A framework for interpreting type I error rates from a product-term model of interaction applied to quantitative traits

Adequate control of type I error rates will be necessary in the increasing genome‐wide search for interactive effects on complex traits. After observing unexpected variability in type I error rates from SNP‐by‐genome interaction scans, we sought to characterize this variability and test the ability of heteroskedasticity‐consistent standard errors to correct it. We performed 81 SNP‐by‐genome interaction scans using a product‐term model on quantitative traits in a sample of 1,053 unrelated European Americans from the NHLBI Family Heart Study, and additional scans on five simulated datasets. We found that the interaction‐term genomic inflation factor (lambda) showed inflation and deflation that varied with sample size and allele frequency; that similar lambda variation occurred in the absence of population substructure; and that lambda was strongly related to heteroskedasticity but not to minor non‐normality of phenotypes. Heteroskedasticity‐consistent standard errors narrowed the range of lambda, with HC3 outperforming HC0, but in individual scans tended to create new P‐value outliers related to sparse two‐locus genotype classes. We explain the lambda variation as a result of non‐independence of test statistics coupled with stochastic biases in test statistics due to a failure of the test to reach asymptotic properties. We propose that one way to interpret lambda is by comparison to an empirical distribution generated from data simulated under the null hypothesis and without population substructure. We further conclude that the interaction‐term lambda should not be used to adjust test statistics and that heteroskedasticity‐consistent standard errors come with limitations that may outweigh their benefits in this setting

Genetics and geography of leukocyte telomere length in sub-Saharan Africans

Leukocyte telomere length (LTL) might be causal in cardiovascular disease and major cancers. To elucidate the roles of genetics and geography in LTL variability across humans, we compared LTL measured in 1295 sub-Saharan Africans (SSAs) with 559 African-Americans (AAms) and 2464 European-Americans (EAms). LTL differed significantly across SSAs (P = 0.003), with the San from Botswana (with the oldest genomic ancestry) having the longest LTL and populations from Ethiopia having the shortest LTL. SSAs had significantly longer LTL than AAms [P = 6.5(e-16)] whose LTL was significantly longer than EAms [P = 2.5(e-7)]. Genetic variation in SSAs explained 52% of LTL variance versus 27% in AAms and 34% in EAms. Adjustment for genetic variation removed the LTL differences among SSAs. LTL genetic variation among SSAs, with the longest LTL in the San, supports the hypothesis that longer LTL was ancestral in humans. Identifying factors driving LTL variation in Africa may have important ramifications for LTL-associated diseases

Microsatellite linkage analysis, single-nucleotide polymorphisms, and haplotype associations with ECB21 in the COGA data

This study, part of the Genetic Analysis Workshop 14 (GAW14), explored real Collaborative Study on the Genetics of Alcoholism data for linkage and association mapping between genetic polymorphisms (microsatellite and single-nucleotide polymorphisms (SNPs)) and beta (16.5–20 Hz) oscillations of the brain rhythms (ecb21). The ecb21 phenotype underwent the statistical adjustments for the age of participants, and for attaining a normal distribution. A total of 1,000 subjects' available phenotypes were included in linkage analysis with microsatellite markers. Linkage analysis was performed only for chromosome 4 where a quantitative trait locus with 5.01 LOD score had been previously reported. Previous findings related this location with the γ-aminobutyric acid type A (GABA(A)) receptor. At the same location, our analysis showed a LOD score of 2.2. This decrease in the LOD score is the result of a drastic reduction (one-third) of the available GAW14 phenotypic data. We performed SNP and haplotype association analyses with the same phenotypic data under the linkage peak region on chromosome 4. Seven Affymetrix and two Illumina SNPs showed significant associations with ecb21 phenotype. A haplotype, a combination of SNPs TSC0044171 and TSC0551006 (the latter almost under the region of GABA(A )genes), showed a significant association with ecb21 (p = 0.015) and a relatively high frequency in the sample studied. Our results affirmed that the GABA region has potential of harboring genes that contribute quantitatively to the beta oscillation of the brain rhythms. The inclusion of the remaining 614 subjects, which in the GAW14 had missing data for the ecb21, can improve the strength of the associations as they have already shown that they contribute quite important information in the linkage analysis

Use of a random coefficient regression (RCR) model to estimate growth parameters

We used a random coefficient regression (RCR) model to estimate growth parameters for the time series of observed serum glucose levels in the Replicate 1 of the Genetic Analysis Workshop 13 simulated data. For comparison, a two time-point interval was also selected and the slope between these two observations was calculated. This process yielded four phenotypes: the RCR growth phenotype, a two time-point slope phenotype, and Time 1 and Time 2 serum glucose level phenotypes. These four phenotypes were used for linkage analyses on simulated chromosomes 5, 7, 9, and 21, those chromosomes that contained loci affecting the growth course for serum glucose levels. The linkage analysis of the RCR-derived phenotype showed overwhelming evidence for linkage at one locus (LOD 65.78 on chromosome 5), while showing elevated but nonsignificant LOD scores for two other loci (LOD 1.25 on chromosome 7, LOD 1.10 on chromosome 9), and no evidence of linkage for the final locus. The two time-point slope phenotype showed evidence for linkage at one locus (LOD 4.16 on chromosome 5) but no evidence for linkage at any of the other loci. A parallel cross-sectional approach, using as input phenotypes the endpoints of the two-point slope phenotype, gave strong linkage results for the major locus on chromosome 5 (maximal LOD scores of 17.90 and 27.24 for Time 1 and Time 2, respectively) while showing elevated but nonsignificant linkage results on chromosome 7 (maximal LOD scores of 1.71 and 1.48) and no evidence for linkage at the two remaining loci. The RCR growth parameter showed more power to detect linkage to the major locus than either the cross-sectional or two-point slope approach, but the cross-sectional approach gave a higher maximal LOD score for one of the minor loci