The kin-cohort study for estimating penetrance

Non-parametric estimation of the age-at-onset distribution from a cross-sectional sample

We propose and study a simple and innovative non-parametric approach to estimate the age-of-onset distribution for a disease from a cross-sectional sample of the population that includes individuals with prevalent disease. First, we estimate the joint distribution of two event times, the age of disease onset and the survival time after disease onset. We accommodate that individuals had to be alive at the time of the study by conditioning on their survival until the age at sampling. We propose a computationally efficient expectation-maximization (EM) algorithm and derive the asymptotic properties of the resulting estimates. From these joint probabilities we then obtain non-parametric estimates of the age-at-onset distribution by marginalizing over the survival time after disease onset to death. The method accommodates categorical covariates and can be used to obtain unbiased estimates of the covariate distribution in the source population. We show in simulations that our method performs well in finite samples even under large amounts of truncation for prevalent cases. We apply the proposed method to data from female participants in the Washington Ashkenazi Study to estimate the age-at-onset distribution of breast cancer associated with carrying BRCA1 or BRCA2 mutations.

Comparison of intergenerational instrumental variable analyses of body mass index and mortality in UK Biobank

BACKGROUND

An increasing proportion of people have a body mass index (BMI) classified as overweight or obese and published studies disagree whether this will be beneficial or detrimental to health. We applied and evaluated two intergenerational instrumental variable methods to estimate the average causal effect of BMI on mortality in a cohort with many deaths: the parents of UK Biobank participants.

METHODS

In Cox regression models, parental BMI was instrumented by offspring BMI using an 'offspring as instrument' (OAI) estimation and by offspring BMI-related genetic variants in a 'proxy-genotype Mendelian randomization' (PGMR) estimation.

RESULTS

Complete-case analyses were performed in parents of 233 361 UK Biobank participants with full phenotypic, genotypic and covariate data. The PGMR method suggested that higher BMI increased mortality with hazard ratios per kg/m2 of 1.02 (95% CI: 1.01, 1.04) for mothers and 1.04 (95% CI: 1.02, 1.05) for fathers. The OAI method gave considerably higher estimates, which varied according to the parent-offspring pairing between 1.08 (95% CI: 1.06, 1.10; mother-son) and 1.23 (95% CI: 1.16, 1.29; father-daughter).

CONCLUSION

Both methods supported a causal role of higher BMI increasing mortality, although caution is required regarding the immediate causal interpretation of these exact values. Evidence of instrument invalidity from measured covariates was limited for the OAI method and minimal for the PGMR method. The methods are complementary for interrogating the average putative causal effects because the biases are expected to differ between them.

CanRisk-Prostate: A Comprehensive, Externally Validated Risk Model for the Prediction of Future Prostate Cancer

PURPOSE

Prostate cancer (PCa) is highly heritable. No validated PCa risk model currently exists. We therefore sought to develop a genetic risk model that can provide personalized predicted PCa risks on the basis of known moderate- to high-risk pathogenic variants, low-risk common genetic variants, and explicit cancer family history, and to externally validate the model in an independent prospective cohort.

MATERIALS AND METHODS

We developed a risk model using a kin-cohort comprising individuals from 16,633 PCa families ascertained in the United Kingdom from 1993 to 2017 from the UK Genetic Prostate Cancer Study, and complex segregation analysis adjusting for ascertainment. The model was externally validated in 170,850 unaffected men (7,624 incident PCas) recruited from 2006 to 2010 to the independent UK Biobank prospective cohort study.

RESULTS

The most parsimonious model included the effects of pathogenic variants in BRCA2, HOXB13, and BRCA1, and a polygenic score on the basis of 268 common low-risk variants. Residual familial risk was modeled by a hypothetical recessively inherited variant and a polygenic component whose standard deviation decreased log-linearly with age. The model predicted familial risks that were consistent with those reported in previous observational studies. In the validation cohort, the model discriminated well between unaffected men and men with incident PCas within 5 years (C-index, 0.790; 95% CI, 0.783 to 0.797) and 10 years (C-index, 0.772; 95% CI, 0.768 to 0.777). The 50% of men with highest predicted risks captured 86.3% of PCa cases within 10 years.

CONCLUSION

To our knowledge, this is the first validated risk model offering personalized PCa risks. The model will assist in counseling men concerned about their risk and can facilitate future risk-stratified population screening approaches.

Germ line DDX41 mutations define a unique subtype of myeloid neoplasms

Germ line DDX41 variants have been implicated in late-onset myeloid neoplasms (MNs). Despite an increasing number of publications, many important features of DDX41-mutated MNs remain to be elucidated. Here we performed a comprehensive characterization of DDX41-mutated MNs, enrolling a total of 346 patients with DDX41 pathogenic/likely-pathogenic (P/LP) germ line variants and/or somatic mutations from 9082 MN patients, together with 525 first-degree relatives of DDX41-mutated and wild-type (WT) patients. P/LP DDX41 germ line variants explained ∼80% of known germ line predisposition to MNs in adults. These risk variants were 10-fold more enriched in Japanese MN cases (n = 4461) compared with the general population of Japan (n = 20 238). This enrichment of DDX41 risk alleles was much more prominent in male than female (20.7 vs 5.0). P/LP DDX41 variants conferred a large risk of developing MNs, which was negligible until 40 years of age but rapidly increased to 49% by 90 years of age. Patients with myelodysplastic syndromes (MDS) along with a DDX41-mutation rapidly progressed to acute myeloid leukemia (AML), which was however, confined to those having truncating variants. Comutation patterns at diagnosis and at progression to AML were substantially different between DDX41-mutated and WT cases, in which none of the comutations affected clinical outcomes. Even TP53 mutations made no exceptions and their dismal effect, including multihit allelic status, on survival was almost completely mitigated by the presence of DDX41 mutations. Finally, outcomes were not affected by the conventional risk stratifications including the revised/molecular International Prognostic Scoring System. Our findings establish that MDS with DDX41-mutation defines a unique subtype of MNs that is distinct from other MNs.

Semiparametric regression analysis of bivariate censored events in a family study of Alzheimer's disease

Assessing disease comorbidity patterns in families represents the first step in gene mapping for diseases and is central to the practice of precision medicine. One way to evaluate the relative contributions of genetic risk factor and environmental determinants of a complex trait (e.g., Alzheimer's disease [AD]) and its comorbidities (e.g., cardiovascular diseases [CVD]) is through familial studies, where an initial cohort of subjects are recruited, genotyped for specific loci, and interviewed to provide extensive disease history in family members. Because of the retrospective nature of obtaining disease phenotypes in family members, the exact time of disease onset may not be available such that current status data or interval-censored data are observed. All existing methods for analyzing these family study data assume single event subject to right-censoring so are not applicable. In this article, we propose a semiparametric regression model for the family history data that assumes a family-specific random effect and individual random effects to account for the dependence due to shared environmental exposures and unobserved genetic relatedness, respectively. To incorporate multiple events, we jointly model the onset of the primary disease of interest and a secondary disease outcome that is subject to interval-censoring. We propose nonparametric maximum likelihood estimation and develop a stable Expectation-Maximization (EM) algorithm for computation. We establish the asymptotic properties of the resulting estimators and examine the performance of the proposed methods through simulation studies. Our application to a real world study reveals that the main contribution of comorbidity between AD and CVD is due to genetic factors instead of environmental factors.

Exploiting family history in aggregation unit-based genetic association tests

The development of sequencing technology calls for new powerful methods to detect disease associations and lower the cost of sequencing studies. Family history (FH) contains information on disease status of relatives, adding valuable information about the probands' health problems and risk of diseases. Incorporating data from FH is a cost-effective way to improve statistical evidence in genetic studies, and moreover, overcomes limitations in study designs with insufficient cases or missing genotype information for association analysis. We proposed family history aggregation unit-based test (FHAT) and optimal FHAT (FHAT-O) to exploit available FH for rare variant association analysis. Moreover, we extended liability threshold model of case-control status and FH (LT-FH) method in aggregated unit-based methods and compared that with FHAT and FHAT-O. The computational efficiency and flexibility of the FHAT and FHAT-O were demonstrated through both simulations and applications. We showed that FHAT, FHAT-O, and LT-FH methods offer reasonable control of the type I error unless case/control ratio is unbalanced, in which case they result in smaller inflation than that observed with conventional methods excluding FH. We also demonstrated that FHAT and FHAT-O are more powerful than LT-FH and conventional methods in many scenarios. By applying FHAT and FHAT-O to the analysis of all cause dementia and hypertension using the exome sequencing data from the UK Biobank, we showed that our methods can improve significance for known regions. Furthermore, we replicated the previous associations in all cause dementia and hypertension and detected novel regions through the exome-wide analysis.

Limited Effect of Y Chromosome Variation on Coronary Artery Disease and Mortality in UK Biobank-Brief Report

BACKGROUND

The effect of genetic variation in the male-specific region of the Y chromosome (MSY) on coronary artery disease and cardiovascular risk factors has been disputed. In this study, we systematically assessed the association of MSY genetic variation on these traits using a kin-cohort analysis of family disease history in the largest sample to date.

METHODS

We tested 90 MSY haplogroups against coronary artery disease, hypertension, blood pressure, classical lipid levels, and all-cause mortality in up to 152 186 unrelated, genomically British individuals from UK Biobank. Unlike previous studies, we did not adjust for heritable lifestyle factors (to avoid collider bias) and instead adjusted for geographic variables and socioeconomic deprivation, given the link between MSY haplogroups and geography. For family history traits, subject MSY haplogroups were tested against father and mother disease as validation and negative control, respectively.

RESULTS

Our models find little evidence for an effect of any MSY haplogroup on cardiovascular risk in participants. Parental models confirm these findings.

CONCLUSIONS

Kin-cohort analysis of the Y chromosome uniquely allows for discoveries in subjects to be validated using family history data. Despite our large sample size, improved models, and parental validation, there is little evidence to suggest cardiovascular risk in UK Biobank is influenced by genetic variation in MSY.

Molecular mechanisms defining penetrance of LRRK2-associated Parkinson's disease

Mutations in Leucine-rich repeat kinase 2 (LRRK2) are the most frequent cause of dominantly inherited Parkinson's disease (PD). LRRK2 mutations, among which p.G2019S is the most frequent, are inherited with reduced penetrance. Interestingly, the disease risk associated with LRRK2 G2019S can vary dramatically depending on the ethnic background of the carrier. While this would suggest a genetic component in the definition of LRRK2-PD penetrance, only few variants have been shown to modify the age at onset of patients harbouring LRRK2 mutations, and the exact cellular pathways controlling the transition from a healthy to a diseased state currently remain elusive. In light of this knowledge gap, recent studies also explored environmental and lifestyle factors as potential modifiers of LRRK2-PD. In this article, we (i) describe the clinical characteristics of LRRK2 mutation carriers, (ii) review known genes linked to LRRK2-PD onset and (iii) summarize the cellular functions of LRRK2 with particular emphasis on potential penetrance-related molecular mechanisms. This section covers LRRK2's involvement in Rab GTPase and immune signalling as well as in the regulation of mitochondrial homeostasis and dynamics. Additionally, we explored the literature with regard to (iv) lifestyle and (v) environmental factors that may influence the penetrance of LRRK2 mutations, with a view towards further exposomics studies. Finally, based on this comprehensive overview, we propose potential future in vivo, in vitro and in silico studies that could provide a better understanding of the processes triggering PD in individuals with LRRK2 mutations.

Kernel density estimation in mixture models with known mixture proportions

In this article, we consider the density estimation for data with a mixture structure, where the component densities are assumed unknown, but for each observation, the probabilities of its membership to the subpopulations are known or estimable from other resources. Data of this kind arise from practice and have wide applications. Motivated from the classical kernel density estimation method for a single population, we propose a weighted kernel density estimation method to estimate the component density functions nonparametrically. Within the framework of the EM algorithm, we derive an algorithm that computes our proposed estimates effectively. Via extensive simulation studies, we demonstrate that our methods outperform the existing methods in most occasions. We further compare our methods with existing methods by real data examples.

Selection models for efficient two-phase design of family studies

Family studies routinely employ biased sampling schemes in which individuals are randomly chosen from a disease registry and genetic and phenotypic data are obtained from their consenting relatives. We view this as a two-phase study and propose the use of an efficient selection model for the recruitment of families to form a phase II sample subject to budgetary constraints. Simple random sampling, balanced sampling and use of an approximately optimal selection model are considered where the latter is chosen to minimize the variance of parameters of interest. We consider the setting where family members provide current status data with respect to the disease and use copula models to address within-family dependence. The efficiency gains from the use of an optimal selection model over simple random sampling and balanced sampling schemes are investigated as is the robustness of optimal sampling to model misspecification. An application to a family study on psoriatic arthritis is given for illustration.

Penetrance of Glucocerebrosidase (GBA) Mutations in Parkinson's Disease: A Kin Cohort Study

BACKGROUND

Homozygous glucocerebrosidase mutations cause Gaucher disease, whereas heterozygous mutations are the most important genetic risk factor for Parkinson's disease (PD). The penetrance of heterozygous glucocerebrosidase mutations for PD is variable (10%-30%), depends on the population studied, and has only been assessed in Gaucher disease or familial PD. The aim of this study was to assess the penetrance of glucocerebrosidase mutations in PD in unselected PD patients.

METHODS

The penetrance of glucocerebrosidase mutations was estimated using the kin-cohort method.

RESULTS

Data on family history were available for 63 of 123 PD glucocerebrosidase mutation carriers, identified among 2843 unrelated consecutive PD patients. Three hundred eighty-one first-degree relatives were analyzed. The risk of developing PD was 10% at 60 years, 16% at 70 years, and 19% at 80 years.

CONCLUSIONS

The estimated penetrance of glucocerebrosidase mutations in unselected PD patients is higher than that estimated in Gaucher disease cohorts and lower than that estimated in familial PD cohorts. © 2020 International Parkinson and Movement Disorder Society.

Dynamic landmark prediction for mixture data

In kin-cohort studies, clinicians want to provide their patients with the most current cumulative risk of death arising from a rare deleterious mutation. Estimating the cumulative risk is difficult when the genetic mutation status is unknown and only estimated probabilities of a patient having the mutation are available. We estimate the cumulative risk for this scenario using a novel nonparametric estimator that incorporates covariate information and dynamic landmark prediction. Our estimator has improved prediction accuracy over existing estimators that ignore covariate information. It is built within a dynamic landmark prediction framework whereby we can obtain personalized dynamic predictions over time. Compared to current standards, a simple transformation of our estimator provides more efficient estimates of marginal distribution functions in settings where patient-specific predictions are not the main goal. We show our estimator is unbiased and has more predictive accuracy compared to methods that ignore covariate information and landmarking. Applying our method to a Huntington disease study of mortality, we develop dynamic survival prediction curves incorporating gender and familial genetic information.

Homeobox B13 G84E Mutation and Prostate Cancer Risk

BACKGROUND

The homeobox B13 (HOXB13) G84E mutation has been recommended for use in genetic counselling for prostate cancer (PCa), but the magnitude of PCa risk conferred by this mutation is uncertain.

OBJECTIVE

To obtain precise risk estimates for mutation carriers and information on how these vary by family history and other factors.

DESIGN, SETTING, AND PARTICIPANTS

Two-fold: a systematic review and meta-analysis of published risk estimates, and a kin-cohort study comprising pedigree data on 11983 PCa patients enrolled during 1993-2014 from 189 UK hospitals and who had been genotyped for HOXB13 G84E.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Relative and absolute PCa risks. Complex segregation analysis with ascertainment adjustment to derive age-specific risks applicable to the population, and to investigate how these vary by family history and birth cohort.

RESULTS AND LIMITATIONS

A meta-analysis of case-control studies revealed significant heterogeneity between reported relative risks (RRs; range: 0.95-33.0, p<0.001) and differences by case selection (p=0.007). Based on case-control studies unselected for PCa family history, the pooled RR estimate was 3.43 (95% confidence interval [CI] 2.78-4.23). In the kin-cohort study, PCa risk for mutation carriers varied by family history (p<0.001). There was a suggestion that RRs decrease with age, but this was not significant (p=0.068). We found higher RR estimates for men from more recent birth cohorts (p=0.004): 3.09 (95% CI 2.03-4.71) for men born in 1929 or earlier and 5.96 (95% CI 4.01-8.88) for men born in 1930 or later. The absolute PCa risk by age 85 for a male HOXB13 G84E carrier varied from 60% for those with no PCa family history to 98% for those with two relatives diagnosed at young ages, compared with an average risk of 15% for noncarriers. Limitations include the reliance on self-reported cancer family history.

CONCLUSIONS

PCa risks for HOXB13 G84E mutation carriers are heterogeneous. Counselling should not be based on average risk estimates but on age-specific absolute risk estimates tailored to individual mutation carriers' family history and birth cohort.

PATIENT SUMMARY

Men who carry a hereditary mutation in the homeobox B13 (HOXB13) gene have a higher than average risk for developing prostate cancer. In our study, we examined a large number of families of men with prostate cancer recruited across UK hospitals, to assess what other factors may contribute to this risk and to assess whether we could create a precise model to help in predicting a man's prostate cancer risk. We found that the risk of developing prostate cancer in men who carry this genetic mutation is also affected by a family history of prostate cancer and their year of birth. This information can be used to assess more personalised prostate cancer risks to men who carry HOXB13 mutations and hence better counsel them on more personalised risk management options, such as tailoring prostate cancer screening frequency.

Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances

We use a genome-wide association of 1 million parental lifespans of genotyped subjects and data on mortality risk factors to validate previously unreplicated findings near CDKN2B-AS1, ATXN2/BRAP, FURIN/FES, ZW10, PSORS1C3, and 13q21.31, and identify and replicate novel findings near ABO, ZC3HC1, and IGF2R. We also validate previous findings near 5q33.3/EBF1 and FOXO3, whilst finding contradictory evidence at other loci. Gene set and cell-specific analyses show that expression in foetal brain cells and adult dorsolateral prefrontal cortex is enriched for lifespan variation, as are gene pathways involving lipid proteins and homeostasis, vesicle-mediated transport, and synaptic function. Individual genetic variants that increase dementia, cardiovascular disease, and lung cancer - but not other cancers - explain the most variance. Resulting polygenic scores show a mean lifespan difference of around five years of life across the deciles.

Editorial note

This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

Breast and ovarian cancer penetrance of BRCA1/2 mutations among Hong Kong women

Germline mutations in BRCA1 and BRCA2 (BRCA1/2) are associated with increased risk of breast and ovarian cancer. The penetrance of breast and ovarian cancer in BRCA1/2 mutation carriers has been well characterized in Caucasian but not in Asian. Two studies have investigated the breast cancer risk in Asian women with BRCA1/2 mutations, and no published estimates are available for ovarian cancer. Therefore, we estimated the age-specific cumulative risk of BRCA1/2-associated breast and ovarian cancer in Chinese women. From Jan 2007 to Nov 2015, the Hong Kong Hereditary Breast Cancer Family Registry identified 1635 families with hereditary breast-ovarian cancer. Among probands in these families, 66 had BRCA1 mutations, 84 had BRCA2 mutations, and 1,485 tested negative for BRCA1/2 mutations. Using the female first-degree relatives of these probands, we estimated the risk of breast and ovarian cancer using a modified marginal likelihood approach. Estimates of breast cancer penetrance by age 70 were 53.7% (95% CI 34.5-71.6%) for BRCA1 mutation carriers and 48.3% (95% CI 31.8-68.5%) for BRCA2. The estimated risk of ovarian cancer by age 70 was 21.5% and 7.3% for Chinese women carrying BRCA1 or BRCA2 mutation respectively. A meta-analysis of available studies in Asian women revealed pooled estimates of breast cancer risk by age 70 of 44.8% (95% CI 33-57.2%) and 40.7% (95% CI 31.3-50.9%) for BRCA1 and BRCA2 mutation carriers respectively. These data suggest that BRCA1/2-associated breast cancer risk for Chinese women is similar to that for Caucasian women, although BRCA1/2-associated ovarian cancer risks are lower for Chinese women.

Statistical modeling of Huntington disease onset

Huntington disease (HD) is caused by a CAG trinucleotide expansion in the huntingtin gene. We now have the power to predict age-at-onset from subject-specific features like motor and neuroimaging measures. In clinical trials, properly modeling onset age is important, because it improves power calculations and directs clinicians to recruit subjects with certain features. The history of modeling onset, from simple linear and logistic regression to advanced survival models, is discussed. We highlight their advantages and disadvantages, emphasizing the methodological challenges when genetic mutation status is unavailable. We also discuss the potential bias and higher variability incurred from the uncertainty associated with subjective definitions for onset. Methods to adjust for the uncertainty in survival models are still in their infancy, but would be beneficial for HD and neurodegenerative diseases with long prodromal periods like Alzheimer's and Parkinson's disease.

Predicting disease Risk by Transformation Models in the Presence of Unspecified Subgroup Membership

Some biomedical studies lead to mixture data. When a discrete covariate defining subgroup membership is missing for some of the subjects in a study, the distribution of the outcome follows a mixture distribution of the subgroup-specific distributions. Taking into account the uncertain distribution of the group membership and the covariates, we model the relation between the disease onset time and the covariates through transformation models in each sub-population, and develop a nonparametric maximum likelihood based estimation implemented through EM algorithm along with its inference procedure. We further propose methods to identify the covariates that have different effects or common effects in distinct populations, which enables parsimonious modeling and better understanding of the difference across populations. The methods are illustrated through extensive simulation studies and a real data example.

Genome-wide meta-analysis associates HLA-DQA1/DRB1 and LPA and lifestyle factors with human longevity

Genomic analysis of longevity offers the potential to illuminate the biology of human aging. Here, using genome-wide association meta-analysis of 606,059 parents’ survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA). We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity. Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated. We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD. Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.

Variability in human longevity is genetically influenced. Using genetic data of parental lifespan, the authors identify associations at HLA-DQA/DRB1 and LPA and find that genetic variants that increase educational attainment have a positive effect on lifespan whereas increasing BMI negatively affects lifespan.

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

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 because of 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 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 the 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. Copyright © 2017 John Wiley & Sons, Ltd.

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

BACKGROUND

Penetrance estimates of the leucine-rich repeat kinase 2 (LRRK2) p.G2019S mutation for PD vary widely (24%-100%). The p.G2019S penetrance in individuals of Ashkenazi Jewish ancestry has been estimated as 25%, adjusted for multiple covariates. It is unknown whether penetrance varies among different ethnic groups. The objective of this study was to estimate the penetrance of p.G2019S in individuals of non-Ashkenazi Jewish ancestry and compare penetrance between Ashkenazi Jews and non-Ashkenazi Jews to age 80.

METHODS

The kin-cohort method was used to estimate penetrance in 474 first-degree relatives of 69 non-Ashkenazi Jewish LRRK2 p.G2019S carrier probands at 8 sites from the Michael J. Fox LRRK2 Cohort Consortium. An identical validated family history interview was administered to assess age at onset of PD, current age, or age at death for relatives in different ethnic groups at each site. Neurological examination and LRRK2 genotype of relatives were included when available.

RESULTS

Risk of PD in non-Ashkenazi Jewish relatives who carry a LRRK2 p.G2019S mutation was 42.5% (95% confidence interval [CI]: 26.3%-65.8%) to age 80, which is not significantly higher than the previously estimated 25% (95% CI: 16.7%-34.2%) in Ashkenazi Jewish carrier relatives. The penetrance of PD to age 80 in LRRK2 p.G2019S mutation carrier relatives was significantly higher than the noncarrier relatives, as seen in Ashkenazi Jewish relatives.

CONCLUSIONS

The similar penetrance of LRRK2 p.G2019S estimated in Ashkenazi Jewish carriers and non-Ashkenazi Jewish carriers confirms that p.G2019S penetrance is 25% to 42.5% at age 80 in all populations analyzed. © 2017 International Parkinson and Movement Disorder Society.

Genetic variants linked to education predict longevity

Educational attainment is associated with many health outcomes, including longevity. It is also known to be substantially heritable. Here, we used data from three large genetic epidemiology cohort studies (Generation Scotland, n = ∼17,000; UK Biobank, n = ∼115,000; and the Estonian Biobank, n = ∼6,000) to test whether education-linked genetic variants can predict lifespan length. We did so by using cohort members' polygenic profile score for education to predict their parents' longevity. Across the three cohorts, meta-analysis showed that a 1 SD higher polygenic education score was associated with ∼2.7% lower mortality risk for both mothers (total ndeaths = 79,702) and ∼2.4% lower risk for fathers (total ndeaths = 97,630). On average, the parents of offspring in the upper third of the polygenic score distribution lived 0.55 y longer compared with those of offspring in the lower third. Overall, these results indicate that the genetic contributions to educational attainment are useful in the prediction of human longevity.

Variants near CHRNA3/5 and APOE have age- and sex-related effects on human lifespan

Lifespan is a trait of enormous personal interest. Research into the biological basis of human lifespan, however, is hampered by the long time to death. Using a novel approach of regressing (272,081) parental lifespans beyond age 40 years on participant genotype in a new large data set (UK Biobank), we here show that common variants near the apolipoprotein E and nicotinic acetylcholine receptor subunit alpha 5 genes are associated with lifespan. The effects are strongly sex and age dependent, with APOE ɛ4 differentially influencing maternal lifespan (P=4.2 × 10⁻¹⁵, effect −1.24 years of maternal life per imputed risk allele in parent; sex difference, P=0.011), and a locus near CHRNA3/5 differentially affecting paternal lifespan (P=4.8 × 10⁻¹¹, effect −0.86 years per allele; sex difference P=0.075). Rare homozygous carriers of the risk alleles at both loci are predicted to have 3.3–3.7 years shorter lives.

Understanding the genetic influences on human aging requires a large number of subjects for a study of sufficient power. Here, Jim Wilson and colleagues use information on parental ages at death to show that common variants near the genes for apolipoprotein E and nicotinic acetylcholine receptor subunit alpha 5 are associated with longer lifespan.

Efficient Estimation of Nonparametric Genetic Risk Function with Censored Data

With an increasing number of causal genes discovered for complex human disorders, it is crucial to assess the genetic risk of disease onset for individuals who are carriers of these causal mutations and compare the distribution of age-at-onset with that in non-carriers. In many genetic epidemiological studies aiming at estimating causal gene effect on disease, the age-at-onset of disease is subject to censoring. In addition, some individuals' mutation carrier or non-carrier status can be unknown due to the high cost of in-person ascertainment to collect DNA samples or death in older individuals. Instead, the probability of these individuals' mutation status can be obtained from various sources. When mutation status is missing, the available data take the form of censored mixture data. Recently, various methods have been proposed for risk estimation from such data, but none is efficient for estimating a nonparametric distribution. We propose a fully efficient sieve maximum likelihood estimation method, in which we estimate the logarithm of the hazard ratio between genetic mutation groups using B-splines, while applying nonparametric maximum likelihood estimation for the reference baseline hazard function. Our estimator can be calculated via an expectation-maximization algorithm which is much faster than existing methods. We show that our estimator is consistent and semiparametrically efficient and establish its asymptotic distribution. Simulation studies demonstrate superior performance of the proposed method, which is applied to the estimation of the distribution of the age-at-onset of Parkinson's disease for carriers of mutations in the leucine-rich repeat kinase 2 gene.

Age-specific penetrance of LRRK2 G2019S in the Michael J. Fox Ashkenazi Jewish LRRK2 Consortium

OBJECTIVE

Estimates of the penetrance of LRRK2 G2019S vary widely (24%-100%), reflective of differences in ascertainment, age, sex, ethnic group, and genetic and environmental modifiers.

METHODS

The kin-cohort method was used to predict penetrance in 2,270 relatives of 474 Ashkenazi Jewish (AJ) Parkinson disease (PD) probands in the Michael J. Fox LRRK2 AJ Consortium in New York and Tel Aviv, Israel. Patients with PD were genotyped for the LRRK2 G2019S mutation and at least 7 founder GBA mutations. GBA mutation carriers were excluded. A validated family history interview, including age at onset of PD and current age or age at death for each first-degree relative, was administered. Neurologic examination and LRRK2 genotype of relatives were included when available.

RESULTS

Risk of PD in relatives predicted to carry an LRRK2 G2019S mutation was 0.26 (95% confidence interval [CI] 0.18-0.36) to age 80 years, and was almost 3-fold higher than in relatives predicted to be noncarriers (hazard ratio [HR] 2.89, 95% CI 1.73-4.55, p < 0.001). The risk among predicted G2019S carrier male relatives (0.22, 95% CI 0.10-0.37) was similar to predicted carrier female relatives (0.29, 95% CI 0.18-0.40; HR male to female: 0.74, 95% CI 0.27-1.63, p = 0.44). In contrast, predicted noncarrier male relatives had a higher risk (0.15, 95% CI 0.11-0.20) than predicted noncarrier female relatives (0.07, 95% CI 0.04-0.10; HR male to female: 2.40, 95% CI 1.50-4.15, p < 0.001).

CONCLUSION

Penetrance of LRRK2 G2019S in AJ is only 26% and lower than reported in other ethnic groups. Further study of the genetic and environmental risk factors that influence G2019S penetrance is warranted.

Breast and ovarian cancer risks in a large series of clinically ascertained families with a high proportion of BRCA1 and BRCA2 Dutch founder mutations

BACKGROUND

BRCA1 or BRCA2 mutations confer increased risks of breast and ovarian cancer, but risks have been found to vary across studies and populations.

METHODS

We ascertained pedigree data of 582 BRCA1 and 176 BRCA2 families and studied the variation in breast and ovarian cancer risks using a modified segregation analysis model.

RESULTS

The average cumulative breast cancer risk by age 70 years was estimated to be 45% (95% CI 36 to 52%) for BRCA1 and 27% (95% CI 14 to 38%) for BRCA2 mutation carriers. The corresponding cumulative risks for ovarian cancer were 31% (95% CI 17 to 43%) for BRCA1 and 6% (95% CI 2 to 11%) for BRCA2 mutation carriers. In BRCA1 families, breast cancer relative risk (RR) increased with more recent birth cohort (p heterogeneity = 0.0006) and stronger family histories of breast cancer (p heterogeneity < 0.001). For BRCA1, our data suggest a significant association between the location of the mutation and the ratio of breast to ovarian cancer (p<0.001). By contrast, in BRCA2 families, no evidence was found for risk heterogeneity by birth cohort, family history or mutation location.

CONCLUSIONS

BRCA1 mutation carriers conferred lower overall breast and ovarian cancer risks than reported so far, while the estimates of BRCA2 mutations were among the lowest. The low estimates for BRCA1 might be due to older birth cohorts, a moderate family history, or founder mutations located within specific regions of the gene. These results are important for a more accurate counselling of BRCA1/2 mutation carriers.

Leveraging family history in population-based case-control association studies

Population-based epidemiologic studies often gather information from study participants on disease history among their family members. Although investigators widely recognize that family history will be associated with genotypes of the participants at disease susceptibility loci, they commonly ignore such information in primary genetic association analyses. In this report, we propose a simple approach to association testing by incorporating family history information as a "phenotype." We account for the expected attenuation in strength of association of the genotype of study participants with family history under Mendelian transmission. The proposed analysis can be performed using standard statistical software adopting either a meta- or pooled-analysis framework. Re-analysis of a total of 115 known susceptibility single-nucleotide polymorphisms, discovered through genome-wide association studies for several disease traits, indicates that incorporation of family history information can increase efficiency by as much as 40%. Efficiency gain depends on the type of design used for conducting the primary study, extent of family history, and accuracy and completeness of reporting.

Comparative study of Parkinson's disease and leucine-rich repeat kinase 2 p.G2019S parkinsonism

Parkinson disease is a progressive neurodegenerative disease for which leucine-rich repeat kinase 2 (LRRK2 carriers) p.G2019S confers substantial genotypic and population attributable risk. With informed consent, we have recruited clinical data from 778 patients from Tunisia (of which 266 have LRRK2 parkinsonism) and 580 unaffected subjects. Motor, autonomic, and cognitive assessments in idiopathic Parkinson disease and LRRK2 patients were compared with regression models. The age-associated cumulative incidence of LRRK2 parkinsonism was also estimated using case-control and family-based designs. LRRK2 parkinsonism patients had slightly less gastrointestinal dysfunction and rapid eye movement sleep disorder. Overall, disease penetrance in LRRK2 carriers was 80% by 70 years but women become affected a median 5 years younger than men. Idiopathic Parkinson disease patients with younger age at diagnosis have slower disease progression. However, age at diagnoses does not predict progression in LRRK2 parkinsonism. LRRK2 p.G2019S mutation is a useful aid to diagnosis and modifiers of disease in LRRK2 parkinsonism may aid in developing therapeutic targets.

Nonparametric modeling and analysis of association between Huntington's disease onset and CAG repeats

Huntington's disease (HD) is a neurodegenerative disorder with a dominant genetic mode of inheritance caused by an expansion of CAG repeats on chromosome 4. Typically, a longer sequence of CAG repeat length is associated with increased risk of experiencing earlier onset of HD. Previous studies of the association between HD onset age and CAG length have favored a logistic model, where the CAG repeat length enters the mean and variance components of the logistic model in a complex exponential-linear form. To relax the parametric assumption of the exponential-linear association to the true HD onset distribution, we propose to leave both mean and variance functions of the CAG repeat length unspecified and perform semiparametric estimation in this context through a local kernel and backfitting procedure. Motivated by including family history of HD information available in the family members of participants in the Cooperative Huntington's Observational Research Trial (COHORT), we develop the methodology in the context of mixture data, where some subjects have a positive probability of being risk free. We also allow censoring on the age at onset of disease and accommodate covariates other than the CAG length. We study the theoretical properties of the proposed estimator and derive its asymptotic distribution. Finally, we apply the proposed methods to the COHORT data to estimate the HD onset distribution using a group of study participants and the disease family history information available on their family members.

Semiparametric inference on the penetrances of rare genetic mutations based on a case-family design

A formal semiparametric statistical inference framework is proposed for the evaluation of the age-dependent penetrance of a rare genetic mutation, using family data generated under a case-family design, where phenotype and genotype information are collected from first-degree relatives of case probands carrying the targeted mutation. The proposed approach allows for unobserved risk factors that are correlated among family members. Some rigorous large sample properties are established, which show that the proposed estimators were asymptotically semi-parametric efficient. A simulation study is conducted to evaluate the performance of the new approach, which shows the robustness of the proposed semiparamteric approach and its advantage over the corresponding parametric approach. As an illustration, the proposed approach is applied to estimating the age-dependent cancer risk among carriers of the MSH2 or MLH1 mutation.

Nonparametric estimation for censored mixture data with application to the Cooperative Huntington's Observational Research Trial

This work presents methods for estimating genotype-specific distributions from genetic epidemiology studies where the event times are subject to right censoring, the genotypes are not directly observed, and the data arise from a mixture of scientifically meaningful subpopulations. Examples of such studies include kin-cohort studies and quantitative trait locus (QTL) studies. Current methods for analyzing censored mixture data include two types of nonparametric maximum likelihood estimators (NPMLEs) which do not make parametric assumptions on the genotype-specific density functions. Although both NPMLEs are commonly used, we show that one is inefficient and the other inconsistent. To overcome these deficiencies, we propose three classes of consistent nonparametric estimators which do not assume parametric density models and are easy to implement. They are based on the inverse probability weighting (IPW), augmented IPW (AIPW), and nonparametric imputation (IMP). The AIPW achieves the efficiency bound without additional modeling assumptions. Extensive simulation experiments demonstrate satisfactory performance of these estimators even when the data are heavily censored. We apply these estimators to the Cooperative Huntington's Observational Research Trial (COHORT), and provide age-specific estimates of the effect of mutation in the Huntington gene on mortality using a sample of family members. The close approximation of the estimated non-carrier survival rates to that of the U.S. population indicates small ascertainment bias in the COHORT family sample. Our analyses underscore an elevated risk of death in Huntington gene mutation carriers compared to non-carriers for a wide age range, and suggest that the mutation equally affects survival rates in both genders. The estimated survival rates are useful in genetic counseling for providing guidelines on interpreting the risk of death associated with a positive genetic testing, and in facilitating future subjects at risk to make informed decisions on whether to undergo genetic mutation testings.

Assessing disease risk in genome-wide association studies using family history

We show how to use reports of cancer in family members to discover additional genetic associations or confirm previous findings in genome-wide association (GWA) studies conducted in case-control, cohort, or cross-sectional studies. Our novel family history-based approach allows economical association studies for multiple cancers, without genotyping of relatives (as required in family studies), follow-up of participants (as required in cohort studies), or oversampling of specific cancer cases (as required in case-control studies). We empirically evaluate the performance of the proposed family history-based approach in studying associations with prostate and ovarian cancers, using data from GWA studies previously conducted within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. The family history-based method may be particularly useful for investigating genetic susceptibility to rare diseases for which accruing cases may be very difficult, by using disease information from nongenotyped relatives of participants in multiple case-control and cohort studies designed primarily for other purposes.

Risk of non-melanoma cancers in first-degree relatives of CDKN2A mutation carriers

The purpose of this study was to quantify the risk of cancers other than melanoma among family members of CDKN2A mutation carriers using data from the Genes, Environment and Melanoma study. Relative risks (RRs) of all non-melanoma cancers among first-degree relatives (FDRs) of melanoma patients with CDKN2A mutations (n = 65) and FDRs of melanoma patients without mutations (n = 3537) were calculated as the ratio of estimated event rates (number of cancers/total person-years) in FDRs of carriers vs noncarriers with exact Clopper-Pearson-type tests and 95% confidence intervals (CIs). All statistical tests were two-sided. There were 56 (13.1%) non-melanoma cancers reported among 429 FDRs of mutation carriers and 2199 (9.4%) non-melanoma cancers in 23 452 FDRs of noncarriers. The FDRs of carriers had an increased risk of any cancer other than melanoma (56 cancers among 429 FDRs of carrier probands vs 2199 cancers among 23 452 FDRs of noncarrier probands; RR = 1.5, 95% CI = 1.2 to 2.0, P = .005), gastrointestinal cancer (20 cancers among 429 FDRs of carrier probands vs 506 cancers among 23 452 FDRs of noncarrier probands; RR = 2.4, 95% CI = 1.4 to 3.7, P = .001), and pancreatic cancer (five cancers among 429 FDRs of carrier probands vs 41 cancers among 23 452 FDRs of noncarrier probands; RR = 7.4, 95% CI = 2.3 to 18.7, P = .002). Wilms tumor was reported in two FDRs of carrier probands and three FDRs of noncarrier probands (RR = 40.4, 95% CI = 3.4 to 352.7, P = .005). The lifetime risk of any cancer other than melanoma among CDKN2A mutation carriers was estimated as 59.0% by age 85 years (95% CI = 39.0% to 75.4%) by the kin-cohort method, under the standard assumptions of Mendelian genetics on the genotype distribution of FDRs conditional on proband genotype.

Efficient distribution estimation for data with unobserved sub-population identifiers

We study efficient nonparametric estimation of distribution functions of several scientifically meaningful sub-populations from data consisting of mixed samples where the sub-population identifiers are missing. Only probabilities of each observation belonging to a sub-population are available. The problem arises from several biomedical studies such as quantitative trait locus (QTL) analysis and genetic studies with ungenotyped relatives where the scientific interest lies in estimating the cumulative distribution function of a trait given a specific genotype. However, in these studies subjects' genotypes may not be directly observed. The distribution of the trait outcome is therefore a mixture of several genotype-specific distributions. We characterize the complete class of consistent estimators which includes members such as one type of nonparametric maximum likelihood estimator (NPMLE) and least squares or weighted least squares estimators. We identify the efficient estimator in the class that reaches the semiparametric efficiency bound, and we implement it using a simple procedure that remains consistent even if several components of the estimator are mis-specified. In addition, our close inspections on two commonly used NPMLEs in these problems show the surprising results that the NPMLE in one form is highly inefficient, while in the other form is inconsistent. We provide simulation procedures to illustrate the theoretical results and demonstrate the proposed methods through two real data examples.

High risk of colorectal and endometrial cancer in Ashkenazi families with the MSH2 A636P founder mutation

BACKGROUND & AIMS

The MSH2 A636P mutation is a founder mutation in Ashkenazi Jews that causes Lynch syndrome, with a prevalence of 0.4%-0.7%. Estimates of age-specific cumulative risk and lifetime risk for colorectal cancer (CRC) and endometrial cancer (EC) specific to carriers of this mutation are not available.

METHODS

We studied 27 families with MSH2 A636P gene mutations identified in Israel; 13 were identified via a population-based, case-control study and 14 were identified from a clinical genetics service. Age-specific cumulative risks (penetrance) and hazard ratio (HR) estimates of CRC and EC risks were calculated and compared with the general Ashkenazi population using modified segregation analysis. An ascertainment-corrected likelihood that combined population-based and clinic-based sampling provided a powerful analysis for estimating penetrance. We analyzed 74 cases of CRC (40 in the clinic series and 34 in the population-based series), diagnosed at median ages of 50 years (men) and 49 years (women) in the combined sample.

RESULTS

The cumulative risk of CRC at age 70 was 61.62% for men (95% confidence interval [CI], 37.49%-76.45%) and 61.08% for women (95% CI, 39.39%-75.14%), with overall HRs of 31.8 (19.9-51.0) and 41.8 (27.4-64.0), respectively. There were 28 cases of EC, diagnosed at a median age of 53.0 years. The cumulative risk of EC was 55.64% (95% CI, 33.07%-70.58%) with an overall HR of 66.7 (41.7-106.7).

CONCLUSIONS

Lifetime risks of CRC and EC in MSH2 A636P carriers are high even after adjusting for ascertainment. These estimates are valuable for patients and providers; specialized cancer screening is necessary for carriers of this mutation.

Estimation of genotype relative risks from pedigree data by retrospective likelihoods

Pedigrees collected for linkage studies are a valuable resource that could be used to estimate genetic relative risks (RRs) for genetic variants recently discovered in case-control genome wide association studies. To estimate RRs from highly ascertained pedigrees, a pedigree "retrospective likelihood" can be used, which adjusts for ascertainment by conditioning on the phenotypes of pedigree members. We explore a variety of approaches to compute the retrospective likelihood, and illustrate a Newton-Raphson method that is computationally efficient particularly for single nucleotide polymorphisms (SNPs) modeled as log-additive effect of alleles on the RR. We also illustrate, by simulations, that a naïve "composite likelihood" method that can lead to biased RR estimates, mainly by not conditioning on the ascertainment process-or as we propose-the disease status of all pedigree members. Applications of the retrospective likelihood to pedigrees collected for a prostate cancer linkage study and recently reported risk-SNPs illustrate the utility of our methods, with results showing that the RRs estimated from the highly ascertained pedigrees are consistent with odds ratios estimated in case-control studies. We also evaluate the potential impact of residual correlations of disease risk among family members due to shared unmeasured risk factors (genetic or environmental) by allowing for a random baseline risk parameter. When modeling only the affected family members in our data, there was little evidence for heterogeneity in baseline risks across families.

A frailty-model-based approach to estimating the age-dependent penetrance function of candidate genes using population-based case-control study designs: an application to data on the BRCA1 gene

The population-based case-control study design is perhaps one of, if not the most, commonly used designs for investigating the genetic and environmental contributions to disease risk in epidemiological studies. Ages at onset and disease status of family members are routinely and systematically collected from the participants in this design. Considering age at onset in relatives as an outcome, this article is focused on using the family history information to obtain the hazard function, i.e., age-dependent penetrance function, of candidate genes from case-control studies. A frailty-model-based approach is proposed to accommodate the shared risk among family members that is not accounted for by observed risk factors. This approach is further extended to accommodate missing genotypes in family members and a two-phase case-control sampling design. Simulation results show that the proposed method performs well in realistic settings. Finally, a population-based two-phase case-control breast cancer study of the BRCA1 gene is used to illustrate the method.

Statistical inference on the penetrances of rare genetic mutations based on a case-family design

We propose a formal statistical inference framework for the evaluation of the penetrance of a rare genetic mutation using family data generated under a kin-cohort type of design, where phenotype and genotype information from first-degree relatives (sibs and/or offspring) of case probands carrying the targeted mutation are collected. Our approach is built upon a likelihood model with some minor assumptions, and it can be used for age-dependent penetrance estimation that permits adjustment for covariates. Furthermore, the derived likelihood allows unobserved risk factors that are correlated within family members. The validity of the approach is confirmed by simulation studies. We apply the proposed approach to estimating the age-dependent cancer risk among carriers of the MSH2 or MLH1 mutation.

Case report: de novo BRCA2 gene mutation in a 35-year-old woman with breast cancer

In this report, we describe a patient with a de novo BRCA2 gene mutation (5301insA) who developed early onset breast cancer with no strong family history of the disease. Only three similar instances have been reported previously. Subsequent site-specific analysis in her parents showed that neither carried the mutation previously identified in their daughter. Various possible explanations for this finding were excluded. Paternity was confirmed using 13 highly polymorphic markers, thereby illustrating that the patient carried a de novo mutation in the BRCA2 gene. The 5301insA mutation has been well described and reported many times in the Breast Cancer Information Core online Breast Cancer Mutation database. This finding illustrates the importance of determining the incidence of de novo BRCA mutations and is of significant clinical value to breast cancer prevention and management. Our case report presents the fourth case in which a de novo germline mutation in a BRCA1/2 gene has been identified.

PEL: an unbiased method for estimating age-dependent genetic disease risk from pedigree data unselected for family history

Providing valid risk estimates of a genetic disease with variable age of onset is a major challenge for prevention strategies. When data are obtained from pedigrees ascertained through affected individuals, an adjustment for ascertainment bias is necessary. This article focuses on ascertainment through at least one affected and presents an estimation method based on maximum likelihood, called the Proband's phenotype exclusion likelihood or PEL for estimating age-dependent penetrance using disease status and genotypic information of family members in pedigrees unselected for family history. We studied the properties of the PEL and compared with another method, the prospective likelihood, in terms of bias and efficiency in risk estimate. For that purpose, family samples were simulated under various disease risk models and under various ascertainment patterns. We showed that, whatever the genetic model and the ascertainment scheme, the PEL provided unbiased estimates, whereas the prospective likelihood exhibited some bias in a number of situations. As an illustration, we estimated the disease risk for transthyretin amyloid neuropathy from a French sample and a Portuguese sample and for BRCA1/2 associated breast cancer from a sample ascertained on early-onset breast cancer cases.

Potential excess mortality in BRCA1/2 mutation carriers beyond breast, ovarian, prostate, and pancreatic cancers, and melanoma

BACKGROUND

Although the increase in risk of developing breast, ovarian, and prostate cancer in BRCA1 and BRCA2 mutation carriers has been studied extensively, its impact on mortality is not well quantified. Further, possible effect of BRCA mutations on non-cancer mortality risk has not been examined.

METHODOLOGY/PRINCIPAL FINDINGS

Using mortality data from the relatives of 5,287 genotyped participants, of whom 120 carried a BRCA Ashkenazi Jewish founder mutation, in a community-based study of the Ashkenazi Jewish population in the Washington D.C area, we examined the association between the three Ashkenazi BRCA founder mutations and risk of overall and non-cancer mortality. To examine risks beyond the established effects of these mutations, we analyzed the data excluding both deaths and follow-up times after reported diagnosis of melanoma and cancer of the breast, ovary, prostate, and pancreas. Using an extension of the kin-cohort method that accounts for informative censoring, we estimated that, in the absence of breast, ovarian, and pancreatic cancers, and melanoma, female carriers had a life expectancy that was 6.8 years lower (95% CI: 1.2-10.5) than non-carriers. In male mutation carriers, the reduction in life expectancy, in the absence of prostate and pancreatic cancers and melanoma, was 3.7 (95% CI: -0.4, 6.8) years. When deaths and follow-up times after any cancer diagnosis were excluded, the difference in life expectancy was 5.7 years for women (95% CI: -0.1, 10.4) and 3.7 years for men (95% CI: -0.4, 6.9). An overall test of association for men and women together showed a statistically significant association between BRCA1/2 mutations and increased non-cancer mortality (p = 0.024).

CONCLUSIONS/SIGNIFICANCE

These findings suggest that there may be unknown effects of BRCA1/2 mutations on non-neoplastic diseases that cause death at older ages.

Risk of Parkinson disease in carriers of parkin mutations: estimation using the kin-cohort method

OBJECTIVE

To estimate the risk of Parkinson disease (PD) in individuals with mutations in the Parkin gene.

DESIGN

We assessed point mutations and exon deletions and duplications in the Parkin gene in 247 probands with PD (age at onset < or =50 years) and 104 control probands enrolled in the Genetic Epidemiology of Parkinson's Disease (GEPD) study. For each first-degree relative, a consensus diagnosis of PD was established. The probability that each relative carried a mutation was estimated from the proband's Parkin carrier status using Mendelian principles and from the relationship of the relative to the proband.

SETTING

Tertiary care movement disorders center. Patients Cases, controls, and their first-degree relatives were enrolled in the GEPD study.

MAIN OUTCOME MEASURES

Estimated age-specific penetrance in first-degree relatives.

RESULTS

Parkin mutations were identified in 25 probands with PD (10.1%), 18 (72.0%) of whom were heterozygotes. One Parkin homozygote was reported in 2 siblings with PD. The cumulative incidence of PD to age 65 years in carrier relatives (age-specific penetrance) was estimated to be 7.0% (95% confidence interval, 0.4%-71.9%), compared with 1.7% (95% confidence interval, 0.8%-3.4%) in noncarrier relatives of the cases (P = .59) and 1.1% (95% confidence interval, 0.3%-3.4%) in relatives of the controls (compared with noncarrier relatives, P = .52).

CONCLUSIONS

The cumulative risk of PD to age 65 years in a noncarrier relative of a case with an age at onset of 50 years or younger is not significantly greater than the general population risk among controls. Age-specific penetrance among Parkin carriers, in particular heterozygotes, deserves further study.

Estimation and interpretation of models of absolute risk from epidemiologic data, including family-based studies

Absolute risk is the chance that a person with given risk factors and free of the disease of interest at age a will be diagnosed with that disease in the interval (a, a + tau]. Absolute risk is sometimes called cumulative incidence. Absolute risk is a "crude" risk because it is reduced by the chance that the person will die of competing causes of death before developing the disease of interest. Cohort studies admit flexibility in modeling absolute risk, either by allowing covariates to affect the cause-specific relative hazards or to affect the absolute risk itself. An advantage of cause-specific relative risk models is that various data sources can be used to fit the required components. For example, case-control data can be used to estimate relative risk and attributable risk, and these can be combined with registry data on age-specific composite hazard rates for the disease of interest and with national data on competing hazards of mortality to estimate absolute risk. Family-based designs, such as the kin-cohort design and collections of pedigrees with multiple affected individuals can be used to estimate the genotype-specific hazard of disease. Such analyses must be adjusted for ascertainment, and failure to take into account residual familial risk, such as might be induced by unmeasured genetic variants or by unmeasured behavioral or environmental exposures that are correlated within families, can lead to overestimates of mutation-specific absolute risk in the general population.

Estimating risk of breast cancer in carriers of BRCA1 and BRCA2 mutations: a meta-analytic approach

Estimates of penetrance (or risk) of breast cancer among BRCA mutation carriers in published studies are heterogeneous, prohibiting direct combined estimates. Estimates of prevalence of BRCA mutations are more homogeneous and could allow combined estimates of prevalence. We propose a combined estimator of penetrance from combined estimates of the prevalence of BRCA mutations in women with and without breast cancer and from the probability of breast cancer by using Bayes' Theorem. The relative risk of having breast cancer with positive family history and the prevalence of positive family history contribute to the combined estimate of penetrance if family history is present. The combined estimate incorporates variation in estimates from different resources. The method is illustrated by using data from Ashkenazi Jewish women unselected for family history and for those with family history. Risks of breast cancer conferred by BRCA1 and BRCA2 mutations are estimated to be 8.39 per cent (6.56, 10.68 per cent) and 2.66 per cent (1.85, 3.82 per cent) by 40 years old, and 47.45 per cent (37.39, 57.72 per cent) and 31.85 per cent (23.72, 41.26 per cent) by 75 years old, respectively. For those with family history, risks of breast cancer conferred by BRCA mutations appear to be higher.

Case-control and case-only designs with genotype and family history data: estimating relative risk, residual familial aggregation, and cumulative risk

In case-control studies of inherited diseases, participating subjects (probands) are often interviewed to collect detailed data about disease history and age-at-onset information in their family members. Genotype data are typically collected from the probands, but not from their relatives. In this article, we introduce an approach that combines case-control analysis of data on the probands with kin-cohort analysis of disease history data on relatives. Assuming a marginally specified multivariate survival model for joint risk of disease among family members, we describe methods for estimating relative risk, cumulative risk, and residual familial aggregation. We also describe a variation of the methodology that can be used for kin-cohort analysis of the family history data from a sample of genotyped cases only. We perform simulation studies to assess performance of the proposed methodologies with correct and mis-specified models for familial aggregation. We illustrate the proposed methodologies by estimating the risk of breast cancer from BRCA1/2 mutations using data from the Washington Ashkenazi Study.

Search on Chromosome 17 Centromere Reveals TNFRSF13B as a Susceptibility Gene for Intracranial Aneurysm

Background— Our previous studies have shown a significant linkage of intracranial aneurysms (IAs) to chromosome 17.

Methods and Results— Nine genes ( TNFRSF13B , M-RIP , COPS3 , RAI1 , SREBF1 , GRAP , MAPK7 , MFAP4 , and AKAP10 ) were selected from 108 genes that are located between D17S1857 and D17S1871 by excluding 99 genes that were pseudogenes, hypothetical genes, or well-characterized genes but not likely associated with IA. Direct sequencing of all coding and regulatory regions in 58 cases (29 pedigree probands and 29 unrelated nonpedigree cases) was performed. Deleterious changes were found only in TNFRSF13B , K154X, and c.585 to 586insA in exon4. The association of IA with TNFRSF13B was further studied in 304 unrelated cases and 332 control subjects. Rare nonsynonymous changes, a splicing acceptor site change and a frame shift, were found in unrelated cases (2.3%; 14 of 608) more frequently than in control subjects (0.8%; 5 of 664; P =0.035). The association study using single-nucleotide polymorphisms in an unrelated case-control cohort revealed a protective haplotype (odds ratio 0.69, 95% confidence interval 0.52 to 0.92, P =0.012) compared with the major haplotype after adjustment for covariates.

Conclusions— We propose that TNFRSF13B is one of the susceptibility genes for IA.