The Four Horsemen of the 'Omicsalypse': ontology, replicability, probability and epistemology.

Much of modern genomics and the other 'omics' that tag along, assert that the causal bases of biomedical outcomes are genomically enumerable lists whose effects are predictable with 'precision', extensible from samples to all, and enabled by ever-greater hypothesis-free data accumulation. The assertion rests on fundamental, if often implicit assumptions, that (1) the phenomena are based on underlying law-like biological causation, and, therefore, are (2) replicable and (3) even if not deterministic, have specifiable, stable, essentially parametric, probabilities, all of which (4) essentially equates induction with deduction, enabling asymptotically accurate prediction based on past observation. These glowing promises are the four horsemen of a genocentric 'Omicsalypse'. But what if the assumptions are wrong or appropriate only to an extent that is unknowable, even in principle? Might there be better ways to understand complex traits?

Genetic Pointillism versus Physiological Form.

Genomics has revealed that biological causation is subtler than a pointillist dream of essentially enumerable, additive precision predictability from constitutive DNA sequences. Instead, data have revealed a higher-dimension interactive genomic landscape, that is more fundamentally fluid than precision predictability requires. This raises epistemological and ontological issues that, if properly accepted, may help leverage new ideas.

Additive genetic variation in the craniofacial skeleton of baboons (genus Papio) and its relationship to body and cranial size.

OBJECTIVES: Determining the genetic architecture of quantitative traits and genetic correlations among them is important for understanding morphological evolution patterns. We address two questions regarding papionin evolution: (1) what effect do body and cranial size, age, and sex have on phenotypic (VP ) and additive genetic (VA ) variation in baboon crania, and (2) how might additive genetic correlations between craniofacial traits and body mass affect morphological evolution? MATERIALS AND METHODS: We use a large captive pedigreed baboon sample to estimate quantitative genetic parameters for craniofacial dimensions (EIDs). Our models include nested combinations of the covariates listed above. We also simulate the correlated response of a given EID due to selection on body mass alone. RESULTS: Covariates account for 1.2-91% of craniofacial VP . EID VA decreases across models as more covariates are included. The median genetic correlation estimate between each EID and body mass is 0.33. Analysis of the multivariate response to selection reveals that observed patterns of craniofacial variation in extant baboons cannot be attributed solely to correlated response to selection on body mass, particularly in males. DISCUSSION: Because a relatively large proportion of EID VA is shared with body mass variation, different methods of correcting for allometry by statistically controlling for size can alter residual VP patterns. This may conflate direct selection effects on craniofacial variation with those resulting from a correlated response to body mass selection. This shared genetic variation may partially explain how selection for increased body mass in two different papionin lineages produced remarkably similar craniofacial phenotypes.

What type of person are you? Old-fashioned thinking even in modern science.

People around the world have folk origin myths, stories that explain where they came from and account for their place in the world and their differences from other peoples. As scientists, however, we claim to be seeking literal historical truth. In Western culture, typological ideas about human variation are at least as ancient as written discussion of the subject, and have dominated both social and scientific thinking about race. From Herodotus to the Biblical lost tribes of Israel, and surprisingly even to today, it has been common to view our species as composed of distinct, or even discrete groups, types, or "races," with other individuals admixed from among those groups. Such rhetoric goes so much against the well-known evolutionary realities that it must reflect something deep about human thought, at least in Western culture. Typological approaches can be convenient for some pragmatic aspects of scientific analysis, but they can be seductively deceiving. We know how to think differently and should do so, given the historical abuses that have occurred as a result of typological thinking that seem always to lurk in the human heart.

Low frequency variants, collapsed based on biological knowledge, uncover complexity of population stratification in 1000 genomes project data.

Analyses investigating low frequency variants have the potential for explaining additional genetic heritability of many complex human traits. However, the natural frequencies of rare variation between human populations strongly confound genetic analyses. We have applied a novel collapsing method to identify biological features with low frequency variant burden differences in thirteen populations sequenced by the 1000 Genomes Project. Our flexible collapsing tool utilizes expert biological knowledge from multiple publicly available database sources to direct feature selection. Variants were collapsed according to genetically driven features, such as evolutionary conserved regions, regulatory regions genes, and pathways. We have conducted an extensive comparison of low frequency variant burden differences (MAF<0.03) between populations from 1000 Genomes Project Phase I data. We found that on average 26.87% of gene bins, 35.47% of intergenic bins, 42.85% of pathway bins, 14.86% of ORegAnno regulatory bins, and 5.97% of evolutionary conserved regions show statistically significant differences in low frequency variant burden across populations from the 1000 Genomes Project. The proportion of bins with significant differences in low frequency burden depends on the ancestral similarity of the two populations compared and types of features tested. Even closely related populations had notable differences in low frequency burden, but fewer differences than populations from different continents. Furthermore, conserved or functionally relevant regions had fewer significant differences in low frequency burden than regions under less evolutionary constraint. This degree of low frequency variant differentiation across diverse populations and feature elements highlights the critical importance of considering population stratification in the new era of DNA sequencing and low frequency variant genomic analyses.