Human life span is a phenotype that integrates many aspects of health and environment into a single ultimate quantity: the elapsed time between birth and death. Though it is widely believed that long life runs in families for genetic reasons, estimates of life span “heritability” are consistently low (∼15-30%). Here, we used pedigree data from Ancestry public trees, including hundreds of millions of historical persons, to estimate the heritability of human longevity. Although “nominal heritability” estimates based on correlations among genetic relatives agreed with prior literature, the majority of that correlation was also captured by correlations among nongenetic (in-law) relatives, suggestive of highly assortative mating around life span-influencing factors (genetic and/or environmental). We used structural equation modeling to account for assortative mating, and concluded that the true heritability of human longevity for birth cohorts across the 1800s and early 1900s was well below 10%, and that it has been generally overestimated due to the effect of assortative mating.
Although the concept of genomic selection relies on linkage disequilibrium (LD) between quantitative trait loci and markers, reliability of genomic predictions is strongly influenced by family relationships. In this study, we investigated the effects of LD and family relationships on reliability of genomic predictions and the potential of deterministic formulas to predict reliability using population parameters in populations with complex family structures. Five groups of selection candidates were simulated taking different information sources from the reference population into account: 1) allele frequencies; 2) LD pattern; 3) haplotypes; 4) haploid chromosomes; 5) individuals from the reference population, thereby having real family relationships with reference individuals. Reliabilities were predicted using genomic relationships among 529 reference individuals and their relationships with selection candidates and with a deterministic formula where the number of effective chromosome segments (M(e)) was estimated based on genomic and additive relationship matrices for each scenario. At a heritability of 0.6, reliabilities based on genomic relationships were 0.002±0.0001 (allele frequencies), 0.015±0.001 (LD pattern), 0.018±0.001 (haplotypes), 0.100±0.008 (haploid chromosomes) and 0.318±0.077 (family relationships). At a heritability of 0.1, relative differences among groups were similar. For all scenarios, reliabilities were similar to predictions with a deterministic formula using estimated M(e). So, reliabilities can be predicted accurately using empirically estimated M(e) and level of relationship with reference individuals has a much higher effect on the reliability than linkage disequilibrium per se. Furthermore, accumulated length of shared haplotypes is more important in determining the reliability of genomic prediction than the individual shared haplotype length.
Members of the Frizzled family of sevenpass transmembrane receptors signal via the canonical Wnt pathway and also via noncanonical pathways of which the best characterized is the planar polarity pathway. Activation of both canonical and planar polarity signaling requires interaction between Frizzled receptors and cytoplasmic proteins of the Dishevelled family; however, there has been some dispute regarding whether the Frizzled-Dishevelled interactions are the same in both cases. Studies looking at mutated forms of Dishevelled suggested that stable recruitment of Dishevelled to membranes by Frizzled was required only for planar polarity activity, implying that qualitatively different Frizzled-Dishevelled interactions underlie canonical signaling. Conversely, studies looking at the sequence requirements of Frizzled receptors in the fruit fly Drosophila melanogaster for canonical and planar polarity signaling have concluded that there is most likely a common mechanism of action. To understand better Frizzled receptor function, we have carried out a large-scale mutagenesis in Drosophila to isolate novel mutations in frizzled that affect planar polarity activity and have identified a group of missense mutations in cytosolic-facing regions of the Frizzled receptor that block Dishevelled recruitment. Interestingly, although some of these affect both planar polarity and canonical activity, as previously reported for similar lesions, we find a subset that affect only planar polarity activity. These results support the view that qualitatively different Frizzled-Dishevelled interactions underlie planar polarity and canonical Wnt signaling.
Segments of identity by descent (IBD) detected from high-density genetic data are useful for many applications, including long-range phase determination, phasing family data, imputation, IBD mapping and heritability analysis in founder populations. We present Refined IBD, a new method for IBD segment detection. Refined IBD achieves both computational efficiency and highly accurate IBD segment reporting by searching for IBD in two steps. The first step (identification) uses the GERMLINE algorithm to find shared haplotypes exceeding a length threshold. The second step (refinement), evaluates candidate segments with a probabilistic approach to assess the evidence for IBD. Like GERMLINE, Refined IBD allows for IBD reporting on a haplotype level, which facilitates determination of multi-individual IBD and allows for haplotype-based downstream analyses. To investigate the properties of Refined IBD, we simulate SNP data from a model with recent super-exponential population growth that is designed to match UK data. The simulation results show that Refined IBD achieves a better power/accuracy profile than fastIBD or GERMLINE. We find that a single run of Refined IBD achieves greater power than 10 runs of fastIBD. We also apply Refined IBD to SNP data for samples from the UK and from Northern Finland, and describe the IBD sharing in these data sets. Refined IBD is powerful, highly accurate, easy to use, and is implemented in Beagle version 4.
Methods for inference and interpretation of evolutionary quantitative genetic parameters, and for prediction of the response to selection, are best developed for traits with normal distributions. Many traits of evolutionary interest, including many life history and behavioural traits, have inherently non-normal distributions. The generalised linear mixed model (GLMM) framework has become a widely used tool for estimating quantitative genetic parameters for non-normal traits. However, whereas GLMMs provide inference on a statistically-convenient latent scale, it is often desirable to express quantitative genetic parameters on the scale upon which traits are measured. The parameters of fitted GLMMs, despite being on a latent scale, fully determine all quantities of potential interest on the scale on which traits are expressed. We provide expressions for deriving each of such quantities, including population means, phenotypic (co)variances, variance components including additive genetic (co)variances, and parameters such as heritability. We demonstrate that fixed effects have a strong impact on those parameters and show how to deal with this by averaging or integrating over fixed effects. The expressions require integration of quantities determined by the link function, over distributions of latent values. In general cases, the required integrals must be solved numerically, but efficient methods are available and we provide an implementation in an R package, QGglmm. We show that known formulae for quantities such as heritability of traits with Binomial and Poisson distributions are special cases of our expressions. Additionally, we show how fittedGLMM can be incorporated into existing methods for predicting evolutionary trajectories. We demonstrate the accuracy of the resulting method for evolutionary prediction by simulation, and apply our approach to data from a wild pedigreed vertebrate population.
While productivity in academia is measured through authorship, not all scientific contributors have been recognized as authors. We consider nonauthor “acknowledged programmers” (APs), who developed, ran, and sometimes analyzed the results of computer programs. We identified APs in Theoretical Population Biology articles published between 1970 and 1990, finding that APs were disproportionately women (P = 4.0 × 10-10). We note recurrent APs who contributed to several highly-cited manuscripts. The occurrence of APs decreased over time, corresponding to the masculinization of computer programming and the shift of programming responsibilities to individuals credited as authors. We conclude that, while previously overlooked, historically, women have made substantial contributions to computational biology. For a video of this abstract, see: https://vimeo.com/313424402.
Approximately 2-4% of genetic material in human populations outside Africa is derived from Neanderthals who interbred with anatomically modern humans. Recent studies have shown that this Neanderthal DNA is depleted around functional genomic regions; this has been suggested to be a consequence of harmful epistatic interactions between human and Neanderthal alleles. However, using published estimates of Neanderthal inbreeding and the distribution of mutational fitness effects, we infer that Neanderthals had at least 40% lower fitness than humans on average; this increased load predicts the reduction in Neanderthal introgression around genes without the need to invoke epistasis. We also predict a residual Neanderthal mutational load in non-Africans, leading to a fitness reduction of at least 0.5%. This effect of Neanderthal admixture has been left out of previous debate on mutation load differences between Africans and non-Africans. We also show that if many deleterious mutations are recessive, the Neanderthal admixture fraction could increase over time due to the protective effect of Neanderthal haplotypes against deleterious alleles that arose recently in the human population. This might partially explain why so many organisms retain gene flow from other species and appear to derive adaptive benefits from introgression.
Ovarian function is directly correlated with survival of the primordial follicle reserve. Women diagnosed with cancer have a primary imperative of treating the cancer, but since the resting oocytes are hypersensitive to the DNA-damaging modalities of certain chemo- and radiotherapeutic regimens, such patients face the collateral outcome of premature loss of fertility and ovarian endocrine function. Current options for fertility preservation primarily include collection and cryopreservation of oocytes or in vitro fertilized oocytes, but this necessitates a delay in cancer treatment and additional assisted reproductive technology (ART) procedures. Here, we evaluated the potential of pharmacological preservation of ovarian function by inhibiting a key element of the oocyte DNA damage checkpoint response, checkpoint kinase 2 (CHK2; CHEK2). Whereas non-lethal doses of ionizing radiation (IR) eradicate immature oocytes in wild type mice, irradiated Chk2(-/-) mice retain their oocytes and thus, fertility. Using an ovarian culture system, we show that transient administration of the CHK2 inhibitor 2-(4-(4-Chlorophenoxy)phenyl)-1H-benzimidazole-5-carboxamide-hydrate (“CHK2iII”) blocked activation of the CHK2 targets TRP53 and TRP63 in response to sterilizing doses of IR, and preserved oocyte viability. After transfer into sterilized host females, these ovaries proved functional and readily yielded normal offspring. These results provide experimental evidence that chemical inhibition of CHK2 is a potentially effective treatment for preserving fertility and ovarian endocrine function of women exposed to DNA-damaging cancer therapies such as IR.
The incidence of diet-induced metabolic disease has soared the last half-century despite national efforts to improve health through universal dietary recommendations. Studies comparing dietary patterns of populations with health outcomes have historically provided the basis for healthy diet recommendations. However, evidence that population-level diet responses are reliable indicators of responses across individuals is lacking. This study investigated how genetic differences influence health responses to several popular diets in mice, which are similar to humans in genetic composition and propensity to develop metabolic disease, but enable precise genetic and environmental control. We designed four human-comparable mouse diets that are representative of those eaten by historical human populations. Across four genetically distinct inbred mouse strains, we compared the American diet’s impact on metabolic health to three alternative diets (Mediterranean, Japanese and Maasai/ketogenic). Furthermore, we investigated metabolomic and epigenetic alterations associated with diet response. Health effects of the diets were highly dependent on genetic background, demonstrating that individualized diet strategies improve health outcomes mice. If similar genetic-dependent diet responses exist in humans, then a personalized, or “precision dietetics,” approach to dietary recommendations may yield better health outcomes than the traditional one-size-fits-all approach.