Using Bayes model averaging to leverage both gene main effects and G
 × 
E
 interactions to identify genomic regions in genome-wide association studies

Dwarfism is a condition defined by low harvest weight in fish, but also results in strange body figures which may have potential for the selective breeding of new ornamental fish strains. The objectives of this study are to reveal the physiological causes of dwarfism and identify the genetic loci controlling this trait in the white sailfin molly. Skeletons of dwarf and normal sailfin mollies were observed by X-ray radioscopy and skeletal staining. Genome-wide association studies based on genotyping-by-sequencing (n = 184) were used to map candidate genomic regions associated with the dwarfism trait. Quantitative real-time PCR was performed to determine the expression level of candidate genes in normal (n = 8) and dwarf (n = 8) sailfin mollies. We found that the dwarf sailfin molly has a short and dysplastic spine in comparison to the normal fish. Two regions, located at NW_015112742.1 and NW_015113621.1, were significantly associated with the dwarfism trait. The expression level of three candidate genes, ADAMTS like 1, Larp7 and PPP3CA, were significantly different between the dwarf and normal sailfin mollies in the hepatopancreas, with PPP3CA also showing significant differences in the vertebrae and Larp7 showing significant differences in the muscle. This study identified genomic regions and candidate genes associated with the dwarfism trait in the white sailfin molly and would provide a reference to determine dwarf-causing variations.

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Genome-Wide Association Studies in Indian Buffalo Revealed Genomic Regions for Lactation and Fertility

Frontiers in Genetics ◽

10.3389/fgene.2021.696109 ◽

2021 ◽

Vol 12 ◽

Author(s):

Vikas Vohra ◽

Supriya Chhotaray ◽

Gopal Gowane ◽

Rani Alex ◽

Anupama Mukherjee ◽

...

Keyword(s):

Milk Yield ◽

Milk Fat ◽

Association Studies ◽

Post Partum ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Improvement Programs ◽

Genomic Regions ◽

Fertility Traits

Murrah breed of buffalo is an excellent dairy germplasm known for its superior milk quality in terms of milk fat and solids-not-fat (SNF); however, it is often reported that Indian buffaloes had lower lactation and fertility potential compared to the non-native cattle of the country. Recent techniques, particularly the genome-wide association studies (GWAS), to identify genomic variations associated with lactation and fertility traits offer prospects for systematic improvement of buffalo. DNA samples were sequenced using the double-digestion restriction-associated DNA (RAD) tag genotyping-by-sequencing. The bioinformatics pipeline was standardized to call the variants, and single-nucleotide polymorphisms (SNPs) qualifying the stringent quality check measures were retained for GWAS. Over 38,000 SNPs were used to perform GWAS on the first two principal components of test-day records of milk yields, fat percentages, and SNF percentages, separately. GWAS was also performed on 305 days’ milk yield; lactation persistency was estimated through the rate of decline after attaining the peak yield method, along with three other standard methods; and breeding efficiency, post-partum breeding interval, and age at sexual maturity were considered fertility traits. Significant association of SNPs was observed for the first principal component, explaining the maximum proportion of variation in milk yield. Furthermore, some potential genomic regions were identified to have a potential role in regulating milk yield and fertility in Murrah. Identification of such genomic regions shall help in carrying out an early selection of high-yielding persistent Murrah buffaloes and, in the long run, would be helpful in shaping their future genetic improvement programs.

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FINEMAP: Efficient variable selection using summary data from genome-wide association studies

10.1101/027342 ◽

2015 ◽

Cited By ~ 1

Author(s):

Christian Benner ◽

Chris C.A. Spencer ◽

Samuli Ripatti ◽

Matti Pirinen

Keyword(s):

Fine Mapping ◽

Molecular Mechanisms ◽

Search Algorithm ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Promising Tool ◽

Genome Wide ◽

Genomic Regions ◽

Summary Data

Motivation: The goal of fine-mapping in genomic regions associated with complex diseases and traits is to identify causal variants that point to molecular mechanisms behind the associations. Recent fine-mapping methods using summary data from genome-wide association studies rely on exhaustive search through all possible causal configurations, which is computationally expensive. Results: We introduce FINEMAP, a software package to efficiently explore a set of the most important causal configurations of the region via a shotgun stochastic search algorithm. We show that FINEMAP produces accurate results in a fraction of processing time of existing approaches and is therefore a promising tool for analyzing growing amounts of data produced in genome-wide association studies. Availability: FINEMAP v1.0 is freely available for Mac OS X and Linux at http://www.christianbenner.com.

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Data Mining in Genome Wide Association Studies

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch073 ◽

2011 ◽

pp. 465-471

Author(s):

Tom Burr

Keyword(s):

Data Mining ◽

Genetic Basis ◽

Association Studies ◽

Causal Variant ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Common Diseases ◽

Genome Wide ◽

Genomic Regions

The genetic basis for some human diseases, in which one or a few genome regions increase the probability of acquiring the disease, is fairly well understood. For example, the risk for cystic fibrosis is linked to particular genomic regions. Identifying the genetic basis of more common diseases such as diabetes has proven to be more difficult, because many genome regions apparently are involved, and genetic effects are thought to depend in unknown ways on other factors, called covariates, such as diet and other environmental factors (Goldstein and Cavalleri, 2005). Genome-wide association studies (GWAS) aim to discover the genetic basis for a given disease. The main goal in a GWAS is to identify genetic variants, single nucleotide polymorphisms (SNPs) in particular, that show association with the phenotype, such as “disease present” or “disease absent” either because they are causal, or more likely, because they are statistically correlated with an unobserved causal variant (Goldstein and Cavalleri, 2005). A GWAS can analyze “by DNA site” or “by multiple DNA sites. ” In either case, data mining tools (Tachmazidou, Verzilli, and De Lorio, 2007) are proving to be quite useful for understanding the genetic causes for common diseases.

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Genome-Wide Association Studies Detect Multiple QTLs for Productivity in Mesoamerican Diversity Panel of Common Bean Under Drought Stress

Frontiers in Plant Science ◽

10.3389/fpls.2020.574674 ◽

2020 ◽

Vol 11 ◽

Author(s):

Paula Arielle Mendes Ribeiro Valdisser ◽

Bárbara S. F. Müller ◽

Janeo Eustáquio de Almeida Filho ◽

Odilon Peixoto Morais Júnior ◽

Cléber Morais Guimarães ◽

...

Keyword(s):

Drought Stress ◽

Common Bean ◽

Genome Wide Association Study ◽

Association Studies ◽

Genetic Material ◽

Genome Wide Association ◽

Abiotic Factor ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Genomic Regions

Drought stress is an important abiotic factor limiting common bean yield, with great impact on the production worldwide. Understanding the genetic basis regulating beans’ yield and seed weight (SW) is a fundamental prerequisite for the development of superior cultivars. The main objectives of this work were to conduct genome-wide marker discovery by genotyping a Mesoamerican panel of common bean germplasm, containing cultivated and landrace accessions of broad origin, followed by the identification of genomic regions associated with productivity under two water regimes using different genome-wide association study (GWAS) approaches. A total of 11,870 markers were genotyped for the 339 genotypes, of which 3,213 were SilicoDArT and 8,657 SNPs derived from DArT and CaptureSeq. The estimated linkage disequilibrium extension, corrected for structure and relatedness (r2sv), was 98.63 and 124.18 kb for landraces and breeding lines, respectively. Germplasm was structured into landraces and lines/cultivars. We carried out GWASs for 100-SW and yield in field environments with and without water stress for 3 consecutive years, using single-, segment-, and gene-based models. Higher number of associations at high stringency was identified for the SW trait under irrigation, totaling ∼185 QTLs for both single- and segment-based, whereas gene-based GWASs showed ∼220 genomic regions containing ∼650 genes. For SW under drought, 18 QTLs were identified for single- and segment-based and 35 genes by gene-based GWASs. For yield, under irrigation, 25 associations were identified, whereas under drought the total was 10 using both approaches. In addition to the consistent associations detected across experiments, these GWAS approaches provided important complementary QTL information (∼221 QTLs; 650 genes; r2 from 0.01% to 32%). Several QTLs were mined within or near candidate genes playing significant role in productivity, providing better understanding of the genetic mechanisms underlying these traits and making available molecular tools to be used in marker-assisted breeding. The findings also allowed the identification of genetic material (germplasm) with better yield performance under drought, promising to a common bean breeding program. Finally, the availability of this highly diverse Mesoamerican panel is of great scientific value for the analysis of any relevant traits in common bean.

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