De novo CNVs are associated with advanced paternal age in a mouse model
While the association between advanced paternal age and an increased risk of various neuropsychiatric disorders such as schizophrenia and autism is now well established, the mechanism underpinning this finding remains unclear. Putative mechanisms include de-novo mutations and/or epigenetic mechanisms. In light of the growing body of evidence linking copy number variants (CNVs) with these same disorders, we used a mouse model to explore the hypothesis that the offspring of older males have an increased risk of de-novo CNVs. C57BL/6J sires that were three- and 12-16 months old were mated with three-month-old dams to create control offspring and offspring of old sires, respectively. Applying genomewide microarray screening technology, seven distinct CNVs were identified in a set of 12 offspring and their parents.

Competitive quantitative PCR confirmed these CNVs in the original set and also established their frequency in an independent set of 77 offspring and their parents. On the basis of...  Read more

De novo CNVs are associated with advanced paternal age in a mouse model
While the association between advanced paternal age and an increased risk of various neuropsychiatric disorders such as schizophrenia and autism is now well established, the mechanism underpinning this finding remains unclear. Putative mechanisms include de-novo mutations and/or epigenetic mechanisms. In light of the growing body of evidence linking copy number variants (CNVs) with these same disorders, we used a mouse model to explore the hypothesis that the offspring of older males have an increased risk of de-novo CNVs. C57BL/6J sires that were three- and 12-16 months old were mated with three-month-old dams to create control offspring and offspring of old sires, respectively. Applying genomewide microarray screening technology, seven distinct CNVs were identified in a set of 12 offspring and their parents.

Competitive quantitative PCR confirmed these CNVs in the original set and also established their frequency in an independent set of 77 offspring and their parents. On the basis of the combined samples, six de-novo CNVs were detected in the offspring of older sires, whereas none were detected in the control group. Two of the CNVs were associated with behavioral and/or neuroanatomical phenotypic features. One of the de-novo CNVs involved Auts2 (autism susceptibility candidate 2), and other CNVs included genes linked to schizophrenia, autism, and brain development.

Our results support the hypothesis that the offspring of older fathers have an increased risk of neurodevelopmental disorders such as schizophrenia and autism by generation of de-novo CNVs in the male germline.

References:

T Flatscher-Bader, CJ Foldi, S Chong, E Whitelaw, RJ Moser, THJ Burne, DW Eyles, JJ McGrath. (2011) Increased de novo copy number variants in the offspring of older males. Translational Psychiatry. View the free full-text article.

This is another excellent genomics study from the Geschwind laboratory, challenging us to think in the context of gene networks (rather than single genes). We always knew that genome deletion, duplications, and mutations will have an effect on development and cellular function only if they ultimately affect gene expression, but rarely has this been proven so eloquently as in this study. Knowing a genetic alteration is not sufficient—the consequences are what matter—and establishing the relevance/causality of mutations and CNVs vis-à-vis a disease process is quite challenging. Combining genetics and genomics can help to achieve this, especially if the expression studies can be performed on peripheral tissues from living patients. Still, even this approach has limitations: the brain has a very different expression profile from peripheral tissues—and the real effect of altered genetic sequence cannot be evaluated if a gene is uniquely expressed in the brain. Furthermore, we know that in schizophrenia, expression events in the periphery and the CNS are only...  Read more

This is another excellent genomics study from the Geschwind laboratory, challenging us to think in the context of gene networks (rather than single genes). We always knew that genome deletion, duplications, and mutations will have an effect on development and cellular function only if they ultimately affect gene expression, but rarely has this been proven so eloquently as in this study. Knowing a genetic alteration is not sufficient—the consequences are what matter—and establishing the relevance/causality of mutations and CNVs vis-à-vis a disease process is quite challenging. Combining genetics and genomics can help to achieve this, especially if the expression studies can be performed on peripheral tissues from living patients. Still, even this approach has limitations: the brain has a very different expression profile from peripheral tissues—and the real effect of altered genetic sequence cannot be evaluated if a gene is uniquely expressed in the brain. Furthermore, we know that in schizophrenia, expression events in the periphery and the CNS are only marginally overlapping, and a number of neurotransmitters and phenotype-specific functional proteins are not expressed outside of the brain. In these cases, inhibitory postsynaptic currents (IPSCs) might be very beneficial to study the cause-effect relationship and in evaluating the significance of the genetic alterations. However, the approach of the Geschwind lab is perfectly suited for evaluation of multifunctional, generic developmental gene networks, where the genes are expressed and play a similar role across various tissues. It is also important to expand these studies to many patients across a variety of human brain disorders; otherwise, due to the staggering number of genetic variations, we might not be able to recognize the common patterns that are essential for understanding mental disorders.

Cognitive function is highly heritable (Devlin et al., 1997), yet we have relatively little understanding of which genes regulate either general intelligence or specific cognitive functions. A long list of mutations can cause the large cognitive impairments that we class as learning disability—including many of the same CNVs associated with cognitive disorders such as autism and schizophrenia (Guilmatre et al., 2009). Prior to the GWAS era, it was generally assumed that normal variation—outside of the range of learning disability—would be regulated by common variants of small effect. Yet GWAS, a technology well suited to detecting common variants of small effect, has not massively increased our understanding of the genetic basis of cognition.

One explanation for this relative lack of success is that, compared with quantitative phenotypes such as height or BMI, the measurement of cognition can be time consuming and therefore costly, so really large-scale studies...  Read more

Cognitive function is highly heritable (Devlin et al., 1997), yet we have relatively little understanding of which genes regulate either general intelligence or specific cognitive functions. A long list of mutations can cause the large cognitive impairments that we class as learning disability—including many of the same CNVs associated with cognitive disorders such as autism and schizophrenia (Guilmatre et al., 2009). Prior to the GWAS era, it was generally assumed that normal variation—outside of the range of learning disability—would be regulated by common variants of small effect. Yet GWAS, a technology well suited to detecting common variants of small effect, has not massively increased our understanding of the genetic basis of cognition.

One explanation for this relative lack of success is that, compared with quantitative phenotypes such as height or BMI, the measurement of cognition can be time consuming and therefore costly, so really large-scale studies are rare. Moreover, any two studies are very unlikely to use identical cognitive tests, introducing error to the phenotypic measurement and making it difficult to combine datasets. In this context, the Nithianantharajah paper is a beautiful example of how translational research using relatively simple but neuroscience-led assays can increase our understanding of the genetics of cognition, while avoiding the need for the ever-increasing sample sizes.

In particular, I was impressed by the building up of related but increasingly complex forms of cognition across the rodent tasks, and the use of the closest possible analogues between mouse and human assays. (Disclosure: I am employed by Cambridge Cognition, the suppliers of the CANTAB tests used in the human phenotyping.) Adding to these very careful phenotyping methodologies, the parallel experiment across both mouse and human "knockouts" is a really elegant piece of translational neuroscience.

Like many traits underlying brain function, it is inherently easy to believe that variants that have large effects on cognition would create strong evolutionary advantages or disadvantages. The authors here demonstrate not only that a related family of genes affects multiple aspects of cognitive function, but also that variation in these genes produces different cognitive tendencies in the mouse, including reciprocal effects of variants of Dlg2 and Dlg3, which seem, at least at first pass, to be conserved in human behavior.

There is a lot to digest in this and the companion paper by Ryan et al., but the methodology appears to have been very useful here in understanding cognition, and may provide useful insights for researchers trying to decipher other aspects of the schizophrenia phenotype.

References:

Devlin B, Daniels M, Roeder K (1997). The heritability of IQ. Nature.388(6641):468-71. Abstract

Guilmatre A, Dubourg C, Mosca AL, Legallic S, Goldenberg A, Drouin-Garraud V, Layet V, Rosier A, Briault S, Bonnet-Brilhault F, Laumonnier F, Odent S, Le Vacon G, Joly-Helas G, David V, Bendavid C, Pinoit JM, Henry C, Impallomeni C, Germano E, Tortorella G, Di Rosa G, Barthelemy C, Andres C, Faivre L, Frébourg T, Saugier Veber P, Campion D. (2009). Recurrent rearrangements in synaptic and neurodevelopmental genes and shared biologic pathways in schizophrenia, autism, and mental retardation. Arch Gen Psychiatry;66(9):947-56. Abstract