Evaluating approaches for constructing polygenic risk scores for prostate cancer in men of African and European ancestry.

Genome-wide polygenic risk scores (GW-PRSs) have been reported to have better predictive ability than PRSs based on genome-wide significance thresholds across numerous traits. We compared the predictive ability of several GW-PRS approaches to a recently developed PRS of 269 established prostate cancer-risk variants from multi-ancestry GWASs and fine-mapping studies (PRS269). GW-PRS models were trained with a large and diverse prostate cancer GWAS of 107,247 cases and 127,006 controls that we previously used to develop the multi-ancestry PRS269. Resulting models were independently tested in 1,586 cases and 1,047 controls of African ancestry from the California Uganda Study and 8,046 cases and 191,825 controls of European ancestry from the UK Biobank and further validated in 13,643 cases and 210,214 controls of European ancestry and 6,353 cases and 53,362 controls of African ancestry from the Million Veteran Program. In the testing data, the best performing GW-PRS approach had AUCs of 0.656 (95% CI = 0.635-0.677) in African and 0.844 (95% CI = 0.840-0.848) in European ancestry men and corresponding prostate cancer ORs of 1.83 (95% CI = 1.67-2.00) and 2.19 (95% CI = 2.14-2.25), respectively, for each SD unit increase in the GW-PRS. Compared to the GW-PRS, in African and European ancestry men, the PRS269 had larger or similar AUCs (AUC = 0.679, 95% CI = 0.659-0.700 and AUC = 0.845, 95% CI = 0.841-0.849, respectively) and comparable prostate cancer ORs (OR = 2.05, 95% CI = 1.87-2.26 and OR = 2.21, 95% CI = 2.16-2.26, respectively). Findings were similar in the validation studies. This investigation suggests that current GW-PRS approaches may not improve the ability to predict prostate cancer risk compared to the PRS269 developed from multi-ancestry GWASs and fine-mapping.

Hierarchical joint analysis of marginal summary statistics-Part II: High-dimensional instrumental analysis of omics data.

Instrumental variable (IV) analysis has been widely applied in epidemiology to infer causal relationships using observational data. Genetic variants can also be viewed as valid IVs in Mendelian randomization and transcriptome-wide association studies. However, most multivariate IV approaches cannot scale to high-throughput experimental data. Here, we leverage the flexibility of our previous work, a hierarchical model that jointly analyzes marginal summary statistics (hJAM), to a scalable framework (SHA-JAM) that can be applied to a large number of intermediates and a large number of correlated genetic variants-situations often encountered in modern experiments leveraging omic technologies. SHA-JAM aims to estimate the conditional effect for high-dimensional risk factors on an outcome by incorporating estimates from association analyses of single-nucleotide polymorphism (SNP)-intermediate or SNP-gene expression as prior information in a hierarchical model. Results from extensive simulation studies demonstrate that SHA-JAM yields a higher area under the receiver operating characteristics curve (AUC), a lower mean-squared error of the estimates, and a much faster computation speed, compared to an existing approach for similar analyses. In two applied examples for prostate cancer, we investigated metabolite and transcriptome associations, respectively, using summary statistics from a GWAS for prostate cancer with more than 140,000 men and high dimensional publicly available summary data for metabolites and transcriptomes.

Clonal hematopoiesis and risk of prostate cancer in large samples of European ancestry men.

Little is known regarding the potential relationship between clonal hematopoiesis (CH) of indeterminate potential (CHIP), which is the expansion of hematopoietic stem cells with somatic mutations, and risk of prostate cancer, the fifth leading cause of cancer death of men worldwide. We evaluated the association of age-related CHIP with overall and aggressive prostate cancer risk in two large whole-exome sequencing studies of 75 047 European ancestry men, including 7663 prostate cancer cases, 2770 of which had aggressive disease, and 3266 men carrying CHIP variants. We found that CHIP, defined by over 50 CHIP genes individually and in aggregate, was not significantly associated with overall (aggregate HR = 0.93, 95% CI = 0.76-1.13, P = 0.46) or aggressive (aggregate OR = 1.14, 95% CI = 0.92-1.41, P = 0.22) prostate cancer risk. CHIP was weakly associated with genetic risk of overall prostate cancer, measured using a polygenic risk score (OR = 1.05 per unit increase, 95% CI = 1.01-1.10, P = 0.01). CHIP was not significantly associated with carrying pathogenic/likely pathogenic/deleterious variants in DNA repair genes, which have previously been found to be associated with aggressive prostate cancer. While findings from this study suggest that CHIP is likely not a risk factor for prostate cancer, it will be important to investigate other types of CH in association with prostate cancer risk.

Identification of proteins associated with type 2 diabetes risk in diverse racial and ethnic populations.

AIMS/HYPOTHESIS: Several studies have reported associations between specific proteins and type 2 diabetes risk in European populations. To better understand the role played by proteins in type 2 diabetes aetiology across diverse populations, we conducted a large proteome-wide association study using genetic instruments across four racial and ethnic groups: African; Asian; Hispanic/Latino; and European. METHODS: Genome and plasma proteome data from the Multi-Ethnic Study of Atherosclerosis (MESA) study involving 182 African, 69 Asian, 284 Hispanic/Latino and 409 European individuals residing in the USA were used to establish protein prediction models by using potentially associated cis- and trans-SNPs. The models were applied to genome-wide association study summary statistics of 250,127 type 2 diabetes cases and 1,222,941 controls from different racial and ethnic populations. RESULTS: We identified three, 44 and one protein associated with type 2 diabetes risk in Asian, European and Hispanic/Latino populations, respectively. Meta-analysis identified 40 proteins associated with type 2 diabetes risk across the populations, including well-established as well as novel proteins not yet implicated in type 2 diabetes development. CONCLUSIONS/INTERPRETATION: Our study improves our understanding of the aetiology of type 2 diabetes in diverse populations. DATA AVAILABILITY: The summary statistics of multi-ethnic type 2 diabetes GWAS of MVP, DIAMANTE, Biobank Japan and other studies are available from The database of Genotypes and Phenotypes (dbGaP) under accession number phs001672.v3.p1. MESA genetic, proteome and covariate data can be accessed through dbGaP under phs000209.v13.p3. All code is available on GitHub ( https://github.com/Arthur1021/MESA-1K-PWAS ).

The power of TOPMed imputation for the discovery of Latino-enriched rare variants associated with type 2 diabetes.

AIMS/HYPOTHESIS: The Latino population has been systematically underrepresented in large-scale genetic analyses, and previous studies have relied on the imputation of ungenotyped variants based on the 1000 Genomes (1000G) imputation panel, which results in suboptimal capture of low-frequency or Latino-enriched variants. The National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) released the largest multi-ancestry genotype reference panel representing a unique opportunity to analyse rare genetic variations in the Latino population. We hypothesise that a more comprehensive analysis of low/rare variation using the TOPMed panel would improve our knowledge of the genetics of type 2 diabetes in the Latino population. METHODS: We evaluated the TOPMed imputation performance using genotyping array and whole-exome sequence data in six Latino cohorts. To evaluate the ability of TOPMed imputation to increase the number of identified loci, we performed a Latino type 2 diabetes genome-wide association study (GWAS) meta-analysis in 8150 individuals with type 2 diabetes and 10,735 control individuals and replicated the results in six additional cohorts including whole-genome sequence data from the All of Us cohort. RESULTS: Compared with imputation with 1000G, the TOPMed panel improved the identification of rare and low-frequency variants. We identified 26 genome-wide significant signals including a novel variant (minor allele frequency 1.7%; OR 1.37, p=3.4 × 10-9). A Latino-tailored polygenic score constructed from our data and GWAS data from East Asian and European populations improved the prediction accuracy in a Latino target dataset, explaining up to 7.6% of the type 2 diabetes risk variance. CONCLUSIONS/INTERPRETATION: Our results demonstrate the utility of TOPMed imputation for identifying low-frequency variants in understudied populations, leading to the discovery of novel disease associations and the improvement of polygenic scores. DATA AVAILABILITY: Full summary statistics are available through the Common Metabolic Diseases Knowledge Portal ( https://t2d.hugeamp.org/downloads.html ) and through the GWAS catalog ( https://www.ebi.ac.uk/gwas/ , accession ID: GCST90255648). Polygenic score (PS) weights for each ancestry are available via the PGS catalog ( https://www.pgscatalog.org , publication ID: PGP000445, scores IDs: PGS003443, PGS003444 and PGS003445).

Including diverse and admixed populations in genetic epidemiology research.

The inclusion of ancestrally diverse participants in genetic studies can lead to new discoveries and is important to ensure equitable health care benefit from research advances. Here, members of the Ethical, Legal, Social, Implications (ELSI) committee of the International Genetic Epidemiology Society (IGES) offer perspectives on methods and analysis tools for the conduct of inclusive genetic epidemiology research, with a focus on admixed and ancestrally diverse populations in support of reproducible research practices. We emphasize the importance of distinguishing socially defined population categorizations from genetic ancestry in the design, analysis, reporting, and interpretation of genetic epidemiology research findings. Finally, we discuss the current state of genomic resources used in genetic association studies, functional interpretation, and clinical and public health translation of genomic findings with respect to diverse populations.

Genetic variants associated with post-traumatic stress symptoms in patients with gynecologic cancer.

OBJECTIVE: Patients with cancer experience symptoms of post-traumatic stress disorder (PTSD) more commonly than the general population. The objective of this study was to identify single nucleotide polymorphisms (SNPs) associated with increased risk of post-traumatic stress disorder (PTSD) in patients with gynecologic cancer. METHODS: A prospective cohort study recruited 181 gynecologic cancer survivors receiving care at the University of Minnesota between 2017 and 2020 who completed PTSD DSM-V surveys to self-report their symptoms of PTSD and provided saliva samples. DNA samples were genotyped for 11 SNPs in 9 genes involved in dopaminergic, serotonergic, and opioidergic systems previously associated with risk of PTSD in populations without cancer. RESULTS: Most participants had either ovarian (42.5%) or endometrial (46.4%) cancer; fewer had cervical (7.7%) or vaginal/vulvar (3.3%) cancer. Two SNPS were identified as statistically significantly associated with higher PTSD scores: rs622337 in HTR2A and rs510769 in OPRM1. CONCLUSIONS: Genetic variation likely plays a role in development of PTSD. HTR2A is involved in the serotonin pathway, and OPRM1 is involved in the opioid receptor pathway. This information can be used by oncologic providers to identify patients at greater risk of developing PTSD and may facilitate referral to appropriate consultants and resources early in their treatment.

Multi-ethnic GWAS and fine-mapping of glycaemic traits identify novel loci in the PAGE Study.

AIMS/HYPOTHESIS: Type 2 diabetes is a growing global public health challenge. Investigating quantitative traits, including fasting glucose, fasting insulin and HbA1c, that serve as early markers of type 2 diabetes progression may lead to a deeper understanding of the genetic aetiology of type 2 diabetes development. Previous genome-wide association studies (GWAS) have identified over 500 loci associated with type 2 diabetes, glycaemic traits and insulin-related traits. However, most of these findings were based only on populations of European ancestry. To address this research gap, we examined the genetic basis of fasting glucose, fasting insulin and HbA1c in participants of the diverse Population Architecture using Genomics and Epidemiology (PAGE) Study. METHODS: We conducted a GWAS of fasting glucose (n = 52,267), fasting insulin (n = 48,395) and HbA1c (n = 23,357) in participants without diabetes from the diverse PAGE Study (23% self-reported African American, 46% Hispanic/Latino, 40% European, 4% Asian, 3% Native Hawaiian, 0.8% Native American), performing transethnic and population-specific GWAS meta-analyses, followed by fine-mapping to identify and characterise novel loci and independent secondary signals in known loci. RESULTS: Four novel associations were identified (p < 5 × 10-9), including three loci associated with fasting insulin, and a novel, low-frequency African American-specific locus associated with fasting glucose. Additionally, seven secondary signals were identified, including novel independent secondary signals for fasting glucose at the known GCK locus and for fasting insulin at the known PPP1R3B locus in transethnic meta-analysis. CONCLUSIONS/INTERPRETATION: Our findings provide new insights into the genetic architecture of glycaemic traits and highlight the continued importance of conducting genetic studies in diverse populations. DATA AVAILABILITY: Full summary statistics from each of the population-specific and transethnic results are available at NHGRI-EBI GWAS catalog ( https://www.ebi.ac.uk/gwas/downloads/summary-statistics ).

Genetic variants predictive of chemotherapy-induced peripheral neuropathy symptoms in gynecologic cancer survivors.

OBJECTIVE: To identify genetic variants associated with chemotherapy-induced peripheral neuropathy (CIPN) symptoms among gynecologic cancer survivors and determine the variants' predictive power in addition to age and clinical factors at time of diagnosis. METHODS: Participants of a prospective cohort study on gynecologic cancers provided a DNA saliva sample and reported CIPN symptoms (FACT/GOG-Ntx). Genotyping of 23 single nucleotide polymorphisms (SNPs) previously identified as related to platinum- or taxane-induced neuropathy was performed using iPLEX Gold method. Risk allele carrier frequencies of 19 SNPs that passed quality checks were compared between those with/without high CIPN symptoms using logistic regression, adjusting for age. Receiver operating characteristic (ROC) curves using clinical risk factors (age, diabetes, BMI, Charlson Comorbidity Index, previous cancer diagnosis) with and without the identified SNPs were compared. RESULTS: 107 individuals received platinum or taxane-based chemotherapy and provided sufficient DNA for analysis. Median age was 65.1 years; 39.6% had obesity and 8.4% diabetes; most had ovarian (58.9%) or uterine cancer (29.0%). Two SNPs were significantly associated with high CIPN symptomatology: rs3753753 in GPX7, OR = 2.55 (1.13, 5.72) and rs139887 in SOX10, 2.66 (1.18, 6.00). Including these two SNPs in a model with clinical characteristics led to an improved AUC for CIPN symptomatology (0.65 vs. 0.74, p = 0.04). CONCLUSIONS: Genetic and clinical characteristics were predictive of higher CIPN symptomatology in gynecologic cancer survivors, and combining these factors resulted in superior predictive power compared with a model with clinical factors only. Prospective validation and assessment of clinical utility are warranted.

Integrated analysis of genomics, longitudinal metabolomics, and Alzheimer's risk factors among 1,111 cohort participants.

Although Alzheimer's disease (AD) is highly heritable, genetic variants are known to be associated with AD only explain a small proportion of its heritability. Genetic factors may only convey disease risk in individuals with certain environmental exposures, suggesting that a multiomics approach could reveal underlying mechanisms contributing to complex traits, such as AD. We developed an integrated network to investigate relationships between metabolomics, genomics, and AD risk factors using Wisconsin Registry for Alzheimer's Prevention participants. Analyses included 1,111 non-Hispanic Caucasian participants with whole blood expression for 11,376 genes (imputed from dense genome-wide genotyping), 1,097 fasting plasma metabolites, and 17 AD risk factors. A subset of 155 individuals also had 364 fastings cerebral spinal fluid (CSF) metabolites. After adjusting each of these 12,854 variables for potential confounders, we developed an undirected graphical network, representing all significant pairwise correlations upon adjusting for multiple testing. There were many instances of genes being indirectly linked to AD risk factors through metabolites, suggesting that genes may influence AD risk through particular metabolites. Follow-up analyses suggested that glycine mediates the relationship between carbamoyl-phosphate synthase 1 and measures of cardiovascular and diabetes risk, including body mass index, waist-hip ratio, inflammation, and insulin resistance. Further, 38 CSF metabolites explained more than 60% of the variance of CSF levels of tau, a detrimental protein that accumulates in the brain of AD patients and is necessary for its diagnosis. These results further our understanding of underlying mechanisms contributing to AD risk while demonstrating the utility of generating and integrating multiple omics data types.

Carrier frequency estimation of Zellweger spectrum disorder using ExAC database and bioinformatics tools.

PURPOSE: We aimed to estimate the carrier frequency of Zellweger spectrum disorder (ZSD), a rare autosomal recessive disease, and the associated disease incidence based on data from the Exome Aggregation Consortium (ExAC) of approximately 60,000 individuals. METHODS: We obtained variants from ExAC in 13 PEX genes associated with ZSD. Potentially pathogenic missense variants were identified with computational variant analysis tools according to three stringency levels. Using variants classified as potentially pathogenic, we estimated the carrier frequency and the associated incidence for the entire ExAC population and its subpopulations. We also evaluated variants based on pathogenicity criteria for sequence variant interpretation outlined by the American College of Medical Genetics and Genomics (ACMG) and calculated the carrier frequency and incidence based on those variants. RESULTS: The bioinformatically estimated incidence rate of ZSD in the ExAC population is 1 in 83,841 using our least stringent pathogenicity cutoff. Under clinical guidelines outlined by ACMG, the estimated incidence is 1 in 3,275,751 births. CONCLUSION: We outlined a process for estimating the ZSD disease carrier frequency and incidence in a large consortium using bioinformatics tools. Our results are close to current newborn screening estimates in New York of 1 in 90,000 births, estimated from 1.08 million screenings.

Using recursive feature elimination in random forest to account for correlated variables in high dimensional data.

BACKGROUND: Random forest (RF) is a machine-learning method that generally works well with high-dimensional problems and allows for nonlinear relationships between predictors; however, the presence of correlated predictors has been shown to impact its ability to identify strong predictors. The Random Forest-Recursive Feature Elimination algorithm (RF-RFE) mitigates this problem in smaller data sets, but this approach has not been tested in high-dimensional omics data sets. RESULTS: We integrated 202,919 genotypes and 153,422 methylation sites in 680 individuals, and compared the abilities of RF and RF-RFE to detect simulated causal associations, which included simulated genotype-methylation interactions, between these variables and triglyceride levels. Results show that RF was able to identify strong causal variables with a few highly correlated variables, but it did not detect other causal variables. CONCLUSIONS: Although RF-RFE decreased the importance of correlated variables, in the presence of many correlated variables, it also decreased the importance of causal variables, making both hard to detect. These findings suggest that RF-RFE may not scale to high-dimensional data.

Data mining and machine learning approaches for the integration of genome-wide association and methylation data: methodology and main conclusions from GAW20.

BACKGROUND: Multiple layers of genetic and epigenetic variability are being simultaneously explored in an increasing number of health studies. We summarize here different approaches applied in the Data Mining and Machine Learning group at the GAW20 to integrate genome-wide genotype and methylation array data. RESULTS: We provide a non-intimidating introduction to some frequently used methods to investigate high-dimensional molecular data and compare the different approaches tried by group members: random forest, deep learning, cluster analysis, mixed models, and gene-set enrichment analysis. Group contributions were quite heterogeneous regarding investigated data sets (real vs simulated), conducted data quality control and assessed phenotypes (eg, metabolic syndrome vs relative differences of log-transformed triglyceride concentrations before and after fenofibrate treatment). However, some common technical issues were detected, leading to practical recommendations. CONCLUSIONS: Different sources of correlation were identified by group members, including population stratification, family structure, batch effects, linkage disequilibrium and correlation of methylation values at neighboring cytosine-phosphate-guanine (CpG) sites, and the majority of applied approaches were able to take into account identified correlation structures. The ability to efficiently deal with high-dimensional omics data, and the model free nature of the approaches that did not require detailed model specifications were clearly recognized as the main strengths of applied methods. A limitation of random forest is its sensitivity to highly correlated variables. The parameter setup and the interpretation of results from deep learning methods, in particular deep neural networks, can be extremely challenging. Cluster analysis and mixed models may need some predimension reduction based on existing literature, data filtering, and supplementary statistical methods, and gene-set enrichment analysis requires biological insight.

A genome sequencing program for novel undiagnosed diseases.

PURPOSE: The Scripps Idiopathic Diseases of Man (IDIOM) study aims to discover novel gene-disease relationships and provide molecular genetic diagnosis and treatment guidance for individuals with novel diseases using genome sequencing integrated with clinical assessment and multidisciplinary case review. Here we describe the operational protocol and initial results of the IDIOM study. METHODS: A total of 121 cases underwent first-tier review by the principal investigators to determine whether the primary inclusion criteria were satisfied, 59 (48.8%) underwent second-tier review by our clinician-scientist review panel, and 17 patients (14.0%) and their family members were enrolled. RESULTS: 60% of cases resulted in a plausible molecular diagnosis, and 18% of cases resulted in a confirmed molecular diagnosis. Two of three confirmed cases led to the identification of novel gene-disease relationships. In the third confirmed case a previously described but unrecognized disease was revealed. In all three confirmed cases a new clinical management strategy was initiated based on the genetic findings. CONCLUSION: Genome sequencing provides tangible clinical benefit for individuals with idiopathic genetic disease, not only in the context of molecular genetic diagnosis of known rare conditions but also in cases where prior clinical information regarding a new genetic disorder is lacking.

Long-term influence of normal variation in neonatal characteristics on human brain development.

It is now recognized that a number of cognitive, behavioral, and mental health outcomes across the lifespan can be traced to fetal development. Although the direct mediation is unknown, the substantial variance in fetal growth, most commonly indexed by birth weight, may affect lifespan brain development. We investigated effects of normal variance in birth weight on MRI-derived measures of brain development in 628 healthy children, adolescents, and young adults in the large-scale multicenter Pediatric Imaging, Neurocognition, and Genetics study. This heterogeneous sample was recruited through geographically dispersed sites in the United States. The influence of birth weight on cortical thickness, surface area, and striatal and total brain volumes was investigated, controlling for variance in age, sex, household income, and genetic ancestry factors. Birth weight was found to exert robust positive effects on regional cortical surface area in multiple regions as well as total brain and caudate volumes. These effects were continuous across birth weight ranges and ages and were not confined to subsets of the sample. The findings show that (i) aspects of later child and adolescent brain development are influenced at birth and (ii) relatively small differences in birth weight across groups and conditions typically compared in neuropsychiatric research (e.g., Attention Deficit Hyperactivity Disorder, schizophrenia, and personality disorders) may influence group differences observed in brain parameters of interest at a later stage in life. These findings should serve to increase our attention to early influences.

Multimodal imaging of the self-regulating developing brain.

Self-regulation refers to the ability to control behavior, cognition, and emotions, and self-regulation failure is related to a range of neuropsychiatric problems. It is poorly understood how structural maturation of the brain brings about the gradual improvement in self-regulation during childhood. In a large-scale multicenter effort, 735 children (4-21 y) underwent structural MRI for quantification of cortical thickness and surface area and diffusion tensor imaging for quantification of the quality of major fiber connections. Brain development was related to a standardized measure of cognitive control (the flanker task from the National Institutes of Health Toolbox), a critical component of self-regulation. Ability to inhibit responses and impose cognitive control increased rapidly during preteen years. Surface area of the anterior cingulate cortex accounted for a significant proportion of the variance in cognitive performance. This finding is intriguing, because characteristics of the anterior cingulum are shown to be related to impulse, attention, and executive problems in neurodevelopmental disorders, indicating a neural foundation for self-regulation abilities along a continuum from normality to pathology. The relationship was strongest in the younger children. Properties of large-fiber connections added to the picture by explaining additional variance in cognitive control. Although cognitive control was related to surface area of the anterior cingulate independently of basic processes of mental speed, the relationship between white matter quality and cognitive control could be fully accounted for by speed. The results underscore the need for integration of different aspects of brain maturation to understand the foundations of cognitive development.

Candidate SNP associations of optimism and resilience in older adults: exploratory study of 935 community-dwelling adults.

OBJECTIVE: Optimism and resilience promote health and well-being in older adults, and previous reports suggest that these traits are heritable. We examined the association of selected single-nucleotide polymorphisms (SNPs) with optimism and resilience in older adults. DESIGN: Candidate gene association study that was a follow-on at the University of California, San Diego, sites of two NIH-funded multi-site longitudinal investigations: Women's Health Initiative (WHI) and SELenium and vitamin E Cancer prevention Trial (SELECT). PARTICIPANTS: 426 women from WHI older than age 50 years, and 509 men older than age 55 years (age 50 years for African American men) from SELECT. MEASUREMENTS: 65 candidate gene SNPs that were judged by consensus, based on a literature review, as being related to predisposition to optimism and resilience, and 31 ancestry informative marker SNPs, genotyped from blood-based DNA samples and self-report scales for trait optimism, resilience, and depressive symptoms. RESULTS: Using a Bonferroni threshold for significant association (p = 0.00089), there were no significant associations for individual SNPs with optimism or resilience in single-locus analyses. Exploratory multi-locus polygenic analyses with p <0.05 showed an association of optimism with SNPs in MAOA, IL10, and FGG genes, and an association of resilience with a SNP in MAOA gene. CONCLUSIONS: Correcting for Type I errors, there were no significant associations of optimism and resilience with specific gene SNPs in single-locus analyses. Positive psychological traits are likely to be genetically complex, with many loci having small effects contributing to phenotypic variation. Our exploratory multi-locus polygenic analyses suggest that larger sample sizes and complementary approaches involving methods such as sequence-based association studies, copy number variation analyses, and pathway-based analyses could be useful for better understanding the genetic basis of these positive psychological traits.

Impact of direct-to-consumer genomic testing at long term follow-up.

BACKGROUND: There are few empirical data to inform the debate surrounding the use and regulation of direct-to-consumer (DTC) genome-wide disease risk tests. This study aimed to determine the long term psychological, behavioural, and clinical impacts of genomic risk testing for common disease. METHODS: The Scripps Genomic Health Initiative is a prospective longitudinal cohort study of adults who purchased the Navigenics Health Compass, a commercially available genomic test. Web based assessments were administered at baseline, short (3 months), and long term (1 year) follow-up. RESULTS: 2240 participants completed either or both follow-ups and a subset of 1325 completed long term follow-up. There were no significant differences from baseline in anxiety (p=0.50), fat intake (p=0.34), or exercise (p=0.39) at long term follow-up, and 96.8% of the sample had no test related distress. Longitudinal linear mixed model analyses were consistent with results of cross-sectional analyses. Screening test completion was associated with sharing genomic test results with a physician (36.0% shared; p<0.001) and perceived utility of the test (61.5% high perceived utility; p=0.002), but was not associated with the genomic risk estimate values themselves. CONCLUSIONS: Over a third of DTC genomic test recipients shared their results with their own physician during an approximate 1 year follow-up period, and this sharing was associated with higher screening test completion. Genomic testing was not associated with long term psychological risks, and most participants reportedly perceived the test to be of high personal utility.

Characteristics of genomic test consumers who spontaneously share results with their health care provider.

The purpose of this study was to evaluate the characteristics of direct-to-consumer (DTC) genomic test consumers who spontaneously shared their test results with their health care provider. Utilizing data from the Scripps Genomic Health Initiative, we compared demographic, behavioral, and attitudinal characteristics of DTC genomic test consumers who shared their results with their physician or health care provider versus those who did not share. We also compared genomic risk estimates between the two groups. Of 2,024 individuals assessed at approximately 6 months post testing, 540 individuals (26.5%) reported sharing their results with their physician or health care provider. Those who shared were older (p < .001), had a higher income (p = .01), were more likely to be married (p = .005), and were more likely to identify with a religion (p = .004). As assessed prior to undergoing testing, sharers also reported higher levels of exercise (p = .003), lower fat intake (p = .02), fewer overall concerns about testing (p = .001), and fewer concerns related to the privacy of their genomic information (p = .03). The genomic disease risk estimates disclosed were not associated with sharing. Thus, in a DTC genomic testing context, physicians and other health care providers may be more likely to encounter patients who are more health conscious and have fewer concerns about the privacy of their genomic information. Genomic risk itself does not appear to be a primary determinant of sharing behavior among consumers.

An extension of latent unknown clustering integrating multi-omics data (LUCID) incorporating incomplete omics data.

Motivation: Latent unknown clustering integrating multi-omics data is a novel statistical model designed for multi-omics data analysis. It integrates omics data with exposures and an outcome through a latent cluster, elucidating how exposures influence processes reflected in multi-omics measurements, ultimately affecting an outcome. A significant challenge in multi-omics analysis is the issue of list-wise missingness. To address this, we extend the model to incorporate list-wise missingness within an integrated imputation framework, which can also handle sporadic missingness when necessary. Results: Simulation studies demonstrate that our integrated imputation approach produces consistent and less biased estimates, closely reflecting true underlying values. We applied this model to data from the ISGlobal/ATHLETE "Exposome Data Challenge Event" to explore the association between maternal exposure to hexachlorobenzene and childhood body mass index by integrating incomplete proteomics data from 1301 children. The model successfully estimated proteomics profiles for two clusters representing higher and lower body mass index, characterizing the potential profiles linking prenatal hexachlorobenzene levels and childhood body mass index. Availability and implementation: The proposed methods have been implemented in the R package LUCIDus. The source code is available at https://github.com/USCbiostats/LUCIDus.