Germ line genetic NBN variation and predisposition to B-cell acute lymphoblastic leukemia in children.

ABSTRACT: Biallelic mutation in the DNA-damage repair gene NBN is the genetic cause of Nijmegen breakage syndrome, which is associated with predisposition to lymphoid malignancies. Heterozygous carriers of germ line NBN variants may also be at risk for leukemia development, although this is much less characterized. By sequencing 4325 pediatric patients with B-cell acute lymphoblastic leukemia (B-ALL), we systematically examined the frequency of germ line NBN variants and identified 25 unique, putatively damaging NBN coding variants in 50 patients. Compared with the frequency of NBN variants in gnomAD noncancer controls (189 unique, putatively damaging NBN coding variants in 472 of 118 479 individuals), we found significant overrepresentation in pediatric B-ALL (P = .004; odds ratio, 1.8). Most B-ALL-risk variants were missense and cluster within the NBN N-terminal domains. Using 2 functional assays, we verified 14 of 25 variants with severe loss-of-function phenotypes and thus classified these as nonfunctional or partially functional. Finally, we found that germ line NBN variant carriers, all of whom were identified as heterozygous genotypes, showed similar survival outcomes relative to those with wild type status. Taken together, our findings provide novel insights into the genetic predisposition to B-ALL, and the impact of NBN variants on protein function and suggest that heterozygous NBN variant carriers may safely receive B-ALL therapy. These trials were registered at www.clinicaltrials.gov as #NCT01225874, NCT00075725, NCT00103285, NCI-T93-0101D, and NCT00137111.

Discovery of novel predisposing coding and noncoding variants in familial Hodgkin lymphoma.

Familial aggregation of Hodgkin lymphoma (HL) has been demonstrated in large population studies, pointing to genetic predisposition to this hematological malignancy. To understand the genetic variants associated with the development of HL, we performed whole genome sequencing on 234 individuals with and without HL from 36 pedigrees that had 2 or more first-degree relatives with HL. Our pedigree selection criteria also required at least 1 affected individual aged <21 years, with the median age at diagnosis of 21.98 years (3-55 years). Family-based segregation analysis was performed for the identification of coding and noncoding variants using linkage and filtering approaches. Using our tiered variant prioritization algorithm, we identified 44 HL-risk variants in 28 pedigrees, of which 33 are coding and 11 are noncoding. The top 4 recurrent risk variants are a coding variant in KDR (rs56302315), a 5' untranslated region variant in KLHDC8B (rs387906223), a noncoding variant in an intron of PAX5 (rs147081110), and another noncoding variant in an intron of GATA3 (rs3824666). A newly identified splice variant in KDR (c.3849-2A>C) was observed for 1 pedigree, and high-confidence stop-gain variants affecting IRF7 (p.W238∗) and EEF2KMT (p.K116∗) were also observed. Multiple truncating variants in POLR1E were found in 3 independent pedigrees as well. Whereas KDR and KLHDC8B have previously been reported, PAX5, GATA3, IRF7, EEF2KMT, and POLR1E represent novel observations. Although there may be environmental factors influencing lymphomagenesis, we observed segregation of candidate germline variants likely to predispose HL in most of the pedigrees studied.

The genomic landscape of acute lymphoblastic leukemia with intrachromosomal amplification of chromosome 21.

Intrachromosomal amplification of chromosome 21 defines a subtype of high-risk childhood acute lymphoblastic leukemia (iAMP21-ALL) characterized by copy number changes and complex rearrangements of chromosome 21. The genomic basis of iAMP21-ALL and the pathogenic role of the region of amplification of chromosome 21 to leukemogenesis remains incompletely understood. In this study, using integrated whole genome and transcriptome sequencing of 124 patients with iAMP21-ALL, including rare cases arising in the context of constitutional chromosomal aberrations, we identified subgroups of iAMP21-ALL based on the patterns of copy number alteration and structural variation. This large data set enabled formal delineation of a 7.8 Mb common region of amplification harboring 71 genes, 43 of which were differentially expressed compared with non-iAMP21-ALL ones, including multiple genes implicated in the pathogenesis of acute leukemia (CHAF1B, DYRK1A, ERG, HMGN1, and RUNX1). Using multimodal single-cell genomic profiling, including single-cell whole genome sequencing of 2 cases, we documented clonal heterogeneity and genomic evolution, demonstrating that the acquisition of the iAMP21 chromosome is an early event that may undergo progressive amplification during disease ontogeny. We show that UV-mutational signatures and high mutation load are characteristic secondary genetic features. Although the genomic alterations of chromosome 21 are variable, these integrated genomic analyses and demonstration of an extended common minimal region of amplification broaden the definition of iAMP21-ALL for more precise diagnosis using cytogenetic or genomic methods to inform clinical management.

Metabolic heterogeneity underlies reciprocal fates of TH17 cell stemness and plasticity.

A defining feature of adaptive immunity is the development of long-lived memory T cells to curtail infection. Recent studies have identified a unique stem-like T-cell subset amongst exhausted CD8-positive T cells in chronic infection1-3, but it remains unclear whether CD4-positive T-cell subsets with similar features exist in chronic inflammatory conditions. Amongst helper T cells, TH17 cells have prominent roles in autoimmunity and tissue inflammation and are characterized by inherent plasticity4-7, although how such plasticity is regulated is poorly understood. Here we demonstrate that TH17 cells in a mouse model of autoimmune disease are functionally and metabolically heterogeneous; they contain a subset with stemness-associated features but lower anabolic metabolism, and a reciprocal subset with higher metabolic activity that supports transdifferentiation into TH1-like cells. These two TH17-cell subsets are defined by selective expression of the transcription factors TCF-1 and T-bet, and by discrete levels of CD27 expression. We also identify signalling via the kinase complex mTORC1 as a central regulator of TH17-cell fate decisions by coordinating metabolic and transcriptional programmes. TH17 cells with disrupted mTORC1 signalling or anabolic metabolism fail to induce autoimmune neuroinflammation or to develop into TH1-like cells, but instead upregulate TCF-1 expression and acquire stemness-associated features. Single-cell RNA sequencing and experimental validation reveal heterogeneity in fate-mapped TH17 cells, and a developmental arrest in the TH1 transdifferentiation trajectory upon loss of mTORC1 activity or metabolic perturbation. Our results establish that the dichotomy of stemness and effector function underlies the heterogeneous TH17 responses and autoimmune pathogenesis, and point to previously unappreciated metabolic control of plasticity in helper T cells.

From genome-wide associations to candidate causal variants by statistical fine-mapping.

Advancing from statistical associations of complex traits with genetic markers to understanding the functional genetic variants that influence traits is often a complex process. Fine-mapping can select and prioritize genetic variants for further study, yet the multitude of analytical strategies and study designs makes it challenging to choose an optimal approach. We review the strengths and weaknesses of different fine-mapping approaches, emphasizing the main factors that affect performance. Topics include interpreting results from genome-wide association studies (GWAS), the role of linkage disequilibrium, statistical fine-mapping approaches, trans-ethnic studies, genomic annotation and data integration, and other analysis and design issues.

Accurate genomic variant detection in single cells with primary template-directed amplification.

Improvements in whole genome amplification (WGA) would enable new types of basic and applied biomedical research, including studies of intratissue genetic diversity that require more accurate single-cell genotyping. Here, we present primary template-directed amplification (PTA), an isothermal WGA method that reproducibly captures >95% of the genomes of single cells in a more uniform and accurate manner than existing approaches, resulting in significantly improved variant calling sensitivity and precision. To illustrate the types of studies that are enabled by PTA, we developed direct measurement of environmental mutagenicity (DMEM), a tool for mapping genome-wide interactions of mutagens with single living human cells at base-pair resolution. In addition, we utilized PTA for genome-wide off-target indel and structural variant detection in cells that had undergone CRISPR-mediated genome editing, establishing the feasibility for performing single-cell evaluations of biopsies from edited tissues. The improved precision and accuracy of variant detection with PTA overcomes the current limitations of accurate WGA, which is the major obstacle to studying genetic diversity and evolution at cellular resolution.

Cancer germline predisposing variants and late mortality from subsequent malignant neoplasms among long-term childhood cancer survivors: a report from the St Jude Lifetime Cohort and the Childhood Cancer Survivor Study.

BACKGROUND: Carriers of cancer predisposing variants are at an increased risk of developing subsequent malignant neoplasms among those who have survived childhood cancer. We aimed to investigate whether cancer predisposing variants contribute to the risk of subsequent malignant neoplasm-related late mortality (5 years or more after diagnosis). METHODS: In this analysis, data were included from two retrospective cohort studies, St Jude Lifetime Cohort (SJLIFE) and the Childhood Cancer Survivor Study (CCSS), with prospective follow-up of patients who were alive for at least 5 years after diagnosis with childhood cancer (ie, long-term childhood cancer survivors) with corresponding germline whole genome or whole exome sequencing data. Cancer predisposing variants affecting 60 genes associated with well-established autosomal-dominant cancer-predisposition syndromes were characterised. Subsequent malignant neoplasms were graded using the National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) version 4.03 with modifications. Cause-specific late mortality was based on linkage with the US National Death Index and systematic cohort follow up. Fine-Gray subdistribution hazard models were used to estimate subsequent malignant neoplasm-related late mortality starting from the first biospecimen collection, treating non-subsequent malignant neoplasm-related deaths as a competing risk, adjusting for genetic ancestry, sex, age at diagnosis, and cancer treatment exposures. SJLIFE (NCT00760656) and CCSS (NCT01120353) are registered with ClinicalTrials.gov. FINDINGS: 12 469 (6172 male and 6297 female) participants were included, 4402 from the SJLIFE cohort (median follow-up time since collection of the first biospecimen 7·4 years [IQR 3·1-9·4]) and 8067 from the CCSS cohort (median follow-up time since collection of the first biospecimen 12·6 years [2·2-16·6]). 641 (5·1%) of 12 469 participants carried cancer predisposing variants (294 [6·7%] in the SJLIFE cohort and 347 [4·3%] in the CCSS cohort), which were significantly associated with an increased severity of subsequent malignant neoplasms (CTCAE grade ≥4 vs grade <4: odds ratio 2·15, 95% CI 1·18-4·19, p=0·0085). 263 (2·1%) subsequent malignant neoplasm-related deaths (44 [1·0%] in the SJLIFE cohort; and 219 [2·7%] in the CCSS cohort) and 426 (3·4%) other-cause deaths (103 [2·3%] in SJLIFE; and 323 [4·0%] in CCSS) occurred. Cumulative subsequent malignant neoplasm-related mortality at 10 years after the first biospecimen collection in carriers of cancer predisposing variants was 3·7% (95% CI 1·2-8·5) in SJLIFE and 6·9% (4·1-10·7) in CCSS versus 1·5% (1·0-2·1) in SJLIFE and 2·1% (1·7-2·5) in CCSS in non-carriers. Carrying a cancer predisposing variant was associated with an increased risk of subsequent malignant neoplasm-related mortality (SJLIFE: subdistribution hazard ratio 3·40 [95% CI 1·37-8·43]; p=0·0082; CCSS: 3·58 [2·27-5·63]; p<0·0001). INTERPRETATION: Identifying participants at increased risk of subsequent malignant neoplasms via genetic counselling and clinical genetic testing for cancer predisposing variants and implementing early personalised cancer surveillance and prevention strategies might reduce the substantial subsequent malignant neoplasm-related mortality burden. FUNDING: American Lebanese Syrian Associated Charities and US National Institutes of Health.

Latent cellular analysis robustly reveals subtle diversity in large-scale single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for characterizing the cell-to-cell variation and cellular dynamics in populations which appear homogeneous otherwise in basic and translational biological research. However, significant challenges arise in the analysis of scRNA-seq data, including the low signal-to-noise ratio with high data sparsity, potential batch effects, scalability problems when hundreds of thousands of cells are to be analyzed among others. The inherent complexities of scRNA-seq data and dynamic nature of cellular processes lead to suboptimal performance of many currently available algorithms, even for basic tasks such as identifying biologically meaningful heterogeneous subpopulations. In this study, we developed the Latent Cellular Analysis (LCA), a machine learning-based analytical pipeline that combines cosine-similarity measurement by latent cellular states with a graph-based clustering algorithm. LCA provides heuristic solutions for population number inference, dimension reduction, feature selection, and control of technical variations without explicit gene filtering. We show that LCA is robust, accurate, and powerful by comparison with multiple state-of-the-art computational methods when applied to large-scale real and simulated scRNA-seq data. Importantly, the ability of LCA to learn from representative subsets of the data provides scalability, thereby addressing a significant challenge posed by growing sample sizes in scRNA-seq data analysis.

Estimated number of adult survivors of childhood cancer in United States with cancer-predisposing germline variants.

PURPOSE: To estimate the absolute number of adult survivors of childhood cancer in the U.S. population who carry a pathogenic or likely pathogenic variant in a cancer predisposition gene. METHODS: Using the Surveillance, Epidemiology, and End Results (SEER) Program, we estimated the number of childhood cancer survivors on December 31, 2016 for each childhood cancer diagnosis, multiplied this by the proportion of carriers of pathogenic/likely pathogenic variants in the St. Jude Lifetime Cohort (SJLIFE) study, and projected the resulting number onto the U.S. RESULTS: Based on genome sequence data, 11.8% of 2450 SJLIFE participants carry a pathogenic/likely pathogenic variant in one of 156 cancer predisposition genes. Given this information, we estimate that 21 800 adult survivors of childhood cancer in the United States carry a pathogenic/likely pathogenic variant in one of these genes. The highest estimated absolute number of variant carriers are among survivors of central nervous system tumors (n = 4300), particularly astrocytoma (n = 1800) and other gliomas (n = 1700), acute lymphoblastic leukemia (n = 4300), and retinoblastoma (n = 3500). The most frequently mutated genes are RB1 (n = 3000), NF1 (n = 2300), and BRCA2 (n = 800). CONCLUSION: Given the increasing number of childhood cancer survivors in the United States, clinicians should counsel survivors regarding their potential genetic risk, consider referral for genetic counseling and testing, and, as appropriate, implement syndrome-specific cancer surveillance or risk-reducing measures.

Small Sample Kernel Association Tests for Human Genetic and Microbiome Association Studies.

Kernel machine based association tests (KAT) have been increasingly used in testing the association between an outcome and a set of biological measurements due to its power to combine multiple weak signals of complex relationship with the outcome through the specification of a relevant kernel. Human genetic and microbiome association studies are two important applications of KAT. However, the classic KAT framework relies on large sample theory, and conservativeness has been observed for small sample studies, especially for microbiome association studies. The common approach for addressing the small sample problem relies on computationally intensive resampling methods. Here, we derive an exact test for KAT with continuous traits, which resolve the small sample conservatism of KAT without the need for resampling. The exact test has significantly improved power to detect association for microbiome studies. For binary traits, we propose a similar approximate test, and we show that the approximate test is very powerful for a wide range of kernels including common variant- and microbiome-based kernels, and the approximate test controls the type I error well for these kernels. In contrast, the sequence kernel association tests have slightly inflated genomic inflation factors after small sample adjustment. Extensive simulations and application to a real microbiome association study are used to demonstrate the utility of our method.

A Generalized Sequential Bonferroni Procedure for GWAS in Admixed Populations Incorporating Admixture Mapping Information into Association Tests.

OBJECTIVE: To develop effective methods for GWAS in admixed populations such as African Americans. METHODS: We show that, when testing the null hypothesis that the test SNP is not in background linkage disequilibrium with the causal variants, several existing methods cannot control well the family-wise error rate (FWER) in the strong sense in GWAS. These existing methods include association tests adjusting for global ancestry and joint association tests that combine statistics from admixture mapping tests and association tests that correct for local ancestry. Furthermore, we describe a generalized sequential Bonferroni (smooth-GSB) procedure for GWAS that incorporates smoothed weights calculated from admixture mapping tests into association tests that correct for local ancestry. We have applied the smooth-GSB procedure to analyses of GWAS data on American Africans from the Atherosclerosis Risk in Communities (ARIC) Study. RESULTS: Our simulation studies indicate that the smooth-GSB procedure not only control the FWER, but also improves statistical power compared with association tests correcting for local ancestry. CONCLUSION: The smooth-GSB procedure can result in a better performance than several existing methods for GWAS in admixed populations.

PedBLIMP: extending linear predictors to impute genotypes in pedigrees.

Recently, Wen and Stephens (Wen and Stephens [2010] Ann Appl Stat 4(3):1158-1182) proposed a linear predictor, called BLIMP, that uses conditional multivariate normal moments to impute genotypes with accuracy similar to current state-of-the-art methods. One novelty is that it regularized the estimated covariance matrix based on a model from population genetics. We extended multivariate moments to impute genotypes in pedigrees. Our proposed method, PedBLIMP, utilizes both the linkage-disequilibrium (LD) information estimated from external panel data and the pedigree structure or identity-by-descent (IBD) information. The proposed method was evaluated on a pedigree design where some individuals were genotyped with dense markers and the rest with sparse markers. We found that incorporating the pedigree/IBD information can improve imputation accuracy compared to BLIMP. Because rare variants usually have low LD with other single-nucleotide polymorphisms (SNPs), incorporating pedigree/IBD information largely improved imputation accuracy for rare variants. We also compared PedBLIMP with IMPUTE2 and GIGI. Results show that when sparse markers are in a certain density range, our method can outperform both IMPUTE2 and GIGI.

Combined Whole Methylome and Genomewide Association Study Implicates CNTN4 in Alcohol Use.

BACKGROUND: Methylome-wide association (MWAS) studies present a new way to advance the search for biological correlates for alcohol use. A challenge with methylation studies of alcohol involves the causal direction of significant methylation-alcohol associations. One way to address this issue is to combine MWAS data with genomewide association study (GWAS) data. METHODS: Here, we combined MWAS and GWAS results for alcohol use from 619 individuals. Our MWAS data were generated by next-generation sequencing of the methylated genomic DNA fraction, producing over 60 million reads per subject to interrogate methylation levels at ~27 million autosomal CpG sites in the human genome. Our GWAS included 5,571,786 single nucleotide polymorphisms (SNPs) imputed with 1000 Genomes. RESULTS: When combining the MWAS and GWAS data, our top finding was a region in an intron of CNTN4 (p = 2.55 × 10(-8) ), located between chr3: 2,555,403 and 2,555,524, encompassing SNPs rs1382874 and rs1382875. This finding was then replicated in an independent sample of 730 individuals. We used bisulfite pyrosequencing to measure methylation and found significant association with regular alcohol use in the same direction as the MWAS (p = 0.021). Rs1382874 and rs1382875 were genotyped and found to be associated in the same direction as the GWAS (p = 0.008 and p = 0.009). After integrating the MWAS and GWAS findings from the replication sample, we replicated our combined analysis finding (p = 0.0017) in CNTN4. CONCLUSIONS: Through combining methylation and SNP data, we have identified CNTN4 as a risk factor for regular alcohol use.

A rapid association test procedure robust under different genetic models accounting for population stratification.

OBJECTIVE: For genome-wide association studies (GWAS) using case-control data with stratification, a commonly used association test is the generalized Armitage (GA) trend test implemented in the software EIGENSTRAT. The GA trend test uses principal component analysis to correct for population stratification. It usually assumes an additive disease model and can have high power when the underlying disease model is additive or multiplicative, but may have relatively low power when the underlying disease model is recessive or dominant. The purpose of this paper is to provide a test procedure for GWAS with increased power over the GA trend test under the recessive and dominant models, while maintaining the power of the GA trend test under the additive and multiplicative models. METHODS: We extend a Hardy-Weinberg disequilibrium (HWD) trend test for a homogeneous population to account for population stratification, and then propose a robust association test procedure for GWAS that incorporates information from the extended HWD trend test into the GA trend test. RESULTS AND CONCLUSIONS: Our simulation studies and application of our method to a GWAS data set indicate that our proposed method can achieve the purpose described above.

Protocol for immunophenotyping out-of-hospital cardiac arrest patients.

Immunophenotyping of out-of-hospital cardiac arrest (OHCA) patients is of increasing interest but has challenges. Here, we describe steps for the design of the clinical cohort, planning patient enrollment and sample collection, and ethical review of the study protocol. We detail procedures for blood sample collection and cryopreservation of peripheral blood mononuclear cells (PBMCs). We detail steps to modulate immune checkpoints in OHCA PBMC ex vivo. This protocol also has relevance for immunophenotyping other types of critical illness. For complete details on the use and execution of this protocol, please refer to Tamura et al. (2023).1.

Genomic Determinants of Outcome in Acute Lymphoblastic Leukemia.

PURPOSE: Although cure rates for childhood acute lymphoblastic leukemia (ALL) exceed 90%, ALL remains a leading cause of cancer death in children. Half of relapses arise in children initially classified with standard-risk (SR) disease. MATERIALS AND METHODS: To identify genomic determinants of relapse in children with SR ALL, we performed genome and transcriptome sequencing of diagnostic and remission samples of children with SR (n = 1,381) or high-risk B-ALL with favorable cytogenetic features (n = 115) enrolled on Children's Oncology Group trials. We used a case-control study design analyzing 439 patients who relapsed and 1,057 who remained in complete remission for at least 5 years. RESULTS: Genomic subtype was associated with relapse, which occurred in approximately 50% of cases of PAX5-altered ALL (odds ratio [OR], 3.31 [95% CI, 2.17 to 5.03]; P = 3.18 × 10-8). Within high-hyperdiploid ALL, gain of chromosome 10 with disomy of chromosome 7 was associated with favorable outcome (OR, 0.27 [95% CI, 0.17 to 0.42]; P = 8.02 × 10-10; St Jude Children's Research Hospital validation cohort: OR, 0.22 [95% CI, 0.05 to 0.80]; P = .009), and disomy of chromosomes 10 and 17 with gain of chromosome 6 was associated with relapse (OR, 7.16 [95% CI, 2.63 to 21.51]; P = 2.19 × 10-5; validation cohort: OR, 21.32 [95% CI, 3.62 to 119.30]; P = .0004). Genomic alterations were associated with relapse in a subtype-dependent manner, including alterations of INO80 in ETV6::RUNX1 ALL, IKZF1, and CREBBP in high-hyperdiploid ALL and FHIT in BCR::ABL1-like ALL. Genomic alterations were also associated with the presence of minimal residual disease, including NRAS and CREBBP in high-hyperdiploid ALL. CONCLUSION: Genetic subtype, patterns of aneuploidy, and secondary genomic alterations determine risk of relapse in childhood ALL. Comprehensive genomic analysis is required for optimal risk stratification.

MethylPCA: a toolkit to control for confounders in methylome-wide association studies.

BACKGROUND: In methylome-wide association studies (MWAS) there are many possible differences between cases and controls (e.g. related to life style, diet, and medication use) that may affect the methylome and produce false positive findings. An effective approach to control for these confounders is to first capture the major sources of variation in the methylation data and then regress out these components in the association analyses. This approach is, however, computationally very challenging due to the extremely large number of methylation sites in the human genome. RESULT: We introduce MethylPCA that is specifically designed to control for potential confounders in studies where the number of methylation sites is extremely large. MethylPCA offers a complete and flexible data analysis including 1) an adaptive method that performs data reduction prior to PCA by empirically combining methylation data of neighboring sites, 2) an efficient algorithm that performs a principal component analysis (PCA) on the ultra high-dimensional data matrix, and 3) association tests. To accomplish this MethylPCA allows for parallel execution of tasks, uses C++ for CPU and I/O intensive calculations, and stores intermediate results to avoid computing the same statistics multiple times or keeping results in memory. Through simulations and an analysis of a real whole methylome MBD-seq study of 1,500 subjects we show that MethylPCA effectively controls for potential confounders. CONCLUSIONS: MethylPCA provides users a convenient tool to perform MWAS. The software effectively handles the challenge in memory and speed to perform tasks that would be impossible to accomplish using existing software when millions of sites are interrogated with the sample sizes required for MWAS.

Rare SERINC2 variants are specific for alcohol dependence in individuals of European descent.

OBJECTIVES: We have previously reported a top-ranked risk gene [i.e., serine incorporator 2 gene (SERINC2)] for alcohol dependence in individuals of European descent by analyzing the common variants in a genome-wide association study. In the present study, we comprehensively examined the rare variants [minor allele frequency (MAF)<0.05] in the NKAIN1-SERINC2 region to confirm our previous finding. MATERIALS AND METHODS: A discovery sample (1409 European-American patients with alcohol dependence and 1518 European-American controls) and a replication sample (6438 European-Australian family participants with 1645 alcohol-dependent probands) were subjected to an association analysis. A total of 39,903 individuals from 19 other cohorts with 11 different neuropsychiatric and neurological disorders served as contrast groups. The entire NKAIN1-SERINC2 region was imputed in all cohorts using the same reference panels of genotypes that included rare variants from the whole-genome sequencing data. We stringently cleaned the phenotype and genotype data, and obtained a total of about 220 single-nucleotide polymorphisms in individuals of European descent and about 450 single-nucleotide polymorphisms in the individuals of African descent with 0

A generalized sequential Bonferroni procedure using smoothed weights for genome-wide association studies incorporating information on Hardy-Weinberg disequilibrium among cases.

BACKGROUND/OBJECTIVES: For genome-wide association studies (GWAS) with case-control designs, one of the most widely used association tests is the Cochran-Armitage (CA) trend test assuming an additive mode of inheritance. The CA trend test often has higher power than other association tests under additive and multiplicative disease models. However, it can have very low power under a recessive disease model in GWAS. Although tests (such as MAX3) robust to different genetic models have been developed, they often have relatively lower power than the CA trend test under additive and multiplicative models. The goal of this study is to propose an efficient method that not only has higher power than the CA trend test under dominant and recessive models but also maintains the power of the CA trend test under additive and multiplicative models. METHODS: We employed the generalized sequential Bonferroni (GSB) procedure of Holm to incorporate information from a Hardy-Weinberg disequilibrium (HWD) test into the CA trend test based on estimating weights from the p values of the HWD test. We proposed to smooth the weights to reduce possible noise. RESULTS AND CONCLUSIONS: Results from extensive simulation studies showed that the proposed GSB procedure can achieve the goal described above.

A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell-Cell Communication.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular biology at an unprecedented resolution, enabling the characterization of cellular heterogeneity, identification of rare but significant cell types, and exploration of cell-cell communications and interactions. Its broad applications span both basic and clinical research domains. In this comprehensive review, we survey the current landscape of scRNA-seq analysis methods and tools, focusing on count modeling, cell-type annotation, data integration, including spatial transcriptomics, and the inference of cell-cell communication. We review the challenges encountered in scRNA-seq analysis, including issues of sparsity or low expression, reliability of cell annotation, and assumptions in data integration, and discuss the potential impact of suboptimal clustering and differential expression analysis tools on downstream analyses, particularly in identifying cell subpopulations. Finally, we discuss recent advancements and future directions for enhancing scRNA-seq analysis. Specifically, we highlight the development of novel tools for annotating single-cell data, integrating and interpreting multimodal datasets covering transcriptomics, epigenomics, and proteomics, and inferring cellular communication networks. By elucidating the latest progress and innovation, we provide a comprehensive overview of the rapidly advancing field of scRNA-seq analysis.