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?

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.

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.

Biomineralization in humans: making the hard choices in life.

The skeleton, teeth, and otoconia are normally the only mineralized tissues or organs in the human body. We describe physiological biomineralization in collagenous matrices as well as a more derived noncollagenous matrix. The origin of the collagenous matrices used in mineralized skeletal tissues can be traced to a soft tissue in early Metazoa. In early vertebrates, a genetic system coding for ancient soft collagenous tissue was co-opted for biomineralization using redundant genes resulting from whole genome duplication. However, genes more specific to mineralized tissues arose subsequent to the genome duplication by genomically local tandem duplication. These new genes are the basis for a novel genetic system for various mineralized tissues in skeleton and teeth. In addition, any tissue can be abnormally mineralized, and many pathologies of mineralization in humans are known.

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.

Attitudes on DNA ancestry tests.

The DNA ancestry testing industry is more than a decade old, yet details about it remain a mystery: there remain no reliable, empirical data on the number, motivations, and attitudes of customers to date, the number of products available and their characteristics, or the industry customs and standard practices that have emerged in the absence of specific governmental regulations. Here, we provide preliminary data collected in 2009 through indirect and direct participant observation, namely blog post analysis, generalized survey analysis, and targeted survey analysis. The attitudes include the first available data on attitudes of those of individuals who have and have not had their own DNA ancestry tested as well as individuals who are members of DNA ancestry-related social networking groups. In a new and fluid landscape, the results highlight the need for empirical data to guide policy discussions and should be interpreted collectively as an invitation for additional investigation of (1) the opinions of individuals purchasing these tests, individuals obtaining these tests through research participation, and individuals not obtaining these tests; (2) the psychosocial and behavioral reactions of individuals obtaining their DNA ancestry information with attention given both to expectations prior to testing and the sociotechnical architecture of the test used; and (3) the applications of DNA ancestry information in varying contexts.

When the time seems ripe: eugenics, the annals, and the subtle persistence of typological thinking.

This journal began in 1925 as the Annals of Eugenics. Much has changed since then. The original Editors' primary eugenic objective was not achieved, and eugenics justifiably became notorious for racism and gross abuse of human rights. But one founding aim was to publish advances in statistical genetics, and that objective prospered in the journal's pages from its beginning to the present day. The online availability of the original issues will be useful to those interested in the history of both eugenics and human genetics and will provide a reminder of how the careless use of genetical concepts can go astray.

The Red Queen and her king: cooperation at all levels of life.

The Red Queen in "Through the Looking Glass" is often used as a metaphor for the relentless, unremitting competitive struggle by which Darwin described life. That imagery fits comfortably in our culture, with its emphasis on competition and inequity, but less so for nature herself. Life is manifestly much more about cooperation, at all levels and through a variety of ubiquitous mechanisms, than it is about competition. Most organisms of most species are nowhere near the proverbial Malthusian edge of survival, such that selection will detect the tiniest difference in their performance and enhance its genetic basis. Cooperation through interaction of multiple entities is inherent in many fundamental aspects of life, and its importance is not widely enough appreciated. Here we discuss a set of principles by which this works. We illustrate the points with a computer simulation of a topic of interest to anthropology, the development of the head. In a sense, our culture has its metaphors reversed. The red royal family is a more accurate symbol for the true nature of life, human or otherwise.

What are genes "for" or where are traits "from"? What is the question?

For at least a century it has been known that multiple factors play a role in the development of complex traits, and yet the notion that there are genes "for" such traits, which traces back to Mendel, is still widespread. In this paper, we illustrate how the Mendelian model has tacitly encouraged the idea that we can explain complexity by reducing it to enumerable genes. By this approach many genes associated with simple as well as complex traits have been identified. But the genetic architecture of biological traits, or how they are made, remains largely unknown. In essence, this reflects the tension between reductionism as the current "modus operandi" of science, and the emerging knowledge of the nature of complex traits. Recent interest in systems biology as a unifying approach indicates a reawakened acceptance of the complexity of complex traits, though the temptation is to replace "gene for" thinking by comparably reductionistic "network for" concepts. Both approaches implicitly mix concepts of variants and invariants in genetics. Even the basic question is unclear: what does one need to know to "understand" the genetic basis of complex traits? New operational ideas about how to deal with biological complexity are needed.

ForSim: a tool for exploring the genetic architecture of complex traits with controlled truth.

UNLABELLED: Many important problems in biology involve complex traits affected by multiple coding or regulatory parts of the genome. How well the underlying genetic architecture can be inferred by statistical methods such as mapping and association studies are active research areas. ForSim is a flexible forward evolutionary simulation tool for exploring the consequences of evolution by phenotype, whereby demographic, chance, behavioral and selective effects mold genetic architecture. Simulation is useful for exploring these issues as well as the choice of study design inferential methods. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Tilting at quixotic trait loci (QTL): an evolutionary perspective on genetic causation.

Recent years have seen great advances in generating and analyzing data to identify the genetic architecture of biological traits. Human disease has understandably received intense research focus, and the genes responsible for most Mendelian diseases have successfully been identified. However, the same advances have shown a consistent if less satisfying pattern, in which complex traits are affected by variation in large numbers of genes, most of which have individually minor or statistically elusive effects, leaving the bulk of genetic etiology unaccounted for. This pattern applies to diverse and unrelated traits, not just disease, in basically all species, and is consistent with evolutionary expectations, raising challenging questions about the best way to approach and understand biological complexity.