The goal is to identify associations between pre-existing conditions and COVID-19 severity.

METHODS

Drawing on a sample of more than 500,000 individuals from the three biobanks, we conducted a phenome-wide association study (PheWAS) to identify associations between a PRS for COVID-19 severity, derived from a genome-wide association study on COVID-19 hospitalization, and clinical pre-existing, pre-pandemic phenotypes. We performed cohort-specific PRS PheWAS and a subsequent fixed-effects meta-analysis.

RESULTS

The current study uncovered 23 pre-existing conditions significantly associated with the COVID-19 severity PRS in cohort-specific analyses, of which 21 were observed in the UKB cohort and two in the MGI cohort. The meta-analysis yielded 27 significant phenotypes predominantly related to obesity, metabolic disorders, and cardiovascular conditions. After adjusting for body mass index, several clinical phenotypes, such as hypercholesterolemia and gastrointestinal disorders, remained associated with an increased risk of hospitalization following COVID-19 infection.

CONCLUSION

By employing PRS as a proxy for COVID-19 severity, we corroborated known risk factors and identified novel associations between pre-existing clinical phenotypes and COVID-19 severity. Our study highlights the potential value of using PRS when actual outcome data may be limited or inadequate for robust analyses.

Aberrant landscapes of maternal meiotic crossovers contribute to aneuploidies in human embryos

Meiotic recombination is crucial for human genetic diversity and chromosome segregation accuracy. Understanding its variation across individuals and the processes by which it goes awry are long-standing goals in human genetics. Current approaches for inferring recombination landscapes either rely on population genetic patterns of linkage disequilibrium (LD)-capturing a time-averaged view-or direct detection of crossovers in gametes or multi-generation pedigrees, which limits dataset scale and availability. Here, we introduce an approach for inferring sex-specific recombination landscapes using data from preimplantation genetic testing for aneuploidy (PGT-A). This method relies on low-coverage (<0.05×) whole-genome sequencing of in vitro fertilized (IVF) embryo biopsies. To overcome the data sparsity, our method exploits its inherent relatedness structure, knowledge of haplotypes from external population reference panels, as well as the frequent occurrence of monosomies in embryos, whereby the remaining chromosome is phased by default. Extensive simulations demonstrate our method's high accuracy, even at coverages as low as 0.02×. Applying this method to PGT-A data from 18,967 embryos, we mapped 70,660 recombination events with ~150 kbp resolution, replicating established sex-specific recombination patterns. We observed a reduced total length of the female genetic map in trisomies compared to disomies, as well as chromosome-specific alterations in crossover distributions. Based on haplotype configurations in pericentromeric regions, our data indicate chromosome-specific propensities for different mechanisms of meiotic error. Our results provide a comprehensive view of the role of aberrant meiotic recombination in the origins of human aneuploidies and offer a versatile tool for mapping crossovers in low-coverage sequencing data from multiple siblings.

Recent positive selection signatures reveal phenotypic evolution in the Han Chinese population

Characterizing natural selection signatures and relationships with phenotype spectra is important for understanding human evolution and both biological and pathological mechanisms. Here, we identified 24 genetic loci under recent selection by analyzing rare singletons in 3946 high-depth whole-genome sequencing data of Han Chinese. The loci include immune-related gene regions (MHC cluster, IGH cluster, STING1, and PSG), alcohol metabolism-related gene regions (ADH1B, ALDH2, and ALDH3B2), and the olfactory perception gene OR4C16, in which the MHC cluster, ADH1B, and ALDH2 were also identified by TOPMed and WestLake Biobank. Among the signals, the IGH cluster is particularly interesting, in which the favored allele of variant 14_105737776_C_T (rs117518546, IgG1-G396R) promotes immune response, but also increases the risk of an autoimmune disease systemic lupus erythematosus (SLE). It is also surprising that our newly discovered ALDH3B2 evolved in the opposite direction to ALDH2 for alcohol metabolism. Besides monogenic traits, we found that multiple complex traits experienced polygenic adaptation. Particularly, multi-methods consistently revealed that lower blood pressure was favored in natural selection. Finally, we built a database named RePoS (recent positive selection, http://bigdata.ibp.ac.cn/RePoS/) to integrate and display multi-population selection signals. Our study extended our understanding of natural evolution and phenotype adaptation in Han Chinese as well as other populations.

Stratified genetic analysis reveals sex differences in MPO-ANCA-associated vasculitis

OBJECTIVE

To identify and genetically characterize subgroups of patients with ANCA-associated vasculitides (AAV) based on sex and ANCA subtype.

METHODS

A previously established SNP dataset derived from DNA sequencing of 1853 genes and genotyping of 1088 Scandinavian cases with AAV and 1589 controls was stratified for sex and ANCA subtype and analysed for association with five top AAV SNPs. rs9274619, a lead variant at the HLA-DQB1/HLA-DQA2 locus previously associated with AAV positive for myeloperoxidase (MPO)-ANCA, was analysed for association with the cumulative disease involvement of ten different organ systems.

RESULTS

rs9274619 showed a significantly stronger association to MPO-ANCA-positive females than males [P = 2.0 × 10-4, OR = 2.3 (95% CI 1.5, 3.5)], whereas proteinase 3 (PR3)-ANCA-associated variants rs1042335, rs9277341 (HLA-DPB1/A1) and rs28929474 (SERPINA1) were equally associated with females and males with PR3-ANCA. In MPO-ANCA-positive cases, carriers of the rs9274619 risk allele were more prone to disease engagement of eyes [P = 0.021, OR = 11 (95% CI 2.2, 205)] but less prone to pulmonary involvement [P = 0.026, OR = 0.52 (95% CI 0.30, 0.92)]. Moreover, AAV with both MPO-ANCA and PR3-ANCA was associated with the PR3-ANCA lead SNP rs1042335 [P = 0.0015, OR = 0.091 (95% CI 0.0022, 0.55)] but not with rs9274619.

CONCLUSIONS

Females and males with MPO-ANCA-positive AAV differ in genetic predisposition to disease, suggesting at least partially distinct disease mechanisms between the sexes. Double ANCA-positive AAV cases are genetically similar to PR3-ANCA-positive cases, providing clues to the clinical follow-up and treatment of these patients.

Associations of polygenic inheritance of physical activity with aerobic fitness, cardiometabolic risk factors and diseases: the HUNT study

Physical activity (PA), aerobic fitness, and cardiometabolic diseases (CMD) are highly heritable multifactorial phenotypes. Shared genetic factors may underlie the associations between higher levels of PA and better aerobic fitness and a lower risk for CMDs. We aimed to study how PA genotype associates with self-reported PA, aerobic fitness, cardiometabolic risk factors and diseases. PA genotype, which combined variation in over one million of gene variants, was composed using the SBayesR polygenic scoring methodology. First, we constructed a polygenic risk score for PA in the Trøndelag Health Study (N = 47,148) using UK Biobank single nucleotide polymorphism-specific weights (N = 400,124). The associations of the PA PRS and continuous variables were analysed using linear regression models and with CMD incidences using Cox proportional hazard models. The results showed that genotypes predisposing to higher amount of PA were associated with greater self-reported PA (Beta [B] = 0.282 MET-h/wk per SD of PRS for PA, 95% confidence interval [CI] = 0.211, 0.354) but not with aerobic fitness. These genotypes were also associated with healthier cardiometabolic profile (waist circumference [B = -0.003 cm, 95% CI = -0.004, -0.002], body mass index [B = -0.002 kg/m2, 95% CI = -0.004, -0.001], high-density lipoprotein cholesterol [B = 0.004 mmol/L, 95% CI = 0.002, 0.006]) and lower incidence of hypertensive diseases (Hazard Ratio [HR] = 0.97, 95% CI = 0.951, 0.990), stroke (HR = 0.94, 95% CI = 0.903, 0.978) and type 2 diabetes (HR = 0.94, 95 % CI = 0.902, 0.970). Observed associations were independent of self-reported PA. These results support earlier findings suggesting small pleiotropic effects between PA and CMDs and provide new evidence about associations of polygenic inheritance of PA and intermediate cardiometabolic risk factors.

Identifying risk genes for embryo aneuploidy using ultra-low coverage whole-genome sequencing

Background

Aneuploidy, the state of a cell containing extra or missing chromosomes, frequently arises during human meiosis and is the primary cause of early miscarriage and maternal age-related in vitro fertilization (IVF) failure. IVF patients exhibit significant variability in aneuploidy rates, although the exact genetic causes of the variability in aneuploid egg production remain unclear. Preimplantation genetic testing for aneuploidy (PGT-A) using ultra-low coverage whole-genome sequencing (ulc-WGS) is a standard test for identifying and selecting IVF-derived embryos with a normal chromosome complement. The wealth of embryo aneuploidy data and ulc-WGS data from PGT-A has potential for discovering variants in paternal genomes that are associated with aneuploidy risk in their embryos.

Methods

Using ulc-WGS data from ∼10,000 PGT-A biopsies, we imputed genotype likelihoods of genetic variants in parental genomes. We then used the imputed variants and aneuploidy calls from the embryos to perform a genome-wide association study of aneuploidy incidence. Finally, we carried out functional evaluation of the identified candidate gene in a mouse oocyte system.

Results

We identified one locus on chromosome 3 that is significantly associated with maternal meiotic aneuploidy risk. One candidate gene, CCDC66, encompassed by this locus, is involved in chromosome segregation during meiosis. Using mouse oocytes, we showed that CCDC66 regulates meiotic progression and chromosome segregation fidelity, especially in older mice.

Conclusions

Our work extended the research utility of PGT-A ulc-WGS data by allowing robust association testing and improved the understanding of the genetic contribution to maternal meiotic aneuploidy risk. Importantly, we introduce a generalizable method that can be leveraged for similar association studies using ulc-WGS data.

A Varying Coefficient Model to Jointly Test Genetic and Gene-Environment Interaction Effects

Most human traits are influenced by the interplay between genetic and environmental factors. Many statistical methods have been proposed to screen for gene-environment interaction (GxE) in the post genome-wide association study era. However, most of the existing methods assume a linear interaction between genetic and environmental factors toward phenotypic variations, which diminishes statistical power in the case of nonlinear GxE. In this paper, we present a flexible statistical procedure to detect GxE regardless of whether the underlying relationship is linear or not. By modeling the joint genetic and GxE effects as a varying-coefficient function of the environmental factor, the proposed model is able to capture dynamic trajectories of GxE. We employ a likelihood ratio test with a fast Monte Carlo algorithm for hypothesis testing. Simulations were conducted to evaluate validity and power of the proposed model in various settings. Real data analysis was performed to illustrate its power, in particular, in the case of nonlinear GxE.

Environmental and genetic associations with aberrant early-life gut microbial maturation in childhood asthma

BACKGROUND

Environmental, genetic, and microbial factors are independently associated with childhood asthma.

OBJECTIVE

We sought to determine the roles of environmental exposures and 17q12-21 locus genotype in the maturation of the early-life microbiome in childhood asthma.

METHODS

We analyzed fecal 16s rRNA sequencing at age 3 to 6 months and age 1 year to characterize microbial maturation of offspring of participants in the Vitamin D Antenatal Reduction Trial. We determined associations of microbial maturation and environmental exposures in the mediation of asthma risk at age 3 years. We examined 17q12-21 genotype and microbial maturation associations with asthma risk in Vitamin D Antenatal Reduction Trial and the replication cohort Copenhagen Prospective Studies on Childhood Asthma 2010.

RESULTS

Accelerated fecal microbial maturation at age 3 to 6 months and delayed maturation at age 1 year were associated with asthma (P < .001). Fecal Bacteroides was reduced at age 3 to 6 months in association with subsequent asthma (P = .006) and among subjects with lower microbial maturation at age 1 year (q = 0.009). Sixty-one percent of the association between breast-feeding and asthma was mediated by microbial maturation at age 3 to 6 months. Microbial maturation and 17q12-21 genotypes exhibited independent, additive effects on childhood asthma risk.

CONCLUSIONS

The intestinal microbiome and its maturation mediates associations between environmental exposures including breast-feeding and asthma. The intestinal microbiome and 17q12-21 genotype appear to exert additive and independent effects on childhood asthma risk.

Omega-3 Fatty Acids Interact with DPP10 Region Genotype in Association with Childhood Atopy

Associations of omega-3 fatty acids (n-3) with allergic diseases are inconsistent, perhaps in part due to genetic variation. We sought to identify and validate genetic variants that modify associations of n-3 with childhood asthma or atopy in participants in the Vitamin D Antenatal Asthma Reduction Trial (VDAART) and the Copenhagen Prospective Studies on Asthma in Childhood 2010 (COPSAC). Dietary n-3 was derived from food frequency questionnaires and plasma n-3 was measured via untargeted mass spectrometry in early childhood and children aged 6 years old. Interactions of genotype with n-3 in association with asthma or atopy at age 6 years were sought for six candidate genes/gene regions and genome-wide. Two SNPs in the region of DPP10 (rs958457 and rs1516311) interacted with plasma n-3 at age 3 years in VDAART (p = 0.007 and 0.003, respectively) and with plasma n-3 at age 18 months in COPSAC (p = 0.01 and 0.02, respectively) in associationwith atopy. Another DPP10 region SNP, rs1367180, interacted with dietary n-3 at age 6 years in VDAART (p = 0.009) and with plasma n-3 at age 6 years in COPSAC (p = 0.004) in association with atopy. No replicated interactions were identified for asthma. The effect of n-3 on reducing childhood allergic disease may differ by individual factors, including genetic variation in the DPP10 region.

ERStruct: a fast Python package for inferring the number of top principal components from whole genome sequencing data

BACKGROUND

Large-scale multi-ethnic DNA sequencing data is increasingly available owing to decreasing cost of modern sequencing technologies. Inference of the population structure with such sequencing data is fundamentally important. However, the ultra-dimensionality and complicated linkage disequilibrium patterns across the whole genome make it challenging to infer population structure using traditional principal component analysis based methods and software.

RESULTS

We present the ERStruct Python Package, which enables the inference of population structure using whole-genome sequencing data. By leveraging parallel computing and GPU acceleration, our package achieves significant improvements in the speed of matrix operations for large-scale data. Additionally, our package features adaptive data splitting capabilities to facilitate computation on GPUs with limited memory.

CONCLUSION

Our Python package ERStruct is an efficient and user-friendly tool for estimating the number of top informative principal components that capture population structure from whole genome sequencing data.

Hybrid autoencoder with orthogonal latent space for robust population structure inference

Analysis of population structure and genomic ancestry remains an important topic in human genetics and bioinformatics. Commonly used methods require high-quality genotype data to ensure accurate inference. However, in practice, laboratory artifacts and outliers are often present in the data. Moreover, existing methods are typically affected by the presence of related individuals in the dataset. In this work, we propose a novel hybrid method, called SAE-IBS, which combines the strengths of traditional matrix decomposition-based (e.g., principal component analysis) and more recent neural network-based (e.g., autoencoders) solutions. Namely, it yields an orthogonal latent space enhancing dimensionality selection while learning non-linear transformations. The proposed approach achieves higher accuracy than existing methods for projecting poor quality target samples (genotyping errors and missing data) onto a reference ancestry space and generates a robust ancestry space in the presence of relatedness. We introduce a new approach and an accompanying open-source program for robust ancestry inference in the presence of missing data, genotyping errors, and relatedness. The obtained ancestry space allows for non-linear projections and exhibits orthogonality with clearly separable population groups.

The Michigan Genomics Initiative: A biobank linking genotypes and electronic clinical records in Michigan Medicine patients

Biobanks of linked clinical patient histories and biological samples are an efficient strategy to generate large cohorts for modern genetics research. Biobank recruitment varies by factors such as geographic catchment and sampling strategy, which affect biobank demographics and research utility. Here, we describe the Michigan Genomics Initiative (MGI), a single-health-system biobank currently consisting of >91,000 participants recruited primarily during surgical encounters at Michigan Medicine. The surgical enrollment results in a biobank enriched for many diseases and ideally suited for a disease genetics cohort. Compared with the much larger population-based UK Biobank, MGI has higher prevalence for nearly all diagnosis-code-based phenotypes and larger absolute case counts for many phenotypes. Genome-wide association study (GWAS) results replicate known findings, thereby validating the genetic and clinical data. Our results illustrate that opportunistic biobank sampling within single health systems provides a unique and complementary resource for exploring the genetics of complex diseases.

Using Genomic Structural Equation Modeling to Partition the Genetic Covariance Between Birthweight and Cardiometabolic Risk Factors into Maternal and Offspring Components in the Norwegian HUNT Study

The Barker Hypothesis posits that adverse intrauterine environments result in fetal growth restriction and increased risk of cardiometabolic disease through developmental compensations. Here we introduce a new statistical model using the genomic SEM software that is capable of simultaneously partitioning the genetic covariation between birthweight and cardiometabolic traits into maternally mediated and offspring mediated contributions. We model the covariance between birthweight and later life outcomes, such as blood pressure, non-fasting glucose, blood lipids and body mass index in the Norwegian HUNT study, consisting of 15,261 mother-eldest offspring pairs with genetic and phenotypic data. Application of this model showed some evidence for maternally mediated effects of systolic blood pressure on offspring birthweight, and pleiotropy between birthweight and non-fasting glucose mediated through the offspring genome. This underscores the importance of genetic links between birthweight and cardiometabolic phenotypes and offer alternative explanations to environmentally based hypotheses for the phenotypic correlation between these variables.

Polygenic Liability to Depression Is Associated With Multiple Medical Conditions in the Electronic Health Record: Phenome-wide Association Study of 46,782 Individuals

BACKGROUND

Major depressive disorder (MDD) is a leading cause of disease-associated disability, with much of the increased burden due to psychiatric and medical comorbidity. This comorbidity partly reflects common genetic influences across conditions. Integrating molecular-genetic tools with health records enables tests of association with the broad range of physiological and clinical phenotypes. However, standard phenome-wide association studies analyze associations with individual genetic variants. For polygenic traits such as MDD, aggregate measures of genetic risk may yield greater insight into associations across the clinical phenome.

METHODS

We tested for associations between a genome-wide polygenic risk score for MDD and medical and psychiatric traits in a phenome-wide association study of 46,782 unrelated, European-ancestry participants from the Michigan Genomics Initiative.

RESULTS

The MDD polygenic risk score was associated with 211 traits from 15 medical and psychiatric disease categories at the phenome-wide significance threshold. After excluding patients with depression, continued associations were observed with respiratory, digestive, neurological, and genitourinary conditions; neoplasms; and mental disorders. Associations with tobacco use disorder, respiratory conditions, and genitourinary conditions persisted after accounting for genetic overlap between depression and other psychiatric traits. Temporal analyses of time-at-first-diagnosis indicated that depression disproportionately preceded chronic pain and substance-related disorders, while asthma disproportionately preceded depression.

CONCLUSIONS

The present results can inform the biological links between depression and both mental and systemic diseases. Although MDD polygenic risk scores cannot currently forecast health outcomes with precision at the individual level, as molecular-genetic discoveries for depression increase, these tools may augment risk prediction for medical and psychiatric conditions.

Fibromuscular Dysplasia and Abdominal Aortic Aneurysms Are Dimorphic Sex-Specific Diseases With Shared Complex Genetic Architecture

BACKGROUND

The risk of arterial diseases may be elevated among family members of individuals having multifocal fibromuscular dysplasia (FMD). We sought to investigate the risk of arterial diseases in families of individuals with FMD.

METHODS

Family histories for 73 probands with FMD were obtained, which included an analysis of 463 total first-degree relatives focusing on FMD and related arterial disorders. A polygenic risk score for FMD (PRSFMD) was constructed from prior genome-wide association findings of 584 FMD cases and 7139 controls and evaluated for association with an abdominal aortic aneurysm (AAA) in a cohort of 9693 AAA cases and 294 049 controls. A previously published PRSAAA was also assessed among the FMD cases and controls.

RESULTS

Of all first degree relatives of probands, 9.3% were diagnosed with FMD, aneurysms, and dissections. Aneurysmal disease occurred in 60.5% of affected relatives and 5.6% of all relatives. Among 227 female first-degree relatives of probands, 4.8% (11) had FMD, representing a relative risk (RR)FMD of 1.5 ([95% CI, 0.75-2.8]; P=0.19) compared with the estimated population prevalence of 3.3%, though not of statistical significance. Of all fathers of FMD probands, 11% had AAAs resulting in a RRAAA of 2.3 ([95% CI, 1.12-4.6]; P=0.014) compared with population estimates. The PRSFMD was found to be associated with an AAA (odds ratio, 1.03 [95% CI, 1.01-1.05]; P=2.6×10-3), and the PRSAAA was found to be associated with FMD (odds ratio, 1.53 [95% CI, 1.2-1.9]; P=9.0×10-5) as well.

CONCLUSIONS

FMD and AAAs seem to be sex-dimorphic manifestations of a heritable arterial disease with a partially shared complex genetic architecture. Excess risk of having an AAA according to a family history of FMD may justify screening in family members of individuals having FMD.

Maternal vitamin D during pregnancy and offspring autism and autism-associated traits: a prospective cohort study

BACKGROUND

There has been a growing interest in the association between maternal levels of vitamin D during pregnancy and offspring autism. However, whether any associations reflect causal effects is still inconclusive.

METHODS

We used data from a UK-based pregnancy cohort study (Avon Longitudinal Study of Parents and Children) comprising 7689 births between 1991 and 1992 with maternal blood vitamin D levels recorded during pregnancy and at least one recorded outcome measure, including autism diagnosis and autism-associated traits. The association between each outcome with seasonal and gestational age-adjusted maternal serum 25-hydroxyvitamin D during pregnancy was estimated using confounder-adjusted regression models. Multiple imputation was used to account for missing data, and restricted cubic splines were used to investigate nonlinear associations. Mendelian randomization was used to strengthen causal inference.

RESULTS

No strong evidence of an association between maternal serum 25-hydroxyvitamin D during pregnancy and any offspring autism-associated outcome was found using multivariable regression analysis (autism diagnosis: adjusted OR = 0.98, 95% CI = 0.90-1.06), including with multiple imputation (autism diagnosis: adjusted OR = 0.99, 95% CI = 0.93-1.06), and no evidence of a causal effect was suggested by Mendelian randomization (autism diagnosis: causal OR = 1.08, 95% CI = 0.46-2.55). Some evidence of increased odds of autism-associated traits at lower levels of maternal serum 25-hydroxyvitamin D was found using spline analysis.

LIMITATIONS

Our study was potentially limited by low power, particularly for diagnosed autism cases as an outcome. The cohort may not have captured the extreme lows of the distribution of serum 25-hydroxyvitamin D, and our analyses may have been biased by residual confounding and missing data.

CONCLUSIONS

The present study found no strong evidence of a causal link between maternal vitamin D levels in pregnancy and offspring diagnosis or traits of autism.

Genomes from a medieval mass burial show Ashkenazi-associated hereditary diseases pre-date the 12th century

We report genome sequence data from six individuals excavated from the base of a medieval well at a site in Norwich, UK. A revised radiocarbon analysis of the assemblage is consistent with these individuals being part of a historically attested episode of antisemitic violence on 6 February 1190 CE. We find that four of these individuals were closely related and all six have strong genetic affinities with modern Ashkenazi Jews. We identify four alleles associated with genetic disease in Ashkenazi Jewish populations and infer variation in pigmentation traits, including the presence of red hair. Simulations indicate that Ashkenazi-associated genetic disease alleles were already at appreciable frequencies, centuries earlier than previously hypothesized. These findings provide new insights into a significant historical crime, into Ashkenazi population history, and into the origins of genetic diseases associated with modern Jewish populations.

The HUNT study: A population-based cohort for genetic research

The Trøndelag Health Study (HUNT) is a population-based cohort of ∼229,000 individuals recruited in four waves beginning in 1984 in Trøndelag County, Norway. Approximately 88,000 of these individuals have available genetic data from array genotyping. HUNT participants were recruited during four community-based recruitment waves and provided information on health-related behaviors, self-reported diagnoses, family history of disease, and underwent physical examinations. Linkage via the Norwegian personal identification number integrates digitized health care information from doctor visits and national health registries including death, cancer and prescription registries. Genome-wide association studies of HUNT participants have provided insights into the mechanism of cardiovascular, metabolic, osteoporotic, and liver-related diseases, among others. Unique features of this cohort that facilitate research include nearly 40 years of longitudinal follow-up in a motivated and well-educated population, family data, comprehensive phenotyping, and broad availability of DNA, RNA, urine, fecal, plasma, and serum samples.

ExPRSweb: An online repository with polygenic risk scores for common health-related exposures

Complex traits are influenced by genetic risk factors, lifestyle, and environmental variables, so-called exposures. Some exposures, e.g., smoking or lipid levels, have common genetic modifiers identified in genome-wide association studies. Because measurements are often unfeasible, exposure polygenic risk scores (ExPRSs) offer an alternative to study the influence of exposures on various phenotypes. Here, we collected publicly available summary statistics for 28 exposures and applied four common PRS methods to generate ExPRSs in two large biobanks: the Michigan Genomics Initiative and the UK Biobank. We established ExPRSs for 27 exposures and demonstrated their applicability in phenome-wide association studies and as predictors for common chronic conditions. Especially the addition of multiple ExPRSs showed, for several chronic conditions, an improvement compared to prediction models that only included traditional, disease-focused PRSs. To facilitate follow-up studies, we share all ExPRS constructs and generated results via an online repository called ExPRSweb.

Large-Scale Targeted Sequencing Study of Ischemic Stroke in the Han Chinese Population

Background

Ischemic stroke is likely caused by interactions of multiple genes and environmental determinants. However, large‐scale sequencing studies to discern functional genetic variants and their interactions with clinical and lifestyle risk factors on ischemic stroke are limited.

Methods and Results

We sequenced functional regions of 740 previously identified genes associated with atherosclerotic disease among 999 ischemic stroke cases and 1001 controls of Chinese ancestry. Multiple logistic regression models were used to examine the associations between variants and ischemic stroke and test interactions between variants and clinical and lifestyle risk factors. Functional variants achieving suggestive significance were replicated in an independent sample of 4724 ischemic stroke cases and 5029 controls. Driven by variant main effects, each minor allele of the correlated rs174535, rs174545, and rs3834458 variants at MYRF‐FADS1‐FADS2 conferred an average 0.83‐fold (95% CI, 0.78–0.88) decreased odds of stroke. Significant main effects of MTHFR rs1801133 missense variant were also observed, with each copy of the A allele associated with a 1.20‐fold (95% CI, 1.13–1.27) higher odds of ischemic stroke. The functional ALDH2 rs671 variant was identified in interaction analyses with alcohol drinking (Meta‐P=3.39×10⁻¹⁷). Each minor allele conferred a 0.54‐fold (95% CI, 0.45–0.64) decreased odds of stroke among drinkers and a 0.89‐fold (95% CI, 0.83–0.97) decreased odds among nondrinkers.

Conclusions

Significant associations at MYRF‐FADS1‐FADS2 indicate that genetically elevated polyunsaturated fatty acids may decrease ischemic stroke risk in East Asians. Significant associations at MTHFR and ALDH2 robustly confirm deleterious effects of genetically elevated homocysteine and alcohol intake, respectively, on ischemic stroke.

GWAS in the southern African context

Researchers would generally adjust for the possible confounding effect of population structure by considering global ancestry proportions or top principle components. Alternatively, researchers would conduct admixture mapping to increase the power to detect variants with an ancestry effect. This is sufficient in simple admixture scenarios, however, populations from southern Africa can be complex multi-way admixed populations. Duan et al. (2018) first described local ancestry adjusted allelic (LAAA) analysis as a robust method for discovering association signals, while producing minimal false positive hits. Their simulation study, however, was limited to a two-way admixed population. Realizing that their findings might not translate to other admixture scenarios, we simulated a three- and five-way admixed population to compare the LAAA model to other models commonly used in genome-wide association studies (GWAS). We found that, given our admixture scenarios, the LAAA model identifies the most causal variants in most of the phenotypes we tested across both the three-way and five-way admixed populations. The LAAA model also produced a high number of false positive hits which was potentially caused by the ancestry effect size that we assumed. Considering the extent to which the various models tested differed in their results and considering that the source of a given association is unknown, we recommend that researchers use multiple GWAS models when analysing populations with complex ancestry.

Dissecting the Interplay Between Genetic Ancestry and Neighborhood Socioeconomic Status on Triple Negative Breast Cancer

OBJECTIVE

To investigate the impact of global and local genetic ancestry and neighborhood socioeconomic status (nSES), on breast cancer (BC) subtype, and gene expression.

SUMMARY OF BACKGROUND DATA

Higher rates of aggressive BC subtypes (TNBC) and worse overall BC survival are seen in black women [Hispanic (HB) and non-Hispanic (NHB)] and women from low nSES. However, the complex relationship between genetic ancestry, nSES, and BC subtype etiology remains unknown.

METHODS

Genomic analysis was performed on the peripheral blood from a cohort of 308 stage I-IV non-Hispanic White (NHW), Hispanic White (HW), HB and NHB women with BC. Patient and tumor characteristics were collected. Global and local ancestral estimates were calculated. Multinomial logistic regression was performed to determine associations between age, stage, genetic ancestry, and nSES on rates of TNBC compared to ER+/HER2-, ER+/HER2+, and ER-/HER2+ disease.

RESULTS

Among 308 women, we identified a significant association between increasing West African (WA) ancestry and odds of TNBC (OR 1.06,95%CI 1.001-1.126, P=0.046) as well as an inverse relationship between higher nSES and TNBC (OR 0.343,95%CI 0.151-0.781, P=0.011). WA ancestry remained significantly associated with TNBC when adjusting for patient age and tumor stage, but not when adjusting for nSES (OR 1.049, 95%CI-0.987-1.116, P=0.120). Local ancestry analysis revealed nSES-independent enriched WA ancestral segment centered at chr2:42004914 (P=3.70×10-5) in patients with TNBC.

CONCLUSIONS

In this translational epidemiologic study of genetic ancestry and nSES on BC subtype, we discovered associations between increasing WA ancestry, low nSES, and higher rates of TNBC compared to other BC subtypes. Moreover, on admixture mapping, specific chromosomal segments were associated with WA ancestry and TNBC, independent of nSES. However, on multinomial logistic regression adjusting for WA ancestry, women from low nSES were more likely to have TNBC, independent of genetic ancestry. These findings highlight the complex nature of TNBC and the importance of studying potential gene-environment interactions as drivers of TNBC.

Cross-continental admixture in the Kho population from northwest Pakistan

Northern Pakistan is home to many diverse ethnicities and languages. The region acted as a prime corridor for ancient invasions and population migrations between Western Eurasia and South Asia. Kho, one of the major ethnic groups living in this region, resides in the remote and isolated mountainous region in the Chitral Valley of the Hindu Kush Mountain range. They are culturally and linguistically distinct from the rest of the Pakistani population groups and their genetic ancestry is still unknown. In this study, we generated genome-wide genotype data of ~1 M loci (Illumina WeGene array) for 116 unrelated Kho individuals and carried out comprehensive analyses in the context of worldwide extant and ancient anatomically modern human populations across Eurasia. The results inferred that the Kho can trace a large proportion of their ancestry to the population who migrated south from the Southern Siberian steppes during the second millennium BCE ~110 generations ago. An additional wave of gene flow from a population carrying East Asian ancestry was also identified in the Kho that occurred ~60 generations ago and may possibly be linked to the expansion of the Tibetan Empire during 7th to 9th centuries CE (current era) in the northwestern regions of the Indian sub-continent. We identified several candidate regions suggestive of positive selection in the Kho, that included genes mainly involved in pigmentation, immune responses, muscular development, DNA repair, and tumor suppression.

An eigenvalue ratio approach to inferring population structure from whole genome sequencing data

Inference of population structure from genetic data plays an important role in population and medical genetics studies. With the advancement and decreasing cost of sequencing technology, the increasingly available whole genome sequencing data provide much richer information about the underlying population structure. The traditional method (Patterson et al., 2006) originally developed for array-based genotype data for computing and selecting top principal components that capture population structure may not perform well on sequencing data for two reasons. First, the number of genetic variants p is much larger than the sample size n in sequencing data such that the sample-to-marker ratio n/p is nearly zero, violating the assumption of the Tracy-Widom test used in their method. Second, their method might not be able to handle the linkage disequilibrium well in sequencing data. To resolve those two practical issues, we propose a new method called ERStruct to determine the number of top informative principal components based on sequencing data. More specifically, we propose to use the ratio of consecutive eigenvalues as a more robust test statistic, and then we approximate its null distribution using modern random matrix theory. Both simulation studies and applications to two public data sets from the HapMap 3 and the 1000 Genomes Projects demonstrate the empirical performance of our ERStruct method. This article is protected by copyright. All rights reserved.

Rare variant association tests for ancestry-matched case-control data based on conditional logistic regression

With the increasing volume of human sequencing data available, analysis incorporating external controls becomes a popular and cost-effective approach to boost statistical power in disease association studies. To prevent spurious association due to population stratification, it is important to match the ancestry backgrounds of cases and controls. However, rare variant association tests based on a standard logistic regression model are conservative when all ancestry-matched strata have the same case-control ratio and might become anti-conservative when case-control ratio varies across strata. Under the conditional logistic regression (CLR) model, we propose a weighted burden test (CLR-Burden), a variance component test (CLR-SKAT) and a hybrid test (CLR-MiST). We show that the CLR model coupled with ancestry matching is a general approach to control for population stratification, regardless of the spatial distribution of disease risks. Through extensive simulation studies, we demonstrate that the CLR-based tests robustly control type 1 errors under different matching schemes and are more powerful than the standard Burden, SKAT and MiST tests. Furthermore, because CLR-based tests allow for different case-control ratios across strata, a full-matching scheme can be employed to efficiently utilize all available cases and controls to accelerate the discovery of disease associated genes.

Investigating a Potential Causal Relationship Between Maternal Blood Pressure During Pregnancy and Future Offspring Cardiometabolic Health

Observational epidemiological studies have reported that higher maternal blood pressure (BP) during pregnancy is associated with increased future risk of offspring cardiometabolic disease. However, it is unclear whether this association represents a causal relationship through intrauterine mechanisms. We used a Mendelian randomization (MR) framework to examine the relationship between unweighted maternal genetic scores for systolic BP and diastolic BP and a range of cardiometabolic risk factors in the offspring of up to 29 708 genotyped mother-offspring pairs from the UKB study (UK Biobank) and the HUNT study (Trøndelag Health). We conducted similar analyses in up to 21 423 father-offspring pairs from the same cohorts. We confirmed that the BP-associated genetic variants from the general population sample also had similar effects on maternal BP during pregnancy in independent cohorts. We did not detect any association between maternal (or paternal) unweighted genetic scores and cardiometabolic offspring outcomes in the meta-analysis of UKB and HUNT after adjusting for offspring genotypes at the same loci. We find little evidence to support the notion that maternal BP is a major causal risk factor for adverse offspring cardiometabolic outcomes in later life.

Expression of Endogenous Retroviral RNA in Prostate Tumors has Prognostic Value and Shows Differences among Americans of African Versus European/Middle Eastern Ancestry

Simple Summary

Endogenous retroviruses (ERVs) are viral sequences that have been incorporated into the human genome over millions of years via integrations in germ-line cells. In this study, we investigated whether the expression of ERVs was associated with two different aspects of prostate cancer (PCa). First, Black American men have a higher incidence and poorer outcome of PCa compared to White men. We identified differences in ERV expression among prostate tumors between men of primarily African versus primarily European or Middle Eastern ancestry, which may be associated with differences in the mechanism of cancer progression in patients of these distinct ancestries. Second, we determined whether ERV expression might be correlated with the progression of disease, regardless of ancestry. We identified the ERV expression signatures that correlated with biochemical relapse among PCa patients of all ancestries, indicating that ERVs may be useful for identifying cancer patients at greatest risk of progression. The utility of ERV expression for studying cancer progression may extend to other cancers.

Abstract

Endogenous retroviruses (ERVs) are abundant, repetitive elements dispersed across the human genome and are implicated in various diseases. We investigated two potential roles for ERVs in prostate cancer (PCa). First, the PCa of Black Americans (BA) is diagnosed at an earlier median age and at a more advanced stage than the PCa of White Americans (WA). We used publicly available RNA-seq data from tumor-enriched samples of 27 BA and 65 WA PCa patients in order to identify 12 differentially expressed ERVs (p_adj < 0.1) and used a tissue microarray of the PCa cores from an independent set of BA and WA patients to validate the differential protein expression of one of these ERVs, ERV3-1 (p = 2.829 × 10⁻⁷). Second, we used 57 PCa tumors from patients of all ancestries from one hospital as a training set to identify the ERVs associated with time to biochemical relapse. A 29-ERV prognostic panel was then tested and validated on 35 separate PCa tumors from patients obtained in two different hospitals with a dramatic increase in prognostic power relative to clinical parameters alone (p = 7.4 × 10⁻¹¹). In summary, ERV RNA expression differences in the prostate tumors of patients of different ancestries may be associated with dissimilarities in the mechanism of cancer progression. In addition, the correlation of expression of certain ERVs in prostate tumors with the risk of biochemical relapse indicates a possible role for ERV expression in cancer progression.

Genetic variants at the RTP4/MASP1 locus are associated with fatigue in Scandinavian patients with primary Sjögren's syndrome

OBJECTIVES

Fatigue is common and severe in primary Sjögren's syndrome (pSS). The aim of this study was to identify genetic determinants of fatigue in pSS through a genome-wide association study.

METHODS

Patients with pSS from Norway, Sweden, UK and USA with fatigue and genotype data available were included. After genotype imputation and quality control, 682 patients and 4 966 157 genetic markers were available. Association analysis in each cohort using linear regression with fatigue as a continuous variable and meta-analyses between the cohorts were performed.

RESULTS

Meta-analysis of the Norwegian and Swedish cohorts identified five polymorphisms within the same linkage disequilibrium block at the receptor transporter protein 4 (RTP4)/MASP1 locus associated with fatigue with genome-wide significance (GWS) (p<5×10^-8). Patients homozygous for the major allele scored 25 mm higher on the fatigue Visual Analogue Scale than patients homozygous for the minor allele. There were no variants associated with fatigue with GWS in meta-analyses of the US/UK cohorts, or all four cohorts. RTP4 expression in pSS B cells was upregulated and positively correlated with the type I interferon score. Expression quantitative trait loci effects in whole blood for fatigue-associated variants at RTP4/MASP1 and levels of RTP4 and MASP1 expression were identified.

CONCLUSION

Genetic variations at RTP4/MASP1 are associated with fatigue in Scandinavian pSS patients. RTP4 encodes a Golgi chaperone that influences opioid pain receptor function and MASP1 is involved in complement activation. These results add evidence for genetic influence over fatigue in pSS.

PPIL4 is essential for brain angiogenesis and implicated in intracranial aneurysms in humans

Intracranial aneurysm (IA) rupture leads to subarachnoid hemorrhage, a sudden-onset disease that often causes death or severe disability. Although genome-wide association studies have identified common genetic variants that increase IA risk moderately, the contribution of variants with large effect remains poorly defined. Using whole-exome sequencing, we identified significant enrichment of rare, deleterious mutations in PPIL4, encoding peptidyl-prolyl cis-trans isomerase-like 4, in both familial and index IA cases. Ppil4 depletion in vertebrate models causes intracerebral hemorrhage, defects in cerebrovascular morphology and impaired Wnt signaling. Wild-type, but not IA-mutant, PPIL4 potentiates Wnt signaling by binding JMJD6, a known angiogenesis regulator and Wnt activator. These findings identify a novel PPIL4-dependent Wnt signaling mechanism involved in brain-specific angiogenesis and maintenance of cerebrovascular integrity and implicate PPIL4 gene mutations in the pathogenesis of IA.

Inferred expression regulator activities suggest genes mediating cardiometabolic genetic signals

Expression QTL (eQTL) analyses have suggested many genes mediating genome-wide association study (GWAS) signals but most GWAS signals still lack compelling explanatory genes. We have leveraged an adipose-specific gene regulatory network to infer expression regulator activities and phenotypic master regulators (MRs), which were used to detect activity QTLs (aQTLs) at cardiometabolic trait GWAS loci. Regulator activities were inferred with the VIPER algorithm that integrates enrichment of expected expression changes among a regulator's target genes with confidence in their regulator-target network interactions and target overlap between different regulators (i.e., pleiotropy). Phenotypic MRs were identified as those regulators whose activities were most important in predicting their respective phenotypes using random forest modeling. While eQTLs were typically more significant than aQTLs in cis, the opposite was true among candidate MRs in trans. Several GWAS loci colocalized with MR trans-eQTLs/aQTLs in the absence of colocalized cis-QTLs. Intriguingly, at the 1p36.1 BMI GWAS locus the EPHB2 cis-aQTL was stronger than its cis-eQTL and colocalized with the GWAS signal and 35 BMI MR trans-aQTLs, suggesting the GWAS signal may be mediated by effects on EPHB2 activity and its downstream effects on a network of BMI MRs. These MR and aQTL analyses represent systems genetic methods that may be broadly applied to supplement standard eQTL analyses for suggesting molecular effects mediating GWAS signals.

Haplotype-aware inference of human chromosome abnormalities

Extra or missing chromosomes-a phenomenon termed aneuploidy-frequently arise during human meiosis and embryonic mitosis and are the leading cause of pregnancy loss, including in the context of in vitro fertilization (IVF). While meiotic aneuploidies affect all cells and are deleterious, mitotic errors generate mosaicism, which may be compatible with healthy live birth. Large-scale abnormalities such as triploidy and haploidy also contribute to adverse pregnancy outcomes, but remain hidden from standard sequencing-based approaches to preimplantation genetic testing for aneuploidy (PGT-A). The ability to reliably distinguish meiotic and mitotic aneuploidies, as well as abnormalities in genome-wide ploidy, may thus prove valuable for enhancing IVF outcomes. Here, we describe a statistical method for distinguishing these forms of aneuploidy based on analysis of low-coverage whole-genome sequencing data, which is the current standard in the field. Our approach overcomes the sparse nature of the data by leveraging allele frequencies and linkage disequilibrium (LD) measured in a population reference panel. The method, which we term LD-informed PGT-A (LD-PGTA), retains high accuracy down to coverage as low as 0.05 × and at higher coverage can also distinguish between meiosis I and meiosis II errors based on signatures spanning the centromeres. LD-PGTA provides fundamental insight into the origins of human chromosome abnormalities, as well as a practical tool with the potential to improve genetic testing during IVF.

Genome-wide analysis identifies impaired axonogenesis in chronic overlapping pain conditions

Chronic pain is often present at more than one anatomical location, leading to chronic overlapping pain conditions (COPC). Whether COPC represents a distinct pathophysiology from the occurrence of pain at only one site is unknown. Using genome-wide approaches, we compared genetic determinants of chronic single-site vs. multisite pain in the UK Biobank. We found that different genetic signals underlie chronic single-site and multisite pain with much stronger genetic contributions for the latter. Among 23 loci associated with multisite pain, 9 loci replicated in the HUNT cohort, with the DCC netrin-1 receptor (DCC) as the top gene. Functional genomics identified axonogenesis in brain tissues as the major contributing pathway to chronic multisite pain. Finally, multimodal structural brain imaging analysis showed that DCC is most strongly expressed in subcortical limbic regions and is associated with alterations in the uncinate fasciculus microstructure, suggesting that DCC-dependent axonogenesis may contribute to COPC via cortico-limbic circuits.

Genetic Variants Associated With Human Eye Size Are Distinct From Those Conferring Susceptibility to Myopia

Purpose

Emmetropization requires coordinated scaling of the major ocular components, corneal curvature and axial length. This coordination is achieved in part through a shared set of genetic variants that regulate eye size. Poorly coordinated scaling of corneal curvature and axial length results in refractive error. We tested the hypothesis that genetic variants regulating eye size in emmetropic eyes are distinct from those conferring susceptibility to refractive error.

Methods

A genome-wide association study (GWAS) for corneal curvature in 22,180 adult emmetropic individuals was performed as a proxy for a GWAS for eye size. A polygenic score created using lead GWAS variants was tested for association with corneal curvature and axial length in an independent sample: 437 classified as emmetropic and 637 as ametropic. The genetic correlation between eye size and refractive error was calculated using linkage disequilibrium score regression for approximately 1 million genetic variants.

Results

The GWAS for corneal curvature in emmetropes identified 32 independent genetic variants (P < 5.0e-08). A polygenic score created using these 32 genetic markers explained 3.5% (P < 0.001) and 2.0% (P = 0.001) of the variance in corneal curvature and axial length, respectively, in the independent sample of emmetropic individuals but was not predictive of these traits in ametropic individuals. The genetic correlation between eye size and refractive error was close to zero (r_g = 0.00; SE = 0.06; P = 0.95).

Conclusions

These results support the hypothesis that genetic variants regulating eye size in emmetropic eyes do not overlap with those conferring susceptibility to myopia. This suggests that distinct biological pathways regulate normal eye growth and myopia development.

Ancestral Spectrum Analysis With Population-Specific Variants

With the advance of sequencing technology, an increasing number of populations have been sequenced to study the histories of worldwide populations, including their divergence, admixtures, migration, and effective sizes. The variants detected in sequencing studies are largely rare and mostly population specific. Population-specific variants are often recent mutations and are informative for revealing substructures and admixtures in populations; however, computational methods and tools to analyze them are still lacking. In this work, we propose using reference populations and single nucleotide polymorphisms (SNPs) specific to the reference populations. Ancestral information, the best linear unbiased estimator (BLUE) of the ancestral proportion, is proposed, which can be used to infer ancestral proportions in recently admixed target populations and measure the extent to which reference populations serve as good proxies for the admixing sources. Based on the same panel of SNPs, the ancestral information is comparable across samples from different studies and is not affected by genetic outliers, related samples, or the sample sizes of the admixed target populations. In addition, ancestral spectrum is useful for detecting genetic outliers or exploring co-ancestry between study samples and the reference populations. The methods are implemented in a program, Ancestral Spectrum Analyzer (ASA), and are applied in analyzing high-coverage sequencing data from the 1000 Genomes Project and the Human Genome Diversity Project (HGDP). In the analyses of American populations from the 1000 Genomes Project, we demonstrate that recent admixtures can be dissected from ancient admixtures by comparing ancestral spectra with and without indigenous Americans being included in the reference populations.

Controlling for human population stratification in rare variant association studies

Population stratification is a confounder of genetic association studies. In analyses of rare variants, corrections based on principal components (PCs) and linear mixed models (LMMs) yield conflicting conclusions. Studies evaluating these approaches generally focused on limited types of structure and large sample sizes. We investigated the properties of several correction methods through a large simulation study using real exome data, and several within- and between-continent stratification scenarios. We considered different sample sizes, with situations including as few as 50 cases, to account for the analysis of rare disorders. Large samples showed that accounting for stratification was more difficult with a continental than with a worldwide structure. When considering a sample of 50 cases, an inflation of type-I-errors was observed with PCs for small numbers of controls (≤ 100), and with LMMs for large numbers of controls (≥ 1000). We also tested a novel local permutation method (LocPerm), which maintained a correct type-I-error in all situations. Powers were equivalent for all approaches pointing out that the key issue is to properly control type-I-errors. Finally, we found that power of analyses including small numbers of cases can be increased, by adding a large panel of external controls, provided an appropriate stratification correction was used.

Trans-ethnic genome-wide association study of severe COVID-19

COVID-19 has caused numerous infections with diverse clinical symptoms. To identify human genetic variants contributing to the clinical development of COVID-19, we genotyped 1457 (598/859 with severe/mild symptoms) and sequenced 1141 (severe/mild: 474/667) patients of Chinese ancestry. We further incorporated 1401 genotyped and 948 sequenced ancestry-matched population controls, and tested genome-wide association on 1072 severe cases versus 3875 mild or population controls, followed by trans-ethnic meta-analysis with summary statistics of 3199 hospitalized cases and 897,488 population controls from the COVID-19 Host Genetics Initiative. We identified three significant signals outside the well-established 3p21.31 locus: an intronic variant in FOXP4-AS1 (rs1853837, odds ratio OR = 1.28, P = 2.51 × 10-10, allele frequencies in Chinese/European AF = 0.345/0.105), a frameshift insertion in ABO (rs8176719, OR = 1.19, P = 8.98 × 10-9, AF = 0.422/0.395) and a Chinese-specific intronic variant in MEF2B (rs74490654, OR = 8.73, P = 1.22 × 10-8, AF = 0.004/0). These findings highlight an important role of the adaptive immunity and the ABO blood-group system in protection from developing severe COVID-19.

RNA expression differences in prostate tumors and tumor-adjacent stroma between Black and White Americans

Prostate cancer (PCa) in Black Americans (BA) is diagnosed at an earlier median age and a more advanced stage than PCa in White Americans (WA). Tumor-adjacent stroma (TAS) plays a critical role in tumorigenesis of prostate cancer. We examined RNA expression in both tumor and TAS of BA compared to WA. After evaluating the geographical ancestry of each sample, preliminary analysis of our own RNA-seq data of 7 BA and 7 WA TAS revealed 1706 downregulated and 1844 upregulated genes in BA relative to WA PCa patients (p_adj < 0.05). An assessment of published RNA-seq data of clinically matched tumor-enriched tissues from 15 BA and 30 WA patients revealed 932 upregulated and 476 downregulated genes in BA relative to WA (p_adj < 0.05). When TAS and tumor epithelial cohorts were compared for the top 2500 statistically significant genes, immune responses were downregulated in BA vs WA TAS, while T cell-exhaustion pathways and the immune checkpoint gene CTLA4 were upregulated in BA vs WA tumors. We found fewer activated dendritic cells in tumor and more CD8 T-cells in TAS of BA versus WA PCa patients. Further characterization of these differences in the immune response of PCa patients of distinct geographical ancestry could help to improve diagnostics, prognostics, and therapy.

The Role of FER rs4957796 in the Risk of Developing and Dying from a Bloodstream Infection: A 23-Year Follow-up of the Population-based Nord-Trøndelag Health Study

BACKGROUND

Bloodstream infection and sepsis are major causes of health loss worldwide, and it is important to identify patients at risk of developing and dying from these conditions. The single-nucleotide polymorphism most strongly associated with sepsis mortality is FER rs4957796. However, it is not known how this variant is associated with bloodstream infection incidence and mortality.

METHODS

We used prospective data from 1995-2017 from the population-based HUNT Study. Genotypes were ascertained from blood samples, and additional genotypes were imputed. Information on bloodstream infection and diagnosis codes at hospitalization were collected through record linkage with all hospitals in the area.

RESULTS

A total of 69 294 patients were included. Patients with the rs4957796 CC genotype had an increased risk of developing a bloodstream infection compared with the TT genotype (hazard ratio [HR], 1.20; 95% confidence interval [CI], 1.00-1.43). However, there was a protective additive effect of the C allele in terms of mortality in the total study population (HR, 0.77; 95% CI, .64-.92 per copy of the C allele) and among bloodstream infection patients (odds ratio, 0.70; 95% CI, .58-.85 per copy of the C allele). The results did not appear to be affected by selection bias.

CONCLUSIONS

The rs4957796 CC genotype was associated with an increased risk of contracting a bloodstream infection but with a reduced risk of dying from one. The latter finding is in line with studies of sepsis case fatality, while the former expands our understanding of the immunoregulatory role of this polymorphism.

Genetic admixture in the culturally unique Peranakan Chinese population in Southeast Asia

The Peranakan Chinese are culturally unique descendants of immigrants from China who settled in the Malay Archipelago ∼300-500 years ago. Today, among large communities in Southeast Asia, the Peranakans have preserved Chinese traditions with strong influence from the local indigenous Malays. Yet, whether or to what extent genetic admixture co-occurred with the cultural mixture has been a topic of ongoing debate. We performed whole-genome sequencing (WGS) on 177 Singapore (SG) Peranakans and analyzed the data jointly with WGS data of Asian and European populations. We estimated that Peranakan Chinese inherited ∼5.62% (95% confidence interval [CI]: 4.75-6.46%) Malay ancestry, much higher than that in SG Chinese (1.08%, 0.69-1.53%), southern Chinese (0.86%, 0.57-1.31%), and northern Chinese (0.25%, 0.18-0.33%). A sex-biased admixture history, in which the Malay ancestry was contributed primarily by females, was supported by X chromosomal variants, and mitochondrial (MT) and Y haplogroups. Finally, we identified an ancient admixture event shared by Peranakan Chinese and SG Chinese ∼1,612 (95% CI: 1,345-1,923) years ago, coinciding with the settlement history of Han Chinese in southern China, apart from the recent admixture event with Malays unique to Peranakan Chinese ∼190 (159-213) years ago. These findings greatly advance our understanding of the dispersal history of Chinese and their interaction with indigenous populations in Southeast Asia.

Towards fine-scale population stratification modeling based on kernel principal component analysis and random forest

BACKGROUND

Population stratification modeling is essential in Genome-Wide Association Studies.

OBJECTIVE

In this paper, we aim to build a fine-scale population stratification model to efficiently infer individual genetic ancestry.

METHODS

Kernel Principal Component Analysis (PCA) and random forest are adopted to build the population stratification model, together with parameter optimization. We explore different PCA methods, including standard PCA and kernel PCA to extract relevant features from the genotype data that is transformed by vcf2geno, a pipeline from LASER software. These extracted features are fed into a random forest for ensemble learning. Parameter tuning is performed to jointly find the optimal number of principal components, kernel function for PCA and parameters of the random forest.

RESULTS

Experiments based on HGDP dataset show that kernel PCA with Sigmoid function and Gaussian function can achieve higher prediction accuracy than the standard PCA. Compared to standard PCA with the two principal components, the accuracy by using KPCA-Sigmoid with the optimal number of principal components can achieve around 100% and 200% improvement for East Asian and European populations, respectively.

CONCLUSION

With the optimal parameter configuration on both PCA and random forest, our proposed method can infer the individual genetic ancestry more accurately, given their variants.

Emerging roles of rare and low-frequency genetic variants in type 1 diabetes mellitus

Type 1 diabetes mellitus (T1DM) is defined as an autoimmune disorder and has enormous complexity and heterogeneity. Although its precise pathogenic mechanisms are obscure, this disease is widely acknowledged to be precipitated by environmental factors in individuals with genetic susceptibility. To date, the known susceptibility loci, which have mostly been identified by genome-wide association studies, can explain 80%–85% of the heritability of T1DM. Researchers believe that at least a part of its missing genetic component is caused by undetected rare and low-frequency variants. Most common variants have only small to modest effect sizes, which increases the difficulty of dissecting their functions and restricts their potential clinical application. Intriguingly, many studies have indicated that rare and low-frequency variants have larger effect sizes and play more significant roles in susceptibility to common diseases, including T1DM, than common variants do. Therefore, better recognition of rare and low-frequency variants is beneficial for revealing the genetic architecture of T1DM and for providing new and potent therapeutic targets for this disease. Here, we will discuss existing challenges as well as the great significance of this field and review current knowledge of the contributions of rare and low-frequency variants to T1DM.

Genetic Ancestry Contributes to Somatic Mutations in Lung Cancers from Admixed Latin American Populations

Inherited lung cancer risk, particularly in nonsmokers, is poorly understood. Genomic and ancestry analysis of 1,153 lung cancers from Latin America revealed striking associations between Native American ancestry and their somatic landscape, including tumor mutational burden, and specific driver mutations in EGFR, KRAS, and STK11. A local Native American ancestry risk score was more strongly correlated with EGFR mutation frequency compared with global ancestry correlation, suggesting that germline genetics (rather than environmental exposure) underlie these disparities. SIGNIFICANCE: The frequency of somatic EGFR and KRAS mutations in lung cancer varies by ethnicity, but we do not understand why. Our study suggests that the variation in EGFR and KRAS mutation frequency is associated with genetic ancestry and suggests further studies to identify germline alleles that underpin this association.See related commentary by Gomez et al., p. 534.This article is highlighted in the In This Issue feature, p. 521.