Rare-variant association testing for sequencing data with the sequence kernel association test

Whole exome sequencing and replication for breast cancer among Hispanic/Latino women identifies FANCM as a susceptibility gene for estrogen-receptor-negative breast cancer

Introduction

Breast cancer (BC) is one of the most common cancers globally. Genetic testing can facilitate screening and risk-reducing recommendations, and inform use of targeted treatments. However, genes included in testing panels are from studies of European-ancestry participants. We sequenced Hispanic/Latina (H/L) women to identify BC susceptibility genes.

Methods

We conducted a pooled BC case-control analysis in H/L women from the San Francisco Bay area, Los Angeles County, and Mexico (4,178 cases and 4,344 controls). Whole exome sequencing was conducted on 1,043 cases and 1,188 controls and a targeted 857-gene panel on the remaining samples. Using ancestry-adjusted SKAT-O analyses, we tested the association of loss of function (LoF) variants with overall, estrogen receptor (ER)-positive, and ER-negative BC risk. We calculated odds ratios (OR) for BC using ancestry-adjusted logistic regression models. We also tested the association of single variants with BC risk.

Results

We saw a strong association of LoF variants in FANCM with ER-negative BC (p=4.1×10-7, OR [CI]: 6.7 [2.9-15.6]) and a nominal association with overall BC risk. Among known susceptibility genes, BRCA1 (p=2.3×10-10, OR [CI]: 24.9 [6.1-102.5]), BRCA2 (p=8.4×10-10, OR [CI]: 7.0 [3.5-14.0]), and PALB2 (p=1.8×10-8, OR [CI]: 6.5 [3.2-13.1]) were strongly associated with BC. There were nominally significant associations with CHEK2, RAD51D, and TP53.

Conclusion

In H/L women, LoF variants in FANCM were strongly associated with ER-negative breast cancer risk. It previously was proposed as a possible susceptibility gene for ER-negative BC, but is not routinely tested in clinical practice. Our results demonstrate that FANCM should be added to BC gene panels.

FGF20 and PGM2 variants are associated with childhood asthma in family-based whole-genome sequencing studies

BACKGROUND

Asthma is a heterogeneous common respiratory disease that remains poorly understood. The established genetic associations fail to explain the high estimated heritability, and the prevalence of asthma differs between populations and geographic regions. Robust association analyses incorporating different genetic ancestries and whole-genome sequencing data may identify novel genetic associations.

METHODS

We performed family-based genome-wide association analyses of childhood-onset asthma based on whole-genome sequencing (WGS) data for the 'The Genetic Epidemiology of Asthma in Costa Rica' study (GACRS) and the Childhood Asthma Management Program (CAMP). Based on parent-child trios with children diagnosed with asthma, we performed a single variant analysis using an additive and a recessive genetic model and a region-based association analysis of low-frequency and rare variants.

RESULTS

Based on 1180 asthmatic trios (894 GACRS trios and 286 CAMP trios, a total of 3540 samples with WGS data), we identified three novel genetic loci associated with childhood-onset asthma: rs4832738 on 4p14 ($P=1.72\ast{10}^{-9}$, recessive model), rs1581479 on 8p22 ($P=1.47\ast{10}^{-8}$, additive model) and rs73367537 on 10q26 ($P=1.21\ast{10}^{-8}$, additive model in GACRS only). Integrative analyses suggested potential novel candidate genes underlying these associations: PGM2 on 4p14 and FGF20 on 8p22.

CONCLUSION

Our family-based whole-genome sequencing analysis identified three novel genetic loci for childhood-onset asthma. Gene expression data and integrative analyses point to PGM2 on 4p14 and FGF20 on 8p22 as linked genes. Furthermore, region-based analyses suggest independent potential low-frequency/rare variant associations on 8p22. Follow-up analyses are needed to understand the functional mechanisms and generalizability of these associations.

Multimarker omnibus tests by leveraging individual marker summary statistics from large biobanks

As biobanks become increasingly popular, access to genotypic and phenotypic data continues to increase in the form of precomputed summary statistics (PCSS). Widespread accessibility of PCSS alleviates many issues related to biobank data, including that of data privacy and confidentiality, as well as high computational costs. However, questions remain about how to maximally leverage PCSS for downstream statistical analyses. Here we present a novel method for testing the association of an arbitrary number of single nucleotide variants (SNVs) on a linear combination of phenotypes after adjusting for covariates for common multimarker tests (e.g., SKAT, SKAT-O) without access to individual patient-level data (IPD). We validate exact formulas for each method, and demonstrate their accuracy through simulation studies and an application to fatty acid phenotypic data from the Framingham Heart Study.

Germline loss-of-function PAM variants are enriched in subjects with pituitary hypersecretion

Pituitary adenomas (PAs) are common, usually benign tumors of the anterior pituitary gland which, for the most part, have no known genetic cause. PAs are associated with major clinical effects due to hormonal dysregulation and tumoral impingement on vital brain structures. Following the identification of a loss-of-function variant (p.Arg703Gln) in the PAM gene in a family with pituitary gigantism, we investigated 299 individuals with sporadic PAs and 17 familial isolated pituitary adenomas kindreds for PAM variants. PAM encodes a multifunctional protein responsible for the essential C-terminal amidation of secreted peptides. Genetic screening was performed by germline and tumor sequencing and germline copy number variation (CNV) analysis. No germline CNVs or somatic single nucleotide variants (SNVs) were identified. We detected seven likely pathogenic heterozygous missense, truncating, and regulatory SNVs. These SNVs were found in sporadic subjects with GH excess (p.Gly552Arg and p.Phe759Ser), pediatric Cushing disease (c.-133T>C and p.His778fs), or with different types of PAs (c.-361G>A, p.Ser539Trp, and p.Asp563Gly). The SNVs were functionally tested in vitro for protein expression and trafficking by Western blotting, for splicing by minigene assays, and for amidation activity in cell lysates and serum samples. These analyses confirmed a deleterious effect on protein expression and/or function. By interrogating 200,000 exomes from the UK Biobank, we confirmed a significant association of the PAM gene and rare PAM SNVs to diagnoses linked to pituitary gland hyperfunction. Identification of PAM as a candidate gene associated with pituitary hypersecretion opens the possibility of developing novel therapeutics based on altering PAM function.

From SNP to pathway-based GWAS meta-analysis: do current meta-analysis approaches resolve power and replication in genetic association studies?

Genome-wide association studies (GWAS) have benefited greatly from enhanced high-throughput technology in recent decades. GWAS meta-analysis has become increasingly popular to highlight the genetic architecture of complex traits, informing about the replicability and variability of effect estimations across human ancestries. A wealth of GWAS meta-analysis methodologies have been developed depending on the input data and the outcome information of interest. We present a survey of current approaches from SNP to pathway-based meta-analysis by acknowledging the range of resources and methodologies in the field, and we provide a comprehensive review of different categories of Genome-Wide Meta-analysis methods employed. These methods highlight different levels at which GWAS meta-analysis may be done, including Single Nucleotide Polymorphisms, Genes and Pathways, for which we describe their framework outline. We also discuss the strengths and pitfalls of each approach and make suggestions regarding each of them.

Whole-exome sequence analysis of anthropometric traits illustrates challenges in identifying effects of rare genetic variants

Anthropometric traits, measuring body size and shape, are highly heritable and significant clinical risk factors for cardiometabolic disorders. These traits have been extensively studied in genome-wide association studies (GWASs), with hundreds of genome-wide significant loci identified. We performed a whole-exome sequence analysis of the genetics of height, body mass index (BMI) and waist/hip ratio (WHR). We meta-analyzed single-variant and gene-based associations of whole-exome sequence variation with height, BMI, and WHR in up to 22,004 individuals, and we assessed replication of our findings in up to 16,418 individuals from 10 independent cohorts from Trans-Omics for Precision Medicine (TOPMed). We identified four trait associations with single-nucleotide variants (SNVs; two for height and two for BMI) and replicated the LECT2 gene association with height. Our expression quantitative trait locus (eQTL) analysis within previously reported GWAS loci implicated CEP63 and RFT1 as potential functional genes for known height loci. We further assessed enrichment of SNVs, which were monogenic or syndromic variants within loci associated with our three traits. This led to the significant enrichment results for height, whereas we observed no Bonferroni-corrected significance for all SNVs. With a sample size of ∼20,000 whole-exome sequences in our discovery dataset, our findings demonstrate the importance of genomic sequencing in genetic association studies, yet they also illustrate the challenges in identifying effects of rare genetic variants.

Whole-exome sequencing in 415,422 individuals identifies rare variants associated with mitochondrial DNA copy number

Inter-individual variation in the number of copies of the mitochondrial genome, called mitochondrial DNA copy number (mtDNA-CN), reflects mitochondrial function and has been associated with various aging-related diseases. We examined 415,422 exomes of self-reported White ancestry individuals from the UK Biobank and tested the impact of rare variants, at the level of single variants and through aggregate variant-set tests, on mtDNA-CN. A survey across nine variant sets tested enrichment of putatively causal variants and identified 14 genes at experiment-wide significance and three genes at marginal significance. These included associations at known mtDNA depletion syndrome genes (mtDNA helicase TWNK, p = 1.1 × 10-30; mitochondrial transcription factor TFAM, p = 4.3 × 10-15; mtDNA maintenance exonuclease MGME1, p = 2.0 × 10-6) and the V617F dominant gain-of-function mutation in the tyrosine kinase JAK2 (p = 2.7 × 10-17), associated with myeloproliferative disease. Novel genes included the ATP-dependent protease CLPX (p = 8.4 × 10-9), involved in mitochondrial proteome quality, and the mitochondrial adenylate kinase AK2 (p = 4.7 × 10-8), involved in hematopoiesis. The most significant association was a missense variant in SAMHD1 (p = 4.2 × 10-28), found on a rare, 1.2-Mb shared ancestral haplotype on chromosome 20. SAMHD1 encodes a cytoplasmic host restriction factor involved in viral defense response and the mitochondrial nucleotide salvage pathway, and is associated with Aicardi-Goutières syndrome 5, a childhood encephalopathy and chronic inflammatory response disorder. Rare variants were enriched in Mendelian mtDNA depletion syndrome loci, and these variants implicated core processes in mtDNA replication, nucleoid structure formation, and maintenance. These data indicate that strong-effect mutations from the nuclear genome contribute to the genetic architecture of mtDNA-CN.

Targeted next-generation sequencing for genetic variants of left ventricular mass status among community-based adults in Taiwan

Background: Left ventricular mass is a highly heritable disease. Previous studies have suggested common genetic variants to be associated with left ventricular mass; however, the roles of rare variants are still unknown. We performed targeted next-generation sequencing using the TruSight Cardio panel, which provides comprehensive coverage of 175 genes with known associations to 17 inherited cardiac conditions. Methods: We conducted next-generation sequencing using the Illumina TruSight Cardiomyopathy Target Genes platform using the 5% and 95% extreme values of left ventricular mass from community-based participants. After removing poor-quality next-generation sequencing subjects, including call rate <98% and Mendelian errors, 144 participants were used for the analysis. We performed downstream analysis, including quality control, alignment, coverage length, and annotation; after setting filtering criteria for depths more than 60, we found a total of 144 samples and 165 target genes for further analysis. Results: Of the 12,287 autosomal variants, most had minor allele frequencies of <1% (rare frequency), and variants had minor allele frequencies ranging from 1% to 5%. In the multi-allele variant analyses, 16 loci in 15 genes were significant using the false discovery rate of less than .1. In addition, gene-based analyses using continuous and binary outcomes showed that three genes (CASQ2, COL5A1, and FXN) remained to be associated with left ventricular mass status. One single-nucleotide polymorphism (rs7538337) was enriched for the CASQ2 gene expressed in aorta artery (p = 4.6 × 10-18), as was another single-nucleotide polymorphism (rs11103536) for the COL5A1 gene expressed in aorta artery (p = 2.0 × 10-9). Among the novel genes discovered, CASQ2, COL5A1, and FXN are within a protein-protein interaction network with known cardiovascular genes. Conclusion: We clearly demonstrated candidate genes to be associated with left ventricular mass. Further studies to characterize the target genes and variants for their functional mechanisms are warranted.

Gene-Folic Acid Interactions and Risk of Conotruncal Heart Defects: Results from the National Birth Defects Prevention Study

Conotruncal heart defects (CTDs) are heart malformations that affect the cardiac outflow tract and typically cause significant morbidity and mortality. Evidence from epidemiological studies suggests that maternal folate intake is associated with a reduced risk of heart defects, including CTD. However, it is unclear if folate-related gene variants and maternal folate intake have an interactive effect on the risk of CTDs. In this study, we performed targeted sequencing of folate-related genes on DNA from 436 case families with CTDs who are enrolled in the National Birth Defects Prevention Study and then tested for common and rare variants associated with CTD. We identified risk alleles in maternal MTHFS (OR_meta = 1.34; 95% CI 1.07 to 1.67), maternal NOS2 (OR_meta = 1.34; 95% CI 1.05 to 1.72), fetal MTHFS (OR_meta = 1.35; 95% CI 1.09 to 1.66), and fetal TCN2 (OR_meta = 1.38; 95% CI 1.12 to 1.70) that are associated with an increased risk of CTD among cases without folic acid supplementation. We detected putative de novo mutations in genes from the folate, homocysteine, and transsulfuration pathways and identified a significant association between rare variants in MGST1 and CTD risk. Results suggest that periconceptional folic acid supplementation is associated with decreased risk of CTD among individuals with susceptible genotypes.

The Contribution of Germline Pathogenic Variants in Breast Cancer Genes to Contralateral Breast Cancer Risk in BRCA1/BRCA2/PALB2-Negative Women

BACKGROUND

Contralateral breast cancer (CBC) is associated with younger age at first diagnosis, family history and pathogenic germline variants (PGVs) in genes such as BRCA1, BRCA2 and PALB2. However, data regarding genetic factors predisposing to CBC among younger women who are BRCA1/2/PALB2-negative remain limited.

METHODS

In this nested case-control study, participants negative for BRCA1/2/PALB2 PGVs were selected from the WECARE Study. The burden of PGVs in established breast cancer risk genes was compared in 357 cases with CBC and 366 matched controls with unilateral breast cancer (UBC). The samples were sequenced in two phases. Whole exome sequencing was used in Group 1, 162 CBC and 172 UBC (mean age at diagnosis: 42 years). A targeted panel of genes was used in Group 2, 195 CBC and 194 UBC (mean age at diagnosis: 50 years). Comparisons of PGVs burdens between CBC and UBC were made in these groups, and additional stratified sub-analysis was performed within each group according to the age at diagnosis and the time from first breast cancer (BC).

RESULTS

The PGVs burden in Group 1 was significantly higher in CBC than in UBC (p = 0.002, OR = 2.5, 95CI: 1.2-5.6), driven mainly by variants in CHEK2 and ATM. The proportions of PGVs carriers in CBC and UBC in this group were 14.8% and 5.8%, respectively. There was no significant difference in PGVs burden between CBC and UBC in Group 2 (p = 0.4, OR = 1.4, 95CI: 0.7-2.8), with proportions of carriers being 8.7% and 8.2%, respectively. There was a significant association of PGVs in CBC with younger age. Metanalysis combining both groups confirmed the significant association between the burden of PGVs and the risk of CBC (p = 0.006) with the significance driven by the younger cases (Group 1).

CONCLUSION

In younger BRCA1/BRCA2/PALB2-negative women, the aggregated burden of PGVs in breast cancer risk genes was associated with the increased risk of CBC and was inversely proportional to the age at onset.

APOE4, Age & Sex Regulate Respiratory Plasticity Elicited By Acute Intermittent Hypercapnic-Hypoxia

Rationale

Acute intermittent hypoxia (AIH) is a promising strategy to induce functional motor recovery following chronic spinal cord injuries and neurodegenerative diseases. Although significant results are obtained, human AIH trials report considerable inter-individual response variability.

Objectives

Identify individual factors ( e.g. , genetics, age, and sex) that determine response magnitude of healthy adults to an optimized AIH protocol, acute intermittent hypercapnic-hypoxia (AIHH).

Methods

Associations of individual factors with the magnitude of AIHH (15, 1-min O 2 =9.5%, CO 2 =5% episodes) induced changes in diaphragm motor-evoked potential amplitude (MEP) and inspiratory mouth occlusion pressures (P 0.1 ) were evaluated in 17 healthy individuals (age=27±5 years) compared to Sham. Single nucleotide polymorphisms (SNPs) in genes linked with mechanisms of AIH induced phrenic motor plasticity ( BDNF, HTR 2A , TPH 2 , MAOA, NTRK 2 ) and neuronal plasticity (apolipoprotein E, APOE ) were tested. Variations in AIHH induced plasticity with age and sex were also analyzed. Additional experiments in humanized ( h ) ApoE knock-in rats were performed to test causality.

Results

AIHH-induced changes in diaphragm MEP amplitudes were lower in individuals heterozygous for APOE 4 ( i.e., APOE 3/4 ) allele versus other APOE genotypes (p=0.048). No significant differences were observed between any other SNPs investigated, notably BDNFval/met ( all p>0.05 ). Males exhibited a greater diaphragm MEP enhancement versus females, regardless of age (p=0.004). Age was inversely related with change in P 0.1 within the limited age range studied (p=0.007). In hApoE 4 knock-in rats, AIHH-induced phrenic motor plasticity was significantly lower than hApoE 3 controls (p<0.05).

Conclusions

APOE 4 genotype, sex and age are important biological determinants of AIHH-induced respiratory motor plasticity in healthy adults.

ADDITION TO KNOWLEDGE BASE

Acute intermittent hypoxia (AIH) is a novel rehabilitation strategy to induce functional recovery of respiratory and non-respiratory motor systems in people with chronic spinal cord injury and/or neurodegenerative diseases. Since most AIH trials report considerable inter-individual variability in AIH outcomes, we investigated factors that potentially undermine the response to an optimized AIH protocol, acute intermittent hypercapnic-hypoxia (AIHH), in healthy humans. We demonstrate that genetics (particularly the lipid transporter, APOE ), age and sex are important biological determinants of AIHH-induced respiratory motor plasticity.

Type 2 cytokine genes as allergic asthma risk factors after viral bronchiolitis in early childhood

Background

Genome-wide association studies of asthma have identified associations with variants in type-2 related genes. Also, specific interactions between genetic variants and viral bronchiolitis in the development of asthma has been suggested.

Objective

To conduct a gene-based analysis of genetic variants in type 2 cytokine related genes as risk factors for allergic asthma at school age, and further, to study their interaction with specific viral infections in early childhood.

Methods

A prospectively investigated cohort of children with previous bronchiolitis and controls came for follow-up at school age. The research visit, blinded to viral exposure, included detailed lung function tests, laboratory investigation, and questionnaires. Allergic asthma was defined as typical symptoms plus objective variable airway obstruction, in addition to laboratory verified atopy (elevated eosinophil count or sensitization to an allergen). Targeted and complete sequencing was performed for nine type 2 cytokine candidate genes: IL4, 5, 13, 25, 33 and 37, IL17RB, CRLF2 and TSLP.

Results

At follow-up, there were 109 children with genetic data, 91 with a history of bronchiolitis (46% respiratory syncytial virus, 24% human rhinovirus, 15% human metapneumovirus and 14% mixed viral etiology) and 18 without. The median age was 9.4 years (range 6-13) and 41 (38%) had laboratory verified atopy. Twenty-one children (19%) met the definition of allergic asthma. After adjusting for age, sex and five viral categories, IL33 achieved nominal significance (p = 0.017) for a positive association with allergic asthma development. In the gene-virus interaction analysis, the variant set in IL17RB demonstrated a nominally significant positive interaction with human metapneumovirus infection (p=0.05).

Conclusion

The results highlight the multifactorial nature of allergic asthma risk, with both viral infection and inherited genetic variants contributing to increasing risk. Results for IL33 and IL17RB were nominally significant and are potential candidate targets for designing therapeutics and early screening, but these results must be replicated in an independent study.

Identification of Novel Genetic Risk Factors for Focal Segmental Glomerulosclerosis in Children: Results From the Chronic Kidney Disease in Children (CKiD) Cohort

RATIONALE & OBJECTIVE

Focal segmental glomerulosclerosis (FSGS) is a major cause of pediatric nephrotic syndrome, and African Americans exhibit an increased risk for developing FSGS compared with other populations. Predisposing genetic factors have previously been described in adults. Here we performed genomic screening of primary FSGS in a pediatric African American population.

STUDY DESIGN

Prospective cohort with case-control genetic association study design.

SETTING & PARTICIPANTS

140 African American children with chronic kidney disease from the Chronic Kidney Disease in Children (CKiD) cohort, including 32 cases with FSGS.

PREDICTORS

Over 680,000 common single-nucleotide polymorphisms (SNPs) were tested for association. We also ran a pathway enrichment analysis and a human leucocyte antigen (HLA)-focused association study.

OUTCOME

Primary biopsy-proven pediatric FSGS.

ANALYTICAL APPROACH

Multivariate logistic regression models.

RESULTS

The genome-wide association study revealed 169 SNPs from 14 independent loci significantly associated with FSGS (false discovery rate [FDR]<5%). We observed notable signals for genetic variants within the APOL1 (P=8.6×10^-7; OR, 25.8 [95% CI, 7.1-94.0]), ALMS1 (P=1.3×10^-7; 13.0% in FSGS cases vs 0% in controls), and FGFR4 (P=4.3×10^-6; OR, 24.8 [95% CI, 6.3-97.7]) genes, all of which had previously been associated with adult FSGS, kidney function, or chronic kidney disease. We also highlighted novel, functionally relevant genes, including GRB2 (which encodes a slit diaphragm protein promoting podocyte structure through actin polymerization) and ITGB1 (which is linked to renal injuries). Our results suggest a major role for immune responses and antigen presentation in pediatric FSGS through (1) associations with SNPs in PTPRJ (or CD148, P=3.5×10^-7), which plays a role in T-cell receptor signaling, (2) HLA-DRB1∗11:01 association (P=6.1×10^-3; OR, 4.5 [95% CI, 1.5-13.0]), and (3) signaling pathway enrichment (P=1.3×10^-6).

LIMITATIONS

Sample size and no independent replication cohort with genomic data readily available.

CONCLUSIONS

Our genetic study has identified functionally relevant risk factors and the importance of immune regulation for pediatric primary FSGS, which contributes to a better description of its molecular pathophysiological mechanisms.

A gene based combination test using GWAS summary data

BACKGROUND

Gene-based association tests provide a useful alternative and complement to the usual single marker association tests, especially in genome-wide association studies (GWAS). The way of weighting for variants in a gene plays an important role in boosting the power of a gene-based association test. Appropriate weights can boost statistical power, especially when detecting genetic variants with weak effects on a trait. One major limitation of existing gene-based association tests lies in using weights that are predetermined biologically or empirically. This limitation often attenuates the power of a test. On another hand, effect sizes or directions of causal genetic variants in real data are usually unknown, driving a need for a flexible yet robust methodology of gene based association tests. Furthermore, access to individual-level data is often limited, while thousands of GWAS summary data are publicly and freely available.

RESULTS

To resolve these limitations, we propose a combination test named as OWC which is based on summary statistics from GWAS data. Several traditional methods including burden test, weighted sum of squared score test [SSU], weighted sum statistic [WSS], SNP-set Kernel Association Test [SKAT], and the score test are special cases of OWC. To evaluate the performance of OWC, we perform extensive simulation studies. Results of simulation studies demonstrate that OWC outperforms several existing popular methods. We further show that OWC outperforms comparison methods in real-world data analyses using schizophrenia GWAS summary data and a fasting glucose GWAS meta-analysis data. The proposed method is implemented in an R package available at https://github.com/Xuexia-Wang/OWC-R-package CONCLUSIONS: We propose a novel gene-based association test that incorporates four different weighting schemes (two constant weights and two weights proportional to normal statistic Z) and includes several popular methods as its special cases. Results of the simulation studies and real data analyses illustrate that the proposed test, OWC, outperforms comparable methods in most scenarios. These results demonstrate that OWC is a useful tool that adapts to the underlying biological model for a disease by weighting appropriately genetic variants and combination of well-known gene-based tests.

Pharmacogenomics of GLP-1 receptor agonists: a genome-wide analysis of observational data and large randomised controlled trials

BACKGROUND

In the treatment of type 2 diabetes, GLP-1 receptor agonists lower blood glucose concentrations, body weight, and have cardiovascular benefits. The efficacy and side effects of GLP-1 receptor agonists vary between people. Human pharmacogenomic studies of this inter-individual variation can provide both biological insight into drug action and provide biomarkers to inform clinical decision making. We therefore aimed to identify genetic variants associated with glycaemic response to GLP-1 receptor agonist treatment.

METHODS

In this genome-wide analysis we included adults (aged ≥18 years) with type 2 diabetes treated with GLP-1 receptor agonists with baseline HbA_1c of 7% or more (53 mmol/mol) from four prospective observational cohorts (DIRECT, PRIBA, PROMASTER, and GoDARTS) and two randomised clinical trials (HARMONY phase 3 and AWARD). The primary endpoint was HbA_1c reduction at 6 months after starting GLP-1 receptor agonists. We evaluated variants in GLP1R, then did a genome-wide association study and gene-based burden tests.

FINDINGS

4571 adults were included in our analysis, of these, 3339 (73%) were White European, 449 (10%) Hispanic, 312 (7%) American Indian or Alaskan Native, and 471 (10%) were other, and around 2140 (47%) of the participants were women. Variation in HbA_1c reduction with GLP-1 receptor agonists treatment was associated with rs6923761G→A (Gly168Ser) in the GLP1R (0·08% [95% CI 0·04-0·12] or 0·9 mmol/mol lower reduction in HbA_1c per serine, p=6·0 × 10^-5) and low frequency variants in ARRB1 (optimal sequence kernel association test p=6·7 × 10^-8), largely driven by rs140226575G→A (Thr370Met; 0·25% [SE 0·06] or 2·7 mmol/mol  [SE 0·7] greater HbA_1c reduction per methionine, p=5·2 × 10^-6). A similar effect size for the ARRB1 Thr370Met was seen in Hispanic and American Indian or Alaska Native populations who have a higher frequency of this variant (6-11%) than in White European populations. Combining these two genes identified 4% of the population who had a 30% greater reduction in HbA_1c than the 9% of the population with the worse response.

INTERPRETATION

This genome-wide pharmacogenomic study of GLP-1 receptor agonists provides novel biological and clinical insights. Clinically, when genotype is routinely available at the point of prescribing, individuals with ARRB1 variants might benefit from earlier initiation of GLP-1 receptor agonists.

FUNDING

Innovative Medicines Initiative and the Wellcome Trust.

Integrative Post-Genome-Wide Association Study Analyses Relevant to Psychiatric Disorders: Imputing Transcriptome and Proteome Signals

Background

The genome-wide association study (GWAS) is a common tool to identify genetic variants associated with complex traits, including psychiatric disorders (PDs). However, post-GWAS analyses are needed to extend the statistical inference to biologically relevant entities, e.g., genes, proteins, and pathways. To achieve this goal, researchers developed methods that incorporate biologically relevant intermediate molecular phenotypes, such as gene expression and protein abundance, which are posited to mediate the variant-trait association. Transcriptome-wide association study (TWAS) and proteome-wide association study (PWAS) are commonly used methods to test the association between these molecular mediators and the trait.

Summary

In this review, we discuss the most recent developments in TWAS and PWAS. These methods integrate existing "omic" information with the GWAS summary statistics for trait(s) of interest. Specifically, they impute transcript/protein data and test the association between imputed gene expression/protein level with phenotype of interest by using (i) GWAS summary statistics and (ii) reference transcriptomic/proteomic/genomic datasets. TWAS and PWAS are suitable as analysis tools for (i) primary association scan and (ii) fine-mapping to identify potentially causal genes for PDs.

Key Messages

As post-GWAS analyses, TWAS and PWAS have the potential to highlight causal genes for PDs. These prioritized genes could indicate targets for the development of novel drug therapies. For researchers attempting such analyses, we recommend Mendelian randomization tools that use GWAS statistics for both trait and reference datasets, e.g., summary Mendelian randomization (SMR). We base our recommendation on (i) being able to use the same tool for both TWAS and PWAS, (ii) not requiring the pre-computed weights (and thus easier to update for larger reference datasets), and (iii) most larger transcriptome reference datasets are publicly available and easy to transform into a compatible format for SMR analysis.

Statistical and Computational Methods for Microbial Strain Analysis

Microbial strains are interpreted as a lineage derived from a recent ancestor that have not experienced "too many" recombination events and can be successfully retrieved with culture-independent techniques using metagenomic sequencing. Such a strain variability has been increasingly shown to display additional phenotypic heterogeneities that affect host health, such as virulence, transmissibility, and antibiotics resistance. New statistical and computational methods have recently been developed to track the strains in samples based on shotgun metagenomics data either based on reference genome sequences or Metagenome-assembled genomes (MAGs). In this paper, we review some recent statistical methods for strain identifications based on frequency counts at a set of single nucleotide variants (SNVs) within a set of single-copy marker genes. These methods differ in terms of whether reference genome sequences are needed, how SNVs are called, what methods of deconvolution are used and whether the methods can be applied to multiple samples. We conclude our review with areas that require further research.

A fast and efficient approach for gene-based association studies of ordinal phenotypes

Many human disease conditions need to be measured by ordinal phenotypes, so analysis of ordinal phenotypes is valuable in genome-wide association studies (GWAS). However, existing association methods for dichotomous or quantitative phenotypes are not appropriate to ordinal phenotypes. Therefore, based on an aggregated Cauchy association test, we propose a fast and efficient association method to test the association between genetic variants and an ordinal phenotype. To enrich association signals of rare variants, we first use the burden method to aggregate rare variants. Then we respectively test the significance of the aggregated rare variants and other common variants. Finally, the combination of transformed variant-level P values is taken as test statistic, that approximately follows Cauchy distribution under the null hypothesis. Extensive simulation studies and analysis of GAW19 show that our proposed method is powerful and computationally fast as a gene-based method. Especially, in the presence of an extremely low proportion of causal variants in a gene, our method has better performance.

Robust genetic model-based SNP-set association test using CauchyGM

MOTIVATION

Association testing on genome-wide association studies (GWAS) data is commonly performed under a single (mostly additive) genetic model framework. However, the underlying true genetic mechanisms are often unknown in practice for most complex traits. When the employed inheritance model deviates from the underlying model, statistical power may be reduced. To overcome this challenge, an integrative association test that directly infers the underlying genetic model from GWAS data has previously been proposed for single-SNP analysis.

RESULTS

In this article, we propose a Cauchy combination Genetic Model-based association test (CauchyGM) under a generalized linear model framework for SNP-set level analysis. CauchyGM does not require prior knowledge on the underlying inheritance pattern of each SNP. It performs a score test that first estimates an individual P-value of each SNP in an SNP-set with both minor allele frequency (MAF) > 1% and three genotypes and further aggregates the rest SNPs using SKAT. CauchyGM then combines the correlated P-values across multiple SNPs and different genetic models within the set using Cauchy Combination Test. To further accommodate both sparse and dense signal patterns, we also propose an omnibus association test (CauchyGM-O) by combining CauchyGM with SKAT and the burden test. Our extensive simulations show that both CauchyGM and CauchyGM-O maintain the type I error well at the genome-wide significance level and provide substantial power improvement compared to existing methods. We apply our methods to a pharmacogenomic GWAS data from a large cardiovascular randomized clinical trial. Both CauchyGM and CauchyGM-O identify several novel genome-wide significant genes.

AVAILABILITY AND IMPLEMENTATION

The R package CauchyGM is publicly available on github: https://github.com/ykim03517/CauchyGM.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Pharmacogenomic and Statistical Analysis

Genetic variants can alter response to drugs and other therapeutic interventions. The study of this phenomenon, called pharmacogenomics, is similar in many ways to other types of genetic studies but has distinct methodological and statistical considerations. Genetic variants involved in the processing of exogenous compounds exhibit great diversity and complexity, and the phenotypes studied in pharmacogenomics are also more complex than typical genetic studies. In this chapter, we review basic concepts in pharmacogenomic study designs, data generation techniques, statistical analysis approaches, and commonly used methods and briefly discuss the ultimate translation of findings to clinical care.

Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies

Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.

Disease patterns of coronary heart disease and type 2 diabetes harbored distinct and shared genetic architecture

BACKGROUND

Coronary heart disease (CHD) and type 2 diabetes (T2D) are two complex diseases with complex interrelationships. However, the genetic architecture of the two diseases is often studied independently by the individual single-nucleotide polymorphism (SNP) approach. Here, we presented a genotypic-phenotypic framework for deciphering the genetic architecture underlying the disease patterns of CHD and T2D.

METHOD

A data-driven SNP-set approach was performed in a genome-wide association study consisting of subpopulations with different disease patterns of CHD and T2D (comorbidity, CHD without T2D, T2D without CHD and all none). We applied nonsmooth nonnegative matrix factorization (nsNMF) clustering to generate SNP sets interacting the information of SNP and subject. Relationships between SNP sets and phenotype sets harboring different disease patterns were then assessed, and we further co-clustered the SNP sets into a genetic network to topologically elucidate the genetic architecture composed of SNP sets.

RESULTS

We identified 23 non-identical SNP sets with significant association with CHD or T2D (SNP-set based association test, P < 3.70 × [Formula: see text]). Among them, disease patterns involving CHD and T2D were related to distinct SNP sets (Hypergeometric test, P < 2.17 × [Formula: see text]). Accordingly, numerous genes (e.g., KLKs, GRM8, SHANK2) and pathways (e.g., fatty acid metabolism) were diversely implicated in different subtypes and related pathophysiological processes. Finally, we showed that the genetic architecture for disease patterns of CHD and T2D was composed of disjoint genetic networks (heterogeneity), with common genes contributing to it (pleiotropy).

CONCLUSION

The SNP-set approach deciphered the complexity of both genotype and phenotype as well as their complex relationships. Different disease patterns of CHD and T2D share distinct genetic architectures, for which lipid metabolism related to fibrosis may be an atherogenic pathway that is specifically activated by diabetes. Our findings provide new insights for exploring new biological pathways.

A Poisson reduced-rank regression model for association mapping in sequencing data

BACKGROUND

Single-cell RNA-sequencing (scRNA-seq) technologies allow for the study of gene expression in individual cells. Often, it is of interest to understand how transcriptional activity is associated with cell-specific covariates, such as cell type, genotype, or measures of cell health. Traditional approaches for this type of association mapping assume independence between the outcome variables (or genes), and perform a separate regression for each. However, these methods are computationally costly and ignore the substantial correlation structure of gene expression. Furthermore, count-based scRNA-seq data pose challenges for traditional models based on Gaussian assumptions.

RESULTS

We aim to resolve these issues by developing a reduced-rank regression model that identifies low-dimensional linear associations between a large number of cell-specific covariates and high-dimensional gene expression readouts. Our probabilistic model uses a Poisson likelihood in order to account for the unique structure of scRNA-seq counts. We demonstrate the performance of our model using simulations, and we apply our model to a scRNA-seq dataset, a spatial gene expression dataset, and a bulk RNA-seq dataset to show its behavior in three distinct analyses.

CONCLUSION

We show that our statistical modeling approach, which is based on reduced-rank regression, captures associations between gene expression and cell- and sample-specific covariates by leveraging low-dimensional representations of transcriptional states.

Kalpra: A kernel approach for longitudinal pathway regression analysis integrating network information with an application to the longitudinal PsyCourse Study

A popular approach to reduce the high dimensionality resulting from genome-wide association studies is to analyze a whole pathway in a single test for association with a phenotype. Kernel machine regression (KMR) is a highly flexible pathway analysis approach. Initially, KMR was developed to analyze a simple phenotype with just one measurement per individual. Recently, however, the investigation into the influence of genomic factors in the development of disease-related phenotypes across time (trajectories) has gained in importance. Thus, novel statistical approaches for KMR analyzing longitudinal data, i.e. several measurements at specific time points per individual are required. For longitudinal pathway analysis, we extend KMR to long-KMR using the estimation equivalence of KMR and linear mixed models. We include additional random effects to correct for the dependence structure. Moreover, within long-KMR we created a topology-based pathway analysis by combining this approach with a kernel including network information of the pathway. Most importantly, long-KMR not only allows for the investigation of the main genetic effect adjusting for time dependencies within an individual, but it also allows to test for the association of the pathway with the longitudinal course of the phenotype in the form of testing the genetic time-interaction effect. The approach is implemented as an R package, kalpra. Our simulation study demonstrates that the power of long-KMR exceeded that of another KMR method previously developed to analyze longitudinal data, while maintaining (slightly conservatively) the type I error. The network kernel improved the performance of long-KMR compared to the linear kernel. Considering different pathway densities, the power of the network kernel decreased with increasing pathway density. We applied long-KMR to cognitive data on executive function (Trail Making Test, part B) from the PsyCourse Study and 17 candidate pathways selected from Reactome. We identified seven nominally significant pathways.

Exploiting family history in aggregation unit-based genetic association tests

The development of sequencing technology calls for new powerful methods to detect disease associations and lower the cost of sequencing studies. Family history (FH) contains information on disease status of relatives, adding valuable information about the probands' health problems and risk of diseases. Incorporating data from FH is a cost-effective way to improve statistical evidence in genetic studies, and moreover, overcomes limitations in study designs with insufficient cases or missing genotype information for association analysis. We proposed family history aggregation unit-based test (FHAT) and optimal FHAT (FHAT-O) to exploit available FH for rare variant association analysis. Moreover, we extended liability threshold model of case-control status and FH (LT-FH) method in aggregated unit-based methods and compared that with FHAT and FHAT-O. The computational efficiency and flexibility of the FHAT and FHAT-O were demonstrated through both simulations and applications. We showed that FHAT, FHAT-O, and LT-FH methods offer reasonable control of the type I error unless case/control ratio is unbalanced, in which case they result in smaller inflation than that observed with conventional methods excluding FH. We also demonstrated that FHAT and FHAT-O are more powerful than LT-FH and conventional methods in many scenarios. By applying FHAT and FHAT-O to the analysis of all cause dementia and hypertension using the exome sequencing data from the UK Biobank, we showed that our methods can improve significance for known regions. Furthermore, we replicated the previous associations in all cause dementia and hypertension and detected novel regions through the exome-wide analysis.

Association of rare RNF213 variants and intracranial aneurysm risk in a Chinese population

Background

Genetic factors play important roles in the development of intracranial aneurysm (IA). Rare RNF213 variants have been identified as being susceptible to Moyamoya disease (MMD), non-MMD intracranial artery stenosis/occlusion disease, and other vascular disorders. This study aimed to investigate the association between rare RNF213 variants and the risk of IA in a Chinese population.

Methods

We recruited 174 patients with IA for RNF213 target exome sequencing. Information on the control subjects was obtained from the 1,000 Genome Project and GeneSky in-house database. After prioritizing rare RNF213 variants, the filtered variants were confirmed by Sanger sequencing. Gene-based association analyses were performed to identify the association between variants and the disease using burden and variance component methods; that is, the weighted-sum statistic (WSS) and the sequence kernel association test (SKAT), respectively. The Student's t-test, Chi-squared test, and Fisher's exact test were used to compare the clinical characteristics between carriers and non-carriers of the RNF213 variants.

Results

After filtering, there were 14 RNF213 variants in 18 patients with IA, which were significantly associated with the disease after the gene-based association tests [minor allele frequency (MAF) <0.01, WSS P value 5.08×10^-9; SKAT P value 2.96×10^-6; SKAT-O P value 3.56×10^-8]. Significant difference was not obtained between the carriers and non-carriers of the RNF213 variants in terms of the clinical characteristics.

Conclusions

Rare RNF213 variants were associated with sporadic IA in a Chinese population. Our findings suggest that these rare RNF213 variants might have potentially important roles in IA. However, more comprehensive studies need to be conducted to confirm this association and causality.

Genetic heterogeneity: Challenges, impacts, and methods through an associative lens

Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.

A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.

Whole-exome sequencing study identifies rare variants and genes associated with intraocular pressure and glaucoma

Elevated intraocular pressure (IOP) is a major risk factor for glaucoma, the leading cause of irreversible blindness worldwide. IOP is also the only modifiable risk factor for glaucoma. Previous genome-wide association studies have established the contribution of common genetic variants to IOP. The role of rare variants for IOP was unknown. Using whole exome sequencing data from 110,260 participants in the UK Biobank (UKB), we conducted the largest exome-wide association study of IOP to date. In addition to confirming known IOP genes, we identified 40 novel rare-variant genes for IOP, such as BOD1L1, ACAD10 and HLA-B, demonstrating the power of including and aggregating rare variants in gene discovery. About half of these IOP genes are also associated with glaucoma phenotypes in UKB and the FinnGen cohort. Six of these genes, i.e. ADRB1, PTPRB, RPL26, RPL10A, EGLN2, and MTOR, are drug targets that are either established for clinical treatment or in clinical trials. Furthermore, we constructed a rare-variant polygenic risk score and showed its significant association with glaucoma in independent participants (n = 312,825). We demonstrated the value of rare variants to enhance our understanding of the biological mechanisms regulating IOP and uncovered potential therapeutic targets for glaucoma.

Deconstructing a Syndrome: Genomic Insights Into PCOS Causal Mechanisms and Classification

Polycystic ovary syndrome (PCOS) is among the most common disorders in women of reproductive age, affecting up to 15% worldwide, depending on the diagnostic criteria. PCOS is characterized by a constellation of interrelated reproductive abnormalities, including disordered gonadotropin secretion, increased androgen production, chronic anovulation, and polycystic ovarian morphology. It is frequently associated with insulin resistance and obesity. These reproductive and metabolic derangements cause major morbidities across the lifespan, including anovulatory infertility and type 2 diabetes (T2D). Despite decades of investigative effort, the etiology of PCOS remains unknown. Familial clustering of PCOS cases has indicated a genetic contribution to PCOS. There are rare Mendelian forms of PCOS associated with extreme phenotypes, but PCOS typically follows a non-Mendelian pattern of inheritance consistent with a complex genetic architecture, analogous to T2D and obesity, that reflects the interaction of susceptibility genes and environmental factors. Genomic studies of PCOS have provided important insights into disease pathways and have indicated that current diagnostic criteria do not capture underlying differences in biology associated with different forms of PCOS. We provide a state-of-the-science review of genetic analyses of PCOS, including an overview of genomic methodologies aimed at a general audience of non-geneticists and clinicians. Applications in PCOS will be discussed, including strengths and limitations of each study. The contributions of environmental factors, including developmental origins, will be reviewed. Insights into the pathogenesis and genetic architecture of PCOS will be summarized. Future directions for PCOS genetic studies will be outlined.

Clinical significance of the series of CYP2C9*non3 variants, an unignorable predictor of warfarin sensitivity in Chinese population

Backgrounds

Gene polymorphisms are critical for variations in warfarin dose. To date, more than 70 CYP2C9 alleles have been identified. This study was designed to clarify the clinical significance of CYP2C9*non-3 variants to warfarin sensitivity in Chinese Han patients.

Methods

The entire CYP2C9 gene region was sequenced in 1,993 individuals, and clinical data and VKORC1 genotypes were collected from 986 patients with atrial fibrillation treated with warfarin. The SKAT-O method was used to analyze the effects of CYP2C9*non-3 variants on warfarin sensitivity.

Results

A total of 20 CYP2C9 variants were identified, of which four were novel. Carriers with CYP2C9*non-3 variants may have lower warfarin dose requirements, and similar to CYP2C9*3, CYP2C9*non-3 variants are clearly relevant to warfarin-sensitive and highly sensitive responders.

Conclusion

Our results showed that, besides CYP2C9*3, the series of CYP2C9*non-3 variants is an unignorable predictor for warfarin sensitivity in Chinese population. From a safety consideration, people carried such variants may need a preferred choice of NOACs when they started anticoagulation therapy.

Whole-exome sequencing identifies novel protein-altering variants associated with serum apolipoprotein and lipid concentrations

BACKGROUND

Dyslipidemia is a major risk factor for cardiovascular disease, and diabetes impacts the lipid metabolism through multiple pathways. In addition to the standard lipid measurements, apolipoprotein concentrations provide added awareness of the burden of circulating lipoproteins. While common genetic variants modestly affect the serum lipid concentrations, rare genetic mutations can cause monogenic forms of hypercholesterolemia and other genetic disorders of lipid metabolism. We aimed to identify low-frequency protein-altering variants (PAVs) affecting lipoprotein and lipid traits.

METHODS

We analyzed whole-exome (WES) and whole-genome sequencing (WGS) data of 481 and 474 individuals with type 1 diabetes, respectively. The phenotypic data consisted of 79 serum lipid and apolipoprotein phenotypes obtained with clinical laboratory measurements and nuclear magnetic resonance spectroscopy.

RESULTS

The single-variant analysis identified an association between the LIPC p.Thr405Met (rs113298164) and serum apolipoprotein A1 concentrations (p=7.8×10-8). The burden of PAVs was significantly associated with lipid phenotypes in LIPC, RBM47, TRMT5, GTF3C5, MARCHF10, and RYR3 (p<2.9×10-6). The RBM47 gene is required for apolipoprotein B post-translational modifications, and in our data, the association between RBM47 and apolipoprotein C-III concentrations was due to a rare 21 base pair p.Ala496-Ala502 deletion; in replication, the burden of rare deleterious variants in RBM47 was associated with lower triglyceride concentrations in WES of >170,000 individuals from multiple ancestries (p=0.0013). Two PAVs in GTF3C5 were highly enriched in the Finnish population and associated with cardiovascular phenotypes in the general population. In the previously known APOB gene, we identified novel associations at two protein-truncating variants resulting in lower serum non-HDL cholesterol (p=4.8×10-4), apolipoprotein B (p=5.6×10-4), and LDL cholesterol (p=9.5×10-4) concentrations.

CONCLUSIONS

We identified lipid and apolipoprotein-associated variants in the previously known LIPC and APOB genes, as well as PAVs in GTF3C5 associated with LDLC, and in RBM47 associated with apolipoprotein C-III concentrations, implicated as an independent CVD risk factor. Identification of rare loss-of-function variants has previously revealed genes that can be targeted to prevent CVD, such as the LDL cholesterol-lowering loss-of-function variants in the PCSK9 gene. Thus, this study suggests novel putative therapeutic targets for the prevention of CVD.

Simultaneous detection of novel genes and SNPs by adaptive p-value combination

Combining SNP p-values from GWAS summary data is a promising strategy for detecting novel genetic factors. Existing statistical methods for the p-value-based SNP-set testing confront two challenges. First, the statistical power of different methods depends on unknown patterns of genetic effects that could drastically vary over different SNP sets. Second, they do not identify which SNPs primarily contribute to the global association of the whole set. We propose a new signal-adaptive analysis pipeline to address these challenges using the omnibus thresholding Fisher’s method (oTFisher). The oTFisher remains robustly powerful over various patterns of genetic effects. Its adaptive thresholding can be applied to estimate important SNPs contributing to the overall significance of the given SNP set. We develop efficient calculation algorithms to control the type I error rate, which accounts for the linkage disequilibrium among SNPs. Extensive simulations show that the oTFisher has robustly high power and provides a higher balanced accuracy in screening SNPs than the traditional Bonferroni and FDR procedures. We applied the oTFisher to study the genetic association of genes and haplotype blocks of the bone density-related traits using the summary data of the Genetic Factors for Osteoporosis Consortium. The oTFisher identified more novel and literature-reported genetic factors than existing p-value combination methods. Relevant computation has been implemented into the R package TFisher to support similar data analysis.

Identification of PCSK9-like human gene knockouts using metabolomics, proteomics, and whole-genome sequencing in a consanguineous population

Natural human knockouts of genes associated with desirable outcomes, such as PCSK9 with low levels of LDL-cholesterol, can lead to the discovery of new drug targets and treatments. Rare loss-of-function variants are more likely to be found in the homozygous state in consanguineous populations, and deep molecular phenotyping of blood samples from homozygous carriers can help to discriminate between silent and functional variants. Here, we combined whole-genome sequencing with proteomics and metabolomics for 2,935 individuals from the Qatar Biobank (QBB) to evaluate the power of this approach for finding genes of clinical and pharmaceutical interest. As proof-of-concept, we identified a homozygous carrier of a very rare PCSK9 variant with extremely low circulating PCSK9 levels and low LDL. Our study demonstrates that the chances of finding such variants are about 168 times higher in QBB compared with GnomAD and emphasizes the potential of consanguineous populations for drug discovery.

Brody J, Chen H, Gottlieb DJ, Evans DS, Guo X, Gharib SA, Hale L, Hillman DR, Lutsey PL, Mukherjee S, Ochs-Balcom HM, Palmer LJ, Purcell S, Saxena R, Patel SR, Stone KL, Tranah GJ, Boerwinkle E, Lin X, Liu Y, Psaty BM, Vasan RS, Manichaikul A, Rich SS, Rotter JI, Sofer T, Redline S, Zhu X. Targeted Genome Sequencing Identifies Multiple Rare Variants in Caveolin-1 Associated with Obstructive Sleep Apnea

Rationale: Obstructive sleep apnea (OSA) is a common disorder associated with increased risk for cardiovascular disease, diabetes, and premature mortality. There is strong clinical and epidemiologic evidence supporting the importance of genetic factors influencing OSA but limited data implicating specific genes. Objectives: To search for rare variants contributing to OSA severity. Methods: Leveraging high-depth genomic sequencing data from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and imputed genotype data from multiple population-based studies, we performed linkage analysis in the CFS (Cleveland Family Study), followed by multistage gene-based association analyses in independent cohorts for apnea-hypopnea index (AHI) in a total of 7,708 individuals of European ancestry. Measurements and Main Results: Linkage analysis in the CFS identified a suggestive linkage peak on chromosome 7q31 (LOD = 2.31). Gene-based analysis identified 21 noncoding rare variants in CAV1 (Caveolin-1) associated with lower AHI after accounting for multiple comparisons (P = 7.4 × 10-8). These noncoding variants together significantly contributed to the linkage evidence (P < 10-3). Follow-up analysis revealed significant associations between these variants and increased CAV1 expression, and increased CAV1 expression in peripheral monocytes was associated with lower AHI (P = 0.024) and higher minimum overnight oxygen saturation (P = 0.007). Conclusions: Rare variants in CAV1, a membrane-scaffolding protein essential in multiple cellular and metabolic functions, are associated with higher CAV1 gene expression and lower OSA severity, suggesting a novel target for modulating OSA severity.

Impact of conditioning chemotherapy on lymphocyte kinetics and outcomes in LBCL patients treated with CAR T-cell therapy

Conditioning chemotherapy (CCT) has been shown to be essential for optimal efficacy of chimeric antigen receptor (CAR) T-cell therapy. Here, we determined whether the change in absolute lymphocyte count, referred to as delta lymphocyte index (DLIx), may serve as a surrogate marker for pharmacodynamic effects of CCT and whether it associated with germline genetic variants in patients with large B-cell lymphoma (LBCL). One-hundred and seventy-one patients were included, of which 86 (50%) received bridging therapy post-leukapheresis. Median DLIx was 0.5 × 10⁹/L (range, 0.01-2.75 × 10⁹/L) and was significantly higher in patients who achieved complete response (p = 0.04). On multivariate analysis, low DLIx was associated only with use of bridging therapy (odds ratio 0.4, 95% CI 0.2-0.8, p = 0.007). Low DLIx was independently associated with shorter progression-free (p = 0.02) and overall survival (p = 0.02). DLIx was associated with genetic variations related to drug metabolism and macrophage biology such as ABCB1, MISP and CPVL. The impact of CCT on lymphocyte count is affected by use of bridging therapy but change in lymphocyte count is independently associated with efficacy. Studies aimed at investigating macrophage biology in this setting may suggest strategies to increase the efficacy of CCT and improve outcomes.

Rare Variants in Genes Encoding Subunits of the Epithelial Na+ Channel Are Associated With Blood Pressure and Kidney Function

BACKGROUND

The epithelial Na+ channel (ENaC) is intrinsically linked to fluid volume homeostasis and blood pressure. Specific rare mutations in SCNN1A, SCNN1B, and SCNN1G, genes encoding the α, β, and γ subunits of ENaC, respectively, are associated with extreme blood pressure phenotypes. No associations between blood pressure and SCNN1D, which encodes the δ subunit of ENaC, have been reported. A small number of sequence variants in ENaC subunits have been reported to affect functional transport in vitro or blood pressure. The effects of the vast majority of rare and low-frequency ENaC variants on blood pressure are not known.

METHODS

We explored the association of low frequency and rare variants in the genes encoding ENaC subunits, with systolic blood pressure, diastolic blood pressure, mean arterial pressure, and pulse pressure. Using whole-genome sequencing data from 14 studies participating in the Trans-Omics in Precision Medicine Whole-Genome Sequencing Program, and sequence kernel association tests.

RESULTS

We found that variants in SCNN1A and SCNN1B were associated with diastolic blood pressure and mean arterial pressure (P<0.00625). Although SCNN1D is poorly expressed in human kidney tissue, SCNN1D variants were associated with systolic blood pressure, diastolic blood pressure, mean arterial pressure, and pulse pressure (P<0.00625). ENaC variants in 2 of the 4 subunits (SCNN1B and SCNN1D) were also associated with estimated glomerular filtration rate (P<0.00625), but not with stroke.

CONCLUSIONS

Our results suggest that variants in extrarenal ENaCs, in addition to ENaCs expressed in kidneys, influence blood pressure and kidney function.

Genome-wide rare variant score associates with morphological subtypes of autism spectrum disorder

Defining different genetic subtypes of autism spectrum disorder (ASD) can enable the prediction of developmental outcomes. Based on minor physical and major congenital anomalies, we categorize 325 Canadian children with ASD into dysmorphic and nondysmorphic subgroups. We develop a method for calculating a patient-level, genome-wide rare variant score (GRVS) from whole-genome sequencing (WGS) data. GRVS is a sum of the number of variants in morphology-associated coding and non-coding regions, weighted by their effect sizes. Probands with dysmorphic ASD have a significantly higher GRVS compared to those with nondysmorphic ASD (P = 0.03). Using the polygenic transmission disequilibrium test, we observe an over-transmission of ASD-associated common variants in nondysmorphic ASD probands (P = 2.9 × 10^-3). These findings replicate using WGS data from 442 ASD probands with accompanying morphology data from the Simons Simplex Collection. Our results provide support for an alternative genomic classification of ASD subgroups using morphology data, which may inform intervention protocols.

Whole genome sequence analysis of blood lipid levels in >66,000 individuals

Blood lipids are heritable modifiable causal factors for coronary artery disease. Despite well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing (WGS), partly due to limited sample sizes, ancestral diversity, and interpretation of clinical significance. Among 66,329 ancestrally diverse (56% non-European) participants, we associate 428M variants from deep-coverage WGS with lipid levels; ~400M variants were not assessed in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with blood lipids through analysis of common and rare coding variants. We discover several associated rare non-coding variants, largely at Mendelian lipid genes. Notably, we observe rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for blood lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.

Genetic and clinical determinants of abdominal aortic diameter: genome-wide association studies, exome array data and Mendelian randomization study

Progressive dilation of the infrarenal aortic diameter is a consequence of the ageing process and is considered the main determinant of abdominal aortic aneurysm (AAA). We aimed to investigate the genetic and clinical determinants of abdominal aortic diameter (AAD). We conducted a meta-analysis of genome-wide association studies in 10 cohorts (n = 13 542) imputed to the 1000 Genome Project reference panel including 12 815 subjects in the discovery phase and 727 subjects [Partners Biobank cohort 1 (PBIO)] as replication. Maximum anterior-posterior diameter of the infrarenal aorta was used as AAD. We also included exome array data (n = 14 480) from seven epidemiologic studies. Single-variant and gene-based associations were done using SeqMeta package. A Mendelian randomization analysis was applied to investigate the causal effect of a number of clinical risk factors on AAD. In genome-wide association study (GWAS) on AAD, rs74448815 in the intronic region of LDLRAD4 reached genome-wide significance (beta = -0.02, SE = 0.004, P-value = 2.10 × 10-8). The association replicated in the PBIO1 cohort (P-value = 8.19 × 10-4). In exome-array single-variant analysis (P-value threshold = 9 × 10-7), the lowest P-value was found for rs239259 located in SLC22A20 (beta = 0.007, P-value = 1.2 × 10-5). In the gene-based analysis (P-value threshold = 1.85 × 10-6), PCSK5 showed an association with AAD (P-value = 8.03 × 10-7). Furthermore, in Mendelian randomization analyses, we found evidence for genetic association of pulse pressure (beta = -0.003, P-value = 0.02), triglycerides (beta = -0.16, P-value = 0.008) and height (beta = 0.03, P-value < 0.0001), known risk factors for AAA, consistent with a causal association with AAD. Our findings point to new biology as well as highlighting gene regions in mechanisms that have previously been implicated in the genetics of other vascular diseases.

Recent advances and challenges of rare variant association analysis in the biobank sequencing era

Causal variants for rare genetic diseases are often rare in the general population. Rare variants may also contribute to common complex traits and can have much larger per-allele effect sizes than common variants, although power to detect these associations can be limited. Sequencing costs have steadily declined with technological advancements, making it feasible to adopt whole-exome and whole-genome profiling for large biobank-scale sample sizes. These large amounts of sequencing data provide both opportunities and challenges for rare-variant association analysis. Herein, we review the basic concepts of rare-variant analysis methods, the current state-of-the-art methods in utilizing variant annotations or external controls to improve the statistical power, and particular challenges facing rare variant analysis such as accounting for population structure, extremely unbalanced case-control design. We also review recent advances and challenges in rare variant analysis for familial sequencing data and for more complex phenotypes such as survival data. Finally, we discuss other potential directions for further methodology investigation.

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.

Genetics in parkinson's disease: From better disease understanding to machine learning based precision medicine

Parkinson's Disease (PD) is a neurodegenerative disorder with highly heterogeneous phenotypes. Accordingly, it has been challenging to robustly identify genetic factors associated with disease risk, prognosis and therapy response via genome-wide association studies (GWAS). In this review we first provide an overview of existing statistical methods to detect associations between genetic variants and the disease phenotypes in existing PD GWAS. Secondly, we discuss the potential of machine learning approaches to better quantify disease phenotypes and to move beyond disease understanding towards a better-personalized treatment of the disease.

SAIGE-GENE+ improves the efficiency and accuracy of set-based rare variant association tests

Several biobanks, including UK Biobank (UKBB), are generating large-scale sequencing data. An existing method, SAIGE-GENE, performs well when testing variants with minor allele frequency (MAF) ≤ 1%, but inflation is observed in variance component set-based tests when restricting to variants with MAF ≤ 0.1% or 0.01%. Here, we propose SAIGE-GENE+ with greatly improved type I error control and computational efficiency to facilitate rare variant tests in large-scale data. We further show that incorporating multiple MAF cutoffs and functional annotations can improve power and thus uncover new gene-phenotype associations. In the analysis of UKBB whole exome sequencing data for 30 quantitative and 141 binary traits, SAIGE-GENE+ identified 551 gene-phenotype associations.

A tree-based gene-environment interaction analysis with rare features

Gene-environment (G-E) interaction analysis plays a critical role in understanding and modeling complex diseases. Compared to main-effect-only analysis, it is more seriously challenged by higher dimensionality, weaker signals, and the unique "main effects, interactions" variable selection hierarchy. In joint G-E interaction analysis under which a large number of G factors are analysed in a single model, effort tailored to rare features (e.g., SNPs with low minor allele frequencies) has been limited. Existing investigations on rare features have been mostly focused on marginal analysis, where various data aggregation techniques have been developed, and hypothesis testings have been conducted to identify significant aggregated features. However, such techniques cannot be extended to joint G-E interaction analysis. In this study, building on a very recent tree-based data aggregation technique, which has been developed for main-effect-only analysis, we develop a new G-E interaction analysis approach tailored to rare features. The adopted data aggregation technique allows for more efficient information borrowing from neighboring rare features. Similar to some existing state-of-the-art ones, the proposed approach adopts penalization for variable selection, regularized estimation, and respect of the variable selection hierarchy. Simulation shows that it has more accurate identification of important interactions and main effects than several competing alternatives. In the analysis of NFBC1966 study, the proposed approach leads to findings different from the alternatives and with satisfactory prediction and stability performance.

Rare germline variants in PALB2 and BRCA2 in familial and sporadic chordoma

Chordoma is a rare bone tumor with genetic risk factors largely unknown. We conducted a whole-exome sequencing (WES) analysis of germline DNA from 19 familial chordoma cases in five pedigrees and 137 sporadic chordoma patients and identified 17 rare germline variants in PALB2 and BRCA2, whose products play essential roles in homologous recombination (HR) and tumor suppression. One PALB2 variant showed disease cosegregation in a family with four affected people or obligate gene carrier. Chordoma cases had a significantly increased burden of rare variants in both genes when compared to population-based controls. Four of the six PALB2 variants identified from chordoma patients modestly affected HR function and three of the 11 BRCA2 variants caused loss of function in experimental assays. These results, together with previous reports of abnormal morphology and Brachyury expression of the notochord in Palb2 knockout mouse embryos and genomic signatures associated with HR defect and HR gene mutations in advanced chordomas, suggest that germline mutations in PALB2 and BRCA2 may increase chordoma susceptibility. Our data shed light on the etiology of chordoma and support the previous finding that PARP-1 inhibitors may be a potential therapy for some chordoma patients.

Detecting associated genes for complex traits shared across East Asian and European populations under the framework of composite null hypothesis testing

Background

Detecting trans-ethnic common associated genetic loci can offer important insights into shared genetic components underlying complex diseases/traits across diverse continental populations. However, effective statistical methods for such a goal are currently lacking.

Methods

By leveraging summary statistics available from global-scale genome-wide association studies, we herein proposed a novel genetic overlap detection method called CONTO (COmposite Null hypothesis test for Trans-ethnic genetic Overlap) from the perspective of high-dimensional composite null hypothesis testing. Unlike previous studies which generally analyzed individual genetic variants, CONTO is a gene-centric method which focuses on a set of genetic variants located within a gene simultaneously and assesses their joint significance with the trait of interest. By borrowing the similar principle of joint significance test (JST), CONTO takes the maximum P value of multiple associations as the significance measurement.

Results

Compared to JST which is often overly conservative, CONTO is improved in two aspects, including the construction of three-component mixture null distribution and the adjustment of trans-ethnic genetic correlation. Consequently, CONTO corrects the conservativeness of JST with well-calibrated P values and is much more powerful validated by extensive simulation studies. We applied CONTO to discover common associated genes for 31 complex diseases/traits between the East Asian and European populations, and identified many shared trait-associated genes that had otherwise been missed by JST. We further revealed that population-common genes were generally more evolutionarily conserved than population-specific or null ones.

Conclusion

Overall, CONTO represents a powerful method for detecting common associated genes across diverse ancestral groups; our results provide important implications on the transferability of GWAS discoveries in one population to others.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03637-8.

Testing for association with rare variants in the coding and non-coding genome: RAVA-FIRST, a new approach based on CADD deleteriousness score

Rare variant association tests (RVAT) have been developed to study the contribution of rare variants widely accessible through high-throughput sequencing technologies. RVAT require to aggregate rare variants in testing units and to filter variants to retain only the most likely causal ones. In the exome, genes are natural testing units and variants are usually filtered based on their functional consequences. However, when dealing with whole-genome sequence (WGS) data, both steps are challenging. No natural biological unit is available for aggregating rare variants. Sliding windows procedures have been proposed to circumvent this difficulty, however they are blind to biological information and result in a large number of tests. We propose a new strategy to perform RVAT on WGS data: “RAVA-FIRST” (RAre Variant Association using Functionally-InfoRmed STeps) comprising three steps. (1) New testing units are defined genome-wide based on functionally-adjusted Combined Annotation Dependent Depletion (CADD) scores of variants observed in the gnomAD populations, which are referred to as “CADD regions”. (2) A region-dependent filtering of rare variants is applied in each CADD region. (3) A functionally-informed burden test is performed with sub-scores computed for each genomic category within each CADD region. Both on simulations and real data, RAVA-FIRST was found to outperform other WGS-based RVAT. Applied to a WGS dataset of venous thromboembolism patients, we identified an intergenic region on chromosome 18 enriched for rare variants in early-onset patients. This region that was missed by standard sliding windows procedures is included in a TAD region that contains a strong candidate gene. RAVA-FIRST enables new investigations of rare non-coding variants in complex diseases, facilitated by its implementation in the R package Ravages.

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.

G × EBLUP: A novel method for exploring genotype by environment interactions and genomic prediction

Genotype by environment (G × E) interaction is fundamental in the biology of complex traits and diseases. However, most of the existing methods for genomic prediction tend to ignore G × E interaction (GEI). In this study, we proposed the genomic prediction method G × EBLUP by considering GEI. Meanwhile, G × EBLUP can also detect the genome-wide single nucleotide polymorphisms (SNPs) subject to GEI. Using comprehensive simulations and analysis of real data from pigs and maize, we showed that G × EBLUP achieved higher efficiency in mapping GEI SNPs and higher prediction accuracy than the existing methods, and its superiority was more obvious when the GEI variance was large. For pig and maize real data, compared with GBLUP, G × EBLUP showed improvement by 3% in the prediction accuracy for backfat thickness, while our findings indicated that the trait of days to 100 kg of pig was not affected by GEI and G × EBLUP did not improve the accuracy of genomic prediction for the trait. A significant advantage was observed for G × EBLUP in maize; the prediction accuracy was improved by ∼5.0 and 7.7% for grain weight and water content, respectively. Furthermore, G × EBLUP was not influenced by the number of environment levels. It could determine a favourable environment using SNP Bayes factors for each environment, implying that it is a robust and useful method for market-specific animal and plant breeding. We proposed G × EBLUP, a novel method for the estimation of genomic breeding value by considering GEI. This method identified the genome-wide SNPs that were susceptible to GEI and yielded higher genomic prediction accuracies and lower mean squared error compared with the GBLUP method.