The phylogeny fallacy and the ontogeny fallacy

In 1990 Robert Lickliter and Thomas Berry identified the phylogeny fallacy, an empirically untenable dichotomy between proximate and evolutionary causation, which locates proximate causes in the decoding of ‘genetic programs’, and evolutionary causes in the historical events that shaped these programs. More recently, Lickliter and Hunter Honeycutt (Psychol Bull 129:819–835, 2003a) argued that Evolutionary Psychologists commit this fallacy, and they proposed an alternative research program for evolutionary psychology. For these authors the phylogeny fallacy is the proximate/evolutionary distinction itself, which they argue constitutes a misunderstanding of development, and its role in the evolutionary process. In this article I argue that the phylogeny fallacy should be relocated to an error of reasoning that this causal framework sustains: the conflation of proximate and evolutionary explanation. Having identified this empirically neutral form of the phylogeny fallacy, I identify its mirror image, the ontogeny fallacy. Through the lens of these fallacies I attempt to solve several outstanding problems in the debate that ensued from Lickliter and Honeycutt’s provocative article.     

A comparison of approaches to control for confounding factors by regression models

A common technique to control for confounding factors in practice is by regression adjustment. There are various versions of regression modeling in the literature, and in this paper we considered four approaches often seen in genetic association studies. We carried out both analytical and simulation studies comparing the bias of effect size estimates and examining the test sizes under the null hypothesis of no association between an outcome and an exposure. Further, we compared the methods in a nonsynonymous genome-wide scan for plasma lipoprotein(a) levels using a dataset from the Dallas Heart Study. We found that a widely employed approach that models the covariate-adjusted outcome and the exposure leads to an infranominal test size and underestimation of the exposure effect size. In conclusion, we recommend either using multiple regression models or modeling the covariate-adjusted outcome and the covariate-adjusted exposure to control for confounding factors.     

Interdependence of welfare outcome measures and potential confounding factors on finishing pig farms

The factors influencing the observed prevalence of some welfare outcome measures were investigated as part of a larger project examining the feasibility and benefits of incorporating on-farm welfare outcome assessments into UK Pig Farm Assurance Schemes. All finishing pigs (>40 kg) on six farms that had pens containing 25 pigs or less were assessed for dirtiness, body lesions, tail lesions, bursitis, lameness, oral behaviour and pigs requiring hospitalisation. The overall small number of significant correlations between the measures across all farms (29 out of a possible 120) and the lack of a significant correlation between any pair of measures on all six or even five farms suggests that no measure can be reliably replaced by another. On the three farms with the highest farm prevalence of dirtiness there were significant negative correlations between pen prevalences of dirtiness and recorded tail and/or body lesions (p < 0.02). The prevalence of pigs requiring hospitalisation was positively correlated with lameness on four farms (p < 0.001) and tail lesions on three farms (p < 0.005). Using a univariate general linear model the time of observation was only found to correlate with one measure, the dirtiness of the pigs (p = 0.004), with the prevalence of dirtiness in pens on the three ‘dirtiest’ farms increasing as the day progressed. There was no effect of the stocking density, group size, time since feeding and time since straw provision on the prevalence of any of the outcome measures. It is recommended that to prevent bias in the recording of tail and body lesions farm assurance assessments are conducted on pigs with a prevalence for dirtiness of less than 17%. Lower prevalences of dirtiness are more likely to occur on some farms first thing in the morning.     

Using the realized relationship matrix to disentangle confounding factors for the estimation of genetic variance components of complex traits

Background

In the analysis of complex traits, genetic effects can be confounded with non-genetic effects, especially when using full-sib families. Dominance and epistatic effects are typically confounded with additive genetic and non-genetic effects. This confounding may cause the estimated genetic variance components to be inaccurate and biased.

Methods

In this study, we constructed genetic covariance structures from whole-genome marker data, and thus used realized relationship matrices to estimate variance components in a heterogenous population of ~ 2200 mice for which four complex traits were investigated. These mice were genotyped for more than 10,000 single nucleotide polymorphisms (SNP) and the variances due to family, cage and genetic effects were estimated by models based on pedigree information only, aggregate SNP information, and model selection for specific SNP effects.

Results and conclusions

We show that the use of genome-wide SNP information can disentangle confounding factors to estimate genetic variances by separating genetic and non-genetic effects. The estimated variance components using realized relationship were more accurate and less biased, compared to those based on pedigree information only. Models that allow the selection of individual SNP in addition to fitting a relationship matrix are more efficient for traits with a significant dominance variance.     

The sex-linked black cat fallacy: a textbook case

Textbook presentations of the genetics of coat color in cats are compared with research literature on cat genetics. There is general failure of the textbook authors to identify mutants by contrast with wild-type standard, thus leading to the erroneous conclusion that black is sex-linked and allelic with yellow.     

Dynamic BTPS correction factors for spirometric data

Because it is often difficult to completely control ambient temperature, a study was conducted to investigate dynamic body temperature pressure saturated (BTPS) correction factors for spirometric data. A forced expiratory simulator system was heated to 37 degrees C and loaded with air saturated with water vapor. This air was then forced from the simulator into a dry rolling-seal spirometer maintained at various ambient temperatures from 3 to 32 degrees C. Errors in forced expiratory volume in 1 s (FEV1) and peak flow from assuming a constant BTPS correction ranged from 7.7 and 14.1% at 3 degrees C to 2.1 and 4.6% at 23 degrees C. Differences between errors observed when saturated and dry air were forced into the spirometer indicate that water vapor condensation introduces an added heat load to the spirometer, adding approximately one percent to the error in FEV1 at lower temperatures. By use of a model to estimate the dynamic BTPS correction factor, errors in FEV1 at all temperatures between 3 and 32 degrees C were reduced to less than 1.5%.     

The correction factors for sucrose gap measurements and their practical applications.

The distribution of extracellular and intracellular potential in the sucrose gap apparatus, previously established for a single fiber using the cable equations for a core conductor model (Jirounek and Straub, Biophys. J., 11:1, 1971), is obtained for a multifiber preparation. The exact equation is derived relating the true membrane potential change to the measured potential differences across the sucrose gap, the junction potentials between sucrose and physiological solution, the membrane potential in the sucrose region, and the electrical parameters of the preparation in each region of the sucrose gap. The extracellular potential distribution has been measured using a modified sucrose gap apparatus for the frog sciatic nerve and the rabbit vagus nerve. The results indicate a hyperpolarization of the preparations in the sucrose region, of 60--75 mV. The hyperpolarization is independent of the presence of junction potentials. The calculation of the correction terms in the equation relating the actual to the measured potential change is illustrated for the case of complete depolarization by KC1 on one side of the sucrose gap. The correction terms in the equation are given for various experimental conditions, and a number of nomographic charts are presented, by means of which the correction factors can be rapidly evaluated.