What could our genomes actually tell about disease risk?

Despite the recent advances in whole-genome sequencing, two recent studies let us think that we are far from uncovering the genetic basis of common diseases risk. In fact, information relevant to complex diseases might hide within rare or even private genome variations, often too scarce to be studied statistically. We might thus have to change radically our way of thinking of genes-diseases associations to make a step forward and make the DNA talk.

Whereas a few, usually rare and severe “genetic disorders” can be traced to variations at one or two locations, or “loci”, in the DNA sequence, most common diseases are the result of complex interactions between protein-coding genes, non-coding DNA and environmental effects. These well-named “complex diseases” include cardiovascular, metabolic, neurologic and psychiatric conditions of great concern to health policies, such as early-onset stroke, myocardial infarction, diabetes, dyslipemia, Alzeihmer's, bipolar disorder or schizophrenia.

Some of these complex diseases have a high heritability, which means that a great part of individual differences in the probability to develop the disease can be explained by differences in genomes. For example, the heritability of early-onset myocardial infarction is about 60% [1]: genomes are more important than environment in explaining the differences in early-onset infarction between individuals. Thus a lot of work has been going into identifying the changes in DNA sequences involved in complex disease heritability. Especially, the development of new sequencing technologies has allowed for comparison of hundreds of individual sequences and their mapping to various symptoms, a method known as “genome-wide association studies”. Hundreds of disease-related genetic variations have been identified this way. However they explain only a very small fraction of the heritability: in the case of early-onset myocardial infarction, only 2.8% of the heritability has already been linked to particular genes [2].

To explain the low power of association studies to identify genetic variants contributing to complex diseases, it was hypothesized that most variation in disease predisposition were due to “high risk” variants, that have a strong negative impact on health, but remain rare in a population because they are counter-selected [3]. In consequence, we would only need to increase sample size and therefore our power to detect rare variants to better explain the genetic basis of common diseases. In that scope, two studies published in the July issue of Science have used large datasets (respectively 2 440  and 14 002 genomes) to investigate the potential role of rare variants, defined when one of the variants at one locus is present in less than 0.5% of the individuals sampled. The large sample sizes allowed for detection of lots of previously unknown variants, thus highlighting the limits of previous smaller-scale studies: 90% of rare variants, but only 5% of common variants, found in 202 drug-target genes were novel, and estimates of discovery rates showed that lots of new variants are still to discover (Fig. 1). 

Nelson et al., An Abundance of Rare Functional Variants in 202 Drug Target Genes Sequenced in 14,002 People, Science 337, 2012. 

Fig. 1 Number of variants discovered per kilobase of sequence with sample sizes increasing to 5000 people for multiple populations.

The studies also confirmed that variants with an potential impact on health remained rare: the proportion of non-synonymous variants, which result in an alteration of the protein synthesized, was higher in rare than in common variants (Fig. 2).

Nelson et al., An Abundance of Rare Functional Variants in 202 Drug Target Genes Sequenced in 14,002 People, Science 337, 2012. 

Fig. 2 Expected ratios of non-synonymous to synonymous variants in the absence of selection and observed ratios for rare to common alleles, from left to right. MAF (Minor Allele Frequency) is the frequency of the rarest version of a variant.

However, rare variants were found to be more numerous than previously thought: around 90% of variants were rare. Interestingly, individuals of African ancestry exhibited less rare variants, but more variants of intermediate frequency than those of European ancestry. Moreover, most rare variants were population-specific (Fig. 3 and 4) and about 60% of all variants were only present in one individual. 

Casals and Bertranpetit, Human Genetic Variation, Shared and Private,  Science 337, 2012. Data from Tennessen et al., Science 2012.

Fig. 3 Proportion of shared and unshared (private) variants between the African-American and the European-American populations.

Nelson et al., An Abundance of Rare Functional Variants in 202 Drug Target Genes Sequenced in 14,002 People, Science 2012. 

Fig. 4 Allele sharing and variant abundance. (A-C) The average allele sharing between pairs of populations for rare (A), intermediate (B) and common (C) variants computed as the frequency in the pooled population pair. (D) The number of variants per kilobase found in population samples of 2,500 individuals.

Such figures are in contradiction with the current estimates derived from recent population growth. In fact, human demography is currently described by the “Out-of-Africa” model, that posits an emergence of European and Asian populations from a small population in Africa about 60,000 years ago [4]. As the individuals that migrated represented only a small fraction of the ancestral African population, some genetic variants, especially the rarest one, were lost. Then African and European populations were supposed to increase regularly, all the while acquiring new population-specific variants by mutation that would increase in frequency only if they are not deleterious, or else eventually disappear. Such “bottleneck effect” can be observed in the Finns that have less variants, but more population specific variants than other Europeans (Fig. 4). But the overall excess in rare variants in Europeans does not fit the model: most of these variants should have either disappeared or increased in frequency over such a time-scale. Such pattern can however be explained by accelerated population growth in the last thousands year, during which lots of new mutations could occur in a short time (Fig. 5). Therefore, rare variants provide a precious insight into recent demography.

Tennessen et al., Evolution and Functional Impact of Rare Coding Variation from Deep Sequencing of Human Exomes, Science 2012.

Fig. 5 Schematic representation (not to scale) of the inferred demographic model. kya, thousand years ago.

These findings are not very good news for complex disease research. Of course, the rare variants discovered in protein-coding genes are numerous and often deleterious, and could therefore play an important role in disease risk. However they rarity makes it difficult to actually test that role: over thousands of genomes, less than 5% protein-coding genes afforded a sufficient power to detect the effect of rare variations on disease risk, even when that effect is relatively strong. Consistently, no significant association was found for 202 drug-target genes. Moreover, most variants are population- or even individual-specific. Thus association studies should be at least replicated across populations, with a careful determination of ancestry, to be universal and avoid false associations between population-specific traits and variants.

The high number of individual-specific variants and the low power of association for other rare variants highlight the importance of genome-wide functional studies to accurately estimate disease risk where association studies fail. Functional predictions might be the next prevailing tool in the study of genes and disease association. Ideally, such studies would directly estimate the functional impact of a given variant, but the methods currently implemented are rather inconsistent and have a high false-positive rate, that is they often detect a functional impact where there is none. Such caveats make them still unsuitable for applied uses in medical diagnosis. A better knowledge of molecular biology and its link to physiology seems still necessary to assess the actual impact of rare variants on complex diseases.

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1       Nora J.J. et al. (1980). Genetic-epidemiologic study of early onset ischemic heart disease. Circulation 61:503 – 508.

2       Myocardial Infarction Genetics Consortium (2009). Genome-wide association of early-onset myocardial infarction with single-nucleotide polymorphisms and copy number variants. Nature Genetics 41:334-341.

3       Manolio T.A. et al. (2009). Finding the missing heritability of complex diseases. Nature 461:747-753.

4       Laval G. et al. (2010). Formulating a historical and demographic model of recent human evolution based on resequencing data from noncoding regions. PLoS ONE 5(4): e10284.

Nelson et al. (2012). An Abundance of Rare Functional Variants in 202 Drug Target Genes Sequenced in 14,002 People. Science, 337, 100-104 DOI: 10.1126/science.1217876

Tennessen et al. (2012). Evolution and Functional Impact of Rare Coding Variation from Deep Sequencing of Human Exomes. Science, 337, 64-69 DOI: 10.1126/science.1219240

Casals and Bertranpetit (2012). Human Genetic Variation, Shared and Private Science, 337, 39-40 DOI: 10.1126/science.1224528

Evolutionary consequences of sex: It's not about what you're doing, but who you're doing it with...

Bacteria are one of the most ubiquitous living group and exhibit finely tuned adaptations to a wide range of habitats, even the most inhospitable ones. Their ability to evolve rapidly is at the roots of many public health issues, such as the development of resistances to antibiotics or the rapid evolution of seasonal diseases, but can also be of great help to humans by creating new metabolic pathways to transform human-made pollutants and harmful substances. In the early 20^th century, new bacterial genomes were still thought to be the result of mutations only, and to be then transmitted vertically within a clonal strain. In the 40’s, the discovery of bacterial DNA recombination through transformation (Avery, MacLeod and McCarty experiment in 1944) or conjugation (Lederberg and Tatum experiment in 1946) shed light on the processes responsible for the rapid ecological differentiation of bacterial strains: an individual can acquire new genes or alleles through recombination that allow it to stand new ecological conditions.

In Eucaryotes, genetic exchange and recombination through sexual reproduction is considered the basis of gene-specific transmission and selection among a population. However, the importance of genetic exchange between bacteria in uncoupling selection processes between different genes remains a controversial issue. In fact, contradictory observations have elicited two models of selection:

1. On one hand, the ecological clustering of bacterial biodiversity in genetically consistent ecotypes support the traditional view that adaptive mutations are selected through whole-genome clonal selection. Moreover the low measured levels of recombination are insufficient to unlink a gene from the rest of the genome.
2. On the other hand, the existence of environment specific genes and alleles suggests that recombination can unlink parts of the genome. Moreover, some loci exhibit low nucleotidic diversities compared to the rest of the genome, with suggest purifying selection on these regions. Thus adaptive mutations seems to be selected quite independently of the rest of the genome.

To disentangle those apparently incompatible observations and assess the degree of gene uncoupling in bacteria, researchers of the MIT examined in a recently published study[i] the genomes of 20 strains representing two ecotypes in the marine species Vibrio cyclitrophicus. As the genomes of these ecotypes are extremely similar, they can be considered the result of recent ecological differentiation, thus giving us a snapshot of this evolutionary process. Based on the comparison of these sequences, the authors claim that gene-specific sweeps do occur and can lead to environment specific-genes on a short time scale, but also to ecological clustering, through preferential within-habitat recombination, on a longer time scale.

In fact, different parts of the genome have different evolutionary histories. Especially, ecotype-specific SNPs are only found on a few locations in the genome, whereas the rest of the polymorphic genome supports a genetic intermingling between the ecotypes. Moreover, the two chromosomes of V. cyclitrophicus support different phylogenies, with chromosome 1 grouping the ecotypes, whereas chromosome 2 splits one ecotype into two groups. The phylogeny within one of these two groups is strongly supported by chr2 but not by chr1. Thus, habitat-specific genes are evolving quite independently and do not drive genomewide selective sweeps, an observation consistent with the environment specific genes and alleles that have already been documented[ii].

These results highlight the need for high quality sequencing data and fine grained analysis to understand the evolutionary histories of different parts of the genome. In fact, the authors show that a few loci with consistent phylogeny, such as the ecotype-specific SNPs here, are sufficient to drive the whole-genome phylogeny, if the signal of clonal ancestry in the rest of the genome has been blurred by homologous recombination (Fig. 1). Therefore, the ecotype theory might be based on phylogenies biased toward the history of a few loci under purifying habitat-driven selection rather than on neutral loci with inconsistent histories accounting for most of the genome.

Fig. 1: A. Maximum-likelihood phylogeny for the core genome (genes presents in all strains) of chromosome I in V. cyclitrophicus. Scale is substitution per site. All nodes have a 100% bootstrap support unless indicated. B. Genome regions with uninterrupted support for (black points) or against (grey points) the ecological split. ML trees for three major regions are shown. Adapted from Shapiro et al. 2012.

The most important point made by the authors remains however their evidences for preferential within-ecotype recombination. When examining recombination events affecting recently diverged pairs of strains, recombination rates were found to be higher within than between habitats. The authors make here an essential point toward a unified theory of bacterial genomes evolution. Such preferential recombination indeed provides an explanation for the development of ecotypes, usually considered an evidence of genomewide genetic sweeps, from gene-specific sweeps. Even if the mechanisms involved in genes transmission are quite different between Eubacteria and Eucaryotes, they seem to converge in allowing gene-specific selective sweeps and in restricting genetic exchange between habitat. This study show a more universal picture of selective pressures on evolutionary mechanisms than previously thought between Eucaryotes and Eubacteria (Fig. 2).

Fig. 2: Model of ecological differentiation between bacterial ecotypes (from Shapiro et al. 2012). Thin grey (resp. black) arrows represent recombination within (resp. between) ecologically associated populations. Thick coloured arrows represent acquisition of adaptive alleles for red or green habitat.

Eventually, this new insight into bacterial evolution plead for a new structure of bacterial diversity. Whether and how a species-level could be defined in Eubacteria has been a long standing controversy. The current species criterion dates back to 1987: it defines a species as a group of clonal strains characterized by at least one phenotypic trait and 70% DNA–DNA hybridization[iii]. However this definition was often criticized for grouping within one species extremely diverse phenotypes[iv]. Moreover, the very idea of bacterial species was sometimes rejected on the basis of common genetic exchanges between morphologically and ecologically distinct bacteria[v]. That last assertion is here contradicted by demonstrating the existence of barriers to gene flow between habitats. If recombination, that is “bacterial sex”, is more frequent within than between habitats, ecotypes are quite close to the definition of a species in Eucaryotes, an “ecotype-hypothesis” already put forward about ten years ago[vi]. Therefore this study provides a first step toward the “solid understanding of the genetic basis of the ecological distinctiveness of the ecotype” advocated by Konstantinidis et al. in 2006[iv], although it does not exclude the possibility that some ecotypes be defined by gene expression rather than gene content.

New paths for research are here opened, especially to define the barriers to gene flow that could explain habitat-specific recombination. The bacteria studied here are not known well enough to define the ecological differentiation observed as sympatric or allopatric: if their habitats within sea water are distinct enough, a physical barrier could be considered. If the differentiation can be regarded as sympatric, the mechanisms that prevent recombination between ecotypes are still to be investigated.

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[i] Shapiro B.J. et al. (2012). Population genomics of early events in the ecological differentiation of bacteria. Science 336:48-51.

[ii] Coleman M.L. and Chisholm S.W. (2010). Ecosystem-specific selection pressures revealed through comparative population genomics. Proc. Natl. Acad. Sci. U.S.A. 107(43):18634-18639.

[iii] Wayne L.G. et al. (1987). Report of the ad hoc committee on reconciliation of approaches to bacterial systematics. Int. J. Syst. Bacteriol. 37:463-464.

[iv] Konstantinidis K.T., Ramette A. and Tiedje J.M. (2006). The bacterial species definition in the genomic era. Philos Trans R Soc Lond B Biol Sci. 361(1475): 1929-1940.

[v] Margulis L. and Sagan D. (1997). Microcosmos: Four Billion Years of Microbial Evolution. University of California Press.

[vi] Cohan F.M. (2002). What are bacterial species? Annu Rev Microbiol. 56:457-487.

 
Shapiro BJ, Friedman J, Cordero OX, Preheim SP, Timberlake SC, Szabó G, Polz MF, & Alm EJ (2012). Population genomics of early events in the ecological differentiation of bacteria. Science (New York, N.Y.), 336 (6077), 48-51 PMID: 22491847

The yak genome and adaptation to life at high altitude

The domestic yak (Bos grunniens) is an important domesticated species for Tibetans. Domestic yaks provide meat and other basic resources of necessity. The analysis of yak genome provides important insights into adaptation to a high altitude. Here discussed study was published in Nature Genetics.

The study compares the yak genome with the genome of taurine cattle (B. taurus). Yak and cattle are cross-fertile, that means that they are genetically very similar. However the cattle suffer from hypertension when living in the yak habitat, thus, comparing this two species can provide the information about evolutionary adaptation to high altitude.

In the study, researches sequenced genome of a female yak. They found three genes that help the animal to deal with a low concentration of oxygen that is typical for high altitude. Five further genes provide a better nutritional assimilation, as a consequence of the limited herbal resources available in the mountains where they live.

Fig.1 Qiu et al., The yak genome and adaptation to life at high altitude., Nature Genetics 44, 2012

Venn diagram showing unique and shared gene families between the yak, cattle, dog and human genoms.

One the Fig.1 unique and shared gene families from four different species (yak, cattle, human and dog) are shown. Gene family is a set of genes that show sequence similarity, and generally (not always) have similar functions. We discussed the choice of the species on the Venn diagram. The two other species for comparison, beside of yak and cattle, were chosen probably because of the good annotated genome. It would be also nice to see the comparison to chimp, to see the relationships yak-cattle and human-chimp together. However the authors mention that yak and cattle have diverged approximately 4.9 million years ago, which is comparable to the time at which humans and chimpanzees diverged. Why the comparison is made to mammals and not other species? The comparison was done to show influence of adaptation, and for this purpose it is better to take more related species.

Fig.2 Qiu et al., The yak genome and adaptation to life at high altitude., Nature Genetics 44, 2012

Gene expasion and contraction in the yak genome

In the Fig.2 a neighbor-joining tree of mammalian Hig domain sequences is presented. The Hig domain is known to play role in hypoxia, so adaptation to high altitude. Hig domain sequences group in different clusters, but yak and cattle sequences seem to be similar. Some brunches of the tree are long, that means that the sequences were very diverged.

Authors also show that yak has three more positively selected genes then cattle, and it is more then the rest of positive selected genes. The paper also compares Ka/Ks ratio of GO categories of yak and cattle. Ka/Ks ratio is a proportion of synonymous to nonsynonymous substitutions. It is assumed that synonymous substitutions represent the background and the selection influence nonsynonymous substitutions.

For the further studies it would be interesting to compare the results from other species, such as goats in mountains and fields, or even wild species. We wondered why there is now comparison of the results of the current study with already known result from the studies of human genome on adaptation to high altitude?

Summary

The study present analysis for the genome adapted to high altitude. The authors sequenced genome of yak using a whole-genome shotgun strategy and the Illumina platform. The scientist hope that this results can help in research of hypoxia-related diseases in humans.

Qiu Q, Zhang G, Ma T, Qian W, Wang J, Ye Z, Cao C, Hu Q, Kim J, Larkin DM, Auvil L, Capitanu B, Ma J, Lewin HA, Qian X, Lang Y, Zhou R, Wang L, Wang K, Xia J, Liao S, Pan S, Lu X, Hou H, Wang Y, Zang X, Yin Y, Ma H, Zhang J, Wang Z, Zhang Y, Zhang D, Yonezawa T, Hasegawa M, Zhong Y, Liu W, Zhang Y, Huang Z, Zhang S, Long R, Yang H, Wang J, Lenstra JA, Cooper DN, Wu Y, Wang J, Shi P, Wang J, & Liu J (2012). The yak genome and adaptation to life at high altitude. Nature genetics, 44 (8), 946-9 PMID: 22751099

The evolutionary history of polar bears

The study of the Ursus lineage, including brown bear (Ursus arctos), black bear (Ursus americanus) and polar bear (Ursus maritimus), provides the ability of addressing the subject of adaptation to extreme (salty and glacial) environments in mammals. Moreover, in last few decades, polar bears won public and media attention, being one of the most charismatic species endangered by global warming and Arctic ice melting. To trace history of innovations and determine response to environmental changes in populations of polar bears, two articles published in Science and Proceedings of the National Academy of Sciences in April and June 2012 provide new data and insights to resolve this question.

The absence of fossil of polar bears dating before the late Pleistocene (circa 126 000 years ago) and mitochondrial data, suggesting that polar bear were very closely related to a group of brown bear living in Admiralty, Baranof and Chichagof (ABC) islands in Alaska, previously led to believe that polar bears recently emerged from brown bears. The consequences of this hypotheses would be :

1. Polar bear underwent a very rapid and recent (less than 200 ky ago) adaptation to extreme environment (previously not seen in mammals)
2. Brown bear is a paraphyletic taxon, as polar bear is the sister specie of the ABC bears (see Fig. 1)

Fig. 1: Miller et al., Polar and brown bear genomes reveal ancient admixture and demographic footprints of past climate change, PNAS 2012

 Phylogeny of bear lineage with mitochondrial DNA and Bayesian maximum clade credibility model

The blue box contains polar individuals coming from Svalbard and Alaska and an ancient sample 130ky to 110 ky old, the yellow box ABC individuals and the pink box other brown bear individuals. The outgroup is made of black bears individuals.

Nevertheless, both fossil data, as it can be incomplete, and mitochondrial data, as it sensitive to hybridization, are not sufficient to confirm this hypothesis. Thus the two publishing groups led in parallel projects aiming to collect nuclear data and test its agreement with mitochondrial data.

Hailer et al., in their work Nuclear Genomic Sequences Reveal that Polar Bears Are an Old and Distinct Bear Lineage published in Science, sequenced 9116 nucleotides from 14 independent introns in 45 individuals of black, brown and polar bears. Introns were sequenced to provide more variation between individuals: given the low amount of time since the divergence of the last common ancestor of bears (estimated between 559 to 1 429 ky ago in their study), choosing exons, whose evolution being more likely bounded by selection, would have yielded less information.

Using this data and various phylogenetic reconstructions (bayesian multilocus coalescent approach, bayesian inference for the concatenated data and neighbour-joining of the differentiation estimates between species) that all led to the same conclusion, they recovered the three species of bears as being monophyletic and observed in the species tree the polar bear clade being sister to the brown bear clade. They estimated the divergence time of the two species around 603 ky ago (338 to 934 ky being the 99% highest credibility range) and clearly revealed a discrepancy with the mitochondrial data.

The authors resolved this incongruence by stating that the most probable scenario was a divergence between polar and brown species 600 ky ago and an hybridization event between 111 to 166 ky ago between polar bears and ABC bears leading to the complete replacement of the former mtDNA by the latter. The opposite phenomenon (several and severe introgression events of polar bears mtDNA into brown bears leading to all extant mtDNA being of polar origin) is judged very unlikely by the authors given the extended range of distribution of the brown bear. The lack of finding of older fossil from polar bears was explained by their constantly changing living environment. 

Despite the recent hybridization event, Hailer et al. found very few common nuclear haplotypes between polar and brown bears: out of the 35 polar and 79 brown haplotypes, only 6 of them were shared across both species. Nevertheless, we must bear in mind that given the relatively low amount of nuclear data analysed, those findings might not reflect the entire picture of polar and brown bears nuclear DNA ancestry.

In Polar and brown bear genomes reveal ancient admixture and demographics footprints of past climate change, published in PNAS by Miller et al., a genome-wide sequencing project was adopted to unravel the same problem. In this extensive study, the authors assembled a reference genome of a polar bear individual, deeply sequenced the genome of two ABC, one black and one non-ABC brown bear (GRZ). Finally, they produced low coverage data from 23 other polar bear individuals, one of them being an ancient specimen 110 to 130 ky old found in Svalbard.

Having aligned all reads from every samples to the polar bear genome reference, they identified 12 millions of what they called "SNPs" (even though they are dealing with three different species) and constructed the following phylogeny (Fig. 2).

Fig. 2: Miller et al., Polar and brown bear genomes reveal ancient admixture and demographic footprints of past climate change, PNAS 2012

Phylogeny based on the matrix of distances of the 12 millions SNP and using a neighbour-joining algorithm (probably given the amount of data and computational time needed with more sophisticated algorithms)

We observe that, as in the previous paper, the nuclear data is not in agreement with the mitochondrial data. A scenario where polar bears emerged as a sister species of the brown species and later experienced a massive and unique event of mtDNA introgression from ABC bears (as the polar bear individuals form only one group in Fig. 1) is again strongly favoured. Regarding the ancient polar bear specimen, both trees inform us that it dates after the mtDNA introgression event and that the modern individuals living in Svalbard are actually more closely related to the modern individuals in Alaska than to the ancient one.

Though up to this point both articles seem consistent, following findings radically differ with the previous study. Indeed, Miller et al., used  a coalescence hidden Markov model for four of their deeply-covered genomes (one ABC, one polar bear, one brown bear, one black bear) to assess the history of the lineage. They estimated both the splits of polar bears with brown bears and the common ancestor of those two species with black bears to have occurred around 4 to 5 My ago, as shown in Fig. 3.

Fig. 3: Miller et al., Polar and brown bear genomes reveal ancient admixture and demographic footprints of past climate change, PNAS 2012

Reconstructed evolutionnary history of polar, brown and black bears

The black solid line represent the specie tree and the brown dashed lines the mtDNA tree

The X represents the introgression event, the shortened branch of the specie tree the disappearance of the ancient Svalbard lineage  

It is however true that Hailer et al. reported on their article (that pre-dates the PNAS one) that other studies hint that the 600 ky-value is an underestimate of the splitting time of the two lineages under consideration, without it weakening their own conclusion. 

Nevertheless, other discrepancies arise : Hailer et al. stated that no evidence of on going gene flow was found between polar bears and brown bears, whereas the coalescent model used by Miller et al. yielded that the time when this gene flow stopped was not significantly different from zero. Following the Science article, a comment arose relating two very recent cases of documented hybridization of polar/brown bears in the wild, among them a second generation hybrid. Interestingly, both crosses involved a polar bear female with a brown bear male: thus no cross leading to the introgression of brown bear mtDNA onto polar bear populations has yet been described.

Besides, where Hailer et al. found relatively few shared nuclear data between polar and brown bears, a PCA analysis of the SNPs identified in the ABC, non-ABC and polar bear genomes yielded that 5.5% of one of the ABC genome and 9.4% of the other one are related to the polar bear genome (Fig. 4).

Fig. 4: Miller et al., Polar and brown bear genomes reveal ancient admixture and demographic footprints of past climate change, PNAS 2012

PCA plot of SNP data for ABC1 & 2, polar and non-ABC brown bear (GRZ)

Following this PCA analysis, it is interesting to focus more precisely on the differentiation of populations of polar and brown bears, as the ABC and GRZ seem pretty much apart on the second component axis. Thus Miller et al. arbitrarily chose a subset of 100 SNPs identified from the genomes of all polar bear individuals and resequenced them for 118 individuals (58 polar bears, 9 ABC bears, 51 non-ABC brown bears). The PCA analysis yielded the following plot (Fig. 5).

Fig. 5: Miller et al., Polar and brown bear genomes reveal ancient admixture and demographic footprints of past climate change, PNAS 2012

On the one hand, ABC and brown bears cluster together even if we can still discriminate them into two groups. On the other hand, polar bear populations seem much more genetically heterogenous than their sister species counterparts. However one must always remain careful when drawing conclusion on such a low amount of data (100 SNPs). Focusing on the polar populations, the authors performed a structured analysis upon this data (Fig. 6).

Fig. 5: Miller et al., Polar and brown bear genomes reveal ancient admixture and demographic footprints of past climate change, PNAS 2012

Structure analysis of 58 polar bear individuals grouped into 4 population

The number of genetic population was set to 3

Here again lies a very striking difference between the two papers. Whereas Miller et al. clearly identified genetic structuring between the populations of polar bears, Hailer et al. used the same type of analysis upon the nuclear variation of their 45 individuals and it led them to conclude that the polar bears were much more genetically homogeneous than the brown bears.

Given the respective data set of both papers, only Miller et al. were able to address the point of adaptation to extreme environment. To do so, they aligned their deeply sequenced genome to the dog genome, choice resulting from a compromise between evolutionary distance and quality of the annotation (as the panda genome has been fully sequenced but being of less good quality). Having thus preserved sinteny accross the bear genomes, they were able to carry admixture analysis for the two ABC genomes (Fig. 6).

Fig. 6: Miller et al., Polar and brown bear genomes reveal ancient admixture and demographic footprints of past climate change, PNAS 2012

Admixture map of the ABC 1 & 2 diploid genomes region homologous to dog chromosome 11

Blue: polar bear origin, red: brown bear origin

In this particular example, based on the annotation of the dog genome, the authors focus on a gene (ALDH7A1) involved in salt resistance. It appears that copies of this gene in the two ABC bears come from the polar bear. As ABC bears live in a marine environment, the idea hinted behind this plot is that during the hybridization event between polar bear and ABC bears, polar bear (being already adapted to salty environment) copies of this gene introgressed into the ABC population and were subsequently selected for, thus appearing in modern ABC individuals.

Then, using Fst values, they were able to identify a few other genes that might have been selected for during the evolution of polar bears, such as DAG1 (involved in the muscular dystrophy) or BTN1A1 (involved in milk producing).

I think that to address the subject of adaptation in polar bear, a study of positive selection in protein-coding gene is lacking. As authors already conducted transcriptome sequencing of polar and brown bears, annotating gene in their genome, selecting orthologous genes together with other copies from completely sequenced genomes, as dog, panda and other mammals, and then using a model to test for positive selection such as implemented in PAML would be an efficient way to identify genes of interest in the polar (or ABC) bears. Nevertheless, I am very well aware of the tremendous amount of work already performed in this PNAS paper. 

Regarding the evolution of the population size in bears, Miller et al. used a pairwise sequentially markovian coalescent model (that uses the length of homozygoteous regions of a diploid genome) to reconstruct the effective population size (number of individual in a perfectly panmictic population leading to the same genetic diversity as our observed population) from the four bear genomes (Fig. 7).

Fig. 6: Miller et al., Polar and brown bear genomes reveal ancient admixture and demographic footprints of past climate change, PNAS 2012

We observe the very closely related trends of both brown bear genomes and the continuous decline of non polar bears during the Early Pleistocene cooling. Conversly, the population of polar bears increased during this period but seemed very sensitive to the following warming period. Two points were raised when discussing this graph:

1. The bump in the polar bear curve signified as the "Post Eemian increase" was not significant when looking at the 95% interval range in the supplementary material
2. Knowing from the previous part of the article the extended hybridization between ABC and polar bears, would not the diversity introduced during those event affect the effective population size reconstruction ?

Putting those two papers in parallel allowed us to realize the difficulties of putting in agreement data from various origin, as in this case nuclear, mitochondrial, palaeontological and ecological. The amount of data needed to reconstruct the whole evolutionary history of such a complicated case becomes striking in the light of the work already performed here.


Hailer F, Kutschera VE, Hallström BM, Klassert D, Fain SR, Leonard JA, Arnason U, & Janke A (2012). Nuclear genomic sequences reveal that polar bears are an old and distinct bear lineage. Science (New York, N.Y.), 336 (6079), 344-347 PMID: 22517859  

Miller W, Schuster SC, Welch AJ, Ratan A, Bedoya-Reina OC, Zhao F, Kim HL, Burhans RC, Drautz DI, Wittekindt NE, Tomsho LP, Ibarra-Laclette E, Herrera-Estrella L, Peacock E, Farley S, Sage GK, Rode K, Obbard M, Montiel R, Bachmann L, Ingólfsson O, Aars J, Mailund T, Wiig O, Talbot SL, & Lindqvist C (2012). Polar and brown bear genomes reveal ancient admixture and demographic footprints of past climate change. Proceedings of the National Academy of Sciences of the United States of America, 109 (36) PMID: 22826254