Computation of ancestry scores with mixed families and unrelated individuals.

The issue of robustness to family relationships in computing genotype ancestry scores such as eigenvector projections has received increased attention in genetic association, and is particularly challenging when sets of both unrelated individuals and closely related family members are included. The current standard is to compute loadings (left singular vectors) using unrelated individuals and to compute projected scores for remaining family members. However, projected ancestry scores from this approach suffer from shrinkage toward zero. We consider two main novel strategies: (i) matrix substitution based on decomposition of a target family-orthogonalized covariance matrix, and (ii) using family-averaged data to obtain loadings. We illustrate the performance via simulations, including resampling from 1000 Genomes Project data, and analysis of a cystic fibrosis dataset. The matrix substitution approach has similar performance to the current standard, but is simple and uses only a genotype covariance matrix, while the family-average method shows superior performance. Our approaches are accompanied by novel ancillary approaches that provide considerable insight, including individual-specific eigenvalue scree plots.

Statistical significance for hierarchical clustering.

Cluster analysis has proved to be an invaluable tool for the exploratory and unsupervised analysis of high-dimensional datasets. Among methods for clustering, hierarchical approaches have enjoyed substantial popularity in genomics and other fields for their ability to simultaneously uncover multiple layers of clustering structure. A critical and challenging question in cluster analysis is whether the identified clusters represent important underlying structure or are artifacts of natural sampling variation. Few approaches have been proposed for addressing this problem in the context of hierarchical clustering, for which the problem is further complicated by the natural tree structure of the partition, and the multiplicity of tests required to parse the layers of nested clusters. In this article, we propose a Monte Carlo based approach for testing statistical significance in hierarchical clustering which addresses these issues. The approach is implemented as a sequential testing procedure guaranteeing control of the family-wise error rate. Theoretical justification is provided for our approach, and its power to detect true clustering structure is illustrated through several simulation studies and applications to two cancer gene expression datasets.

Image and statistical analysis of melanocytic histology.

AIMS: We applied digital image analysis techniques to study selected types of melanocytic lesions. METHODS AND RESULTS: We used advanced digital image analysis to compare melanocytic lesions as follows: (i) melanoma to nevi, (ii) melanoma subtypes to nevi, (iii) severely dysplastic nevi to other nevi and (iv) melanoma to severely dysplastic nevi. We were successful in differentiating melanoma from nevi [receiver operating characteristic area (ROC) 0.95] using image-derived features, among which those related to nuclear size and shape and distance between nuclei were most important. Dividing melanoma into subtypes, even greater separation was obtained (ROC area 0.98 for superficial spreading melanoma; 0.95 for lentigo maligna melanoma; and 0.99 for unclassified). Severely dysplastic nevi were best differentiated from conventional and mildly dysplastic nevi by differences in cellular staining qualities (ROC area 0.84). We found that melanomas were separated from severely dysplastic nevi by features related to shape and staining qualities (ROC area 0.95). All comparisons were statistically significant (P < 0.0001). CONCLUSIONS: We offer a unique perspective into the evaluation of melanocytic lesions and demonstrate a technological application with increasing prevalence, and with potential use as an adjunct to traditional diagnosis in the future.

Measured visual acuity of fellow eyes as a prognostic factor in macular hole surgery.

PURPOSE: To investigate the effect of the vision of the fellow eye on the visual acuity of eyes after successful macular hole surgery. DESIGN: Prospective consecutive series. METHODS: A consecutive series of eyes with successful macular hole closure were studied and assigned to one of two groups according to the visual acuity of the fellow eye; a group with visual acuity less than 20/200 and a group with visual acuity of 20/200 or better. Preoperative and postoperative visual acuity in the two groups was measured based on the logarithm of the minimal angle of resolution (LogMAR), and postoperative visual acuity was also determined by using the multiple-letter visual acuity chart, which permitted measurement of visual acuity at an extrafoveal point. RESULTS: Group 1 (<20/200) consisted of 19 eyes and group 2 (>20/200) consisted of 51 eyes. LogMAR visual acuity at 6 months postoperatively was significantly better in group 1 than in group 2 (0.21 vs 0.41, P <.01). The logMAR change 6 months after surgery was significantly greater in group 1 than in group 2 (0.49 vs 0.23, P <.01). There was no significant difference between two groups in logMAR visual acuity at 6 months postoperatively determined with the multiple-letter visual acuity chart (0.20 vs 0.29, P >.05). CONCLUSIONS: Visual recovery after successful macular hole surgery is inversely correlated with vision in the fellow eye. Learning to use eccentric fixation may contribute to visual improvement after macular hole surgery.