Uncertainty in Breast Cancer Risk Prediction: A Conformal Prediction Study of Race Stratification.

The use of Artificial Intelligence (AI) in medicine has attracted a great deal of attention in the medical literature, but less is known about how to assess the uncertainty of individual predictions in clinical applications. This paper demonstrates the use of Conformal Prediction (CP) to provide insight on racial stratification of uncertainty quantification for breast cancer risk prediction. The results presented here show that CP methods provide important information about the diminished quality of predictions for individuals of minority racial backgrounds.

A Rare Variant in MDH2 (rs111879470) Is Associated with Predisposition to Recurrent Breast Cancer in an Extended High-Risk Pedigree.

A significant fraction of breast cancer recurs, with lethal outcome, but specific genetic variants responsible have yet to be identified. Five cousin pairs with recurrent breast cancer from pedigrees with a statistical excess of recurrent breast cancer were sequenced to identify rare, shared candidate predisposition variants. The candidates were tested for association with breast cancer risk with UKBiobank data. Additional breast cancer cases were assayed for a subset of candidate variants to test for co-segregation. Three-dimensional protein structure prediction methods were used to investigate how the mutation under consideration is predicted to change structural and electrostatic properties in the mutated protein. One hundred and eighty-one rare candidate predisposition variants were shared in at least one cousin pair from a high-risk pedigree. A rare variant in MDH2 was found to segregate with breast-cancer-affected relatives in one extended pedigree. MDH2 is an estrogen-stimulated gene encoding the protein malate dehydrogenase, which catalyzes the reversible oxidation of malate to oxaloacetate. The molecular simulation results strongly suggest that the mutation changes the NAD+ binding pocket electrostatics of MDH2. This small sequencing study, using a powerful approach based on recurrent breast cancer cases from high-risk pedigrees, identified a set of strong candidate variants for inherited predisposition for breast cancer recurrence, including MDH2, which should be pursued in other resources.

Molecular dynamics study of the mechanical properties of drug loaded model systems: A comparison of a polymersome with a bilayer.

In this work we implement a new methodology to study structural and mechanical properties of systems having spherical and planar symmetries throughout Molecular Dynamics simulations. This methodology is applied here to a drug delivery system based in polymersomes, as an example. The chosen model drug was the local anesthetic prilocaine due to previous parameterization within the used coarse grain scheme. In our approach, mass density profiles (MDPs) are used to obtain key structural parameters of the systems, and pressure profiles are used to estimate the curvature elastic parameters. The calculation of pressure profiles and radial MPDs required the development of specific methods, which were implemented in an in-house built version of the GROMACS 2018 code. The methodology presented in this work is applied to characterize poly(ethylene oxide)-poly(butadiene) polymersomes and bilayers loaded with the model drug prilocaine. Our results show that structural properties of the polymersome membrane could be obtained from bilayer simulations, with significantly lower computational cost compared to whole polymersome simulations, but the bilayer simulations are insufficient to get insights on their mechanical aspects, since the elastic parameters are canceled out for the complete bilayer (as consequence of the symmetry). The simulations of entire polymersomes, although more complex, offer a complementary approach to get insights on the mechanical behavior of the systems.

Prioritization of infectious epitopes for translational investigation in type 1 diabetes etiology.

Molecular mimicry is one mechanism by which infectious agents are thought to trigger islet autoimmunity in type 1 diabetes. With a growing number of reported infectious agents and islet antigens, strategies to prioritize the study of infectious agents are critically needed to expedite translational research into the etiology of type 1 diabetes. In this work, we developed an in-silico pipeline for assessing molecular mimicry in type 1 diabetes etiology based on sequence homology, empirical binding affinity to specific MHC molecules, and empirical potential for T-cell immunogenicity. We then assess whether potential molecular mimics were conserved across other pathogens known to infect humans. Overall, we identified 61 potentially high-impact molecular mimics showing sequence homology, strong empirical binding affinity, and empirical immunogenicity linked with specific MHC molecules. We further found that peptide sequences from 32 of these potential molecular mimics were conserved across several human pathogens. These findings facilitate translational evaluation of molecular mimicry in type 1 diabetes etiology by providing a curated and prioritized list of peptides from infectious agents for etiopathologic investigation. These results may also provide evidence for generation of infectious and HLA-specific preclinical models and inform future screening and preventative efforts in genetically susceptible populations.

The IHI Rochester Report 2022 on Healthcare Informatics Research: Resuming After the CoViD-19.

In 2020, the CoViD-19 pandemic spread worldwide in an unexpected way and suddenly modified many life issues, including social habits, social relationships, teaching modalities, and more. Such changes were also observable in many different healthcare and medical contexts. Moreover, the CoViD-19 pandemic acted as a stress test for many research endeavors, and revealed some limitations, especially in contexts where research results had an immediate impact on the social and healthcare habits of millions of people. As a result, the research community is called to perform a deep analysis of the steps already taken, and to re-think steps for the near and far future to capitalize on the lessons learned due to the pandemic. In this direction, on June 09th-11th, 2022, a group of twelve healthcare informatics researchers met in Rochester, MN, USA. This meeting was initiated by the Institute for Healthcare Informatics-IHI, and hosted by the Mayo Clinic. The goal of the meeting was to discuss and propose a research agenda for biomedical and health informatics for the next decade, in light of the changes and the lessons learned from the CoViD-19 pandemic. This article reports the main topics discussed and the conclusions reached. The intended readers of this paper, besides the biomedical and health informatics research community, are all those stakeholders in academia, industry, and government, who could benefit from the new research findings in biomedical and health informatics research. Indeed, research directions and social and policy implications are the main focus of the research agenda we propose, according to three levels: the care of individuals, the healthcare system view, and the population view.

Sequential data mining of infection patterns as predictors for onset of type 1 diabetes in genetically at-risk individuals.

Infections are implicated in the etiology of type 1 diabetes mellitus (T1DM); however, conflicting epidemiologic evidence makes designing effective strategies for presymptomatic screening and disease prevention difficult. Considering the temporality and combination in which infections occur may provide valuable insights into understanding T1DM etiology but is rarely studied due to limited longitudinal datasets and insufficient analytical techniques. The objective of this work was to demonstrate a computational approach to classify the temporality and combination of infections in presymptomatic T1DM. We present a sequential data mining pipeline that leverages routinely collected infectious disease data from a prospective cohort study, the Environmental Determinants of Diabetes in the Young (TEDDY) study, to extract, interpret, and compare infection sequences. We then utilize this pipeline to assess risk for developing presymptomatic biomarkers of islet autoimmunity and clinical onset of T1DM. Overall, we identified 229 significant sequential rules that increased the risk for developing presymptomatic biomarkers of islet autoimmunity or clinical onset of T1DM. Multiple significant sequential rules involving varicella increased the risk for all presymptomatic biomarker-specific outcomes, while a single significant sequential rule involving parasites significantly increased risk for T1DM. Significant sequential rules involving respiratory illnesses were differentially represented among the presymptomatic biomarkers of islet autoimmunity and clinical onset of T1DM. Risk for T1DM was significantly increased by a single episode of sixth disease at 12 months, representing the only single-event sequence that increased disease risk. Together, these findings provide the first insights into the timing and combination of infections in T1DM etiology, which may ultimately lead to personalized disease screening and prevention strategies. The sequential data mining pipeline developed in this work demonstrates how temporal data mining can be used to address clinically meaningful questions. This method can be adapted to other presymptomatic factors and clinical conditions.

Detecting hypoglycemia-induced electrocardiogram changes in a rodent model of type 1 diabetes using shape-based clustering.

Sudden death related to hypoglycemia is thought to be due to cardiac arrhythmias. A clearer understanding of the cardiac changes associated with hypoglycemia is needed to reduce mortality. The objective of this work was to identify distinct patterns of electrocardiogram heartbeat changes that correlated with glycemic level, diabetes status, and mortality using a rodent model. Electrocardiogram and glucose measurements were collected from 54 diabetic and 37 non-diabetic rats undergoing insulin-induced hypoglycemic clamps. Shape-based unsupervised clustering was performed to identify distinct clusters of electrocardiogram heartbeats, and clustering performance was assessed using internal evaluation metrics. Clusters were evaluated by experimental conditions of diabetes status, glycemic level, and death status. Overall, shape-based unsupervised clustering identified 10 clusters of ECG heartbeats across multiple internal evaluation metrics. Several clusters demonstrating normal ECG morphology were specific to hypoglycemia conditions (Clusters 3, 5, and 8), non-diabetic rats (Cluster 4), or were generalized among all experimental conditions (Cluster 1). In contrast, clusters demonstrating QT prolongation alone or a combination of QT, PR, and QRS prolongation were specific to severe hypoglycemia experimental conditions and were stratified heartbeats by non-diabetic (Clusters 2 and 6) or diabetic status (Clusters 9 and 10). One cluster demonstrated an arrthymogenic waveform with premature ventricular contractions and was specific to heartbeats from severe hypoglycemia conditions (Cluster 7). Overall, this study provides the first data-driven characterization of ECG heartbeats in a rodent model of diabetes during hypoglycemia.

Hypertensive disorders of pregnancy and subsequent risk of Alzheimer's disease and other dementias.

Introduction: Women with hypertensive disorders of pregnancy (HDP) have an increased risk of cardiovascular disease. Whether HDP is also associated with later-life dementia has not been fully explored. Methods: Using the Utah Population Database, we performed an 80-year retrospective cohort study of 59,668 parous women. Results: Women with, versus without, HDP, had a 1.37 higher risk of all-cause dementia (95% confidence interval [CI]: 1.26, 1.50) after adjustment for maternal age at index birth, birth year, and parity. HDP was associated with a 1.64 higher risk of vascular dementia (95% CI: 1.19, 2.26) and 1.49 higher risk of other dementia (95% CI: 1.34, 1.65) but not Alzheimer's disease dementia (adjusted hazard ratio = 1.04; 95% CI: 0.87, 1.24). Gestational hypertension and preeclampsia/eclampsia showed similar increased dementia risk. Nine mid-life cardiometabolic and mental health conditions explained 61% of HDP's effect on subsequent dementia risk. Discussion: Improved HDP and mid-life care could reduce the risk of dementia.

High-Risk Pedigree Study Identifies LRBA (rs62346982) as a Likely Predisposition Variant for Prostate Cancer.

There is evidence for contribution of inherited factors to prostate cancer, and more specifically to lethal prostate cancer, but few responsible genes/variants have been identified. We examined genetic sequence data for 51 affected cousin pairs who each died from prostate cancer and who were members of high-risk prostate cancer pedigrees in order to identify rare variants shared by the cousins as candidate predisposition variants. Candidate variants were tested for association with prostate cancer risk in UK Biobank data. Candidate variants were also assayed in 1195 additional sampled Utah prostate cancer cases. We used 3D protein structure prediction methods to analyze structural changes and provide insights into mechanisms of pathogenicity. Almost 4000 rare (<0.005) variants were identified as shared in the 51 affected cousin pairs. One candidate variant was also significantly associated with prostate cancer risk among the 840 variants with data in UK Biobank, in the gene LRBA (p = 3.2 × 10-5; OR = 2.09). The rare risk variant in LRBA was observed to segregate in five pedigrees. The overall predicted structures of the mutant protein do not show any significant overall changes upon mutation, but the mutated structure loses a helical structure for the two residues after the mutation. This unique analysis of closely related individuals with lethal prostate cancer, who were members of high-risk prostate cancer pedigrees, has identified a strong set of candidate predisposition variants which should be pursued in independent studies. Validation data for a subset of the candidates identified are presented, with strong evidence for a rare variant in LRBA.

A rare FGF5 candidate variant (rs112475347) for predisposition to nonsquamous, nonsmall-cell lung cancer.

A unique approach with rare resources was used to identify candidate variants predisposing to familial nonsquamous nonsmall-cell lung cancers (NSNSCLC). We analyzed sequence data from NSNSCLC-affected cousin pairs belonging to high-risk lung cancer pedigrees identified in a genealogy of Utah linked to statewide cancer records to identify rare, shared candidate predisposition variants. Variants were tested for association with lung cancer risk in UK Biobank. Evidence for linkage with lung cancer was also reviewed in families from the Genetic Epidemiology of Lung Cancer Consortium. Protein prediction modeling compared the mutation with reference. We sequenced NSNSCLC-affected cousin pairs from eight high-risk lung cancer pedigrees and identified 66 rare candidate variants shared in the cousin pairs. One variant in the FGF5 gene also showed significant association with lung cancer in UKBiobank. This variant was observed in 3/163 additional sampled Utah lung cancer cases, 2 of whom were related in another independent pedigree. Modeling of the predicted protein predicted a second binding site for SO4 that may indicate binding differences. This unique study identified multiple candidate predisposition variants for NSNSCLC, including a rare variant in FGF5 that was significantly associated with lung cancer risk and that segregated with lung cancer in the two pedigrees in which it was observed. FGF5 is an oncogenic factor in several human cancers, and the mutation found here (W81C) changes the binding ability of heparan sulfate to FGF5, which might lead to its deregulation. These results support FGF5 as a potential NSNSCLC predisposition gene and present additional candidate predisposition variants.

Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review.

BACKGROUND: Environmental exposures are implicated in diabetes etiology, but are poorly understood due to disease heterogeneity, complexity of exposures, and analytical challenges. Machine learning and data mining are artificial intelligence methods that can address these limitations. Despite their increasing adoption in etiology and prediction of diabetes research, the types of methods and exposures analyzed have not been thoroughly reviewed. OBJECTIVE: We aimed to review articles that implemented machine learning and data mining methods to understand environmental exposures in diabetes etiology and disease prediction. METHODS: We queried PubMed and Scopus databases for machine learning and data mining studies that used environmental exposures to understand diabetes etiology on September 19th, 2022. Exposures were classified into specific external, general external, or internal exposures. We reviewed machine learning and data mining methods and characterized the scope of environmental exposures studied in the etiology of general diabetes, type 1 diabetes, type 2 diabetes, and other types of diabetes. RESULTS: We identified 44 articles for inclusion. Specific external exposures were the most common exposures studied, and supervised models were the most common methods used. Well-established specific external exposures of low physical activity, high cholesterol, and high triglycerides were predictive of general diabetes, type 2 diabetes, and prediabetes, while novel metabolic and gut microbiome biomarkers were implicated in type 1 diabetes. DISCUSSION: The use of machine learning and data mining methods to elucidate environmental triggers of diabetes was largely limited to well-established risk factors identified using easily explainable and interpretable models. Future studies should seek to leverage machine learning and data mining to explore the temporality and co-occurrence of multiple exposures and further evaluate the role of general external and internal exposures in diabetes etiology.

A likely HOXC4 predisposition variant for Chiari malformations.

OBJECTIVE: Inherited variants predisposing patients to type 1 or 1.5 Chiari malformation (CM) have been hypothesized but have proven difficult to confirm. The authors used a unique high-risk pedigree population resource and approach to identify rare candidate variants that likely predispose individuals to CM and protein structure prediction tools to identify pathogenicity mechanisms. METHODS: By using the Utah Population Database, the authors identified pedigrees with significantly increased numbers of members with CM diagnosis. From a separate DNA biorepository of 451 samples from CM patients and families, 32 CM patients belonging to 1 or more of 24 high-risk Chiari pedigrees were identified. Two high-risk pedigrees had 3 CM-affected relatives, and 22 pedigrees had 2 CM-affected relatives. To identify rare candidate predisposition gene variants, whole-exome sequence data from these 32 CM patients belonging to 24 CM-affected related pairs from high-risk pedigrees were analyzed. The I-TASSER package for protein structure prediction was used to predict the structures of both the wild-type and mutant proteins found here. RESULTS: Sequence analysis of the 24 affected relative pairs identified 38 rare candidate Chiari predisposition gene variants that were shared by at least 1 CM-affected pair from a high-risk pedigree. The authors found a candidate variant in HOXC4 that was shared by 2 CM-affected patients in 2 independent pedigrees. All 4 of these CM cases, 2 in each pedigree, exhibited a specific craniocervical bony phenotype defined by a clivoaxial angle less than 125°. The protein structure prediction results suggested that the mutation considered here may reduce the binding affinity of HOXC4 to DNA. CONCLUSIONS: Analysis of unique and powerful Utah genetic resources allowed identification of 38 strong candidate CM predisposition gene variants. These variants should be pursued in independent populations. One of the candidates, a rare HOXC4 variant, was identified in 2 high-risk CM pedigrees, with this variant possibly predisposing patients to a Chiari phenotype with craniocervical kyphosis.

Stratifying risk for onset of type 1 diabetes using islet autoantibody trajectory clustering.

AIMS/HYPOTHESIS: Islet autoantibodies can be detected prior to the onset of type 1 diabetes and are important tools for aetiologic studies, prevention trials and disease screening. Current risk stratification models rely on the positivity status of islet autoantibodies alone, but additional autoantibody characteristics may be important for understanding disease onset. This work aimed to determine if a data-driven model incorporating characteristics of islet autoantibody development, including timing, type and titre, could stratify risk for type 1 diabetes onset. METHODS: Data on autoantibodies against GAD (GADA), tyrosine phosphatase islet antigen-2 (IA-2A) and insulin (IAA) were obtained for 1,415 children enrolled in The Environmental Determinants of Diabetes in the Young study with at least one positive autoantibody measurement from years 1 to 12 of life. Unsupervised machine learning algorithms were trained to identify clusters of autoantibody development based on islet autoantibody timing, type and titre. Risk for type 1 diabetes across each identified cluster was evaluated using time-to-event analysis. RESULTS: We identified 2-4 clusters in each year cohort that differed by autoantibody timing, titre and type. During the first 3 years of life, risk for type 1 diabetes onset was driven by membership in clusters with high titres of all three autoantibodies (1-year risk: 20.87-56.25%, 5-year risk: 67.73-69.19%). Type 1 diabetes risk transitioned to type-specific titres during ages 4 to 8, as clusters with high titres of IA-2A (1-year risk: 20.88-28.93%, 5-year risk: 62.73-78.78%) showed faster progression to diabetes compared with high titres of GADA (1-year risk: 4.38-6.11%, 5-year risk: 25.06-31.44%). The importance of high GADA titres decreased during ages 9 to 12, with clusters containing high titres of IA-2A alone (1-year risk: 14.82-30.93%) or both GADA and IA-2A (1-year risk: 8.27-25.00%) demonstrating increased risk. CONCLUSIONS/INTERPRETATION: This unsupervised machine learning approach provides a novel tool for stratifying risk for type 1 diabetes onset using multiple autoantibody characteristics. These findings suggest that age-dependent changes in IA-2A titres modulate risk for type 1 diabetes onset across 12 years of life. Overall, this work supports incorporation of islet autoantibody timing, type and titre in risk stratification models for aetiologic studies, prevention trials and disease screening.

Conformal Prediction in Clinical Medical Sciences.

The use of machine learning (ML) and artificial intelligence (AI) applications in medicine has attracted a great deal of attention in the medical literature, but little is known about how to use Conformal Predictions (CP) to assess the accuracy of individual predictions in clinical applications. We performed a comprehensive search in SCOPUS® to find papers reporting the use of CP in clinical applications. We identified 14 papers reporting the use of CP for clinical applications, and we briefly describe the methods and results reported in these papers. The literature reviewed shows that CP methods can be used in clinical applications to provide important insight into the accuracy of individual predictions. Unfortunately, the review also shows that most of the studies have been performed in isolation, without input from practicing clinicians, not providing comparisons among different approaches and not considering important socio-technical considerations leading to clinical adoption.

K-means cluster analysis of cooperative effects of CO, NO2, O3, PM2.5, PM10, and SO2 on incidence of type 2 diabetes mellitus in the US.

Air pollution (AP) has been shown to increase the risk of type 2 diabetes mellitus, as well as other cardiometabolic diseases. AP is characterized by a complex mixture of components for which the composition depends on sources and metrological factors. The US Environmental Protection Agency (EPA) monitors and regulates certain components of air pollution known to have negative consequences for human health. Research assessing the health effects of these components of AP often uses traditional regression models, which might not capture more complex and interdependent relationships. Machine learning has the capability to simultaneously assess multiple components and find complex, non-linear patterns that may not be apparent and could not be modeled by other techniques. Here we use k-means clustering to assess the patterns associating PM2.5, PM10, CO, NO2, O3, and SO2 measurements and changes in annual diabetes incidence at a US county level. The average age adjusted annual decrease in diabetes incidence for the entire US populations is -0.25 per 1000 but the change shows a significant geographic variation (range: -17.2 to 5.30 per 1000). In this paper these variations were compared with the local daily AP concentrations of the pollutants listed above from 2005 to 2015, which were matched to the annual change in diabetes incidence for the following year. A total of 134,925 daily air quality observations were included in the cluster analysis, representing 125 US counties and the District of Columbia. K-means successfully clustered AP components and indicated an association between exposure to certain AP mixtures with lower decreases on T2D incidence.

Microproteins: a 3D protein structure prediction analysis.

Microproteins are a novel and expanding group of small proteins encoded by less than 100-150 codons that are translated from small open reading frames (smORFs). It has been shown that smORFs and their corresponding microproteins make up a sizable fraction of the genome and proteome, but very little information on microproteins' structural features exists in the literature. In this paper, we present the results of analyzing the predicted structures of 44 microproteins. The results show that this set of microproteins have a different amino acid composition profiles, similar structural characteristics and fewer small-molecule ligand binding sites than regular proteins.Communicated by Ramaswamy H. Sarma.

Structure analysis of the proteins associated with polyA repeat expansion disorders.

Repeat regions are low-complexity regions in the human genome that largely code for intrinsic disorder in proteins. Expansions outside the normal thresholds in repeat regions are likely to be pathogenic, leading to the so-called repeat expansion diseases. There have been numerous studies on the most common group of repeat expansion diseases, which are the polyglutamine (polyQ) repeat expansion diseases, but there has been much less work done on the second-largest group of expansion repeats disorders, which involves the expansion of polyalanine (polyA) repeat tracts. In this article, we present a comprehensive study of the structural changes predicted using I-TASSER when comparing the wild type and enlarged structures of all known polyA expansion disorders. The results show that there is a reduction in α helices, an increase in extended strands in parallel and/or anti-parallel ß-sheet conformation, an increase in random coils/loops and irregular elements, and a large increase in solvent-accessible surface area. When compared to the findings in polyQ expansions disorders we see similar trends, suggesting that the polyQ and polyA repeat expansion causes similar effects on the respective proteins, which lead to higher misfolding and aggregation propensities.Communicated by Ramaswamy H. Sarma.

Erratum to: Data-driven identification of temporal glucose patterns in a large cohort of nondiabetic patients with COVID-19 using time-series clustering.

[This corrects the article DOI: 10.1093/jamiaopen/ooab063.].

Data-driven identification of temporal glucose patterns in a large cohort of nondiabetic patients with COVID-19 using time-series clustering.

OBJECTIVE: Hyperglycemia has emerged as an important clinical manifestation of coronavirus disease 2019 (COVID-19) in diabetic and nondiabetic patients. Whether these glycemic changes are specific to a subgroup of patients and persist following COVID-19 resolution remains to be elucidated. This work aimed to characterize longitudinal random blood glucose in a large cohort of nondiabetic patients diagnosed with COVID-19. MATERIALS AND METHODS: De-identified electronic medical records of 7502 patients diagnosed with COVID-19 without prior diagnosis of diabetes between January 1, 2020, and November 18, 2020, were accessed through the TriNetX Research Network. Glucose measurements, diagnostic codes, medication codes, laboratory values, vital signs, and demographics were extracted before, during, and after COVID-19 diagnosis. Unsupervised time-series clustering algorithms were trained to identify distinct clusters of glucose trajectories. Cluster associations were tested for demographic variables, COVID-19 severity, glucose-altering medications, glucose values, and new-onset diabetes diagnoses. RESULTS: Time-series clustering identified a low-complexity model with 3 clusters and a high-complexity model with 19 clusters as the best-performing models. In both models, cluster membership differed significantly by death status, COVID-19 severity, and glucose levels. Clusters membership in the 19 cluster model also differed significantly by age, sex, and new-onset diabetes mellitus. DISCUSSION AND CONCLUSION: This work identified distinct longitudinal blood glucose changes associated with subclinical glucose dysfunction in the low-complexity model and increased new-onset diabetes incidence in the high-complexity model. Together, these findings highlight the utility of data-driven techniques to elucidate longitudinal glycemic dysfunction in patients with COVID-19 and provide clinical evidence for further evaluation of the role of COVID-19 in diabetes pathogenesis.

An intronic variant in the CELF4 gene is associated with risk for colorectal cancer.

BACKGROUND: Germline predisposition variants associated with colorectal cancer (CRC) have been identified but all are not yet identified. We sought to identify the responsible predisposition germline variant in an extended high-risk CRC pedigree that exhibited evidence of linkage to the 18q12.2 region (TLOD = +2.81). METHODS: DNA from two distantly related carriers of the hypothesized predisposition haplotype on 18q12.2 was sequenced to identify candidate variants. The candidate rare variants shared by the related sequenced subjects were screened in 3,094 CRC cases and 5x population-matched controls from UKBiobank to test for association. Further segregation of the variant was tested via Taqman assay in other sampled individuals in the pedigree. RESULTS: Analysis of whole genome sequence data for the two related hypothesized predisposition haplotype carriers, restricted to the shared haplotype boundaries, identified multiple (n = 6) rare candidate non-coding variants that were tested for association with CRC risk in UKBiobank. A rare intronic variant ofCELF4 gene, rs568643870, was significantly associated with CRC (p = 0.004, OR = 5.0), and segregated with CRC in other members of the linked pedigree. CONCLUSION: Evidence of segregation in a high-risk pedigree, case-control association in an external dataset, and identification of additional CRC-affected carriers in the linked pedigree support a role for a rareCELF4 intronic variant in CRC risk.