High throughput genotyping and sequencing has led to the discovery of numerous sequence variants associated to human traits and diseases. An important type of variants involved are Loss of Function (LoF) mutations (frameshift indels, stop-gain and essential sites variants), which are predicted to completely disrupt the function of protein-coding genes. In case of Mendelian recessive diseases, for the condition to occur, the LoF variants must be biallelic, i.e. affecting both copies of a gene. The affected gene is then defined as “knockout”.

By studying the Icelandic population, authors aim to identify rare LoF mutations (Minor Allele Frequency, MAF < 2%) present in individuals participating in various disease projects. They then investigate at which frequency in the population these LoF mutations are homozygous (i.e. knockout) in the germline genome.

The Icelandic population Iceland is well-suited for genetic studies for three main reasons. The island was colonized by human population around the 9^th century by 8-20 thousand settlers. Since then the population grew to around 320’000 inhabitants today. The initial founder effect and rare genetic admixture make the Icelandic population a genetic isolate. In addition to an unusual genetic isolation, Iceland’s population benefits of a genealogical database containing family histories reaching centuries back in time, as well as a broad access to nationwide healthcare information.

These characteristics led to the development of large-scale genomic studies of Icelanders by deCODE Genetics. This biopharmaceutical company has published various studies, including this paper, related to genetic variants and diseases in Icelanders.

Loss of function mutation and rare complete knockouts Authors sequenced the whole genome of 2’626 Icelanders participating in various disease projects and identified variants in protein coding genes. These variants were annotated with the predicted impact that they have on the gene: LoF, moderate or low impact. A total of 6’795 LoF mutations in 4’924 genes were identified, with most of these variants (6’285) being rare (MAF < 2%).

The identified LoF variants were imputed into an additional 101’584 chip-genotyped and phased Icelanders, allowing the identification of the number of knockout genes in the population. Authors found that 1’485 previously identified LoF mutations (MAF <2%) are contributing to the knockout of 1’171 genes and that 8’041 individuals possess at least 1 of these knockout genes. Out of these 1’171 genes, 88 had been already linked by previous studies to conditions through a recessive mode of inheritance.

Double transmission deficit of LoF variants Because knockout genes should be deleterious for an organisms, we expect a deficit of homozygous for these genes in the population due to embryonic/fetal, perinatal or juvenile lethality. To investigate whether such a deficit was present, authors calculated the transmission probability of LoF variants from parents to their offspring.

Under Mendelian inheritance, the expected percent of transmission of the LoF mutated gene from heterozygous parents to their offspring (i.e. double transmission) is of 25%. However, results show a statistically significant deficit in double transmission, the observed double transmission probability being of 23.6%.

The rare LoF mutations were ranked according to the Residual Variation Intolerance Score (RVIS) percentiles and essentiality score percentiles. Both measures attempt to classify genes according to their tolerance to functional variation, with the lowest rank corresponding to genes being more sensitive to mutations. As expected, the lowest double transmission rate was found for the most sensitive genes (first percentile), suggesting that a homozygous state of LoF mutation in these genes is deleterious.

Tissue specific expression of knockout genes Authors investigated if genes were more likely to be knockout when expressed in specific tissues. By retrieving the information from previous studies of the number of genes that are highly expressed in 1 or more – but not all – 27 tissues, they calculated the fraction of these genes that were knockout in each tissue. They found that the brain and placenta were the tissue with the lowest fraction of knockout genes (3.1% and 3.9%, respectively), and that in testis, small intestine and duodenum were observed the highest fraction of biallelic LoF mutations (5.8%, 6.4%, and 6.9% respectively).

Conclusion and Comments The characteristics of Icelandic population and the incredibly large sample size (~ 1/3 of the total population) allowed authors to identify a large number of new and rare LoF mutations. Part of these mutations was shown to contribute to the knockout of an unexpected large number of genes in an unexpected large number of people. This study is the first to shed a light on the astonishing number of knockout present in human populations. In addition, by investigating the transmission probability, a deficit in homozygous loss-of function offspring was identified, especially when LoF mutations affected essential genes. This result was expected because of the predicted deleterious effect of biallelic LoF mutations.

Besides the aforementioned interesting results of the paper, some aspects were slightly disappointing. First, I was expecting authors to focus more on the genotype-phenotype aspects. Even if they pinpoint a deficit in double transmission, suggesting deleterious consequences for the organism, authors did not discuss the function of the identified knockout genes and their effect on the phenotype. Second, the paper was not an easy read. Many results were only mentioned without additional information on the methods or data used, and it was sometimes difficult to link them with the main aim of the study. Additionally, figures were sometimes misleading because of different axis scales or incomplete legends.

Finally, authors suggested that important tissues, such as the brain, have a lesser number of knockout compared to other tissues, writing that “genes that are highly expressed in the brain are less often completely knocked out than other genes”. However, this result is questionable as we do not have any measure of the number of knockout genes that we expect to be expressed only by chance in the tissues. In other words, the brain could have a lower number of knockout genes expressed compared to other tissues only because the total number of expressed genes in the brain is lower. Therefore we do not know if the lower number of knockout genes in the brain is due to chance or to biological reasons.

Nevertheless, this study opens the door to understanding how many knockout genes occur without phenotypic consequences in humans, what are the genes function and essentiality, and the role of the environment in the buildup of phenotype. The classical search for genetic variants associated to a phenotype, as in GWAS studies, could be reversed by first identifying individuals with the same genetic variants and then precisely phenotyping them.

Sulem, P., Helgason, H., Oddson, A., Stefansson, H., Gudjonsson, S., Zink, F., Hjartarson, E., Sigurdsson, G., Jonasdottir, A., Jonasdottir, A., Sigurdsson, A., Magnusson, O., Kong, A., Helgason, A., Holm, H., Thorsteinsdottir, U., Masson, G., Gudbjartsson, D., & Stefansson, K. (2015). Identification of a large set of rare complete human knockouts Nature Genetics, 47 (5), 448-452 DOI: 10.1038/ng.3243

Understanding the evolutionary history of our own species, how migration and mixture of ancestral populations have shaped modern human populations is a key question in evolutionary biology. Here we present three articles related to this topic, the first two dealing with India and the third one focusing on a single Ethiopian group :

1) Moorjani et al 2013 Genetic Evidence for Recent Population Mixture in India AJHG 93,: 422–438

2) Basu et al 2016 Genomic reconstruction of the history of extant populations of India reveals five distinct ancestral components and a complex structure PNAS online before print

3) Van Dorp et al 2016 Evidence for a Common Origin of Blacksmiths and Cultivators in the Ethiopian Ari within the Last 4500 Years: Lessons for Clustering-Based Inference PLOS Genetics 11(8): e1005397

All of them use genome wide data from micro array. After a brief abstract of  each paper, showing their similarities and differences, we discuss their methodological approaches.

Ancestral populations of India

The aim of the first two articles is to understand the history of the populations of the Indian subcontinent. The first one (Moorjani et al 2013) reports data from 73 groups living in India for more than 570 individuals sampled. The authors filtered out  the data by removing all individuals with evidence of recent admixture or recent ancestry from out of India. The populations that were included in the analysis can be classified into two linguistic categories: the ones speaking Indo-European languages and the ones speaking Dravidian languages.

Previous genetic evidence indicates that most of the groups of India descend from a mixture of two distinct ancestral populations: Ancestral North Indians (ANI) and Ancestral South Indians (ASI). Three different hypothesis exist for the date of mixture of these two populations:

1) arrival of ANI is due to migration prior to agriculture about 30,000-40,000 years ago

2) ANI arrived with the spread of agriculture who probably began around 8,000 and 9,000 years ago

3) ANI arrived very recently (3,000-4,000 years ago) when the Indo-European languages presumably began to be spoken in India.

To prove the admixed origin of Indian groups and estimate the proportion of each ancestry in each population they use a PCA and a statistic called F4 ratio that infers the mixture proportion measuring the correlation in allele frequencies between each pair of groups. They demonstrated that all populations are admixed and lie along an “Indian cline”, that is a gradient going from 17% of ANI ancestry to 71%. These results correlate well with geography and language, with the northern Indo-European populations having more ANI ancestry than the southern Dravidian ones. Then they use linkage disequilibrium (LD) to estimate the dates of admixture : LD blocs are longer if the admixture is younger. By fitting an exponential function to the decay of LD (that is expected from a sudden cessation of admixture) they could estimate that admixture occurred between 1,856 and 4,176 years ago, supporting the third hypothesis. These results correspond with demographic and cultural changes observed in India with the establishment of the caste system leading to strong endogamy that stopped the admixture rapidly. Moreover they found that Indo-Europeans groups have more recent admixture dates, which could be explained by multiple waves of mixture in these populations. Another finding of this paper is that aboriginal Andaman Islanders (Onge) belong to a sister group of ASI.

The second article (Basu et al 2016) has the same focus region and use the same basic dataset, except that the authors kept the all populations in the analyses, including the austro asiatic (AA) and tibeto burman (TB) speakers. They first ran ADMIXTURE on all populations and showed that islanders and mainland populations have distinct ancestral components (islanders share ancestry with oceanic peoples like Papuans). In a second time they ran the same analysis on mainland populations only (thus excluding population from the Andaman and Nicobar islands). The best model was composed of four ancestral components, the ANI, the ASI as well as the ancestral AA and TB and they found that several present day populations are almost pure representatives of these ancestral components (figure 2).

They further estimated the time and extent of admixture using the degree of fragmentation (due to recombination) of haplotypes blocs originating from a donor population into the recipient population. In each population, the distribution fitted again with an exponential curve. They showed that admixture abruptly came to an end about 1575 years ago in upper-caste populations, most likely due to the establishment of endogamy, while tribal populations seemed to have admixed until 1500-1000 years ago.

In short, although they share a common topic, these two papers propose divergent versions of the history of Indian population : while the first considers a priori that austro asiatic and tibeto burman speakers are not component of the ancestral populations of India and only focuses on the mixture between the ANI and ASI components, the second paper claims that the genetic structure of Indian population is the result of admixture events between four ancestral components. However the two views converge on the idea that admixture was a common phenomenon in India that ceased rapidly with the establishment of the caste systems that enforced endogamy.

Common origin of two subgroups of Ari people

The 3rd paper investigates the history of human populations at a smaller scale, focusing on a single ethnic group, the Ari people of Ethiopia. The Ari are composed of two socially and genetically distinct subgroups : the cultivators (Aric) and the blacksmiths (Arib). Anthropologists have proposed two alternatives hypothesis to explain the division of the Ari : under the remnant hypothesis (RN), the blacksmiths are the remnants of an indigenous group that was assimilated by the more recently arrived cultivators, whereas the marginalization (MA) hypothesis proposes that the two groups share a common ancestry but the blacksmith were recently marginalized due to their activity. While anthropologists traditionally favour the MA hypothesis, recent genetic studies have provided support for the RN hypothesis. In this article the authors use a new methodology on the same genetic dataset to bring evidence for the MA hypothesis. They show that when ADMIXTURE, fineSTRUCTURE or CHROMOPAINTER analysis are run on a complete dataset of 237 samples of 12 Ethiopian and neighbouring populations, the Arib are grouped into a single homogeneous cluster. But when the patterns of haplotype sharing are inferred by composing the Ari as a genetic mixture of all other groups, except themselves, the genetic differences between Arib and Aric disappear. In fact, their analyses reveal that the two Ari groups have the same mixture events with non Ari populations (figure 3).

To explain this pattern they propose that the genetic differentiation of the blacksmith is due to a bottleneck effect. Their hypothesis is supported by the fact that identity-by-descent (IBD) is stronger in blacksmiths than cultivators which is consistent with reduced genetic diversity in the blacksmiths. Using the D-statistic, they also show that the Arib and Aric are more closely related to each other than they are to any other Ethiopian group. Therefore they conclude that the observed genetic differentiation between the Arib and Aric does not represent separate ancestry but is rather the result of strong genetic drift due to a bottleneck effect induced by the social marginalization of the blacksmiths.

Methodological discussion

What stands out from reading these three articles is that selection of a proper methodology is crucial within an hypothesis testing framework. While the two articles on Indian populations use the same initial dataset, the way they filter and analyse it results in very different conclusions. The inclusion or exclusion of some populations from an admixture analysis or outgroup selection for an f4 ratio estimation directly impact the output of these analysis and can lead the authors to tell very different stories. Before disclaiming or putting forward one hypothesis, it is important to be aware of the limitations of the method that is used to produce the results. For example the authors of the second paper on India’s ancestral populations, claim to demonstrate a more complex history than shown in the first paper but their result is solely based on a clustering analyse (implemented in various softwares such as STRUCTURE or ADMIXTURE).

The basic principle of those STRUCTURE/ADMIXTURE like programs is to take the K most different groups of the dataset, consider them as the pure ancestral groups and force the others to be a combination of those. This means that the results depend on the populations and the number of clusters K that are input in the program. There are different methods to determine which K provide the best fit to the data (cross-validation error, delta K …) but in numerous cases the inferred mixture proportions are wrong. Only in very simple cases, like the African American genetic history (well explained in Daniel Falush’s blog) that involves three clearly defined and very differentiated ancestral populations (West Africans, Europeans and Native Americans) we can be confident in the results of the clustering analyse.

But in many cases the history is more complex and no current population actually corresponds to a pure ancestral population because of multiple waves of admixtures. In this case the most differentiated groups correspond only to the most extreme groups but it does not mean that these groups are pure or ancestral. This is well explained in Razib Khan’s blog using the simple example of Uygurs and Europeans :  it is known that the Uygurs are a recently mixed group (between European and Asian) but if K is fixed to 2 with Uygurs and Europeans, STRUCTURE will form two different clusters at 100% levels, one with the Uygurs and one with Europeans. This is  why, in the 2nd paper, the apparently pure AAA, ATB, ASI and ANI populations and all the clustering implications are probably meaningless.  In fact, when using the  f4 ratio (as in the first paper) all groups are found to be admixed to a certain extent (with the smallest rate of admixture being 17%).

This critic of clustering analysis is a key element of the study on the Ari people where the authors point out that results from such methods should not be taken for granted but interpreted with caution. Indeed this kind of method cannot discriminate between alternative scenarios of recent mixture of separate populations or shared ancestry followed by population divergence. Therefore support for one of these hypotheses should rely on additional tests. Instead of directly accepting the story suggested by a clustering analysis, a more reasonable work-flow would be to use other methods in order to address the specific implications of one hypothesis. This is exactly what is done in the third article where, as we previously explained, the authors constrain the analysis of mixture by forbidding self ancestry in the two groups of interest which remove the confounding effect of recent bottleneck. In such complex cases, associating PCA and STRUCTURE-like analyses with F-statistics and simulations allow to draw a more robust conclusion. Indeed statistics such as Fst or Dxy that estimate the genetic differentiation between two populations can be simulated under alternative scenarios, representing competing hypothesis (figure 5). These simulated statistics can be subsequently compared with the ones estimated from real data to favour one hypothesis over the other.  Simulations can also give an idea of how difficult it is to discriminate between the different hypothesis, which avoid over interpretation of the results. In the second paper, where the authors put forward an new hypothesis, radically different from the classical hypothesis of anthropology and other genetic studies, additional tests like these seem necessary to strengthen their conclusions.

Although it was not mentioned in any of the articles, the quality of the data and the way to obtain them, i.e. the kind of sequencing methodology, should also be a matter of precaution. Indeed, they all use micro arrays designed from European populations. These micro arrays consist of thousands of DNA spots containing a predefined sequence, known to be polymorphic in Europeans and only the complementary sequence can fix to this spot and be sequenced. So using these micro arrays to study the history of non european populations may be problematic as only SNPs that are variable for europeans will be targeted, probably leading to the exclusion of meaningful information for non European populations. Today, with New Generation Sequencing (NGS) there are many alternatives, such as RAD sequencing or Whole Genome Sequencing, that allow to sequence tens of thousands non-predefined SNPs.

Conclusion

To conclude, the take home messages from these three articles are :

– Social systems leading to endogamy can influence and modify rapidly and dramatically the genetic structure and patterns of humans populations.

– It is difficult to reconstruct the ancestry of human populations, especially when they involve a complex process with multiple waves of admixture.

– Clustering methods are designed to find a structure in a genetic dataset but they do not necessarily reflect real shared ancestry. Further test using other methods are required to robustly support one hypothesis.

 

Despite being the world’s most genetically diverse continent, only a handful of studies attempted to understand the genetic risks for diseases of the African populations. This study shines light not only on the genetic diversity to help learn more about the variants that are associated with malaria and hypertension, but also on the population history across sub-Saharan African populations. Beside the comprehensive map of the African variants obtained from genotypes of 1,481 individuals and whole-genome sequences of 320 individuals, authors offered a design of the array suitable to capturing variants of African populations.

Summary and comments of the paper

Population structure in SSA. Comparing ~2.2 million variants of 18 ethno-linguistic groups from sub-Saharan Africa (SSA), authors found modest differentiation among SSA populations (mean pairwise Fst = 0.019) and among Niger-Congo language groups (mean pairwise Fst = 0.009). In the article, authors suggested that the modest differentiation among Niger-Congo language group showed evidence for ‘Bantu expansion’. However, the Fig1.a shows sample distribution mostly next to the Western, East and South African coasts, rather then inside of continent where the Bantu expansion occurred, therefore indicating the sampling bias.

Furthermore, the authors found a high proportion of unshared and novel variants in Ethiopian population raising the importance of sequencing individuals across Africa.

Extending the analysis on population history in Africa, authors performed PCA analysis among African populations. The results suggested Euroasian gene flow and possible hunter-gatherer (HG) ancestry. To support the results from PCA analysis, unsupervised ADMIXTURE analysis (Fig1.c) showed similar results, with Euroasian admixture in Ethiopian population (Oromo, Amhara, Somali) and HG admixture in Biaka and Mbuti rainforest HG. Also, it is noticeable that Western Europeans and Central/Eastern Asian are well separated, indicating two branches of migration. ADMIXTURE analysis also pointed out the heterogeneous American population. The authors found that the most probable number of clusters of worlds populations in ADMIXTURE analysis is k = 18. Unfortunately, it is not clearly seen from the supplementary data that the CV error of clusters was lowest for k = 18.

The authors were interested in the more detailed gene flow effect among the African populations by masking Euroasian admixture. The results showed reduced population differentiation, suggesting that Euroasian admixture has a significant impact on those populations. Nevertheless, the authors did not discuss other possibilities of gene flow effects, such as allele surfing or allele fixation.

Population admixture in SSA. Using three population tests (f3 statistics), authors identified greatest proportion of Euroasian admixture in East Africa and HG admixture among Zulu and Sotho populations in South Africa. In the Fig2., authors showed that ancient Euroasian admixture appears in Yoruba population (~7,500-10,500 years), which gives support to Neanderthal ancestry in this African population.

Beside the observed HG admixture in South African samples, a HG admixture was also detected in Igbo populations and more recent in East Africa. The explanation of HG admixture in West and South Africa is related to Khoe-San populations, while in the East Africa is related to Mbuti rainforest HG populations dating to ~3,000 years ago.

Moreover, in the Fig 2. is observed an overlap of Euroasian and HG admixtures in East African populations (Barundi, Banyarwanda and Baganda) both dating to ~2,400-3,900 years ago. However, it was not commented in article do these populations have a presence of both admixtures or not and how is it possible.

Positive selection in SSA. The authors observed highly differentiated SNPs in two population structure approaches to inspect the positive selection due to local adaptive forces.

One approach was to observe highly differentiated SNPs between Euroasian and African populations. Beside some other locus-specific differentiations, they found evidence of differentiation in CR1 gene (chemokine receptor 1), previously reported as a gene implicated in malaria susceptibility. The authors also identified locus-specific differentiation within genes active in osmoregulation, specifically in hypertension. Given these results, the authors speculate that changes in these gene regions give basic support in differences of salt sensitivity and hypertension in sub-Saharian African populations.

Second approach observed highly differentiated SNPs among the African populations when Euroasian admixture was masked. It has not escaped to notice that the most of Euroasian admixture had main proportion in Ethiopian populations (as seen in Fig2. and Fig1c.). For that reason, masked Euroasian admixture might affect only Ethiopian population, but certainly cannot be generalized for other African populations that actually might have had a process of local adaptation. Consequently, the quote from paper “This suggests that a large proportion of differentiation observed among African populations could be due to Euroasian admixture, rather than adaptation to selective forces.” should be taken cum grano salis. The speculative reason why there is an observed Euroasian admixture in Ethiopian population is that nomadic groups survived the migration from North and cross the Sahara to inhabit current Eastern African territory.

However, the analysis of African populations with masked Euroasian admixture revealed 56 loci, together with highly differentiated variant in CSK gene region, involved in hypertension. The variant in CKS gene region showed complete linkage disequilibrium (LD) with another risk allele that correlates with latitude, giving the evidence of temperature local adaptation as a mechanism of hypertension.

Next, the authors were interested in comparison of populations situated in endemic and non-endemic regions to distinguish loci related to infectious diseases. They identified set of loci signals in gene regions for malaria, Lassa fever, trypanosomiasis and trachoma.

Designing medical genetics studies in Africa. Taking into consideration that there is a high genetic diversity on African continent, the importance to build the reference genome panel across African populations cannot be stressed enough since it enable us to shed light on most of the worlds variation. Current reference genome panels, such as HapMap and 1000Genome, were mostly built on European, American and Asian populations and they miss the African polymorphisms. This makes more difficult to recognize certain polymorphic biomarkers associated to spectrum of diseases in African populations.

Therefore, authors investigated imputation accuracy of two African populations using two different reference genome panels – 1000Genome project and ‘merged’ 1000Genome project with 320 whole genome-sequenced African individuals, respectively. They observed the slight improvement in imputation accuracy of the Sotho and Igbo populations using ‘merged’ reference genome panel (Fig3.).

Moreover, the authors compared the usefulness of current array chips to define the most favorable array design capturing African variants. Their results showed efficiency of HumanOmni2.5M array capturing >80% of common variation. Surprisingly, authors did not mention future possibilities of whole-genome sequencing in Africa that play a crucial role in modern research nor the drawbacks of microarray noisy data. The dropping costs of sequencing technology and its development would certainly bring more precise results.

Conclusion

In spite of the nicely presented results with plenty of supplementary data, the article raises lots of speculations and thoughtful discussions on migration of African populations. Furthermore, the PCA analysis in extended and supplementary data are hard to read due to many different symbols and colors. Easier representation of PCA analysis would help to distinct the patterns of African populations. However, the study provides invaluable resource of variant association information for several diseases that will increasingly improve medical diagnostics in African populations.

Gurdasani, D., Carstensen, T., Tekola-Ayele, F., Pagani, L., Tachmazidou, I., Hatzikotoulas, K., Karthikeyan, S., Iles, L., Pollard, M., Choudhury, A., Ritchie, G., Xue, Y., Asimit, J., Nsubuga, R., Young, E., Pomilla, C., Kivinen, K., Rockett, K., Kamali, A., Doumatey, A., Asiki, G., Seeley, J., Sisay-Joof, F., Jallow, M., Tollman, S., Mekonnen, E., Ekong, R., Oljira, T., Bradman, N., Bojang, K., Ramsay, M., Adeyemo, A., Bekele, E., Motala, A., Norris, S., Pirie, F., Kaleebu, P., Kwiatkowski, D., Tyler-Smith, C., Rotimi, C., Zeggini, E., & Sandhu, M. (2014). The African Genome Variation Project shapes medical genetics in Africa Nature, 517 (7534), 327-332 DOI: 10.1038/nature13997

All contents refer to the original paper (Carbone et al. Nature. 2014 Sep 11;513(7517):195-201)

Summary and personal comments

This paper concerns a study of gibbon karyotype in the perspective of their divergent evolution from ancestral primates. Gibbons, small monkeys living in South-East Asia, differ from other primates, such as great apes and Old World monkeys, for a surprising number of chromosomal rearrangements. The authors aimed to study the mechanisms underlying such an important plasticity in gibbon genome gibbon.

1) The authors sequenced and assembled the genome of a white-cheeked gibbon female (Nomascus leucogenys), ordered in 26 chromosomes (against human reference), and analyzed gibbon-human synteny breakpoints (= rupture of synteny=physical co-localization of genetic loci on the same chromosome within gibbon and human).

Fig 2a shows Oxford plots for human (axys y) versus other primates chromosomes (axys x), expressed in terms of collinear blocks of > 10 Mb. It is evident from the graphic that, when compared to other primates, gibbons present the highest rate of chromosome rearrangements, graphically visualized as a scattered instead of a linear plot (Fig2a), in particular large-scale reshuffling (as shown in Fig 2b, right part of the graphic). Examples of synteny breakpoints, such as chromosomal inversion, are shown in Fig 2c.

2) The authors analyzed various transposable elements of different primates and found that one retrotransposon, the LAVA element, is exclusive to gibbon genome. Intragenic LAVA insertions are observed particularly in genes that are important for cell division and chromosome segregation, as shown in Table 1 of the Extended Data. Authors hypothesized that antisense insertions of LAVA elements into introns could determine an early transcription termination by polyadenilation. They provided evidence supporting their hypothesis through a gene construct involving a luciferase: LAVA insertions into luciferase gene determined an early termination of luciferase transcription, as suggested by lower enzymatic activity (Fig 3 b right).

4) Moreover, authors explored LAVA families across 4 gibbon genera in order to study gibbon lineage evolution. They identified 22 LAVA subfamilies and used a maximum likelihood method to estimate LAVA age and to locate the divergence of gibbons from great apes at 16.8 Myr ago. Furthermore, they performed a WGS of the genome of 4 gibbon genera (from 2 individuals per genera) and constructed the most probable gene trees through a UPGMA method (unweighted pair group method with arithmetic mean) from a coalescent-based analysis (ABC), as shown in Fig 4a. Fig 5 from Extended Data shows the 15 top UPGMA trees for 100 kb non-overlapping sliding windows of gibbon genome. Interestingly, the most probable bifurcating species topology suggest a strikingly rapid speciation process for all 4 gibbon genera, with a beginning of speciation placed at 5 Myr ago (Fig 4b).

5) Finally, in order to investigate the features of such an adaptive evolution, authors analyzed genomic regions which could have undergone lineage-specific modifications. They identified 240 regions with gibbon-specific accelerated substitution rates (gibARs) that were not only intragenic but also co-localized with LAVA elements. They also identified genes (TBX5, COL1A1, CHRNA1, SNX19) that might have been undergone a positive selection related to gibbon-specific traits, such as longer arms or stronger shoulder/elbow muscles compared to humans.

This paper underlines important characteristics of gibbon genomes and provides novel insights into genome plasticity mechanisms of those small apes. Nevertheless, it remains largely unclear under which circumstances gibbons had undergone such an accelerated evolution and how speciation and fixation of specific traits could have been produced so rapidly.

Carbone, L., Alan Harris, R., Gnerre, S., Veeramah, K., Lorente-Galdos, B., Huddleston, J., Meyer, T., Herrero, J., Roos, C., Aken, B., Anaclerio, F., Archidiacono, N., Baker, C., Barrell, D., Batzer, M., Beal, K., Blancher, A., Bohrson, C., Brameier, M., Campbell, M., Capozzi, O., Casola, C., Chiatante, G., Cree, A., Damert, A., de Jong, P., Dumas, L., Fernandez-Callejo, M., Flicek, P., Fuchs, N., Gut, I., Gut, M., Hahn, M., Hernandez-Rodriguez, J., Hillier, L., Hubley, R., Ianc, B., Izsvák, Z., Jablonski, N., Johnstone, L., Karimpour-Fard, A., Konkel, M., Kostka, D., Lazar, N., Lee, S., Lewis, L., Liu, Y., Locke, D., Mallick, S., Mendez, F., Muffato, M., Nazareth, L., Nevonen, K., O’Bleness, M., Ochis, C., Odom, D., Pollard, K., Quilez, J., Reich, D., Rocchi, M., Schumann, G., Searle, S., Sikela, J., Skollar, G., Smit, A., Sonmez, K., Hallers, B., Terhune, E., Thomas, G., Ullmer, B., Ventura, M., Walker, J., Wall, J., Walter, L., Ward, M., Wheelan, S., Whelan, C., White, S., Wilhelm, L., Woerner, A., Yandell, M., Zhu, B., Hammer, M., Marques-Bonet, T., Eichler, E., Fulton, L., Fronick, C., Muzny, D., Warren, W., Worley, K., Rogers, J., Wilson, R., & Gibbs, R. (2014). Gibbon genome and the fast karyotype evolution of small apes Nature, 513 (7517), 195-201 DOI: 10.1038/nature13679

In a recent paper, Moreno-Estrada et al. 2014 performed a detailed genetic study of Mexican genetic diversity. The results showed the genetic stratification among indigenous populations and an association between subcontinental ancestry and lung function.

In the first part of the study, to estimate the genetic diversity, researchers examined autosomal single-nucleotide polymorphisms for more than 500 Native Mexican individuals from all around Mexico. Statistical analysis of genomic data showed that some populations within Mexico are more differentiated than European and East Asian populations. This extreme differentiation thought to be a result of isolation followed by a bottleneck and small effective population sizes.

The data was analyzed in various ways (ROH and IBD analysis, PCA etc.) and revealed the population substructure of Mexico. In all of the analysis, the results confirmed that Seri (northernmost) and Lacandon (southernmost) have the highest level of differentiation. Also, the differentiation between Seri and Lacandon was greater than average differentiation between human populations. The relationships between other populations were accordance with geography, migration and language history. When African and European genetic data were included in the analysis of native Mexicans, it had been shown that most individuals have the genetic composition of Native and European ancestry. Further analysis indicated the ancient Native American substructure was recapitulated even after postcolonial admixture.

In the second part of the study, Moreno-Estrada et al. 2014 investigated the potential biomedical applications of genetic substructure information. Previous studies indicated the relationship between forced expiratory volume in 1 second (FEV₁) could be an indicative of pulmonary disease and another study suggested that the proportion of European ancestry was associated with FEV₁ in Mexicans. Researchers measured the lung function in Mexican and Mexican-American children with asthma and correlated these findings with native ancestry. Results showed 7.3% change in FEV₁ moving from Sonora to Yucatan and researchers proposed that native ancestry could alone have effects on lung function in admixed individuals within Mexico.

Personal Comments

This paper provides novel insights to Mexican genetic diversity and proposes the biomedical applications of genetic data. The sampling locations cover most of the country and the analysis of the data in various methods gives confidence to reader. The paper is easy to follow and the figures are quite helpful.

However, I think there is a critical point that needs to be discussed from a medical point of view. As far as I know, asthma is a complex disease and thought to be caused by both genetic and environmental factors. In this study, I could not find any information about the developmental and medical history of patients. I think this is a critical point because of heterogeneous geography of the county. Where were they raised – in volcano towns, Pacific shores, Sumidero Canyon, Laguna Salada (-10m) or piedmont plains of Pico de Orizaba (5636m)? Did their mothers smoke during pregnancy? Were they born in Mexico City – the city named as “the most polluted city on the planet” by United Nations in 1992? I hope the researchers have already checked for this type information and found them unnecessary to include.

Nevertheless, this is an interesting paper and shows the genetic history of Mexico – before and after 16^th century. I recommend reading this paper and discussing with a medical doctor

Moreno-Estrada, A., Gignoux, C., Fernandez-Lopez, J., Zakharia, F., Sikora, M., Contreras, A., Acuna-Alonzo, V., Sandoval, K., Eng, C., Romero-Hidalgo, S., Ortiz-Tello, P., Robles, V., Kenny, E., Nuno-Arana, I., Barquera-Lozano, R., Macin-Perez, G., Granados-Arriola, J., Huntsman, S., Galanter, J., Via, M., Ford, J., Chapela, R., Rodriguez-Cintron, W., Rodriguez-Santana, J., Romieu, I., Sienra-Monge, J., Navarro, B., London, S., Ruiz-Linares, A., Garcia-Herrera, R., Estrada, K., Hidalgo-Miranda, A., Jimenez-Sanchez, G., Carnevale, A., Soberon, X., Canizales-Quinteros, S., Rangel-Villalobos, H., Silva-Zolezzi, I., Burchard, E., & Bustamante, C. (2014). The genetics of Mexico recapitulates Native American substructure and affects biomedical traits Science, 344 (6189), 1280-1285 DOI: 10.1126/science.1251688

Gibbons are small apes living in southeast Asia that diverged between Old Monkeys and great apes and whose most distinctive feature is the high rate of evolutionary chromosomal rearrangement.

The aim of this study was threefold: First, the authors looked into the mechanisms that could explain the extraordinary rate of chromosomal rearrangement of gibbons. Second, they explored their evolutionary history to shed light into the timing and order of splitting of the gibbon genera. Third, they looked into the functional evolution of genes that might be associated with gibbon-specific adaptations.

To do so, they sequenced and assembled the genome of the white-cheeked gibbon (Nomascus leucogenys), showing that the quality and statistics of the assembled genome was comparable to that of other primates (Table 1 and Fig.S1).

Chromosomal rearrangement and LAVA insertions

Chromosomal rearrangement was confirmed by comparing the karyotype of the assembled Gibbon genome (Nleu1.0) to that of human. Figure 2A shows the extraordinarily high number of rearrangements compared to other primates. Furthermore these reshuffling events affect long stretches of chromosomes (displayed in Fig.2A are collinear blocks larger than 10Mb), whereas short-scale rearrangement events occur at levels comparable to other primates (Fig.2B).

Since the four Gibbon genera of this study differ themselves in chromosome number (ranging from 38 to 52), it would be interesting to have also a global view of the large-scale chromosomal rearrangement of the other genera compared to human, as well as the differences in karyotype among the four species.

Next the authors classified the 94 identified gibbon-human synteny breakpoints in two classes, depending on whether the breakpoint could be defined at base-pair level or at interval level (exemplified in Fig.2C). In the latter case, authors observed that repetitive sequences tend to accumulate at the synteny intervals.

In order to investigate the possible mechanism underlying the increased rate of chromosomal reshuffling, Carbone et al. searched for LAVA insertions in the gibbon genome. LAVA elements are retrotransposons unique to gibbons, with a structure that combines parts of other repeats (Fig. 3A). More than 1200 functional LAVA insertions were found in the assembled genome, of which a significant proportion overlap with genes related to chromosome segregation. Moreover LAVA elements were found to lie within introns and mostly in the antisense orientation.

In a series of reporter assays using a luciferase construct in which the transcription termination site has been replaced with the 3’ end of LAVA elements (LAVA_E or LAVA_F, Fig.3B) in antisense orientation, the authors showed that the termination site provided by the LAVA element can cause the premature termination of the transcript (Fig.3B). However this was the case for only one of the two constructs (LAVA_F but not LAVA_E). Given the presence of several subfamilies of LAVA elements (as illustrated in Fig.3C) it would then be interesting to see if their hypothesis of intronic antisense LAVA insertions causing early transcription termination in genes related to chromosome segregation holds for more of these elements or whether LAVA_F and not LAVA_E elements are specifically enriched in the genes of interest.

Evolutionary history of gibbons

In order to study gibbon phylogeny and demography, Carbone et al. sequenced the genomes of two individuals from each genus (Nomascus, Hylobates, Hoolock, Symphalangus, see figure 1 for geographic distribution) to a medium coverage and constructed phylogenetic trees by UPGMA and ABC analyses. At least three UPGMA trees are observed with similar frequency (Fig.4A), therefore leaving still open the debate of the splitting order of the genera.

On the other hand, the short length of the internal branches in the best phylogenetic tree is suggestive of a fast speciation process, or even a nearly instantaneous appearance of all four genera around 5 millions years ago (Fig.4B) that would explain the difficulty to discern the order and timing of speciation.

Functional genome evolution

Carbone et al. found 240 short regions with and increased substitution rate, a proxy of adaptive and functional evolution. Moreover these regions co-localized in genes containing LAVA elements and therefore enriched for chromosome segregation-related pathways. The authors hypothesized that, similar to humans, these hotspots of accelerated substitutions can have a functional role by modulating the transcriptional termination of LAVA insertions. However, it is important to notice that the functional relevance of such accelerated regions in human is still a matter of debate.

Finally the study revealed the positive selection, solely in gibbons, of a series of genes responsible for the specific features of these animals, such as the longer and powerful arm muscles.

In summary, the fundamental finding of this study is the presence of gibbon-specific LAVA insertions in genes responsible for chromosome organization which, although it does not prove causality, provides an interesting and plausible molecular mechanisms that would explain the strikingly high rate of large chromosomal rearrangements observed in these species.

Carbone, L., Alan Harris, R., Gnerre, S., Veeramah, K., Lorente-Galdos, B., Huddleston, J., Meyer, T., Herrero, J., Roos, C., Aken, B., Anaclerio, F., Archidiacono, N., Baker, C., Barrell, D., Batzer, M., Beal, K., Blancher, A., Bohrson, C., Brameier, M., Campbell, M., Capozzi, O., Casola, C., Chiatante, G., Cree, A., Damert, A., de Jong, P., Dumas, L., Fernandez-Callejo, M., Flicek, P., Fuchs, N., Gut, I., Gut, M., Hahn, M., Hernandez-Rodriguez, J., Hillier, L., Hubley, R., Ianc, B., Izsvák, Z., Jablonski, N., Johnstone, L., Karimpour-Fard, A., Konkel, M., Kostka, D., Lazar, N., Lee, S., Lewis, L., Liu, Y., Locke, D., Mallick, S., Mendez, F., Muffato, M., Nazareth, L., Nevonen, K., O’Bleness, M., Ochis, C., Odom, D., Pollard, K., Quilez, J., Reich, D., Rocchi, M., Schumann, G., Searle, S., Sikela, J., Skollar, G., Smit, A., Sonmez, K., Hallers, B., Terhune, E., Thomas, G., Ullmer, B., Ventura, M., Walker, J., Wall, J., Walter, L., Ward, M., Wheelan, S., Whelan, C., White, S., Wilhelm, L., Woerner, A., Yandell, M., Zhu, B., Hammer, M., Marques-Bonet, T., Eichler, E., Fulton, L., Fronick, C., Muzny, D., Warren, W., Worley, K., Rogers, J., Wilson, R., & Gibbs, R. (2014). Gibbon genome and the fast karyotype evolution of small apes Nature, 513 (7517), 195-201 DOI: 10.1038/nature13679