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50 results for “adaptive introgression”
Adaptive Introgression in Modern Human Circadian Rhythm Genes Datasets
<p><strong>README:</strong></p> <p>Modern human genetic data with evidence of adaptive introgression from Neanderthals or Denisovans within circadian rhythm genes. The data was generated from the phased gnomAD 1KGP + HGDP callset (Koenig <em>et al</em>., 2024) and introgressed segments were identified by SPrime (Browning <em>et al</em>., 2018). Genes of interest were downloaded from the Circadian Genome Database (CGDB) (Li <em>et al</em>., 2017). Additional variants, haplotypes, and genes that have been previously reported to influence circadian rhythm or chronotype that are thought to be derived from Neanderthals and Denisovans were compiled from Dannemann & Kelso (2017), McArthur et al. (2021), Dannemann et al. (2022), and Velazquez-Arcelay et al. (2023).</p> <p><strong>SPrime ND_Match Files</strong></p> <p>Raw SPrime identified files that we used for our entire analysis. These were modified to include the archaic allele, archaic allele frequency, and average introgressed segment allele frequency. Note that these have been lifted over (Hinrichs <em>et</em> <em>al</em>., 2006) from GRCh38 (hg38) to GRCh37 (hg19) coordinates to match the genome builds of the archaic samples used in our study. As such, any manually generated variant IDs (chromosome:position:ReferenceAllele_AlternativeAllele naming convention) may no longer match the position they are currently sitting on as they were generated with hg38 coordinates. However, all of these were subsequently filtered out of our final results and any proper SNP IDs (dbSNP labels) will be accurate.</p> <p><strong>Supplementary Tables</strong></p> <p>All supplementary tables have an associated README as the first sheet that explains in detail the contents.</p> <p><strong>NEXUS Files</strong></p> <p>NEXUS files were used to generate haplotype networks in PopArt (Leigh & Bryant, 2015). There is a larger, master haplotype file and a smaller subset file. The larger file contains 668 haplotypes from all populations generated in the phased gnomAD 1KGP + HGDP callset (Koenig <em>et al</em>., 2024) for the <em>SUSD1 </em>core haplotype. The smaller subset file is the top 50 haplotypes and ties based on frequency, all Oceanic haplotypes with frequencies of at least 2, and the Neanderthal and Denisovan haplotypes for <em>SUSD1</em>. </p> <p><strong>TRAITS file</strong></p> <p>Accompanies the NEXUS files to create pie graphs for the haplotype network and contains frequency counts of number of haplotypes per region.</p>
Adaptive introgression from maize has facilitated the establishment of teosinte as a noxious weed in Europe
<p>This is the total genotyoping matrix we used for the analyses.<br> The first line of the file contains the identifiers of the samples and each subsequent line the genotype at each SNP The first column contains the identifier of the SNPs.</p> <p>Genotype data for the 70 French teosintes was combined with published and available data for the following material: 40 accessions of Spanish teosintes (1), 314 accessions of parviglumis (2, 3), 332 accessions of mexicana (2, 3), 94 maize landraces from Meso- and Central-America (4) and 155 maize inbred lines from North-America and Europe (5)</p> <ol> <li> <p>Trtikova M, Lohn A, Binimelis R, Chapela I, Oehen B, Zemp N, Widmer A, Hilbeck A (2017) Teosinte in Europe – searching for the origin of a novel weed. Scientific Reports 7, 1560. DOI: https://doi.org/10.1038/s41598-017-01478-w</p> </li> <li> <p>Aguirre-Liguori JA, Tenaillon MI, Vásquez-Lobo A, Gaut BS, Jaramillo-Correa JP, Montes-Hernandez S, Souza V, Eguiarte LE (2017) Connecting genomic patterns of local adaptation and niche suitability in teosintes. Molecular Ecology 26, 4226-4240. DOI: https://doi.org/10.1111/mec.14203</p> </li> <li> <p>Pyhäjärvi T, Hufford MB, Mezmouk S, Ross-Ibarra J (2013) Complex patterns of local adaptation in teosinte. Genome Biology and Evolution 5, 1594–1609. DOI: https://doi.org/10.1093/gbe/evt109</p> </li> <li> <p>Takuno S, Ralph P, Swarts K, Elshire RJ, Glaubitz JC, Buckler ES, Hufford MB, Ross-Ibarra J (2015) Independent molecular basis of convergent highland adaptation in maize. Genetics 200, 1297–1312. DOI: https://doi.org/10.1534/genetics.115.17832</p> </li> <li> <p>Unterseer S, Pophaly SD, Peis R, Westermeier P, Mayer M, Seidel MA, Haberer G, Mayer KFX, Ordas B, Pausch H, Tellier A, Bauer , Schön CC (2016) A comprehensive study of the genomic differentiation between temperate Dent and Flint maize. Genome Biology 17, 137. DOI: https://doi.org/10.1186/s13059-016-1009-x</p> </li> </ol>
Supplementary Information for "Performance evaluation of adaptive introgression classification methods"
<p>Supplementary information : supplementary figures and tables from "<em>Performance evaluation of adaptive introgression classification methods</em>", Romieu <em>et al., </em>2024 manuscript. ROC values, curves and score value by non-AI windows type for various demographic scenarios.</p>
Local adaptation and archaic introgression shape global diversity at human structural variant loci
<p>Supporting data associated with the manuscript "Local adaptation and archaic introgression shape global diversity at human structural variant loci". These include:</p> <ul> <li>structural variant genotypes (Paragraph; <a href="https://github.com/Illumina/paragraph">https://github.com/Illumina/paragraph</a>)</li> <li>eQTL mapping results (fastqtl permutation pass; see <a href="http://fastqtl.sourceforge.net/">http://fastqtl.sourceforge.net/</a> for column descriptions)</li> <li>eQTL fine-mapping results (CAVIAR; see <a href="http://genetics.cs.ucla.edu/caviar/index.html">http://genetics.cs.ucla.edu/caviar/index.html</a>)</li> <li>structural variant selection scan results (Ohana; <a href="https://github.com/jade-cheng/ohana">https://github.com/jade-cheng/ohana</a>)</li> </ul> <p>Description of files in this directory:</p> <p><strong>Structural variant genotypes</strong></p> <p><code>SVs_paragraphFormat.vcf.gz</code> - merged long-read structural variant calls</p> <p><code>SVs_1KGP_pgGTs.vcf.gz</code> - genotypes for 1000 Genomes samples in VCF format</p> <p><strong>eQTL mapping results</strong></p> <p><code>fastqtl_out.txt</code> - results from fastQTL permutation pass; see <a href="http://fastqtl.sourceforge.net/">http://fastqtl.sourceforge.net/</a> for column descriptions</p> <p><code>caviar_out.txt</code> - results from fine-mapping SNPs and SVs at significant SV eQTL loci with CAVIAR. Description of columns:</p> <ul> <li>query_sv: SV that was a significant eQTL and underwent fine-mapping</li> <li>gene_id: gene exhibiting an expression association with the query_sv</li> <li>var_id: variant (SNV or SV) that was tested for expression association with the above gene in the fine-mapping analysis</li> <li>var_in_credible_causal_set: Boolean variable denoting whether the above variant is in the 95% credible causal set</li> <li>prob_in_pcausal_set: the amount that this variant contributes to 95% credible causal set</li> <li>causal_post_prob: the posterior probability that the variant is causal in the expression association</li> </ul> <p><strong>Structural variant selection scan results</strong></p> <p><code>chr21_pruned_50_Q.matrix</code> - admixture proportion matrix (generated by Ohana; <a href="https://github.com/jade-cheng/ohana">https://github.com/jade-cheng/ohana</a>)</p> <p><code>chr21_pruned_50_F.matrix</code> - matrix of inferred ancestral allele frequencies (generated by Ohana)</p> <p><code>chr21_pruned_50_C.matrix</code> - matrix of ancestry component covariances (generated by Ohana) Entries of the matrix can be modified to produce "selection hypothesis" matrices where allele frequencies are allowed to vary in one ancestry component (<a href="https://github.com/jade-cheng/ohana/wiki/Population-or-ancestry-specific-selection-scan">https://github.com/jade-cheng/ohana/wiki/Population-or-ancestry-specific-selection-scan</a>).</p> <p><code>selscan_50_k8_p*.txt.gz</code> - raw output of Ohana selscan (see <a href="https://github.com/jade-cheng/ohana">https://github.com/jade-cheng/ohana</a>)</p> <p><code>selscan_res.txt.gz</code> - Ohana selection scan results. These results have been filtered to exclude SVs that have low genotyping rates (<50% of samples), violate Hardy-Weinberg equilibrium expectations (excess of heterozygotes) in more than half of populations, or have extreme global log likelihood estimate (LLE) values. Description of columns:</p> <ul> <li>ID: SV ID</li> <li>#CHROM: SV chromosome</li> <li>POS: SV start position</li> <li>SVLEN: SV length (negative for deletions)</li> <li>step: number of steps needed to interpolate between genome-wide and selection hypothesis models</li> <li>lle_ratio: likelihood ratio statistic (LRS) of the genome-wide vs. selection hypothesis model</li> <li>global-lle: log likelihood of the genome-wide model</li> <li>local-lle: log likelihood of the selection hypothesis model</li> <li>f-pop0: inferred allele frequency in ancestry component 0</li> <li>f-pop1: inferred allele frequency in ancestry component 1</li> <li>f-pop2: inferred allele frequency in ancestry component 2</li> <li>f-pop3: inferred allele frequency in ancestry component 3</li> <li>f-pop4: inferred allele frequency in ancestry component 4</li> <li>f-pop5: inferred allele frequency in ancestry component 5</li> <li>f-pop6: inferred allele frequency in ancestry component 6</li> <li>f-pop7: inferred allele frequency in ancestry component 7</li> <li>ancestry_component: ancestry component tested by the selection hypothesis model. Note that we have added 1 to the ancestry component numbers to match the terminology used in paper (which orders the components from 1-8 rather than 0-7 for interpretability)</li> <li>snp_perc: SV's percentile in the LRS distribution for frequency-matched SNPs</li> <li>p_nominal: nominal p-value calculated from the likelihood ratio</li> <li>p_adj: adjusted p-value calculated from the likelihood ratio</li> </ul> <p> </p>
Supplementary Data of "Introgression between highly divergent sea squirt genomes: an adaptive breakthrough?"
<p><strong>Datasets used in the analyses (see Table S5 for a detailed description).</strong></p> <p>► Dataset #1: phased SNPs with offspring.<br> joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2.clean.biallelic.noindels_filter_phased_phasedBeagle.vcf<br> ► Dataset #2: all SNPs with missing data.<br> joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2.clean.biallelic.noindels_filter_parents_oNA5.vcf<br> ► Dataset #3a: phased SNPs.<br> joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2.clean.biallelic.noindels_filter_phased_phasedBeagle_parents.vcf<br> ► Dataset #3b: CDS version of “phased SNPs”.<br> joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2.clean.biallelic.noindels_filter_phased_phasedBeagle_parents_SNP_CHRall.orf.cds<br> ► Dataset #3c: FASTA version of “phased SNPs”.<br> joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2.clean.biallelic.noindels_filter_phased_phasedBeagle_parents_SNP_chr5.sub_${START}-${END}.fasta<br> ► Dataset #4: ancestry informative phased SNPs.<br> joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2.clean.biallelic.noindels_filter_phased_phasedBeagle_parents.frq.fixed.vcf<br> ► Dataset #5: all SNPs.<br> joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2.clean.biallelic.noindels_filter_parents_oNA.vcf<br> ► Dataset #6: all polarized SNPs.<br> joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2.clean.biallelic.noindels_filter_parents_raw_refine_HQ_edwardsi_oNA3_polar.vcf<br> ► Dataset #7: unfiltered mapping files.<br> ciona_bwa-mapping_${IND}_bwa_mem_mdup_IR_chromosome5:700000-1500000_sorted_nodup.bam</p>
Supplementary Information of "Introgression between highly divergent sea squirt genomes: an adaptive breakthrough?"
<p><strong>Supplementary Figures</strong></p> <p><strong>Figure S1</strong> Population genetic statistics calculated in non-overlapping 10 Kb windows along the 14 chromosomes in the sea squirt genome.<br> <strong>Figure S2</strong> <em>C. robusta</em> introgression into <em>C. intestinalis</em> shown across the 14 chromosomes.<br> <strong>Figure S3</strong> Population genetic statistics of the <em>C. robusta</em> introgressed coding sequences.<br> <strong>Figure S4 </strong>ABBA-BABA introgression patterns using<em> C. edwardsi </em>as an outgroup.<br> <strong>Figure S5</strong> Inference of the divergence history between <em>C. robusta</em> and <em>C. intestinalis</em> with moments.<br> <strong>Figure S6 </strong>Selection tests.<br> <strong>Figure S7 </strong><em>C. robusta</em> ancestry along chromosome 5 in <em>C. intestinalis</em> individuals.<br> <strong>Figure S8</strong> Neighbor-joining trees of 50 Kb windows framing the “missing data region” (grey band) at the center of the chromosome 5 hotspot.<br> <strong>Figure S9</strong> Copy number variation at candidate SNPs in the introgression hotspot on chromosome 5 (700 Kb - 1.5 Mb).<br> <strong>Figure S10</strong> Structural analysis of the “missing data region” on chromosome 5 (from 1,009,000 to 1,055,000 bp).</p> <p> </p> <p><strong>Supplementary Tables</strong></p> <p><strong>Table S1</strong> Sample information.<br> <strong>Table S2 </strong>Correlation between chromosomes of the individual <em>C. robusta </em>ancestry fraction.<br> <strong>Table S3</strong> Demographic results with moments – excluding chromosome 5.<br> <strong>Table S4</strong> Demographic results with moments – including chromosome 5.<br> <strong>Table S5 </strong>Description of the Supplementary Data.</p> <p> </p> <p><strong>Supplementary Scripts</strong></p> <p><em>Bioinformatic pipeline used for genotyping and haplotyping.</em></p> <p><strong>Script #1</strong>: prepare the reference genome for BWA and GATK.<br> reference_bwa_GATK_CF.sh<br> <strong>Script #2</strong>: mapping the reads to the reference with BWA.<br> mapping_bwa-mem_CF.sh<br> <strong>Script #3</strong>: indel realignment with GATK.<br> indel_realignment_CF.sh<br> <strong>Script #4</strong>: individual variant calling in gVCF format with GATK.<br> snpindel_callingGVCF_raw_CF.sh<br> <strong>Script #5</strong>: joint genotyping with GATK.<br> joint_genotyping_raw_CF.sh<br> <strong>Script #6</strong>: genotype refinement with GATK.<br> genotype_refinement_raw_CF.sh<br> <strong>Script #7</strong>: SNPs and indels recalibration with GATK.<br> snpindel_recalibration_CF.sh<br> <strong>Script #8</strong>: genotype refinement after recalibration with GATK.<br> genotype_refinement_recal_CF.sh<br> <strong>Script #9</strong>: genotype correction.<br> phase_by_transmission_correctCalling_CF@2020.sh<br> <strong>Script #10</strong>: phasing with GATK and BEAGLE.<br> phase_by_transmission_clean_CF@2020.sh</p> <p><em>Pipeline used for the demographic inferences with moments.</em></p> <p><strong>Script #11</strong>: define the demographic models.<br> moments_models_2pop_bb_parallel_folded_2periods.py<br> <strong>Script #12</strong>: run the demographic inferences.<br> moments_inference_dualanneal_bb_parallel_folded_2periods_bounds.py</p>
Adaptive alien genes are maintained amidst a vanishing introgression footprint in a sea squirt
<p>This zenodo archive contains the multi-locus genotype tables for the paper "Falling shoulders ahead: adaptive alien genes maintained amidst vanishing introgression footprint in sea squirts". There are three files in this archive:</p> <ul> <li>Ci_KASP_genotypes_2012.csv: genotype table for individuals sampled in 2012</li> <li>Ci_KASP_genotypes_2021.csv: genotype table for individuals sampled in 2021 as well as control individuals from the two <em>Ciona</em> species</li> <li>Ci_KASP_genotypes_hybrids.csv: genotype table for control hybrids</li> </ul>
Multigenerational hybridisation results in heterosis and facilitates adaptive introgression, with no evidence of outbreeding depression in a pair of marine gastropods
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Data from: Introgression between divergent corn borer species in a region of sympatry: implications on the evolution and adaptation of pest arthropods
The Asian corn borer, Ostrinia furnacalis, and European corn borer, O. nubilalis (Lepidoptera: Crambidae), cause damage to cultivated maize in spatially distinct geographies, and have evolved divergent hydrocarbons as the basis of sexual communication. The Yili area of Xinjiang Uyghur Autonomous Region in China represents the only known region where O. furnacalis has invaded a native O. nubilalis range, and these two corn borer species have made secondary contact. Genetic differentiation was estimated between Ostrinia larvae collected from maize plants at 11 locations in Xinjiang Province, and genotyped using high throughput SNP and microsatellite markers. Maternal lineages were assessed by direct sequencing of mitochondrial cytochrome c oxidase subunit I and II haplotypes, and a high degree of genotypic diversity was demonstrated between lineages based on SNP genotypes. Furthermore, historical introgression was predicted among SNP genotypes only at sympatric locations in the Yili area, whereas in Xinjiang populations wherein only O. furnacalis haplotypes were detected no analogous introgressed genotypes were predicted. Our detection of putative hybrids and historical evidence of introgression defines Yili area as a hybrid zone between the species in normal ecological interactions, and furthermore might indicate that adaptive traits could spread even between seemingly divergent species through horizontal transmission. Results of this study indicate there may be a continuum in the degree of reproductive isolation between Ostrinia species, and that the elegance of distinct and complete speciation based on modifications to the pheromone communication might need to be reconsidered.
Data from: Hybridization and adaptive introgression in a marine invasive species in native habitats
<p><span>Hybridization</span> <span>of distinct populations or species is an important evolutionary driving force. For invasive species, hybridization can enhance their competitive advantage in the non-native range as a source of adaptive novelty by introgression of selectively favoured alleles. </span><span>W</span><span>e use </span><span>single-nucleotide polymorphism arrays (SNP-chips) to assess genetic diversity and population structure in the invasive ctenophore <em>Mnemiopsis</em> <em>leidyi</em> </span><span>in native habitats along the USA east coast. H</span><span>ybrids are present at the distribution border of the two lineages. However, our data suggests selection against hybrids in stable habitats, while hybrids are selected for in fluctuating environments. H</span><span>ybrid populations thriving in extreme and unstable environments of the native range, such as the Chesapeake Bay, could accelerate the invasion success if translocated. For <em>M. leidyi</em>, this is especially relevant as low salinity currently limits its invasion range in western Eurasia. </span><span>Hybridization status is thus important but currently disregarded to determine high-risk areas for ballast water exchange.</span></p>
Data from: Introgression between divergent corn borer species in a region of sympatry: implications on the evolution and adaptation of pest arthropods
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Data from: Recent range expansion and genomic admixture in a kleptoparasitic spider, Argyrodes lanyuensis: A case of adaptive introgression on isolated small island of the Taiwan-Philippine transition zone?
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Data from: Hybridization and adaptive introgression in a marine invasive species in native habitats
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Data for: Pangenome analysis reveals local adaptation to climate driven by introgression in oak species
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Yosemite Toad (Anaxyrus canorus) transcriptome reveals interplay between speciation genes and adaptive introgression
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Data from: VolcanoFinder: genomic scans for adaptive introgression
<p>Recent research shows that introgression between closely-related species is an important source of adaptive alleles for a wide range of taxa. Typically, detection of adaptive introgression from genomic data relies on comparative analyses that require sequence data from both the recipient and the donor species. However, in many cases, the donor is unknown or the data is not currently available. Here, we introduce a genome-scan method---VolcanoFinder---to detect recent events of adaptive introgression using polymorphism data from the recipient species only. VolcanoFinder detects adaptive introgression sweeps from the pattern of excess intermediate-frequency polymorphism they produce in the flanking region of the genome, a pattern which appears as a volcano-shape in pairwise genetic diversity. Using coalescent theory, we derive analytical predictions for these patterns. Based on these results, we develop a composite-likelihood test to detect signatures of adaptive introgression relative to the genomic background. Simulation results show that VolcanoFinder has high statistical power to detect these signatures, even for older sweeps and for soft sweeps initiated by multiple migrant haplotypes. Finally, we implement VolcanoFinder to detect archaic introgression in European and sub-Saharan African human populations, and uncovered interesting candidates in both populations, such as TSHR in Europeans and TCHH-RPTN in Africans. We discuss their biological implications and provide guidelines for identifying and circumventing artifactual signals during empirical applications of VolcanoFinder.</p>
Genomic evidence of introgression and adaptation in a model subtropical tree species, Eucalyptus grandis
<p>The genetic consequences of adaptation to changing environments can be deciphered using landscape genomics, which may help predict species' responses to global climate change. Towards this, we used genome-wide SNP marker analysis to determine population structure and patterns of genetic differentiation in terms of neutral and adaptive genetic variation in the natural range of Eucalyptus grandis, a widely cultivated subtropical and temperate species, serving as genomic reference for the genus. We analysed introgression patterns at subchromosomal resolution using a modified ancestry mapping approach and identified provenances with extensive interspecific introgression, suggesting early hybrid speciation in response to increased aridity. Furthermore, we describe potentially adaptive genetic variation as explained by environment-associated SNP markers, which also led to the discovery of a large structural variant. Finally, we show that genes linked to these markers are enriched for biotic and abiotic stress responses.</p>
Data from: QTL mapping identifies candidate alleles involved in adaptive introgression and range expansion in a wild sunflower
The wild North American sunflowers Helianthus annuus and H. debilis are participants in one of the earliest identified examples of adaptive trait introgression, and the exchange is hypothesized to have triggered a range expansion in H. annuus. However, the genetic basis of the adaptive exchange has not been examined. Here, we combine quantitative trait locus (QTL) mapping with field measurements of fitness to identify candidate H. debilis QTL alleles likely to have introgressed into H. annuus to form the natural hybrid lineage H. a. texanus. Two 500-individual BC1 mapping populations were grown in central Texas, genotyped for 384 single nucleotide polymorphism (SNP) markers and then phenotyped in the field for two fitness and 22 herbivore resistance, ecophysiological, phenological and architectural traits. We identified a total of 110 QTL, including at least one QTL for 22 of the 24 traits. Over 75% of traits exhibited at least one H. debilis QTL allele that would shift the trait in the direction of the wild hybrid H. a. texanus. We identified three chromosomal regions where H. debilis alleles increased both female and male components of fitness; these regions are expected to be strongly favoured in the wild. QTL for a number of other ecophysiological, phenological and architectural traits colocalized with these three regions and are candidates for the actual traits driving adaptive shifts. G × E interactions played a modest role, with 17% of the QTL showing potentially divergent phenotypic effects between the two field sites. The candidate adaptive chromosomal regions identified here serve as explicit hypotheses for how the genetic architecture of the hybrid lineage came into existence.
Data from: Genomic and functional approaches reveal a case of adaptive introgression from Populus balsamifera (balsam poplar) in P. trichocarpa (black cottonwood)
Natural hybrid zones in forest trees provide systems to study the transfer of adaptive genetic variation by introgression. Previous landscape genomic studies in Populus trichocarpa, a keystone tree species, indicated genomic footprints of admixture with its sister species P. balsamifera and identified candidate genes for local adaptation. Here, we explored patterns of introgression and signals of local adaptation in P. trichocarpa and P. balsamifera, employing genome resequencing data from three chromosomes in pure species and admixed individuals from wild populations. Local ancestry analysis in admixed P. trichocarpa revealed a telomeric region in chromosome 15 with P. balsamifera ancestry, containing several candidate genes for local adaptation. Genomic analyses revealed signals of selection in certain genes in this region (e.g. PRR5, COMT1), and functional analyses based on gene expression variation and correlations with adaptive phenotypes suggest distinct functions of the introgressed alleles. In contrast, a block of genes in chromosome 12 paralogous to the introgressed region showed no signs of introgression or signatures of selection. We hypothesize that the introgressed region in chromosome 15 has introduced modular, or cassette-like variation into P. trichocarpa. These linked adaptive mutations are associated with a block of genes in chromosome 15 that appear to have undergone neo- or sub-functionalization relative to paralogs in a duplicated region on chromosome 12 that show no signatures of adaptive variation. The association between P. balsamifera introgressed alleles with the expression of adaptive traits in P. trichocarpa supports the hypothesis that this is a case of adaptive introgression in an ecologically important foundation species.
Introgression, admixture and selection facilitate genetic adaptation to high-altitude environments in Chinese cattle
<p>Domestication and subsequent selection of cattle to form breeds and biological types that can adapt to different environments partitioned ancestral genetic diversity into distinct modern lineages. Genome-wide selection particularly for adaptation to extreme environments left detectable signatures genome-wide. We used high-density genotype data for 42 cattle breeds and identified the influence of <em>Bos grunniens</em> and <em>Bos javanicus</em> on the formation of Chinese indicine breeds that led to their divergence from India-origin Zebu. We also found evidence for introgression, admixture, and migration in most of the Chinese breeds. Selection signature analyses between high-altitude (>1800m) and low-altitude adapted breeds (<1500m) revealed candidate genes (<em>ACSS2</em>, <em>ALDOC,</em> <em>EPAS1</em>,<em> EGLN1, NUCB2</em>) and pathways that are putatively involved in hypoxia adaptation. Immunohistochemical, real-time PCR and CRISPR/cas9 <em>ACSS2</em>-knockout analyses suggests that the up-regulation of <em>ACSS2</em> expression in the liver promotes the metabolic adaptation of cells to hypoxia via the hypoxia-inducible factor pathway. High altitude adaptation involved the introgression of alleles from high-altitude adapted Yaks into Chinese <em>B. t. taurus </em>prior to their formation into recognized breeds and followed by selection. In addition to selection, adaptation to high altitude environments has been facilitated by admixture and introgression with locally adapted cattle populations.</p>
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