Regional, Local and Microgeographic Allele Frequency Variation between Apple and Hawthorn Populations of Rhagoletis pomonella in Western Michigan

Evolution ◽  
1990 ◽  
Vol 44 (3) ◽  
pp. 595 ◽  
Author(s):  
Jeffrey L. Feder ◽  
Charley A. Chilcote ◽  
Guy L. Bush
Keyword(s):  
Allele Frequency ◽  
Frequency Variation ◽  
Rhagoletis Pomonella ◽  
Allele Frequency Variation

scholarly journals Genetic diversity of ‘Very Important Pharmacogenes’ in two South-Asian populations

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12294
Author(s):  
Neeraj Bharti ◽  
Ruma Banerjee ◽  
Archana Achalere ◽  
Sunitha Manjari Kasibhatla ◽  
Rajendra Joshi
Keyword(s):  
Genetic Diversity ◽  
Allele Frequency ◽  
Population Stratification ◽  
Drug Response ◽  
Fixation Index ◽  
Frequency Variation ◽  
1000 Genomes Project ◽  
1000 Genomes ◽  
Link Type ◽  
Allele Frequency Variation

Objectives Reliable identification of population-specific variants is important for building the single nucleotide polymorphism (SNP) profile. In this study, genomic variation using allele frequency differences of pharmacologically important genes for Gujarati Indians in Houston (GIH) and Indian Telugu in the U.K. (ITU) from the 1000 Genomes Project vis-à-vis global population data was studied to understand its role in drug response. Methods Joint genotyping approach was used to derive variants of GIH and ITU independently. SNPs of both these populations with significant allele frequency variation (minor allele frequency ≥ 0.05) with super-populations from the 1000 Genomes Project and gnomAD based on Chi-square distribution with p-value of ≤ 0.05 and Bonferroni’s multiple adjustment tests were identified. Population stratification and fixation index analysis was carried out to understand genetic differentiation. Functional annotation of variants was carried out using SnpEff, VEP and CADD score. Results Population stratification of VIP genes revealed four clusters viz., single cluster of GIH and ITU, one cluster each of East Asian, European, African populations and Admixed American was found to be admixed. A total of 13 SNPs belonging to ten pharmacogenes were identified to have significant allele frequency variation in both GIH and ITU populations as compared to one or more super-populations. These SNPs belong to VKORC1 (rs17708472, rs2359612, rs8050894) involved in Vitamin K cycle, cytochrome P450 isoforms CYP2C9 (rs1057910), CYP2B6 (rs3211371), CYP2A2 (rs4646425) and CYP2A4 (rs4646440); ATP-binding cassette (ABC) transporter ABCB1 (rs12720067), DPYD1 (rs12119882, rs56160474) involved in pyrimidine metabolism, methyltransferase COMT (rs9332377) and transcriptional factor NR1I2 (rs6785049). SNPs rs1544410 (VDR), rs2725264 (ABCG2), rs5215 and rs5219 (KCNJ11) share high fixation index (≥ 0.5) with either EAS/AFR populations. Missense variants rs1057910 (CYP2C9), rs1801028 (DRD2) and rs1138272 (GSTP1), rs116855232 (NUDT15); intronic variants rs1131341 (NQO1) and rs115349832 (DPYD) are identified to be ‘deleterious’. Conclusions Analysis of SNPs pertaining to pharmacogenes in GIH and ITU populations using population structure, fixation index and allele frequency variation provides a premise for understanding the role of genetic diversity in drug response in Asian Indians.

Porcine LIF gene polymorphisms and their association with litter size traits in four pig breeds

2020 ◽  
Vol 100 (1) ◽  
pp. 85-92
Author(s):  
Yueyun Ding ◽  
Chong Ding ◽  
Xudong Wu ◽  
Chaodong Wu ◽  
Li Qian ◽  
...  
Keyword(s):  
Allele Frequency ◽  
Litter Size ◽  
Fragment Length Polymorphism ◽  
Frequency Variation ◽  
Related Gene ◽  
Chain Reaction ◽  
Large White ◽  
Potential Marker ◽  
Polymerase Chain ◽  
Allele Frequency Variation

Leukemia inhibitory factor (LIF) is an important productivity-related gene in pigs. We found two polymorphisms — g.6646C>T and g.6988C>T — in exon 3 of LIF in pigs by using DNA sequencing and polymerase chain reaction-restriction fragment length polymorphism. Three genotypes were obtained and associated with litter size traits in Anqing Six-end-white (AQ), Wei (W), Wannan Black (WNB), and Large White (LW) pigs. At locus g.6646C>T, the g.6646C allele frequency variation was 0.6869 (AQ), 0.7473 (W), 1 (WNB), and 0.6852 (LW). In AQ pigs, sows with the TT genotype had higher total number of piglets born (TNB) and number of piglets born alive (NBA) in the first parity and multiparities (P < 0.01). In W and LW pigs, sows with the CC genotype had higher TNB and NBA in multiparities (P < 0.01). At locus g.6988C>T, the g.6988C allele frequency variation was 1 (AQ), 0.6154 (W), 1 (WNB), and 0.6667 (LW). The CC genotype significantly differed from CT or TT genotypes (P < 0.01) for TNB and NBA in W and LW pigs. Thus, LIF was shown to have a significant influence on litter size. Therefore, g.6646C>T and g.6988C>T loci of LIF could be potential marker-assisted selection tools for improving litter size in pig production.

scholarly journals Geographic patterns of human allele frequency variation: a variant-centric perspective

2020 ◽  
Author(s):  
Arjun Biddanda ◽  
Daniel P. Rice ◽  
John Novembre
Keyword(s):  
Genetic Variation ◽  
Gene Flow ◽  
Genetic Structure ◽  
Allele Frequency ◽  
Human Genetics ◽  
Geographic Region ◽  
Frequency Variation ◽  
Human Genetic Variation ◽  
Alternative Representation ◽  
Geographic Patterns

AbstractA key challenge in human genetics is to describe and understand the distribution of human genetic variation. Often genetic variation is described by showing relationships among populations or individuals, in each case drawing inferences over a large number of variants. Here, we present an alternative representation of human genetic variation that reveals the relative abundance of different allele frequency patterns across populations. This approach allows viewers to easily see several features of human genetic structure: (1) most variants are rare and geographically localized, (2) variants that are common in a single geographic region are more likely to be shared across the globe than to be private to that region, and (3) where two individuals differ, it is most often due to variants that are common globally, regardless of whether the individuals are from the same region or different regions. To guide interpretation of the results, we also apply the visualization to contrasting theoretical scenarios with varying levels of divergence and gene flow. Our variant-centric visualization clarifies the major geographic patterns of human variation and can be used to help correct potential misconceptions about the extent and nature of genetic differentiation among populations.

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