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Background and aims While the prevalence of major depression is elevated among cannabis users, the role of genetics in this pattern of comorbidity is not clear. This study aimed to estimate the heritability of cannabis use and major depression, quantify the genetic overlap between these two traits and localize regions of the genome that segregate in families with cannabis use and major depression. Design Family-based univariate and bivariate genetic analysis. Setting San Antonio, Texas, USA. Participants Genetics of Brain Structure and Function study (GOBS) participants: 1284 Mexican Americans from 75 large multi-generation families and an additional 57 genetically unrelated spouses. Measurements Phenotypes of life-time history of cannabis use and major depression, measured using the semistructured MINI-Plus interview. Genotypes measured using ~1 M single nucleotide polymorphisms (SNPs) on Illumina BeadChips. A subselection of these SNPs were used to build multi-point identity-by-descent matrices for linkage analysis. Findings Both cannabis use [ h2 = 0.614, P = 1.00 × 10−6, standard error (SE) = 0.151] and major depression ( h2 = 0.349, P = 1.06 × 10−5, SE = 0.100) are heritable traits, and there is significant genetic correlation between the two (ρg = 0.424, P = 0.0364, SE = 0.195). Genome-wide linkage scans identify a significant univariate linkage peak for major depression on chromosome 22 [logarithm of the odds (LOD) = 3.144 at 2 centimorgans (cM)], with a suggestive peak for cannabis use on chromosome 21 (LOD = 2.123 at 37 cM). A significant pleiotropic linkage peak influencing both cannabis use and major depression was identified on chromosome 11 using a bivariate model (LOD = 3.229 at 112 cM). Follow-up of this pleiotropic signal identified a SNP 20 kb upstream of NCAM1 (rs7932341) that shows significant bivariate association ( P = 3.10 × 10−5). However, this SNP is rare (seven minor allele carriers) and does not drive the linkage signal observed. Conclusions There appears to be a significant genetic overlap between cannabis use and major depression among Mexican Americans, a pleiotropy that appears to be localized to a region on chromosome 11q23 that has been linked previously to these phenotypes. [ABSTRACT FROM AUTHOR]

Background: Italian ryegrass (Lolium perenne ssp. multiflorum) is one of the major winter annual weeds worldwide. In this research, diversity for seed morpho-physiological traits such as seed weight, seed size, awnedness, dormancy, speed of germination, and seed vigor among Italian ryegrass populations collected from the Texas Blacklands region were assessed, and potential association with herbicide resistance was investigated., Results: A high degree of diversity was observed among the populations for 100-seed weight (125-256 mg), seed length (4.8-6.6 mm), awn length (0-6 mm), and total seedling length (9-14 cm at 21 days after seed germination). Inter-population range for seed dormancy was higher in the freshly harvested seed (31-85%), which reduced to 18 to 62% at 9 months after harvest. Populations with high initial seed dormancy (> 70% dormancy) released dormancy at a faster rate than the low dormancy group (< 40%). Percent survival status to multiple postemergence herbicides was positively correlated with 100-seed weight and fresh or initial seed dormancy., Conclusion: Early emerging cohorts are easily controlled by pre-plant tillage and preemergence herbicides, whereas late emerging cohorts (facilitated by seed dormancy) are exposed to postemergence herbicides wherein greater opportunities exist for resistance evolution, likely explaining the occurrence of high seed dormancy in Italian ryegrass populations resistant to postemergence herbicides. High seed weights can further allow seedling emergence from greater burial depth, thereby exposing more seedlings to postemergence herbicides and increasing the likelihood of resistance evolution. Results provide unique insights into the association between seed traits and herbicide resistance in this species. © 2021 Society of Chemical Industry., (© 2021 Society of Chemical Industry.)

Recent technological advances equipped researchers with capabilities that go beyond traditional genotyping of loci known to be polymorphic in a general population. Genetic sequences of study participants can now be assessed directly. This capability removed technology-driven bias toward scoring predominantly common polymorphisms and let researchers reveal a wealth of rare and sample-specific variants. Although the relative contributions of rare and common polymorphisms to trait variation are being debated, researchers are faced with the need for new statistical tools for simultaneous evaluation of all variants within a region. Several research groups demonstrated flexibility and good statistical power of the functional linear model approach. In this work we extend previous developments to allow inclusion of multiple traits and adjustment for additional covariates. Our functional approach is unique in that it provides a nuanced depiction of effects and interactions for the variables in the model by representing them as curves varying over a genetic region. We demonstrate flexibility and competitive power of our approach by contrasting its performance with commonly used statistical tools and illustrate its potential for discovery and characterization of genetic architecture of complex traits using sequencing data from the Dallas Heart Study., (Published 2016. This article is a U.S. Government work and is in the public domain in the USA.)