A few weeks ago I commented on the paper about the origin of the small dog phenotype in the Middle East. Now The New York Times has an article on a newer paper, New Finding Puts Origins Of Dogs in Middle East. Here's the conclusion:

Dog domestication and human settlement occurred at the same time, some 15,000 years ago, raising the possibility that dogs may have had a complex impact on the structure of human society. Dogs could have been the sentries that let hunter gatherers settle without fear of surprise attack. They may also have been the first major item of inherited wealth, preceding cattle, and so could have laid the foundations for the gradations of wealth and social hierarchy that differentiated settled groups from the egalitarianism of their hunter-gatherer predecessors. Notions of inheritance and ownership, Dr. Driscoll said, may have been prompted by the first dogs to permeate human society, laying an unexpected track from wolf to wealth.

Humans are often conceived of as the selection pressure on our domesticates, but clearly this is a two-way street. Cows have strongly shaped the human genome in the form of lactase persistence. And of course there have been many pathogens which have jumped from domesticates to humans, including ones which might change human behavior. The evolutionary process in this conception is a complex series of interactive feedback loops, and the task of reconstruction is going to be a laborious, but fascinating one. And luckily, we have "control" populations who have been little impacted by domesticated animals.

Here's the letter in Nature.

Over the past week I've been asked via email and on message boards about about David Shenk's new book, . Since I haven't read the book I can't really comment, but I did finally listen to Will Wilkinson's interview with Shenk on bloggingheads.tv. It seems to me that Will exhibited more clarity and precision in one sentence in relation to the term heritability than Shenk did in 10 minutes. It is true there are many people who don't understand that 80% heritable does not mean that a trait is "80% genetic." In fact, I really don't know what a trait being "80% genetic" means in a precise sense, but I also know that long time readers of this weblog do fall into this trap.

Instead of reading Shenk's book I strongly suspect that people might gain some more genuine insight about heritability and the genetics of complex traits by looking at what we know about height. We don't know much in terms of the underlying genes; height seems to be controlled by many genes of small effect. But, we do know that in the developed world, where nutritional intakes have saturated, height is about ~80% heritable. That is, most of the variation in the population can be accounted for by variation in genes. There are probably gene-environment interactions in regards to the trait of height. For example, there may be individuals whose genotypes are more sensitive to nutritional deprivation than others, so that changing uniform nutritional intakes across a population may not change just the median of the distribution, but also the general shape. But those interaction effects are obviously not as important today in the developed world where malnutrition is very rare.

At least judging by the conversation with Wilkinson, and the title of the book, Shenk seems to want to spotlight people who are many standard deviations from the norm. For example, Mozart and Michael Jordan are arguably not 1 in 100, or even 1 in 1,000, in regards to their domains of virtuosity. I think that focusing this far out to the tails is interesting, and makes for good narrative as one can populate it with illustrative anecdotes, but on any given quantitative trait most people are going to be much closer to the median. Variation on the margins of the normal are very significant, and all too often ignored. In here that I think that the simplest models have the most utility. So you want to complexify, just focus on the outliers....

Note: Using Amazon's search inside feature I see that Shenk mentions gene-environment interaction quite a bit, but not gene-environment correlation.

Apparently there'll be a new Skip Gates documentary on personal genomics on PBS, Faces of America.

Personal genomics comes to TV:

Carl Zimmer has a nice write up of the a new paper in Science which characterizes the nature of the cells which are manifest during devil facial tumor disease. The Tasmanian Devil Transcriptome Reveals Schwann Cell Origins of a Clonally Transmissible Cancer:

The Tasmanian devil, a marsupial carnivore, is endangered because of the emergence of a transmissible cancer known as devil facial tumor disease (DFTD). This fatal cancer is clonally derived and is an allograft transmitted between devils by biting. We performed a large-scale genetic analysis of DFTD with microsatellite genotyping, a mitochondrial genome analysis, and deep sequencing of the DFTD transcriptome and microRNAs. These studies confirm that DFTD is a monophyletic clonally transmissible tumor and suggest that the disease is of Schwann cell origin. On the basis of these results, we have generated a diagnostic marker for DFTD and identify a suite of genes relevant to DFTD pathology and transmission. We provide a genomic data set for the Tasmanian devil that is applicable to cancer diagnosis, disease evolution, and conservation biology.

In Carl's article, he reports:

The cancer, devil's facial tumor disease, is transmitted when the animals bite one another's faces during fights. It grows rapidly, choking off the animal's mouth and spreading to other organs. The disease has wiped out 60 percent of all Tasmanian devils since it was first observed in 1996, and some ecologists predict that it could obliterate the entire wild population within 35 years.

I think that the ecologists need to be careful here, as the public might think that the cancer itself is going to be the immediate proximate cause of extinction. Rather, it seems more likely that the disease will reduce the numbers of the devils, of which there are on the order of 10 to 100 thousand on the island. And small populations, say less than a 1,000, are subject to random fluctuations in population size which could drive them to extinction (imagine a short-term climatic regime which reduces the food supply). It seems that some individuals are already immune to the disease, so over time if nature took its course the population would probably bounce back. Projecting extinction because of disease necessarily and sufficiently is just part of the linear fallacy, which isn't really good at predicting over the long term in biological contexts. Australia still has rabbits. It's called evolution.

The Properties of Adaptive Walks in Evolving Populations of Fungus:

The rarity of beneficial mutations has frustrated efforts to develop a quantitative theory of adaptation. Recent models of adaptive walks, the sequential substitution of beneficial mutations by selection, make two compelling predictions: adaptive walks should be short, and fitness increases should become exponentially smaller as successive mutations fix. We estimated the number and fitness effects of beneficial mutations in each of 118 replicate lineages of Aspergillus nidulans evolving for approximately 800 generations at two population sizes using a novel maximum likelihood framework, the results of which were confirmed experimentally using sexual crosses. We find that adaptive walks do indeed tend to be short, and fitness increases become smaller as successive mutations fix. Moreover, we show that these patterns are associated with a decreasing supply of beneficial mutations as the population adapts. We also provide empirical distributions of fitness effects among mutations fixed at each step. Our results provide a first glimpse into the properties of multiple steps in an adaptive walk in asexual populations and lend empirical support to models of adaptation involving selection towards a single optimum phenotype. In practical terms, our results suggest that the bulk of adaptation is likely to be accomplished within the first few steps.

I've discussed this issue before. The general logic here is that when a population is subject to new selection pressures it uses whatever tricks and tools are handy in the short term even if they're suboptimal in the long term. Over time adaptation should "refine" the phenotype so that there are fewer trade-offs so that fitness gradually converges upon an idealized peak. Consider the various malaria adaptations, which arose in the past 5,000 years, some of which still have major side effects such as sickle cell anemia in homozygotes. But in a malarial environment the side effects, the risk of morbidity and mortality, is worth the overall the reduction in mortality. One imagines that over time new mutations would emerge to mask the deleterious consequences of new adaptations, which are basically evolutionary kludges.

They illustrate this process experimentally:

Disease Gene Characterization through Large-Scale Co-Expression Analysis:

Celsius, the largest co-normalized microarray dataset of Affymetrix based gene expression, was used to calculate the correlation between all possible gene pairs on all platforms, and generate stored indexes in a web searchable format. The size of Celsius makes UGET a powerful gene characterization tool. Using a small seed list of known cartilage-selective genes, UGET extended the list of known genes by identifying 32 new highly cartilage-selective genes. Of these, 7 of 10 tested were validated by qPCR including the novel cartilage-specific genes SDK2 and FLJ41170. In addition, we retrospectively tested UGET and other gene expression based prioritization tools to identify disease-causing genes within known linkage intervals. We first demonstrated this utility with UGET using genetically heterogeneous disorders such as Joubert syndrome, microcephaly, neuropsychiatric disorders and type 2 limb girdle muscular dystrophy (LGMD2) and then compared UGET to other gene expression based prioritization programs which use small but discrete and well annotated datasets. Finally, we observed a significantly higher gene correlation shared between genes in disease networks associated with similar complex or Mendelian disorders.

Citation: Day A, Dong J, Funari VA, Harry B, Strom SP, et al. 2009 Disease Gene Characterization through Large-Scale Co-Expression Analysis. PLoS ONE 4(12): e8491. doi:10.1371/journal.pone.0008491

I referenced a paper in PNAS yesterday, and I thought it might be good to actually point to it today. There's nothing that new in the paper. It confirms the finding that ~20% of the ancestry of African Americans is European, and, that African ancestry seems to be much more dominant when it comes to components of the genome presumably disproportionately contributed by females (2/3 of X chromosomes). In any case, the paper, Genome-wide patterns of population structure and admixture in West Africans and African Americans:

Quantifying patterns of population structure in Africans and African Americans illuminates the history of human populations and is critical for undertaking medical genomic studies on a global scale. To obtain a fine-scale genome-wide perspective of ancestry, we analyze Affymetrix GeneChip 500K genotype data from African Americans (n = 365) and individuals with ancestry from West Africa (n = 203 from 12 populations) and Europe (n = 400 from 42 countries). We find that population structure within the West African sample reflects primarily language and secondarily geographical distance, echoing the Bantu expansion. Among African Americans, analysis of genomic admixture by a principal component-based approach indicates that the median proportion of European ancestry is 18.5% (25th-75th percentiles: 11.6-27.7%), with very large variation among individuals. In the African-American sample as a whole, few autosomal regions showed exceptionally high or low mean African ancestry, but the X chromosome showed elevated levels of African ancestry, consistent with a sex-biased pattern of gene flow with an excess of European male and African female ancestry. We also find that genomic profiles of individual African Americans afford personalized ancestry reconstructions differentiating ancient vs. recent European and African ancestry. Finally, patterns of genetic similarity among inferred African segments of African-American genomes and genomes of contemporary African populations included in this study suggest African ancestry is most similar to non-Bantu Niger-Kordofanian-speaking populations, consistent with historical documents of the African Diaspora and trans-Atlantic slave trade.

One of the value-adds from this paper is that the authors explored how African Americans related to disparate African populations. The historical records indicate that American slaves arrived disproportionately from the regions to the west of the Bight of Bonny. In other words, black Americans derive predominantly from the non-Bantu populations of West Africa, from Senegal down to Nigeria. This is in contrast to Brazil, where the black population was reputedly of more diverse origin, including many Bantu speakers from Angola as well as West Africans.

I reedited part of figure 1 to show which African groups are in the study and how they relate to each other genetically:

Over at Genetic Future Dr. Daniel MacArthur points out some errors in deCODE's interpretation services. Dr. MacArthur presumably knows his maternity, though if the X chromosome results were correct one would guess that Dr. MacArthur is actually adopted and that his mother might be a Lumbee Indian.

But it makes me wonder how confused people are going to be due to problems with false results. In particular, as these technologies become very cheap many families with make recourse to them. Sometimes this will highlight "extrapair paternity events," but sometimes there will be errors and siblings may face a period of uncertainty in relation to possibly discordant results. The likelihood of a false result creating an unexpected situation is conditional on various probabilities, the error rate of the results, and the likelihood of an extrapair paternity event (which varies from demographic to demographic and family to family). I guess we'll have more data in the near future....