Quantitative trait loci (QTL)/association mapping aims at finding genomic loci associated with the phenotypes, whereas genomic selection focuses on breeding value prediction based on genomic data. Variable selection is a key to both of these tasks as it allows to (1) detect clear mapping signals of QTL activity, and (2) predict the genome-enhanced breeding values accurately. In this paper, we provide an overview of a statistical method called least absolute shrinkage and selection operator (LASSO) and two of its generalizations named elastic net and adaptive LASSO in the contexts of QTL mapping and genomic breeding value prediction in plants (or animals). We also briefly summarize the Bayesian interpretation of LASSO, and the inspired hierarchical Bayesian models. We illustrate the implementation and examine the performance of methods using three public data sets: (1) North American barley data with 127 individuals and 145 markers, (2) a simulated QTLMAS XII data with 5,865 individuals and 6,000 markers for both QTL mapping and genomic selection, and (3) a wheat data with 599 individuals and 1,279 markers only for genomic selection.

Detecting and estimating the degree of genetic differentiation among populations of highly mobile marine fish having pelagic larval stages is challenging because their effective population sizes can be large, and thus, little genetic drift and differentiation is expected in neutral genomic sites. However, genomic sites subject to directional selection stemming from variation in local environmental conditions can still show substantial genetic differentiation, yet these signatures can be hard to detect with low-throughput approaches. Using a pooled RAD-seq approach, we investigated genomewide patterns of genetic variability and differentiation within and among 20 populations of Atlantic herring in the Baltic Sea (and adjacent Atlantic sites), where previous low-throughput studies and/or studies based on few populations have found limited evidence for genetic differentiation. Stringent quality control was applied in the filtering of 1 791 254 SNPs, resulting in a final data set of 68 182 polymorphic loci. Clear differentiation was identified between Atlantic and Baltic populations in many genomic sites, while differentiation within the Baltic Sea area was weaker and geographically less structured. However, outlier analyses - whether including all populations or only those within the Baltic Sea - uncovered hundreds of directionally selected loci in which variability was associated with either salinity, temperature or both. Hence, our results support the view that although the degree of genetic differentiation among Baltic Sea herring populations is low, there are many genomic regions showing elevated divergence, apparently as a response to temperature- and salinity-related natural selection. As such, the results add to the increasing evidence of local adaptation in highly mobile marine organisms, and those in the young Baltic Sea in particular.

Genomewide association studies (GWAS) aim to identify genetic markers strongly associated with quantitative traits by utilizing linkage disequilibrium (LD) between candidate genes and markers. However, because of LD between nearby genetic markers, the standard GWAS approaches typically detect a number of correlated SNPs covering long genomic regions, making corrections for multiple testing overly conservative. Additionally, the high dimensionality of modern GWAS data poses considerable challenges for GWAS procedures such as permutation tests, which are computationally intensive. We propose a cluster-based GWAS approach that first divides the genome into many large nonoverlapping windows and uses linkage disequilibrium network analysis in combination with principal component (PC) analysis as dimensional reduction tools to summarize the SNP data to independent PCs within clusters of loci connected by high LD. We then introduce single- and multilocus models that can efficiently conduct the association tests on such high-dimensional data. The methods can be adapted to different model structures and used to analyse samples collected from the wild or from biparental F populations, which are commonly used in ecological genetics mapping studies. We demonstrate the performance of our approaches with two publicly available data sets from a plant (Arabidopsis thaliana) and a fish (Pungitius pungitius), as well as with simulated data.

ObjectiveFactors that lead to metabolic dysregulation are associated with increased risk of early-onset colorectal cancer (CRC diagnosed under age 50). However, the association between metabolic syndrome (MetS) and early-onset CRC remains unexamined.DesignWe conducted a nested case–control study among participants aged 18–64 in the IBM MarketScan Commercial Database (2006–2015). Incident CRC was identified using pathologist-coded International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes, and controls were frequency matched. MetS was defined as presence of ≥3 conditions among obesity, hypertension, hyperlipidaemia and hyperglycaemia/type 2 diabetes, based on ICD-9-CM and use of medications. Multivariable logistic regressions were used to estimate ORs and 95% CIs.ResultsMetS was associated with increased risk of early-onset CRC (n=4673; multivariable adjusted OR 1.25; 95% CI 1.09 to 1.43), similar to CRC diagnosed at age 50–64 (n=14 928; OR 1.21; 95% CI 1.15 to 1.27). Compared with individuals without a metabolic comorbid condition, those with 1, 2 or ≥3 conditions had a 9% (1.09; 95% CI 1.00 to 1.17), 12% (1.12; 95% CI 1.01 to 1.24) and 31% (1.31; 95% CI 1.13 to 1.51) higher risk of early-onset CRC (ptrend <0.001). No associations were observed for one or two metabolic comorbid conditions and CRC diagnosed at age 50–64. These positive associations were driven by proximal (OR per condition 1.14; 95% CI 1.06 to 1.23) and distal colon cancer (OR 1.09; 95% CI 1.00 to 1.18), but not rectal cancer (OR 1.03; 95% CI 0.97 to 1.09).ConclusionsMetabolic dysregulation was associated with increased risk of early-onset CRC, driven by proximal and distal colon cancer, thus at least in part contribute to the rising incidence of early-onset CRC.