Common variation near ROBO2 is associated with expressive vocabulary in infancy

Pourcain, Beaté St; Cents, Rolieke A. M.; Whitehouse, Andrew J. O.; Haworth, Claire M. A.; Davis, Oliver S. P.; O’Reilly, Paul; Roulstone, Sue; Wren, Yvonne; Ang, Q.W.; Velders, Fleur P.; Evans, David M.; Kemp, John P.; Warrington, Nicole M.; Miller, Laura L.; Timpson, Nicholas J.; Ring, Susan M.; Verhulst, Frank C.; Hofman, Albert; Rivadeneira, Fernando; Meaburn, Emma L.; Price, Thomas S.; Dale, Philip S.; Pillas, Demetris; Yliherva, Anneli; Rodriguez, Alina; Golding, Jean; Jaddoe, Vincent W.V.; Järvelin, Marjo‐Riitta; Plomin, Robert; Pennell, Craig E.; Tiemeier, Henning; Smith, George Davey

doi:10.1038/ncomms5831

Cited by 89 publications

(89 citation statements)

References 61 publications

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“…SLITs proteins act as attractive or repulsive signals for axons expressing different ROBO receptors. ROBO2 has been further associated with vocabulary growth (St Pourcain et al 2014), autism (Suda et al 2011), and dyslexia (Fisher and DeFries 2002) and is involved in the development of neural circuits related to vocal learning in birds (Wang et al 2015). Interestingly, ROBO2 is also in a long desert of both Denisovan and Neandertal ancestry in non-Africans.…”

Section: Discussionmentioning

confidence: 99%

Detecting ancient positive selection in humans using extended lineage sorting

et al. 2017

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Natural selection that affected modern humans early in their evolution has likely shaped some of the traits that set presentday humans apart from their closest extinct and living relatives. The ability to detect ancient natural selection in the human genome could provide insights into the molecular basis for these human-specific traits. Here, we introduce a method for detecting ancient selective sweeps by scanning for extended genomic regions where our closest extinct relatives, Neandertals and Denisovans, fall outside of the present-day human variation. Regions that are unusually long indicate the presence of lineages that reached fixation in the human population faster than expected under neutral evolution. Using simulations, we show that the method is able to detect ancient events of positive selection and that it can differentiate those from background selection. Applying our method to the 1000 Genomes data set, we find evidence for ancient selective sweeps favoring regulatory changes and present a list of genomic regions that are predicted to underlie positively selected human specific traits.[Supplemental material is available for this article.]Modern humans differ from their closest extinct relatives, Neandertals, in several aspects, including skeletal and skull morphology (Weaver 2009), and may also differ in other traits that are not preserved in the archeological record (Varki et al. 2008;Laland et al. 2010). Natural selection may have played a role in fixing these traits on the modern human lineage. However, the selection events driving the fixation would have been restricted to a specific timeframe, extending from the split between archaic and modern humans ca. 650,000 yr ago to the split of modern human populations from each other around 100,000 yr ago . While methods exist that can be used to scan the genome for the remnants of past or ongoing positive selection (Nielsen et al. 2007; Pybus and Shapiro 2009), current methods have limited power to detect positive selection on the human lineage that acted during this older timeframe (see Sabeti et al. 2006 for a review on detection methods and their timeframes): an unusually high ratio of functional changes to nonfunctional changes, such as the d N /d S test, requires millions of years and often multiple events of selection to generate detectable signals (Kryazhimskiy and Plotkin 2008), while unusual patterns of genetic diversity between individuals and populations (e.g., extended homozygosity, Tajima's D, F ST ) are most powerful during the selective sweep or shortly after (Sabeti et al. 2006;Oleksyk et al. 2010). A favorable substitution is not expected to leave a mark on linked neutral variation beyond 250,000 yr in humans (Przeworski 2002(Przeworski , 2003.The genome sequencing of archaic humans (Neandertals and Denisovans) to high coverage (Meyer et al. 2012;Prüfer et al. 2014) has spawned new methods to investigate the genetic basis of modern human traits that are not shared by the archaics (Pääbo 2014). One method, called 3P-CLR, models allele ...

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Section: Discussionmentioning

confidence: 99%

Detecting ancient positive selection in humans using extended lineage sorting

et al. 2017

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“…But there are many other disorders with genetic bases where genomes of afflicted individuals shed light on genes involved in language (I will call these Blanguage-related genes,^or LRGs). Relevant disorders can either be rather specific to components of language, like FOXP2, specific language impairment (van der Lely & Pinker, 2014), or dyslexia (Mozzi et al, 2016;St Pourcain et al, 2014;Wang, Chen, et al, 2015), or they can be broader disorders like autism, which have important consequences for, but are not specific to, language (Graham & Fisher, 2015;Raff, 2014;RodenasCuadrado, Ho, & Vernes, 2014). A particularly interesting result involves CNTNAP2, a gene coding for a neurexin specifically expressed in the human cortex and involved in cortical development.…”

Section: The Short Time Scale: Comparing Human Populationsmentioning

confidence: 99%

Empirical approaches to the study of language evolution

2017

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The study of language evolution, and human cognitive evolution more generally, has often been ridiculed as unscientific, but in fact it differs little from many other disciplines that investigate past events, such as geology or cosmology. Well-crafted models of language evolution make numerous testable hypotheses, and if the principles of strong inference (simultaneous testing of multiple plausible hypotheses) are adopted, there is an increasing amount of relevant data allowing empirical evaluation of such models. The articles in this special issue provide a concise overview of current models of language evolution, emphasizing the testable predictions that they make, along with overviews of the many sources of data available to test them (emphasizing comparative, neural, and genetic data). The key challenge facing the study of language evolution is not a lack of data, but rather a weak commitment to hypothesistesting approaches and strong inference, exacerbated by the broad and highly interdisciplinary nature of the relevant data. This introduction offers an overview of the field, and a summary of what needed to evolve to provide our species with language-ready brains. It then briefly discusses different contemporary models of language evolution, followed by an overview of different sources of data to test these models. I conclude with my own multistage model of how different components of language could have evolved.

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“…Attention must be given to the encapsulation and crosslinking materials, as they must remain inert in the long run and should not lead to any immune responses. Considerable evidences suggest [70][71][72][73][74][75][76][77][78] the association of pancreatic β cells with other networks, indicating certain 'cross-talks' among the heterodermal derivatives. These studies warn that the islets, albeit unique, are not isolated entities, but are in continuous equilibrium with other physiological systems.…”

Section: Discussionmentioning

confidence: 99%

The Islet Confidential: Recent Trends and Perspectives in Pancreatic Islet Transplantation

Radha¹,

Muniraj

Rengasamy

et al. 2016

IJPPE

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Diabetes ranks among the top 5 killer diseases of the current world population. Transplantation of pancreatic islets is a common surgical procedure used to combat the late stage diabetic complications. A successful and long lasting islet transplant is an enigma as the complex immunoactivation mechanisms against the transplants, the subsequent graft rejection and the proper maturation and functioning of the islets in the host microenvironment, are the subjects of research for many years. This review details certain recent studies performed upon primate, porcine, murine and rabbit models, in relation to islet transplantation, with a critical standpoint.

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Common variation near ROBO2 is associated with expressive vocabulary in infancy

Cited by 89 publications

References 61 publications

Detecting ancient positive selection in humans using extended lineage sorting

Detecting ancient positive selection in humans using extended lineage sorting

Empirical approaches to the study of language evolution

The Islet Confidential: Recent Trends and Perspectives in Pancreatic Islet Transplantation

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