Comparison of missing data approaches in linkage analysis

BACKGROUND: Observational cohort studies have been little used in linkage analyses due to their general lack of large, disease-specific pedigrees. Nevertheless, the longitudinal nature of such studies makes them potentially valuable for assessing the linkage between genotypes and temporal trends in phenotypes. The repeated phenotype measures in cohort studies (i.e., across time), however, can have extensive missing information. Existing methods for handling missing data in observational studies may decrease efficiency, introduce biases, and give spurious results. The impact of such methods when undertaking linkage analysis of cohort studies is unclear. Therefore, we compare here six methods of imputing missing repeated phenotypes on results from genome-wide linkage analyses of four quantitative traits from the Framingham Heart Study cohort. RESULTS: We found that simply deleting observations with missing values gave many more nominally statistically significant linkages than the other five approaches. Among the latter, those with similar underlying methodology (i.e., imputation- versus model-based) gave the most consistent results, although some discrepancies remained. CONCLUSION: Different methods for addressing missing values in linkage analyses of cohort studies can give substantially diverse results, and must be carefully considered to protect against biases and spurious findings

Comparison of microsatellites, single-nucleotide polymorphisms (SNPs) and composite markers derived from SNPs in linkage analysis

There is growing evidence that a map of dense single-nucleotide polymorphisms (SNPs) can outperform a map of sparse microsatellites for linkage analysis. There is also argument as to whether a clustered SNP map can outperform an evenly spaced SNP map. Using Genetic Analysis Workshop 14 simulated data, we compared for linkage analysis microsatellites, SNPs, and composite markers derived from SNPs. We encoded the composite markers in a two-step approach, in which the maximum identity length contrast method was employed to allow for recombination between loci. A SNP map 2.3 times as dense as a microsatellite map (~2.9 cM compared to ~6.7 cM apart) provided slightly less information content (~0.83 compared to ~0.89). Most inheritance information could be extracted when the SNPs were spaced < 1 cM apart. Comparing the linkage results on using SNPs or composite markers derived from them based on both 3 cM and 0.3 cM resolution maps, we showed that the inter-SNP distance should be kept small (< 1 cM), and that for multipoint linkage analysis the original markers and the derived composite markers had similar power; but for single point linkage analysis the resulting composite markers lead to more power. Considering all factors, such as information content, flexibility of analysis method, map errors, and genotyping errors, a map of clustered SNPs can be an efficient design for a genome-wide linkage scan

HNF1B and Endometrial Cancer Risk: Results from the PAGE study

We examined the association between HNF1B variants identified in a recent genome-wide association study and endometrial cancer in two large case-control studies nested in prospective cohorts: the Multiethnic Cohort Study (MEC) and the Women's Health Initiative (WHI) as part of the Population Architecture using Genomics and Epidemiology (PAGE) study. A total of 1,357 incident cases of invasive endometrial cancer and 7,609 controls were included in the analysis (MEC: 426 cases/3,854 controls; WHI: 931cases/3,755 controls). The majority of women in the WHI were European American, while the MEC included sizable numbers of African Americans, Japanese and Latinos. We estimated the odds ratios (ORs) per allele and 95% confidence intervals (CIs) of each SNP using unconditional logistic regression adjusting for age, body mass index, and four principal components of ancestry informative markers. The combined ORs were estimated using fixed effect models. Rs4430796 and rs7501939 were associated with endometrial cancer risk in MEC and WHI with no heterogeneity observed across racial/ethnic groups (P≥0.21) or between studies (P≥0.70). The ORper allele was 0.82 (95% CI: 0.75, 0.89; P = 5.63×10−6) for rs4430796 (G allele) and 0.79 (95% CI: 0.73, 0.87; P = 3.77×10−7) for rs7501939 (A allele). The associations with the risk of Type I and Type II tumors were similar (P≥0.19). Adjustment for additional endometrial cancer risk factors such as parity, oral contraceptive use, menopausal hormone use, and smoking status had little effect on the results. In conclusion, HNF1B SNPs are associated with risk of endometrial cancer and that the associated relative risks are similar for Type I and Type II tumors

Colorectal cancer linkage on chromosomes 4q21, 8q13, 12q24, and 15q22

A substantial proportion of familial colorectal cancer (CRC) is not a consequence of known susceptibility loci, such as mismatch repair (MMR) genes, supporting the existence of additional loci. To identify novel CRC loci, we conducted a genome-wide linkage scan in 356 white families with no evidence of defective MMR (i.e., no loss of tumor expression of MMR proteins, no microsatellite instability (MSI)-high tumors, or no evidence of linkage to MMR genes). Families were ascertained via the Colon Cancer Family Registry multi-site NCI-supported consortium (Colon CFR), the City of Hope Comprehensive Cancer Center, and Memorial University of Newfoundland. A total of 1,612 individuals (average 5.0 per family including 2.2 affected) were genotyped using genome-wide single nucleotide polymorphism linkage arrays; parametric and non-parametric linkage analysis used MERLIN in a priori-defined family groups. Five lod scores greater than 3.0 were observed assuming heterogeneity. The greatest were among families with mean age of diagnosis less than 50 years at 4q21.1 (dominant HLOD = 4.51, α = 0.84, 145.40 cM, rs10518142) and among all families at 12q24.32 (dominant HLOD = 3.60, α = 0.48, 285.15 cM, rs952093). Among families with four or more affected individuals and among clinic-based families, a common peak was observed at 15q22.31 (101.40 cM, rs1477798; dominant HLOD = 3.07, α = 0.29; dominant HLOD = 3.03, α = 0.32, respectively). Analysis of families with only two affected individuals yielded a peak at 8q13.2 (recessive HLOD = 3.02, α = 0.51, 132.52 cM, rs1319036). These previously unreported linkage peaks demonstrate the continued utility of family-based data in complex traits and suggest that new CRC risk alleles remain to be elucidated. © 2012 Cicek et al

Mendelian randomization study of adiposity-related traits and risk of breast, ovarian, prostate, lung and colorectal cancer

Background: Adiposity traits have been associated with risk of many cancers in observational studies, but whether these associations are causal is unclear. Mendelian randomization (MR) uses genetic predictors of risk factors as instrumental variables to eliminate reverse causation and reduce confounding bias. We performed MR analyses to assess the possible causal relationship of birthweight, childhood and adult body mass index (BMI), and waist-hip ratio (WHR) on the risks of breast, ovarian, prostate, colorectal and lung cancers. Methods: We tested the association between genetic risk scores and each trait using summary statistics from published genome-wide association studies (GWAS) and from 51 537 cancer cases and 61 600 controls in the Genetic Associations and Mechanisms in Oncology (GAME-ON) Consortium. Results: We found an inverse association between the genetic score for childhood BMI and risk of breast cancer [odds ratio (OR)=0.71 per standard deviation (s.d.) increase in childhood BMI; 95% confidence interval (CI): 0.60, 0.80; P=6.5×10-5). We also found the genetic score for adult BMI to be inversely associated with breast cancer risk (OR=0.66 per s.d. increase in BMI; 95% CI: 0.57, 0.77; P=2.5×10-7), and positively associated with ovarian cancer (OR=1.35; 95% CI: 1.05, 1.72; P=0.017), lung cancer (OR=1.27; 95% CI: 1.09, 1.49; P=2.9×10-3) and colorectal cancer (OR=1.39; 95% CI: 1.06, 1.82, P=0.016). The inverse association between genetically predicted adult BMI and breast cancer risk remained even after adjusting for directional pleiotropy via MR-Egger regression. Conclusions: Findings from this study provide additional understandings of the complex relationship between adiposity and cancer risks. Our results for breast and lung cancer are particularly interesting, given previous reports of effect heterogeneity by menopausal status and smoking status.</p