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PubMed1990

Power of daughter and granddaughter designs for determining linkage between marker loci and quantitative trait loci in dairy cattle.

There is considerable interest in bovine DNA-level polymorphic marker loci as a means of mapping quantitative trait loci (QTL) of economic importance in cattle. Progeny of a sire heterozygous for both a marker locus and a linked QTL, which inherit different alleles for the marker, will have different trait means. Based on this, power to detect QTL, as a function of QTL effect, heritability of the trait, and number of animals tested was determined for 1) daughter design, marker genotype and quantitative trait values assessed on daughters of sires heterozygous for the markers; and 2) granddaughter design, a newly devised alternative design in which marker genotype is determined on sons of heterozygous sires and quantitative trait value measured on daughters of the sons. For equal numbers of assays, power increased with the number of daughters per sire (design 1) and sons per grandsire (design 2). For equal power and heritability less than or equal to .2, design 2 required half as many marker assays as design 1, e.g., with heritability of .2, QTL effect of .2 SD units, and type 1 error of .01, power was .70 if 400 daughters of each of 10 sires were assayed for the markers and .95 if markers were assayed on 100 sons of each of 20 sires with 50 granddaughters per son.

Alleles↗

PubMed2024

Complementation testing identifies genes mediating effects at quantitative trait loci underlying fear-related behavior.

Knowing the genes involved in quantitative traits provides an entry point to understanding the biological bases of behavior, but there are very few examples where the pathway from genetic locus to behavioral change is known. To explore the role of specific genes in fear behavior, we mapped three fear-related traits, tested fourteen genes at six quantitative trait loci (QTLs) by quantitative complementation, and identified six genes. Four genes, Lamp, Ptprd, Nptx2, and Sh3gl, have known roles in synapse function; the fifth, Psip1, was not previously implicated in behavior; and the sixth is a long non-coding RNA, 4933413L06Rik, of unknown function. Variation in transcriptome and epigenetic modalities occurred preferentially in excitatory neurons, suggesting that genetic variation is more permissible in excitatory than inhibitory neuronal circuits. Our results relieve a bottleneck in using genetic mapping of QTLs to uncover biology underlying behavior and prompt a reconsideration of expected relationships between genetic and functional variation.

Animals↗

PubMed1987

Molecular-marker-facilitated investigations of quantitative-trait loci in maize. I. Numbers, genomic distribution and types of gene action.

Individual genetic factors which underlie variation in quantitative traits of maize were investigated in each of two F2 populations by examining the mean trait expressions of genotypic classes at each of 17-20 segregating marker loci. It was demonstrated that the trait expression of marker locus classes could be interpreted in terms of genetic behavior at linked quantitative trait loci (QTLs). For each of 82 traits evaluated, QTLs were detected and located to genomic sites. The numbers of detected factors varied according to trait, with the average trait significantly influenced by almost two-thirds of the marked genomic sites. Most of the detected associations between marker loci and quantitative traits were highly significant, and could have been detected with fewer than the 1800-1900 plants evaluated in each population. The cumulative, simple effects of marker-linked regions of the genome explained between 8 and 40% of the phenotypic variation for a subset of 25 traits evaluated. Single marker loci accounted for between 0.3% and 16% of the phenotypic variation of traits. Individual plant heterozygosity, as measured by marker loci, was significantly associated with variation in many traits. The apparent types of gene action at the QTLs varied both among traits and between loci for given traits, although overdominance appeared frequently, especially for yield-related traits. The prevalence of apparent overdominance may reflect the effects of multiple QTLs within individual marker-linked regions, a situation which would tend to result in overestimation of dominance. Digenic epistasis did not appear to be important in determining the expression of the quantitative traits evaluated. Examination of the effects of marked regions on the expression of pairs of traits suggests that genomic regions vary in the direction and magnitudes of their effects on trait correlations, perhaps providing a means of selecting to dissociate some correlated traits. Marker-facilitated investigations appear to provide a powerful means of examining aspects of the genetic control of quantitative traits. Modifications of the methods employed herein will allow examination of the stability of individual gene effects in varying genetic backgrounds and environments.

Chromosome Mapping↗

PubMed2026

Identifying Single-Cell Expression Quantitative Trait Loci Using a Bootstrap Penalized Hurdle Model.

BACKGROUND: Expression quantitative trait loci (eQTL) analysis links genetic variants to gene expression levels, helping to uncover how genetic variation contributes to gene regulation. While traditional eQTL analyses rely on bulk RNA-seq data, recent advances in single-cell RNA sequencing (scRNA-seq) have made it possible to detect cell-type-specific eQTLs. However, the inherent sparsity and heterogeneity of scRNA-seq data present major challenges for standard modeling approaches. METHODS: In this paper, we propose a novel statistical framework, Bootstrap Penalized Hurdle regression model (BPHurdle), designed specifically for scRNA-seq data. BPHurdle employs a hurdle modeling framework, where a logistic component accounts for the excess zeros in single-cell expression data, and a Poisson component jointly evaluates the effects of multiple SNPs on positive gene expression levels. RESULTS: Through simulation studies, we show that BPHurdle achieves high accuracy and robustness in identifying regulatory variants. We further demonstrate its utility on a real dataset through a case study focusing on a subset of differentially expressed genes, where it successfully identifies reliable cell-type-specific eQTLs. CONCLUSIONS: Overall, BPHurdle offers an advanced and flexible approach for single-cell eQTL mapping, providing deeper insight into the genetic regulation of gene expression at cellular resolution.

Quantitative Trait Loci↗

PubMed2026

Quantitative trait loci mapping of gene expression and chromatin accessibility in primary fibroblasts reveals shared allelic effects between Latin American and European ancestries.

BACKGROUND: Quantitative Trait Locus (QTL) analysis of molecular data has identified genetic variants associated with traits such as gene expression, and colocalization of these functional QTL with GWAS risk loci has offered insights into the genetic basis of human disease. We employed gene expression (RNA-seq) and chromatin accessibility (ATAC-seq) obtained from human primary fibroblasts to investigate quantitative trait loci (QTLs) in cohorts ascertained for bipolar disorder of European (n = 150) and Latin American (n = 96) ancestries. RESULTS: Leveraging data from three countries of origin (The Netherlands, Colombia, Costa Rica) within our cohort, we characterized differences among individuals at the SNP, gene, and accessible-chromatin levels to compute ancestry-specific expression (e)QTLs and chromatin-accessibility (ca)QTLs. Across ancestries, we observed R2 ≥ 0.93 for eQTL effect sizes and R2 ≥ 0.95 for caQTLs, indicating a high degree of concordance. Integrating chromatin data with expression and genotype information enabled precise fine-mapping of eQTLs, yielding 203 genes with high-confidence (posterior probability > 90%) candidate regulatory pathways. In downstream analyses, transcriptome-wide (TWAS) and chromatin-wide (CWAS) association studies with brain- and skin-related GWAS identified 36 TWAS-significant genes and 77 CWAS-significant open chromatin regions. CONCLUSIONS: These findings underscore the shared genetic regulatory mechanisms across European and Latin American ancestries, while demonstrating that ancestry-specific reference panels enhance the accuracy of TWAS and CWAS in diverse populations. More broadly, this study highlights the value of paired multi-omic datasets from diverse cohorts for interpreting disease-associated genetic variation.

Humans↗

PubMed1990

Fine mapping of quantitative trait loci using selected overlapping recombinant chromosomes, in an interspecies cross of tomato.

Quantitative trait loci (QTLs) have been mapped to small intervals along the chromosomes of tomato (Lycopersicon esculentum), by a method we call substitution mapping. The size of the interval to which a QTL can be mapped is determined primarily by the number and spacing of previously mapped genetic markers in the region surrounding the QTL. We demonstrate the method using tomato genotypes carrying chromosomal segments from Lycopersicon chmielewskii, a wild relative of tomato with high soluble solids concentration but small fruit and low yield. Different L. chmielewskii chromosomal segments carrying a common restriction fragment length polymorphism were identified, and their regions of overlap determined using all available genetic markers. The effect of these chromosomal segments on soluble solids concentration, fruit mass, yield, and pH, was determined in the field. Many overlapping chromosomal segments had very different phenotypic effects, indicating QTLs affecting the phenotype(s) to lie in intervals of as little as 3 cM by which the segments differed. Some associations between different traits were attributed to close linkage between two or more QTLs, rather than pleiotropic effects of a single QTL: in such cases, recombination should separate desirable QTLs from genes with undesirable effects. The prominence of such trait associations in wide crosses appears partly due to infrequent reciprocal recombination between heterozygous chromosomal segments flanked by homozygous regions. Substitution mapping is particularly applicable to gene introgression from wild to domestic species, and generally useful in narrowing the gap between linkage mapping and physical mapping of QTLs.

Alleles↗

PubMed1991

Identification of markers associated with quantitative trait loci in chickens by DNA fingerprinting.

Three approaches for identifying VNTR alleles associated with quantitative traits in chickens are described. One approach is based on the comparison of well-defined selected and non-selected control strains. The second approach is based on analyzing chickens within a breeding population ranked according to specific traits and the third approach involves segregation analysis. In this latter approach a large number of offspring of a single male segregating for a quantitative trait are produced and tested for trait association of the male DNA fingerprinting bands. In all cases pooled DNA samples of birds, rather than individual samples, are analyzed and band intensity is assumed to reflect the relative frequency of an allele. Examples from the literature and from our laboratory indicate that these methods permit the identification of DNA fingerprinting bands associated with quantitative traits. After developing locus-specific probes for these bands it should ultimately be possible to detect and map quantitative trait loci.

Animals↗

PubMed1992

Maximum likelihood mapping of quantitative trait loci using full-sib families.

A maximum likelihood method is presented for the detection of quantitative trait loci (QTL) using flanking markers in full-sib families. This method incorporates a random component for common family effects due to additional QTL or the environment. Simulated data have been used to investigate this method. With a fixed total number of full sibs power of detection decreased substantially with decreasing family size. Increasing the number of alleles at the marker loci (i.e., polymorphism information content) and decreasing the interval size about the QTL increased power. Flanking markers were more powerful than single markers. In testing for a linked QTL the test must be made against a model which allows for between family variation (i.e., including an unlinked QTL or a between family variance component) or the test statistic may be grossly inflated. Mean parameter estimates were close to the simulated values in all situations when fitting the full model (including a linked QTL and common family effect). If the common family component was omitted the QTL effect was overestimated in data in which additional genetic variance was simulated and when compared with an unlinked QTL model there was reduced power. The test statistic curves, reflecting the likelihood of the QTL at each position along the chromosome, have discontinuities at the markers caused by adjacent pairs of markers providing different amounts of information. This must be accounted for when using flanking markers to search for a QTL in an outbred population.

Animals↗

PubMed1991

Use of recombinant inbred strains to identify quantitative trait loci in psychopharmacology.

Unlike simple Mendelian characteristics, individual differences in complex quantitative phenotypes studied in psychopharmacology are generally distributed continuously and are likely to be influenced by many genes. Recombinant inbred (RI) strains are valuable not only for their traditional use of detecting major gene segregation and linkage but also for identifying associations between quantitative traits and quantitative trait loci (QTL) that account for relatively small amounts of variation in phenotypes as well as loci that account for greater amounts of variation. When applied to published data on genetic markers and on amphetamine, alcohol, and morphine responses in BXD RI strains (RI strains developed from the cross between C57BL/6J and DBA/2J progenitor inbred strains), the RI QTL approach identified several significant associations beyond known major gene effects. Together, significant associations explain more than half of the genetic variance for these measures. The RI QTL approach is especially valuable for investigating the QTL underpinnings of genetic correlations among measures. It is recommended that psychopharmacogenetic research focus on the BXD RI strains. The cumulative and integrative nature of such a program of research is the major benefit of the RI QTL association approach for molecular genetic analysis of psychopharmacological processes, their physiological infrastructure, and their interface with other behavioral and biological systems.

Animals↗

PubMed1992

The use of CXB recombinant inbred mice to detect quantitative trait loci in behavior.

Although recombinant inbred (RI) series of mice have been developed to identify and map single-gene characteristics, they can also be used to identify quantitative trait loci (QTL) that account for small amounts of variance in quantitative traits such as behavior. We applied an RI QTL approach to the analysis of published behavioral data from seven studies that used the CXB RI series of mice. Nearly all of the behaviors showed strain distribution patterns indicative of multiple-gene rather than single-gene influence. Although the CXB series is limited to seven RI strains, RI QTL association analysis suggests QTL candidate markers for several behaviors, including avoidance and exploration.

Animals↗

PubMed1991

Use of recombinant inbred strains to detect quantitative trait loci associated with behavior.

Recombinant inbred (RI) strains are valuable not only for detecting major gene segregation and linkage but also for identifying associations between behavior and quantitative trait loci (QTL) that account for relatively small amounts of variation in behaviors for which strain distribution patterns are not bimodal. When applied to published data on genetic markers and on behavior for BXD RI strains, the RI QTL association approach suggests the presence of QTLs on chromosomes 6 and 12 for open-field activity and on chromosomes 1, 2, and 17 for high-pressure seizure susceptibility. Because the RI QTL approach does not require that the progenitor inbred strains of a particular RI series differ, researchers could focus on the BXD RI series, for which the greatest number of genetic markers are available. Focusing on BXD would capitalize on the cumulative nature of RI research which permits analyses of QTL sources of genetic correlations across studies.

Acoustic Stimulation↗

PubMed2025

Quantitative trait loci for Globodera pallida resistance derived from wild potato species Solanum gourlayi.

Globodera pallida is a major pest that is responsible for huge losses in potato yields worldwide. Expanding the gene pool of cultivated potatoes with clones resistant to this pest is made possible by searching for resistance genes in wild Solanum species. The aim of this study was to identify quantitative trait loci (QTLs) for potato resistance to Globodera pallida derived from Solanum gourlayi. A resistant diploid potato clone, Sg 2/7 (Solanum gourlayi, accession CGN17592), was crossed with a susceptible potato hybrid clone, DW 94-4235, to generate an F1 mapping population. All clones were tested for nematode resistance using G. pallida, pathotypes Pa2 and Pa3, in 2 or 3 years (2017-2019), respectively. Diversity Array Technology (DArTseq) was used for genotyping and genetic map construction. QTLs for nematode resistance were identified on potato chromosomes II, IV, V, VI, VII, X, XI, and XII, explaining from 10.1 to 21.5% of phenotypic variance. The most significant QTL for resistance to G. pallida pathotype Pa2 was identified on chromosome XII, explaining 20.9% of the phenotypic variance in the dataset from 2017. The most significant QTL for resistance to the G. pallida Pa3 pathotype was identified on chromosome VI, with a CAPS marker Exp928 in its peak, explaining 21.5% of the phenotypic variance in the dataset from 2017. The novel QTLs for resistance to S. gourlayi may be useful for breeding resistant potato cultivars, further studies of candidate genes, and host responses of potato to G. pallida infection.

Quantitative Trait Loci↗

PubMed2026

X chromosome-wide association studies for quantitative trait loci based on the mixture of general pedigrees and additional unrelated individuals.

Genome-wide association studies have successfully identified many genetic variants associated with complex traits. However, most existing methods target autosomes rather than X chromosome, and several existing X chromosome-wide association studies (XWAS) at quantitative trait loci (QTL) largely focus on unrelated individuals, with limited attention to general pedigrees or mixture of general pedigrees and additional unrelated individuals (called the mixed data for brevity). In this study, we propose nine novel methods for XWAS at QTL in the mixed data (${\mathrm{MQX}}_{\mathrm{cat}}$, ${\mathrm{MQZ}}_{\mathrm{max}}$, ${\mathrm{MT}}_{\mathrm{plinkw}}$, ${\mathrm{MT}}_{\mathrm{chenw}}$, $\mathrm{MwM}3\mathrm{VNA}$, ${\mathrm{MQMVX}}_{\mathrm{cat}}$, ${\mathrm{MQMVZ}}_{\mathrm{max}}$, $\mathrm{MpMV}$, and $\mathrm{McMV}$), also applicable to general pedigrees alone. The first four methods test for mean differences across genotypes; the latter four test for differences in both means and variances; $\mathrm{MwM}3\mathrm{VNA}$ tests for variance differences only. All mean-based and mean-variance-based methods incorporate X chromosome inactivation information, and all nine methods consider genetic relatedness in pedigrees. Simulation studies confirm well-controlled type I error rates, and inclusion of pedigrees significantly improves statistical power. Note that there has been no study focusing on X chromosome for the mixed data or general pedigrees from UK Biobank database, so we apply our proposed methods to this dataset, which identify five total cholesterol (TC)-associated and 13 low-density lipoprotein cholesterol (LDL-C)-associated single nucleotide polymorphisms (SNPs). Linkage disequilibrium (LD) analysis reveals that these SNPs fall into three distinct LD blocks. Functional annotation and gene ontology enrichment analysis reveal 16 and 28 enriched pathways for TC-associated and LDL-C-associated genes, respectively. These methods provide robust and powerful tools for XWAS at QTL in both mixed data and general pedigrees.

Quantitative Trait Loci↗

PubMed1992

Mapping quantitative trait loci for behavioral traits in the mouse.

After many years of studying various behavioral characters in the mouse, it is clear that most are heritable and are specified by complexes of genes or quantitative trait loci (QTLs). In order to attain a more complete understanding of the genetic causes of individual differences in behavior, the mechanism of action of these QTLs must be elucidated. The most straightforward approach to determining the mechanism of action of a particular QTL is to identify and molecularly clone the gene; this can be done by positional cloning, which depends on precise knowledge of the genetic map position. As the genetic data base for the mouse genome continues to develop, such strategies will become increasingly easy to perform. Here we suggest a multistage strategy for QTL mapping using recombinant-inbred strains of mice: (1) characterize genomic DNA from parental strains originally used to generate the RI strains; (2) characterize the RI strains for a quantitative character and for DNA markers that differ in the parental strains; and (3) assess the quantitative character in F2 mice derived from crosses between the parental strains, then determine the genotypes of extreme F2 mice for markers that account for at least 5% of the additive genetic variance. Data from these F2 crosses can be used to test hypotheses from the analysis of RI strains, i.e., that a QTL maps to a particular region. Using data from the mouse genome data base, this strategy should allow the molecular identification of the gene based on a candidate-gene approach. We illustrate the approach with examples from our work in mapping QTLs specifying neural sensitivity to the anesthetic effects of ethanol.

Alcoholism↗

PubMed2026

Tonsillar expression quantitative trait loci verify and expand genetic contributors to childhood atopic diseases.

BACKGROUND: The spectrum of causal variants, mechanisms, and immunologic gene networks that influence pediatric atopic traits is not completely understood. Human genetic variation associated with transcript abundance (expression quantitative trait loci [eQTLs]) can help to advance our understanding, yet prior work has focused on profiling immune cell populations collected from peripheral blood primarily in adult populations, leaving tissue-resident lymphocytes collected from children uncharacterized. OBJECTIVE: We sought to characterize gene expression of 4 populations of tonsil-derived immune cell types collected from pediatric patients. METHODS: We collected naive B, germinal center B, naive T, and T follicular helper cells from the discarded tonsils of 103 children across development (age range 1-19). Following genotyping and RNA sequencing of samples, we performed differential expression and eQTL analysis, then statistically linked eQTL signals to relevant atopic traits via colocalization. RESULTS: We found differentially expressed genes across cell types and identified 13,393 expression genes (eGenes) (1,793 eGenes not previously reported in similar datasets) influenced by 27,603 eQTLs (5,199 eQTLs not previously reported). We linked eQTLs to associations identified in pediatric and adult asthma and atopy traits, nominating 78 eGenes including TRAF3, ZBTB10, and JAZF1 in disease-relevant cell types. CONCLUSIONS: Our freely available resource exemplifies the importance of discovery in native tissues and across human development.

Expression quantitative trait locus↗

PubMed2026

Genome-wide cis-expression Quantitative Trait Loci (eQTL) and transcriptomic signals reveal distinct molecular regulation across correlated feed efficiency traits.

INTRODUCTION: Feed efficiency (FE) is a complex trait which determines livestock production profitability, yet the molecular mechanisms behind it remain unclear. This study investigated the blood transcriptomic profile of lambs, alongside genotype data with the aim to uncover the genetic basis of FE traits such as absolute dry matter intake (DMIabsolute), DMI adjusted for body size (DMIadjusted), average daily live weight gain (ADG), and residual feed intake (RFI). MATERIALS AND METHODS: Bulk RNA-Seq and genotype data were analysed using three complementary approaches: differential gene expression (DGE) analysis, weighted gene co-expression network analysis (WGCNA), and cis-expression Quantitative Trait Loci (cis-eQTL) mapping. These methods were used independently to identify genes and regulatory networks associated with FE traits and to investigate evidence supporting multi-trait candidate gene selection. RESULTS: DGE analysis revealed 2, 24, 85 and 4 differentially expressed genes for DMIabsolute, DMIadjusted, ADG, and RFI (Padjusted < 0.05), functionally enriched in sensory perception, ATP-dependent chromatin remodeling, Notch signaling and immune response pathways. 9 gene modules significantly associated with the FE traits (P ≤ 0.05) with correlations ranging from r = -0.56 to 0.49, were identified using WGCNA. Single nucleotide polymorphism (SNP)-level cis-eQTL analysis identified 93 eSNPs associated with 74 genes (false discovery rate (FDR) < 0.05), while permutation-derived gene level analysis identified 280 eGenes (FDR < 0.2, empirical P < 0.03). Across the three analyses, applying thresholds of DGE (Padjusted < 0.05), WGCNA (correlation, P ≤ 0.05), and cis-eQTL gene-level significance (empirical P < 0.05), multiple overlapping genes were identified including DNMT3A, KANSL1, NCOR1 for DMIadjusted, ACOX2, FANCF, CIMIP2B, LOC101115106, ARMH2, LOC132657496 for ADG, and LOC114114576 for RFI representing regulators of variations in FE. DISCUSSION: The integration of DGE, WGCNA, and cis-eQTL analyses identified key genes and regulatory mechanisms associated with variation in FE traits. These results highlight that integrated multi-trait candidate gene identification approaches can reveal key genes that lower feed intake while maintaining animal growth, supporting breeding strategies aimed at improving efficiency and long-term economic sustainability in sheep.

average daily gain (ADG)↗

PubMed1986

Maximum likelihood techniques for the mapping and analysis of quantitative trait loci with the aid of genetic markers.

A method is presented to estimate the biometric parameters of a quantitative trait locus linked to a genetic marker when both loci are segregating in the F-2 generation of a cross between two inbred lines. The method, which assumes underlying normal distributions, is a combination of maximum likelihood and moments methods and uses the statistics of the genetic marker genotype samples for the quantitative trait to estimate the recombination frequency between the two loci and the means and variances of the genotypes of the quantitative trait locus. With this method, the genetic parameters of a locus affecting plant height linked to an electrophoretic marker for esterase were accurately estimated from a sample of 1596 F-2 progeny of a cross between two species of Lycopersicon (tomato). Linkage distance between the two loci was 38 map units and the effect of the quantitative trait locus was 1.6 phenotypic standard deviation units. Accurate estimates of the genetic parameters and linkage distance for populations of 2000 individuals simulated with a segregating codominant locus with an effect of 1.63 standard deviations linked to a genetic marker with .2 recombination were also derived by this method. The method is not effective in distinguishing between complete and partial linkage in samples of only 500 individuals or for quantitative loci with effects less than a phenotypic standard deviation. The method is more effective for codominant than for dominant loci.

Algorithms↗

PubMed2026

Integrative multi-omics quantitative trait loci prioritize CASP7 as a candidate protective gene for cataract.

Cataracts are the leading cause of vision loss worldwide. Despite surgery being the only effective treatment, its economic burden highlights the necessity of exploring the pathogenesis of cataracts. In this study, we analyzed 4 large-scale GWAS (genome-wide association study) datasets for cataracts and performed SMR analysis along with heterogeneity in dependent instruments (HEIDI) testing to explore the effects of methylation, expression, and protein QTLs on cataracts. We further validated shared genetic variants through COLOC analysis. Additionally, we searched datasets related to cataracts from the Gene Expression Omnibus (GEO) database for differentially expressed genes (DEGs) and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses. By integrating summary-based Mendelian randomization (SMR) results with bioinformatics findings, CASP7 showed a consistent protective-direction association with cataract risk (mQTL: OR [95% CI] = 0.959 [0.941-0.977], FDR-adjusted P = .039; eQTL: OR [95% CI] = 0.897 [0.860-0.937], FDR-adjusted P = .0046; pQTL: OR [95% CI] = 0.597 [0.483-0.738], FDR-adjusted P = .00083). GEO-based analyses provided transcriptomic support for CASP7 involvement in cataract-related lens biology. These findings prioritize CASP7 as a genetically supported candidate protective gene associated with cataract risk. Because this study is based on public summary-level and transcriptomic datasets, the results should be interpreted cautiously and require functional validation in human lens-relevant systems.

Quantitative Trait Loci↗