Subgroups of pelvic pain are differentially associated with endometriosis and inflammatory comorbidities: a latent class analysis.

ABSTRACT: Chronic pelvic pain is heterogeneous with potentially clinically informative subgroups. We aimed to identify subgroups of pelvic pain based on symptom patterns and investigate their associations with inflammatory and chronic pain-related comorbidities. Latent class analysis (LCA) identified subgroups of participants (n = 1255) from the Adolescence to Adulthood (A2A) cohort. Six participant characteristics were included in the LCA: severity, frequency, and impact on daily activities of both menstruation-associated (cyclic) and non-menstruation-associated (acyclic) pelvic pain. Three-step LCA quantified associations between LC subgroups, demographic and clinical variables, and 18 comorbidities (10 with prevalence ≥10%). Five subgroups were identified: none or minimal (23%), moderate cyclic only (28%), severe cyclic only (20%), moderate or severe acyclic plus moderate cyclic (9%), and severe acyclic plus severe cyclic (21%). Endometriosis prevalence within these 5 LCA-pelvic pain-defined subgroups ranged in size from 4% in "none or minimal pelvic pain" to 24%, 72%, 70%, and 94%, respectively, in the 4 pain subgroups, with statistically significant odds of membership only for the latter 3 subgroups. Migraines were associated with significant odds of membership in all 4 pelvic pain subgroups relative to those with no pelvic pain (adjusted odds ratios = 2.92-7.78), whereas back, joint, or leg pain each had significantly greater odds of membership in the latter 3 subgroups. Asthma or allergies had three times the odds of membership in the most severe pain group. Subgroups with elevated levels of cyclic or acyclic pain are associated with greater frequency of chronic overlapping pain conditions, suggesting an important role for central inflammatory and immunological mechanisms.

Design, analysis, and interpretation of treatment response heterogeneity in personalized nutrition and obesity treatment research.

It is increasingly assumed that there is no one-size-fits-all approach to dietary recommendations for the management and treatment of chronic diseases such as obesity. This phenomenon that not all individuals respond uniformly to a given treatment has become an area of research interest given the rise of personalized and precision medicine. To conduct, interpret, and disseminate this research rigorously and with scientific accuracy, however, requires an understanding of treatment response heterogeneity. Here, we define treatment response heterogeneity as it relates to clinical trials, provide statistical guidance for measuring treatment response heterogeneity, and highlight study designs that can quantify treatment response heterogeneity in nutrition and obesity research. Our goal is to educate nutrition and obesity researchers in how to correctly identify and consider treatment response heterogeneity when analyzing data and interpreting results, leading to rigorous and accurate advancements in the field of personalized medicine.

Breastfeeding history and adenomyosis risk using a novel case-control study design.

OBJECTIVE: To evaluate the association between breastfeeding history, including lifetime exclusive breastfeeding, and risk of adenomyosis. DESIGN: We used data from a case-control study designed with 2 control groups to address the challenge of selecting noncases for a valid epidemiologic study when cases are identified by hysterectomy. The case-control study was conducted among premenopausal and postmenopausal enrollees aged 18-59 years in a large, integrated health care system in western Washington state. PATIENT(S): Cases were enrollees with incident, pathology-confirmed adenomyosis diagnosed during 2001-2006 (n = 386). The 2 control groups were as follows: (1) randomly selected age-matched enrollees with intact uteri ("population controls," n = 323) and (2) hysterectomy controls (n = 233). INTERVENTION(S): Data on breastfeeding history were collected by in-person interviews. For each reported live birth, participants were asked whether they breastfed, along with infant age at supplemental feeding introduction and breastfeeding discontinuation. MAIN OUTCOME MEASURE(S): Among participants with at least 1 live birth (330 cases, 246 population controls, and 198 hysterectomy controls), we used unconditional logistic regression to estimate adjusted odds ratios and 95% confidence intervals (CIs) for the associations between the following: (1) ever breastfeeding, (2) ever breastfeeding for ≥8 weeks, (3) lifetime breastfeeding, and (4) lifetime exclusive breastfeeding and risk of adenomyosis. Analyses were adjusted for age, reference year, smoking, education, and parity. RESULT(S): In analyses comparing cases with population controls, we observed a 40% decreased odds of adenomyosis with a history of ever breastfeeding (adjusted odds ratio, 0.6; 95% CI, 0.3-1.0) and breastfeeding for ≥8 weeks (adjusted odds ratio, 0.6; 95% CI, 0.4-0.8). The strongest associations, 60%-70% decreased odds of adenomyosis, were observed with ≥12 months of lifetime breastfeeding (vs. <3 months) (adjusted odds ratio, 0.4; 95% CI, 0.2-0.6) and 9 to <12 months of lifetime exclusive breastfeeding (vs. <3 months) (adjusted odds ratio, 0.3; 95% CI, 0.2-0.6), comparing cases to population controls. In analyses using hysterectomy controls, we observed similar patterns of associations slightly attenuated in magnitude. CONCLUSION(S): Breastfeeding history was associated with a 40% decreased odds of adenomyosis, a condition that can confer substantial morbidity and requires hysterectomy for definitive treatment. The consistency of our findings with that of a previous study lends support that breastfeeding may modify risk of adenomyosis.

Association of Gout Polygenic Risk Score With Age at Disease Onset and Tophaceous Disease in European and Polynesian Men With Gout.

OBJECTIVE: To determine whether a gout polygenic risk score (PRS) is associated with age at gout onset and tophaceous disease in European, East Polynesian, and West Polynesian men and women with gout. METHODS: A 19-variant gout PRS was produced in 7 European gout cohorts (N = 4,016), 2 East Polynesian gout cohorts (N = 682), and 1 West Polynesian gout cohort (N = 490). Sex-stratified regression models were used to estimate the relationship between the PRS and age at gout onset and tophaceous disease. RESULTS: The PRS was associated with earlier age at gout onset in men (ß = -3.61 in years per unit PRS [95% confidence interval (95% CI) -4.32, -2.90] in European men; ß = -6.35 [95% CI -8.91, -3.80] in East Polynesian men; ß = -3.51 [95% CI -5.46, -1.57] in West Polynesian men) but not in women (ß = 0.07 [95% CI -2.32, 2.45] in European women; ß = 0.20 [95% CI -7.21, 7.62] in East Polynesian women; ß -3.33 [95% CI -9.28, 2.62] in West Polynesian women). The PRS showed a positive association with tophaceous disease in men (odds ratio [OR] for the association 1.15 [95% CI 1.00, 1.31] in European men; OR 2.60 [95% CI 1.66, 4.06] in East Polynesian men; OR 1.53 [95% CI 1.07, 2.19] in West Polynesian men) but not in women (OR for the association 0.68 [95% CI 0.42, 1.10] in European women; OR 1.45 [95% CI 0.39, 5.36] in East Polynesian women). The PRS association with age at gout onset was robust to the removal of ABCG2 variants from the PRS in European and East Polynesian men (ß = -2.42 [95% CI -3.37, -1.46] and ß = -6.80 [95% CI -10.06, -3.55], respectively) but not in West Polynesian men (ß = -1.79 [95% CI -4.74, 1.16]). CONCLUSION: Genetic risk variants for gout also harbor risk for earlier age at gout onset and tophaceous disease in European and Polynesian men. Our findings suggest that earlier gout onset involves the accumulation of gout risk alleles in men but perhaps not in women, and that this genetic risk is shared across multiple ancestral groups.

Local genetic covariance between serum urate and kidney function estimated with Bayesian multitrait models.

Hyperuricemia (serum urate >6.8 mg/dl) is associated with several cardiometabolic and renal diseases, such as gout and chronic kidney disease. Previous studies have examined the shared genetic basis of chronic kidney disease and hyperuricemia in humans either using single-variant tests or estimating whole-genome genetic correlations between the traits. Individual variants typically explain a small fraction of the genetic correlation between traits, thus the ability to map pleiotropic loci is lacking power for available sample sizes. Alternatively, whole-genome estimates of genetic correlation indicate a moderate correlation between these traits. While useful to explain the comorbidity of these traits, whole-genome genetic correlation estimates do not shed light on what regions may be implicated in the shared genetic basis of traits. Therefore, to fill the gap between these two approaches, we used local Bayesian multitrait models to estimate the genetic covariance between a marker for chronic kidney disease (estimated glomerular filtration rate) and serum urate in specific genomic regions. We identified 134 overlapping linkage disequilibrium windows with statistically significant covariance estimates, 49 of which had positive directionalities, and 85 negative directionalities, the latter being consistent with that of the overall genetic covariance. The 134 significant windows condensed to 64 genetically distinct shared loci which validate 17 previously identified shared loci with consistent directionality and revealed 22 novel pleiotropic genes. Finally, to examine potential biological mechanisms for these shared loci, we have identified a subset of the genomic windows that are associated with gene expression using colocalization analyses. The regions identified by our local Bayesian multitrait model approach may help explain the association between chronic kidney disease and hyperuricemia.

MOSS: multi-omic integration with sparse value decomposition.

SUMMARY: This article presents multi-omic integration with sparse value decomposition (MOSS), a free and open-source R package for integration and feature selection in multiple large omics datasets. This package is computationally efficient and offers biological insight through capabilities, such as cluster analysis and identification of informative omic features. AVAILABILITY AND IMPLEMENTATION: https://CRAN.R-project.org/package=MOSS. SUPPLEMENTARY INFORMATION: Supplementary information can be found at https://github.com/agugonrey/GonzalezReymundez2021.

Multimodal characterization of Yucatan minipig behavior and physiology through maturation.

Brain injuries induced by external forces are particularly challenging to model experimentally. In recent decades, the domestic pig has been gaining popularity as a highly relevant animal model to address the pathophysiological mechanisms and the biomechanics associated with head injuries. Understanding cognitive, motor, and sensory aspects of pig behavior throughout development is crucial for evaluating cognitive and motor deficits after injury. We have developed a comprehensive battery of tests to characterize the behavior and physiological function of the Yucatan minipig throughout maturation. Behavioral testing included assessments of learning and memory, executive functions, circadian rhythms, gait analysis, and level of motor activity. We applied traditional behavioral apparatus and analysis methods, as well as state-of-the-art sensor technologies to report on motion and activity, and artificial intelligent approaches to analyze behavior. We studied pigs from 16 weeks old through sexual maturity at 35 weeks old. The results show multidimensional characterization of minipig behavior, and how it develops and changes with age. This animal model may capitulate the biomechanical consideration and phenotype of head injuries in the developing brain and can drive forward the field of understanding pathophysiological mechanisms and developing new therapies to accelerate recovery in children who have suffered head trauma.

Gene-Based Association Testing of Dichotomous Traits With Generalized Functional Linear Mixed Models Using Extended Pedigrees: Applications to Age-Related Macular Degeneration.

Genetics plays a role in age-related macular degeneration (AMD), a common cause of blindness in the elderly. There is a need for powerful methods for carrying out region-based association tests between a dichotomous trait like AMD and genetic variants on family data. Here, we apply our new generalized functional linear mixed models (GFLMM) developed to test for gene-based association in a set of AMD families. Using common and rare variants, we observe significant association with two known AMD genes: CFH and ARMS2. Using rare variants, we find suggestive signals in four genes: ASAH1, CLEC6A, TMEM63C, and SGSM1. Intriguingly, ASAH1 is down-regulated in AMD aqueous humor, and ASAH1 deficiency leads to retinal inflammation and increased vulnerability to oxidative stress. These findings were made possible by our GFLMM which model the effect of a major gene as a fixed mean, the polygenic contributions as a random variation, and the correlation of pedigree members by kinship coefficients. Simulations indicate that the GFLMM likelihood ratio tests (LRTs) accurately control the Type I error rates. The LRTs have similar or higher power than existing retrospective kernel and burden statistics. Our GFLMM-based statistics provide a new tool for conducting family-based genetic studies of complex diseases. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Immunophenotype-associated gene signature in ductal breast tumors varies by receptor subtype, but the expression of individual signature genes remains consistent.

BACKGROUND: In silico deconvolution of invasive immune cell infiltration in bulk breast tumors helps characterize immunophenotype, expands treatment options, and influences survival endpoints. In this study, we identify the differential expression (DE) of the LM22 signature to classify immune-rich and -poor breast tumors and evaluate immune infiltration by receptor subtype and lymph node metastasis. METHODS: Using publicly available data, we applied the CIBERSORT algorithm to estimate immune cells infiltrating the tumor into immune-rich and immune-poor groups. We then tested the association of receptor subtype and nodal status with immune-rich/poor phenotype. We used DE to test individual signature genes and over-representation analysis for related pathways. RESULTS: CCL19 and CXCL9 expression differed between rich/poor signature groups regardless of subtype. Overexpression of CHI3L2 and FES was observed in triple negative breast cancers (TNBCs) relative to other subtypes in immune-rich tumors. Non-signature genes, LYZ, C1QB, CORO1A, EVI2B, GBP1, PSMB9, and CD52 were consistently overexpressed in immune-rich tumors, and SCUBE2 and GRIA2 were associated with immune-poor tumors. Immune-rich tumors had significant upregulation of genes/pathways while none were identified in immune-poor tumors. CONCLUSIONS: Overall, the proportion of immune-rich/poor tumors differed by subtype; however, a subset of 10 LM22 genes that marked immune-rich status remained the same across subtype. Non-LM22 genes differentially expressed between the phenotypes suggest that the biologic processes responsible for immune-poor phenotype are not yet well characterized.

Mapping pleiotropic loci using a fast-sequential testing algorithm.

Pleiotropy (i.e., genes with effects on multiple traits) leads to genetic correlations between traits and contributes to the development of many syndromes. Identifying variants with pleiotropic effects on multiple health-related traits can improve the biological understanding of gene action and disease etiology, and can help to advance disease-risk prediction. Sequential testing is a powerful approach for mapping genes with pleiotropic effects. However, the existing methods and the available software do not scale to analyses involving millions of SNPs and large datasets. This has limited the adoption of sequential testing for pleiotropy mapping at large scale. In this study, we present a sequential test and software that can be used to test pleiotropy in large systems of traits with biobank-sized data. Using simulations, we show that the methods implemented in the software are powerful and have adequate type-I error rate control. To demonstrate the use of the methods and software, we present a whole-genome scan in search of loci with pleiotropic effects on seven traits related to metabolic syndrome (MetS) using UK-Biobank data (n~300 K distantly related white European participants). We found abundant pleiotropy and report 170, 44, and 18 genomic regions harboring SNPs with pleiotropic effects in at least two, three, and four of the seven traits, respectively. We validate our results using previous studies documented in the GWAS-catalog and using data from GTEx. Our results confirm previously reported loci and lead to several novel discoveries that link MetS-related traits through plausible biological pathways.

Genetic correlations between traits associated with hyperuricemia, gout, and comorbidities.

Hypertension, obesity, chronic kidney disease and type 2 diabetes are comorbidities that have very high prevalence among persons with hyperuricemia (serum urate > 6.8 mg/dL) and gout. Here we use multivariate genetic models to test the hypothesis that the co-association of traits representing hyperuricemia and its comorbidities is genetically based. Using Bayesian whole-genome regression models, we estimated the genetic marker-based variance and the covariance between serum urate, serum creatinine, systolic blood pressure (SBP), blood glucose and body mass index (BMI) from two independent family-based studies: The Framingham Heart Study-FHS and the Hypertension Genetic Epidemiology Network study-HyperGEN. The main genetic findings that replicated in both FHS and HyperGEN, were (1) creatinine was genetically correlated only with urate and (2) BMI was genetically correlated with urate, SBP, and glucose. The environmental covariance among the traits was generally highest for trait pairs involving BMI. The genetic overlap of traits representing the comorbidities of hyperuricemia and gout appears to cluster in two separate axes of genetic covariance. Because creatinine is genetically correlated with urate but not with metabolic traits, this suggests there is one genetic module of shared loci associated with hyperuricemia and chronic kidney disease. Another module of shared loci may account for the association of hyperuricemia and metabolic syndrome. This study provides a clear quantitative genetic basis for the clustering of comorbidities with hyperuricemia.

ANOVA-HD: Analysis of variance when both input and output layers are high-dimensional.

Modern genomic data sets often involve multiple data-layers (e.g., DNA-sequence, gene expression), each of which itself can be high-dimensional. The biological processes underlying these data-layers can lead to intricate multivariate association patterns. We propose and evaluate two methods to determine the proportion of variance of an output data set that can be explained by an input data set when both data panels are high dimensional. Our approach uses random-effects models to estimate the proportion of variance of vectors in the linear span of the output set that can be explained by regression on the input set. We consider a method based on an orthogonal basis (Eigen-ANOVA) and one that uses random vectors (Monte Carlo ANOVA, MC-ANOVA) in the linear span of the output set. Using simulations, we show that the MC-ANOVA method gave nearly unbiased estimates. Estimates produced by Eigen-ANOVA were also nearly unbiased, except when the shared variance was very high (e.g., >0.9). We demonstrate the potential insight that can be obtained from the use of MC-ANOVA and Eigen-ANOVA by applying these two methods to the study of multi-locus linkage disequilibrium in chicken (Gallus gallus) genomes and to the assessment of inter-dependencies between gene expression, methylation, and copy-number-variants in data from breast cancer tumors from humans (Homo sapiens). Our analyses reveal that in chicken breeding populations ~50,000 evenly-spaced SNPs are enough to fully capture the span of whole-genome-sequencing genomes. In the study of multi-omic breast cancer data, we found that the span of copy-number-variants can be fully explained using either methylation or gene expression data and that roughly 74% of the variance in gene expression can be predicted from methylation data.

How did Different Generations Cope with the COVID-19 Pandemic? Early Stages of the Pandemic in Spain.

BACKGROUND: The World Health Organization has highlighted the importance of studying the consequences of the COVID-19 pandemic on mental health. The aim of this study is to examine the role of age in the early psychological responses to the pandemic in a Spanish community sample, focusing on how different generations coped with it. METHOD: An online survey was conducted during the early stages of the quarantine. Sociodemographic, health and behavioral variables were compared for five age groups. Mental health was assessed by the Depression, Anxiety and Stress Scale (DASS-21) and psychological impacts were assessed by the Impact of Event Scale-Revised (IES-R). RESULTS: 3,524 participants were included (Mage = 39.24, SDage = 12.00). Participants aged between 18 and 33 years old showed more hyperactivation and evitation, were more depressed, anxious and stressed. Those aged between 26 and 33 years old showed more intrusion. Those aged between 18 and 25 years old suffered more sleep disturbances, claustrophobia and somatization and maintained worse routines. Elderly people showed better psychological responses in general. CONCLUSIONS: This study provides initial evidence that the negative psychological impact of COVID-19 pandemic hits young people harder. These results should be taken into account when developing specific evidence-based strategies.

DNA Methylation and Gene Expression with Clinical Covariates Explain Variation in Aggressiveness and Survival of Pancreatic Cancer Patients.

Pancreatic cancer (PC) is associated with a high mortality rate. We explored the interindividual variation of cancer outcomes, attributable to DNA methylation, gene expression, and clinical factors among PC patients. We aim to determine whether we could differentiate subjects with greater nodal involvement, higher cancer staging, and subsequent survival. We modeled every response variable as a function of a linear predictor involving the effects of clinical variables, methylation, and gene expression in a Bayesian framework. Our results highlight the overall importance of wide-spread alterations in methylation and gene expression patterns associated with survival, nodal metastasis, and staging.

Xylem systems genetics analysis reveals a key regulator of lignin biosynthesis in Populus deltoides.

Despite the growing resources and tools for high-throughput characterization and analysis of genomic information, the discovery of the genetic elements that regulate complex traits remains a challenge. Systems genetics is an emerging field that aims to understand the flow of biological information that underlies complex traits from genotype to phenotype. In this study, we used a systems genetics approach to identify and evaluate regulators of the lignin biosynthesis pathway in Populus deltoides by combining genome, transcriptome, and phenotype data from a population of 268 unrelated individuals of P. deltoides The discovery of lignin regulators began with the quantitative genetic analysis of the xylem transcriptome and resulted in the detection of 6706 and 4628 significant local- and distant-eQTL associations, respectively. Among the locally regulated genes, we identified the R2R3-MYB transcription factor MYB125 (Potri.003G114100) as a putative trans-regulator of the majority of genes in the lignin biosynthesis pathway. The expression of MYB125 in a diverse population positively correlated with lignin content. Furthermore, overexpression of MYB125 in transgenic poplar resulted in increased lignin content, as well as altered expression of genes in the lignin biosynthesis pathway. Altogether, our findings indicate that MYB125 is involved in the control of a transcriptional coexpression network of lignin biosynthesis genes during secondary cell wall formation in P. deltoides.

Multi-omic signatures identify pan-cancer classes of tumors beyond tissue of origin.

Despite recent advances in treatment, cancer continues to be one of the most lethal human maladies. One of the challenges of cancer treatment is the diversity among similar tumors that exhibit different clinical outcomes. Most of this variability comes from wide-spread molecular alterations that can be summarized by omic integration. Here, we have identified eight novel tumor groups (C1-8) via omic integration, characterized by unique cancer signatures and clinical characteristics. C3 had the best clinical outcomes, while C2 and C5 had poorest. C1, C7, and C8 were upregulated for cellular and mitochondrial translation, and relatively low proliferation. C6 and C4 were also downregulated for cellular and mitochondrial translation, and had high proliferation rates. C4 was represented by copy losses on chromosome 6, and had the highest number of metastatic samples. C8 was characterized by copy losses on chromosome 11, having also the lowest lymphocytic infiltration rate. C6 had the lowest natural killer infiltration rate and was represented by copy gains of genes in chromosome 11. C7 was represented by copy gains on chromosome 6, and had the highest upregulation in mitochondrial translation. We believe that, since molecularly alike tumors could respond similarly to treatment, our results could inform therapeutic action.

Deciphering Sex-Specific Genetic Architectures Using Local Bayesian Regressions.

Many complex human traits exhibit differences between sexes. While numerous factors likely contribute to this phenomenon, growing evidence from genome-wide studies suggest a partial explanation: that males and females from the same population possess differing genetic architectures. Despite this, mapping gene-by-sex (G×S) interactions remains a challenge likely because the magnitude of such an interaction is typically and exceedingly small; traditional genome-wide association techniques may be underpowered to detect such events, due partly to the burden of multiple test correction. Here, we developed a local Bayesian regression (LBR) method to estimate sex-specific SNP marker effects after fully accounting for local linkage-disequilibrium (LD) patterns. This enabled us to infer sex-specific effects and G×S interactions either at the single SNP level, or by aggregating the effects of multiple SNPs to make inferences at the level of small LD-based regions. Using simulations in which there was imperfect LD between SNPs and causal variants, we showed that aggregating sex-specific marker effects with LBR provides improved power and resolution to detect G×S interactions over traditional single-SNP-based tests. When using LBR to analyze traits from the UK Biobank, we detected a relatively large G×S interaction impacting bone mineral density within ABO, and replicated many previously detected large-magnitude G×S interactions impacting waist-to-hip ratio. We also discovered many new G×S interactions impacting such traits as height and body mass index (BMI) within regions of the genome where both male- and female-specific effects explain a small proportion of phenotypic variance (R2 < 1 × 10-4), but are enriched in known expression quantitative trait loci.

The Shared Genetic Basis of Hyperuricemia, Gout, and Kidney Function.

Increased urate levels and gout correlate with chronic kidney disease with consensus that the primary driver of this relationship is reduced kidney function. However, a comparison of results of genome-wide association studies in serum urate levels and kidney function indicate a more complex situation. Approximately 20% of loci are shared-comprised of those in which the urate-raising allele associates with reduced kidney function, the vice versa situation, and those in which the signals/alleles are different. Although there is very little known regarding the molecular basis of the shared genetic relationship, it is clear that there is no major role for urate transporters and associated transportasome machinery. Some loci, however, do provide clues. The ATXN2 locus, with a shared signal, is one of only a small number of master regulators of expression by chromatin interaction, regulating expression of genes relevant for cholesterol and blood pressure. This suggests a role for systemic metabolic alteration. At HNF4A there is genetic heterogeneity with different genetic variants conferring risk to hyperuricemia and chronic kidney disease, suggesting different pathways. Interestingly, the shared loci congregate in the olfactory receptor pathway. The genome-wide association studies have generated a range of experimentally testable hypotheses that should provide insights into the shared pathogenesis of hyperuricemia/gout and chronic kidney disease.

Gain of function in somatic TP53 mutations is associated with immune-rich breast tumors and changes in tumor-associated macrophages.

BACKGROUND: Somatic mutations in TP53 are present in 20%-30% of all breast tumors. While there are numerous population-based analyses of TP53, yet none have examined the relationship between somatic mutations in TP53 and tumor invasive immune cells. METHODS: Clinical and genetic data from 601 women drawn from The Cancer Genome Atlas (TCGA) were used to test the association between somatic TP53 mutation and immune-rich or immune-poor tumor status; determined using the CIBERSORT-based gene expression signature of 22 immune cell types. Our validation dataset, the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), used a pathologist-determined measure of lymphocyte infiltration. RESULTS: Within TP53-mutated samples, a mutation at codon p.R175H was shown to be present at higher frequency in immune-rich tumors. In validation analysis, any somatic mutation in TP53 was associated with immune-rich status, and the mutation at p.R175H had a significant association with tumor-invasive lymphocytes. TCGA-only analysis of invasive immune cell type identified an increase in M0 macrophages associated with p.R175H. CONCLUSIONS: These findings suggest that TP53 somatic mutations, particularly at codon p.R175H, are enriched in tumors with infiltrating immune cells. Our results confirm recent research showing inflammation-related gain of function in specific TP53 mutations.

Modeling Heterogeneity in the Genetic Architecture of Ethnically Diverse Groups Using Random Effect Interaction Models.

In humans, most genome-wide association studies have been conducted using data from Caucasians and many of the reported findings have not replicated in other populations. This lack of replication may be due to statistical issues (small sample sizes or confounding) or perhaps more fundamentally to differences in the genetic architecture of traits between ethnically diverse subpopulations. What aspects of the genetic architecture of traits vary between subpopulations and how can this be quantified? We consider studying effect heterogeneity using Bayesian random effect interaction models. The proposed methodology can be applied using shrinkage and variable selection methods, and produces useful information about effect heterogeneity in the form of whole-genome summaries (e.g., the proportions of variance of a complex trait explained by a set of SNPs and the average correlation of effects) as well as SNP-specific attributes. Using simulations, we show that the proposed methodology yields (nearly) unbiased estimates when the sample size is not too small relative to the number of SNPs used. Subsequently, we used the methodology for the analyses of four complex human traits (standing height, high-density lipoprotein, low-density lipoprotein, and serum urate levels) in European-Americans (EAs) and African-Americans (AAs). The estimated correlations of effects between the two subpopulations were well below unity for all the traits, ranging from 0.73 to 0.50. The extent of effect heterogeneity varied between traits and SNP sets. Height showed less differences in SNP effects between AAs and EAs whereas HDL, a trait highly influenced by lifestyle, exhibited a greater extent of effect heterogeneity. For all the traits, we observed substantial variability in effect heterogeneity across SNPs, suggesting that effect heterogeneity varies between regions of the genome.