Estimating narrow-sense heritability using family data from admixed populations

Finding genes and pathways that underlie coral adaptation

Mass coral bleaching is one of the clearest threats of climate change to the persistence of marine biodiversity. Despite the negative impacts of bleaching on coral health and survival, some corals may be able to rapidly adapt to warming ocean temperatures. Thus, a significant focus in coral research is identifying the genes and pathways underlying coral heat adaptation. Here, we review state-of-the-art methods that may enable the discovery of heat-adaptive loci in corals and identify four main knowledge gaps. To fill these gaps, we describe an experimental approach combining seascape genomics with CRISPR/Cas9 gene editing to discover and validate heat-adaptive loci. Finally, we discuss how information on adaptive genotypes could be used in coral reef conservation and management strategies.

Multiple Sclerosis Heritability Estimation on Sardinian Ascertained Extended Families Using Bayesian Liability Threshold Model

Heritability studies represent an important tool to investigate the main sources of variability for complex diseases, whose etiology involves both genetics and environmental factors. In this paper, we aimed to estimate multiple sclerosis (MS) narrow-sense heritability (h2), on a liability scale, using extended families ascertained from affected probands sampled in the Sardinian province of Nuoro, Italy. We also investigated the sources of MS liability variability among shared environment effects, sex, and categorized year of birth (<1946, ≥1946). The latter can be considered a proxy for different early environmental exposures. To this aim, we implemented a Bayesian liability threshold model to obtain posterior distributions for the parameters of interest adjusting for ascertainment bias. Our analysis highlighted categorized year of birth as the main explanatory factor, explaining ~70% of MS liability variability (median value = 0.69, 95% CI: 0.64, 0.73), while h2 resulted near to 0% (median value = 0.03, 95% CI: 0.00, 0.09). By performing a year of birth-stratified analysis, we found a high h2 only in individuals born on/after 1946 (median value = 0.82, 95% CI: 0.68, 0.93), meaning that the genetic variability acquired a high explanatory role only when focusing on this subpopulation. Overall, the results obtained highlighted early environmental exposures, in the Sardinian population, as a meaningful factor involved in MS to be further investigated.

Rare genetic variants explain missing heritability in smoking

Common genetic variants explain less variation in complex phenotypes than inferred from family-based studies, and there is a debate on the source of this 'missing heritability'. We investigated the contribution of rare genetic variants to tobacco use with whole-genome sequences from up to 26,257 unrelated individuals of European ancestries and 11,743 individuals of African ancestries. Across four smoking traits, single-nucleotide-polymorphism-based heritability ([Formula: see text]) was estimated from 0.13 to 0.28 (s.e., 0.10-0.13) in European ancestries, with 35-74% of it attributable to rare variants with minor allele frequencies between 0.01% and 1%. These heritability estimates are 1.5-4 times higher than past estimates based on common variants alone and accounted for 60% to 100% of our pedigree-based estimates of narrow-sense heritability ([Formula: see text], 0.18-0.34). In the African ancestry samples, [Formula: see text] was estimated from 0.03 to 0.33 (s.e., 0.09-0.14) across the four smoking traits. These results suggest that rare variants are important contributors to the heritability of smoking.

Heritability Estimation of Multiple Sclerosis Related Plasma Protein Levels in Sardinian Families with Immunochip Genotyping Data

This work aimed at estimating narrow-sense heritability, defined as the proportion of the phenotypic variance explained by the sum of additive genetic effects, via Haseman–Elston regression for a subset of 56 plasma protein levels related to Multiple Sclerosis (MS). These were measured in 212 related individuals (with 69 MS cases and 143 healthy controls) obtained from 20 Sardinian families with MS history. Using pedigree information, we found seven statistically significant heritable plasma protein levels (after multiple testing correction), i.e., Gc (h2 = 0.77; 95%CI: 0.36, 1.00), Plat (h2 = 0.70; 95%CI: 0.27, 0.95), Anxa1 (h2 = 0.68; 95%CI: 0.27, 1.00), Sod1 (h2 = 0.58; 95%CI: 0.18, 0.96), Irf8 (h2 = 0.56; 95%CI: 0.19, 0.99), Ptger4 (h2 = 0.45; 95%CI: 0.10, 0.96), and Fadd (h2 = 0.41; 95%CI: 0.06, 0.84). A subsequent analysis was performed on these statistically significant heritable plasma protein levels employing Immunochip genotyping data obtained in 155 healthy controls (92 related and 63 unrelated); we found a meaningful proportion of heritable plasma protein levels’ variability explained by a small set of SNPs. Overall, the results obtained, for these seven MS-related proteins, emphasized a high additive genetic variance component explaining plasma levels’ variability.

Ancestry-inclusive dog genomics challenges popular breed stereotypes

Behavioral genetics in dogs has focused on modern breeds, which are isolated subgroups with distinctive physical and, purportedly, behavioral characteristics. We interrogated breed stereotypes by surveying owners of 18,385 purebred and mixed-breed dogs and genotyping 2155 dogs. Most behavioral traits are heritable [heritability (h²) > 25%], and admixture patterns in mixed-breed dogs reveal breed propensities. Breed explains just 9% of behavioral variation in individuals. Genome-wide association analyses identify 11 loci that are significantly associated with behavior, and characteristic breed behaviors exhibit genetic complexity. Behavioral loci are not unusually differentiated in breeds, but breed propensities align, albeit weakly, with ancestral function. We propose that behaviors perceived as characteristic of modern breeds derive from thousands of years of polygenic adaptation that predates breed formation, with modern breeds distinguished primarily by aesthetic traits.

Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure

Single nucleotide polymorphisms (SNPs) are the most abundant type of genomic variation and the most accessible to genotype in large cohorts. However, they individually explain a small proportion of phenotypic differences between individuals. Ancestry, collective SNP effects, structural variants, somatic mutations or even differences in historic recombination can potentially explain a high percentage of genomic divergence. These genetic differences can be infrequent or laborious to characterize; however, many of them leave distinctive marks on the SNPs across the genome allowing their study in large population samples. Consequently, several methods have been developed over the last decade to detect and analyze different genomic structures using SNP arrays, to complement genome-wide association studies and determine the contribution of these structures to explain the phenotypic differences between individuals. We present an up-to-date collection of available bioinformatics tools that can be used to extract relevant genomic information from SNP array data including population structure and ancestry; polygenic risk scores; identity-by-descent fragments; linkage disequilibrium; heritability and structural variants such as inversions, copy number variants, genetic mosaicisms and recombination histories. From a systematic review of recently published applications of the methods, we describe the main characteristics of R packages, command-line tools and desktop applications, both free and commercial, to help make the most of a large amount of publicly available SNP data.