Estimating disease prevalence in large datasets using genetic risk scores

AbstractClinical classification is essential for estimating disease prevalence but is difficult, often requiring complex investigations. The widespread availability of population level genetic data makes novel genetic stratification techniques a highly attractive alternative. We propose a generalizable mathematical framework for determining disease prevalence within a cohort using genetic risk scores. We compare and evaluate methods based on the means of genetic risk scores’ distributions; the Earth Mover’s Distance between distributions; a linear combination of kernel density estimates of distributions; and an Excess method. We demonstrate the performance of genetic stratification to produce robust prevalence estimates. Specifically, we show that robust estimates of prevalence are still possible even with rarer diseases, smaller cohort sizes and less discriminative genetic risk scores, highlighting the general utility of these approaches. Genetic stratification techniques offer exciting new research tools, enabling unbiased insights into disease prevalence and clinical characteristics unhampered by clinical classification criteria.

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Estimating population level disease prevalence using genetic risk scores

10.1101/2020.02.20.20025528 ◽

2020 ◽

Author(s):

Benjamin D. Evans ◽

Piotr Słowiński ◽

Andrew T. Hattersley ◽

Samuel E. Jones ◽

Seth Sharp ◽

...

Keyword(s):

Genetic Risk ◽

Population Level ◽

Disease Prevalence ◽

Risk Scores ◽

Attractive Alternative ◽

Mathematical Framework ◽

Clinical Classification ◽

Genetic Risk Scores ◽

Genetic Stratification ◽

The Impact

AbstractClinical classification is essential for estimating disease prevalence in a population but is difficult, often requiring complex investigations. The widespread availability of population level genetic data makes novel genetic stratification techniques a highly attractive alternative. We propose a generalizable mathematical framework for determining the prevalence of a disease within a population using genetic risk scores. We compare and evaluate methods based on the means of the genetic risk scores’ distributions; the Earth Mover’s Distance between distributions; a linear combination of kernel density estimates of distributions; and an Excess method. We assess the impact on estimates resulting from the population size and proportion of cases to non-cases. Using less discriminative genetic risk scores still results in robust estimates of proportion. Genetic stratification techniques provide exciting research tools enabling unbiased insights into disease prevalence and clinical characteristics unhampered by clinical classification criteria.

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Assessing the clinical utility of genetic risk scores for targeted cancer screening

Journal of Translational Medicine ◽

10.1186/s12967-020-02699-w ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Carly A. Conran ◽

Zhuqing Shi ◽

William Kyle Resurreccion ◽

Rong Na ◽

Brian T. Helfand ◽

...

Keyword(s):

Colorectal Cancer ◽

Primary Care ◽

High Risk ◽

Risk Score ◽

Genetic Risk ◽

Clinical Utility ◽

Low Risk ◽

Risk Scores ◽

Average Risk ◽

Genetic Risk Scores

Abstract Background Genome-wide association studies have identified thousands of disease-associated single nucleotide polymorphisms (SNPs). A subset of these SNPs may be additively combined to generate genetic risk scores (GRSs) that confer risk for a specific disease. Although the clinical validity of GRSs to predict risk of specific diseases has been well established, there is still a great need to determine their clinical utility by applying GRSs in primary care for cancer risk assessment and targeted intervention. Methods This clinical study involved 281 primary care patients without a personal history of breast, prostate or colorectal cancer who were 40–70 years old. DNA was obtained from a pre-existing biobank at NorthShore University HealthSystem. GRSs for colorectal cancer and breast or prostate cancer were calculated and shared with participants through their primary care provider. Additional data was gathered using questionnaires as well as electronic medical record information. A t-test or Chi-square test was applied for comparison of demographic and key clinical variables among different groups. Results The median age of the 281 participants was 58 years and the majority were female (66.6%). One hundred one (36.9%) participants received 2 low risk scores, 99 (35.2%) received 1 low risk and 1 average risk score, 37 (13.2%) received 1 low risk and 1 high risk score, 23 (8.2%) received 2 average risk scores, 21 (7.5%) received 1 average risk and 1 high risk score, and no one received 2 high risk scores. Before receiving GRSs, younger patients and women reported significantly more worry about risk of developing cancer. After receiving GRSs, those who received at least one high GRS reported significantly more worry about developing cancer. There were no significant differences found between gender, age, or GRS with regards to participants’ reported optimism about their future health neither before nor after receiving GRS results. Conclusions Genetic risk scores that quantify an individual’s risk of developing breast, prostate and colorectal cancers as compared with a race-defined population average risk have potential clinical utility as a tool for risk stratification and to guide cancer screening in a primary care setting.

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Has the time come to integrate genetic risk scores into clinical practice?

European Heart Journal ◽

10.1093/ehjci/ehaa946.3761 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

F.V Moniz Mendonca ◽

M.I Mendonca ◽

A Pereira ◽

J Monteiro ◽

J Sousa ◽

...

Keyword(s):

Risk Factors ◽

Clinical Practice ◽

Genetic Variants ◽

Genetic Risk ◽

Odds Ratio ◽

Risk Scores ◽

Cumulative Number ◽

Genetic Risk Scores ◽

Risk Alleles ◽

Cad Risk

Abstract Background The risk for Coronary Artery Disease (CAD) is determined by both genetic and environmental factors, as well as by the interaction between them. It is estimated that genetic factors could account for 40% to 55% of the existing variability among the population (inheritability). Therefore, some authors have advised that it is time we integrated genetic risk scores into clinical practice. Aim The aim of this study was to evaluate the magnitude of the association between an additive genetic risk score (aGRS) and CAD based on the cumulative number of risk alleles in these variants, and to estimate whether their use is valuable in clinical practice. Methods A case-control study was performed in a Portuguese population. We enrolled 3120 participants, of whom 1687 were CAD patients and 1433 were normal controls. Controls were paired to cases with respect to gender and age. 33 genetic variants known to be associated with CAD were selected, and an aGRS was calculated for each individual. The aGRS was further subdivided into deciles groups, in order to estimate the CAD risk in each decile, defined by the number of risk alleles. The magnitude of the risk (odds ratio) was calculated for each group by multiple logistic regression using the 5th decile as the reference group (median). In order to evaluate the ability of the aGRS to discriminate susceptibility to CAD, two genetic models were performed, the first with traditional risk factors (TRF) and second with TRF plus aGRS. The AUC of the two ROC curves was calculated. Results A higher prevalence of cases over controls became apparent from the 6th decile of the aGRS, reflecting the higher number of risk alleles present (see figure). The difference in CAD risk was only significant from the 6th decile, increasing gradually until the 10th decile. The odds ratio (OR) for the last decile related to 5th decile (median) was 1.87 (95% CI:1.36–2.56; p<0.0001). The first model yielded an AUC=0.738 (95% CI:0.720–0.755) and the second model was slightly more discriminative for CAD risk (AUC=0.748; 95% CI:0.730–0.765). The DeLong test was significant (p=0.0002). Conclusion Adding an aGRS to the non-genetic risk factors resulted in a modest improvement in the ability to discriminate the risk of CAD. Such improvement, even if statistically significant, does not appear to be of real value in clinical practice yet. We anticipate that with the development of further knowledge about different SNPs and their complex interactions, and with the inclusion of rare genetic variants, genetic risk scores will be better suited for use in a clinical setting. Funding Acknowledgement Type of funding source: None

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Predictive Value of Genetic Risk Scores in the Development of Colorectal Adenomas

Digestive Diseases and Sciences ◽

10.1007/s10620-021-07218-5 ◽

2021 ◽

Author(s):

Carla J. Gargallo-Puyuelo ◽

Rocío Aznar-Gimeno ◽

Patricia Carrera-Lasfuentes ◽

Ángel Lanas ◽

Ángel Ferrández ◽

...

Keyword(s):

Predictive Value ◽

Genetic Risk ◽

Colorectal Adenomas ◽

Risk Scores ◽

Genetic Risk Scores

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The patterns of family genetic risk scores for eleven major psychiatric and substance use disorders in a Swedish national sample

Translational Psychiatry ◽

10.1038/s41398-021-01454-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Kenneth S. Kendler ◽

Henrik Ohlsson ◽

Jan Sundquist ◽

Kristina Sundquist

Keyword(s):

Genetic Risk ◽

Molecular Genetic ◽

National Sample ◽

Autism Spectrum ◽

Risk Scores ◽

Pedigree Data ◽

Obsessive Compulsive ◽

Swedish Population ◽

Compulsive Disorder ◽

Genetic Risk Scores

AbstractTo clarify the structure of genetic risks for 11 major psychiatric disorders, we calculated, from morbidity risks for disorders in 1st–5th degree relatives controlling for cohabitation effects, in the Swedish population born between 1932 and 1995 (n = 5,830,014), the family genetic risk scores (FGRS) for major depression (MD), anxiety disorders (AD), obsessive-compulsive disorder (OCD), bipolar disorder (BD), schizophrenia (SZ), bulimia (BUL), anorexia nervosa (AN), alcohol use disorder (AUD), drug use disorder (DUD), ADHD, and autism-spectrum disorder (ASD). For all affected individuals, we calculated their mean standardized FGRS for each disorder. The patterns of FGRS were quite similar for MD and AD, and for AUD and DUD, but substantially less similar for BUL and AN, BD and SZ, and ADHD and ASD. While OCD had high levels of FGRS for MD and AD, the overall FGRS profile differed considerably from MD and AD. ADHD FGRS scores were substantially elevated in AUD and DUD. FGRS scores for BD, OCD, AN, ASD, ADHD, and especially SZ were relatively disorder-specific while genetic risk for MD and AD had more generalized effects. The levels of FGRS for BMI, coronary artery disease, and educational attainment across our disorders replicated prior associations found using molecular genetic methods. All diagnostic categories examined had elevated FGRS for many disorders producing, for each condition, an informative FGRS profile. Using a novel method which approximates, from pedigree data, aggregate genetic risk, we have replicated and extended prior insights into the structure of genetic risk factors for key psychiatric illnesses.

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