The effect of phenotype variation on detection of linkage in the COGA data

Error in phenotypic measurement can significantly compromise ability to detect linkage. We assessed the impact of introducing phenotypic measurement error on our ability to detect a quantitative trait locus in the Collaborative Study on the Genetics of Alcoholism (COGA) data. The impact of introducing three different types of errors was evaluated: 1) errors generated by sampling from a normal distribution; 2) errors generated by permuting phenotype values between subjects; and 3) errors generated by sampling from a uniform error distribution.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/101827/1/1370170711_ftp.pd

Identifying influential individuals in linkage analysis: Application to a quantitative trait locus detected in the COGA data

Once linkage is detected to a quantitative trait locus (QTL), the next step towards localizing the gene involved may be to identify those families, or individuals, in whom the putative mutations are segregating. In this paper, we describe a jackknife procedure for identifying individuals (and families) who contribute disproportionately to the linkage. Following initial detection of linkage to a QTL, the strategy involves sequentially removing each individual (or each family) from the analysis and recomputing the lod score associated with the linked region using data from all remaining subjects (or families). This procedure can be used to determine if particular observations have substantial impact on evidence for linkage. Identification of such observations may provide insights for further efforts to localize the QTL.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/101831/1/1370170744_ftp.pd

Application of an ordered subset analysis approach to the genetics of alcoholism

For complex diseases, underlying etiologic heterogeneity may reduce power to detect linkage. Thus, methods to identify more homogeneous subgroups within a given sample in a linkage study may improve detection of putative susceptibility loci. In this study we describe an ordered subsetting approach that utilizes disease‐related quantitative trait data to complement traditional linkage analysis. This approach uses family‐based lod scores derived from the initial genome screen and a family‐based descriptor of the trait of interest. The goal of the approach is to identify more homogeneous subgroups of the data by ranking families based on their quantitative trait data. Permutation testing is used to assess statistical significance. This approach can be adapted to a variety of linkage methods and may provide a means to dissect some of the underlying heterogeneity in complex disease genetics.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/101874/1/1370170765_ftp.pd

Case report: A patient with Delayed Sleep-Wake Phase Disorder and Optic Nerve Hypoplasia treated with tasimelteon: a case study

We present a case of an adult female diagnosed with Delayed Sleep-Wake Phase Disorder (DSWPD) and Optic Nerve Hypoplasia (ONH), with a confirmed delayed Dim Light Melatonin Onset (DLMO), who reports the inability to fall asleep at their desired bedtime and obtain adequate sleep nightly, despite the ability to have a full night’s sleep when not required to be up at a specific time for societal requirements. The participant was enrolled in an 11-month Open-Label Extension (OLE) following the randomized portion of a clinical study and was successfully treated with tasimelteon. DSWPD symptoms were resolved, and their previously delayed sleep-wake cycle was advanced.Clinical trial registrationhttps://clinicaltrials.gov/ct2/show/NCT04652882, identifier NCT04652882