The goal of the Integrated Biostatistics and Bioinformatics Analysis Core (IBBAC; pronounced "eye-back") is to[unreadable] provide analysis tools, data analyses, and access to state-of-the-field tools, expertise, and leadership for the[unreadable] integrated or combined analysis of data arising from the Clinical Phenotype: Recruitment and Clinical[unreadable] Assessment (B) and Clinical Phenotype: Treatment Response (C) Cores as well as the four proposed projects.[unreadable] The IBBAC will take advantage of both existing analysis methods and tools for high-dimensional data types[unreadable] (e.g., partial least squares, support vector machines, cluster analysis techniques, etc.) as well as novel[unreadable] methods and extensions of existing approaches for analyzing the data generated by the UCSD ACE[unreadable] researchers. The ultimate aim of this research is to identify a unique set of clinical, subclinical (e.g.,[unreadable] imaging-based phenotypes), and genomic endpoints (or "fingerprints") that are correlated with Autism[unreadable] Spectrum Disorder (ASD) and/or Developmental Delay (DD) and are distinct from features found in[unreadable] typically-developing children. The biological-meaning of the identified "fingerprints" of ASD and DD[unreadable] emerging from these analyses will be a major consideration in assessing their validity; i.e., consistency of[unreadable] these fingerprints with the main motivating hypothesis of the center, which is that early postnatal brain[unreadable] overgrowth is the hallmark of ASD/DD pathogenesis. The need for novel multivariate data analysis methods in[unreadable] neuropsychiatric and behavioral genetics research of the type proposed has grown considerably with the[unreadable] introduction of data intensive technologies such as large-scale genotyping assays and gene expression[unreadable] microarrays. In addition, information-intensive phenotyping assays such as imaging technologies, multiplex[unreadable] behavorial assessments/elaborate psychometric exams, and large-scale endophenotype and/or cognitive[unreadable] assessment strategies - that could be used to complement genomic technologies - have been introduced[unreadable] which create further needs for appropriate multivariate analysis methods. Although there is considerable[unreadable] research in the development of mathematical models of multiparameter biological processes (e.g., gene[unreadable] transcription) as well as data mining/pattern discovery strategies for genomic technologies, there is less[unreadable] research on, and actual implementation of, the development of hypothesis-oriented multivariate data analysis[unreadable] methodologies that consider the information produced by genomic and multiplex phenotyping technologies[unreadable] either in isolation or in combination. The proposed IBBAC activity will consider the development, deployment,[unreadable] and interpretation of novel multivariate analysis methods appropriate for drawing meaningful inferences from[unreadable] the high-dimensional genomic and phenotypic data generated as part of the proposed UCSD ACE research.[unreadable] Some of the proposed data analysis methodologies build off and extend a few fundamental multivariate[unreadable] techniques (e.g., the analysis of similarity and distance, multivariate regression, and variance component[unreadable] models).