Att_8-STO2b_SOW_01_2024.pdf

75N91024R00009

Attachment 8 - Sample Task Order 2b Statement of Work October 1, 2024

STATEMENT OF WORK

Sample Task Order 2b, Advanced Statistical and Bioinformatic Analysis of Genome-Wide Association Studies (GWAS)

Independently and not as an agent of the Government, the Contractor shall furnish all the necessary services, qualified personnel, material, equipment, and facilities, not otherwise provided by the Government as needed to perform the Statement of Work (SOW).

I. PERIOD OF PERFORMANCE

The period of performance for this task order will be for a base period of twelve (12) months with one (1) option period of twelve (12) months.

II. TASK AREA(S)

C: Data Analytics, Statistical and Bioinformatic Analysis and Tool Development

III. BACKGROUND

The Division of Cancer Epidemiology and Genetics (DCEG), National Cancer Institute (NCI) conducts broad-based, high quality, high impact research to uncover the causes of cancer and the means of its prevention. DCEG maintains a national and international perspective, giving priority to emergent issues identified through epidemiologic, clinical, and laboratory observations, as well as to public health concerns identified by the NCI, Congress, regulatory agencies, and other appropriate bodies.

The Division develops research resources and strategic partnerships in cancer epidemiology and genetics across NCI, National Institutes of Health (NH), and the global research community.

DCEG programs include domestic and international studies with diverse study designs in epidemiology and genetics research including prospective cohort studies, case-control studies, family studies, and randomized clinical trials. These studies span several geographic areas (potentially in any continent), are of varying size and complexity, and include dense, high-dimensional data on exposures, endpoints, clinical information, and biomarkers from molecular technologies.

IV. SCOPE

The goal of this Task Order is to provide advanced statistical and bioinformatics data analysis to investigate the genomic features associated with multiple independent studies across the Division of Cancer Epidemiology and Genetics (DCEG). Specifically, data collected by various -omic based assays, including combining data from multiple platforms; utilization of public datasets to compare/replicate analysis; merging datasets with clinical and epidemiologic data; and assistance with preparing, formatting, and transferring the data to the appropriate repositories for public accessibility shall be provided.

V. TECHNICAL REQUIREMENTS

The Contractor shall:

1. As determined by the COR, provide a review of the data analysis plan provided by the DCEG primary investigator. The data analysis plans could include something simple or complex (e.g.

provide descriptive statistics, run logistic regression analyses, etc.).

2. Meet with Contracting Officer Representative (COR), Subject Matter Expert (SME), or relevant scientific teams from DCEG as necessary, but at least once per week virtually, to report on progress/barriers.

3. Provide Analysis for genome-wide array genotyping data, including but are not limited to:

a. Basic genome-wide association (GWAS) analyses (including Quality Control (QC), imputation, and association), meta-analyses and visualization using tools such as Manhattan plot and LocusZoom plot.

b. Advanced approaches to address needs for multi-ethnic analyses, including African, African American, Hispanic, Asian, and other populations shall require additional focus on population substructure.

c. Case/control matching

i. Principle component analysis

d. Cross-trait associations analyses using tools such as Association Analysis based on SubSETs (ASSET), Mixed effect two-stage polytomous (MTOP) model score test.

e. Association testing by gene/pathways

f. Gene-level test for rare variants using tools such as SNP-Set (Sequence) Kernel Association

Test (SKAT)

g. Integrative analysis of genetic and genomic data for quantitative trait association testing, e.g. expression quantitative trait loci (eQTL), methylation quantitative trait loci (meQTL), transcriptome-wide Association Studies (TWAS), methylation-wide Association Studies (MWAS), protein quantitative trait loci (pQTL) in Proteome-Wide Association Study (PWAS).

h. Colocalization analysis between QTLs and GWAS using tools such as eQTL and GWAS CAusal Variants Identification in Associated Regions (eCAVIAR), Colocalization (COLOC), and Hypothesis Prioritization for multi-trait Colocalization (HyPrColoc).

i. Fine mapping analyses

i. Analysis of heritability, co-heritability, mixed models and Linkage Disequilibrium (LD) LD-score regressions (Tools: fast Genome-wideF Complex Trait Analysis (fastGCTA), LD-score)

ii. Polygenic risk prediction (LDPred) / cross-ancestry methods, LDPred)

j. Integration of PRS and risk factor data for risk prediction using: Polygenetic Risk Score

(PRS)/Gene by Environment (GxE) analyses and the Individualized Coherent Absolute Risk Estimators (iCARE) package

k. Genetic mosaicism/clonal hematopoiesis analyses

l. Mendelian Randomization (MR) using the MRBase tool.

m. Analyses of GWAS of metabolomic data including, meta-analyses of individual study results and data visualization.

4. Provide analysis for next-generation sequencing (NGS) data, including but not limited to:

a. Quality Control, basic alignment, variant calling, and annotation identification of single nucleotide variants (SNVs), insertions and deletions (Indels), copy number variations (CNVs), and structural variations (SVs)

b. Transcriptomics analysis: spatial transcriptomics, single-cell sequencing transcriptomics, and whole genome transcriptome profiling

c. ChIP-seq: identification of the binding sites of a transcription factor on the genome

d. Epigenetic analysis: identifying DNA methylation, histone modification, and chromatin conformation at both the whole genome and single cell level.

e. Spatial biology analysis for protein biomarkers and transcriptomics

f. NGS-based proteomics analysis (i.e., proximity extension assays)

g. Microbiome analysis: including both metagenomics and 16s rRNA analysis.

h. For all NGS based analysis, downstream support for:

i. Functional annotation: Annotate the variants with gene and functional information using opensource tools and databases.

ii. Variant prioritization: Prioritize and classify variants identified from NGS data by their functional impact, frequency, and relevance to the phenotype of interest.

iii. Burden testing: Develop algorithms or use state-of-art tools to collapse variants across genes or regions of interest and testing for significant differences in their distribution between cases and controls.

iv. Gene-based pathway analysis: Identify enriched biological pathways or gene sets that are associated with the phenotype of interest.

v. Integration of NGS data with phenotypic, clinical, epidemiological, and other sources of information to aid in interpretation and drive biological insights.

vi. Build user-friendly tools for no-code variant filtering and prioritization based on variant, clinical, phenotypic and other relevant metadata.

vii. Reporting: Generate summary reports that include QC metrics, alignment statistics, variant calling metrics and functional annotations.

5. Provide the methodology used to conduct the analysis for replication of the results of the analysis. Apply the Findability, Accessibility, Interoperability, and Reusability (FAIR) guiding principles to scientific software development and data management, which includes but is not limited to:

a. Use of version control tools and sharing via code repository such as GitHub

b. Use of automated unit testing for all major components/modules of any pipelines developed for analysis.

c. Ensure reusability and portability of the scientific software at different computing environments including computing clusters and public cloud environments, use containers and package managers when necessary.

6. Improve the efficiency and portability of existing code, examples include but not limited to:

a. Code review: A thorough review of the current code base shall be conducted to identify areas where performance can be improved, such as in algorithms or data structures.

b. Optimization: Based on the code review, optimizations shall be implemented to reduce computational time, memory usage, and network traffic.

c. Refactoring: The code shall be refactored to ensure it is readable, maintainable, and modular, making it easier to update and maintain in the future.

d. Portability: To make the code more portable, dependencies shall be assessed, and any necessary modifications made to allow the code to run on different platforms and environments (ie. on HPC Slurm, Cloud platforms such as GCP and AWS, analysis platforms such as DNANexus). Platform independent solution such as containers (Docker/Singularity) and/or WebAssembly are preferred.

e. Usability: Modification of the code to improve software usability. This includes factors such as the design of the interface, the quality of the documentation and error messages, the ease of finding information, and the speed and responsiveness of the software. This also includes the design of the functionality of the API and web interface.

f. Testing: The code shall be thoroughly tested to ensure it performs correctly and meets the requirements of the project.

7. Manage and merge genomic datasets from different platforms and with clinical and epidemiologic data and perform analyses for associations between genomic features and individual characteristics.

8. Assistance in preparing the genomic and phenotype datasets for public posting in accordance with the National Institutes of Health (NIH) Genomic Data Sharing Policy.

9. Work with DCEG staff and NCI contractors as required.

10. Include descriptions of the methods used for analysis, code developed for analysis, version control and analytic services rendered in all documentation. Documentation of analysis via Rmarkdown, Jupyter notebooks or similar tools shall be required. All results and documentation of project progress, analytical decisions, results should be stored in a centrally accessible location.

VI. REPORTING REQUIREMENTS / DELIVERABLES

Documentation for work conducted under this task order should be maintained for the life of the task order. Pertinent documentation may include, but is not limited to, methods, coding and version control for analytic services rendered.

The Contractor shall prepare and submit the following during the period of performance of this Task Order in accordance with ARTICLE C.2 REPORTING REQUIREMENTS of the base contract:

a. Technical Progress Reports

1. Monthly Task Order Progress Reports Include status updates of all projects within period- of-performance, as well as the corresponding costs related to those tasks. Data elements that may be required include, but are not limited to, project codes, descriptions of services rendered, requestor name, hours billed for each activity, and total amount.

2. Annual Task Order Progress Report

Summary of 12-month progress

3. Final Task Order Report Report shall include the work performed and the results obtained for the entire task order period of performance. The report shall be in sufficient detail to describe comprehensively the results achieved.

b. Other Reports / Deliverables

4. Analytic Dataset(s)

Final delivery of the analytic output as defined by the COR or designee.

5. Final Documentation Deposit Include descriptions of the methods used for analysis, code developed for analysis, version control and analytic services rendered.

c. Information and/ or Physical Security

The Contractor shall submit the following reports as required by the base contract. Additional information can be found at https://wiki.nci.nih.gov/display/NSCI/ATO+Package+Artifact+Checklist

6. Roster

7. IT Security Training All contractor (and/or subcontractors) assigned to work on this contract shall complete the applicable HHS/NIH contractor information security awareness, privacy, and Records Management training at http://irtsectraining.nih.gov/.

Item Description Delivery Schedule

Technical Progress Reports 1 Monthly Task Order Progress Report 15 calendar days following each reporting period 2 Annual Task Order Progress Report 15 calendar days following reporting period 3 Final Summary of all Progress Report On or before Task Order expiration date Other Reports/Deliverables 4 Analytic Dataset(s) Due no later than 10 working days prior to the end of the

Task Order unless designated by the COR during Task Order.

5 Final Documentation Deposit Due no later 30 calendar days of the completion date of the Task Order.

Information and/ or Physical Security 6 Roster 14 calendar days after contract award. Revisions to Roster due within 7 calendar days of any change 7 IT Security Training http://irtsectraining.nih.gov/ Prior to any activity and annually after Task Order award https://wiki.nci.nih.gov/display/NSCI/ATO%2BPackage%2BArtifact%2BChecklist http://irtsectraining.nih.gov/ http://irtsectraining.nih.gov/