A germline variant at 8q24 contributes to familial clustering of prostate cancer in men of African ancestry

Although men of African ancestry have a high risk of prostate cancer (PCa), no genes or mutations have been identified that contribute to familial clustering of PCa in this population. We investigated whether the African ancestry-specific PCa risk variant at 8q24, rs72725854, is enriched in men with a PCa family history in 9052 cases, 143 cases from high-risk families, and 8595 controls of African ancestry. We found the risk allele to be significantly associated with earlier age at diagnosis, more aggressive disease, and enriched in men with a PCa family history (32% of high-risk familial cases carried the variant vs 23% of cases without a family history and 12% of controls). For cases with two or more first-degree relatives with PCa who had at least one family member diagnosed at age \u3c60 yr, the odds ratios for TA heterozygotes and TT homozygotes were 3.92 (95% confidence interval [CI] = 2.13-7.22) and 33.41 (95% CI = 10.86-102.84), respectively. Among men with a PCa family history, the absolute risk by age 60 yr reached 21% (95% CI = 17-25%) for TA heterozygotes and 38% (95% CI = 13-65%) for TT homozygotes. We estimate that in men of African ancestry, rs72725854 accounts for 32% of the total familial risk explained by all known PCa risk variants. PATIENT SUMMARY: We found that rs72725854, an African ancestry-specific risk variant, is more common in men with a family history of prostate cancer and in those diagnosed with prostate cancer at younger ages. Men of African ancestry may benefit from the knowledge of their carrier status for this genetic risk variant to guide decisions about prostate cancer screening

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

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

Liver-Specific Polygenic Risk Score Is Associated with Alzheimer's Disease Diagnosis

BACKGROUND: Our understanding of the pathophysiology underlying Alzheimer's disease (AD) has benefited from genomic analyses, including those that leverage polygenic risk score (PRS) models of disease. The use of functional annotation has been able to improve the power of genomic models. OBJECTIVE: We sought to leverage genomic functional annotations to build tissue-specific AD PRS models and study their relationship with AD and its biomarkers. METHODS: We built 13 tissue-specific AD PRS and studied the scores' relationships with AD diagnosis, cerebrospinal fluid (CSF) amyloid, CSF tau, and other CSF biomarkers in two longitudinal cohort studies of AD. RESULTS: The AD PRS model that was most predictive of AD diagnosis (even without APOE) was the liver AD PRS: n = 1,115; odds ratio = 2.15 (1.67-2.78), p = 3.62×10-9. The liver AD PRS was also statistically significantly associated with cerebrospinal fluid biomarker evidence of amyloid-β (Aβ 42:Aβ 40 ratio, p = 3.53×10-6) and the phosphorylated tau:amyloid-β ratio (p = 1.45×10-5). CONCLUSION: These findings provide further evidence of the role of the liver-functional genome in AD and the benefits of incorporating functional annotation into genomic research

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

Genetic Discovery and Risk Characterization in Type 2 Diabetes across Diverse Populations

Genomic discovery and characterization of risk loci for type 2 diabetes (T2D) have been conducted primarily in individuals of European ancestry. We conducted a multiethnic genome-wide association study of T2D among 53,102 cases and 193,679 control subjects from African, Hispanic, Asian, Native Hawaiian, and European population groups in the Population Architecture Genomics and Epidemiology (PAGE) and Diabetes Genetics Replication and Meta-analysis (DIAGRAM) Consortia. In individuals of African ancestry, we discovered a risk variant in th

Fast and efficient QTL mapper for thousands of molecular phenotypes

In order to discover quantitative trait loci, multi-dimensional genomic datasets combining DNA-seq and ChiP-/RNA-seq require methods that rapidly correlate tens of thousands of molecular phenotypes with millions of genetic variants while appropriately controlling for multiple testing

Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction.

Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction