Impact of low-frequency coding variants on human facial shape

The contribution of low-frequency variants to the genetic architecture of normal-range facial traits is unknown. We studied the influence of low-frequency coding variants (MAF < 1%) in 8091 genes on multi-dimensional facial shape phenotypes in a European cohort of 2329 healthy individuals. Using three-dimensional images, we partitioned the full face into 31 hierarchically arranged segments to model facial morphology at multiple levels, and generated multi-dimensional phenotypes representing the shape variation within each segment. We used MultiSKAT, a multivariate kernel regression approach to scan the exome for face-associated low-frequency variants in a gene-based manner. After accounting for multiple tests, seven genes (AR, CARS2, FTSJ1, HFE, LTB4R, TELO2, NECTIN1) were significantly associated with shape variation of the cheek, chin, nose and mouth areas. These genes displayed a wide range of phenotypic effects, with some impacting the full face and others affecting localized regions. The missense variant rs142863092 in NECTIN1 had a significant effect on chin morphology and was predicted bioinformatically to have a deleterious effect on protein function. Notably, NECTIN1 is an established craniofacial gene that underlies a human syndrome that includes a mandibular phenotype. We further showed that nectin1a mutations can affect zebrafish craniofacial development, with the size and shape of the mandibular cartilage altered in mutant animals. Findings from this study expanded our understanding of the genetic basis of normal-range facial shape by highlighting the role of low-frequency coding variants in several novel genes.

Wind Turbine Systematic Yaw Error: Operation Data Analysis Techniques for Detecting It and Assessing Its Performance Impact

The widespread availability of wind turbine operation data has considerably boosted the research and the applications for wind turbine monitoring. It is well established that a systematic misalignment of the wind turbine nacelle with respect to the wind direction has a remarkable impact in terms of down-performance, because the extracted power is in first approximation proportional to the cosine cube of the yaw angle. Nevertheless, due to the fact that in the wind farm practice the wind field facing the rotor is estimated through anemometers placed behind the rotor, it is challenging to robustly detect systematic yaw errors without the use of additional upwind sensory systems. Nevertheless, this objective is valuable because it involves the use of data that are available to wind farm practitioners at zero cost. On these grounds, the present work is a two-steps test case discussion. At first, a new method for systematic yaw error detection through operation data analysis is presented and is applied for individuating a misaligned multi-MW wind turbine. After the yaw error correction on the test case wind turbine, operation data of the whole wind farm are employed for an innovative assessment method of the performance improvement at the target wind turbine. The other wind turbines in the farm are employed as references and their operation data are used as input for a multivariate Kernel regression whose target is the power of the wind turbine of interest. Training the model with pre-correction data and validating on post-correction data, it is estimated that a systematic yaw error of 4 ∘ affects the performance up to the order of the 1.5% of the Annual Energy Production.

Multi-SKAT: General framework to test multiple phenotype associations of rare variants

In genetic association analysis, a joint test of multiple distinct phenotypes can increase power to identify sets of trait-associated variants within genes or regions of interest. Existing multi-phenotype tests for rare variants make specific assumptions about the patterns of association of underlying causal variants, and the violation of these assumptions can reduce power to detect association. Here we develop a general framework for testing pleiotropic effects of rare variants based on multivariate kernel regression (Multi-SKAT). Multi-SKAT models effect sizes of variants on the phenotypes through a kernel matrix and performs a variance component test of association. We show that many existing tests are equivalent to specific choices of kernel matrices with the Multi-SKAT framework. To increase power to detect association across tests with different kernel matrices, we developed a fast and accurate approximation of the significance of the minimum observed p-value across tests. To account for related individuals, our framework uses a random effects for the kinship matrix. Using simulated data and amino acid and exome-array data from the METSIM study, we show that Multi-SKAT can improve power over single-phenotype SKAT-O test and existing multiple phenotype tests, while maintaining type I error rate.