Recommended joint and meta-analysis strategies for case-control association testing of single low-count variants

Systematic identification of pathogenic variants of non-small cell lung cancer in the promoters of DNA-damage repair genes

BACKGROUND

Deficiency in DNA-damage repair (DDR) genes, often due to disruptive coding variants, is linked to higher cancer risk. Our previous study has revealed the association between rare loss-of-function variants in DDR genes and the risk of lung cancer. However, it is still challenging to study the predisposing role of rare regulatory variants of these genes.

METHODS

Based on whole-genome sequencing data from 2984 patients with non-small cell lung cancer (NSCLC) and 3020 controls, we performed massively parallel reporter assays on 1818 rare variants located in the promoters of DDR genes. Pathway- or gene-level burden analyses were performed using Firth's logistic regression or generalized linear model.

FINDINGS

We identified 750 rare functional regulatory variants (frVars) that showed allelic differences in transcriptional activity within the promoter regions of DDR genes. Interestingly, the burden of frVars was significantly elevated in cases (odds ratio [OR] = 1.17, p = 0.026), whereas the burden of variants prioritized solely based on bioinformatics annotation was comparable between cases and controls (OR = 1.04, p = 0.549). Among the frVars, 297 were down-regulated transcriptional activity (dr-frVars) and 453 were up-regulated transcriptional activity (ur-frVars); especially, dr-frVars (OR = 1.30, p = 0.008) rather than ur-frVars (OR = 1.06, p = 0.495) were significantly associated with risk of NSCLC. Individuals with NSCLC carried more dr-frVars from Fanconi anemia, homologous recombination, and nucleotide excision repair pathways. In addition, we identified seven genes (i.e., BRCA2, GTF2H1, DDB2, BLM, ALKBH2, APEX1, and RAD51B) with promoter dr-frVars that were associated with lung cancer susceptibility.

INTERPRETATION

Our findings indicate that functional promoter variants in DDR genes, in addition to protein-truncating variants, can be pathogenic and contribute to lung cancer susceptibility.

FUNDING

National Natural Science Foundation of China, Youth Foundation of Jiangsu Province, Research Unit of Prospective Cohort of Cardiovascular Diseases and Cancer of Chinese Academy of Medical Sciences, and Natural Science Foundation of Jiangsu Province.

Bidirectional Risk Modulator and Modifier Variant of Dilated and Hypertrophic Cardiomyopathy in BAG3

Importance

The genetic factors that modulate the reduced penetrance and variable expressivity of heritable dilated cardiomyopathy (DCM) are largely unknown. BAG3 genetic variants have been implicated in both DCM and hypertrophic cardiomyopathy (HCM), nominating BAG3 as a gene that harbors potential modifier variants in DCM.

Objective

To interrogate the clinical traits and diseases associated with BAG3 coding variation.

Design, Setting, and Participants

This was a cross-sectional study in the Penn Medicine BioBank (PMBB) enrolling patients of the University of Pennsylvania Health System's clinical practice sites from 2014 to 2023. Whole-exome sequencing (WES) was linked to electronic health record (EHR) data to associate BAG3 coding variants with EHR phenotypes. This was a health care population-based study including individuals of European and African genetic ancestry in the PMBB with WES linked to EHR phenotypes, with replication studies in BioVU, UK Biobank, MyCode, and DCM Precision Medicine Study.

Exposures

Carrier status for BAG3 coding variants.

Main Outcomes and Measures

Association of BAG3 coding variation with clinical diagnoses, echocardiographic traits, and longitudinal outcomes.

Results

In PMBB (n = 43 731; median [IQR] age, 65 [50-76] years; 21 907 female [50.1%]), among 30 324 European and 11 198 African individuals, the common C151R variant was associated with decreased risk for DCM (odds ratio [OR], 0.85; 95% CI, 0.78-0.92) and simultaneous increased risk for HCM (OR, 1.59; 95% CI, 1.25-2.02), which was confirmed in the replication cohorts. C151R carriers exhibited improved longitudinal outcomes compared with noncarriers as assessed by age at death (hazard ratio [HR], 0.85; 95% CI, 0.74-0.96; median [IQR] age, 71.8 [63.1-80.7] in carriers and 70.3 [61.6-79.2] in noncarriers) and heart transplant (HR, 0.81; 95% CI, 0.66-0.99; median [IQR] age, 56.7 [46.1-63.1] in carriers and 55.6 [45.2-62.9] in noncarriers). C151R was associated with reduced risk of DCM (OR, 0.42; 95% CI, 0.24-0.74) and heart failure (OR, 0.27; 95% CI, 0.14-0.50) among individuals harboring truncating TTN variants in exons with high cardiac expression (n = 358).

Conclusions and Relevance

BAG3 C151R was identified as a bidirectional modulator of risk along the DCM-HCM spectrum, as well as an important genetic modifier variant in TTN-mediated DCM. This work expands on the understanding of the etiology and penetrance of DCM, suggesting that BAG3 C151R is an important genetic modifier variant contributing to the variable expressivity of DCM, warranting further exploration of its mechanisms and of genetic modifiers in DCM more broadly.

Diverse ancestry GWAS for advanced age-related macular degeneration in TOPMed-imputed and Ophthalmologically-confirmed 16,108 cases and 18,038 controls

Age-related macular degeneration (AMD) is a leading cause of blindness with $344 billion dollars global costs. In 2016, the International Age-related Macular Degeneration Genomics Consortium devised genomic data on ∼50,000 individuals (IAMDGC 1.0) and identified 52 variants across 34 loci associated with advanced AMD in European ancestry. We have now analyzed a more densely imputed version (IAMDGC 2.0) and performed cross-ancestry GWAS in 16,108 advanced AMD cases and 18,038 AMD-free controls. This identified 28 loci at P<5×10 -8 , including two additional AMD loci compared to IAMDGC 1.0 ( SERPINA1 and CPN1 ). Fine-mapping supported one ancestry-shared signal around HTRA1/ARMS2 and nine signals around CFH without African ancestry contribution. The 52-variant genetic risk score with and the 44-variant score without CFH -variants predicted advanced AMD not only in EUR, but also in AFR and ASN (AUC=0.80/0.75, 0.65/0.64, 0.80/0.79, respectively). Our results indicate that the genetic underpinning of advanced AMD is mostly shared between ancestries.

Genetic links between ovarian ageing, cancer risk and de novo mutation rates

Human genetic studies of common variants have provided substantial insight into the biological mechanisms that govern ovarian ageing1. Here we report analyses of rare protein-coding variants in 106,973 women from the UK Biobank study, implicating genes with effects around five times larger than previously found for common variants (ETAA1, ZNF518A, PNPLA8, PALB2 and SAMHD1). The SAMHD1 association reinforces the link between ovarian ageing and cancer susceptibility1, with damaging germline variants being associated with extended reproductive lifespan and increased all-cause cancer risk in both men and women. Protein-truncating variants in ZNF518A are associated with shorter reproductive lifespan-that is, earlier age at menopause (by 5.61 years) and later age at menarche (by 0.56 years). Finally, using 8,089 sequenced trios from the 100,000 Genomes Project (100kGP), we observe that common genetic variants associated with earlier ovarian ageing associate with an increased rate of maternally derived de novo mutations. Although we were unable to replicate the finding in independent samples from the deCODE study, it is consistent with the expected role of DNA damage response genes in maintaining the genetic integrity of germ cells. This study provides evidence of genetic links between age of menopause and cancer risk.

Whole-genome sequencing identifies variants in ANK1, LRRN1, HAS1, and other genes and regulatory regions for stroke in type 1 diabetes

Individuals with type 1 diabetes (T1D) carry a markedly increased risk of stroke, with distinct clinical and neuroimaging characteristics as compared to those without diabetes. Using whole-exome or whole-genome sequencing of 1,051 individuals with T1D, we aimed to find rare and low-frequency genomic variants associated with stroke in T1D. We analysed the genome comprehensively with single-variant analyses, gene aggregate analyses, and aggregate analyses on genomic windows, enhancers and promoters. In addition, we attempted replication in T1D using a genome-wide association study (N = 3,945) and direct genotyping (N = 3,263), and in the general population from the large-scale population-wide FinnGen project and UK Biobank summary statistics. We identified a rare missense variant on SREBF1 exome-wide significantly associated with stroke (rs114001633, p.Pro227Leu, p-value = 7.30 × 10-8), which replicated for hemorrhagic stroke in T1D. Using gene aggregate analysis, we identified exome-wide significant genes: ANK1 and LRRN1 displayed replication evidence in T1D, and LRRN1, HAS1 and UACA in the general population (UK Biobank). Furthermore, we performed sliding-window analyses and identified 14 genome-wide significant windows for stroke on 4q33-34.1, of which two replicated in T1D, and a suggestive genomic window on LINC01500, which replicated in T1D. Finally, we identified a suggestively stroke-associated TRPM2-AS promoter (p-value = 5.78 × 10-6) with borderline significant replication in T1D, which we validated with an in vitro cell-based assay. Due to the rarity of the identified genetic variants, future replication of the genomic regions represented here is required with sequencing of individuals with T1D. Nevertheless, we here report the first genome-wide analysis on stroke in individuals with diabetes.

The landscape of rare genetic variation associated with inflammatory bowel disease and Parkinson's disease comorbidity

BACKGROUND

Inflammatory bowel disease (IBD) and Parkinson's disease (PD) are chronic disorders that have been suggested to share common pathophysiological processes. LRRK2 has been implicated as playing a role in both diseases. Exploring the genetic basis of the IBD-PD comorbidity through studying high-impact rare genetic variants can facilitate the identification of the novel shared genetic factors underlying this comorbidity.

METHODS

We analyzed whole exomes from the BioMe BioBank and UK Biobank, and whole genomes from a cohort of 67 European patients diagnosed with both IBD and PD to examine the effects of LRRK2 missense variants on IBD, PD and their co-occurrence (IBD-PD). We performed optimized sequence kernel association test (SKAT-O) and network-based heterogeneity clustering (NHC) analyses using high-impact rare variants in the IBD-PD cohort to identify novel candidate genes, which we further prioritized by biological relatedness approaches. We conducted phenome-wide association studies (PheWAS) employing BioMe BioBank and UK Biobank whole exomes to estimate the genetic relevance of the 14 prioritized genes to IBD-PD.

RESULTS

The analysis of LRRK2 missense variants revealed significant associations of the G2019S and N2081D variants with IBD-PD in addition to several other variants as potential contributors to increased or decreased IBD-PD risk. SKAT-O identified two significant genes, LRRK2 and IL10RA, and NHC identified 6 significant gene clusters that are biologically relevant to IBD-PD. We observed prominent overlaps between the enriched pathways in the known IBD, PD, and candidate IBD-PD gene sets. Additionally, we detected significantly enriched pathways unique to the IBD-PD, including MAPK signaling, LPS/IL-1 mediated inhibition of RXR function, and NAD signaling. Fourteen final candidate IBD-PD genes were prioritized by biological relatedness methods. The biological importance scores estimated by protein-protein interaction networks and pathway and ontology enrichment analyses indicated the involvement of genes related to immunity, inflammation, and autophagy in IBD-PD. Additionally, PheWAS provided support for the associations of candidate genes with IBD and PD.

CONCLUSIONS

Our study confirms and uncovers new LRRK2 associations in IBD-PD. The identification of novel inflammation and autophagy-related genes supports and expands previous findings related to IBD-PD pathogenesis, and underscores the significance of therapeutic interventions for reducing systemic inflammation.

Multi-ancestry Whole-exome Sequencing Study of Alcohol Use Disorder in Two Cohorts

Alcohol use disorder (AUD) is a leading cause of death and disability worldwide. There has been substantial progress in identifying genetic variants underlying AUD. However, there are few whole-exome sequencing (WES) studies of AUD. We analyzed WES of 4,530 samples from the Yale-Penn cohort and 469,835 samples from the UK Biobank (UKB). After quality control, 1,420 AUD cases and 619 controls of European ancestry (EUR) and 1,142 cases and 608 controls of African ancestry (AFR) from Yale-Penn were retained for subsequent analyses. WES data from 415,617 EUR samples (12,861 cases), 6,142 AFR samples (130 cases) and 4,607 South Asian (SAS) samples (130 cases) from UKB were also analyzed. Single-variant association analysis identified the well-known functional variant rs1229984 in ADH1B ( P =4.88×10 -31 ) and several other common variants in ADH1C . Gene-based tests identified ADH1B ( P =1.00×10 -31 ), ADH1C ( P =5.23×10 -7 ), CNST ( P =1.19×10 -6 ), and IFIT5 (3.74×10 -6 ). This study extends our understanding of the genetic basis of AUD.

Decoding and reconstructing disease relations between dry eye and depression: a multimodal investigation comprising meta-analysis, genetic pathways and Mendelian randomization

INTRODUCTION

The clinical presentations of dry eye disease (DED) and depression (DEP) often comanifest. However, the robustness and the mechanisms underlying this association were undetermined.

OBJECTIVES

To this end, we set up a three-segment study that employed multimodality results (meta-analysis, genome-wide association study [GWAS] and Mendelian randomization [MR]) to elucidate the association, common pathways and causality between DED and DEP.

METHODS

A meta-analysis comprising 26 case-control studies was first conducted to confirm the DED-DEP association. Next, we performed a linkage disequilibrium (LD)-adjusted GWAS and targeted phenotype association study (PheWAS) in East Asian TW Biobank (TWB) and European UK Biobank (UKB) populations. Single-nucleotide polymorphisms (SNPs) were further screened for molecular interactions and common pathways at the functional gene level. To further elucidate the activated pathways in DED and DEP, a systemic transcriptome review was conducted on RNA sequencing samples from the Gene Expression Omnibus. Finally, 48 MR experiments were implemented to examine the bidirectional causation between DED and DEP.

RESULTS

Our meta-analysis showed that DED patients are associated with an increased DEP prevalence (OR = 1.83), while DEP patients have a concurrent higher risk of DED (OR = 2.34). Notably, cross-disease GWAS analysis revealed that similar genetic architecture (rG = 0.19) and pleiotropic functional genes contributed to phenotypes in both diseases. Through protein-protein interaction and ontology convergence, we summarized the pleiotropic functional genes under the ontology of immune activation, which was further validated by a transcriptome systemic review. Importantly, the inverse variance-weighted (IVW)-MR experiments in both TWB and UKB populations (p value <0.001) supported the bidirectional exposure-outcome causation for DED-to-DEP and DEP-to-DED. Despite stringent LD-corrected instrumental variable re-selection, the bidirectional causation between DED and DEP remained.

CONCLUSION

With the multi-modal evidence combined, we consolidated the association and causation between DED and DEP.

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.

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

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

Strong protective effect of the APOL1 p.N264K variant against G2-associated focal segmental glomerulosclerosis and kidney disease

African Americans have a significantly higher risk of developing chronic kidney disease, especially focal segmental glomerulosclerosis -, than European Americans. Two coding variants (G1 and G2) in the APOL1 gene play a major role in this disparity. While 13% of African Americans carry the high-risk recessive genotypes, only a fraction of these individuals develops FSGS or kidney failure, indicating the involvement of additional disease modifiers. Here, we show that the presence of the APOL1 p.N264K missense variant, when co-inherited with the G2 APOL1 risk allele, substantially reduces the penetrance of the G1G2 and G2G2 high-risk genotypes by rendering these genotypes low-risk. These results align with prior functional evidence showing that the p.N264K variant reduces the toxicity of the APOL1 high-risk alleles. These findings have important implications for our understanding of the mechanisms of APOL1-associated nephropathy, as well as for the clinical management of individuals with high-risk genotypes that include the G2 allele.

Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of a diverse feature set

Gain-of-function (GOF) variants give rise to increased/novel protein functions whereas loss-of-function (LOF) variants lead to diminished protein function. Experimental approaches for identifying GOF and LOF are generally slow and costly, whilst available computational methods have not been optimized to discriminate between GOF and LOF variants. We have developed LoGoFunc, a machine learning method for predicting pathogenic GOF, pathogenic LOF, and neutral genetic variants, trained on a broad range of gene-, protein-, and variant-level features describing diverse biological characteristics. LoGoFunc outperforms other tools trained solely to predict pathogenicity for identifying pathogenic GOF and LOF variants and is available at https://itanlab.shinyapps.io/goflof/ .

A benchmark study on current GWAS models in admixed populations

OBJECTIVE

The performances of popular genome-wide association study (GWAS) models have not been examined yet in a consistent manner under the scenario of genetic admixture, which introduces several challenging aspects: heterogeneity of minor allele frequency (MAF), wide spectrum of case-control ratio, varying effect sizes, etc.

METHODS

We generated a cohort of synthetic individuals (N = 19 234) that simulates (i) a large sample size; (ii) two-way admixture (Native American and European ancestry) and (iii) a binary phenotype. We then benchmarked three popular GWAS tools [generalized linear mixed model associated test (GMMAT), scalable and accurate implementation of generalized mixed model (SAIGE) and Tractor] by computing inflation factors and power calculations under different MAFs, case-control ratios, sample sizes and varying ancestry proportions. We also employed a cohort of Peruvians (N = 249) to further examine the performances of the testing models on (i) real genetic and phenotype data and (ii) small sample sizes.

RESULTS

In the synthetic cohort, SAIGE performed better than GMMAT and Tractor in terms of type-I error rate, especially under severe unbalanced case-control ratio. On the contrary, power analysis identified Tractor as the best method to pinpoint ancestry-specific causal variants but showed decreased power when the effect size displayed limited heterogeneity between ancestries. In the Peruvian cohort, only Tractor identified two suggestive loci (P-value $\le 1\ast{10}^{-5}$) associated with Native American ancestry.

DISCUSSION

The current study illustrates best practice and limitations for available GWAS tools under the scenario of genetic admixture. Incorporating local ancestry in GWAS analyses boosts power, although careful consideration of complex scenarios (small sample sizes, imbalance case-control ratio, MAF heterogeneity) is needed.

Increase in power by obtaining 10 or more controls per case when type-1 error is small in large-scale association studies

BACKGROUND

The rule of thumb that there is little gain in statistical power by obtaining more than 4 controls per case, is based on type-1 error α = 0.05. However, association studies that evaluate thousands or millions of associations use smaller α and may have access to plentiful controls. We investigate power gains, and reductions in p-values, when increasing well beyond 4 controls per case, for small α.

METHODS

We calculate the power, the median expected p-value, and the minimum detectable odds-ratio (OR), as a function of the number of controls/case, as α decreases.

RESULTS

As α decreases, at each ratio of controls per case, the increase in power is larger than for α = 0.05. For α between 10-6 and 10-9 (typical for thousands or millions of associations), increasing from 4 controls per case to 10-50 controls per case increases power. For example, a study with power = 0.2 (α = 5 × 10-8) with 1 control/case has power = 0.65 with 4 controls/case, but with 10 controls/case has power = 0.78, and with 50 controls/case has power = 0.84. For situations where obtaining more than 4 controls per case provides small increases in power beyond 0.9 (at small α), the expected p-value can decrease by orders-of-magnitude below α. Increasing from 1 to 4 controls/case reduces the minimum detectable OR toward the null by 20.9%, and from 4 to 50 controls/case reduces by an additional 9.7%, a result which applies regardless of α and hence also applies to "regular" α = 0.05 epidemiology.

CONCLUSIONS

At small α, versus 4 controls/case, recruiting 10 or more controls/cases can increase power, reduce the expected p-value by 1-2 orders of magnitude, and meaningfully reduce the minimum detectable OR. These benefits of increasing the controls/case ratio increase as the number of cases increases, although the amount of benefit depends on exposure frequencies and true OR. Provided that controls are comparable to cases, our findings suggest greater sharing of comparable controls in large-scale association studies.

HostSeq: a Canadian whole genome sequencing and clinical data resource

HostSeq was launched in April 2020 as a national initiative to integrate whole genome sequencing data from 10,000 Canadians infected with SARS-CoV-2 with clinical information related to their disease experience. The mandate of HostSeq is to support the Canadian and international research communities in their efforts to understand the risk factors for disease and associated health outcomes and support the development of interventions such as vaccines and therapeutics. HostSeq is a collaboration among 13 independent epidemiological studies of SARS-CoV-2 across five provinces in Canada. Aggregated data collected by HostSeq are made available to the public through two data portals: a phenotype portal showing summaries of major variables and their distributions, and a variant search portal enabling queries in a genomic region. Individual-level data is available to the global research community for health research through a Data Access Agreement and Data Access Compliance Office approval. Here we provide an overview of the collective project design along with summary level information for HostSeq. We highlight several statistical considerations for researchers using the HostSeq platform regarding data aggregation, sampling mechanism, covariate adjustment, and X chromosome analysis. In addition to serving as a rich data source, the diversity of study designs, sample sizes, and research objectives among the participating studies provides unique opportunities for the research community.

Large multi-ethnic genetic analyses of amyloid imaging identify new genes for Alzheimer disease

Amyloid PET imaging has been crucial for detecting the accumulation of amyloid beta (Aβ) deposits in the brain and to study Alzheimer's disease (AD). We performed a genome-wide association study on the largest collection of amyloid imaging data (N = 13,409) to date, across multiple ethnicities from multicenter cohorts to identify variants associated with brain amyloidosis and AD risk. We found a strong APOE signal on chr19q.13.32 (top SNP: APOE ɛ4; rs429358; β = 0.35, SE = 0.01, P = 6.2 × 10-311, MAF = 0.19), driven by APOE ɛ4, and five additional novel associations (APOE ε2/rs7412; rs73052335/rs5117, rs1081105, rs438811, and rs4420638) independent of APOE ɛ4. APOE ɛ4 and ε2 showed race specific effect with stronger association in Non-Hispanic Whites, with the lowest association in Asians. Besides the APOE, we also identified three other genome-wide loci: ABCA7 (rs12151021/chr19p.13.3; β = 0.07, SE = 0.01, P = 9.2 × 10-09, MAF = 0.32), CR1 (rs6656401/chr1q.32.2; β = 0.1, SE = 0.02, P = 2.4 × 10-10, MAF = 0.18) and FERMT2 locus (rs117834516/chr14q.22.1; β = 0.16, SE = 0.03, P = 1.1 × 10-09, MAF = 0.06) that all colocalized with AD risk. Sex-stratified analyses identified two novel female-specific signals on chr5p.14.1 (rs529007143, β = 0.79, SE = 0.14, P = 1.4 × 10-08, MAF = 0.006, sex-interaction P = 9.8 × 10-07) and chr11p.15.2 (rs192346166, β = 0.94, SE = 0.17, P = 3.7 × 10-08, MAF = 0.004, sex-interaction P = 1.3 × 10-03). We also demonstrated that the overall genetic architecture of brain amyloidosis overlaps with that of AD, Frontotemporal Dementia, stroke, and brain structure-related complex human traits. Overall, our results have important implications when estimating the individual risk to a population level, as race and sex will needed to be taken into account. This may affect participant selection for future clinical trials and therapies.

Saddlepoint approximations to score test statistics in logistic regression for analyzing genome-wide association studies

We investigate saddlepoint approximations of tail probabilities of the score test statistic in logistic regression for genome-wide association studies. The inaccuracy in the normal approximation of the score test statistic increases with increasing imbalance in the response and with decreasing minor allele counts. Applying saddlepoint approximation methods greatly improve the accuracy, even far out in the tails of the distribution. By using exact results for a simple logistic regression model, as well as simulations for models with nuisance parameters, we compare double saddlepoint methods for computing two-sided P $$ P $$ -values and mid- P $$ P $$ -values. These methods are also compared to a recent single saddlepoint procedure. We investigate the methods further on data from UK Biobank with skin and soft tissue infections as phenotype, using both common and rare variants.

Aberrant activation of TCL1A promotes stem cell expansion in clonal haematopoiesis

Mutations in a diverse set of driver genes increase the fitness of haematopoietic stem cells (HSCs), leading to clonal haematopoiesis1. These lesions are precursors for blood cancers2-6, but the basis of their fitness advantage remains largely unknown, partly owing to a paucity of large cohorts in which the clonal expansion rate has been assessed by longitudinal sampling. Here, to circumvent this limitation, we developed a method to infer the expansion rate from data from a single time point. We applied this method to 5,071 people with clonal haematopoiesis. A genome-wide association study revealed that a common inherited polymorphism in the TCL1A promoter was associated with a slower expansion rate in clonal haematopoiesis overall, but the effect varied by driver gene. Those carrying this protective allele exhibited markedly reduced growth rates or prevalence of clones with driver mutations in TET2, ASXL1, SF3B1 and SRSF2, but this effect was not seen in clones with driver mutations in DNMT3A. TCL1A was not expressed in normal or DNMT3A-mutated HSCs, but the introduction of mutations in TET2 or ASXL1 led to the expression of TCL1A protein and the expansion of HSCs in vitro. The protective allele restricted TCL1A expression and expansion of mutant HSCs, as did experimental knockdown of TCL1A expression. Forced expression of TCL1A promoted the expansion of human HSCs in vitro and mouse HSCs in vivo. Our results indicate that the fitness advantage of several commonly mutated driver genes in clonal haematopoiesis may be mediated by TCL1A activation.

Schizophrenia risk conferred by rare protein-truncating variants is conserved across diverse human populations

Schizophrenia (SCZ) is a chronic mental illness and among the most debilitating conditions encountered in medical practice. A recent landmark SCZ study of the protein-coding regions of the genome identified a causal role for ten genes and a concentration of rare variant signals in evolutionarily constrained genes1. This recent study-and most other large-scale human genetics studies-was mainly composed of individuals of European (EUR) ancestry, and the generalizability of the findings in non-EUR populations remains unclear. To address this gap, we designed a custom sequencing panel of 161 genes selected based on the current knowledge of SCZ genetics and sequenced a new cohort of 11,580 SCZ cases and 10,555 controls of diverse ancestries. Replicating earlier work, we found that cases carried a significantly higher burden of rare protein-truncating variants (PTVs) among evolutionarily constrained genes (odds ratio = 1.48; P = 5.4 × 10-6). In meta-analyses with existing datasets totaling up to 35,828 cases and 107,877 controls, this excess burden was largely consistent across five ancestral populations. Two genes (SRRM2 and AKAP11) were newly implicated as SCZ risk genes, and one gene (PCLO) was identified as shared by individuals with SCZ and those with autism. Overall, our results lend robust support to the rare allelic spectrum of the genetic architecture of SCZ being conserved across diverse human populations.

AWOT and CWOT for genotype and genotype-by-treatment interaction joint analysis in pharmacogenetics GWAS

MOTIVATION

Pharmacogenomics (PGx) research holds the promise for detecting association between genetic variants and drug responses in randomized clinical trials, but it is limited by small populations and thus has low power to detect signals. It is critical to increase the power of PGx genome-wide association studies (GWAS) with small sample sizes so that variant-drug-response association discoveries are not limited to common variants with extremely large effect.

RESULTS

In this article, we first discuss the challenges of PGx GWAS studies and then propose the adaptively weighted joint test (AWOT) and Cauchy Weighted jOint Test (CWOT), which are two flexible and robust joint tests of the single nucleotide polymorphism main effect and genotype-by-treatment interaction effect for continuous and binary endpoints. Two analytic procedures are proposed to accurately calculate the joint test P-value. We evaluate AWOT and CWOT through extensive simulations under various scenarios. The results show that the proposed AWOT and CWOT control type I error well and outperform existing methods in detecting the most interesting signal patterns in PGx settings (i.e. with strong genotype-by-treatment interaction effects, but weak genotype main effects). We demonstrate the value of AWOT and CWOT by applying them to the PGx GWAS from the Bezlotoxumab Clostridium difficile MODIFY I/II Phase 3 trials.

AVAILABILITY AND IMPLEMENTATION

The R package COWT is publicly available on CRAN https://cran.r-project.org/web/packages/cwot/index.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Analyses of rare predisposing variants of lung cancer in 6,004 whole genomes in Chinese

We present the largest whole-genome sequencing (WGS) study of non-small cell lung cancer (NSCLC) to date among 6,004 individuals of Chinese ancestry, coupled with 23,049 individuals genotyped by SNP array. We construct a high-quality haplotype reference panel for imputation and identify 20 common and low-frequency loci (minor allele frequency [MAF] ≥ 0.5%), including five loci that have never been reported before. For rare loss-of-function (LoF) variants (MAF < 0.5%), we identify BRCA2 and 18 other cancer predisposition genes that affect 5.29% of individuals with NSCLC, and 98.91% (181 of 183) of LoF variants have not been linked previously to NSCLC risk. Promoter variants of BRCA2 also have a substantial effect on NSCLC risk, and their prevalence is comparable with BRCA2 LoF variants. The associations are validated in an independent case-control study including 4,410 individuals and a prospective cohort study including 23,826 individuals. Our findings not only provide a high-quality reference panel for future array-based association studies but depict the whole picture of rare pathogenic variants for NSCLC.

Efficient and accurate frailty model approach for genome-wide survival association analysis in large-scale biobanks

With decades of electronic health records linked to genetic data, large biobanks provide unprecedented opportunities for systematically understanding the genetics of the natural history of complex diseases. Genome-wide survival association analysis can identify genetic variants associated with ages of onset, disease progression and lifespan. We propose an efficient and accurate frailty model approach for genome-wide survival association analysis of censored time-to-event (TTE) phenotypes by accounting for both population structure and relatedness. Our method utilizes state-of-the-art optimization strategies to reduce the computational cost. The saddlepoint approximation is used to allow for analysis of heavily censored phenotypes (>90%) and low frequency variants (down to minor allele count 20). We demonstrate the performance of our method through extensive simulation studies and analysis of five TTE phenotypes, including lifespan, with heavy censoring rates (90.9% to 99.8%) on ~400,000 UK Biobank participants with white British ancestry and ~180,000 individuals in FinnGen. We further analyzed 871 TTE phenotypes in the UK Biobank and presented the genome-wide scale phenome-wide association results with the PheWeb browser.

Identifying genetic variants associated with the ICD10 (International Classification of Diseases10)-based diagnosis of cerebrovascular disease using a large-scale biomedical database

OBJECTIVES

To utilize the UK Biobank to identify genetic variants associated with the ICD10 (International Classification of Diseases10)-based diagnosis of cerebrovascular disease (CeVD).

BACKGROUND

Cerebrovascular disease occurs because of a complex interplay between vascular, environmental, and genetic factors. It is the second leading cause of disability worldwide. Understanding who may be genetically predisposed to cerebrovascular disease can help guide preventative efforts. Moreover, there is considerable interest in the use of real-world data, such as EHR (electronic health records) to better understand disease mechanisms and to discover new treatment strategies, but whether ICD10-based diagnosis can be used to study CeVD genetics is unknown.

METHODS

Using the UK Biobank, we conducted a genome-wide association study (GWAS) where we analyzed the genomes of 11,155 cases and 122,705 controls who were sex, age and ancestry-matched in a 1:11 case: control design. Genetic variants were identified by Plink's firth logistic regression and assessed for association with the ICD10 codes corresponding to CeVD.

RESULTS

We identified two groups of SNPs closely linked to PITX2 and LRRTM4 that were significantly associated with CeVD in this study (p < 5 x 10-8) and had a minor allele frequency of > 0.5%.

DISCUSSION

Disease assignment based on ICD10 codes may underestimate prevalence; however, for CeVD, this does not appear to be the case. Compared to the age- and sex-matched control population, individuals with CeVD were more frequently diagnosed with comorbid conditions, such as hypertension, hyperlipidemia & atrial fibrillation or flutter, confirming their contribution to CeVD. The UK Biobank based ICD10 study identified 2 groups of variants that were associated with CeVD. The association between PITX2 and CeVD is likely explained by the increased rates of atrial fibrillation and flutter. While the mechanism explaining the relationship between LRRTM4 and CeVD is unclear, this has been documented in previous studies.

Cross-ancestry genome-wide meta-analysis of 61,047 cases and 947,237 controls identifies new susceptibility loci contributing to lung cancer

To identify new susceptibility loci to lung cancer among diverse populations, we performed cross-ancestry genome-wide association studies in European, East Asian and African populations and discovered five loci that have not been previously reported. We replicated 26 signals and identified 10 new lead associations from previously reported loci. Rare-variant associations tended to be specific to populations, but even common-variant associations influencing smoking behavior, such as those with CHRNA5 and CYP2A6, showed population specificity. Fine-mapping and expression quantitative trait locus colocalization nominated several candidate variants and susceptibility genes such as IRF4 and FUBP1. DNA damage assays of prioritized genes in lung fibroblasts indicated that a subset of these genes, including the pleiotropic gene IRF4, potentially exert effects by promoting endogenous DNA damage.

Revisiting the Wald Test in Small Case-Control Studies With a Skewed Covariate

The Wald test is routinely used in case-control studies to test for association between a covariate and disease. However, when the evidence for association is high, the Wald test tends to inflate small P values as a result of the Hauck-Donner effect (HDE). Here, we investigate the HDE in the context of genetic burden, both with and without additional covariates. First, we examine the burden-based P values in the absence of association using whole-exome sequence data from 1000 Genomes Project reference samples (n = 54) and selected preterm infants with neonatal complications (n = 74). Our careful analysis of the burden-based P values shows that the HDE is present and that the cause of the HDE in this setting is likely a natural extension of the well-known cause of the HDE in 2 × 2 contingency tables. Second, in a reanalysis of real data, we find that the permutation test provides increased power over the Wald, Firth, and likelihood ratio tests, which agrees with our intuition since the permutation test is valid for any sample size and since it does not suffer from the HDE. Therefore, we propose a powerful and computationally efficient permutation-based approach for the analysis and reanalysis of small case-control association studies.

Identification of Novel Genetic Variants and Comorbidities Associated With ICD-10-Based Diagnosis of Hypertrophic Cardiomyopathy Using the UK Biobank Cohort

Objectives: To identify previously unrecognized genetic variants and clinical variables associated with the ICD-10 (International Classification of Diseases 10)-based diagnosis of hypertrophic cardiomyopathy in the UK Biobank cohort. Background: Hypertrophic cardiomyopathy (HCM) is the most common genetic cardiovascular disorder with more than 2000 known mutations in one of eight genes encoding sarcomeric proteins. However, there is considerable variation in disease manifestation, suggesting the role of additional unrecognized contributors, genetic and otherwise. There is substantial interest in the use of real-world data, such as electronic health records to better understand disease mechanisms and discover new treatment strategies, but whether ICD-10-based diagnosis can be used to study HCM genetics is unknown. Methods: In a genome-wide association study (GWAS) using the UK Biobank, we analyzed the genomes of 363 individuals diagnosed with HCM based on ICD-10 coding compared to 7,260 age, ancestry, and sex-matched controls in a 1:20 case:control design. Genetic variants were analyzed by Plink's firth logistic regression and assessed for association with HCM. We also examined 61 biomarkers and other diagnoses in the 363 HCM cases and matched controls. Results: The prevalence of ICD-10-based diagnosis of HCM in the UK Biobank cohort was 1 in 1,342, suggesting disease assignment based on the two ICD-10 codes underestimates HCM prevalence. In addition, common cardiovascular comorbidities were more prevalent in ICD-10-based HCM cases in comparison to controls. We identified two novel, non-sarcomeric genetic variants in KMT2C rs78630626, and PARD3B rs188937806 that were associated with ICD-10 codes for HCM with genome-wide significance (p < 5 x 10-8). These are associated with an increased odds ratio (OR) of ∼3.8 for being diagnosed with HCM. Minor allele frequency (MAF) of each variant was >1%. Discussion: Disease assignment based strictly on ICD-10 codes may underestimate HCM prevalence. Individuals with HCM were more frequently diagnosed with several comorbid conditions, such as hypertension, atherosclerotic heart disease, diabetes, and kidney failure, suggesting they may contribute to disease manifestation. This UK Biobank database-based GWAS identified common variants in KMT2C and PARD3B that are associated with HCM diagnosis, which may represent novel modifier genes. Our study demonstrates the feasibility and limitations of conducting phenotypic and genotypic characterization of HCM based on ICD-10 diagnosis in a large population-based cohort.

Reconstructing SNP allele and genotype frequencies from GWAS summary statistics

The emergence of genome-wide association studies (GWAS) has led to the creation of large repositories of human genetic variation, creating enormous opportunities for genetic research and worldwide collaboration. Methods that are based on GWAS summary statistics seek to leverage such records, overcoming barriers that often exist in individual-level data access while also offering significant computational savings. Such summary-statistics-based applications include GWAS meta-analysis, with and without sample overlap, and case-case GWAS. We compare performance of leading methods for summary-statistics-based genomic analysis and also introduce a novel framework that can unify usual summary-statistics-based implementations via the reconstruction of allelic and genotypic frequencies and counts (ReACt). First, we evaluate ASSET, METAL, and ReACt using both synthetic and real data for GWAS meta-analysis (with and without sample overlap) and find that, while all three methods are comparable in terms of power and error control, ReACt and METAL are faster than ASSET by a factor of at least hundred. We then proceed to evaluate performance of ReACt vs an existing method for case-case GWAS and show comparable performance, with ReACt requiring minimal underlying assumptions and being more user-friendly. Finally, ReACt allows us to evaluate, for the first time, an implementation for calculating polygenic risk score (PRS) for groups of cases and controls based on summary statistics. Our work demonstrates the power of GWAS summary-statistics-based methodologies and the proposed novel method provides a unifying framework and allows further extension of possibilities for researchers seeking to understand the genetics of complex disease.

Maturation and application of phenome-wide association studies

In the past 10 years since its introduction, phenome-wide association studies (PheWAS) have uncovered novel genotype-phenotype relationships. Along the way, PheWAS have evolved in many aspects as a study design with the expanded availability of large data repositories with genome-wide data linked to detailed phenotypic data. Advancement in methods, including algorithms, software, and publicly available integrated resources, makes it feasible to more fully realize the potential of PheWAS, overcoming the previous computational and analytical limitations. We review here the most recent improvements and notable applications of PheWAS since the second half of the decade from its inception. We also note the challenges that remain embedded along the entire PheWAS analytical pipeline that necessitate further development of tools and resources to further advance the understanding of the complex genetic architecture underlying human diseases and traits.

Genetic Contributors of Incident Stroke in 10,700 African Americans With Hypertension: A Meta-Analysis From the Genetics of Hypertension Associated Treatments and Reasons for Geographic and Racial Differences in Stroke Studies

Background: African Americans (AAs) suffer a higher stroke burden due to hypertension. Identifying genetic contributors to stroke among AAs with hypertension is critical to understanding the genetic basis of the disease, as well as detecting at-risk individuals. Methods: In a population comprising over 10,700 AAs treated for hypertension from the Genetics of Hypertension Associated Treatments (GenHAT) and Reasons for Geographic and Racial Differences in Stroke (REGARDS) studies, we performed an inverse variance-weighted meta-analysis of incident stroke. Additionally, we tested the predictive accuracy of a polygenic risk score (PRS) derived from a European ancestral population in both GenHAT and REGARDS AAs aiming to evaluate cross-ethnic performance. Results: We identified 10 statistically significant (p < 5.00E-08) and 90 additional suggestive (p < 1.00E-06) variants associated with incident stroke in the meta-analysis. Six of the top 10 variants were located in an intergenic region on chromosome 18 (LINC01443-LOC644669). Additional variants of interest were located in or near the COL12A1, SNTG1, PCDH7, TMTC1, and NTM genes. Replication was conducted in the Warfarin Pharmacogenomics Cohort (WPC), and while none of the variants were directly validated, seven intronic variants of NTM proximal to our target variants, had a p-value <5.00E-04 in the WPC. The inclusion of the PRS did not improve the prediction accuracy compared to a reference model adjusting for age, sex, and genetic ancestry in either study and had lower predictive accuracy compared to models accounting for established stroke risk factors. These results demonstrate the necessity for PRS derivation in AAs, particularly for diseases that affect AAs disproportionately. Conclusion: This study highlights biologically plausible genetic determinants for incident stroke in hypertensive AAs. Ultimately, a better understanding of genetic risk factors for stroke in AAs may give new insight into stroke burden and potential clinical tools for those among the highest at risk.

Genetically regulated expression in late-onset Alzheimer's disease implicates risk genes within known and novel loci

Late-onset Alzheimer disease (LOAD) is highly polygenic, with a heritability estimated between 40 and 80%, yet risk variants identified in genome-wide studies explain only ~8% of phenotypic variance. Due to its increased power and interpretability, genetically regulated expression (GReX) analysis is an emerging approach to investigate the genetic mechanisms of complex diseases. Here, we conducted GReX analysis within and across 51 tissues on 39 LOAD GWAS data sets comprising 58,713 cases and controls from the Alzheimer's Disease Genetics Consortium (ADGC) and the International Genomics of Alzheimer's Project (IGAP). Meta-analysis across studies identified 216 unique significant genes, including 72 with no previously reported LOAD GWAS associations. Cross-brain-tissue and cross-GTEx models revealed eight additional genes significantly associated with LOAD. Conditional analysis of previously reported loci using established LOAD-risk variants identified eight genes reaching genome-wide significance independent of known signals. Moreover, the proportion of SNP-based heritability is highly enriched in genes identified by GReX analysis. In summary, GReX-based meta-analysis in LOAD identifies 216 genes (including 72 novel genes), illuminating the role of gene regulatory models in LOAD.

Improved Estimation of Phenotypic Correlations Using Summary Association Statistics

Estimating the phenotypic correlations between complex traits and diseases based on their genome-wide association summary statistics has been a useful technique in genetic epidemiology and statistical genetics inference. Two state-of-the-art strategies, Z-score correlation across null-effect single nucleotide polymorphisms (SNPs) and LD score regression intercept, were widely applied to estimate phenotypic correlations. Here, we propose an improved Z-score correlation strategy based on SNPs with low minor allele frequencies (MAFs), and show how this simple strategy can correct the bias generated by the current methods. The low MAF estimator improves phenotypic correlation estimation, thus it is beneficial for methods and applications using phenotypic correlations inferred from summary association statistics.

BinomiRare: A robust test for association of a rare genetic variant with a binary outcome for mixed models and any case-control proportion

Whole-genome sequencing (WGS) and whole-exome sequencing studies have become increasingly available and are being used to identify rare genetic variants associated with health and disease outcomes. Investigators routinely use mixed models to account for genetic relatedness or other clustering variables (e.g., family or household) when testing genetic associations. However, no existing tests of the association of a rare variant with a binary outcome in the presence of correlated data control the type 1 error where there are (1) few individuals harboring the rare allele, (2) a small proportion of cases relative to controls, and (3) covariates to adjust for. Here, we address all three issues in developing a framework for testing rare variant association with a binary trait in individuals harboring at least one risk allele. In this framework, we estimate outcome probabilities under the null hypothesis and then use them, within the individuals with at least one risk allele, to test variant associations. We extend the BinomiRare test, which was previously proposed for independent observations, and develop the Conway-Maxwell-Poisson (CMP) test and study their properties in simulations. We show that the BinomiRare test always controls the type 1 error, while the CMP test sometimes does not. We then use the BinomiRare test to test the association of rare genetic variants in target genes with small-vessel disease (SVD) stroke, short sleep, and venous thromboembolism (VTE), in whole-genome sequence data from the Trans-Omics for Precision Medicine (TOPMed) program.

An atlas of mitochondrial DNA genotype-phenotype associations in the UK Biobank

Mitochondrial DNA (mtDNA) variation in common diseases has been underexplored, partly due to a lack of genotype calling and quality-control procedures. Developing an at-scale workflow for mtDNA variant analyses, we show correlations between nuclear and mitochondrial genomic structures within subpopulations of Great Britain and establish a UK Biobank reference atlas of mtDNA-phenotype associations. A total of 260 mtDNA-phenotype associations were new (P < 1 × 10-5), including rs2853822 /m.8655 C>T (MT-ATP6) with type 2 diabetes, rs878966690 /m.13117 A>G (MT-ND5) with multiple sclerosis, 6 mtDNA associations with adult height, 24 mtDNA associations with 2 liver biomarkers and 16 mtDNA associations with parameters of renal function. Rare-variant gene-based tests implicated complex I genes modulating mean corpuscular volume and mean corpuscular hemoglobin. Seven traits had both rare and common mtDNA associations, where rare variants tended to have larger effects than common variants. Our work illustrates the value of studying mtDNA variants in common complex diseases and lays foundations for future large-scale mtDNA association studies.

Scalable and Robust Regression Methods for Phenome-Wide Association Analysis on Large-Scale Biobank Data

With the advances in genotyping technologies and electronic health records (EHRs), large biobanks have been great resources to identify novel genetic associations and gene-environment interactions on a genome-wide and even a phenome-wide scale. To date, several phenome-wide association studies (PheWAS) have been performed on biobank data, which provides comprehensive insights into many aspects of human genetics and biology. Although inspiring, PheWAS on large-scale biobank data encounters new challenges including computational burden, unbalanced phenotypic distribution, and genetic relationship. In this paper, we first discuss these new challenges and their potential impact on data analysis. Then, we summarize approaches that are scalable and robust in GWAS and PheWAS. This review can serve as a practical guide for geneticists, epidemiologists, and other medical researchers to identify genetic variations associated with health-related phenotypes in large-scale biobank data analysis. Meanwhile, it can also help statisticians to gain a comprehensive and up-to-date understanding of the current technical tool development.

PCAmatchR: a flexible R package for optimal case-control matching using weighted principal components

SUMMARY

A concern when conducting genome-wide association studies (GWAS) is the potential for population stratification, i.e. ancestry-based genetic differences between cases and controls, that if not properly accounted for, could lead to biased association results. We developed PCAmatchR as an open source R package for performing optimal case-control matching using principal component analysis (PCA) to aid in selecting controls that are well matched by ancestry to cases. PCAmatchR takes user supplied PCA outputs and selects matching controls for cases by utilizing a weighted Mahalanobis distance metric which weights each principal component by the percentage of genetic variation explained. Results from the 1000 Genomes Project data demonstrate both the functionality and performance of PCAmatchR for selecting matching controls for case populations as well as reducing inflation of association test statistics. PCAmatchR improves genomic similarity between matched cases and controls, which minimizes the effects of population stratification in GWAS analyses.

AVAILABILITY AND IMPLEMENTATION

PCAmatchR is freely available for download on GitHub (https://github.com/machiela-lab/PCAmatchR) or through CRAN (https://CRAN.R-project.org/package=PCAmatchR).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Statistical Learning Methods Applicable to Genome-Wide Association Studies on Unbalanced Case-Control Disease Data

Despite the fact that imbalance between case and control groups is prevalent in genome-wide association studies (GWAS), it is often overlooked. This imbalance is getting more significant and urgent as the rapid growth of biobanks and electronic health records have enabled the collection of thousands of phenotypes from large cohorts, in particular for diseases with low prevalence. The unbalanced binary traits pose serious challenges to traditional statistical methods in terms of both genomic selection and disease prediction. For example, the well-established linear mixed models (LMM) yield inflated type I error rates in the presence of unbalanced case-control ratios. In this article, we review multiple statistical approaches that have been developed to overcome the inaccuracy caused by the unbalanced case-control ratio, with the advantages and limitations of each approach commented. In addition, we also explore the potential for applying several powerful and popular state-of-the-art machine-learning approaches, which have not been applied to the GWAS field yet. This review paves the way for better analysis and understanding of the unbalanced case-control disease data in GWAS.

A catalog of associations between rare coding variants and COVID-19 outcomes

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease-19 (COVID-19), a respiratory illness that can result in hospitalization or death. We investigated associations between rare genetic variants and seven COVID-19 outcomes in 543,213 individuals, including 8,248 with COVID-19. After accounting for multiple testing, we did not identify any clear associations with rare variants either exome-wide or when specifically focusing on (i) 14 interferon pathway genes in which rare deleterious variants have been reported in severe COVID-19 patients; (ii) 167 genes located in COVID-19 GWAS risk loci; or (iii) 32 additional genes of immunologic relevance and/or therapeutic potential. Our analyses indicate there are no significant associations with rare protein-coding variants with detectable effect sizes at our current sample sizes. Analyses will be updated as additional data become available, with results publicly browsable at https://rgc-covid19.regeneron.com.

An evaluation of approaches for rare variant association analyses of binary traits in related samples

Recognizing that family data provide unique advantage of identifying rare risk variants in genetic association studies, many cohorts with related samples have gone through whole genome sequencing in large initiatives such as the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. Analyzing rare variants poses challenges for binary traits in that some genotype categories may have few or no observed events, causing bias and inflation in commonly used methods. Several methods have recently been proposed to better handle rare variants while accounting for family relationship, but their performances have not been thoroughly evaluated together. Here we compare several existing approaches including SAIGE but not limited to related samples using simulations based on the Framingham Heart Study samples and genotype data from Illumina HumanExome BeadChip where rare variants are the majority. We found that logistic regression with likelihood ratio test applied to related samples was the only approach that did not have inflated type I error rates in both single variant test (SVT) and gene-based tests, followed by Firth logistic regression that had inflation in its direction insensitive gene-based test at prevalence 0.01 only, applied to either related or unrelated samples, though theoretically logistic regression and Firth logistic regression do not account for relatedness in samples. SAIGE had inflation in SVT at prevalence 0.1 or lower and the inflation was eliminated with a minor allele count filter of 5. As for power, there was no approach that outperformed others consistently among all single variant tests and gene-based tests.

Mutational Analysis of Known ALS Genes in an Italian Population-Based Cohort

OBJECTIVE

To assess the burden of rare genetic variants and to estimate the contribution of known amyotrophic lateral sclerosis (ALS) genes in an Italian population-based cohort, we performed whole genome sequencing in 959 patients with ALS and 677 matched healthy controls.

METHODS

We performed genome sequencing in a population-based cohort (Piemonte and Valle d'Aosta Registry for ALS [PARALS]). A panel of 40 ALS genes was analyzed to identify potential disease-causing genetic variants and to evaluate the gene-wide burden of rare variants among our population.

RESULTS

A total of 959 patients with ALS were compared with 677 healthy controls from the same geographical area. Gene-wide association tests demonstrated a strong association with SOD1, whose rare variants are the second most common cause of disease after C9orf72 expansion. A lower signal was observed for TARDBP, proving that its effect on our cohort is driven by a few known causal variants. We detected rare variants in other known ALS genes that did not surpass statistical significance in gene-wise tests, thus highlighting that their contribution to disease risk in our cohort is limited.

CONCLUSIONS

We identified potential disease-causing variants in 11.9% of our patients. We identified the genes most frequently involved in our cohort and confirmed the contribution of rare variants in disease risk. Our results provide further insight into the pathologic mechanism of the disease and demonstrate the importance of genome-wide sequencing as a diagnostic tool.

Analysis of Glucocorticoid-Related Genes Reveal CCHCR1 as a New Candidate Gene for Type 2 Diabetes

Glucocorticoids have multiple therapeutic benefits and are used both for immunosuppression and treatment purposes. Notwithstanding their benefits, glucocorticoid use often leads to hyperglycemia. Owing to the pathophysiologic overlap in glucocorticoid-induced hyperglycemia (GIH) and type 2 diabetes (T2D), we hypothesized that genetic variation in glucocorticoid pathways contributes to T2D risk. To determine the genetic contribution of glucocorticoid action on T2D risk, we conducted multiple genetic studies. First, we performed gene-set enrichment analyses on 3 collated glucocorticoid-related gene sets using publicly available genome-wide association and whole-exome data and demonstrated that genetic variants in glucocorticoid-related genes are associated with T2D and related glycemic traits. To identify which genes are driving this association, we performed gene burden tests using whole-exome sequence data. We identified 20 genes within the glucocorticoid-related gene sets that are nominally enriched for T2D-associated protein-coding variants. The most significant association was found in coding variants in coiled-coil α-helical rod protein 1 (CCHCR1) in the HLA region (P = .001). Further analyses revealed that noncoding variants near CCHCR1 are also associated with T2D at genome-wide significance (P = 7.70 × 10-14), independent of type 1 diabetes HLA risk. Finally, gene expression and colocalization analyses demonstrate that variants associated with increased T2D risk are also associated with decreased expression of CCHCR1 in multiple tissues, implicating this gene as a potential effector transcript at this locus. Our discovery of a genetic link between glucocorticoids and T2D findings support the hypothesis that T2D and GIH may have shared underlying mechanisms.

Rare variants association testing for a binary outcome when pooling individual level data from heterogeneous studies

Whole genome sequencing (WGS) and whole exome sequencing studies are used to test the association of rare genetic variants with health traits. Many existing WGS efforts now aggregate data from heterogeneous groups, for example, combining sets of individuals of European and African ancestries. We here investigate the statistical implications on rare variant association testing with a binary trait when combining together heterogeneous studies, defined as studies with potentially different disease proportion and different frequency of variant carriers. We study and compare in simulations the Type 1 error control and power of the naïve score test, the saddlepoint approximation to the score test, and the BinomiRare test in a range of settings, focusing on low numbers of variant carriers. We show that Type 1 error control and power patterns depend on both the number of carriers of the rare allele and on disease prevalence in each of the studies. We develop recommendations for association analysis of rare genetic variants. (1) The Score test is preferred when the case proportion in the sample is 50%. (2) Do not down-sample controls to balance case-control ratio, because it reduces power. Rather, use a test that controls the Type 1 error. (3) Conduct stratified analysis in parallel with combined analysis. Aggregated testing may have lower power when the variant effect size differs between strata.

Evaluation of population stratification adjustment using genome-wide or exonic variants

Population stratification may cause an inflated type-I error and spurious association when assessing the association between genetic variations with an outcome. Many genetic association studies are now using exonic variants, which captures only 1% of the genome, however, population stratification adjustments have not been evaluated in the context of exonic variants. We compare the performance of two established approaches: principal components analysis (PCA) and mixed-effects models and assess the utility of genome-wide (GW) and exonic variants, by simulation and using a data set from the Framingham Heart Study. Our results illustrate that although the PCs and genetic relationship matrices computed by GW and exonic markers are different, the type-I error rate of association tests for common variants with additive effect appear to be properly controlled in the presence of population stratification. In addition, by considering single nucleotide variants (SNVs) that have different levels of confounding by population stratification, we also compare the power across multiple association approaches to account for population stratification such as PC-based corrections and mixed-effects models. We find that while these two methods achieve a similar power for SNVs that have a low or medium level of confounding by population stratification, mixed-effects model can reach a higher power for SNVs highly confounded by population stratification.

Chances and challenges of machine learning-based disease classification in genetic association studies illustrated on age-related macular degeneration

Imaging technology and machine learning algorithms for disease classification set the stage for high-throughput phenotyping and promising new avenues for genome-wide association studies (GWAS). Despite emerging algorithms, there has been no successful application in GWAS so far. We establish machine learning-based phenotyping in genetic association analysis as misclassification problem. To evaluate chances and challenges, we performed a GWAS based on automatically classified age-related macular degeneration (AMD) in UK Biobank (images from 135,500 eyes; 68,400 persons). We quantified misclassification of automatically derived AMD in internal validation data (4,001 eyes; 2,013 persons) and developed a maximum likelihood approach (MLA) to account for it when estimating genetic association. We demonstrate that our MLA guards against bias and artifacts in simulation studies. By combining a GWAS on automatically derived AMD and our MLA in UK Biobank data, we were able to dissect true association (ARMS2/HTRA1, CFH) from artifacts (near HERC2) and identified eye color as associated with the misclassification. On this example, we provide a proof-of-concept that a GWAS using machine learning-derived disease classification yields relevant results and that misclassification needs to be considered in analysis. These findings generalize to other phenotypes and emphasize the utility of genetic data for understanding misclassification structure of machine learning algorithms.

Acquired Personality Disturbances After Meningioma Resection Are Strongly Associated With Impaired Quality of Life

BACKGROUND

Some patients experience long-term declines in quality of life following meningioma resection, but associated factors are not well understood.

OBJECTIVE

To investigate whether long-term declines in quality of life (specifically impaired adaptive functioning) after meningioma resection are associated with specific personality disturbances that often develop with lesions in ventromedial prefrontal cortex (vmPFC).

METHODS

We studied 38 patients who underwent resection of meningioma, 18 of whom had vmPFC lesions and 20 with lesions elsewhere (non-vmPFC). A total of 30 personality characteristics were rated by spouse or family, and a neuropsychologist blindly rated adaptive functioning an average of 3.8 yr postresection. Relevant personality disturbance was defined by a priori process: the presence of "conjoint personality disturbance" required specific disturbances in at least 2 of 4 types of disturbance: executive disorders, disturbed social behavior, emotional dysregulation, and hypoemotionality.

RESULTS

Fourteen patients had impaired adaptive functioning: 12 had vmPFC lesions and 2 had non-vmPFC lesions. Fourteen patients had conjoint personality disturbance, and 12 of them had impaired adaptive functioning. By contrast, among the 24 patients who did not have conjoint personality disturbance, only 2 had impaired adaptive functioning. Mediation analysis showed that the association between vmPFC lesions and impaired adaptive functioning was mediated by the negative impact of acquired personality disturbance on adaptive functioning.

CONCLUSION

Anterior skull base meningiomas plus resection surgery may result in specific personality disturbances that are highly associated with impaired adaptive functioning at long-term follow-up. These patients may benefit from early counseling regarding potential personality changes and their implications for adaptive functioning.

Chromosomal alterations among age-related haematopoietic clones in Japan

The extent to which the biology of oncogenesis and ageing are shaped by factors that distinguish human populations is unknown. Haematopoietic clones with acquired mutations become common with advancing age and can lead to blood cancers^1-10. Here we describe shared and population-specific patterns of genomic mutations and clonal selection in haematopoietic cells on the basis of 33,250 autosomal mosaic chromosomal alterations that we detected in 179,417 Japanese participants in the BioBank Japan cohort and compared with analogous data from the UK Biobank. In this long-lived Japanese population, mosaic chromosomal alterations were detected in more than 35.0% (s.e.m., 1.4%) of individuals older than 90 years, which suggests that such clones trend towards inevitability with advancing age. Japanese and European individuals exhibited key differences in the genomic locations of mutations in their respective haematopoietic clones; these differences predicted the relative rates of chronic lymphocytic leukaemia (which is more common among European individuals) and T cell leukaemia (which is more common among Japanese individuals) in these populations. Three different mutational precursors of chronic lymphocytic leukaemia (including trisomy 12, loss of chromosomes 13q and 13q, and copy-neutral loss of heterozygosity) were between two and six times less common among Japanese individuals, which suggests that the Japanese and European populations differ in selective pressures on clones long before the development of clinically apparent chronic lymphocytic leukaemia. Japanese and British populations also exhibited very different rates of clones that arose from B and T cell lineages, which predicted the relative rates of B and T cell cancers in these populations. We identified six previously undescribed loci at which inherited variants predispose to mosaic chromosomal alterations that duplicate or remove the inherited risk alleles, including large-effect rare variants at NBN, MRE11 and CTU2 (odds ratio, 28-91). We suggest that selective pressures on clones are modulated by factors that are specific to human populations. Further genomic characterization of clonal selection and cancer in populations from around the world is therefore warranted.

Identification of type 2 diabetes loci in 433,540 East Asian individuals

Meta-analyses of genome-wide association studies (GWAS) have identified more than 240 loci that are associated with type 2 diabetes (T2D)1,2; however, most of these loci have been identified in analyses of individuals with European ancestry. Here, to examine T2D risk in East Asian individuals, we carried out a meta-analysis of GWAS data from 77,418 individuals with T2D and 356,122 healthy control individuals. In the main analysis, we identified 301 distinct association signals at 183 loci, and across T2D association models with and without consideration of body mass index and sex, we identified 61 loci that are newly implicated in predisposition to T2D. Common variants associated with T2D in both East Asian and European populations exhibited strongly correlated effect sizes. Previously undescribed associations include signals in or near GDAP1, PTF1A, SIX3, ALDH2, a microRNA cluster, and genes that affect the differentiation of muscle and adipose cells3. At another locus, expression quantitative trait loci at two overlapping T2D signals affect two genes-NKX6-3 and ANK1-in different tissues4-6. Association studies in diverse populations identify additional loci and elucidate disease-associated genes, biology, and pathways.

Insights into the genetic basis of retinal detachment

Retinal detachment (RD) is a serious and common condition, but genetic studies to date have been hampered by the small size of the assembled cohorts. In the UK Biobank data set, where RD was ascertained by self-report or hospital records, genetic correlations between RD and high myopia or cataract operation were, respectively, 0.46 (SE = 0.08) and 0.44 (SE = 0.07). These correlations are consistent with known epidemiological associations. Through meta-analysis of genome-wide association studies using UK Biobank RD cases (N = 3 977) and two cohorts, each comprising ~1 000 clinically ascertained rhegmatogenous RD patients, we uncovered 11 genome-wide significant association signals. These are near or within ZC3H11B, BMP3, COL22A1, DLG5, PLCE1, EFEMP2, TYR, FAT3, TRIM29, COL2A1 and LOXL1. Replication in the 23andMe data set, where RD is self-reported by participants, firmly establishes six RD risk loci: FAT3, COL22A1, TYR, BMP3, ZC3H11B and PLCE1. Based on the genetic associations with eye traits described to date, the first two specifically impact risk of a RD, whereas the last four point to shared aetiologies with macular condition, myopia and glaucoma. Fine-mapping prioritized the lead common missense variant (TYR S192Y) as causal variant at the TYR locus and a small set of credible causal variants at the FAT3 locus. The larger study size presented here, enabled by resources linked to health records or self-report, provides novel insights into RD aetiology and underlying pathological pathways.

Burden of rare exome sequence variants in PROC gene is associated with venous thromboembolism: a population-based study

BACKGROUND

Rare coding mutations underlying deficiencies of antithrombin and proteins C and S contribute to familial venous thromboembolism (VTE). It is uncertain whether rare variants play a role in the etiology of VTE in the general population.

OBJECTIVES

We conducted a deep whole-exome sequencing (WES) study to investigate the associations between rare coding variants and the risk of VTE in two population-based prospective cohorts.

PATIENTS/METHODS

Whole-exome sequencing was performed in the Longitudinal Investigation of Thromboembolism Etiology (LITE), which combines the Atherosclerosis Risk in Communities (ARIC) study (316 incident VTE events among 3159 African Americans [AAs] and 458 incident VTEs among 7772 European Americans [EAs]) and the Cardiovascular Healthy Study (CHS; 60 incident VTEs among 1751 EAs). We performed gene-based tests of rare variants (allele frequency < 1%, exome-wide significance P < 1.47 × 10-6 ) separately in each study and ancestry group, and meta-analyzed the results for the EAs in ARIC and CHS.

RESULTS

In the meta-analysis of EAs, we identified one gene, PROC, in which the burden of rare, coding variants was significantly associated with increased risk of VTE (HR = 5.42 [3.11, 9.42] for carriers versus non-carriers, P = 2.27 × 10-9 ). In ARIC EAs, carriers of the PROC rare variants had on average 0.75 standard deviation (SD) lower concentrations of plasma protein C and 0.28 SD higher D-dimer (P < .05) than non-carriers. Adjustment for low protein C status did not eliminate the association of PROC burden with VTE. In AAs, rare coding PROC variants were not associated with VTE.

CONCLUSIONS

Rare coding variants in PROC contribute to increased VTE risk in EAs in this general population sample.

Whole-Genome Sequencing of Finnish Type 1 Diabetic Siblings Discordant for Kidney Disease Reveals DNA Variants associated with Diabetic Nephropathy

BACKGROUND

Several genetic susceptibility loci associated with diabetic nephropathy have been documented, but no causative variants implying novel pathogenetic mechanisms have been elucidated.

METHODS

We carried out whole-genome sequencing of a discovery cohort of Finnish siblings with type 1 diabetes who were discordant for the presence (case) or absence (control) of diabetic nephropathy. Controls had diabetes without complications for 15-37 years. We analyzed and annotated variants at genome, gene, and single-nucleotide variant levels. We then replicated the associated variants, genes, and regions in a replication cohort from the Finnish Diabetic Nephropathy study that included 3531 unrelated Finns with type 1 diabetes.

RESULTS

We observed protein-altering variants and an enrichment of variants in regions associated with the presence or absence of diabetic nephropathy. The replication cohort confirmed variants in both regulatory and protein-coding regions. We also observed that diabetic nephropathy-associated variants, when clustered at the gene level, are enriched in a core protein-interaction network representing proteins essential for podocyte function. These genes include protein kinases (protein kinase C isoforms ε and ι) and protein tyrosine kinase 2.

CONCLUSIONS

Our comprehensive analysis of a diabetic nephropathy cohort of siblings with type 1 diabetes who were discordant for kidney disease points to variants and genes that are potentially causative or protective for diabetic nephropathy. This includes variants in two isoforms of the protein kinase C family not previously linked to diabetic nephropathy, adding support to previous hypotheses that the protein kinase C family members play a role in diabetic nephropathy and might be attractive therapeutic targets.

UK Biobank Whole-Exome Sequence Binary Phenome Analysis with Robust Region-Based Rare-Variant Test

In biobank data analysis, most binary phenotypes have unbalanced case-control ratios, and this can cause inflation of type I error rates. Recently, a saddle point approximation (SPA) based single-variant test has been developed to provide an accurate and scalable method to test for associations of such phenotypes. For gene- or region-based multiple-variant tests, a few methods exist that can adjust for unbalanced case-control ratios; however, these methods are either less accurate when case-control ratios are extremely unbalanced or not scalable for large data analyses. To address these problems, we propose SKAT- and SKAT-O- type region-based tests; in these tests, the single-variant score statistic is calibrated based on SPA and efficient resampling (ER). Through simulation studies, we show that the proposed method provides well-calibrated p values. In contrast, when the case-control ratio is 1:99, the unadjusted approach has greatly inflated type I error rates (90 times that of exome-wide sequencing α = 2.5 × 10-6). Additionally, the proposed method has similar computation time to the unadjusted approaches and is scalable for large sample data. In our application, the UK Biobank whole-exome sequence data analysis of 45,596 unrelated European samples and 791 PheCode phenotypes identified 10 rare-variant associations with p value < 10-7, including the associations between JAK2 and myeloproliferative disease, HOXB13 and cancer of prostate, and F11 and congenital coagulation defects. All analysis summary results are publicly available through a web-based visual server, and this availability can help facilitate the identification of the genetic basis of complex diseases.