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> Genes & Molecular Sequences > Gene or Genome > Quantitative Trait Loci

Quantitative Trait Loci

Quantitative Trait Loci (QTLs) are regions of the genome that contain genes influencing quantitative traits, such as height, weight, or disease susceptibility.
QTLs are identified through genetic linkage analysis, allowing researchers to pinpoint genomic locations associated with specific phenotypes.
Locating and studying QTLs is crucial for understanding the genetic basis of complex traits and developing targeted interventions.
PubCompare.ai's AI-driven platform streamlines the QTL research process, helping scientists easily identify and compare protocols from literature, preprints, and patents to enhance reproducibility and optimize experimental design.
This cutting-edg AI technology takes the guesswork out of protocol selection, empowering researchers to make informed decisions and drive their QTL studies forward with confidence.

Most cited protocols related to «Quantitative Trait Loci»

1
Advancing eQTL Analysis with GTEx Consortium
To promote the analysis of eQTL results across a wide range of human tissues, the NIH funded five centers to develop improved methods for statistical analysis. Investigators funded through this RFA form an analysis consortium that will provide innovative approaches to analyses of GTEx data and other similar datasets. Investigators also collaborate with the LDACC to perform data quality assessment/quality control before release into dbGaP. The initial GTEx Consortium publications, anticipated in 2013, will include genome-wide analysis of cis- and trans-eQTLs, allele-specific expression, splicing quantitative trait loci, and a comparison of array and RNA-Seq based gene expression results.
The Genotype-Tissue Expression (GTEx) project. (2013). Nature genetics, 45(6), 580-585.
Publication 2013
Alleles Gene Expression Genome Homo sapiens Quantitative Trait Loci RNA-Seq Tissues
2
Genome-Wide Analysis of Lipid Traits
The full Methods are in Supplementary Information and provide information about: (1) study samples and phenotypes; (2) genotyping and imputation; (3) genome-wide association analyses; (4) meta-analyses of directly typed and imputed SNPs; (5) estimation of effect sizes; (6) conditional analyses of top signals; (7) sex-specific analyses; (8) cis-expression quantitative trait locus analyses; (9) analyses of lipid-associated SNPs in European and non-European samples; (10) analyses of lipid-associated SNPs in individuals with and without CAD; (11) analyses of associated SNPs in patients with extreme LDL-C, HDL-C, or TG levels; (12) simulation studies to assess overlap between GWAS signals and Mendelian disease loci; and (13) details of mouse studies.
Teslovich T.M., Musunuru K., Smith A.V., Edmondson A.C., Stylianou I.M., Koseki M., Pirruccello J.P., Ripatti S., Chasman D.I., Willer C.J., Johansen C.T., Fouchier S.W., Isaacs A., Peloso G.M., Barbalic M., Ricketts S.L., Bis J.C., Aulchenko Y.S., Thorleifsson G., Feitosa M.F., Chambers J., Orho-Melander M., Melander O., Johnson T., Li X., Guo X., Li M., Cho Y.S., Go M.J., Kim Y.J., Lee J.Y., Park T., Kim K., Sim X., Ong R.T., Croteau-Chonka D.C., Lange L.A., Smith J.D., Song K., Zhao J.H., Yuan X., Luan J., Lamina C., Ziegler A., Zhang W., Zee R.Y., Wright A.F., Witteman J.C., Wilson J.F., Willemsen G., Wichmann H.E., Whitfield J.B., Waterworth D.M., Wareham N.J., Waeber G., Vollenweider P., Voight B.F., Vitart V., Uitterlinden A.G., Uda M., Tuomilehto J., Thompson J.R., Tanaka T., Surakka I., Stringham H.M., Spector T.D., Soranzo N., Smit J.H., Sinisalo J., Silander K., Sijbrands E.J., Scuteri A., Scott J., Schlessinger D., Sanna S., Salomaa V., Saharinen J., Sabatti C., Ruokonen A., Rudan I., Rose L.M., Roberts R., Rieder M., Psaty B.M., Pramstaller P.P., Pichler I., Perola M., Penninx B.W., Pedersen N.L., Pattaro C., Parker A.N., Pare G., Oostra B.A., O'Donnell C.J., Nieminen M.S., Nickerson D.A., Montgomery G.W., Meitinger T., McPherson R., McCarthy M.I., McArdle W., Masson D., Martin N.G., Marroni F., Mangino M., Magnusson P.K., Lucas G., Luben R., Loos R.J., Lokki M., Lettre G., Langenberg C., Launer L.J., Lakatta E.G., Laaksonen R., Kyvik K.O., Kronenberg F., König I.R., Khaw K.T., Kaprio J., Kaplan L.M., Johansson Å., Jarvelin M.R., Janssens A.C., Ingelsson E., Igl W., Hovingh G.K., Hottenga J.J., Hofman A., Hicks A.A., Hengstenberg C., Heid I.M., Hayward C., Havulinna A.S., Hastie N.D., Harris T.B., Haritunians T., Hall A.S., Gyllensten U., Guiducci C., Groop L.C., Gonzalez E., Gieger C., Freimer N.B., Ferrucci L., Erdmann J., Elliott P., Ejebe K.G., Döring A., Dominiczak A.F., Demissie S., Deloukas P., de Geus E.J., de Faire U., Crawford G., Collins F.S., Chen Y.D., Caulfield M.J., Campbell H., Burtt N.P., Bonnycastle L.L., Boomsma D.I., Boekholdt S.M., Bergman R.N., Barroso I., Bandinelli S., Ballantyne C.M., Assimes T.L., Quertermous T., Altshuler D., Seielstad M., Wong T.Y., Tai E.S., Feranil A.B., Kuzawa C.W., Adair L.S., Taylor HA J.r., Borecki I.B., Gabriel S.B., Wilson J.G., Stefansson K., Thorsteinsdottir U., Gudnason V., Krauss R.M., Mohlke K.L., Ordovas J.M., Munroe P.B., Kooner J.S., Tall A.R., Hegele R.A., Kastelein J.J., Schadt E.E., Rotter J.I., Boerwinkle E., Strachan D.P., Mooser V., Holm H., Reilly M.P., Samani N.J., Schunkert H., Cupples L.A., Sandhu M.S., Ridker P.M., Rader D.J., van Duijn C.M., Peltonen L., Abecasis G.R., Boehnke M, & Kathiresan S. (2010). Biological, Clinical, and Population Relevance of 95 Loci for Blood Lipids. Nature, 466(7307), 707-713.
Publication 2010
Europeans Genome-Wide Association Study Lipids Mice, Laboratory Patients Phenotype Quantitative Trait Loci Single Nucleotide Polymorphism
3
Genome-Wide Analysis of Lipid Traits
The full Methods are in Supplementary Information and provide information about: (1) study samples and phenotypes; (2) genotyping and imputation; (3) genome-wide association analyses; (4) meta-analyses of directly typed and imputed SNPs; (5) estimation of effect sizes; (6) conditional analyses of top signals; (7) sex-specific analyses; (8) cis-expression quantitative trait locus analyses; (9) analyses of lipid-associated SNPs in European and non-European samples; (10) analyses of lipid-associated SNPs in individuals with and without CAD; (11) analyses of associated SNPs in patients with extreme LDL-C, HDL-C, or TG levels; (12) simulation studies to assess overlap between GWAS signals and Mendelian disease loci; and (13) details of mouse studies.
Teslovich T.M., Musunuru K., Smith A.V., Edmondson A.C., Stylianou I.M., Koseki M., Pirruccello J.P., Ripatti S., Chasman D.I., Willer C.J., Johansen C.T., Fouchier S.W., Isaacs A., Peloso G.M., Barbalic M., Ricketts S.L., Bis J.C., Aulchenko Y.S., Thorleifsson G., Feitosa M.F., Chambers J., Orho-Melander M., Melander O., Johnson T., Li X., Guo X., Li M., Cho Y.S., Go M.J., Kim Y.J., Lee J.Y., Park T., Kim K., Sim X., Ong R.T., Croteau-Chonka D.C., Lange L.A., Smith J.D., Song K., Zhao J.H., Yuan X., Luan J., Lamina C., Ziegler A., Zhang W., Zee R.Y., Wright A.F., Witteman J.C., Wilson J.F., Willemsen G., Wichmann H.E., Whitfield J.B., Waterworth D.M., Wareham N.J., Waeber G., Vollenweider P., Voight B.F., Vitart V., Uitterlinden A.G., Uda M., Tuomilehto J., Thompson J.R., Tanaka T., Surakka I., Stringham H.M., Spector T.D., Soranzo N., Smit J.H., Sinisalo J., Silander K., Sijbrands E.J., Scuteri A., Scott J., Schlessinger D., Sanna S., Salomaa V., Saharinen J., Sabatti C., Ruokonen A., Rudan I., Rose L.M., Roberts R., Rieder M., Psaty B.M., Pramstaller P.P., Pichler I., Perola M., Penninx B.W., Pedersen N.L., Pattaro C., Parker A.N., Pare G., Oostra B.A., O'Donnell C.J., Nieminen M.S., Nickerson D.A., Montgomery G.W., Meitinger T., McPherson R., McCarthy M.I., McArdle W., Masson D., Martin N.G., Marroni F., Mangino M., Magnusson P.K., Lucas G., Luben R., Loos R.J., Lokki M., Lettre G., Langenberg C., Launer L.J., Lakatta E.G., Laaksonen R., Kyvik K.O., Kronenberg F., König I.R., Khaw K.T., Kaprio J., Kaplan L.M., Johansson Å., Jarvelin M.R., Janssens A.C., Ingelsson E., Igl W., Hovingh G.K., Hottenga J.J., Hofman A., Hicks A.A., Hengstenberg C., Heid I.M., Hayward C., Havulinna A.S., Hastie N.D., Harris T.B., Haritunians T., Hall A.S., Gyllensten U., Guiducci C., Groop L.C., Gonzalez E., Gieger C., Freimer N.B., Ferrucci L., Erdmann J., Elliott P., Ejebe K.G., Döring A., Dominiczak A.F., Demissie S., Deloukas P., de Geus E.J., de Faire U., Crawford G., Collins F.S., Chen Y.D., Caulfield M.J., Campbell H., Burtt N.P., Bonnycastle L.L., Boomsma D.I., Boekholdt S.M., Bergman R.N., Barroso I., Bandinelli S., Ballantyne C.M., Assimes T.L., Quertermous T., Altshuler D., Seielstad M., Wong T.Y., Tai E.S., Feranil A.B., Kuzawa C.W., Adair L.S., Taylor HA J.r., Borecki I.B., Gabriel S.B., Wilson J.G., Stefansson K., Thorsteinsdottir U., Gudnason V., Krauss R.M., Mohlke K.L., Ordovas J.M., Munroe P.B., Kooner J.S., Tall A.R., Hegele R.A., Kastelein J.J., Schadt E.E., Rotter J.I., Boerwinkle E., Strachan D.P., Mooser V., Holm H., Reilly M.P., Samani N.J., Schunkert H., Cupples L.A., Sandhu M.S., Ridker P.M., Rader D.J., van Duijn C.M., Peltonen L., Abecasis G.R., Boehnke M, & Kathiresan S. (2010). Biological, Clinical, and Population Relevance of 95 Loci for Blood Lipids. Nature, 466(7307), 707-713.
Publication 2010
Europeans Genome-Wide Association Study Lipids Mice, Laboratory Patients Phenotype Quantitative Trait Loci Single Nucleotide Polymorphism
4
Conditional Analysis for Multiple QTLs
The two mapping approaches above only report a single candidate QTL per phenotype or group of phenotypes. In some cases, this limitation may reduce significantly the number of discoveries. For example, it is relatively frequent that expression for a given gene is affected by multiple proximal eQTLs1 (link). A well-established approach to discover multiple QTLs with independent effects on a given phenotype relies on conditional analysis: new discoveries are made by conditioning on previous ones. In QTLtools, we implemented a conditional analysis scheme based on stepwise linear regression that is fast, accounts for multiple testing and automatically learns the number of independent signals per phenotype. Specifically, we implemented it as follows for both grouped and ungrouped phenotypes:

Initialization. We determine a nominal P value threshold of significance on a per-phenotype basis. To do so, we first perform a permutation pass as described above which gives us an adjusted P value per phenotype (or group of phenotypes) together with its most likely beta parameter values. Next, we determine the adjusted P value threshold corresponding to the targeted FDR level (for example, 5% FDR) and feed the beta quantile function (for example, R/q beta) with it to get a specific nominal P value threshold for each phenotype. Here, the beta quantile function allows us to use the Beta distribution in a reversed way: from adjusted P value to nominal P value. Note that the resulting nominal P value thresholds vary from one phenotype to the other depending on the complexity of the cis regions being tested and the effective number of independent tests they encapsulate.

Forward pass. We next learn the number of independent signals per phenotype using stepwise regressions with forward variable selection. More specifically, we start from the original phenotype quantifications and search for the variant in cis with the strongest association. When the corresponding nominal P value of association is below the threshold defined in step (1), we store the variant as additional and independent discovery and residualize its genotypes out from the phenotype quantifications. We then repeat these two steps until no more significant discovery is made: this immediately gives us the number of independent molQTLs together with a best candidate variant for each.

Backward pass. Finally, we try to assign nearby genetic variants to the various independent signals we discovered in step (2). To do so, we define a linear regression model that contains all candidate QTLs discovered so far in the forward pass: P=Q1+…+Qi+…+QR where R is the number of independent signals and {Q1, …, Qi, …, QR} are the corresponding best molQTL candidates. Then, we test all possible hypotheses by fitting this model Rx(L-R) times each time fixing { Q1, …, Qi−1, Qi+1,…, QR } and setting Qi as another variant in cis (L–R variants in cis not being a candidate molQTL times R independent signals). We then end up with a vector of R nominal P values for each variant in cis which allows us to determine the signal the variant belongs to by simply finding the smallest P value in this vector and comparing it to the significance threshold obtained in step (1).

Delaneau O., Ongen H., Brown A.A., Fort A., Panousis N.I, & Dermitzakis E.T. (2017). A complete tool set for molecular QTL discovery and analysis. Nature Communications, 8, 15452.
Publication 2017
Cloning Vectors Gene Expression Genetic Diversity Genotype Phenotype Quantitative Trait Loci
5
Genome-wide Genotyping and Imputation for Lung Health
We undertook genome-wide genotyping of variants using a new custom Affymetrix Axiom array (UK BiLEVE array; Santa Clara, CA, USA; appendix pp 5–8) that was designed to (1) measure rare coding variation; (2) provide a framework for optimum imputation of non-genotyped variants that are common (MAF >5%) or of low frequency (MAF 1–5%) in the European population, when used in conjunction with a large imputation reference panel of individuals with whole-genome sequence data;21 (link) and (3) optimise coverage of genes and genomic regions with established or putative roles in lung health and disease to enable fine mapping. After thorough sample and variant quality control (appendix pp 8–15), we imputed non-genotyped variants using a combined 1000 Genomes Project Phase 122 (link) and UK10K Project23,24 reference panel (appendix pp 15–16). The data were used to finalise the design of the UK Biobank array, which is being used for genome-wide genotyping and imputation of the remaining UK Biobank participants.
Using data from previously published studies of whole-genome gene expression and genome-wide genotyping,25–29 we assessed whether variants at associated loci (identified as described in the Statistical analysis) regulate levels of mRNA. These expression quantitative trait loci (eQTL) studies included non-tumour lung tissue, blood, and, for variants associated with smoking behaviour, brain. For genes close to peaks of novel signals or genes implicated through eQTL, we assessed differential expression in the lungs of individuals with and without COPD and differential expression in the pseudoglandular and canalicular stages of development of the fetal lung.30,31 Additionally, we generated RNA sequencing data to discover novel transcripts of these genes in human bronchial epithelial cells. We tested all genome-wide meta-analysis p values for enrichment in biological pathways defined in publicly available databases. All functional analyses are described in detail in the appendix (pp 21–23).
Wain L.V., Shrine N., Miller S., Jackson V.E., Ntalla I., Artigas M.S., Billington C.K., Kheirallah A.K., Allen R., Cook J.P., Probert K., Obeidat M., Bossé Y., Hao K., Postma D.S., Paré P.D., Ramasamy A., Mägi R., Mihailov E., Reinmaa E., Melén E., O'Connell J., Frangou E., Delaneau O., Freeman C., Petkova D., McCarthy M., Sayers I., Deloukas P., Hubbard R., Pavord I., Hansell A.L., Thomson N.C., Zeggini E., Morris A.P., Marchini J., Strachan D.P., Tobin M.D, & Hall I.P. (2015). Novel insights into the genetics of smoking behaviour, lung function, and chronic obstructive pulmonary disease (UK BiLEVE): a genetic association study in UK Biobank. The Lancet. Respiratory Medicine, 3(10), 769-781.
Publication 2015
Biopharmaceuticals BLOOD Brain Bronchi Chronic Obstructive Airway Disease Epithelial Cells Europeans Fetal Development Gene Expression Genes Genome Homo sapiens Lung Neoplasms Quantitative Trait Loci RNA, Messenger Tissues

Most recents protocols related to «Quantitative Trait Loci»

1
Marker-Assisted Backcross Breeding for Drought Tolerance
Initially, a cross was made between HD2733 and C306 to transfer drought stress tolerance QTLs into HD2733. The MABB procedure followed here is represented in Figure 1. The true F1s were identified using foreground SSR markers and backcrossed to the recurrent parent. The BC1F1s were subjected to foreground and initial background selection with a set of 64 polymorphic markers. Twenty-five lines positive for target QTLs with maximum recurrent parent genome (RPG) recovery coupled with phenotypic similarity to the recipient parent were selected. The 21 selected lines were backcrossed to the recurrent parent and selfed to generate BC2F1 and BC1F2 seeds. BC2F1 and BC1F2 lines were repeated for the MABB process involving foreground and background selection with 120 polymorphic SSR markers.
The polymorphic SSR markers were used to construct a schematic map illustrating the genomic contributions of donor and recurrent parents with Graphical GenoType (GGT) v2.045 software to identify backcross-derived lines possessing the maximum recurrent parent genome. The positive foreground-selected plants genotyped for polymorphic markers at each backcross/selfing generation and recurrent parent genome recovery (G) were estimated using the following formula: G = [(X + ½Y) × 100]/N; here, N is the total number of parental polymorphic markers screened, X is the number of markers showing homozygosity for recurrent parental alleles, and Y is the number of markers showing heterozygosity for parental alleles. Based on the recovery of the recurrent parental genome and the presence of targeted donor genomic regions, 50 lines were selected from BC2F1 and BC1F2 plants and advanced through selfing.
Thirteen plants were selected from advanced BC2F2 lines based on maximum recovery for RPG through background and foreground selection and visible phenotypic similarity with the recurrent parent strain, HD2733, while BC1F3 lines were selfed and advanced to BC1F4 generations. A total of 10 BC1F4 plants were again selected based on foreground selection and maximum background recovery of the recurrent parental genome. The selected 13 BC2F3 and 10 BC1F4 plants were evaluated for morphological and physiological traits and yield performance and further advanced through selfing for evaluation under a national testing trial.
Kumar P.K., Bellundagi A., Krishna H., Mallikarjuna M.G., Thimmappa R.K., Rai N., Shashikumara P., Sinha N., Jain N., Singh P.K., Singh G.P, & Prabhu K.V. (2023). Development of bread wheat (Triticum aestivum L) variety HD3411 following marker-assisted backcross breeding for drought tolerance. Frontiers in Genetics, 14, 1046624.
Publication 2023
Alleles Drought Tolerance Genome Heterozygote Homozygote Neutrophil Parent Phenotype physiology Plant Embryos Plants Quantitative Trait Loci Strains Tissue Donors
2
Identifying Splicing-Linked Genetic Variants
A splicing quantitative trait locus (sQTL) is a SNP that predicts alternative mRNA splicing associated with a trait. Similar to Li et al.23 (link), we standardized excision-splicing ratios and then quantile normalized splicing data across individuals. Our analyses used default settings on MatrixQTL to find cis-acting sQTLs that may affect mRNA splicing in a nearby gene, which tests all SNPs within 1 megabase (Mb) of a genomic region. sQTLs were defined as a SNP associated with a differentially spliced gene that survived a BH-FDR correction for multiple testing per SNP. To determine whether sQTLs resided in specific regions of the genome we annotated sQTLs in 11 annotation categories from ANNOVAR (version 4.1)29 (link). The annotation categories that were built on hg18 genome coordinates were updated to their corresponding hg19 values using CrossMap (version 0.5.1)30 (link). Genetic analyses (polygenic score and sQTLs) controlled for sex, age, and two ancestral principal components.
Huggett S.B., Ikeda A.S., Yuan Q., Benca-Bachman C.E, & Palmer R.H. (2023). Genome- and transcriptome-wide splicing associations with alcohol use disorder. Scientific Reports, 13, 3950.
Publication 2023
Genes Genes, Spliced Genome MLL protein, human Quantitative Trait Loci Reproduction RNA, Messenger
3
Genome-Wide Association Study of Bovine Traits
The EuroG MD Bead chip is based on the University of Maryland UMD 3.1 Bos taurus reference genome; hence, the coordinates of the GWAS-identified SNPs were converted to the ARS-UCD1.2 genome by using liftOver software (https://genome.ucsc.edu). The location of the identified SNPs (e.g. upstream or downstream of a transcript, in the coding sequence, in non-coding RNA, in regulatory regions) in the ARS-UCD1.2 genome was determined using the Ensembl Variant Effect predictor (VEP). None of the identified SNPs were within 500,000 base pairs of each other or on linkage disequilibrium. The candidate genes located within 50,000 base pairs to each side of the SNPs were identified using Ensembl (https://www.ensembl.org). The function of all the identified genes was searched in GeneCards (http://www.genecards.org) by searching their gene symbol. Since there is evidence that some allelic variants may contribute to resistance to multiple pathogens, the identified SNPs and candidate genes were compared with QTLs and candidate genes previously associated with other bovine diseases, longevity, and reproductive and health traits (http://www.animalgenome.org). In addition, the identified candidate genes were also compared with human candidate genes previously identified for CD, IBD, and colorectal cancer (http://www.ebi.ac.uk/gwas).
Badia-Bringué G., Canive M, & Alonso-Hearn M. (2023). Control of Mycobacterium avium subsp. paratuberculosis load within infected bovine monocyte-derived macrophages is associated with host genetics. Frontiers in Immunology, 14, 1042638.
Publication 2023
Alleles Cattle Cattle Diseases Colorectal Carcinoma DNA Chips Genes Genome Genome-Wide Association Study Open Reading Frames Pathogenicity Quantitative Trait Loci Regulatory Sequences, Nucleic Acid Reproduction RNA, Untranslated Single Nucleotide Polymorphism
4
Tissue-specific expression regulation in PTB
The Genotype-Tissue Expression (GTEx) database cataloged a large number of tissue-specific genotypes and shared regulatory expression quantitative trait loci (eQTL) variants. We used GTEx to identify the best expressed gene in the female reproductive tissues (bladder, cervix–ectocervix, endocerivx, fallopian tubes, ovaries, uterus, and vagina) that could impact PTB status at protein level [41 (link)]. After investigating the concerned genes/proteins with identified tissue-specific regulatory expression in female reproductive tissues, the Protein Data Bank (PDB) structural coverage details from previous reported studies were investigated from PDB [42 (link)].
Azmi M.B., Khan W., Azim M.K., Nisar M.I, & Jehan F. (2023). Identification of potential therapeutic intervening targets by in-silico analysis of nsSNPs in preterm birth-related genes. PLOS ONE, 18(3), e0280305.
Publication 2023
Cervix Uteri Ectocervix Fallopian Tubes Females Gene Expression Regulation Genes Genotype Ovary Polypyrimidine Tract-Binding Protein Proteins Quantitative Trait Loci Reproduction Tissues Tissue Specificity Urinary Bladder Uterus Vagina
5
Genetic Associations with Circulating Proteins
Summary-level statistics of genetic associations with levels of 4907 circulating proteins were extracted from a large-scale protein quantitative trait loci (pQTL) study in 35,559 Icelanders.6 (link) Proteomic profiling was performed by a multiplexed, modified aptamer-based binding assay (SOMAscan version 4). The levels of protein were rank-inverse normal transformed by age and sex. The residuals were standardized using rank-inverse normal transformation and the standardized values were treated as phenotypes in the genome-wide association analyses under the BOLT-LMM linear mixed model. Details on the GWAS can be found in the original publication.6 (link) The current study included the proteins with pQTLs available at the genome-wide significance level (P < 5 × 10−8) in two-sample MR. All proteins with summary-level data were included in the colocalization analysis.
Chen J., Xu F., Ruan X., Sun J., Zhang Y., Zhang H., Zhao J., Zheng J., Larsson S.C., Wang X., Li X, & Yuan S. (2023). Therapeutic targets for inflammatory bowel disease: proteome-wide Mendelian randomization and colocalization analyses. eBioMedicine, 89, 104494.
Publication 2023
Biological Assay Genome Genome-Wide Association Study Phenotype Proteins Quantitative Trait Loci Staphylococcal Protein A

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More about "Quantitative Trait Loci"

Quantitative Trait Loci (QTLs) are regions of the genome that contain genes influencing quantitative traits, such as height, weight, or disease susceptibility.
QTLs are identified through genetic linkage analysis, allowing researchers to pinpoint genomic locations associated with specific phenotypes.
Locating and studying QTLs is crucial for understanding the genetic basis of complex traits and developing targeted interventions.
PubCompare.ai's AI-driven platform streamlines the QTL research process, helping scientists easily identify and compare protocols from literature, preprints, and patents to enhance reproducibility and optimize experimental design.
This cutting-edge AI technology takes the guesswork out of protocol selection, empowering researchers to make informed decisions and drive their QTL studies forward with confidence.
The process of QTL identification often involves the use of statistical software like SAS 9.4, which can be used to perform linkage analysis and identify regions of the genome associated with quantitative traits.
Additionally, genotyping technologies such as the GoldenGate assay and the Infinium assay, as well as the NanoDrop 1000 spectrophotometer and the MassARRAY iPLEX platform, can be used to generate and analyze genetic data for QTL studies.
Other software tools like SPSS Statistics 19 and GenomeStudio, a suite of analysis tools for microarray data, can also be utilized in the QTL research process.
The GenomeStudio software, particularly the version 2011.1, has been widely used in QTL studies to visualize and analyze genetic data.
Overall, the combination of advanced statistical analysis, high-throughput genotyping technologies, and intuitive data visualization tools has been instrumental in the advancement of QTL research, enabling researchers to uncover the genetic underpinnings of complex traits and develop more effective interventions.