Genome Scan for Selection in Structured Layer Chicken Populations Exploiting Linkage Disequilibrium Information

Role of Calcium Homeostasis in Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disease associated with senile plaques (SP) and neurofibrillary tangles (NFTs) in the brain. With aging of the population, AD has become the most common form of dementia. However, the mechanisms leading to AD are still under investigation, and there are currently no specific drugs for its treatment. Therefore, further study on the pathogenesis of AD to develop new drugs for AD treatment remains a top priority. Several studies have suggested that intracellular calcium homeostasis is dysregulated in AD, and this has been implicated in the deposition of amyloid β (Aβ), hyperphosphorylation of tau protein, abnormal synaptic plasticity, and apoptosis, all of which are involved in the occurrence and development of AD. In addition, some based on pathways linking calcium homeostasis and AD have achieved results in AD treatment. This review comprehensively explores the relationship between calcium homeostasis and the pathogenesis of AD to provide a theoretical basis for the future exploration of AD and the development of novel therapeutic drugs.

A genome-wide scan to identify signatures of selection in two Iranian indigenous chicken ecotypes

Background

Various regions of the chicken genome have been under natural and artificial selection for thousands of years. The substantial diversity that exits among chickens from different geographic regions provides an excellent opportunity to investigate the genomic regions under selection which, in turn, will increase our knowledge about the mechanisms that underlie chicken diversity and adaptation. Several statistics have been developed to detect genomic regions that are under selection. In this study, we applied approaches based on differences in allele or haplotype frequencies (F_ST and hapFLK, respectively) between populations, differences in long stretches of consecutive homozygous sequences (ROH), and differences in allele frequencies within populations (composite likelihood ratio (CLR)) to identify inter- and intra-populations traces of selection in two Iranian indigenous chicken ecotypes, the Lari fighting chicken and the Khazak or creeper (short-leg) chicken.

Results

Using whole-genome resequencing data of 32 individuals from the two chicken ecotypes, approximately 11.9 million single nucleotide polymorphisms (SNPs) were detected and used in genomic analyses after quality processing. Examination of the distribution of ROH in the two populations indicated short to long ROH, ranging from 0.3 to 5.4 Mb. We found 90 genes that were detected by at least two of the four applied methods. Gene annotation of the detected putative regions under selection revealed candidate genes associated with growth (DCN, MEOX2 and CACNB1), reproduction (ESR1 and CALCR), disease resistance (S1PR1, ALPK1 and MHC-B), behavior pattern (AGMO, GNAO1 and PSEN1), and morphological traits (IHH and NHEJ1).

Conclusions

Our findings show that these two phenotypically different indigenous chicken populations have been under selection for reproduction, immune, behavioral, and morphology traits. The results illustrate that selection can play an important role in shaping signatures of differentiation across the genomic landscape of two chicken populations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-021-00664-9.

Identification of regions under selection and loci controlling agronomic traits in a soft red winter wheat population

Comprehensive information of a breeding population is a necessity to design promising crosses. This study was conducted to characterize a soft red winter wheat breeding population that was subject of intensive germplasm introductions and introgression from exotic germplasm. We used genome-wide markers and phenotypic assessment to identify signatures of selection and loci controlling agronomic traits in a soft red winter wheat population. The study of linkage disequilibrium (LD) revealed that the extent of LD and its decay varied among chromosomes with chromosomes 2B and 7D showing the most extended islands of high-LD with slow rates of decay. Four sub-populations, two with North American origin and two with Australian and Chinese origins, were identified. Genome-wide scans for selection signatures using F_ST and hapFLK identified 13 genomic regions under selection, of which five loci (LT, Fr-A2, Vrn-A1, Vrn-B1, Vrn3) were associated with environmental adaptation and two loci were associated with disease resistance genes (Sr36 and Fhb1). Genome-wide association studies identified major loci controlling yield and yield related traits. For days to heading and plant height, major loci with effects sizes of 2.2 days and 5 cm were identified on chromosomes 7B and 6A respectively. For test weight, number of spikes per square meter, and number of kernels per square meter, large effect loci were identified on chromosomes 1A, 4B, and 5A, respectively. However, for yield alone, no major loci were detected. A combination of selection for large effect loci for yield components and genomic selection could be a promising approach for yield improvement.

Important candidate genes for abdominal fat content identified by linkage disequilibrium and fixation index information

Selection for rapid growth in chickens has always been accompanied by increased fat deposition and excessive fat deposition, especially abdominal fat, cannot only decrease feed efficiency but also cause many diseases. Finding the candidate genes associated with abdominal fat deposition is essential for breeding. To identify these candidate genes, we applied linkage disequilibrium and selection signature analysis using chicken 60 k single nucleotide polymorphism (SNP) chips in two broiler lines divergently selected for abdominal fat content for 11 generations. After quality control, 46,033 SNPs were left for analysis. Using these SNPs, we found that r2 was 0.06 to 0.14 in the lean line and 0.07 to 0.13 in the fat line for all 28 chromosomes (except GGA16). Pairwise SNP distances <25 kb showed a mean r2 = 0.33 in the lean line and r2 = 0.32 in the fat line. The fixation index (FST) analysis was carried out and 46 SNPs with the top 0.1% of the FST value was detected as the loci with selection signatures. Besides FST, hapFLK was also used to detect selection signatures for abdominal fat content. A total of 11 genes, including transient receptor potential cation channel subfamily C member 4, estrogen related receptor gamma, fibroblast growth factor 13, G-protein-signaling modulator 2, RAR related orphan receptor A, phospholipase A2 group X, mitochondrial ribosomal protein L28, metadherin, calcitonin receptor like receptor, serine/threonine kinase 39, and nuclear factor I A, were detected as the important candidate genes for abdominal fat deposition based on their basic functions. The results of the present study may benefit the understanding of genetic mechanism of abdominal fat deposition in chicken.

Detection of Selection Signatures Among Brazilian, Sri Lankan, and Egyptian Chicken Populations Under Different Environmental Conditions

Extreme environmental conditions are a major challenge in livestock production. Changes in climate, particularly those that contribute to weather extremes like drought or excessive humidity, may result in reduced performance and reproduction and could compromise the animal's immune function. Animal survival within extreme environmental conditions could be in response to natural selection and to artificial selection for production traits that over time together may leave selection signatures in the genome. The aim of this study was to identify selection signatures that may be involved in the adaptation of indigenous chickens from two different climatic regions (Sri Lanka = Tropical; Egypt = Arid) and in non-indigenous chickens that derived from human migration events to the generally tropical State of São Paulo, Brazil. To do so, analyses were conducted using fixation index (Fst) and hapFLK analyses. Chickens from Brazil (n = 156), Sri Lanka (n = 92), and Egypt (n = 96) were genotyped using the Affymetrix Axiom^®600k Chicken Genotyping Array. Pairwise Fst analyses among countries did not detect major regions of divergence between chickens from Sri Lanka and Brazil, with ecotypes/breeds from Brazil appearing to be genetically related to Asian-Indian (Sri Lanka) ecotypes. However, several differences were detected in comparisons of Egyptian with either Sri Lankan or Brazilian populations, and common regions of difference on chromosomes 2, 3 and 8 were detected. The hapFLK analyses for the three separate countries suggested unique regions that are potentially under selection on chromosome 1 for all three countries, on chromosome 4 for Sri Lankan, and on chromosomes 3, 5, and 11 for the Egyptian populations. Some of identified regions under selection with hapFLK analyses contained genes such as TLR3, SOCS2, EOMES, and NFAT5 whose biological functions could provide insights in understanding adaptation mechanisms in response to arid and tropical environments.

Integrating Selection Mapping With Genetic Mapping and Functional Genomics

Genomic scans for signatures of selection allow us to, in principle, detect variants and genes that underlie recent adaptations. By combining selection mapping with genetic mapping of traits known to be relevant to adaptation, we can simultaneously investigate whether genes and variants show signals of recent selection and whether they impact traits that have likely been selected. There are three ways to integrate selection mapping with genetic mapping or functional genomics: (1) To use genetic mapping data from other populations as a form of genome annotation. (2) To perform experimental evolution or artificial selection to be able to study selected variants when they segregate, either by performing genetic mapping before selection or by crossing the selected individuals to some reference population. (3) To perform a comparative study of related populations facing different selection regimes. This short review discusses these different ways of integrating selection mapping with genetic mapping and functional genomics, with examples of how each has been done.

Identification of genomic regions and candidate genes for chicken meat ultimate pH by combined detection of selection signatures and QTL

BACKGROUND

The understanding of the biological determinism of meat ultimate pH, which is strongly related to muscle glycogen content, is a key point for the control of muscle integrity and meat quality in poultry. In the present study, we took advantage of a unique model of two broiler lines divergently selected for the ultimate pH of the pectoralis major muscle (PM-pHu) in order to decipher the genetic control of this trait. Two complementary approaches were used: detection of selection signatures generated during the first five generations and genome-wide association study for PM-pHu and Sartorius muscle pHu (SART-pHu) at the sixth generation of selection.

RESULTS

Sixty-three genomic regions showed significant signatures of positive selection. Out of the 10 most significant regions (detected by HapFLK or FLK method with a p-value below 1e-6), 4 were detected as soon as the first generation (G1) and were recovered at each of the four following ones (G2-G5). Another four corresponded to a later onset of selection as they were detected only at G5. In total, 33 SNPs, located in 24 QTL regions, were significantly associated with PM-pHu. For SART-pHu, we detected 18 SNPs located in 10 different regions. These results confirmed a polygenic determinism for these traits and highlighted two major QTL: one for PM-pHu on GGA1 (with a Bayes Factor (BF) of 300) and one for SART-pHu on GGA4 (with a BF of 257). Although selection signatures were enriched in QTL for PM-pHu, several QTL with strong effect haven't yet responded to selection, suggesting that the divergence between lines might be further increased.

CONCLUSIONS

A few regions of major interest with significant selection signatures and/or strong association with PM-pHu or SART-pHu were evidenced for the first time in chicken. Their gene content suggests several candidates associated with diseases of glycogen storage in humans. The impact of these candidate genes on meat quality and muscle integrity should be further investigated in chicken.

Genome-wide characterization of genetic variants and putative regions under selection in meat and egg-type chicken lines

Background

Meat and egg-type chickens have been selected for several generations for different traits. Artificial and natural selection for different phenotypes can change frequency of genetic variants, leaving particular genomic footprints throghtout the genome. Thus, the aims of this study were to sequence 28 chickens from two Brazilian lines (meat and white egg-type) and use this information to characterize genome-wide genetic variations, identify putative regions under selection using Fst method, and find putative pathways under selection.

Results

A total of 13.93 million SNPs and 1.36 million INDELs were identified, with more variants detected from the broiler (meat-type) line. Although most were located in non-coding regions, we identified 7255 intolerant non-synonymous SNPs, 512 stopgain/loss SNPs, 1381 frameshift and 1094 non-frameshift INDELs that may alter protein functions. Genes harboring intolerant non-synonymous SNPs affected metabolic pathways related mainly to reproduction and endocrine systems in the white-egg layer line, and lipid metabolism and metabolic diseases in the broiler line. Fst analysis in sliding windows, using SNPs and INDELs separately, identified over 300 putative regions of selection overlapping with more than 250 genes. For the first time in chicken, INDEL variants were considered for selection signature analysis, showing high level of correlation in results between SNP and INDEL data. The putative regions of selection signatures revealed interesting candidate genes and pathways related to important phenotypic traits in chicken, such as lipid metabolism, growth, reproduction, and cardiac development.

Conclusions

In this study, Fst method was applied to identify high confidence putative regions under selection, providing novel insights into selection footprints that can help elucidate the functional mechanisms underlying different phenotypic traits relevant to meat and egg-type chicken lines. In addition, we generated a large catalog of line-specific and common genetic variants from a Brazilian broiler and a white egg layer line that can be used for genomic studies involving association analysis with phenotypes of economic interest to the poultry industry.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4444-0) contains supplementary material, which is available to authorized users.

High-resolution analysis of selection sweeps identified between fine-wool Merino and coarse-wool Churra sheep breeds

BACKGROUND

With the aim of identifying selection signals in three Merino sheep lines that are highly specialized for fine wool production (Australian Industry Merino, Australian Merino and Australian Poll Merino) and considering that these lines have been subjected to selection not only for wool traits but also for growth and carcass traits and parasite resistance, we contrasted the OvineSNP50 BeadChip (50 K-chip) pooled genotypes of these Merino lines with the genotypes of a coarse-wool breed, phylogenetically related breed, Spanish Churra dairy sheep. Genome re-sequencing datasets of the two breeds were analyzed to further explore the genetic variation of the regions initially identified as putative selection signals.

RESULTS

Based on the 50 K-chip genotypes, we used the overlapping selection signals (SS) identified by four selection sweep mapping analyses (that detect genetic differentiation, reduced heterozygosity and patterns of haplotype diversity) to define 18 convergence candidate regions (CCR), five associated with positive selection in Australian Merino and the remainder indicating positive selection in Churra. Subsequent analysis of whole-genome sequences from 15 Churra and 13 Merino samples identified 142,400 genetic variants (139,745 bi-allelic SNPs and 2655 indels) within the 18 defined CCR. Annotation of 1291 variants that were significantly associated with breed identity between Churra and Merino samples identified 257 intragenic variants that caused 296 functional annotation variants, 275 of which were located across 31 coding genes. Among these, four synonymous and four missense variants (NPR2_His847Arg, NCAPG_Ser585Phe, LCORL_Asp1214Glu and LCORL_Ile1441Leu) were included.

CONCLUSIONS

Here, we report the mapping and genetic variation of 18 selection signatures that were identified between Australian Merino and Spanish Churra sheep breeds, which were validated by an additional contrast between Spanish Merino and Churra genotypes. Analysis of whole-genome sequencing datasets allowed us to identify divergent variants that may be viewed as candidates involved in the phenotypic differences for wool, growth and meat production/quality traits between the breeds analyzed. The four missense variants located in the NPR2, NCAPG and LCORL genes may be related to selection sweep regions previously identified and various QTL reported in sheep in relation to growth traits and carcass composition.

Genetic Mapping of Quantitative Trait Loci for Egg Production and Egg Quality Traits in Chickens: a Review

Chickens display a wide spectrum of phenotypic variations in quantitative traits such as egg-related traits. Quantitative trait locus (QTL) analysis is a statistical method used to understand the relationship between phenotypic (trait measurements) and genotypic data (molecular markers). We have performed QTL analyses for egg-related traits using an original resource population based on the Japanese Large Game (Oh-Shamo) and the White Leghorn breeds of chickens. In this article, we summarize the results of our extensive QTL analyses for 11 and 66 traits for egg production and egg quality, respectively. We reveal that at least 30 QTL regions on 17 different chromosomes affect phenotypic variation in egg-related traits. Each locus had an age-specific effect on traits, and a variety in effects was also apparent, such as additive, dominance, and epistatic-interaction effects. Although genome-wide association study (GWAS) is suitable for gene-level resolution mapping of GWAS loci with additive effects, QTL mapping studies enable us to comprehensively understand genetic control, such as chromosomal regions, genetic contribution to phenotypic variance, mode of inheritance, and age-specificity of both common and rare alleles. QTL analyses also describe the relationship between genotypes and phenotypes in experimental populations. Accumulation of QTL information, including GWAS loci, is also useful for studies of population genomics approached without phenotypic data in order to validate the identified genomic signatures of positive selection. The combination of QTL studies and next-generation sequencing techniques with uncharacterized genetic resources will enhance current understanding of the relationship between genotypes and phenotypes in livestock animals.

The association of very low-density lipoprotein receptor (VLDLR) haplotypes with egg production indicates VLDLR is a candidate gene for modulating egg production

The very low-density lipoprotein receptor (VLDLR) transports egg yolk precursors into oocytes. However, our knowledge of the distribution patterns of VLDLR variants among breeds and their relationship to egg production is still incomplete. In this study, eight single nucleotide polymorphisms (SNPs) that account for 87% of all VLDLR variants were genotyped in Nick Chick (NC, n=91), Lohmann Brown (LohB, n=50) and Lueyang (LY, n=381) chickens, the latter being an Chinese indigenous breed. Egg production by NC and LY chickens was recorded from 17 to 50 weeks. Only four similar haplotypes were found in NC and LohB, of which two accounted for 100% of all NC haplotypes and 92.5% of LohB haplotypes. In contrast, there was considerable haplotypic diversity in LY. Comparison of egg production in LY showed that hens with NC-like haplotypes had a significantly higher production (p < 0.05) than those without the haplotypes. However, VLDLR expression was not significantly different between the haplotypes. These findings indicate a divergence in the distribution of VLDLR haplotypes between selected and non-selected breeds and suggest that the near fixation of VLDLR variants in NC and LohB is compatible with signature of selection. These data also support VLDLR as a candidate gene for modulating egg production.

Population structure of eleven Spanish ovine breeds and detection of selective sweeps with BayeScan and hapFLK

The goals of the current work were to analyse the population structure of 11 Spanish ovine breeds and to detect genomic regions that may have been targeted by selection. A total of 141 individuals were genotyped with the Infinium 50 K Ovine SNP BeadChip (Illumina). We combined this dataset with Spanish ovine data previously reported by the International Sheep Genomics Consortium (N = 229). Multidimensional scaling and Admixture analyses revealed that Canaria de Pelo and, to a lesser extent, Roja Mallorquina, Latxa and Churra are clearly differentiated populations, while the remaining seven breeds (Ojalada, Castellana, Gallega, Xisqueta, Ripollesa, Rasa Aragonesa and Segureña) share a similar genetic background. Performance of a genome scan with BayeScan and hapFLK allowed us identifying three genomic regions that are consistently detected with both methods i.e. Oar3 (150–154 Mb), Oar6 (4–49 Mb) and Oar13 (68–74 Mb). Neighbor-joining trees based on polymorphisms mapping to these three selective sweeps did not show a clustering of breeds according to their predominant productive specialization (except the local tree based on Oar13 SNPs). Such cryptic signatures of selection have been also found in the bovine genome, posing a considerable challenge to understand the biological consequences of artificial selection.

Whole-genome resequencing of honeybee drones to detect genomic selection in a population managed for royal jelly

Four main evolutionary lineages of A. mellifera have been described including eastern Europe (C) and western and northern Europe (M). Many apiculturists prefer bees from the C lineage due to their docility and high productivity. In France, the routine importation of bees from the C lineage has resulted in the widespread admixture of bees from the M lineage. The haplodiploid nature of the honeybee Apis mellifera, and its small genome size, permits affordable and extensive genomics studies. As a pilot study of a larger project to characterise French honeybee populations, we sequenced 60 drones sampled from two commercial populations managed for the production of honey and royal jelly. Results indicate a C lineage origin, whilst mitochondrial analysis suggests two drones originated from the O lineage. Analysis of heterozygous SNPs identified potential copy number variants near to genes encoding odorant binding proteins and several cytochrome P450 genes. Signatures of selection were detected using the hapFLK haplotype-based method, revealing several regions under putative selection for royal jelly production. The framework developed during this study will be applied to a broader sampling regime, allowing the genetic diversity of French honeybees to be characterised in detail.