- A genome-wide scan for common alleles affecting risk for autism (2010)
- Although autism spectrum disorders (ASDs) have a substantial genetic basis, most of the known genetic risk has been traced to rare variants, principally copy number variants (CNVs). To identify common risk variation, the Autism Genome Project (AGP) Consortium genotyped 1558 rigorously defined ASD families for 1 million single-nucleotide polymorphisms (SNPs) and analyzed these SNP genotypes for association with ASD. In one of four primary association analyses, the association signal for marker rs4141463, located within MACROD2, crossed the genome-wide association significance threshold of P < 5 3 10 exp -8. When a smaller replication sample was analyzed, the risk allele at rs4141463 was again over-transmitted; yet, consistent with the winner’s curse, its effect size in the replication sample was much smaller; and, for the combined samples, the association signal barely fell below the P < 5 3 10 exp -8 threshold. Exploratory analyses of phenotypic subtypes yielded no significant associations after correction for multiple testing. They did, however, yield strong signals within several genes, KIAA0564, PLD5, POU6F2, ST8SIA2 and TAF1C.
- Individual common variants exert weak effects on the risk for autism spectrum disorders (2012)
- While it is apparent that rare variation can play an important role in the genetic architecture of autism spectrum disorders (ASDs), the contribution of common variation to the risk of developing ASD is less clear. To produce a more comprehensive picture, we report Stage 2 of the Autism Genome Project genome-wide association study, adding 1301 ASD families and bringing the total to 2705 families analysed (Stages 1 and 2). In addition to evaluating the association of individual single nucleotide polymorphisms (SNPs), we also sought evidence that common variants, en masse, might affect the risk. Despite genotyping over a million SNPs covering the genome, no single SNP shows significant association with ASD or selected phenotypes at a genome-wide level. The SNP that achieves the smallest P-value from secondary analyses is rs1718101. It falls in CNTNAP2, a gene previously implicated in susceptibility for ASD. This SNP also shows modest association with age of word/phrase acquisition in ASD subjects, of interest because features of language development are also associated with other variation in CNTNAP2. In contrast, allele scores derived from the transmission of common alleles to Stage 1 cases significantly predict case status in the independent Stage 2 sample. Despite being significant, the variance explained by these allele scores was small (Vm< 1%). Based on results from individual SNPs and their en masse effect on risk, as inferred from the allele score results, it is reasonable to conclude that common variants affect the risk for ASD but their individual effects are modest.
- Using high resolution digital aerial imagery interpreted in a 3-D digital GIS environment to map predefined plant communities in central-southern New South Wales (2012)
- Aerial photo interpretation of high resolution airborne imagery (ADS40) was used in a three-dimensional (3-D) digital Geographic Information System (GIS) environment to map native plant communities defined in the NSW Vegetation Classification and Assessment (NSW VCA) in central-southern New South Wales. NSW VCA plant community types form part of the NSW BioMetric vegetation type dataset underpinning NSW natural resource management (NRM) planning frameworks. This region was previously devoid of detailed vegetation mapping. In addition to developing a novel method for mapping plant communities, the use of ADS40 imagery allowed for capture of multiple attributes in each map polygon including attributes pertaining to dominant species and vegetation condition. Such data informs multi-attribute models used in conservation planning, providing utility beyond that of a singular plant community map. A total of 546,150 hectares of native vegetation in 100 native plant communities was mapped across the study area (Coolamon, Cootamundra, Junee, Lockhart, Narrandera, Tarcutta, Urana, Wagga Wagga and Yanco 1:100,000 mapsheets and Ariah Park, Wallaroobie Range and Yoogali 1:50,000 mapsheets). Exotic pine plantations and native species plantings were also mapped. Remnants of greater than one hectare were captured through on-screen GIS digitising at scales of approximately 1:4,000. The plant community type mapping was independently assessed using random blind validation points as having a user accuracy of 87%. This level of accuracy demonstrates the applicability of the methodology for mapping open forests, woodlands and open woodlands of south-eastern Australia and probably other vegetation elsewhere. Such accurate mapping provides end users with confidence when using vegetation maps in environmental assessment and land use planning.