Possible biases in heritability estimates from intraclass correlation

Summary There is an inherent bias in intraclass correlations since the expectation of a ratio does not equal the ratio of expectations. A simple accurate approximation for this bias is derived, and it is found that the inherent bias is usually negligible. Selection of sires is known to bias half-sib heritability estimates, and appropriate formulae are given and discussed.     

Estimating major gene effects with partial information using Gibbs sampling

A method for estimating major gene effects using Gibbs sampling to infer genotype of individuals with unknown values, was compared with a standard mixed-model analysis. The purpose of this study was to evaluate the effect of including information of individuals with unknown genotypes on the estimates and their error variances (Ve) of the single-gene effects. When genotypes were known for all the individuals, results using the Gibbs method (GS) were similar to those obtained with the mixed model (MM). In the absence of selection, when information from individuals with unknown genotypes was included, GS yielded unbiased estimates of the major gene effects while reducing the Ve associated with them. This reduction in Ve depended on the gene frequency and mode of action of the major locus. For the additive effect, the reduction in Ve ranged from 29 to 69% of the total reduction which would have been obtained if all individuals had had a known genotype. Similarly the reduction in Ve found for the dominance effect ranged from 12 to 58%. Estimates using GS generally had small detectable biases when the polygenic heritability used in the analysis was inflated or estimated simultaneously. However, the benefit of using information from individuals with unknown genotypes was still maintained when comparing the mean square error of the estimates using either GS or MM when genotypes are only known for a subset of the population. When the population has been under selection, the use of Gibbs sampling to incorporate information of individuals without genotypes reduced substantially the bias and mean square error found for MM analysis on partial data. Nevertheless, there was some bias detected using Gibbs sampling. The gene frequency of the major gene in the base population was also well estimated despite its change over generations due to selection.     

Substitutional bias confounds inference of cyanelle origins from sequence data

Summary Available molecular and biochemical data offer conflicting evidence for the origin of the cyanelle of Cyanophora paradoxa. We show that the similarity of cyanelle and green chloroplast sequences is probably a result of these two lineages independently developing the same pattern of directional nucleotide change (substitutional bias). This finding suggests caution should be exercised in the interpretation of nucleotide sequence analyses that appear to favor the view of a common endosymbiont for the cyanelle and chlorophyll-b-containing chloroplasts. The data and approaches needed to resolve the issue of cyanelle origins are discussed. Our findings also have general implications for phylogenetic inference under conditions where the base compositions (compositional bias) of the sequences analyzed differ. Offprint requests to: C.J. Howe     

Potential role of selection bias in the association between childhood leukemia and residential magnetic fields exposure: A population-based assessment

PurposeData from the Northern California Childhood Leukemia Study (NCCLS) were used to assess whether selection bias may explain the association between residential magnetic fields (assessed by wire codes) and childhood leukemia as previously observed in case–control studies.MethodsWiring codes were calculated for participating cases, n = 310; and non-participating cases, n = 66; as well as for three control groups: first-choice participating, n = 174; first-choice non-participating, n = 252; and replacement (non-first choice participating controls), n = 220.ResultsParticipating controls tended to be of higher socioeconomic status than non-participating controls, and lower socioeconomic status was related to higher wire-codes. The odds ratio (OR) for developing childhood leukemia associated with high wire-codes was 1.18 (95% CI: 0.85, 1.64) when all cases were compared to all first-choice controls (participating and non-participating). The OR for developing childhood leukemia in the high current category was 1.43 (95% CI: 0.91, 2.26) when participating cases were compared to first-choice participating controls, but no associations were observed when participating cases were compared to non-participating controls (OR = 1.06, 95% CI: 0.71, 1.57) or to replacement controls (OR = 1.06, 95% CI: 0.71, 1.60).ConclusionsThe observed risk estimates vary by type of control group, and no statistically significant association between wire codes and childhood leukemia is observed in the California population participating in the NCCLS.     

An assessment of accuracy, error, and conflict with support values from genome-scale phylogenetic data

Despite the importance of molecular phylogenetics, few of its assumptions have been tested with real data. It is commonly assumed that nonparametric bootstrap values are an underestimate of the actual support, Bayesian posterior probabilities are an overestimate of the actual support, and among-gene phylogenetic conflict is low. We directly tested these assumptions by using a well-supported yeast reference tree. We found that bootstrap values were not significantly different from accuracy. Bayesian support values were, however, significant overestimates of accuracy but still had low false-positive error rates (0% to 2.8%) at the highest values (>99%). Although we found evidence for a branch-length bias contributing to conflict, there was little evidence for widespread, strongly supported among-gene conflict from bootstraps. The results demonstrate that caution is warranted concerning conclusions of conflict based on the assumption of underestimation for support values in real data.     

The jackknife estimate of a Kaplan--Meier integral

We derive an explicit formula for the jackknife estimate ofa Kaplan-Meier integral. From this the asymptotic analysis ofthe jackknifed Kaplan-Meier process becomes straightforward.In a small simulation study it is demonstrated that jackknifingmay lead to a considerable reduction of the bias.     

Analysis of a molecular genetic neuro-oncology study with partially biased selection

Oligodendrogliomas are a common variant of malignant brain tumors, and are unique for their relative sensitivity to chemotherapy and better prognosis. For these reasons, the identification of an objective oligodendroglial marker has been a long sought-after goal in the field of neuro-oncology. To this end, 75 patients who received chemotherapy at the London Regional Cancer Centre between 1984 and 1999 were studied (Ino et al., Clinical Cancer Research, 7, 839-845, 2001). Of these 75 patients, 50 were initially treated with chemotherapy (the current practice) and comprise a population-based sample. The remaining 25 patients were initially treated with radiation and were included in the study only because their tumor recurred, at which time they received chemotherapy. Because this group of 25 patients included neither those radiation patients whose tumors never recurred nor those radiation patients whose tumors recurred but were not treated with chemotherapy, issues of selection bias were of concern. For this reason, the initial analysis of these data included only the 50 population-based patients. This was unsatisfying given the rarity of this disease and of genetic information on this disease and led us to question whether we could undertake an analysis that includes all of the patients. Here we examine approaches for utilizing the entire study population, as well as the assumptions required for doing so. We illustrate that there are both costs and benefits to using the 25 selected patients.