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Genome wide association studies (GWAS) have associated thousands of loci with quantitative human blood trait variation. Loci and related genes that impact blood trait variation may regulate blood cell-intrinsic biological processes, or alternatively impact blood cell development and function via systemic factors. Clinical observations have linked tobacco or alcohol use with altered blood traits, but these trait relationships have not been systematically explored at the genetic level. Applying a Mendelian randomization (MR) framework to GWAS summary statistics, we explore relationships between smoking and drinking behaviors with 15 quantitative blood traits. We find that the effects of smoking and drinking are confined to red blood cell traits. An instrumental variable (IV) comprised of 113 single nucleotide polymorphisms (SNPs) associated with smoking initiation is associated with decreased hemoglobin (HGB: Effect = -0.07 standard deviation units [95% confidence interval = -0.03 to -0.10 SD units], P = 1x10-4), hematocrit (HCT: Effect = -0.06 [-0.03 - -0.09] SD units, P = 4x10-4), and red blood cell count (RBC: Effect = -0.05 [-0.02 - -0.09] SD units, P = 5x10-3) without impacting platelet count (P = 0.9) or white blood cell count (P = 0.6). Similarly, an IV associated with an increased number of alcoholic drinks consumed per week is associated with decreased HGB (Effect = -0.22 [-0.42 - -0.02] SD units, P = 3x10-2) and RBC (Effect = -0.27 [-0.51 - -0.03] SD units, P = 3x10-2). Using multivariable MR and causal mediation analyses, we find that an increased genetic predisposition to smoking initiation is associated with increased alcohol intake, and that alcohol use mediates the genetic effect of smoking initiation on red blood cell traits. These findings demonstrate a novel role for genetically influenced behaviors on human blood traits, revealing opportunities to dissect related pathways and mechanisms that influence hematopoiesis and blood cell biology., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Shivakumar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The identification of genomic regions and genes that have evolved under natural selection is a fundamental objective in the field of evolutionary genetics. While various approaches have been established for the detection of targets of positive selection, methods for identifying targets of balancing selection, a form of natural selection that preserves genetic and phenotypic diversity within populations, have yet to be fully developed. Despite this, balancing selection is increasingly acknowledged as a significant driver of diversity within populations, and the identification of its signatures in genomes is essential for understanding its role in evolution. In recent years, a plethora of sophisticated methods has been developed for the detection of patterns of linked variation produced by balancing selection, such as high levels of polymorphism, altered allele-frequency distributions, and polymorphism sharing across divergent populations. In this review, we provide a comprehensive overview of classical and contemporary methods, offer guidance on the choice of appropriate methods, and discuss the importance of avoiding artifacts and of considering alternative evolutionary processes. The increasing availability of genome-scale datasets holds the potential to assist in the identification of new targets and the quantification of the prevalence of balancing selection, thus enhancing our understanding of its role in natural populations., (© The Author(s) 2023. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.)

Polygenic risk scores (PRS) use the results of genome-wide association studies (GWAS) to predict quantitative phenotypes or disease risk at an individual level, and provide a potential route to the use of genetic data in personalized medical care. However, a major barrier to the use of PRS is that the majority of GWAS come from cohorts of European ancestry. The predictive power of PRS constructed from these studies is substantially lower in non-European ancestry cohorts, although the reasons for this are unclear. To address this question, we investigate the performance of PRS for height in cohorts with admixed African and European ancestry, allowing us to evaluate ancestry-related differences in PRS predictive accuracy while controlling for environment and cohort differences. We first show that the predictive accuracy of height PRS increases linearly with European ancestry and is partially explained by European ancestry segments of the admixed genomes. We show that recombination rate, differences in allele frequencies, and differences in marginal effect sizes across ancestries all contribute to the decrease in predictive power, but none of these effects explain the decrease on its own. Finally, we demonstrate that prediction for admixed individuals can be improved by using a linear combination of PRS that includes ancestry-specific effect sizes, although this approach is at present limited by the small size of non-European ancestry discovery cohorts., (Copyright © 2020 Bitarello and Mathieson.)

Inversions are one type of structural variants linked to phenotypic differences and adaptation in multiple organisms. However, there is still very little information about polymorphic inversions in the human genome due to the difficulty of their detection. Here, we develop a new high-throughput genotyping method based on probe hybridization and amplification, and we perform a complete study of 45 common human inversions of 0.1-415 kb. Most inversions promoted by homologous recombination occur recurrently in humans and great apes and they are not tagged by SNPs. Furthermore, there is an enrichment of inversions showing signatures of positive or balancing selection, diverse functional effects, such as gene disruption and gene-expression changes, or association with phenotypic traits. Therefore, our results indicate that the genome is more dynamic than previously thought and that human inversions have important functional and evolutionary consequences, making possible to determine for the first time their contribution to complex traits.

Balancing selection maintains advantageous diversity in populations through various mechanisms. Although extensively explored from a theoretical perspective, an empirical understanding of its prevalence and targets lags behind our knowledge of positive selection. Here, we describe the Non-central Deviation (NCD), a simple yet powerful statistic to detect long-term balancing selection (LTBS) that quantifies how close frequencies are to expectations under LTBS, and provides the basis for a neutrality test. NCD can be applied to a single locus or genomic data, and can be implemented considering only polymorphisms (NCD1) or also considering fixed differences with respect to an outgroup (NCD2) species. Incorporating fixed differences improves power, and NCD2 has higher power to detect LTBS in humans under different frequencies of the balanced allele(s) than other available methods. Applied to genome-wide data from African and European human populations, in both cases using chimpanzee as an outgroup, NCD2 shows that, albeit not prevalent, LTBS affects a sizable portion of the genome: ∼0.6% of analyzed genomic windows and 0.8% of analyzed positions. Significant windows (P < 0.0001) contain 1.6% of SNPs in the genome, which disproportionally fall within exons and change protein sequence, but are not enriched in putatively regulatory sites. These windows overlap ∼8% of the protein-coding genes, and these have larger number of transcripts than expected by chance even after controlling for gene length. Our catalog includes known targets of LTBS but a majority of them (90%) are novel. As expected, immune-related genes are among those with the strongest signatures, although most candidates are involved in other biological functions, suggesting that LTBS potentially influences diverse human phenotypes.

The classical class I HLA loci of humans show an excess of nonsynonymous with respect to synonymous substitutions at codons of the antigen recognition site (ARS), a hallmark of adaptive evolution. Additionally, high polymporphism, linkage disequilibrium, and disease associations suggest that one or more balancing selection regimes have acted upon these genes. However, several questions about these selective regimes remain open. First, it is unclear if stronger evidence for selection on deep timescales is due to changes in the intensity of selection over time or to a lack of power of most methods to detect selection on recent timescales. Another question concerns the functional entities which define the selected phenotype. While most analyses focus on selection acting on individual alleles, it is also plausible that phylogenetically defined groups of alleles ("lineages") are targets of selection. To address these questions, we analyzed how dN/dS (ω) varies with respect to divergence times between alleles and phylogenetic placement (position of branches). We find that ω for ARS codons of class I HLA genes increases with divergence time and is higher for inter-lineage branches. Throughout our analyses, we used non-selected codons to control for possible effects of inflation of ω associated to intra-specific analysis, and showed that our results are not artifactual. Our findings indicate the importance of considering the timescale effect when analysing ω over a wide spectrum of divergences. Finally, our results support the divergent allele advantage model, whereby heterozygotes with more divergent alleles have higher fitness than those carrying similar alleles.

Background: Kiwi, comprising five species from the genus Apteryx, are endangered, ground-dwelling bird species endemic to New Zealand. They are the smallest and only nocturnal representatives of the ratites. The timing of kiwi adaptation to a nocturnal niche and the genomic innovations, which shaped sensory systems and morphology to allow this adaptation, are not yet fully understood., Results: We sequenced and assembled the brown kiwi genome to 150-fold coverage and annotated the genome using kiwi transcript data and non-redundant protein information from multiple bird species. We identified evolutionary sequence changes that underlie adaptation to nocturnality and estimated the onset time of these adaptations. Several opsin genes involved in color vision are inactivated in the kiwi. We date this inactivation to the Oligocene epoch, likely after the arrival of the ancestor of modern kiwi in New Zealand. Genome comparisons between kiwi and representatives of ratites, Galloanserae, and Neoaves, including nocturnal and song birds, show diversification of kiwi's odorant receptors repertoire, which may reflect an increased reliance on olfaction rather than sight during foraging. Further, there is an enrichment of genes influencing mitochondrial function and energy expenditure among genes that are rapidly evolving specifically on the kiwi branch, which may also be linked to its nocturnal lifestyle., Conclusions: The genomic changes in kiwi vision and olfaction are consistent with changes that are hypothesized to occur during adaptation to nocturnal lifestyle in mammals. The kiwi genome provides a valuable genomic resource for future genome-wide comparative analyses to other extinct and extant diurnal ratites.

Next-generation sequencing (NGS) technologies have become the standard for data generation in studies of population genomics, as the 1000 Genomes Project (1000G). However, these techniques are known to be problematic when applied to highly polymorphic genomic regions, such as the human leukocyte antigen (HLA) genes. Because accurate genotype calls and allele frequency estimations are crucial to population genomics analyses, it is important to assess the reliability of NGS data. Here, we evaluate the reliability of genotype calls and allele frequency estimates of the single-nucleotide polymorphisms (SNPs) reported by 1000G (phase I) at five HLA genes (HLA-A, -B, -C, -DRB1, and -DQB1). We take advantage of the availability of HLA Sanger sequencing of 930 of the 1092 1000G samples and use this as a gold standard to benchmark the 1000G data. We document that 18.6% of SNP genotype calls in HLA genes are incorrect and that allele frequencies are estimated with an error greater than ±0.1 at approximately 25% of the SNPs in HLA genes. We found a bias toward overestimation of reference allele frequency for the 1000G data, indicating mapping bias is an important cause of error in frequency estimation in this dataset. We provide a list of sites that have poor allele frequency estimates and discuss the outcomes of including those sites in different kinds of analyses. Because the HLA region is the most polymorphic in the human genome, our results provide insights into the challenges of using of NGS data at other genomic regions of high diversity., (Copyright © 2015 Brandt et al.)