Colorectal cancer is a common cancer in Indonesia, yet it has been understudied in this resource-constrained setting. We conducted a genome-wide association study focused on evaluation and preliminary discovery of colorectal cancer risk factors in Indonesians. We administered detailed questionnaires and collecting blood samples from 162 colorectal cancer cases throughout Makassar, Indonesia. We also established a control set of 193 healthy individuals frequency matched by age, sex, and ethnicity. A genome-wide association analysis was performed on 84 cases and 89 controls passing quality control. We evaluated known colorectal cancer genetic variants using logistic regression and established a genome-wide polygenic risk model using a Bayesian variable selection technique. We replicate associations for rs9497673, rs6936461 and rs7758229 on chromosome 6; rs11255841 on chromosome 10; and rs4779584, rs11632715, and rs73376930 on chromosome 15. Polygenic modeling identified 10 SNP associated with colorectal cancer risk. This work helps characterize the relationship between variants in the SCL22A3, SCG5, GREM1, and STXBP5-AS1 genes and colorectal cancer in a diverse Indonesian population. With further biobanking and international research collaborations, variants specific to colorectal cancer risk in Indonesians will be identified.

Genomic studies of plants often seek to identify genetic factors associated with desirable traits. The process of evaluating genetic markers one by one (i.e. a marginal analysis) may not identify important polygenic and environmental effects. Further, confounding due to growing conditions/factors and genetic similarities among plant varieties may influence conclusions. When developing new plant varieties to optimize yield or thrive in future adverse conditions (e.g. flood, drought), scientists seek a complete understanding of how the factors influence desirable traits. Motivated by a study design that measures rice yield across different seasons, fields, and plant varieties in Indonesia, we develop a regression method that identifies significant genomic factors, while simultaneously controlling for field factors and genetic similarities in the plant varieties. Our approach develops a Bayesian maximum a posteriori probability (MAP) estimator under a generalized double Pareto shrinkage prior. Through a hierarchical representation of the proposed model, a novel and computationally efficient expectation-maximization (EM) algorithm is developed for variable selection and estimation. The performance of the proposed approach is demonstrated through simulation and is used to analyze rice yields from a pilot study conducted by the Indonesian Center for Rice Research.

Purpose: Colorectal cancer is a common cancer in Indonesia, yet 13 it has been understudied. We conduct a genome-wide association study focused 14 on evaluation and discovery of colorectal cancer risk factors in Indonesians. 15 Methods: We administered detailed questionnaires and collecting blood sam-16 ples from 162 colorectal cancer cases throughout Makassar, Indonesia. We also 17 established a control set of 193 healthy individuals frequency matched by age, 18 sex, and ethnicity. A genome-wide association analysis was performed on 84 19 cases and 89 controls passing quality control. We evaluated known colorectal 20 cancer genetic variants using logistic regression and established a genome-wide 21 polygenic risk model using a Bayesian variable selection technique.22 Results: We replicate associations for rs9497673, rs6936461 and rs7758229 23 on chromosome 6; rs11255841 on chromosome 10; and rs4779584, rs11632715, 24 and rs73376930 on chromosome 15. Polygenic modeling identified 10 SNP as-25 sociated with colorectal cancer risk.26Conclusions: This work helps characterize the relationship between variants

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