Unravelling the complex causal effects of substance use behaviours on common diseases.

BACKGROUND: Substance use behaviours (SUB) including smoking, alcohol consumption, and coffee intake are associated with many health outcomes. However, whether the health effects of SUB are causal remains controversial, especially for alcohol consumption and coffee intake. METHODS: In this study, we assess 11 commonly used Mendelian Randomization (MR) methods by simulation and apply them to investigate the causal relationship between 7 SUB traits and health outcomes. We also combine stratified regression, genetic correlation, and MR analyses to investigate the dosage-dependent effects. RESULTS: We show that smoking initiation has widespread risk effects on common diseases such as asthma, type 2 diabetes, and peripheral vascular disease. Alcohol consumption shows risk effects specifically on cardiovascular diseases, dyslipidemia, and hypertensive diseases. We find evidence of dosage-dependent effects of coffee and tea intake on common diseases (e.g., cardiovascular disease and osteoarthritis). We observe that the minor allele effect of rs4410790 (the top signal for tea intake level) is negative on heavy tea intake ( b ̂ G W A S = - 0.091 , s . e . = 0.007 , P = 4.90 × 10 - 35 ) but positive on moderate tea intake ( b ̂ G W A S = 0.034 , s . e . = 0.006 , P = 3.40 × 10 - 8 ) , compared to the non-tea-drinkers. CONCLUSION: Our study reveals the complexity of the health effects of SUB and informs design for future studies aiming to dissect the causal relationships between behavioural traits and complex diseases.

Many people smoke or consume alcohol, coffee and tea. The relationship between using these types of substance and the development of different diseases is not well understood. Previous studies have suggested that differences in genetics, i.e. inherited characteristics, could have an impact on how each substance impacts a particular person's health. We used a method called Mendelian Randomization to look at the impact of consuming tobacco, alcohol, coffee and tea on the development of various common diseases using genetic information. We found that relationships were complicated and many were dosage-dependent, but that consumption of a large amount of all substances tended to have negative health impacts regardless of lifestyle, behavioural or inherited characteristics.

The potential clinical impact of cell type-specific genetic regulation: Crohn's disease.

Complex diseases are heterogenous due to variation in their genetic and environmental underpinnings, leading to varied treatment responses. Genome-wide association studies (GWAS) integrated with single-cell expression quantitative trait loci analyses (eQTL) can pinpoint cell-type specific candidate disease-relevant genes and pathways. This knowledge can be applied to patient stratification and novel therapeutic target identification. Here, we describe the translational potential of cell-type specific genetic regulation, using Crohn's disease as an example.

Pitfalls and opportunities for applying latent variables in single-cell eQTL analyses.

Using latent variables in gene expression data can help correct unobserved confounders and increase statistical power for expression quantitative trait Loci (eQTL) detection. The probabilistic estimation of expression residuals (PEER) and principal component analysis (PCA) are widely used methods that can remove unwanted variation and improve eQTL discovery power in bulk RNA-seq analysis. However, their performance has not been evaluated extensively in single-cell eQTL analysis, especially for different cell types. Potential challenges arise due to the structure of single-cell RNA-seq data, including sparsity, skewness, and mean-variance relationship. Here, we show by a series of analyses that PEER and PCA require additional quality control and data transformation steps on the pseudo-bulk matrix to obtain valid latent variables; otherwise, it can result in highly correlated factors (Pearson's correlation r = 0.63 ~ 0.99). Incorporating valid PFs/PCs in the eQTL association model would identify 1.7 ~ 13.3% more eGenes. Sensitivity analysis showed that the pattern of change between the number of eGenes detected and fitted PFs/PCs varied significantly in different cell types. In addition, using highly variable genes to generate latent variables could achieve similar eGenes discovery power as using all genes but save considerable computational resources (~ 6.2-fold faster).

Estimation and implications of the genetic architecture of fasting and non-fasting blood glucose.

The genetic regulation of post-prandial glucose levels is poorly understood. Here, we characterise the genetic architecture of blood glucose variably measured within 0 and 24 h of fasting in 368,000 European ancestry participants of the UK Biobank. We found a near-linear increase in the heritability of non-fasting glucose levels over time, which plateaus to its fasting state value after 5 h post meal (h2 = 11%; standard error: 1%). The genetic correlation between different fasting times is > 0.77, suggesting that the genetic control of glucose is largely constant across fasting durations. Accounting for heritability differences between fasting times leads to a ~16% improvement in the discovery of genetic variants associated with glucose. Newly detected variants improve the prediction of fasting glucose and type 2 diabetes in independent samples. Finally, we meta-analysed summary statistics from genome-wide association studies of random and fasting glucose (N = 518,615) and identified 156 independent SNPs explaining 3% of fasting glucose variance. Altogether, our study demonstrates the utility of random glucose measures to improve the discovery of genetic variants associated with glucose homeostasis, even in fasting conditions.

Genetic evidence for a causal relationship between type 2 diabetes and peripheral artery disease in both Europeans and East Asians.

BACKGROUND: Observational studies have revealed that type 2 diabetes (T2D) is associated with an increased risk of peripheral artery disease (PAD). However, whether the two diseases share a genetic basis and whether the relationship is causal remain unclear. It is also unclear as to whether these relationships differ between ethnic groups. METHODS: By leveraging large-scale genome-wide association study (GWAS) summary statistics of T2D (European-based: Ncase = 21,926, Ncontrol = 342,747; East Asian-based: Ncase = 36,614, Ncontrol = 155,150) and PAD (European-based: Ncase = 5673, Ncontrol = 359,551; East Asian-based: Ncase = 3593, Ncontrol = 208,860), we explored the genetic correlation and putative causal relationship between T2D and PAD in both Europeans and East Asians using linkage disequilibrium score regression and seven Mendelian randomization (MR) models. We also performed multi-trait analysis of GWAS and two gene-based analyses to reveal candidate variants and risk genes involved in the shared genetic basis between T2D and PAD. RESULTS: We observed a strong genetic correlation (rg) between T2D and PAD in both Europeans (rg = 0.51; p-value = 9.34 × 10-15) and East Asians (rg = 0.46; p-value = 1.67 × 10-12). The MR analyses provided consistent evidence for a causal effect of T2D on PAD in both ethnicities (odds ratio [OR] = 1.05 to 1.28 for Europeans and 1.15 to 1.27 for East Asians) but not PAD on T2D. This putative causal effect was not influenced by total cholesterol, body mass index, systolic blood pressure, or smoking initiation according to multivariable MR analysis, and the genetic overlap between T2D and PAD was further explored employing an independent European sample through polygenic risk score regression. Multi-trait analysis of GWAS revealed two novel European-specific single nucleotide polymorphisms (rs927742 and rs1734409) associated with the shared genetic basis of T2D and PAD. Gene-based analyses consistently identified one gene ANKFY1 and gene-gene interactions (e.g., STARD10 [European-specific] to AP3S2 [East Asian-specific]; KCNJ11 [European-specific] to KCNQ1 [East Asian-specific]) associated with the trans-ethnic genetic overlap between T2D and PAD, reflecting a common genetic basis for the co-occurrence of T2D and PAD in both Europeans and East Asians. CONCLUSIONS: Our study provides the first evidence for a genetically causal effect of T2D on PAD in both Europeans and East Asians. Several candidate variants and risk genes were identified as being associated with this genetic overlap. Our findings emphasize the importance of monitoring PAD status in T2D patients and suggest new genetic biomarkers for screening PAD risk among patients with T2D.

Mendelian randomization study reveals a population-specific putative causal effect of type 2 diabetes in risk of cataract.

BACKGROUND: The epidemiological association between type 2 diabetes and cataract has been well established. However, it remains unclear whether the two diseases share a genetic basis, and if so, whether this reflects a putative causal relationship. METHODS: We used East Asian population-based genome-wide association studies (GWAS) summary statistics of type 2 diabetes (Ncase = 36 614, Ncontrol = 155 150) and cataract (Ncase = 24 622, Ncontrol = 187 831) to comprehensively investigate the shared genetics between the two diseases. We performed: (i) linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics (ρ-HESS) to estimate the genetic correlation and local genetic correlation pattern between type 2 diabetes and cataract; (ii) multiple Mendelian randomization (MR) analyses to infer the putative causality between type 2 diabetes and cataract; and (iii) summary-data-based Mendelian randomization (SMR) to identify candidate risk genes underling the putative causality. Moreover, to investigate the extent of the population-specific genetic effect size underlying the shared genetics between type 2 diabetes and cataract, we applied the same analytical pipeline to perform a comparative analysis on European population-based GWAS of type 2 diabetes (Ncase = 62 892, Ncontrol = 596 424) and cataract (Ncase = 5045, Ncontrol = 356 096). RESULTS: Using East Asian population-based GWAS summary data, we observed a strong genetic correlation [rg = 0.58, 95% confidence interval (CI) = 0.33, 0.83), P-value = 5.60 × 10-6] between type 2 diabetes and cataract. Both ρ-HESS and multiple MR methods consistently showed a putative causal effect of type 2 diabetes on cataract, with estimated liability-scale MR odds ratios (ORs) at around 1.10 (95% CI = 1.06, 1.17). In contrast, no evidence supports a causal effect of cataract on type 2 diabetes. SMR analysis identified two novel genes MIR4453HG (ßSMR = -0.34, 95% CI = -0.46, -0.22, P-value = 6.41 × 10-8) and KCNK17 (ßSMR = -0.07, 95% CI = -0.09, -0.05, P-value = 2.49 × 10-10), whose expression levels were likely involved in the putative causality of type 2 diabetes on cataract. On the contrary, our comparative analysis on European population provided universally weak evidence on the genetic correlation and causal relationship between the two diseases. CONCLUSIONS: Our results provided robust evidence supporting a putative causal effect of type 2 diabetes on the risk of cataract in East Asians, and revealed potential genetic heterogeneity in the shared genetics underlying type 2 diabetes and cataract between East Asians and Europeans. These findings posed new paths on guiding the prevention and early-stage diagnosis of cataract in type 2 diabetes patients.

Genetic and functional interaction network analysis reveals global enrichment of regulatory T cell genes influencing basal cell carcinoma susceptibility.

BACKGROUND: Basal cell carcinoma (BCC) of the skin is the most common form of human cancer, with more than 90% of tumours presenting with clear genetic activation of the Hedgehog pathway. However, polygenic risk factors affecting mechanisms such as DNA repair and cell cycle checkpoints or which modulate the tumour microenvironment or host immune system play significant roles in determining whether genetic mutations culminate in BCC development. We set out to define background genetic factors that play a role in influencing BCC susceptibility via promoting or suppressing the effects of oncogenic drivers of BCC. METHODS: We performed genome-wide association studies (GWAS) on 17,416 cases and 375,455 controls. We subsequently performed statistical analysis by integrating data from population-based genetic studies of multi-omics data, including blood- and skin-specific expression quantitative trait loci and methylation quantitative trait loci, thereby defining a list of functionally relevant candidate BCC susceptibility genes from our GWAS loci. We also constructed a local GWAS functional interaction network (consisting of GWAS nearest genes) and another functional interaction network, consisting specifically of candidate BCC susceptibility genes. RESULTS: A total of 71 GWAS loci and 46 functional candidate BCC susceptibility genes were identified. Increased risk of BCC was associated with the decreased expression of 26 susceptibility genes and increased expression of 20 susceptibility genes. Pathway analysis of the functional candidate gene regulatory network revealed strong enrichment for cell cycle, cell death, and immune regulation processes, with a global enrichment of genes and proteins linked to TReg cell biology. CONCLUSIONS: Our genome-wide association analyses and functional interaction network analysis reveal an enrichment of risk variants that function in an immunosuppressive regulatory network, likely hindering cancer immune surveillance and effective antitumour immunity.

Widespread signatures of natural selection across human complex traits and functional genomic categories.

Understanding how natural selection has shaped genetic architecture of complex traits is of importance in medical and evolutionary genetics. Bayesian methods have been developed using individual-level GWAS data to estimate multiple genetic architecture parameters including selection signature. Here, we present a method (SBayesS) that only requires GWAS summary statistics. We analyse data for 155 complex traits (n = 27k-547k) and project the estimates onto those obtained from evolutionary simulations. We estimate that, on average across traits, about 1% of human genome sequence are mutational targets with a mean selection coefficient of ~0.001. Common diseases, on average, show a smaller number of mutational targets and have been under stronger selection, compared to other traits. SBayesS analyses incorporating functional annotations reveal that selection signatures vary across genomic regions, among which coding regions have the strongest selection signature and are enriched for both the number of associated variants and the magnitude of effect sizes.

Genome-wide analyses of behavioural traits are subject to bias by misreports and longitudinal changes.

Genome-wide association studies (GWAS) have discovered numerous genetic variants associated with human behavioural traits. However, behavioural traits are subject to misreports and longitudinal changes (MLC) which can cause biases in GWAS and follow-up analyses. Here, we demonstrate that individuals with higher disease burden in the UK Biobank (n = 455,607) are more likely to misreport or reduce their alcohol consumption levels, and propose a correction procedure to mitigate the MLC-induced biases. The alcohol consumption GWAS signals removed by the MLC corrections are enriched in metabolic/cardiovascular traits. Almost all the previously reported negative estimates of genetic correlations between alcohol consumption and common diseases become positive/non-significant after the MLC corrections. We also observe MLC biases for smoking and physical activities in the UK Biobank. Our findings provide a plausible explanation of the controversy about the effects of alcohol consumption on health outcomes and a caution for future analyses of self-reported behavioural traits in biobank data.

Tumor Mutational Burden Is Polygenic and Genetically Associated with Complex Traits and Diseases.

Tumor mutational burden (TMB) is an emerging biomarker of response to immunotherapy in solid tumors. However, the extent to which variation in TMB between patients is attributable to germline genetic variation remains elusive. Here, using 7,004 unrelated patients of European descent across 33 cancer types from The Cancer Genome Atlas, we show that pan-cancer TMB is polygenic with approximately 13% of its variation explained by approximately 1.1 million common variants altogether. We identify germline variants that affect TMB in stomach adenocarcinoma through altering the expression levels of BAG5 and KLC1. Further analyses provide evidence that TMB is genetically associated with complex traits and diseases, such as smoking, rheumatoid arthritis, height, and cancers, and some of the associations are likely causal. Overall, these results provide new insights into the genetic basis of somatic mutations in tumors and may inform future efforts to use genetic variants to stratify patients for immunotherapy. SIGNIFICANCE: This study provides evidence for a polygenic architecture of tumor mutational burden and opens an avenue for the use of whole-genome germline genetic variations to stratify patients with cancer for immunotherapy.

Genome-wide association study identifies 143 loci associated with 25 hydroxyvitamin D concentration.

Vitamin D deficiency is a candidate risk factor for a range of adverse health outcomes. In a genome-wide association study of 25 hydroxyvitamin D (25OHD) concentration in 417,580 Europeans we identify 143 independent loci in 112 1-Mb regions, providing insights into the physiology of vitamin D and implicating genes involved in lipid and lipoprotein metabolism, dermal tissue properties, and the sulphonation and glucuronidation of 25OHD. Mendelian randomization models find no robust evidence that 25OHD concentration has causal effects on candidate phenotypes (e.g. BMI, psychiatric disorders), but many phenotypes have (direct or indirect) causal effects on 25OHD concentration, clarifying the epidemiological relationship between 25OHD status and the health outcomes examined in this study.

Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies.

BACKGROUND: Genome-wide association studies (GWAS) in Parkinson's disease have increased the scope of biological knowledge about the disease over the past decade. We aimed to use the largest aggregate of GWAS data to identify novel risk loci and gain further insight into the causes of Parkinson's disease. METHODS: We did a meta-analysis of 17 datasets from Parkinson's disease GWAS available from European ancestry samples to nominate novel loci for disease risk. These datasets incorporated all available data. We then used these data to estimate heritable risk and develop predictive models of this heritability. We also used large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type, and biological pathway enrichments for the identified risk factors. Additionally, we examined shared genetic risk between Parkinson's disease and other phenotypes of interest via genetic correlations followed by Mendelian randomisation. FINDINGS: Between Oct 1, 2017, and Aug 9, 2018, we analysed 7·8 million single nucleotide polymorphisms in 37 688 cases, 18 618 UK Biobank proxy-cases (ie, individuals who do not have Parkinson's disease but have a first degree relative that does), and 1·4 million controls. We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16-36% of the heritable risk of Parkinson's disease depending on prevalence. Integrating methylation and expression data within a Mendelian randomisation framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested Parkinson's disease loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes (false discovery rate-adjusted p=0·0035 for intracranial volume, p=0·024 for putamen volume), smoking status (p=0·024), and educational attainment (p=0·038). Mendelian randomisation between cognitive performance and Parkinson's disease risk showed a robust association (p=8·00 × 10-7). INTERPRETATION: These data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, to the best of our knowledge, by revealing many additional Parkinson's disease risk loci, providing a biological context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified. These associations derived from European ancestry datasets will need to be followed-up with more diverse data. FUNDING: The National Institute on Aging at the National Institutes of Health (USA), The Michael J Fox Foundation, and The Parkinson's Foundation (see appendix for full list of funding sources).

The Parkinson's Disease Mendelian Randomization Research Portal.

BACKGROUND: Mendelian randomization is a method for exploring observational associations to find evidence of causality. OBJECTIVE: To apply Mendelian randomization between risk factors/phenotypic traits (exposures) and PD in a large, unbiased manner, and to create a public resource for research. METHODS: We used two-sample Mendelian randomization in which the summary statistics relating to single-nucleotide polymorphisms from 5,839 genome-wide association studies of exposures were used to assess causal relationships with PD. We selected the highest-quality exposure genome-wide association studies for this report (n = 401). For the disease outcome, summary statistics from the largest published PD genome-wide association studies were used. For each exposure, the causal effect on PD was assessed using the inverse variance weighted method, followed by a range of sensitivity analyses. We used a false discovery rate of 5% from the inverse variance weighted analysis to prioritize exposures of interest. RESULTS: We observed evidence for causal associations between 12 exposures and risk of PD. Of these, nine were effects related to increasing adiposity and decreasing risk of PD. The remaining top three exposures that affected PD risk were tea drinking, time spent watching television, and forced vital capacity, but these may have been biased and were less convincing. Other exposures at nominal statistical significance included inverse effects of smoking and alcohol. CONCLUSIONS: We present a new platform which offers Mendelian randomization analyses for a total of 5,839 genome-wide association studies versus the largest PD genome-wide association studies available (https://pdgenetics.shinyapps.io/MRportal/). Alongside, we report further evidence to support a causal role for adiposity on lowering the risk of PD. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank.

Genotype-by-environment interaction (GEI) is a fundamental component in understanding complex trait variation. However, it remains challenging to identify genetic variants with GEI effects in humans largely because of the small effect sizes and the difficulty of monitoring environmental fluctuations. Here, we demonstrate that GEI can be inferred from genetic variants associated with phenotypic variability in a large sample without the need of measuring environmental factors. We performed a genome-wide variance quantitative trait locus (vQTL) analysis of ~5.6 million variants on 348,501 unrelated individuals of European ancestry for 13 quantitative traits in the UK Biobank and identified 75 significant vQTLs with P < 2.0 × 10-9 for 9 traits, especially for those related to obesity. Direct GEI analysis with five environmental factors showed that the vQTLs were strongly enriched with GEI effects. Our results indicate pervasive GEI effects for obesity-related traits and demonstrate the detection of GEI without environmental data.

Is Schizophrenia a Risk Factor for Breast Cancer?-Evidence From Genetic Data.

Observational epidemiological studies have found an association between schizophrenia and breast cancer, but it is not known if the relationship is a causal one. We used summary statistics from very large genome-wide association studies of schizophrenia (n = 40675 cases and 64643 controls) and breast cancer (n = 122977 cases and 105974 controls) to investigate whether there is evidence that the association is partly due to shared genetic risk factors and whether there is evidence of a causal relationship. Using LD-score regression, we found that there is a small but significant genetic correlation (rG) between the 2 disorders (rG = 0.14, SE = 0.03, P = 4.75 × 10-8), indicating shared genetic risk factors. Using 142 genetic variants associated with schizophrenia as instrumental variables that are a proxy for having schizophrenia, we estimated a causal effect of schizophrenia on breast cancer on the observed scale as bxy = 0.032 (SE = 0.009, P = 2.3 × 10-4). A 1 SD increase in liability to schizophrenia increases risk of breast cancer 1.09-fold. In contrast, the estimated causal effect of breast cancer on schizophrenia from 191 instruments was not significantly different from zero (bxy = -0.005, SE = 0.012, P = .67). No evidence for pleiotropy was found and adjusting for the effects of smoking or parity did not alter the results. These results provide evidence that the previously observed association is due to schizophrenia causally increasing risk for breast cancer. Genetic variants may provide an avenue to elucidating the mechanism underpinning this relationship.

Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes.

Type 2 diabetes (T2D) is a very common disease in humans. Here we conduct a meta-analysis of genome-wide association studies (GWAS) with ~16 million genetic variants in 62,892 T2D cases and 596,424 controls of European ancestry. We identify 139 common and 4 rare variants associated with T2D, 42 of which (39 common and 3 rare variants) are independent of the known variants. Integration of the gene expression data from blood (n = 14,115 and 2765) with the GWAS results identifies 33 putative functional genes for T2D, 3 of which were targeted by approved drugs. A further integration of DNA methylation (n = 1980) and epigenomic annotation data highlight 3 genes (CAMK1D, TP53INP1, and ATP5G1) with plausible regulatory mechanisms, whereby a genetic variant exerts an effect on T2D through epigenetic regulation of gene expression. Our study uncovers additional loci, proposes putative genetic regulatory mechanisms for T2D, and provides evidence of purifying selection for T2D-associated variants.

Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood.

Understanding the difference in genetic regulation of gene expression between brain and blood is important for discovering genes for brain-related traits and disorders. Here, we estimate the correlation of genetic effects at the top-associated cis-expression or -DNA methylation (DNAm) quantitative trait loci (cis-eQTLs or cis-mQTLs) between brain and blood (r b ). Using publicly available data, we find that genetic effects at the top cis-eQTLs or mQTLs are highly correlated between independent brain and blood samples ([Formula: see text] for cis-eQTLs and [Formula: see text] for cis-mQTLs). Using meta-analyzed brain cis-eQTL/mQTL data (n = 526 to 1194), we identify 61 genes and 167 DNAm sites associated with four brain-related phenotypes, most of which are a subset of the discoveries (97 genes and 295 DNAm sites) using data from blood with larger sample sizes (n = 1980 to 14,115). Our results demonstrate the gain of power in gene discovery for brain-related phenotypes using blood cis-eQTL/mQTL data with large sample sizes.

Signatures of negative selection in the genetic architecture of human complex traits.

We develop a Bayesian mixed linear model that simultaneously estimates single-nucleotide polymorphism (SNP)-based heritability, polygenicity (proportion of SNPs with nonzero effects), and the relationship between SNP effect size and minor allele frequency for complex traits in conventionally unrelated individuals using genome-wide SNP data. We apply the method to 28 complex traits in the UK Biobank data (N = 126,752) and show that on average, 6% of SNPs have nonzero effects, which in total explain 22% of phenotypic variance. We detect significant (P < 0.05/28) signatures of natural selection in the genetic architecture of 23 traits, including reproductive, cardiovascular, and anthropometric traits, as well as educational attainment. The significant estimates of the relationship between effect size and minor allele frequency in complex traits are consistent with a model of negative (or purifying) selection, as confirmed by forward simulation. We conclude that negative selection acts pervasively on the genetic variants associated with human complex traits.

Dissecting genetic architecture of startle response in Drosophila melanogaster using multi-omics information.

Startle behavior is important for survival, and abnormal startle responses are related to several neurological diseases. Drosophila melanogaster provides a powerful system to investigate the genetic underpinnings of variation in startle behavior. Since mechanically induced, startle responses and environmental conditions can be readily quantified and precisely controlled. The 156 wild-derived fully sequenced lines of the Drosophila Genetic Reference Panel (DGRP) were used to identify SNPs and transcripts associated with variation in startle behavior. The results validated highly significant effects of 33 quantitative trait SNPs (QTSs) and 81 quantitative trait transcripts (QTTs) directly associated with phenotypic variation of startle response. We also detected QTT variation controlled by 20 QTSs (tQTSs) and 73 transcripts (tQTTs). Association mapping based on genomic and transcriptomic data enabled us to construct a complex genetic network that underlies variation in startle behavior. Based on principles of evolutionary conservation, human orthologous genes could be superimposed on this network. This study provided both genetic and biological insights into the variation of startle response behavior of Drosophila melanogaster, and highlighted the importance of genetic network to understand the genetic architecture of complex traits.