Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics.

The Cancer Genome Atlas (TCGA) has catalyzed systematic characterization of diverse genomic alterations underlying human cancers. At this historic junction marking the completion of genomic characterization of over 11,000 tumors from 33 cancer types, we present our current understanding of the molecular processes governing oncogenesis. We illustrate our insights into cancer through synthesis of the findings of the TCGA PanCancer Atlas project on three facets of oncogenesis: (1) somatic driver mutations, germline pathogenic variants, and their interactions in the tumor; (2) the influence of the tumor genome and epigenome on transcriptome and proteome; and (3) the relationship between tumor and the microenvironment, including implications for drugs targeting driver events and immunotherapies. These results will anchor future characterization of rare and common tumor types, primary and relapsed tumors, and cancers across ancestry groups and will guide the deployment of clinical genomic sequencing.

Elucidating the structure and function of the nucleus-The NIH Common Fund 4D Nucleome program.

Genomic architecture appears to play crucial roles in health and a variety of diseases. How nuclear structures reorganize over different timescales is elusive, partly because the tools needed to probe and perturb them are not as advanced as needed by the field. To fill this gap, the National Institutes of Health Common Fund started a program in 2015, called the 4D Nucleome (4DN), with the goal of developing and ultimately applying technologies to interrogate the structure and function of nuclear organization in space and time.

Strategic vision for improving human health at The Forefront of Genomics.

Starting with the launch of the Human Genome Project three decades ago, and continuing after its completion in 2003, genomics has progressively come to have a central and catalytic role in basic and translational research. In addition, studies increasingly demonstrate how genomic information can be effectively used in clinical care. In the future, the anticipated advances in technology development, biological insights, and clinical applications (among others) will lead to more widespread integration of genomics into almost all areas of biomedical research, the adoption of genomics into mainstream medical and public-health practices, and an increasing relevance of genomics for everyday life. On behalf of the research community, the National Human Genome Research Institute recently completed a multi-year process of strategic engagement to identify future research priorities and opportunities in human genomics, with an emphasis on health applications. Here we describe the highest-priority elements envisioned for the cutting-edge of human genomics going forward-that is, at 'The Forefront of Genomics'.

Prioritizing diversity in human genomics research.

Recent studies have highlighted the imperatives of including diverse and under-represented individuals in human genomics research and the striking gaps in attaining that inclusion. With its multidecade experience in supporting research and policy efforts in human genomics, the National Human Genome Research Institute is committed to establishing foundational approaches to study the role of genomic variation in health and disease that include diverse populations. Large-scale efforts to understand biology and health have yielded key scientific findings, lessons and recommendations on how to increase diversity in genomic research studies and the genomic research workforce. Increased attention to diversity will increase the accuracy, utility and acceptability of using genomic information for clinical care.

Clinical Sequencing Exploratory Research Consortium: Accelerating Evidence-Based Practice of Genomic Medicine.

Despite rapid technical progress and demonstrable effectiveness for some types of diagnosis and therapy, much remains to be learned about clinical genome and exome sequencing (CGES) and its role within the practice of medicine. The Clinical Sequencing Exploratory Research (CSER) consortium includes 18 extramural research projects, one National Human Genome Research Institute (NHGRI) intramural project, and a coordinating center funded by the NHGRI and National Cancer Institute. The consortium is exploring analytic and clinical validity and utility, as well as the ethical, legal, and social implications of sequencing via multidisciplinary approaches; it has thus far recruited 5,577 participants across a spectrum of symptomatic and healthy children and adults by utilizing both germline and cancer sequencing. The CSER consortium is analyzing data and creating publically available procedures and tools related to participant preferences and consent, variant classification, disclosure and management of primary and secondary findings, health outcomes, and integration with electronic health records. Future research directions will refine measures of clinical utility of CGES in both germline and somatic testing, evaluate the use of CGES for screening in healthy individuals, explore the penetrance of pathogenic variants through extensive phenotyping, reduce discordances in public databases of genes and variants, examine social and ethnic disparities in the provision of genomics services, explore regulatory issues, and estimate the value and downstream costs of sequencing. The CSER consortium has established a shared community of research sites by using diverse approaches to pursue the evidence-based development of best practices in genomic medicine.

Genome-Wide Interaction Analyses between Genetic Variants and Alcohol Consumption and Smoking for Risk of Colorectal Cancer.

Genome-wide association studies (GWAS) have identified many genetic susceptibility loci for colorectal cancer (CRC). However, variants in these loci explain only a small proportion of familial aggregation, and there are likely additional variants that are associated with CRC susceptibility. Genome-wide studies of gene-environment interactions may identify variants that are not detected in GWAS of marginal gene effects. To study this, we conducted a genome-wide analysis for interaction between genetic variants and alcohol consumption and cigarette smoking using data from the Colon Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). Interactions were tested using logistic regression. We identified interaction between CRC risk and alcohol consumption and variants in the 9q22.32/HIATL1 (Pinteraction = 1.76×10-8; permuted p-value 3.51x10-8) region. Compared to non-/occasional drinking light to moderate alcohol consumption was associated with a lower risk of colorectal cancer among individuals with rs9409565 CT genotype (OR, 0.82 [95% CI, 0.74-0.91]; P = 2.1×10-4) and TT genotypes (OR,0.62 [95% CI, 0.51-0.75]; P = 1.3×10-6) but not associated among those with the CC genotype (p = 0.059). No genome-wide statistically significant interactions were observed for smoking. If replicated our suggestive finding of a genome-wide significant interaction between genetic variants and alcohol consumption might contribute to understanding colorectal cancer etiology and identifying subpopulations with differential susceptibility to the effect of alcohol on CRC risk.

Characterizing reduced coverage regions through comparison of exome and genome sequencing data across 10 centers.

PURPOSE: As massively parallel sequencing is increasingly being used for clinical decision making, it has become critical to understand parameters that affect sequencing quality and to establish methods for measuring and reporting clinical sequencing standards. In this report, we propose a definition for reduced coverage regions and describe a set of standards for variant calling in clinical sequencing applications. METHODS: To enable sequencing centers to assess the regions of poor sequencing quality in their own data, we optimized and used a tool (ExCID) to identify reduced coverage loci within genes or regions of particular interest. We used this framework to examine sequencing data from 500 patients generated in 10 projects at sequencing centers in the National Human Genome Research Institute/National Cancer Institute Clinical Sequencing Exploratory Research Consortium. RESULTS: This approach identified reduced coverage regions in clinically relevant genes, including known clinically relevant loci that were uniquely missed at individual centers, in multiple centers, and in all centers. CONCLUSION: This report provides a process road map for clinical sequencing centers looking to perform similar analyses on their data.

Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases.

Recently, many new approaches, study designs, and statistical and analytical methods have emerged for studying gene-environment interactions (G×Es) in large-scale studies of human populations. There are opportunities in this field, particularly with respect to the incorporation of -omics and next-generation sequencing data and continual improvement in measures of environmental exposures implicated in complex disease outcomes. In a workshop called "Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases," held October 17-18, 2014, by the National Institute of Environmental Health Sciences and the National Cancer Institute in conjunction with the annual American Society of Human Genetics meeting, participants explored new approaches and tools that have been developed in recent years for G×E discovery. This paper highlights current and critical issues and themes in G×E research that need additional consideration, including the improved data analytical methods, environmental exposure assessment, and incorporation of functional data and annotations.

Genome-wide diet-gene interaction analyses for risk of colorectal cancer.

Dietary factors, including meat, fruits, vegetables and fiber, are associated with colorectal cancer; however, there is limited information as to whether these dietary factors interact with genetic variants to modify risk of colorectal cancer. We tested interactions between these dietary factors and approximately 2.7 million genetic variants for colorectal cancer risk among 9,287 cases and 9,117 controls from ten studies. We used logistic regression to investigate multiplicative gene-diet interactions, as well as our recently developed Cocktail method that involves a screening step based on marginal associations and gene-diet correlations and a testing step for multiplicative interactions, while correcting for multiple testing using weighted hypothesis testing. Per quartile increment in the intake of red and processed meat were associated with statistically significant increased risks of colorectal cancer and vegetable, fruit and fiber intake with lower risks. From the case-control analysis, we detected a significant interaction between rs4143094 (10p14/near GATA3) and processed meat consumption (OR = 1.17; p = 8.7E-09), which was consistently observed across studies (p heterogeneity = 0.78). The risk of colorectal cancer associated with processed meat was increased among individuals with the rs4143094-TG and -TT genotypes (OR = 1.20 and OR = 1.39, respectively) and null among those with the GG genotype (OR = 1.03). Our results identify a novel gene-diet interaction with processed meat for colorectal cancer, highlighting that diet may modify the effect of genetic variants on disease risk, which may have important implications for prevention.

Common genetic variation and survival after colorectal cancer diagnosis: a genome-wide analysis.

Genome-wide association studies have identified several germline single nucleotide polymorphisms (SNPs) significantly associated with colorectal cancer (CRC) incidence. Common germline genetic variation may also be related to CRC survival. We used a discovery-based approach to identify SNPs related to survival outcomes after CRC diagnosis. Genome-wide genotyping arrays were conducted for 3494 individuals with invasive CRC enrolled in six prospective cohort studies (median study-specific follow-up = 4.2-8.1 years). In pooled analyses, we used Cox regression to assess SNP-specific associations with CRC-specific and overall survival, with additional analyses stratified by stage at diagnosis. Top findings were followed-up in independent studies. A P value threshold of P < 5×10(-8) in analyses combining discovery and follow-up studies was required for genome-wide significance. Among individuals with distant-metastatic CRC, several SNPs at 6p12.1, nearest the ELOVL5 gene, were statistically significantly associated with poorer survival, with the strongest associations noted for rs209489 [hazard ratio (HR) = 1.8, P = 7.6×10(-10) and HR = 1.8, P = 3.7×10(-9) for CRC-specific and overall survival, respectively). No SNPs were statistically significantly associated with survival among all cases combined or in cases without distant-metastases. SNPs in 6p12.1/ELOVL5 were associated with survival outcomes in individuals with distant-metastatic CRC, and merit further follow-up for functional significance. Findings from this genome-wide association study highlight the potential importance of genetic variation in CRC prognosis and provide clues to genomic regions of potential interest.

Genetic simulation tools for post-genome wide association studies of complex diseases.

Genetic simulation programs are used to model data under specified assumptions to facilitate the understanding and study of complex genetic systems. Standardized data sets generated using genetic simulation are essential for the development and application of novel analytical tools in genetic epidemiology studies. With continuing advances in high-throughput genomic technologies and generation and analysis of larger, more complex data sets, there is a need for updating current approaches in genetic simulation modeling. To provide a forum to address current and emerging challenges in this area, the National Cancer Institute (NCI) sponsored a workshop, entitled "Genetic Simulation Tools for Post-Genome Wide Association Studies of Complex Diseases" at the National Institutes of Health (NIH) in Bethesda, Maryland on March 11-12, 2014. The goals of the workshop were to (1) identify opportunities, challenges, and resource needs for the development and application of genetic simulation models; (2) improve the integration of tools for modeling and analysis of simulated data; and (3) foster collaborations to facilitate development and applications of genetic simulation. During the course of the meeting, the group identified challenges and opportunities for the science of simulation, software and methods development, and collaboration. This paper summarizes key discussions at the meeting, and highlights important challenges and opportunities to advance the field of genetic simulation.

A model to determine colorectal cancer risk using common genetic susceptibility loci.

BACKGROUND & AIMS: Risk for colorectal cancer (CRC) can be greatly reduced through screening. To aid in the development of screening strategies, we refined models designed to determine risk of CRC by incorporating information from common genetic susceptibility loci. METHODS: By using data collected from more than 12,000 participants in 6 studies performed from 1990 through 2011 in the United States and Germany, we developed risk determination models based on sex, age, family history, genetic risk score (number of risk alleles carried at 27 validated common CRC susceptibility loci), and history of endoscopic examinations. The model was validated using data collected from approximately 1800 participants in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, conducted from 1993 through 2001 in the United States. RESULTS: We identified a CRC genetic risk score that independently predicted which patients in the training set would develop CRC. Compared with determination of risk based only on family history, adding the genetic risk score increased the discriminatory accuracy from 0.51 to 0.59 (P = .0028) for men and from 0.52 to 0.56 (P = .14) for women. We calculated age- and sex-specific 10-year CRC absolute risk estimates based on the number of risk alleles, family history, and history of endoscopic examinations. A model that included a genetic risk score better determined the recommended starting age for screening in subjects with and without family histories of CRC. The starting age for high-risk men (family history of CRC and genetic risk score, 90%) was 42 years, and for low-risk men (no family history of CRC and genetic risk score, 10%) was 52 years. For men with no family history and a high genetic risk score (90%), the starting age would be 47 years; this is an intermediate value that is 5 years earlier than it would be for men with a genetic risk score of 10%. Similar trends were observed in women. CONCLUSIONS: By incorporating information on CRC risk alleles, we created a model to determine the risk for CRC more accurately. This model might be used to develop screening and prevention strategies.

Identification of a common variant with potential pleiotropic effect on risk of inflammatory bowel disease and colorectal cancer.

Although genome-wide association studies (GWAS) have separately identified many genetic susceptibility loci for ulcerative colitis (UC), Crohn's disease (CD) and colorectal cancer (CRC), there has been no large-scale examination for pleiotropy, or shared genetic susceptibility, for these conditions. We used logistic regression modeling to examine the associations of 181 UC and CD susceptibility variants previously identified by GWAS with risk of CRC using data from the Genetics and Epidemiology of Colorectal Cancer Consortium and the Colon Cancer Family Registry. We also examined associations of significant variants with clinical and molecular characteristics in a subset of the studies. Among 11794 CRC cases and 14190 controls, rs11676348, the susceptibility single nucleotide polymorphism (SNP) for UC, was significantly associated with reduced risk of CRC (P = 7E-05). The multivariate-adjusted odds ratio of CRC with each copy of the T allele was 0.93 (95% CI 0.89-0.96). The association of the SNP with risk of CRC differed according to mucinous histological features (P heterogeneity = 0.008). In addition, the (T) allele was associated with lower risk of tumors with Crohn's-like reaction but not tumors without such immune infiltrate (P heterogeneity = 0.02) and microsatellite instability-high (MSI-high) but not microsatellite stable or MSI-low tumors (P heterogeneity = 0.03). The minor allele (T) in SNP rs11676348, located downstream from CXCR2 that has been implicated in CRC progression, is associated with a lower risk of CRC, particularly tumors with a mucinous component, Crohn's-like reaction and MSI-high. Our findings offer the promise of risk stratification of inflammatory bowel disease patients for complications such as CRC.

Association of aspirin and NSAID use with risk of colorectal cancer according to genetic variants.

IMPORTANCE: Use of aspirin and other nonsteroidal anti-inflammatory drugs (NSAIDs) is associated with lower risk of colorectal cancer. OBJECTIVE: To identify common genetic markers that may confer differential benefit from aspirin or NSAID chemoprevention, we tested gene × environment interactions between regular use of aspirin and/or NSAIDs and single-nucleotide polymorphisms (SNPs) in relation to risk of colorectal cancer. DESIGN, SETTING, AND PARTICIPANTS: Case-control study using data from 5 case-control and 5 cohort studies initiated between 1976 and 2003 across the United States, Canada, Australia, and Germany and including colorectal cancer cases (n=8634) and matched controls (n=8553) ascertained between 1976 and 2011. Participants were all of European descent. EXPOSURES: Genome-wide SNP data and information on regular use of aspirin and/or NSAIDs and other risk factors. MAIN OUTCOMES AND MEASURES: Colorectal cancer. RESULTS: Regular use of aspirin and/or NSAIDs was associated with lower risk of colorectal cancer (prevalence, 28% vs 38%; odds ratio [OR], 0.69 [95% CI, 0.64-0.74]; P = 6.2 × 10(-28)) compared with nonregular use. In the conventional logistic regression analysis, the SNP rs2965667 at chromosome 12p12.3 near the MGST1 gene showed a genome-wide significant interaction with aspirin and/or NSAID use (P = 4.6 × 10(-9) for interaction). Aspirin and/or NSAID use was associated with a lower risk of colorectal cancer among individuals with rs2965667-TT genotype (prevalence, 28% vs 38%; OR, 0.66 [95% CI, 0.61-0.70]; P = 7.7 × 10(-33)) but with a higher risk among those with rare (4%) TA or AA genotypes (prevalence, 35% vs 29%; OR, 1.89 [95% CI, 1.27-2.81]; P = .002). In case-only interaction analysis, the SNP rs16973225 at chromosome 15q25.2 near the IL16 gene showed a genome-wide significant interaction with use of aspirin and/or NSAIDs (P = 8.2 × 10(-9) for interaction). Regular use was associated with a lower risk of colorectal cancer among individuals with rs16973225-AA genotype (prevalence, 28% vs 38%; OR, 0.66 [95% CI, 0.62-0.71]; P = 1.9 × 10(-30)) but was not associated with risk of colorectal cancer among those with less common (9%) AC or CC genotypes (prevalence, 36% vs 39%; OR, 0.97 [95% CI, 0.78-1.20]; P = .76). CONCLUSIONS AND RELEVANCE: In this genome-wide investigation of gene × environment interactions, use of aspirin and/or NSAIDs was associated with lower risk of colorectal cancer, and this association differed according to genetic variation at 2 SNPs at chromosomes 12 and 15. Validation of these findings in additional populations may facilitate targeted colorectal cancer prevention strategies.

Gene-environment interactions in cancer epidemiology: a National Cancer Institute Think Tank report.

Cancer risk is determined by a complex interplay of genetic and environmental factors. Genome-wide association studies (GWAS) have identified hundreds of common (minor allele frequency [MAF] > 0.05) and less common (0.01 < MAF < 0.05) genetic variants associated with cancer. The marginal effects of most of these variants have been small (odds ratios: 1.1-1.4). There remain unanswered questions on how best to incorporate the joint effects of genes and environment, including gene-environment (G × E) interactions, into epidemiologic studies of cancer. To help address these questions, and to better inform research priorities and allocation of resources, the National Cancer Institute sponsored a "Gene-Environment Think Tank" on January 10-11, 2012. The objective of the Think Tank was to facilitate discussions on (1) the state of the science, (2) the goals of G × E interaction studies in cancer epidemiology, and (3) opportunities for developing novel study designs and analysis tools. This report summarizes the Think Tank discussion, with a focus on contemporary approaches to the analysis of G × E interactions. Selecting the appropriate methods requires first identifying the relevant scientific question and rationale, with an important distinction made between analyses aiming to characterize the joint effects of putative or established genetic and environmental factors and analyses aiming to discover novel risk factors or novel interaction effects. Other discussion items include measurement error, statistical power, significance, and replication. Additional designs, exposure assessments, and analytical approaches need to be considered as we move from the current small number of success stories to a fuller understanding of the interplay of genetic and environmental factors.

Variation in the association between colorectal cancer susceptibility loci and colorectal polyps by polyp type.

We conducted a case-control study of the association between subsets of colorectal polyps, including adenomas and serrated polyps, and single-nucleotide polymorphisms (SNPs) related to colorectal cancer through prior genome-wide association studies (GWAS). Participants were enrollees in the Group Health Cooperative (Seattle, Washington) aged 24-79 years who received a colonoscopy from 1998 to 2007, donated a buccal or blood sample, and completed a structured questionnaire. We performed genotyping of 13 colorectal cancer susceptibility SNPs. Polytomous logistic regression models were used to estimate odds ratios and 95% confidence intervals for associations between polyps and the colorectal cancer risk allele for each SNP under a log-additive model. Analyses included 781 controls, 489 cases with adenoma, 401 cases with serrated polyps, and 188 cases with both polyp types. The following SNPs were associated with advanced adenomas: rs10936599, rs10795668, rs16892766, and rs9929218 (P < 0.05). For nonadvanced adenomas and for serrated polyps overall, only rs961253 was statistically significant (P < 0.05). These associations were in the same directions as those in prior colorectal cancer GWAS. No SNP was significantly associated with hyperplastic polyps, and only rs6983267 was significantly associated with sessile serrated polyps, but this association was opposite of that found in colorectal cancer GWAS. Our results suggest that the association between colorectal cancer susceptibility SNPs and colorectal polyps varies by polyp type.

Genome-wide association study of body height in African Americans: the Women's Health Initiative SNP Health Association Resource (SHARe).

Height is a complex trait under strong genetic influence. To date, numerous genetic loci have been associated with height in individuals of European ancestry. However, few large-scale discovery genome-wide association studies (GWAS) of height in minority populations have been conducted and thus information about population-specific height regulation is limited. We conducted a GWA analysis of height in 8149 African-American (AA) women from the Women's Health Initiative. Genetic variants with P< 5 × 10(-5) (n = 169) were followed up in a replication data set (n = 20 809) and meta-analyzed in a total of 28 958 AAs and African-descent individuals. Twelve single-nucleotide polymorphisms (SNPs) representing 7 independent loci were significantly associated with height at P < 5 × 10(-8). We identified novel SNPs in 17q23 (TMEM100/PCTP) and Xp22.3 (ARSE) reflecting population-specific regulation of height in AAs and replicated five loci previously reported in European-descent populations [4p15/LCORL, 11q13/SERPINH1, 12q14/HMGA2, 17q23/MAP3K3 (mitogen-activated protein kinase3) and 18q21/DYM]. In addition, we performed an admixture mapping analysis of height which is both complementary and supportive to the GWA analysis and suggests potential associations between ancestry and height on chromosomes 4 (4q21), 15 (15q26) and 17 (17q23). Our findings provide insight into the genetic architecture of height and support the investigation of non-European-descent populations for identifying genetic factors associated with complex traits. Specifically, we identify new loci that may reflect population-specific regulation of height and report several known height loci that are important in determining height in African-descent populations.

Identification of Genetic Susceptibility Loci for Colorectal Tumors in a Genome-Wide Meta-analysis.

BACKGROUND & AIMS: Heritable factors contribute to the development of colorectal cancer. Identifying the genetic loci associated with colorectal tumor formation could elucidate the mechanisms of pathogenesis. METHODS: We conducted a genome-wide association study that included 14 studies, 12,696 cases of colorectal tumors (11,870 cancer, 826 adenoma), and 15,113 controls of European descent. The 10 most statistically significant, previously unreported findings were followed up in 6 studies; these included 3056 colorectal tumor cases (2098 cancer, 958 adenoma) and 6658 controls of European and Asian descent. RESULTS: Based on the combined analysis, we identified a locus that reached the conventional genome-wide significance level at less than 5.0 × 10(-8): an intergenic region on chromosome 2q32.3, close to nucleic acid binding protein 1 (most significant single nucleotide polymorphism: rs11903757; odds ratio [OR], 1.15 per risk allele; P = 3.7 × 10(-8)). We also found evidence for 3 additional loci with P values less than 5.0 × 10(-7): a locus within the laminin gamma 1 gene on chromosome 1q25.3 (rs10911251; OR, 1.10 per risk allele; P = 9.5 × 10(-8)), a locus within the cyclin D2 gene on chromosome 12p13.32 (rs3217810 per risk allele; OR, 0.84; P = 5.9 × 10(-8)), and a locus in the T-box 3 gene on chromosome 12q24.21 (rs59336; OR, 0.91 per risk allele; P = 3.7 × 10(-7)). CONCLUSIONS: In a large genome-wide association study, we associated polymorphisms close to nucleic acid binding protein 1 (which encodes a DNA-binding protein involved in DNA repair) with colorectal tumor risk. We also provided evidence for an association between colorectal tumor risk and polymorphisms in laminin gamma 1 (this is the second gene in the laminin family to be associated with colorectal cancers), cyclin D2 (which encodes for cyclin D2), and T-box 3 (which encodes a T-box transcription factor and is a target of Wnt signaling to ß-catenin). The roles of these genes and their products in cancer pathogenesis warrant further investigation.

Opportunities for basic, clinical, and bioethics research at the intersection of machine learning and genomics.

The data-intensive fields of genomics and machine learning (ML) are in an early stage of convergence. Genomics researchers increasingly seek to harness the power of ML methods to extract knowledge from their data; conversely, ML scientists recognize that genomics offers a wealth of large, complex, and well-annotated datasets that can be used as a substrate for developing biologically relevant algorithms and applications. The National Human Genome Research Institute (NHGRI) inquired with researchers working in these two fields to identify common challenges and receive recommendations to better support genomic research efforts using ML approaches. Those included increasing the amount and variety of training datasets by integrating genomic with multiomics, context-specific (e.g., by cell type), and social determinants of health datasets; reducing the inherent biases of training datasets; prioritizing transparency and interpretability of ML methods; and developing privacy-preserving technologies for research participants' data.