Browsing by Author "Li, Qingqin"

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    Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers
    (Springer, 2014) Shen, Li; Thompson, Paul M.; Potkin, Steven G.; Bertram, Lars; Farrer, Lindsay A.; Foroud, Tatiana M.; Green, Robert C.; Hu, Xiaolan; Huentelman, Matthew J.; Kim, Sungeun; Kauwe, John S. K.; Li, Qingqin; Liu, Enchi; Macciardi, Fabio; Moore, Jason H.; Munsie, Leanne; Nho, Kwangsik; Ramanan, Vijay K.; Risacher, Shannon L.; Stone, David J.; Swaminathan, Shanker; Toga, Arthur W.; Weiner, Michael W.; Saykin, Andrew J.; Alzheimer’s Disease Neuroimaging Initiative; Medical and Molecular Genetics, School of Medicine
    The Genetics Core of the Alzheimer's Disease Neuroimaging Initiative (ADNI), formally established in 2009, aims to provide resources and facilitate research related to genetic predictors of multidimensional Alzheimer's disease (AD)-related phenotypes. Here, we provide a systematic review of genetic studies published between 2009 and 2012 where either ADNI APOE genotype or genome-wide association study (GWAS) data were used. We review and synthesize ADNI genetic associations with disease status or quantitative disease endophenotypes including structural and functional neuroimaging, fluid biomarker assays, and cognitive performance. We also discuss the diverse analytical strategies used in these studies, including univariate and multivariate analysis, meta-analysis, pathway analysis, and interaction and network analysis. Finally, we perform pathway and network enrichment analyses of these ADNI genetic associations to highlight key mechanisms that may drive disease onset and trajectory. Major ADNI findings included all the top 10 AD genes and several of these (e.g., APOE, BIN1, CLU, CR1, and PICALM) were corroborated by ADNI imaging, fluid and cognitive phenotypes. ADNI imaging genetics studies discovered novel findings (e.g., FRMD6) that were later replicated on different data sets. Several other genes (e.g., APOC1, FTO, GRIN2B, MAGI2, and TOMM40) were associated with multiple ADNI phenotypes, warranting further investigation on other data sets. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future studies employing next-generation sequencing and convergent multi-omics approaches, and for clinical drug and biomarker development.
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    Translating genome-wide association findings into new therapeutics for psychiatry
    (Nature, 2016-11) Breen, Gerome; Li, Qingqin; Roth, Bryan L.; O'Donnell, Patricio; Didriksen, Michael; Dolmetsch, Ricardo; O'Reilly, Paul; Gaspar, Helena; Manji, Husseini; Huebel, Christopher; Kelsoe, John R.; Malhotra, Dheeraj; Bertolino, Alessandro; Posthuma, Danielle; Sklar, Pamela; Kapur, Shitij; Sullivan, Patrick F.; Collier, David A.; Edenberg, Howard J.; Department of Biochemistry & Molecular Biology, IU School of Medicine
    Genome-wide association studies (GWAS) in psychiatry, once they reach sufficient sample size and power, have been enormously successful. The Psychiatric Genomics Consortium (PGC) aims for mega-analyses with sample sizes that will grow to >1 million individuals in the next 5 years. This should lead to hundreds of new findings for common genetic variants across nine psychiatric disorders studied by the PGC. The new targets discovered by GWAS have the potential to restart largely stalled psychiatric drug development pipelines, and the translation of GWAS findings into the clinic is a key aim of the recently funded phase 3 of the PGC. This is not without considerable technical challenges. These approaches complement the other main aim of GWAS studies, risk prediction approaches for improving detection, differential diagnosis, and clinical trial design. This paper outlines the motivations, technical and analytical issues, and the plans for translating PGC phase 3 findings into new therapeutics.