Changes In Allele Frequencies Research Articles

Detecting parallel polygenic adaptation to novel evolutionary pressure in wild populations: a case study in Atlantic cod (Gadus morhua).

Populations can adapt to novel selection pressures through dramatic frequency changes in a few genes of large effect or subtle shifts in many genes of small effect. The latter (polygenic adaptation) is expected to be the primary mode of evolution for many life-history traits but tends to be more difficult to detect than changes in genes of large effect. Atlantic cod (Gadus morhua) were subjected to intense fishing pressure over the twentieth century, leading to abundance crashes and a phenotypic shift toward earlier maturation across many populations. Here, we use spatially replicated temporal genomic data to test for a shared polygenic adaptive response to fishing using methods previously applied to evolve-and-resequence experiments. Cod populations on either side of the Atlantic show covariance in allele frequency change across the genome that are characteristic of recent polygenic adaptation. Using simulations, we demonstrate that the degree of covariance in allele frequency change observed in cod is unlikely to be explained by neutral processes or background selection. As human pressures on wild populations continue to increase, understanding and attributing modes of adaptation using methods similar to those demonstrated here will be important in identifying the capacity for adaptive responses and evolutionary rescue. This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.

Frequency changes in HLA-I alleles: A marker to guide immunotherapy in lung adenocarcinoma patients and its relationship with tumor mutational burden and PD-L1 expression.

The aim of the study was to investigate differences in HLA-I alleles between lung adenocarcinoma patients and healthy controls and determine their association with PD-L1 expression and tumor mutational burden (TMB) to understand the mechanism underlying lung adenocarcinoma susceptibility. Differences in HLA allele frequencies between the two groups were analyzed in a case-control study. PD-L1 expression and TMB in lung adenocarcinoma patients were determined and their relationships with HLA-I were analyzed. The lung adenocarcinoma group showed significantly higher HLA-A*30:01 (p = 0.0067, odds ratio [OR], 1.834; 95% confidence interval [CI]: 1.176-2.860), B*13:02 (p = 0.0050, OR, 1.855; 95% CI: 1.217-2.829), and C*06:02 (p = 0.0260, OR, 1.478; 95% CI: 1.060-2.060) and significantly lower B*51:01 (p = 0.0290, OR, 0.6019; 95% CI: 0.3827-0.9467), and C*14:02 (p = 0.0255, OR, 0.5089; 95% CI: 0.2781-0.9312) than the control group. Haplotype analysis results showed that HLA-A*30:01-B*13:02 (p = 0.0100, OR, 1.909; 95% CI: 1.182-3.085), A*11:01-C*01:02 (p = 0.0056, OR, 1.909; 95% CI: 1.182-3.085), A*30:01-C*06:02 (p = 0.0111, OR, 1.846; 95% CI: 1.147-2.969), and B*13:02-C*06:02 (p = 0.0067, OR, 1.846; 95% CI: 1.147-2.969) frequencies significantly increased and B*51:01-C*14:02 (p = 0.0219, OR, 0.490; 95% CI: 0.263-0.914) frequency significantly decreased in lung adenocarcinoma patients. Three-locus haplotype analysis showed that HLA-A*30:01-B*13:02-C*06:02 frequency (p = 0.0100, OR, 1.909; 95% CI: 1.182-3.085) significantly increased in patients. HLA-A*30:01, B*13:02, and C*06:02 may be the susceptibility genes and HLA-B*51:01 and C*14:01 act as the resistance genes of lung adenocarcinoma. The changes in HLA-I allele frequencies had no association with PD-L1 expression and TMB among these patients.

Self-contained Beta-with-Spikes Approximation for Inference Under a Wright-Fisher Model.

We construct a reliable estimation method for evolutionary parameters within the Wright-Fisher model, which describes changes in allele frequencies due to selection and genetic drift, from time-series data. Such data exist for biological populations, for example via artificial evolution experiments, and for the cultural evolution of behavior, such as linguistic corpora that document historical usage of different words with similar meanings. Our method of analysis builds on a Beta-with-Spikes approximation to the distribution of allele frequencies predicted by the Wright-Fisher model. We introduce a self-contained scheme for estimating parameters in the approximation, and demonstrate its robustness with synthetic data, especially in the strong-selection and near-extinction regimes where previous approaches fail. We further apply the method to allele frequency data for baker's yeast (Saccharomyces cerevisiae), finding a significant signal of selection in cases where independent evidence supports such a conclusion. We further demonstrate the possibility of detecting time-points at which evolutionary parameters change in the context of a historical spelling reform in the Spanish language.

Balancing selection and candidate loci for survival and growth during larval development in the Mediterranean mussel, Mytilus galloprovincialis.

Many marine bivalves have complex life histories with distinct developmental processes and genetic mechanisms. Larval development for most bivalves is often a prolonged and crucial physiological stage, where they suffer mass mortality due to early-acting genetic load. In this study, we describe genetic changes taking place within a single generation of families of the Mediterranean mussel Mytilus galloprovincialis over 23 days of larval development. Using replicated cultures and a pooled sequencing approach, we demonstrate that temporal balancing selection at the majority of loci preserve genetic variation in the early developmental stages of M. galloprovincialis. Balancing selection may be the mechanism which maintains standing genetic variation within the mussel genome and may improve the chances of survival and shield larvae from high levels of genetic load. Additionally, we used changes in allele frequencies to identify potential size-associated SNPs and viability-associated SNPs and found that patterns of genetic changes in directionally selected SNPs cannot be simply explained by traditional theories of genetic purging or directional selection without consideration of balancing selection. Finally, we observed a negative correlation between larval growth rates and survival, implying a potential trade-off relationship between the 2 commercially relevant phenotypes.

Abstract 5360: Improved identification of CHIP mutations from cell-free DNA without matched normal samples using machine learning

Abstract Blood-based liquid biopsies are becoming the standard for noninvasive profiling of tumors from circulating tumor DNA (ctDNA) to facilitate treatment decisions. However, it is known that non-tumor-derived mutations, such as those from clonal hematopoietic (CH) lineage, are also present in cell-free DNA (cfDNA) and can complicate tumor mutation detection and interpretation. Filtering alterations associated with CH can reduce the risk of false positives in ctDNA analysis. Current strategies for filtering CH mutations are based on prior knowledge of those in canonical CH genes, sequencing a matched peripheral blood mononuclear cell sample, or using multiple time points to evaluate changes in allele frequency (AF), with the assumption that AF of CH of indeterminate potential (CHIPs) are unlikely to be impacted by treatments; however, these approaches are either insufficient or cost prohibited. We present a machine learning framework that incorporates prior knowledge from tumor and blood-derived mutations to improve the classification of CH and tumor variants from plasma-only cfDNA samples and that greatly outperforms a simple rule-based approach. We developed a classification framework composed of three main modules to maximize the information gain from ctDNA as non-ctDNA samples: (1) a sequence context-based model where each variant is represented by 128 mutational signatures extracted using a bag-of-words-like modeling (using normal and tumor samples) along with mutational function effect scores from SnpSift and cancer type origin. In total 6,745 CH and 57,210 somatic tumor-derived mutations are used to train a model to distinguish CH, CH-putative cancer driver, and tumor mutations; (2) a ctDNA-context model that includes patient-level information (age, mutational signatures, and variant allele frequencies) from 392 tumor and 481 CH blood-derived mutations extracted exclusively from ctDNA samples to predict blood or tumor origin; and (3) a meta-classifier where models 1 and 2 are aggregated into a single score for predicting the origin of a given mutation. We compared our models against a baseline strategy where mutations were predicted as CH if they fell into a list of 27 canonical CH-related genes. Our proposed metaclassifier that integrates sequence- and ctDNA-based features improved the overall performance of CH detection (AUC = 0.83, compared with the baseline model, AUC = 0.72), highlighting the value of adding ctDNA-derived and mutation-context features to profile somatic mutations. As ctDNA assays and technologies rapidly progress and gain adoption for precision oncology, we anticipate that our work, combined with existing benchmarking efforts, will enable more robust analyses on critical applications such as early disease detection, minimal residual disease, and mutational profiling to ultimately best inform clinical decision-making. Citation Format: Gustavo Arango-Argoty, Gerald Sun, Aleksandra Markovets, Carl Barrett, Zhongwu Lai, Etai Jacob. Improved identification of CHIP mutations from cell-free DNA without matched normal samples using machine learning. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5360.

Abstract 2298: Ultra-sensitive tumor-informed ctDNA assay predicts survival in advanced melanoma patients treated with immune checkpoint inhibition

Abstract Introduction: Immune checkpoint inhibition (ICI) elicits clinical benefit in a subset of cancer patients and monitoring of ctDNA in peripheral blood might improve our ability to predict responses or resistance earlier than imaging. Here, we analyzed melanoma patients receiving ICI over several years using a novel tumor-informed ctDNA platform, and correlated the findings to clinical outcome. Methods: Plasma from 23 advanced melanoma patients was collected at ICI treatment cycles for up to 1,200 days and retrospectively profiled using NeXT Personal™ in this research study. NeXT Personal is a tumor-informed liquid biopsy assay that leverages whole-genome sequencing of tumor/normal samples to generate a personalized liquid biopsy panel for each patient consisting of up to 1,800 selected variants to enable ultra-sensitive molecular residual disease (MRD) detection down to 1-3 parts per million (PPM). Each bespoke panel also covers a fixed set of >2,100 known clinical and resistance loci for detecting variants emerging under therapeutic pressure. We compared NeXT Personal results with imaging and RECIST assessments for each patient. Results: Results showed ultra-high sensitivity for ctDNA with a wide dynamic range of detections from approximately 100,000 PPM down to 2.3 PPM. The median limit of detection was 1.97 PPM. Excluding patients with recent second-line therapy, 94% (17/18) of baseline samples were ctDNA positive (ctDNA+), including all patients who progressed. 37% (22/59) of on-treatment ctDNA+ detections were <100 PPM. 100% of ctDNA+ detections had correlated imaging findings via RECIST confirming presence of tumor, including the lowest PPM detections. Similarly, 100% of complete responses (CR) assessed via RECIST were ctDNA negative for all corresponding plasma timepoints. ctDNA clearance preceded imaging derived CR by an average of 81 days (clearance = 131 days, CR = 212 days). Patients that attained ctDNA clearance at one or more plasma timepoints had significantly longer overall survival (OS) (log-rank test; p = 0.007). Patients with increasing ctDNA levels over the first 25 (or 50) days compared to baseline had significantly reduced OS (p < 0.05). Analysis of the fixed clinical portion of each bespoke panel showed dynamic variant allele frequency changes in 21 variants including therapeutic targets such as BRAF V600E. Conclusions: Advanced melanoma patients exhibit a wide range of ctDNA concentrations at the beginning and during the course of treatment. Ultra-sensitive ctDNA detection down to 2.3 PPM was achieved using the NeXT Personal platform. Most importantly, our pilot study indicates that ctDNA measurements correlated with clinical outcome. The de novo detection of emerging clinically actionable and resistance variants using NeXT Personal can potentially be used to inform treatment once validated in future studies. Citation Format: Laura Keller, Isabel Heidrich, Julian Kött, Charles W. Abbott, Sean Boyle, Jason Pugh, Richard O. Chen, Glen Geidel, Simon Ronald, Stephan W. Schneider, Christoffer Gebhardt, Klaus Pantel. Ultra-sensitive tumor-informed ctDNA assay predicts survival in advanced melanoma patients treated with immune checkpoint inhibition [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2298.

Interdisciplinary approaches to predicting evolutionary biology.

Interdisciplinary approaches to predicting evolutionary biology.

How predictable is adaptation from standing genetic variation? Experimental evolution in Drosophila highlights the central role of redundancy and linkage disequilibrium.

Experimental evolution is well-suited to test the predictability of evolution without the confounding effects of inaccurate forecasts about future environments. Most of the literature about parallel (and thus predictable) evolution has been carried out in asexual microorganisms, which adapt by de novo mutations. Nevertheless, parallel evolution has also been studied in sexual species at the genomic level. Here, I review the evidence for parallel evolution in Drosophila, the best-studied obligatory outcrossing model for adaptation from standing genetic variation in the laboratory. Similar to asexual microorganisms, evidence for parallel evolution varies between the focal hierarchical levels. Selected phenotypes consistently respond in a very predicable way, but the underlying allele frequency changes are much less predictable. The most important insight is that the predictability of the genomic selection response for polygenic traits depends highly on the founder population and to a much lesser extent on the selection regime. This implies that predicting adaptive genomic response is challenging and requires a good understanding of the adaptive architecture (including linkage disequilibrium) in the ancestral populations. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

The effect of cycles of genomic selection on the wheat (T. aestivum) genome

We documented changes in the wheat genome attributed to genomic selection including loss of diversity, and changes in population structure and linkage disequilibrium patterns. We conclude that training and prediction populations need to co-evolve instead of the use of a static training population. Genomic selection (GS) is widely used in plant breeding to shorten breeding cycles. Our objective was to assess the impact of rapid cycling GS on the wheat genome. We used 3927 markers to genotype a training population (YTP) and individuals from five cycles (YC1-YC5) of GS for grain yield. We assessed changes of allele frequency, genetic distance, population structure, and linkage disequilibrium (LD). We found 27.3% of all markers had a significant allele frequency change by YC5, 18% experienced a significant change attributed to selection, and 9.3% had a significant change due to either drift or selection. A total of 725 of 3927 markers were fixed by YC5 with selection fixing 7.3% of the 725 markers. The genetic distance between cycles increased over time. The Fst value of 0.224 between YTP and YC5 indicates their relationship was low. The number LD blocks decreased over time and the correlation between LD matrices also decreased over time. Overall, we found a reduction in genetic diversity, increased genetic differentiation of cycles from the training population, and restructuring of the LD patterns over cycles. The accuracy of GS depends on the genomic similarity of the training population and the prediction populations. Our results show that the similarity can decline rapidly over cycles of GS and compromise the predictive ability of the YTP-based model. Our results support implementing a GS scheme where the training and prediction populations co-evolve instead of the use of a static training population.

Variation in ERAP2 has opposing effects on severe respiratory infection and autoimmune disease

ERAP2 is an aminopeptidase involved in immunological antigen presentation. Genotype data in human samples from before and after the Black Death, an epidemic due to Yersinia pestis, have marked changes in allele frequency of the single-nucleotide polymorphism (SNP) rs2549794, with the T allele suggested to be deleterious during this period, while ERAP2 is also implicated in autoimmune diseases. This study explored the association between variation at ERAP2 and (1) infection, (2) autoimmune disease, and (3) parental longevity. Genome-wide association studies (GWASs) of these outcomes were identified in contemporary cohorts (UK Biobank, FinnGen, and GenOMICC). Effect estimates were extracted for rs2549794 and rs2248374, a haplotype tagging SNP. Additionally, cis expression and protein quantitative trait loci (QTLs) for ERAP2 were used in Mendelian randomization (MR) analyses. Consistent with decreased survival in the Black Death, the T allele of rs2549794 showed evidence of association with respiratory infection (odds ratio; OR for pneumonia 1.03; 95% CI 1.01-1.05). Effect estimates were larger for more severe phenotypes (OR for critical care admission with pneumonia 1.08; 95% CI 1.02-1.14). In contrast, opposing effects were identified for Crohn disease (OR 0.86; 95% CI 0.82-0.90). This allele was shown to associate with decreased ERAP2 expression and protein levels, independent of haplotype. MR analyses suggest that ERAP2 expression may be mediating disease associations. Decreased ERAP2 expression is associated with severe respiratory infection with an opposing association with autoimmune diseases. These data support the hypothesis of balancing selection at this locus driven by autoimmune and infectious disease.

Abstract P5-05-04: Estrogen receptor 1 (ESR1) mutations in circulating tumor DNA (ctDNA) from patients with ER+/HER2- metastatic breast cancer (mBC) treated with lasofoxifene or fulvestrant in the ELAINE 1 study

Abstract Introduction: Acquired ESR1 mutations (mutESR1) after long-term endocrine therapy drive treatment resistance, metastasis, and poor prognosis for patients (pts) with ER+/HER2- metastatic breast cancer (mBC). Lasofoxifene (LAS), a selective estrogen receptor modulator, alone or with a CDK4/6 inhibitor (CDK4/6i) reduced tumor growth better than fulvestrant (Fulv) in mutESR1 BC xenograft models. ELAINE 1 is a randomized trial of LAS vs Fulv in pts with mutESR1 and prior progression on aromatase inhibitor and CDK4/6i. Preliminary results (ESMO 2022) showed that LAS prolonged median progression-free survival (mPFS) compared with Fulv with a favorable safety profile. Here, we report changes in ESR1 ctDNA mutant allele frequency (MAF) from baseline to 8 wks and their associations with clinical benefit (CB) and mPFS. Methods: ELAINE 1 pts were randomized to oral LAS 5 mg daily or IM Fulv 500 mg on days 1, 15, and 29, then every 4 wks, until disease progression or severe toxicity. ctDNA mutESR1 mutations (baseline and 8 wks) were assessed using the Sysmex Inostics OncoBeam or SafeSeq assays—which detect mutESR1 at low allele fractions. MAF changes from baseline to wk 8 were characterized as decreased (decrease in ESR1 MAF or fully cleared), increased (increase in MAF), or equivocal (polyclonal patients [>1 mutESR1] with some increasing and decreasing MAF trends); correlations with PFS and CB were explored. Efficacy measures included objective response rate (ORR), PFS, and CB at 24 wks (CB defined as response or stable disease ≥24 wks). Results: 103 pts received LAS (n=52) or Fulv (n=51). Most common baseline ESR1 variants detected were D538G (56%), Y537S (39%), Y537N (29%), E380Q (22%); 56 (54%) pts were polyclonal. Of the 61 pts with evaluable baseline and wk 8 ctDNA, LAS decreased mutESR1 MAF in 29/35 pts (83% [11 complete clearance]) while Fulv decreased mutESR1 MAF in 16/26 pts (61.5% [6 complete clearance]) (Table). mPFS with LAS was 8 and 4 mos for pts with decreased/cleared MAF and increased MAF, respectively, and with Fulv was 4.5 and 2.8 mos, respectively (Table). LAS decreased the common mutESR1 variants more frequently than Fulv (median relative change -87.1% vs -14.7%). In pts with decreased MAF, CB was observed in 16/29 LAS pts (55%) and 4/16 Fulv pts (25%). The predictiveness of ESR1 MAF clearance for CB was also explored. Of 11 pts with ESR1 MAF clearance taking LAS, 10 achieved CB, yielding a positive predictive value (PPV) of 90.9%. In contrast, 2/6 pts with ESR1 MAF clearance taking Fulv had CB for a PPV of 33.3%. Sensitivity for predicting CB based on direction of ESR1 MAF change was 94% with LAS and 80% with Fulv. In pts with Y537S MAF (n=33), LAS decreased Y537S in 13/15 (87%), with a median relative MAF decrease of 89%. In marked contrast, Fulv increased Y537S MAF in 11/18 pts (61%), corresponding to an MAF relative increase of 82%. LAS and Fulv resulted in complete clearance of Y537S MAF in 33% and 6% of pts, respectively. Conclusion: Our data demonstrate that LAS more effectively decreased or cleared mutESR1 than Fulv. Further, mutESR1 clearance was associated with prolonged PFS and more CB in LAS but not Fulv pts, suggesting that LAS results in robust mutESR1 target engagement. Taken together, our data suggest mutESR1 as a potential liquid biomarker for predicting response to LAS in mutESR1, endocrine-resistant mBC pts. Table. Change from baseline to week 8 in ESR1 MAF and clinical benefit at 24 weeks. CI, confidence interval; MAF, mutant allele fraction; ND, none detected; PFS, progression-free survival. Citation Format: Matthew P. Goetz, Einav Gal-Yam, Daniel Stover, Sarah L. Sammons, Stephanie L. Graff, Grace Wang, Massimo Cristofanilli, Gary Riordan, Hillary S. Sloane, Dominic Carroll, Paul V. Plourde, David J Portman. Estrogen receptor 1 (ESR1) mutations in circulating tumor DNA (ctDNA) from patients with ER+/HER2- metastatic breast cancer (mBC) treated with lasofoxifene or fulvestrant in the ELAINE 1 study [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-05-04.

Where to draw the line? Expanding the delineation of conservation units to highly mobile taxa.

Conservation units (CUs) are an essential tool for maximizing evolutionary potential and prioritizing areas across a species' range for protection when implementing conservation and management measures. However, current workflows for identifying CUs on the basis of neutral and adaptive genomic variation largely ignore information contained in patterns of isolation by distance (IBD), frequently the primary signal of population structure in highly mobile taxa, such as birds, bats, and marine organisms with pelagic larval stages. While individuals located on either end of a species' distribution may exhibit clear genetic, phenotypic, and ecological differences, IBD produces subtle changes in allele frequencies across space, making it difficult to draw clear boundaries for conservation purposes in the absence of discrete population structure. Here, we highlight potential pitfalls that arise when applying common methods for delineating CUs to continuously distributed organisms and review existing methods for detecting subtle breakpoints in patterns of IBD that can indicate barriers to gene flow in highly mobile taxa. In addition, we propose a new framework for identifying CUs in all organisms, including those characterized by continuous genomic differentiation, and suggest several possible ways to harness the information contained in patterns of IBD to guide conservation and management decisions.

Cloning of an Albino Mutation of Arabidopsis thaliana Using Mapping-by-Sequencing.

We report the molecular characterization of an ethyl methanesulfonate (EMS)-induced mutation that causes albinism and lethality at the seedling stage in Arabidopsis thaliana. We identified the mutation using a mapping-by-sequencing approach that uses Fisher's exact tests to detect changes in allele frequencies among the seedlings of an F2 mapping population, which had been pooled according to their phenotypes (wild-type or mutant). After purifying genomic DNA from the plants of both pools, the two samples were sequenced using the Illumina HiSeq 2500 next-generation sequencing platform. The bioinformatic analysis allowed us to identify a point mutation that damages a conserved residue at the acceptor site of an intron of the At2g04030 gene, which encodes the chloroplast-localized AtHsp90.5 protein, a member of the HSP90 family of heat shock proteins. Our RNA-seq analysis demonstrates that the new allele alters the splicing of At2g04030 transcripts in multiple ways, leading to massive deregulation of genes encoding plastid-localized proteins. A search for protein-protein interactions using the yeast two-hybrid method allowed us to identify two members of the GrpE superfamily as potential interactors of AtHsp90.5, as has previously been reported for green algae.

The Difficulty of Predicting Evolutionary Change in Response to Novel Ecological Interactions: A Field Experiment with Anolis Lizards.

AbstractDetermining whether and how evolution is predictable is an important goal, particularly as anthropogenic disturbances lead to novel species interactions that could modify selective pressures. Here, we use a multigeneration field experiment with brown anole lizards (Anolis sagrei) to test hypotheses about the predictability of evolution. We manipulated the presence/absence of predators and competitors of A. sagrei across 16 islands in the Bahamas that had preexisting brown anole populations. Before the experiment and again after roughly five generations, we measured traits related to locomotor performance and habitat use by brown anoles and used double-digest restriction enzyme-associated DNA sequencing to estimate genome-wide changes in allele frequencies. Although previous work showed that predators and competitors had characteristic effects on brown anole behavior, diet, and population sizes, we found that evolutionary change at both phenotypic and genomic levels was difficult to forecast. Phenotypic changes were contingent on sex and habitat use, whereas genetic change was unpredictable and not measurably correlated with phenotypic changes, experimental treatments, or other environmental factors. Our work shows how differences in ecological context can alter evolutionary outcomes over short timescales and underscores the difficulty of forecasting evolutionary responses to multispecies interactions in natural conditions, even in a well-studied system with ample supporting ecological information.

Nonparallel genome changes within subpopulations over time contributed to genetic diversity within the US Holstein population

Maintaining genetic variation in a population is important for long-term genetic gain. The existence of subpopulations within a breed helps maintain genetic variation and diversity. The 20,990 genotyped animals, representing the breeding animals in the year 2014, were identified as the sires of animals born after 2010 with at least 25 progenies, and females measured for type traits within the last 2 yr of data. K-means clustering with 5 clusters (C1, C2, C3, C4, and C5) was applied to the genomic relationship matrix based on 58,990 SNP markers to stratify the selected candidates into subpopulations. The general higher inbreeding resulting from within-cluster mating than across-cluster mating suggests the successful stratification into genetically different groups. The largest cluster (C4) contained animals that were less related to each animal within and across clusters. The average fixation index was 0.03, indicating that the populations were differentiated, and allele differences across the subpopulations were not due to drift alone. Starting with the selected candidates within each cluster, a family unit was identified by tracing back through the pedigree, identifying the genotyped ancestors, and assigning them to a pseudogeneration. Each of the 5 families (F1, F2, F3, F4, and F5) was traced back for 10 generations, allowing for changes in frequency of individual SNPs over time to be observed, which we call allele frequencies change. Alternative procedures were used to identify SNPs changing in a parallel or nonparallel way across families. For example, markers that have changed the most in the whole population, markers that have changed differently across families, and genes previously identified as those that have changed in allele frequency. The genomic trajectory taken by each family involves selective sweeps, polygenic changes, hitchhiking, and epistasis. The replicate frequency spectrum was used to measure the similarity of change across families and showed that populations have changed differently. The proportion of markers that reversed direction in allele frequency change varied from 0.00 to 0.02 if the rate of change was greater than 0.02 per generation, or from 0.14 to 0.24 if the rate of change was greater than 0.005 per generation within each family. Cluster-specific SNP effects for stature were estimated using only females and applied to obtain indirect genomic predictions for males. Reranking occurs depending on SNP effects used. Additive genetic correlations between clusters show possible differences in populations. Further research is required to determine how this knowledge can be applied to maintain diversity and optimize selection decisions in the future.

Genomic decoding of breeding history to guide breeding-by-design in rice.

Deciphering the intrinsic molecular logic of empirical crop breeding from a genomic perspective is a decisive prerequisite for breeding-by-design (BbD), but remains not well established. Here, we decoded the historical features of past rice breeding by phenotyping and haplotyping 546 accessions covering the majority of cultivars bred in the history of Northeast China (NEC). We revealed that three groups founded the genetic diversities in NEC rice with distinct evolution patterns and traced and verified the breeding footprints to known or genome-wide association study (GWAS)-detected quantitative trait loci (QTLs), or introgressions from indica sub-species with chronological changes in allele frequencies. Then we summarized a rice breeding trend/principle in NEC, and combined with the successful example in breeding and application of Zhongkefa5 to demonstrate the guiding value of our conclusion for BbD in practice. Our study provides a paradigm for decoding the breeding history of a specific crop to guide BbD, which may have implications in different crop breeding.

Estimating Temporally Variable Selection Intensity from Ancient DNA Data.

Novel technologies for recovering DNA information from archaeological and historical specimens have made available an ever-increasing amount of temporally spaced genetic samples from natural populations. These genetic time series permit the direct assessment of patterns of temporal changes in allele frequencies and hold the promise of improving power for the inference of selection. Increased time resolution can further facilitate testing hypotheses regarding the drivers of past selection events such as the incidence of plant and animal domestication. However, studying past selection processes through ancient DNA (aDNA) still involves considerable obstacles such as postmortem damage, high fragmentation, low coverage, and small samples. To circumvent these challenges, we introduce a novel Bayesian framework for the inference of temporally variable selection based on genotype likelihoods instead of allele frequencies, thereby enabling us to model sample uncertainties resulting from the damage and fragmentation of aDNA molecules. Also, our approach permits the reconstruction of the underlying allele frequency trajectories of the population through time, which allows for a better understanding of the drivers of selection. We evaluate its performance through extensive simulations and demonstrate its utility with an application to the ancient horse samples genotyped at the loci for coat coloration. Our results reveal that incorporating sample uncertainties can further improve the inference of selection.

Changes in genomic inbreeding and diversity over half a century in Swedish Red and Swedish Holstein dairy cattle.

Swedish Red (SR) and Swedish Holstein (SH) are the dominating commercial dairy cattle breeds in Sweden. Both breeds have undergone substantial changes during the last half century due to intensive selection for breeding goal traits, but also resulting from increased international exchange of breeding animals and genetic drift. The aim of this study was to learn more about changes in genomic diversity and inbreeding in these two breeds over time. Therefore, semen samples from old bulls were genotyped using the 150K Genomic Profiler SNP array and combined with 50K SNP array genotype data, obtained for more recent bulls from the Nordic Cattle Genetic Evaluation. Different measures of level of homozygosity, genomic inbreeding, relatedness and changes in allele frequency were estimated for bulls born during different time periods from the 1950s until 2020. In total, more than 33,000 SNPs for 9737 SR and 5041 SH bulls were included in the analysis using PLINK v1.9. The results showed higher average homozygosity for SR than for SH bulls up to around 2000, but the difference was very small after that. The average inbreeding coefficients based on deviation from expected homozygosity as well as on runs of homozygosity decreased until the early 1980s in both breeds, whereafter they started to increase again for SH, but stayed more stable for SR. From the 1990s onwards, SH displayed higher average inbreeding coefficients than SR. In the last studied birth year group (2015-2020), the mean inbreeding coefficient based on runs of homozygosity was 5.9% for SH and 3.7% for SR. A principal component analysis showed a pattern of genetic relationships related to the birth year period of the bulls, illustrating the gradual change of the genetic material within each breed. The change in allele frequency over time was generally larger for SH than for SR. The results show that the inbreeding level was higher half a century ago than at present, and the inbreeding levels were lower than in some other studied populations. Still, the increase seen for inbreeding coefficients and homozygosity, especially in SH during recent years, should be considered in future breeding strategies.

Whole Mitochondrial Genome Analysis in Non-Small Cell Lung Carcinoma Reveals Unique Tumor-Specific Somatic Mutations.

Mitochondria and mitochondrial DNA have been suggested to play a role in cancer initiation and progression. Knowledge of mitochondrial DNA could provide a breakthrough to advance cancer management. To identify the mitochondrial DNA landscape in non-small cell lung carcinoma. The adenocarcinoma set consisted of 365 pairs of adenocarcinomas and normal lung tissues, whereas the metastasis set included 12 primary non-small cell carcinomas, 15 metastatic tumors, and their normal counterparts. Tumor-specific somatic variants were identified, and if a variant showed heteroplasmy, the proportion of minor alleles was evaluated. Variants with greater than 10% change in allele frequency between tumor and normal pairs were identified as "heteroplasmic shifts." Tumor-specific variants appeared throughout the whole mitochondrial genome, without a common hot spot. Distinct variant profiles were seen in 289 (79.18%) of all individual adenocarcinomas. The presence of a unique profile and the number and loading of heteroplasmic shifts in tumors increased with higher stage or lymph node metastasis, and were related to shorter survival. In the metastasis set, the primary tumor variants were generally found in metastatic tumors. This study shows that somatic mitochondrial DNA mutations present with diverse locations and unique profiles in each individual tumor, implying their clinicopathologic utility.

Estimating linkage disequilibrium and selection from allele frequency trajectories.

Genetic sequences collected over time provide an exciting opportunity to study natural selection. In such studies, it is important to account for linkage disequilibrium to accurately measure selection and to distinguish between selection and other effects that can cause changes in allele frequencies, such as genetic hitchhiking or clonal interference. However, most high-throughput sequencing methods cannot directly measure linkage due to short-read lengths. Here we develop a simple method to estimate linkage disequilibrium from time-series allele frequencies. This reconstructed linkage information can then be combined with other inference methods to infer the fitness effects of individual mutations. Simulations show that our approach reliably outperforms inference that ignores linkage disequilibrium and, with sufficient sampling, performs similarly to inference using the true linkage information. We also introduce two regularization methods derived from random matrix theory that help to preserve its performance under limited sampling effects. Overall, our method enables the use of linkage-aware inference methods even for data sets where only allele frequency time series are available.