Allele frequency misspecification: effect on power and Type I error of model-dependent linkage analysis of quantitative traits under random ascertainment

BACKGROUND: Studies of model-based linkage analysis show that trait or marker model misspecification leads to decreasing power or increasing Type I error rate. An increase in Type I error rate is seen when marker related parameters (e.g., allele frequencies) are misspecified and ascertainment is through the trait, but lod-score methods are expected to be robust when ascertainment is random (as is often the case in linkage studies of quantitative traits). In previous studies, the power of lod-score linkage analysis using the "correct" generating model for the trait was found to increase when the marker allele frequencies were misspecified and parental data were missing. An investigation of Type I error rates, conducted in the absence of parental genotype data and with misspecification of marker allele frequencies, showed that an inflation in Type I error rate was the cause of at least part of this apparent increased power. To investigate whether the observed inflation in Type I error rate in model-based LOD score linkage was due to sampling variation, the trait model was estimated from each sample using REGCHUNT, an automated segregation analysis program used to fit models by maximum likelihood using many different sets of initial parameter estimates. RESULTS: The Type I error rates observed using the trait models generated by REGCHUNT were usually closer to the nominal levels than those obtained when assuming the generating trait model. CONCLUSION: This suggests that the observed inflation of Type I error upon misspecification of marker allele frequencies is at least partially due to sampling variation. Thus, with missing parental genotype data, lod-score linkage is not as robust to misspecification of marker allele frequencies as has been commonly thought

A Recurrent Mutation in PARK2 Is Associated with Familial Lung Cancer

PARK2, a gene associated with Parkinson disease, is a tumor suppressor in human malignancies. Here, we show that c.823C>T (p.Arg275Trp), a germline mutation in PARK2, is present in a family with eight cases of lung cancer. The resulting amino acid change, p.Arg275Trp, is located in the highly conserved RING finger 1 domain of PARK2, which encodes an E3 ubiquitin ligase. Upon further analysis, the c.823C>T mutation was detected in three additional families affected by lung cancer. The effect size for PARK2 c.823C>T (odds ratio = 5.24) in white individuals was larger than those reported for variants from lung cancer genome-wide association studies. These data implicate this PARK2 germline mutation as a genetic susceptibility factor for lung cancer. Our results provide a rationale for further investigations of this specific mutation and gene for evaluation of the possibility of developing targeted therapies against lung cancer in individuals with PARK2 variants by compensating for the loss-of-function effect caused by the associated variation

Focused Analysis of Exome Sequencing Data for Rare Germline Mutations in Familial and Sporadic Lung Cancer

AbstractIntroductionThe association between smoking-induced chronic obstructive pulmonary disease (COPD) and lung cancer (LC) is well documented. Recent genome-wide association studies (GWAS) have identified 28 susceptibility loci for LC, 10 for COPD, 32 for smoking behavior, and 63 for pulmonary function, totaling 107 nonoverlapping loci. Given that common variants have been found to be associated with LC in genome-wide association studies, exome sequencing of these high-priority regions has great potential to identify novel rare causal variants.MethodsTo search for disease-causing rare germline mutations, we used a variation of the extreme phenotype approach to select 48 patients with sporadic LC who reported histories of heavy smoking—37 of whom also exhibited carefully documented severe COPD (in whom smoking is considered the overwhelming determinant)—and 54 unique familial LC cases from families with at least three first-degree relatives with LC (who are likely enriched for genomic effects).ResultsBy focusing on exome profiles of the 107 target loci, we identified two key rare mutations. A heterozygous p.Arg696Cys variant in the coiled-coil domain containing 147 (CCDC147) gene at 10q25.1 was identified in one sporadic and two familial cases. The minor allele frequency (MAF) of this variant in the 1000 Genomes database is 0.0026. The p.Val26Met variant in the dopamine β-hydroxylase (DBH) gene at 9q34.2 was identified in two sporadic cases; the minor allele frequency of this mutation is 0.0034 according to the 1000 Genomes database. We also observed three suggestive rare mutations on 15q25.1: iron-responsive element binding protein neuronal 2 (IREB2); cholinergic receptor, nicotinic, alpha 5 (neuronal) (CHRNA5); and cholinergic receptor, nicotinic, beta 4 (CHRNB4).ConclusionsOur results demonstrated highly disruptive risk-conferring CCDC147 and DBH mutations

Post hoc Analysis for Detecting Individual Rare Variant Risk Associations Using Probit Regression Bayesian Variable Selection Methods in Case-Control Sequencing Studies

Rare variants (RVs) have been shown to be significant contributors to complex disease risk. By definition, these variants have very low minor allele frequencies and traditional single-marker methods for statistical analysis are underpowered for typical sequencing study sample sizes. Multimarker burden-type approaches attempt to identify aggregation of RVs across case-control status by analyzing relatively small partitions of the genome, such as genes. However, it is generally the case that the aggregative measure would be a mixture of causal and neutral variants, and these omnibus tests do not directly provide any indication of which RVs may be driving a given association. Recently, Bayesian variable selection approaches have been proposed to identify RV associations from a large set of RVs under consideration. Although these approaches have been shown to be powerful at detecting associations at the RV level, there are often computational limitations on the total quantity of RVs under consideration and compromises are necessary for large-scale application. Here, we propose a computationally efficient alternative formulation of this method using a probit regression approach specifically capable of simultaneously analyzing hundreds to thousands of RVs. We evaluate our approach to detect causal variation on simulated data and examine sensitivity and specificity in instances of high RV dimensionality as well as apply it to pathway-level RV analysis results from a prostate cancer (PC) risk case-control sequencing study. Finally, we discuss potential extensions and future directions of this work

Genome-wide association of familial prostate cancer cases identifies evidence for a rare segregating haplotype at 8q24.21

Previous genome-wide association studies (GWAS) of prostate cancer risk focused on cases unselected for family history and have reported over 100 significant associations. The International Consortium for Prostate Cancer Genetics (ICPCG) has now performed a GWAS of 2511 (unrelated) familial prostate cancer cases and 1382 unaffected controls from 12 member sites. All samples were genotyped on the Illumina 5M+exome single nucleotide polymorphism (SNP) platform. The GWAS identified a significant evidence for association for SNPs in six regions previously associated with prostate cancer in population-based cohorts, including 3q26.2, 6q25.3, 8q24.21, 10q11.23, 11q13.3, and 17q12. Of note, SNP rs138042437 (p = 1.7e−8) at 8q24.21 achieved a large estimated effect size in this cohort (odds ratio = 13.3). 116 previously sampled affected relatives of 62 risk-allele carriers from the GWAS cohort were genotyped for this SNP, identifying 78 additional affected carriers in 62 pedigrees. A test for an excess number of affected carriers among relatives exhibited strong evidence for co-segregation of the variant with disease (p = 8.5e−11). The majority (92 %) of risk-allele carriers at rs138042437 had a consistent estimated haplotype spanning approximately 100 kb of 8q24.21 that contained the minor alleles of three rare SNPs (dosage minor allele frequencies <1.7 %), rs183373024 (PRNCR1), previously associated SNP rs188140481, and rs138042437 (CASC19). Strong evidence for co-segregation of a SNP on the haplotype further characterizes the haplotype as a prostate cancer pre-disposition locus

REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants

Supplemental Data Supplemental Data include one figure and five tables and can be found with this article online at http://dx.doi.org/10.1016/j.ajhg.2016.08.016. Supplemental Data Document S1. Figure S1 and Tables S1–S5 Download Document S2. Article plus Supplemental Data Download Web Resources ClinVar, https://www.ncbi.nlm.nih.gov/clinvar/ dbNSFP, https://sites.google.com/site/jpopgen/dbNSFP Human Gene Mutation Database, http://www.hgmd.cf.ac.uk/ REVEL, https://sites.google.com/site/revelgenomics/ SwissVar, http://swissvar.expasy.org/ The vast majority of coding variants are rare, and assessment of the contribution of rare variants to complex traits is hampered by low statistical power and limited functional data. Improved methods for predicting the pathogenicity of rare coding variants are needed to facilitate the discovery of disease variants from exome sequencing studies. We developed REVEL (rare exome variant ensemble learner), an ensemble method for predicting the pathogenicity of missense variants on the basis of individual tools: MutPred, FATHMM, VEST, PolyPhen, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP, SiPhy, phyloP, and phastCons. REVEL was trained with recently discovered pathogenic and rare neutral missense variants, excluding those previously used to train its constituent tools. When applied to two independent test sets, REVEL had the best overall performance (p < 10−12) as compared to any individual tool and seven ensemble methods: MetaSVM, MetaLR, KGGSeq, Condel, CADD, DANN, and Eigen. Importantly, REVEL also had the best performance for distinguishing pathogenic from rare neutral variants with allele frequencies <0.5%. The area under the receiver operating characteristic curve (AUC) for REVEL was 0.046–0.182 higher in an independent test set of 935 recent SwissVar disease variants and 123,935 putatively neutral exome sequencing variants and 0.027–0.143 higher in an independent test set of 1,953 pathogenic and 2,406 benign variants recently reported in ClinVar than the AUCs for other ensemble methods. We provide pre-computed REVEL scores for all possible human missense variants to facilitate the identification of pathogenic variants in the sea of rare variants discovered as sequencing studies expand in scale

Lung cancer in ever- and never-smokers: findings from multi-population GWAS studies.

BackgroundClinical, molecular, and genetic epidemiology studies displayed remarkable differences between ever- and never-smoking lung cancer.MethodsWe conducted a stratified multi-population (European, East Asian, and African descent) association study on 44,823 ever-smokers and 20,074 never-smokers to identify novel variants that were missed in the non-stratified analysis. Functional analysis including eQTL colocalization and DNA damage assays, and annotation studies were conducted to evaluate the functional roles of the variants. We further evaluated the impact of smoking quantity on lung cancer risk for the variants associated with ever-smoking lung cancer.ResultsFive novel independent loci, GABRA4, inter-genic region 12q24.33, LRRC4C, LINC01088, and LCNL1 were identified with the association at two or three populations (P 20). Different risk patterns were observed for the variants among the different groups by smoking behavior.ConclusionsWe identified novel variants associated with lung cancer in only ever- or never-smoking groups that were missed by prior main-effect association studies.ImpactOur study highlights the genetic heterogeneity between ever- and never-smoking lung cancer and provides etiological insights into the complicated genetic architecture of this deadly cancer

HOXB13 is a susceptibility gene for prostate cancer: results from the International Consortium for Prostate Cancer Genetics (ICPCG)

Prostate cancer has a strong familial component but uncovering the molecular basis for inherited susceptibility for this disease has been challenging. Recently, a rare, recurrent mutation (G84E) in HOXB13 was reported to be associated with prostate cancer risk. Confirmation and characterization of this finding is necessary to potentially translate this information to the clinic. To examine this finding in a large international sample of prostate cancer families, we genotyped this mutation and 14 other SNPs in or flanking HOXB13 in 2,443 prostate cancer families recruited by the International Consortium for Prostate Cancer Genetics (ICPCG). At least one mutation carrier was found in 112 prostate cancer families (4.6%), all of European descent. Within carrier families, the G84E mutation was more common in men with a diagnosis of prostate cancer (194 of 382, 51%) than those without (42 of 137, 30%), P=9.9×10−8 [odds ratio 4.42 (95% confidence interval 2.56–7.64)]. A family-based association test found G84E to be significantly over-transmitted from parents to affected offspring (P=6.5×10−6). Analysis of markers flanking the G84E mutation indicates that it resides in the same haplotype in 95% of carriers, consistent with a founder effect. Clinical characteristics of cancers in mutation carriers included features of high-risk disease. These findings demonstrate that the HOXB13 G84E mutation is present in ~5% of prostate cancer families, predominantly of European descent, and confirm its association with prostate cancer risk. While future studies are needed to more fully define the clinical utility of this observation, this allele and others like it could form the basis for early, targeted screening of men at elevated risk for this common, clinically heterogeneous cancer.Electronic supplementary materialThe online version of this article (doi:10.1007/s00439-012-1229-4) contains supplementary material, which is available to authorized users

Abstract PR11: Exome sequencing identifies germline mutations in African American families with hereditary prostate cancer

Abstract Prostate cancer (PCa) is the most common malignancy and the second leading cause of cancer death among men in the United States. Although it has been demonstrated that genetics plays a strong role in PCa, the genetic risk factors responsible for PCa remain poorly understood. Family history is a significant risk factor for PCa and African American (AA) men have the highest PCa incidence among ethnic/racial groups. Genetic analysis of AA families with PCa could facilitate the identification of genetic components contributing to PCa susceptibility. However, this area of research has not been sufficiently explored. We performed Exome sequencing on one affected and one unaffected man from one AA hereditary PCa family, which consists of 9 affected and 18 unaffected men in three generations. We identified 226 novel nonsynonymous variants in the affected man. We examined these mutations in remaining family members and narrowed down to 12 novel mutations which cosegregate with the disease in this family. Further analysis of the 12 mutations in affected and unaffected men from an additional 19 AA hereditary PCa families, 95 AA men with sporadic PCa and 95 unaffected AA control subjects identified three candidate gene mutations. One of the mutations was detected in three and the other two in two AA PCa families but they were either absent or were present in a very low frequency in unaffected control subjects. The three candidate mutations are now under further investigation in additional AA PCa families. The detailed bioinformatics analysis and the significance of these mutations in PCa susceptibility will be discussed. This abstract is also presented as Poster C67. Citation Format: Zemin Wang, Chiping Qian, Elisa M. Eledet, George Washington, Jovanny Zabaleta, Jennifer J. Hu, Diptasri Mandal, Wanguo Liu. Exome sequencing identifies germline mutations in African American families with hereditary prostate cancer. [abstract]. In: Proceedings of the Sixth AACR Conference: The Science of Cancer Health Disparities; Dec 6–9, 2013; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2014;23(11 Suppl):Abstract nr PR11. doi:10.1158/1538-7755.DISP13-PR11</jats:p