Estimation of genetic risk function with covariates in the presence of missing genotypes

Abstract

In genetic epidemiological studies, family history data are collected on relatives of study participants and used to estimate the age-specific risk of disease for individuals who carry a causal mutation. However, a family member’s genotype data may not be collected due to the high cost of in-person interview to obtain blood sample or death of a relative. Previously, efficient nonparametric genotype-specific risk estimation in censored mixture data has been proposed without considering covariates. With multiple predictive risk factors available, risk estimation requires a multivariate model to account for additional covariates that may affect disease risk simultaneously. Therefore, it is important to consider the role of covariates in the genotype-specific distribution estimation using family history data. We propose an estimation method that permits more precise risk prediction by controlling for individual characteristics and incorporating interaction effects with missing genotypes in relatives, and thus gene-gene interactions and gene-environment interactions can be handled within the framework of a single model. We examine performance of the proposed methods by simulations and apply them to estimate the age-specific cumulative risk of Parkinson’s disease (PD) in carriers of LRRK2 G2019S mutation using first-degree relatives who are at genetic risk for PD. The utility of estimated carrier risk is demonstrated through designing a future clinical trial under various assumptions. Such sample size estimation is seen in the Huntington’s disease literature using the length of abnormal expansion of a CAG repeat in the HTT gene, but is less common in the PD literature.

Penetrance estimate of LRRK2 p.G2019S mutation in individuals of non‐Ashkenazi Jewish ancestry

“…Here, we estimate the penetrance of LRRK2 p.G2019S in the non-Ashkenazi Jewish population using 474 relatives of 69 carrier probands, adjusting for multiple covariates 9 . We compare the penetrance estimate of LRRK2 p.G2019S in first-degree relatives of non-Ashkenazi Jewish PD probands to Ashkenazi Jewish PD probands and used a newly developed statistical method that accounts for covariates (e.g., demographics or risk factors of PD) simultaneously 9 , thereby improving the precision of the estimates.…”

“…We compare the penetrance estimate of LRRK2 p.G2019S in first-degree relatives of non-Ashkenazi Jewish PD probands to Ashkenazi Jewish PD probands and used a newly developed statistical method that accounts for covariates (e.g., demographics or risk factors of PD) simultaneously 9 , thereby improving the precision of the estimates.…”

“…The genotypes of the remaining 385 relatives were unknown. When most of the genotypes in relatives are not observed, the kin-cohort method 9,12 estimates the probability of missing LRRK2 p.G2019S carrier status in the relatives using the mutation status in PD probands and Mendelian inheritance patterns with the prevalence of mutation in relatives. The method incorporates this estimated probability with age at PD diagnosis in the first-degree relatives to estimate the age-specific cumulative risk of PD in p.G2019S carriers and non-carriers, using an expectation-maximization algorithm 13 developed to handle missing relatives’ genotype data.…”

“…Fox Ashkenazi Jewish LRRK2 Consortium, including family history data on 652 Ashkenazi Jewish relatives of 129 PD probands with p.G2019S mutation and 1,618 Ashkenazi Jewish relatives of 345 PD probands without the p.G2019S mutation 8 without adjusting for covariates. We subsequently proposed statistical methods to account for multiple covariates simultaneously in order to refine the penetrance estimate 9 . We re-estimated the adjusted penetrance of LRRK2 p.G2019S in the Ashkenazi Jewish population as 25% (95% CI: 16.7–34.2%) 9 to age 80 and found it to be similar to that reported initially 8 .…”

“…We subsequently proposed statistical methods to account for multiple covariates simultaneously in order to refine the penetrance estimate 9 . We re-estimated the adjusted penetrance of LRRK2 p.G2019S in the Ashkenazi Jewish population as 25% (95% CI: 16.7–34.2%) 9 to age 80 and found it to be similar to that reported initially 8 .…”