Skip to main content
Premium Trial:

Request an Annual Quote

Genomics in the Journals: Dec 19, 2013

NEW YORK (GenomeWeb News) – In Nature Genetics, an international team led by investigators at the University of Michigan, the Broad Institute, and Massachusetts General Hospital presented a computational strategy for performing rare variant association meta-analyses at the gene level.

The researchers found that bringing together statistics from the individual association studies included in a given meta-analysis made it possible to do centralized gene-level association testing.

After trying out this approach with simulated data, the group took a crack at applying it to data for more than 18,699 individuals of European ancestry with known blood lipid levels who had been genotyped using exome arrays for past studies. There, the strategy unearthed gene-level associations and rare variants at known lipid-related risk loci.

"Using simulation and empirical evaluation, we demonstrate that our approach is well calibrated and provides comparable power to more cumbersome analyses that require that all individual-level data be pooled," the study's authors wrote.

"We envision that this approach (and continued development of related approaches) will facilitate the large sample sizes required to accelerate discoveries in complex trait genetics," they concluded.

Through a meta-analysis of data for roughly 5,000 individuals enrolled through prior genome-wide association studies of cognitive function, members of the Cognitive Genomics consortium (COGENT) developed polygenic risk scores that subsequently proved useful for distinguishing individuals with schizophrenia from those without.

Conversely, when they came up with polygenic schizophrenia risk scores, the investigators found that those variant sets tended to coincide with individuals at the lower end of the memory, attention, and language skills spectrum in cognitive studies.

As the team reported in Molecular Psychiatry, findings from the study not only lend credence to the existence of distinct schizophrenia endophenotypes, but also point to some degree of genetic overlap between variants influencing cognitive abilities and those contributing to schizophrenia risk.

"This research leads us to a deeper understanding of how schizophrenia affects the brain at the molecular level," first author Todd Lencz, a psychiatry researcher with the Feinstein Institute for Medical Research, said in a statement. "Our studies are designed to provide clues to the development of new treatments to improve the lives of our patients."

Researchers reporting in Nature found additional evidence of genetic overlap between schizophrenia and cognitive function: their analysis indicated that some of the same copy number variants that are over-represented in some individuals with schizophrenia or autism spectrum disorder cases may also impact the cognitive performance of seemingly healthy individuals who haven't been diagnosed with those conditions.

Decode Genetics CEO Kári Stefánsson and colleagues from several international centers started by looking at genotyping data for Icelandic individuals born prior to 1968, focusing on 26 CNVs previously linked to schizophrenia and ASD. There, they noted that three of the CNVs tended to turn up more often in individuals who had fewer children by the age of 45 years old.

When the team focused in on neuropsychological test results for a group of nearly 1,300 individuals — including individuals with or without schizophrenia and with or without CNVs implicated in the neuropsychiatric conditions — it saw apparent ties between such CNVs and cognitive function in the healthy control individuals.

Preliminary findings hint that some aspects of that effect may vary depending on the particular CNV involved. For example, in the Icelandic individuals tested, a particular chromosome 15 deletion was more common amongst individuals who experienced problems with reading or mathematics, while half a dozen other CNVs seemed to coincide with lower-than-usual scores on verbal or other tests.

"[T]hese results raise important questions as to why certain mutations impact some individuals more dramatically than others," Stefánsson said in a statement.

"Our results suggest that neuropsychatric CNVs can be used as an instrument for further study of the cognitive abnormalities that characterize schizophrenia," he added. "The findings also provide insight into which cognitive abilities put individuals at risk of developing schizophrenia and demonstrate that control carriers provide an opportunity to study cognitive abnormalities without the confounding effects of psychosis or medication."

A German- and Spanish-led group has generated a reference genome for the sugar beet plant Beta vulgaris ssp. vulgaris. As they reported in Nature, the researchers used a combination of Illumina, Roche 454, and Sanger sequencing to generate both genome sequence and genotyping-by-sequencing data from a double haploid sugar beet plant.

The genome sequence was assembled into a 567 million base RefBeet assembly, including nearly 395 million bases that were successfully assigned to one of the plant's nine chromosomes.

The team's analysis of the sequences revealed a slew of repeat sequences, along with 27,241 predicted protein-coding genes that could be verified using transcriptome sequence data. Re-sequencing data from four more sugar beet accessions made it possible to track down some 7 million variants in the plant's genome, with roughly 2.9 million variants appearing in each newly sequenced non-reference genome.

Along with clues to sugar beet biology, the investigators used the sugar beet reference genome — together with a new genome sequence for spinach — to delve into the diversity, relationships, and evolution of plants from the Caryophylalles order.

A Science study highlighted a mouse mutation that apparently affects the animals' susceptibility to drugs such as cocaine and methamphetamine.

Researchers from the University of Texas Southwestern Medical Center and elsewhere used quantitative trait locus mapping in mouse crosses, followed by genome sequencing and mouse knockout experiments, to narrow in on a variant in the Cyfip2 gene that causes lower-than-usual drug response in a mouse strain known as C57BL/6N — a sub-strain of the inbred mouse strain C57BL/6J that was used to generate the mouse reference genome.

"We propose that CYFIP2 is a key regulator of cocaine response in mammals," the study's authors wrote, "and present a framework to use mouse sub-strains to identify previously unknown genes and alleles regulating behavior."