Oxytocin and its association with reward-based personality traits: A multilocus genetic profile (MLGP) approach

Linked OXTR Variants Are Associated with Social Behavior Differences in Bonobos (Pan paniscus)

Single-nucleotide polymorphisms (SNPs) in forkhead box protein P2 (FOXP2) and oxytocin receptor (OXTR) genes have been associated with linguistic and social development in humans, as well as to symptom severity in autism spectrum disorder (ASD). Studying biobehavioral mechanisms in the species most closely related to humans can provide insights into the origins of human communication, and the impact of genetic variation on complex behavioral phenotypes. Here, we aimed to determine if bonobos (Pan paniscus) exhibit individual variation in FOXP2 and OXTR loci that have been associated with human social development and behavior. Although the ASD-related variants were reported in 13-41% of the human population, we did not find variation at these loci in our sample of 13 bonobos. However, we did identify a novel variant in bonobo FOXP2, as well as four novel variants in bonobo OXTR that were 17-184 base pairs from the human ASD variants. We also found the same linked, homozygous allelic combination across the 4 novel OXTR SNPs (homozygous TGTC) in 6 of the 13 bonobos, indicating that this combination may be under positive selection. When comparing the combined OXTR genotypes, we found significant group differences in social behavior; bonobos with zero copies of the TGTC combination were less social than bonobos with one copy of the TGTC combination. Taken together, our findings suggest that these OXTR variants may influence individual-level social behavior in bonobos and support the notion that linked genetic variants are promising risk factors for social communication deficits in humans.

Simultaneous clustering and variable selection: A novel algorithm and model selection procedure

The growing availability of high-dimensional data sets offers behavioral scientists an unprecedented opportunity to integrate the information hidden in the novel types of data (e.g., genetic data, social media data, and GPS tracks, etc.,) and thereby obtain a more detailed and comprehensive view towards their research questions. In the context of clustering, analyzing the large volume of variables could potentially result in an accurate estimation or a novel discovery of underlying subgroups. However, a unique challenge is that the high-dimensional data sets likely involve a significant amount of irrelevant variables. These irrelevant variables do not contribute to the separation of clusters and they may mask cluster partitions. The current paper addresses this challenge by introducing a new clustering algorithm, called Cardinality K-means or CKM, and by proposing a novel model selection strategy. CKM is able to perform simultaneous clustering and variable selection with high stability. In two simulation studies and an empirical demonstration with genetic data, CKM consistently outperformed competing methods in terms of recovering cluster partitions and identifying signaling variables. Meanwhile, our novel model selection strategy determines the number of clusters based on a subset of variables that are most likely to be signaling variables. Through a simulation study, this strategy was found to result in a more accurate estimation of the number of clusters compared to the conventional strategy that utilizes the full set of variables. Our proposed CKM algorithm, together with the novel model selection strategy, has been implemented in a freely accessible R package.