Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However, identification of disease-susceptibility genes by means of such studies provides limited functional information on how genes lead to disease. In fact, in most cases there is an absence of functional information altogether, preventing a definitive identification of the susceptibility gene or genes. Here we develop an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, we identify gene networks that are perturbed by susceptibility loci and that in turn lead to disease. Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome. Three genes in this network, lipoprotein lipase (Lpl), lactamase beta (Lactb) and protein phosphatase 1-like (Ppm1l), are validated as previously unknown obesity genes, strengthening the association between this network and metabolic disease traits. Our analysis provides direct experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors.

The great majority of the world's music is metrical, i.e., has periodic structure at multiple time scales. Does the metrical structure of a non-isochronous rhythm improve synchronization with a beat compared to synchronization with an isochronous sequence at the beat period? Beat synchronization is usually associated with auditory stimuli, but are people able to extract a beat from rhythmic visual sequences with metrical structure? We addressed these questions by presenting listeners with rhythmic patterns which were either isochronous or non-isochronous in either the auditory or visual modality, and by asking them to tap to the beat, which was prescribed to occur at 800-ms intervals. For auditory patterns, we found that a strongly metrical structure did not improve overall accuracy of synchronization compared with isochronous patterns of the same beat period, though it did influence the higher-level patterning of taps. Synchronization was impaired in weakly metrical patterns in which some beats were silent. For the visual patterns, we found that participants were generally unable to synchronize to metrical non-isochronous rhythms, or to rapid isochronous rhythms. This suggests that beat perception and synchronization have a special affinity with the auditory system.

Consider the task of synchronizing the movement of one's limb to a periodic environmental signal. Using rescaled range analysis and the spectral maximum likelihood estimator, we establish that the experimental errors of human synchronization exhibit 1͞f a type long memory where a is about 0.5, and that the underlying stochastic process can be modeled by fractional Gaussian noise. In addition, we provide a preliminary model of this phenomenon using stochastic delayed differential equations. [S0031-9007(97)04698-X] PACS numbers: 87.22.Jb, 05.40. + j, The wide occurrence of 1͞f a type of long memory (long range correlated) processes in electrical systems and in solid state devices has long posed a challenging problem for physics [1]. A number of mechanisms, ranging from the superposition of many independent relaxation processes [2] to self-organized criticality [3], are proffered to explain this phenomenon. In this Letter we report the manifestation of long memory processes in a human sensorimotor coordination experiment in which a subject synchronizes his finger tapping with an external periodic stimulus. Using an array of diagnostic tools including rescaled range analysis and the spectral maximum likelihood estimator, we show that the error time series, defined as the time between a predetermined point in the tapping cycle and the onset of the stimulus, exhibits long memory of 1͞f a type, and can be modeled as fractional Gaussian noise [4]. The average value of a is found to be about 0.5. This result adds the present human sensorimotor coordination system to a growing list of biological examples in which one observes long range correlated random fluctuations [5][6][7]. In addition, we report our attempt at modeling the experimental findings using stochastic delay differential equations. Our motivation is to present a unifying mechanism for a diverse set of long memory processes, observed under a variety of sensorimotor conditions [7], by incorporating both the inevitable occurrence of noise (white) in the nervous system and delay feedback networks involved in controlling the motor output.Experiment and data collection.-Five right-handed male subjects ranging in age from 25 to 35 took part in the synchronization experiment. Seated in a sound attenuated chamber, each subject was instructed to cyclically press his index finger against a computer key in synchrony with a periodic series of auditory beeps, delivered through a headphone. Two frequency conditions, F 1 2 Hz (T 1 500 ms) and F 2 1.25 Hz (T 2 800 ms), were studied. These frequencies were chosen such that the subject was able to perform the required tapping motion continuously [8,9].Each experimental session consisted of the subject performing 1200 continuous taps for a given frequency.A computer program was used to register the time of a specific point in the tapping cycle in millisecond resolution. The data collected were the interresponse intervals (IRIs) I i , and the synchronization or tapping errors e i . As defined in Fig. 1, e i is the time between th...

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Complex biological systems are best modeled as highly modular, fluid systems exhibiting a plasticity that allows them to adapt to a vast array of changing conditions. Here we highlight several novel network-based approaches to elucidate genetic networks underlying complex traits. These integrative genomic approaches combine large-scale genotypic and gene expression results in segregating mouse populations to reconstruct reliable genetic networks underlying complex traits such as disease or drug response. We apply these novel approaches to one of the most extensive surveys of gene expression studies ever undertaken in whole brain in a segregating mouse population. More than 23,000 genes were monitored in whole brain samples from more than 300 mice derived from an F2 intercross population and genotyped at over 1200 SNP markers uniformly spread over the entire genome. We explore the topological properties of the brain transcriptional network and highlight different approaches to inferring causal associations among genes by integrating genotypic and expression data. We demonstrate the utility of these approaches by identifying and experimentally validating brain gene expression traits predicted to respond to a strong expression quantitative trait locus (eQTL) for the pituitary tumor-transforming 1 gene (Pttg1) that coincides with the physical location of this gene (a cis eQTL). We identify core functional modules making up the brain transcriptional network in mice that are coherent for core biological processes associated with metabolic disease traits including obesity and diabetes. Keywords: genetics, gene expression, integration genomics, QTL, networks, systems biology. J. Neurochem. Complex traits like common human diseases and drug response involve complex interactions among genes and between genes and environment, and also among genetic, genomic, metabolomic, proteomic, and signaling networks. Whereas in the past the reductionist approach to elucidating complex systems was necessary for understanding fundamental building blocks of these systems and because tools did not exist to take on a more holistic approach to dissecting such systems, the explosion of large-scale, high throughput technologies in the biological sciences has motivated a rapid paradigm shift away from reductionism in favor of a systems level view of biology (Hartwell et al. 1999;Kirschner 2005;Schadt et al. 2005a). Recent studies have begun to characterize topological properties of biological networks in mammalian systems, focused primarily on protein interaction and

The authors analyzed fluctuations in timing errors when 8 human participants attempted to coordinate movement with external rhythmic signals. The temporal dynamics of the errors is usually described in terms of simple, self-correcting models. Here the authors demonstrate that timing errors are characterized by a 1/f(alpha) type of long memory process. The value of the exponent alpha differentiates different types of coordination states: synchronization and syncopation. More interesting, evidence was found that alpha can be changed when participants use different coordination strategies. Together with the authors' understanding of the generation mechanism for long memory processes, these results suggest that 1/f(alpha) type of long-range correlated timing errors are of higher cortical origin and are likely the outcome of distributed neural processes acting on multiple time scales.