Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.

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Neurons in inferotemporal cortex (area TE) of the monkey had visual receptive fields which were very large (greater than 10 by 10 degrees) and almost always included the fovea. Some extended well into both halves of the visual field, while others were confined to the ipsilateral or contralateral side. These neurons were differentially sensitive to several of the following dimensions of the stimulus: size and shape, color, orientation, and direction of movement.

We compared the cortical inputs to the superficial and deep compartments of the superior colliculus, asking if the corticotectal system, like the colliculus itself, consists of two functional divisions: visual and visuomotor. We made injections of retrograde tracer extending into both superficial and deep layers in three colliculi: the injection site involved mainly the upper quadrant representation in one case, the lower quadrant representation in a second case, and both quadrants in a third. In a fourth colliculus, the tracer injection was restricted to the lower quadrant representation of the superficial layers. After injections involving both superficial and deep layers, labeled cells were seen over V1, many prestriate visual areas, and in prefrontal and posterior parietal cortex. Both the density of labeled cells and the degree of visuotopic order as inferred from the distribution of labeled cells in cortex varied among areas. In visual areas comprising the lower levels of the cortical hierarchy, visuotopy was preserved, whereas in "higher" areas the distribution of labeled cells did not strongly reflect the visuotopic location of the injection. Despite the widespread distribution of labeled cells, there were several areas with few or no labeled cells: MSTd, 7a, VIP, MIP, and TE. In the case with an injection restricted to superficial layers, labeled cells were seen only in V1 and in striate-recipient areas V2, V3, and MT. The results are consistent with the idea that the corticotectal system consists of two largely nonoverlapping components: a visual component consisting of striate cortex and striate-recipient areas, which projects only to the superficial layers, and a visuomotor component consisting of many other prestriate visual areas as well as frontal and parietal visuomotor areas, which projects to the deep compartment of the colliculus.

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Attentional modulation of neuronal responsiveness is common in many areas of visual cortex. We examined whether attentional modulation in the visual thalamus was quantitatively similar to that in cortex. Identical procedures and apparatus were used to compare attentional modulation of single neurons in seven different areas of the visual system: the lateral geniculate, three visual subdivisions of the pulvinar [inferior, lateral, dorsomedial part of lateral pulvinar (Pdm)], and three areas of extrastriate cortex representing early, intermediate, and late stages of cortical processing (V2, V4/PM, area 7a). A simple fixation task controlled transitions among three attentive states. The animal waited for a fixation point to appear (ready state), fixated the point until it dimmed (fixation state), and then waited idly to begin the next trial (idle state). Attentional modulation was estimated by flashing an identical, irrelevant stimulus in a neuron's receptive field during each of the three states; the three responses defined a "response vector" whose deviation from the line of equal response in all three states (the main diagonal) indicated the character and magnitude of attentional modulation. Attentional modulation was present in all visual areas except the lateral geniculate, indicating that modulation was of central origin. Prevalence of modulation was modest (26%) in pulvinar, and increased from 21% in V2 to 43% in 7a. Modulation had a push-pull character (as many cells facilitated as suppressed) with respect to the fixation state in all areas except Pdm where all cells were suppressed during fixation. The absolute magnitude of attentional modulation, measured by the angle between response vector and main diagonal expressed as a percent of the maximum possible angle, differed among brain areas. Magnitude of modulation was modest in the pulvinar (19-26%), and increased from 22% in V2 to 41% in 7a. However, average trial-to-trial variability of response, measured by the coefficient of variation, also increased across brain areas so that its difference among areas accounted for more than 90% of the difference in modulation magnitude among areas. We also measured attentional modulation by the ratio of cell discharge due to attention divided by discharge variability. The resulting signal-to-noise ratio of attention was small and constant, 1.3 +/- 10%, across all areas of pulvinar and cortex. We conclude that the pulvinar, but not the lateral geniculate, is as strongly affected by attentional state as any area of visual cortex we studied and that attentional modulation amplitude is closely tied to intrinsic variability of response.

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