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Background: Acute lymphoblastic leukemia (ALL) is the most common childhood cancer, suggesting that germline variants influence ALL risk. Although multiple genome-wide association (GWA) studies have identified variants predisposing children to ALL, it remains unclear whether genetic heterogeneity affects ALL susceptibility and how interactions within and among genes containing ALL-associated variants influence ALL risk.Methods: Here, we jointly analyzed two published datasets of case–control GWA summary statistics along with germline data from ALL case–parent trios. We used the gene-level association method PEGASUS to identify genes with multiple variants associated with ALL. We then used PEGASUS gene scores as input to the network analysis algorithm HotNet2 to characterize the genomic architecture of ALL.Results: Using PEGASUS, we confirmed associations previously observed at genes such as ARID5B, IKZF1, CDKN2A/2B, and PIP4K2A, and we identified novel candidate gene associations. Using HotNet2, we uncovered significant gene subnetworks that may underlie inherited ALL risk: a subnetwork involved in B-cell differentiation containing the ALL-associated gene CEBPE, and a subnetwork of homeobox genes, including MEIS1.Conclusions: Gene and network analysis uncovered loci associated with ALL that are missed by GWA studies, such as MEIS1. Furthermore, ALL-associated loci do not appear to interact directly with each other to influence ALL risk, and instead appear to influence leukemogenesis through multiple, complex pathways.Impact: We present a new pipeline for post hoc analysis of association studies that yields new insight into the etiology of ALL and can be applied in future studies to shed light on the genomic underpinnings of cancer. Cancer Epidemiol Biomarkers Prev; 26(10); 1531–9. ©2017 AACR.

Supplementary Tables S1-S8 and Supplementary Figure Legends.

Background Cardiometabolic diseases are highly comorbid, but their relationship with female‐specific or overwhelmingly female‐predominant health conditions (breast cancer, endometriosis, pregnancy complications) is understudied. This study aimed to estimate the cross‐trait genetic overlap and influence of genetic burden of cardiometabolic traits on health conditions unique to women. Methods and Results Using electronic health record data from 71 008 ancestrally diverse women, we examined relationships between 23 obstetrical/gynecological conditions and 4 cardiometabolic phenotypes (body mass index, coronary artery disease, type 2 diabetes, and hypertension) by performing 4 analyses: (1) cross‐trait genetic correlation analyses to compare genetic architecture, (2) polygenic risk score–based association tests to characterize shared genetic effects on disease risk, (3) Mendelian randomization for significant associations to assess cross‐trait causal relationships, and (4) chronology analyses to visualize the timeline of events unique to groups of women with high and low genetic burden for cardiometabolic traits and highlight the disease prevalence in risk groups by age. We observed 27 significant associations between cardiometabolic polygenic scores and obstetrical/gynecological conditions (body mass index and endometrial cancer, body mass index and polycystic ovarian syndrome, type 2 diabetes and gestational diabetes, type 2 diabetes and polycystic ovarian syndrome). Mendelian randomization analysis provided additional evidence of independent causal effects. We also identified an inverse association between coronary artery disease and breast cancer. High cardiometabolic polygenic scores were associated with early development of polycystic ovarian syndrome and gestational hypertension. Conclusions We conclude that polygenic susceptibility to cardiometabolic traits is associated with elevated risk of certain female‐specific health conditions.

Background/aim: The objective of the paper was to develop a comprehensive “Adolescence Stress Scale” and to examine different psychometric issues in the development, initial validation, and standardization of this scale. Method: Exploratory factor analysis was conducted on the data procured from a sample of 634 (11–18 years) school-going adolescents in India. Results: An exploratory analysis provided a 10 factor structure, namely, major loss induced stress, enforcement or conflict induced stress, phobic stress, interpersonal conflict induced stress, punishment induced stress, illness and injury induced stress, performance stress, imposition induced stress, insecurity induced stress, unhealthy environment induced stress. The 10 oblique factor solutions are found to be interrelated and interdependent with good indices of internal consistency, and content validity. Conclusions: This scale development is a novel and powerful measure that taps onto various aspects of stress experienced by school-going adolescents. The scale can facilitate researchers, clinicians, and teachers to identify and quantify the significant sources of stress in adolescents in school, or clinic settings.

Human adult height reflects the outcome of childhood skeletal growth. Growth plate (epiphyseal) chondrocytes are key determinants of height. As epiphyseal chondrocytes mature and proliferate, they pass through three developmental stages, which are organized into three distinct layers in the growth plate: (i) resting (round), (ii) proliferative (flat), and (iii) hypertrophic. Recent genomewide association studies (GWASs) of human height identified numerous associated loci, which are enriched for genes expressed in growth plate chondrocytes. However, it remains unclear which specific genes expressed in which layers of the growth plate regulate skeletal growth and human height. To connect the genetics of height and growth plate biology, we analyzed GWAS data through the lens of gene expression in the three dissected layers of murine newborn tibial growth plate. For each gene, we derived a specificity score for each growth plate layer and regressed these scores against gene-level p values from recent height GWAS data. We found that specificity for expression in the round cell layer, which contains chondrocytes early in maturation, is significantly associated with height GWAS p values (p = 8.5 × 10-9 ); this association remains after conditioning on specificity for the other cell layers. The association also remains after conditioning on membership in an "Online Mendelian Inheritance in Man (OMIM) gene set" (genes known to cause monogenic skeletal growth disorders, p < 9.7 × 10-6 ). We replicated the association in RNA-sequencing (RNA-seq) data from maturing chondrocytes sampled at early and late time points during differentiation in vitro: we found that expression early in differentiation is significantly associated with p values from height GWASs (p = 6.1 × 10-10 ) and that this association remains after conditioning on expression at 10 days in culture and on the OMIM gene set (p < 0.006). These findings newly implicate genes highlighted by GWASs of height and specifically expressed in the round cell layer as being potentially important regulators of skeletal biology. © 2021 American Society for Bone and Mineral Research (ASBMR).

Human adult height reflects the outcome of childhood skeletal growth. Growth plate (epiphyseal) chondrocytes are key determinants of height. As epiphyseal chondrocytes mature and proliferate, they pass through three developmental stages, which are organized into three distinct layers in the growth plate: 1) resting (round), 2) proliferative (flat), and 3) hypertrophic. Recent genome-wide association studies (GWAS) of human height identified numerous associated loci, which are enriched for genes expressed in growth plate chondrocytes. However, it remains unclear which specific genes expressed in which layers of the growth plate regulate skeletal growth and human height.To connect the genetics of height and growth plate biology, we analyzed GWAS data through the lens of gene expression in the three dissected layers of murine newborn tibial growth plate. For each gene, we derived a specificity score for each growth plate layer and regressed these scores against gene-level p-values from recent height GWAS data. We found that specificity for expression in the round cell layer, which contains chondrocytes early in maturation, is significantly associated with height GWAS p-values (p=8.5×10−9); this association remains after conditioning on specificity for the other cell layers. The association also remains after conditioning on membership in an “OMIM gene set” (genes known to cause monogenic skeletal growth disorders, p−6). We replicated the association in RNA-seq data from maturing chondrocytes sampled at early and late time points during differentiation in vitro: we found that expression early in differentiation is significantly associated with p-values from height GWAS (p=6.1×10−10) and that this association remains after conditioning on expression at 10 days in culture and on the OMIM gene set (p

Background: Acute lymphoblastic leukemia (ALL) is the most common childhood cancer, suggesting that germline variants influence ALL risk. Although multiple genome-wide association (GWA) studies have identified variants predisposing children to ALL, it remains unclear whether genetic heterogeneity affects ALL susceptibility and how interactions within and among genes containing ALL-associated variants influence ALL risk. Methods: Here, we jointly analyzed two published datasets of case–control GWA summary statistics along with germline data from ALL case–parent trios. We used the gene-level association method PEGASUS to identify genes with multiple variants associated with ALL. We then used PEGASUS gene scores as input to the network analysis algorithm HotNet2 to characterize the genomic architecture of ALL. Results: Using PEGASUS, we confirmed associations previously observed at genes such as ARID5B, IKZF1, CDKN2A/2B, and PIP4K2A, and we identified novel candidate gene associations. Using HotNet2, we uncovered significant gene subnetworks that may underlie inherited ALL risk: a subnetwork involved in B-cell differentiation containing the ALL-associated gene CEBPE, and a subnetwork of homeobox genes, including MEIS1. Conclusions: Gene and network analysis uncovered loci associated with ALL that are missed by GWA studies, such as MEIS1. Furthermore, ALL-associated loci do not appear to interact directly with each other to influence ALL risk, and instead appear to influence leukemogenesis through multiple, complex pathways. Impact: We present a new pipeline for post hoc analysis of association studies that yields new insight into the etiology of ALL and can be applied in future studies to shed light on the genomic underpinnings of cancer. Cancer Epidemiol Biomarkers Prev; 26(10); 1531–9. ©2017 AACR.

Meiotic nondisjunction and resulting aneuploidy can lead to severe health consequences in humans. Aneuploidy rescue can restore euploidy but may result in uniparental disomy (UPD), the inheritance of both homologs of a chromosome from one parent with no representative copy from the other. Current understanding of UPD is limited to ∼3,300 case subjects for which UPD was associated with clinical presentation due to imprinting disorders or recessive diseases. Thus, the prevalence of UPD and its phenotypic consequences in the general population are unknown. We searched for instances of UPD across 4,400,363 consented research participants from the personal genetics company 23andMe, Inc., and 431,094 UK Biobank participants. Using computationally detected DNA segments identical-by-descent (IBD) and runs of homozygosity (ROH), we identified 675 instances of UPD across both databases. We estimate that UPD is twice as common as previously thought, and we present a machine-learning framework to detect UPD using ROH. While we find a nominally significant association between UPD of chromosome 22 and autism risk, we do not find significant associations between UPD and deleterious traits in the 23andMe database.

SummaryMeiotic nondisjunction and resulting aneuploidy can lead to severe health consequences in humans. Aneuploidy rescue can restore euploidy but may result in uniparental disomy (UPD), the inheritance of both homologs of a chromosome from one parent with no representative copy from the other. Current understanding of UPD is limited to ~3,300 cases for which UPD was associated with clinical presentation due to imprinting disorders or recessive diseases. Thus, the prevalence of UPD and its phenotypic consequences in the general population are unknown. We searched for instances of UPD in over four million consented research participants from the personal genetics company 23andMe, Inc., and 431,094 UK Biobank participants. Using computationally detected DNA segments identical-by-descent (IBD) and runs of homozygosity (ROH), we identified 675 instances of UPD across both databases. Here we present the first characterization of UPD prevalence in the general population, a machine-learning framework to detect UPD using ROH, and a novel association between autism and UPD of chromosome 22.

Human height is a model polygenic trait with thousands of height-related SNPs identified in GWAS to date. An important determinant of height is the proliferation and hypertrophy of growth plate chondrocytes during childhood long bone elongation. Connecting the expression of specific genes that affect skeletal biology to associated variants in GWAS remains a difficult challenge. To connect the genetics of height and growth plate gene expression, we studied the relationship between gene expression in the murine growth plate and common-variant associations from GWAS of height. To obtain gene expression data from the growth plate, we dissected three layers of murine tibial growth plates, extracted RNA from each layer, and measured expression using the Affymetrix GeneChip 430 3.0. For each gene, we derived a specificity score for each growth plate layer, and SNP-level p-values from a published GWAS of height (N~700000) were combined into gene-level p-values using MAGMA. We then used MAGMA to test for association between specificity of expression for each growth plate layer and the GWAS gene level p-values for height. We found that specificity for the round cell layer is significantly associated with height GWAS p-values (p = 8.5x10-9). This association remains when we condition on each of the other cell layers and on membership in a set of genes from OMIM that cause skeletal growth disorders (3.3x10-8 < p < 4.1x10-6). We replicated this result in a RNA-seq dataset of maturing chondrocytes sampled at three time points during development in vitro (days 3, 5, and 10): we found that z-scores for expression in the earliest two days of development are significantly associated with gene-level p-values from height GWAS (pDay3 = 1.2x10-21 and pDay5 = 2.0x10-20) and that this association remains after conditioning on the other timepoints and on the OMIM gene set (3.1x10-20 < pDay3 < 8.3x10-5; 3.7x10-19 < pDay5 < 0.002). We then performed pathway analysis of genes that are both highly specific to the round layer and highly significant in GWAS using Enrichr. Together, our results suggest that genes expressed in early chondrocyte development (the round cell layer) are particularly relevant to the contribution of growth plate-expressed genes to height. This conclusion both sheds light on the regulation of human skeletal growth and also helps prioritize relevant genes implicated from the height GWAS in skeletal biology.

Genome-wide association (GWA) studies typically lack power to detect genotypes significantly associated with complex diseases, where different causal mutations of small effect may be present across cases. A common, tractable approach for identifying genomic elements associated with complex traits is to evaluate combinations of variants in known pathways or gene sets with shared biological function. Such gene-set analyses require the computation of gene-level P-values or gene scores; these gene scores are also useful when generating hypotheses for experimental validation. However, commonly used methods for generating GWA gene scores are computationally inefficient, biased by gene length, imprecise, or have low true positive rate (TPR) at low false positive rates (FPR), leading to erroneous hypotheses for functional validation. Here we introduce a new method, PEGASUS, for analytically calculating gene scores. PEGASUS produces gene scores with as much as 10 orders of magnitude higher numerical precision than competing methods. In simulation, PEGASUS outperforms existing methods, achieving up to 30% higher TPR when the FPR is fixed at 1%. We use gene scores from PEGASUS as input to HotNet2 to identify networks of interacting genes associated with multiple complex diseases and traits; this is the first application of HotNet2 to common variation. In ulcerative colitis and waist–hip ratio, we discover networks that include genes previously associated with these phenotypes, as well as novel candidate genes. In contrast, existing methods fail to identify these networks. We also identify networks for attention-deficit/hyperactivity disorder, in which GWA studies have yet to identify any significant SNPs.

Background Great strides have been made in the effective treatment of HIV-1 with the development of second-generation protease inhibitors (PIs) that are effective against historically multi-PI-resistant HIV-1 variants. Nevertheless, mutation patterns that confer decreasing susceptibility to available PIs continue to arise within the population. Understanding the phenotypic and genotypic patterns responsible for multi-PI resistance is necessary for developing PIs that are active against clinically-relevant PI-resistant HIV-1 variants. Results In this work, we use globally optimal integer programming-based clustering techniques to elucidate multi-PI phenotypic resistance patterns using a data set of 398 HIV-1 protease sequences that have each been phenotyped for susceptibility toward the nine clinically-approved HIV-1 PIs. We validate the information content of the clusters by evaluating their ability to predict the level of decreased susceptibility to each of the available PIs using a cross validation procedure. We demonstrate the finding that as a result of phenotypic cross resistance, the considered clinical HIV-1 protease isolates are confined to ~6% or less of the clinically-relevant phenotypic space. Clustering and feature selection methods are used to find representative sequences and mutations for major resistance phenotypes to elucidate their genotypic signatures. We show that phenotypic similarity does not imply genotypic similarity, that different PI-resistance mutation patterns can give rise to HIV-1 isolates with similar phenotypic profiles. Conclusion Rather than characterizing HIV-1 susceptibility toward each PI individually, our study offers a unique perspective on the phenomenon of PI class resistance by uncovering major multidrug-resistant phenotypic patterns and their often diverse genotypic determinants, providing a methodology that can be applied to understand clinically-relevant phenotypic patterns to aid in the design of novel inhibitors that target other rapidly evolving molecular targets as well., Wellesley College, National Institutes of Health (U.S.) (NIHR01GM086884), National Institutes of Health (U.S.) (Grant AI068581), National Institutes of Health (U.S.) (Grant P01GM066524-06), National Institutes of Health (U.S.) (Grant GM82209), Howard Hughes Medical Institute, Wellesley College (Brachman-Hoffman fund), National Science Foundation (U.S.) (NSF-REU)