scholarly journals Assessing the potential for assisted gene flow using past introduction of Norway spruce in Southern Sweden: Local adaptation and genetic basis of quantitative traits in trees

2018 ◽  
Author(s):  
Pascal Milesi ◽  
Mats Berlin ◽  
Jun Chen ◽  
Marion Orsucci ◽  
Lili Li ◽  
...  
Keyword(s):  
Gene Flow ◽  
Local Adaptation ◽  
Norway Spruce ◽  
Quantitative Traits ◽  
Genetic Basis ◽  
Association Studies ◽  
Phenotypic Traits ◽  
Genome Wide Association Studies ◽  
Southern Sweden ◽  
Breeding Programs

AbstractNorway spruce (Picea abies) is a dominant conifer species of major economic importance in Northern Europe. Extensive breeding programs were established to improve phenotypic traits of interest. In southern Sweden seeds used to create progeny tests were collected on about 3000 trees of outstanding phenotype (“plus” trees) across the region. Some were of local origin but many were recent introductions from the rest of the natural range. The mixed origin of the trees together with partial sequencing of the exome of >1,500 of these trees and phenotypic data retrieved from the Swedish breeding program offered us a unique opportunity to dissect the genetic basis of local adaptation of three quantitative traits (height, diameter and budburst). Through a combination of multivariate analyses and genome-wide association studies, we showed that there was a very strong effect of geographical origin on growth (height and diameter) and phenology (budburst) with trees from southern origins outperforming local provenances. Association studies also indicated that growth traits were highly polygenic and budburst somewhat less. Hence, our results suggest that assisted gene flow and genomic selection approaches could help alleviating the effect of climate change on P. abies breeding programs in Sweden.

scholarly journals Genomic Analysis, Progress and Future Perspectives in Dairy Cattle Selection: A Review

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 599
Author(s):  
Miguel A. Gutierrez-Reinoso ◽  
Pedro M. Aponte ◽  
Manuel Garcia-Herreros
Keyword(s):  
Dairy Cattle ◽  
Production Efficiency ◽  
Association Studies ◽  
Phenotypic Traits ◽  
Progeny Testing ◽  
Genome Wide Association Studies ◽  
Genetic Progress ◽  
Breeding Programs ◽  
Increased Risk ◽  
Selection Of

Genomics comprises a set of current and valuable technologies implemented as selection tools in dairy cattle commercial breeding programs. The intensive progeny testing for production and reproductive traits based on genomic breeding values (GEBVs) has been crucial to increasing dairy cattle productivity. The knowledge of key genes and haplotypes, including their regulation mechanisms, as markers for productivity traits, may improve the strategies on the present and future for dairy cattle selection. Genome-wide association studies (GWAS) such as quantitative trait loci (QTL), single nucleotide polymorphisms (SNPs), or single-step genomic best linear unbiased prediction (ssGBLUP) methods have already been included in global dairy programs for the estimation of marker-assisted selection-derived effects. The increase in genetic progress based on genomic predicting accuracy has also contributed to the understanding of genetic effects in dairy cattle offspring. However, the crossing within inbred-lines critically increased homozygosis with accumulated negative effects of inbreeding like a decline in reproductive performance. Thus, inaccurate-biased estimations based on empirical-conventional models of dairy production systems face an increased risk of providing suboptimal results derived from errors in the selection of candidates of high genetic merit-based just on low-heritability phenotypic traits. This extends the generation intervals and increases costs due to the significant reduction of genetic gains. The remarkable progress of genomic prediction increases the accurate selection of superior candidates. The scope of the present review is to summarize and discuss the advances and challenges of genomic tools for dairy cattle selection for optimizing breeding programs and controlling negative inbreeding depression effects on productivity and consequently, achieving economic-effective advances in food production efficiency. Particular attention is given to the potential genomic selection-derived results to facilitate precision management on modern dairy farms, including an overview of novel genome editing methodologies as perspectives toward the future.

scholarly journals Leveraging breeding programs and genomic data in Norway spruce (Picea abies L. Karst) for GWAS analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Zhi-Qiang Chen ◽  
Yanjun Zan ◽  
Pascal Milesi ◽  
Linghua Zhou ◽  
Jun Chen ◽  
...  
Keyword(s):  
Norway Spruce ◽  
Complex Traits ◽  
Field Experiments ◽  
Association Studies ◽  
Snp Array ◽  
Frost Damage ◽  
Exome Capture ◽  
Genome Wide Association Studies ◽  
Phenotypic Data ◽  
Breeding Programs

Abstract Background Genome-wide association studies (GWAS) identify loci underlying the variation of complex traits. One of the main limitations of GWAS is the availability of reliable phenotypic data, particularly for long-lived tree species. Although an extensive amount of phenotypic data already exists in breeding programs, accounting for its high heterogeneity is a great challenge. We combine spatial and factor-analytics analyses to standardize the heterogeneous data from 120 field experiments of 483,424 progenies of Norway spruce to implement the largest reported GWAS for trees using 134 605 SNPs from exome sequencing of 5056 parental trees. Results We identify 55 novel quantitative trait loci (QTLs) that are associated with phenotypic variation. The largest number of QTLs is associated with the budburst stage, followed by diameter at breast height, wood quality, and frost damage. Two QTLs with the largest effect have a pleiotropic effect for budburst stage, frost damage, and diameter and are associated with MAP3K genes. Genotype data called from exome capture, recently developed SNP array and gene expression data indirectly support this discovery. Conclusion Several important QTLs associated with growth and frost damage have been verified in several southern and northern progeny plantations, indicating that these loci can be used in QTL-assisted genomic selection. Our study also demonstrates that existing heterogeneous phenotypic data from breeding programs, collected over several decades, is an important source for GWAS and that such integration into GWAS should be a major area of inquiry in the future.

scholarly journals Molecular advances on agricultural crop improvement to meet current cultivating demands

2019 ◽  
Vol 12 (2) ◽  
pp. 39-60
Author(s):  
T. Margaritopoulou ◽  
D. Milioni
Keyword(s):  
Genomic Selection ◽  
Quantitative Traits ◽  
Seed Quality ◽  
Association Studies ◽  
Crop Improvement ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Agricultural Crop ◽  
Marker Assisted Breeding ◽  
Breeding Programs

Abstract Sunflower, maize and potato are among the world’s principal crops. In order to improve various traits, these crops have been genetically engineered to a great extent. Even though molecular markers for simple traits such as, fertility, herbicide tolerance or specific pathogen resistance have been successfully used in marker-assisted breeding programs for years, agronomical important complex quantitative traits like yield, biotic and abiotic stress resistance and seed quality content are challenging and require whole genome approaches. Collections of genetic resources for these crops are conserved worldwide and represent valuable resources to study complex traits. Nowadays technological advances and the availability of genome sequence have made novel approaches on the whole genome level possible. Molecular breeding, including both transgenic approach and marker-assisted breeding have facilitated the production of large amounts of markers for high density maps and allowed genome-wide association studies and genomic selection in sunflower, maize and potato. Marker-assisted selection related to hybrid performance has shown that genomic selection is a successful approach to address complex quantitative traits and to facilitate speeding up breeding programs in these crops in the future.

scholarly journals The Genetic Basis of Hypertriglyceridemia

2021 ◽  
Vol 23 (8) ◽  
Author(s):  
Germán D. Carrasquilla ◽  
Malene Revsbech Christiansen ◽  
Tuomas O. Kilpeläinen
Keyword(s):  
Genetic Basis ◽  
Association Studies ◽  
Cumulative Effect ◽  
Genome Wide Association Studies ◽  
Genetic Loci ◽  
Risk Variants ◽  
Genome Wide ◽  
Severe Hypertriglyceridemia ◽  
Increased Risk ◽  
Polygenic Scores

Abstract Purpose of Review Hypertriglyceridemia is a common dyslipidemia associated with an increased risk of cardiovascular disease and pancreatitis. Severe hypertriglyceridemia may sometimes be a monogenic condition. However, in the vast majority of patients, hypertriglyceridemia is due to the cumulative effect of multiple genetic risk variants along with lifestyle factors, medications, and disease conditions that elevate triglyceride levels. In this review, we will summarize recent progress in the understanding of the genetic basis of hypertriglyceridemia. Recent Findings More than 300 genetic loci have been identified for association with triglyceride levels in large genome-wide association studies. Studies combining the loci into polygenic scores have demonstrated that some hypertriglyceridemia phenotypes previously attributed to monogenic inheritance have a polygenic basis. The new genetic discoveries have opened avenues for the development of more effective triglyceride-lowering treatments and raised interest towards genetic screening and tailored treatments against hypertriglyceridemia. Summary The discovery of multiple genetic loci associated with elevated triglyceride levels has led to improved understanding of the genetic basis of hypertriglyceridemia and opened new translational opportunities.

scholarly journals Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

2016 ◽  
Vol 283 (1835) ◽  
pp. 20160569 ◽  
Author(s):  
M. E. Goddard ◽  
K. E. Kemper ◽  
I. M. MacLeod ◽  
A. J. Chamberlain ◽  
B. J. Hayes
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Quantitative Traits ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Crop Breeding ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Phenotype Identification

Complex or quantitative traits are important in medicine, agriculture and evolution, yet, until recently, few of the polymorphisms that cause variation in these traits were known. Genome-wide association studies (GWAS), based on the ability to assay thousands of single nucleotide polymorphisms (SNPs), have revolutionized our understanding of the genetics of complex traits. We advocate the analysis of GWAS data by a statistical method that fits all SNP effects simultaneously, assuming that these effects are drawn from a prior distribution. We illustrate how this method can be used to predict future phenotypes, to map and identify the causal mutations, and to study the genetic architecture of complex traits. The genetic architecture of complex traits is even more complex than previously thought: in almost every trait studied there are thousands of polymorphisms that explain genetic variation. Methods of predicting future phenotypes, collectively known as genomic selection or genomic prediction, have been widely adopted in livestock and crop breeding, leading to increased rates of genetic improvement.

scholarly journals HAPPI GWAS: Holistic Analysis with Pre and Post Integration GWAS

2020 ◽  
Author(s):  
Marianne L. Slaten ◽  
Yen On Chan ◽  
Vivek Shrestha ◽  
Alexander E. Lipka ◽  
Ruthie Angelovici
Keyword(s):  
Association Studies ◽  
Phenotypic Traits ◽  
Genome Wide Association Studies ◽  
Sequencing Data ◽  
Gwas Analysis ◽  
Genome Wide ◽  
Large Populations ◽  
Unbiased Estimates ◽  
Best Linear Unbiased ◽  
Automated Pipeline

AbstractMotivationAdvanced publicly available sequencing data from large populations have enabled in-formative genome-wide association studies (GWAS) that associate SNPs with phenotypic traits of interest. Many publicly available tools able to perform GWAS have been developed in response to increased demand. However, these tools lack a comprehensive pipeline that includes both pre-GWAS analysis such as outlier removal, data transformation, and calculation of Best Linear Unbiased Predictions (BLUPs) or Best Linear Unbiased Estimates (BLUEs). In addition, post-GWAS analysis such as haploblock analysis and candidate gene identification are lacking.ResultsHere, we present HAPPI GWAS, an open-source GWAS tool able to perform pre-GWAS, GWAS, and post-GWAS analysis in an automated pipeline using the command-line interface.AvailabilityHAPPI GWAS is written in R for any Unix-like operating systems and is available on GitHub (https://github.com/Angelovici-Lab/HAPPI.GWAS.git)[email protected]

scholarly journals GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background

2020 ◽  
Author(s):  
Nasa Sinnott-Armstrong ◽  
Sahin Naqvi ◽  
Manuel Rivas ◽  
Jonathan K Pritchard
Keyword(s):  
Complex Traits ◽  
Genetic Basis ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Biological Processes ◽  
Uk Biobank ◽  
The Core ◽  
Genome Wide ◽  
Core Genes

SummaryGenome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. However, for most traits it remains difficult to interpret what genes and biological processes are impacted by the top hits. Here, as a contrast, we describe UK Biobank GWAS results for three molecular traits—urate, IGF-1, and testosterone—that are biologically simpler than most diseases, and for which we know a great deal in advance about the core genes and pathways. Unlike most GWAS of complex traits, for all three traits we find that most top hits are readily interpretable. We observe huge enrichment of significant signals near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of variation in each trait, including insights into differences in testosterone regulation between females and males. Meanwhile, in other respects the results are reminiscent of GWAS for more-complex traits. In particular, even these molecular traits are highly polygenic, with most of the variance coming not from core genes, but from thousands to tens of thousands of variants spread across most of the genome. Given that diseases are often impacted by many distinct biological processes, including these three, our results help to illustrate why so many variants can affect risk for any given disease.

scholarly journals A deletion in GDF7 is associated with a heritable forebrain commissural malformation concurrent with ventriculomegaly and interhemispheric cysts in cats

2020 ◽  
Author(s):  
Yoshihiko Yu ◽  
Erica K. Creighton ◽  
Reuben M. Buckley ◽  
Leslie A. Lyons ◽  
Keyword(s):  
Genetic Basis ◽  
Association Studies ◽  
Homozygosity Mapping ◽  
Mammalian Brain ◽  
Genome Wide Association Studies ◽  
Commissural Axons ◽  
Genome Wide ◽  
Mixed Breed ◽  
Roof Plate ◽  
Control Association

AbstractAn inherited neurologic syndrome in a family of mixed-breed Oriental cats has been characterized as forebrain commissural malformation concurrent with ventriculomegaly and interhemispheric cysts. However, the genetic basis for this autosomal recessive syndrome in cats is unknown. Forty-three cats were genotyped on the Illumina Infinium Feline 63K iSelect DNA Array and used for analyses. Genome-wide association studies, including a sib-transmission disequilibrium test, a case-control association analysis, and homozygosity mapping, identified a critical region on cat chromosome A3. Short-read whole genome sequencing was completed for a cat trio segregating with the syndrome. A homozygous 7 bp deletion in growth differentiation factor 7 (GDF7) (c.221_227delGCCGCGC [p.Arg74Profs]) was identified in affected cats by comparison to the 99 Lives Cat variant dataset, validated using Sanger sequencing, and genotyped by fragment analyses. This variant was not identified in 192 unaffected cats in the 99 Lives dataset. The variant segregated concordantly in an extended pedigree. Obligate carrier cats were heterozygous. In mice, GDF7 mRNA is expressed within the roof plate when commissural axons initiate ventrally-directed growth. This finding emphasizes the importance of GDF7 in the neurodevelopmental process in the mammalian brain. A genetic test can be developed for use by cat breeders to eradicate this variant.

Abstract 367: Extreme High-Density Lipoprotein Cholesterol Genetics: An Assortment of Large and Small Polygenic Effects

2017 ◽  
Vol 37 (suppl_1) ◽  
Author(s):  
Jacqueline S Dron ◽  
Jian Wang ◽  
Cécile Low-Kam ◽  
Sumeet A Khetarpal ◽  
John F Robinson ◽  
...  
Keyword(s):  
Large Scale ◽  
Genetic Basis ◽  
Rare Variants ◽  
Association Studies ◽  
Density Lipoprotein ◽  
Copy Number Variations ◽  
Genome Wide Association Studies ◽  
Common Variants ◽  
Targeted Next Generation Sequencing ◽  
Common Genetic Variants

Rationale: Although HDL-C levels are known to have a complex genetic basis, most studies have focused solely on identifying rare variants with large phenotypic effects to explain extreme HDL-C phenotypes. Objective: Here we concurrently evaluate the contribution of both rare and common genetic variants, as well as large-scale copy number variations (CNVs), towards extreme HDL-C concentrations. Methods: In clinically ascertained patients with low ( N =136) and high ( N =119) HDL-C profiles, we applied our targeted next-generation sequencing panel (LipidSeq TM ) to sequence genes involved in HDL metabolism, which were subsequently screened for rare variants and CNVs. We also developed a novel polygenic trait score (PTS) to assess patients’ genetic accumulations of common variants that have been shown by genome-wide association studies to associate primarily with HDL-C levels. Two additional cohorts of patients with extremely low and high HDL-C (total N =1,746 and N =1,139, respectively) were used for PTS validation. Results: In the discovery cohort, 32.4% of low HDL-C patients carried rare variants or CNVs in primary ( ABCA1 , APOA1 , LCAT ) and secondary ( LPL , LMF1 , GPD1 , APOE ) HDL-C–altering genes. Additionally, 13.4% of high HDL-C patients carried rare variants or CNVs in primary ( SCARB1 , CETP , LIPC , LIPG ) and secondary ( APOC3 , ANGPTL4 ) HDL-C–altering genes. For polygenic effects, patients with abnormal HDL-C profiles but without rare variants or CNVs were ~2-fold more likely to have an extreme PTS compared to normolipidemic individuals, indicating an increased frequency of common HDL-C–associated variants in these patients. Similar results in the two validation cohorts demonstrate that this novel PTS successfully quantifies common variant accumulation, further characterizing the polygenic basis for extreme HDL-C phenotypes. Conclusions: Patients with extreme HDL-C levels have various combinations of rare variants, common variants, or CNVs driving their phenotypes. Fully characterizing the genetic basis of HDL-C levels must extend to encompass multiple types of genetic determinants—not just rare variants—to further our understanding of this complex, controversial quantitative trait.

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