Linear regression in genetic association studies

Associations among the genome, rumen metabolome, ruminal bacteria, and milk production in early-lactation Holsteins

A multicenter observational study to evaluate genome-wide association was conducted in early-lactation Holstein cows (n = 293) from 36 herds in Canada, the USA, and Australia. Phenotypic observations included rumen metabolome, acidosis risk, ruminal bacterial taxa, and milk composition and yield measures. Diets ranged from pasture supplemented with concentrates to total mixed rations (nonfiber carbohydrates = 17 to 47, and neutral detergent fiber = 27 to 58% of dry matter). Rumen samples were collected <3 h after feeding and analyzed for pH, ammonia, d- and l-lactate, volatile fatty acid (VFA) concentrations, and abundance of bacterial phyla and families. Eigenvectors were produced using cluster and discriminant analyses from a combination of pH and ammonia, d-lactate, and VFA concentrations, and were used to estimate the probability of the risk of ruminal acidosis based on proximity to the centroid of 3 clusters, termed high (24.0% of cows), medium (24.2%), and low risk (51.8%) for acidosis. DNA of sufficient quality was successfully extracted from whole blood (218 cows) or hair (65 cows) collected simultaneously with the rumen samples and sequenced using the Geneseek Genomic Profiler Bovine 150K Illumina SNPchip. Genome-wide association used an additive model and linear regression with principal component analysis (PCA) population stratification and a Bonferroni correction for multiple comparisons. Population structure was visualized using PCA plots. Single genomic markers were associated with milk protein percent and the center logged ratio abundance of the phyla Chloroflexi, SR1, and Spirochaetes, and tended to be associated with milk fat yield, rumen acetate, butyrate, and isovalerate concentrations and with the probability of being in the low-risk acidosis group. More than one genomic marker was associated or tended to be associated with rumen isobutyrate and caproate concentrations, and the center log ratio of the phyla Bacteroidetes and Firmicutes and center log ratio of the families Prevotellaceae, BS11, S24-7, Acidaminococcaceae, Carnobacteriaceae, Lactobacillaceae, Leuconostocaceae, and Streptococcaceae. The provisional NTN4 gene, involved in several functions, had pleiotropy with 10 bacterial families, the phyla Bacteroidetes and Firmicutes, and butyrate. The ATP2CA1 gene, involved in the ATPase secretory pathway for Ca²⁺ transport, overlapped for the families Prevotellaceae, S24-7, and Streptococcaceae, the phylum Bacteroidetes, and isobutyrate. No genomic markers were associated with milk yield, fat percentage, protein yield, total solids, energy-corrected milk, somatic cell count, rumen pH, ammonia, propionate, valerate, total VFA, and d-, l-, or total lactate concentrations, or probability of being in the high- or medium-risk acidosis groups. Genome-wide associations with the rumen metabolome, microbial taxa, and milk composition were present across a wide geographical and management range of herds, suggesting the existence of markers for the rumen environment but not for acidosis susceptibility. The variation in pathogenesis of ruminal acidosis in the small population of cattle in the high risk for acidosis group and the dynamic nature of the rumen as cows cycle through a bout of acidosis may have precluded the identification of markers for acidosis susceptibility. Despite a limited sample size, this study provides evidence of interactions between the mammalian genome, the rumen metabolome, ruminal bacteria, and milk protein percentage.

The effect of heteroscedasticity on the prediction efficiency of genome-wide polygenic score for body mass index

Globally, more than 1.9 billion adults are overweight. Thus, obesity is a serious public health issue. Moreover, obesity is a major risk factor for diabetes mellitus, coronary heart disease, and cardiovascular disease. Recently, GWAS examining obesity and body mass index (BMI) have increasingly unveiled many aspects of the genetic architecture of obesity and BMI. Information on genome-wide genetic variants has been used to estimate the genome-wide polygenic score (GPS) for a personalized prediction of obesity. However, the prediction power of GPS is affected by various factors, including the unequal variance in the distribution of a phenotype, known as heteroscedasticity. Here, we calculated a GPS for BMI using LDpred2, which was based on the BMI GWAS summary statistics from a European meta-analysis. Then, we tested the GPS in 354,761 European samples from the UK Biobank and found an effective prediction power of the GPS on BMI. To study a change in the variance of BMI, we investigated the heteroscedasticity of BMI across the GPS via graphical and statistical methods. We also studied the homoscedastic samples for BMI compared to the heteroscedastic sample, randomly selecting samples with various standard deviations of BMI residuals. Further, we examined the effect of the genetic interaction of GPS with environment (GPS×E) on the heteroscedasticity of BMI. We observed the changing variance (i.e., heteroscedasticity) of BMI along the GPS. The heteroscedasticity of BMI was confirmed by both the Breusch-Pagan test and the Score test. Compared to the heteroscedastic sample, the homoscedastic samples from small standard deviation of BMI residuals showed a decreased heteroscedasticity and an improved prediction accuracy, suggesting a quantitatively negative correlation between the phenotypic heteroscedasticity and the prediction accuracy of GPS. To further test the effects of the GPS×E on heteroscedasticity, first we tested the genetic interactions of the GPS with 21 environments and found 8 significant GPS×E interactions on BMI. However, the heteroscedasticity of BMI was not ameliorated after adjusting for the GPS×E interactions. Taken together, our findings suggest that the heteroscedasticity of BMI exists along the GPS and is not affected by the GPS×E interaction.

Methods for statistical fine-mapping and their applications to auto-immune diseases

Although genome-wide association studies (GWAS) have identified thousands of loci in the human genome that are associated with different traits, understanding the biological mechanisms underlying the association signals identified in GWAS remains challenging. Statistical fine-mapping is a method aiming to refine GWAS signals by evaluating which variant(s) are truly causal to the phenotype. Here, we review the types of statistical fine-mapping methods that have been widely used to date, with a focus on recently developed functionally informed fine-mapping (FIFM) methods that utilize functional annotations. We then systematically review the applications of statistical fine-mapping in autoimmune disease studies to highlight the value of statistical fine-mapping in biological contexts.

MF-TOWmuT: Testing an optimally weighted combination of common and rare variants with multiple traits using family data

With rapid advancements of sequencing technologies and accumulations of electronic health records, a large number of genetic variants and multiple correlated human complex traits have become available in many genetic association studies. Thus, it becomes necessary and important to develop new methods that can jointly analyze the association between multiple genetic variants and multiple traits. Compared with methods that only use a single marker or trait, the joint analysis of multiple genetic variants and multiple traits is more powerful since such an analysis can fully incorporate the correlation structure of genetic variants and/or traits and their mutual dependence patterns. However, most of existing methods that simultaneously analyze multiple genetic variants and multiple traits are only applicable to unrelated samples. We develop a new method called MF-TOWmuT to detect association of multiple phenotypes and multiple genetic variants in a genomic region with family samples. MF-TOWmuT is based on an optimally weighted combination of variants. Our method can be applied to both rare and common variants and both qualitative and quantitative traits. Our simulation results show that (1) the type I error of MF-TOWmuT is preserved; (2) MF-TOWmuT outperforms two existing methods such as Multiple Family-based Quasi-Likelihood Score Test and Multivariate Family-based Rare Variant Association Test in terms of power. We also illustrate the usefulness of MF-TOWmuT by analyzing genotypic and phenotipic data from the Genetics of Kidneys in Diabetes study. R program is available at https://github.com/gaochengPRC/MF-TOWmuT.

A rank-based normalization method with the fully adjusted full-stage procedure in genetic association studies

In the area of genetic epidemiology, studies of the genotype-phenotype associations have made significant contributions to human complicated trait genetics. These studies depend on specialized statistical methods for uncover the association between traits and genetic variants, both common and rare variants. Often, in analyzing such studies, potentially confounding factors, such as social and environmental conditions, are required to be involved. Multiple linear regression is the most widely used type of regression analysis when the outcome of interest is quantitative traits. Many statistical tests for identifying genotype-phenotype associations using linear regression rely on the assumption that the traits (or the residuals) of the regression follow a normal distribution. In genomic research, the rank-based inverse normal transformation (INT) is one of the most popular approaches to reach normally distributed traits (or normally distributed residuals). Many researchers believe that applying the INT to the non-normality of the traits (or the non-normality of the residuals) is required for valid inference, because the phenotypic (or residual) outliers and non-normality have the significant influence on both the type I error rate control and statistical power, especially under the situation in rare-variant association testing procedures. Here we propose a test for exploring the association of the rare variant with the quantitative trait by using a fully adjusted full-stage INT. Using simulations we show that the fully adjusted full-stage INT is more appropriate than the existing INT methods, such as the fully adjusted two-stage INT and the INT-based omnibus test, in testing genotype-phenotype associations with rare variants, especially when genotypes are uncorrelated with covariates. The fully adjusted full-stage INT retains the advantages of the fully adjusted two-stage INT and ameliorates the problems of the fully adjusted two-stage INT for analysis of rare variants under non-normality of the trait. We also present theoretical results on these desirable properties. In addition, the two available methods with non-normal traits, the quantile/median regression method and the Yeo-Johnson power transformation, are also included in simulations for comparison with these desirable properties.

A Perception on Genome-Wide Genetic Analysis of Metabolic Traits in Arab Populations

Despite dedicated nation-wide efforts to raise awareness against the harmful effects of fast-food consumption and sedentary lifestyle, the Arab population continues to struggle with an increased risk for metabolic disorders. Unlike the European population, the Arab population lacks well-established genetic risk determinants for metabolic disorders, and the transferability of established risk loci to this population has not been satisfactorily demonstrated. The most recent findings have identified over 240 genetic risk loci (with ~400 independent association signals) for type 2 diabetes, but thus far only 25 risk loci (ADAMTS9, ALX4, BCL11A, CDKAL1, CDKN2A/B, COL8A1, DUSP9, FTO, GCK, GNPDA2, HMG20A, HNF1A, HNF1B, HNF4A, IGF2BP2, JAZF1, KCNJ11, KCNQ1, MC4R, PPARγ, SLC30A8, TCF7L2, TFAP2B, TP53INP1, and WFS1) have been replicated in Arab populations. To our knowledge, large-scale population- or family-based association studies are non-existent in this region. Recently, we conducted genome-wide association studies on Arab individuals from Kuwait to delineate the genetic determinants for quantitative traits associated with anthropometry, lipid profile, insulin resistance, and blood pressure levels. Although these studies led to the identification of novel recessive variants, they failed to reproduce the established loci. However, they provided insights into the genetic architecture of the population, the applicability of genetic models based on recessive mode of inheritance, the presence of genetic signatures of inbreeding due to the practice of consanguinity, and the pleiotropic effects of rare disorders on complex metabolic disorders. This perspective presents analysis strategies and study designs for identifying genetic risk variants associated with diabetes and related traits in Arab populations.

Single Nucleotide Polymorphism in the COL11A2 Gene Associated with Heat Pain Sensitivity in Knee Osteoarthritis

Pain is one of the most prominent symptoms of osteoarthritis. However, there is often discordance between the pain experienced by individuals with osteoarthritis and the degree of articular pathology. This suggests that individual differences, including genetic variability in the central processing of nociceptive stimuli, may impact the presentation of osteoarthritis. Here, we show that the single nucleotide polymorphism rs16868943 in the collagen gene COL11A2 is significantly associated with lowered heat pain tolerance on the arm in participants with knee osteoarthritis (P = 1.21 × 10⁻⁶, P = 0.0053 after Bonferroni correction, beta = −3.42). A total of 161 knee osteoarthritis participants were included and evaluated for heat, punctate and pressure pain sensitivity of the affected knee and the ipsilateral arm. Each participant was genotyped for 4392 single nucleotide polymorphisms in genes implicated in pain perception, inflammation and mood and tested for association with pain sensitivity. The minor A allele of single nucleotide polymorphism rs16868943 was significantly associated with lower arm heat pain tolerance after correction for age, gender, race, and study site. This single nucleotide polymorphism was also nominally associated with other measures of heat pain sensitivity, including lowered knee heat pain tolerance (P = 1.14 × 10⁻⁵, P = 0.05 after Bonferroni correction), lowered arm heat pain threshold (P = 0.0039, uncorrected) and lowered knee heat pain threshold (P = 0.003, uncorrected). Addition of genotypes from 91 participants without knee pain produced a significant interaction between knee osteoarthritis status and the rs16868943 single nucleotide polymorphism in heat pain tolerance (P = 1.71 × 10⁻⁵), such that rs16868943 was not associated with heat pain tolerance in participants without knee pain (P = 0.12, beta = 1.3). This is the first study to show genetic association with heat pain tolerance in individuals with osteoarthritis. The association is specific to participants who have already developed knee osteoarthritis, suggesting that the COL11A2 gene, which has previously been associated with familial osteoarthritis, may play a role in pain sensitization after the development of osteoarthritis.

Genetic Markers Are Associated with the Ruminal Microbiome and Metabolome in Grain and Sugar Challenged Dairy Heifers

Dairy heifers were subjected to a non-life-threatening challenge designed to induce ruminal acidosis by feeding grain and sugar. Large among animal variation in clinical signs of acidosis, rumen metabolite concentrations, and the rumen microbiome occurred. This exploratory study investigates sources of the variation by examining associations between the genome, metabolome, and microbiome, albeit with a limited population. The broader objective is to provide a rationale for a larger field study to identify markers for susceptibility to ruminal acidosis. Initially, heifers (n = 40) allocated to five feed additive groups were fed 20-days pre-challenge with a total mixed ration and additives. Fructose (0.1% of bodyweight/day) was added for the last 10 days pre-challenge. On day 21 heifers were challenged with 1.0% of bodyweight dry matter wheat + 0.2% of bodyweight fructose + additives. Rumen samples were collected via stomach tube weekly (day 0, 7, and 14) and at five times over 3.6 h after challenge and analyzed for pH and volatile fatty acid, ammonia, D-, and L-lactate concentrations. Relative abundance of bacteria and archaea were determined using Illumina MiSeq. Genotyping was undertaken using a 150K Illumina SNPchip. Genome-wide association was performed for metabolite and microbiome measures (n = 33). Few genome associations occurred with rumen pH, concentration of acetate, propionate, total volatile fatty acids, or ammonia, or the relative abundance of the Firmicutes, Bacteroidetes, and Spirochaetes phyla. Metabolites and microbial phyla that had markers associated and quantitative trait loci (QTL) were: acetate to propionate ratio (A:P), D-, L-, and total lactate, butyrate, acidosis eigenvalue, Actinobacteria, Chloroflexi, Euryarchaeota, Fibrobacteres, Planctomycetes, Proteobacteria, and Tenericutes. A putative genomic region overlapped for Actinobacteria, Euryarchaeota, and Fibrobacteres and covered the region that codes for matrix extracellular phosphoglycoprotein (MEPE). Other overlapping regions were: (1) Chloroflexi, Tenericutes, and A:P, (2) L- and total lactate and Actinobacteria, and (3) Actinobacteria, Euryarchaeota, Fibrobacteres, and A:P. Genome-wide associations with the metabolome and microbiome occurred despite the small population, suggesting that markers for ruminal acidosis susceptibility exist. The findings may explain some of the variation in metabolomic and microbial data and provide a rationale for a larger study with a population that has variation in acidosis.

Genome-wide association mapping for seedling and field resistance to Puccinia striiformis f. sp. tritici in elite durum wheat

Genome-wide association analysis in tetraploid wheat revealed novel and diverse loci for seedling and field resistance to stripe rust in elite spring durum wheat accessions from worldwide. Improving resistance to stripe rust, caused by Puccinia striiformis f. sp. tritici, is a major objective for wheat breeding. To identify effective stripe rust resistance loci, a genome-wide association study (GWAS) was conducted using 232 elite durum wheat (Triticum turgidum ssp. durum) lines from worldwide breeding programs. Genotyping with the 90 K iSelect wheat single nucleotide polymorphism (SNP) array resulted in 11,635 markers distributed across the genome. Response to stripe rust infection at the seedling stage revealed resistant and susceptible accessions present in rather balanced frequencies for the six tested races, with a higher frequency of susceptible responses to United States races as compared to Italian races (61.1 vs. 43.1% of susceptible accessions). Resistance at the seedling stage only partially explained adult plant resistance, which was found to be more frequent with 67.7% of accessions resistant across six nurseries in the United States. GWAS identified 82 loci associated with seedling stripe rust resistance, five of which were significant at the false discovery rate adjusted P value <0.1 and 11 loci were detected for the field response at the adult plant stages in at least two environments. Notably, Yrdurum-1BS.1 showed the largest effect for both seedling and field resistance, and is therefore considered as a major locus for resistance in tetraploid wheat. Our GWAS study is the first of its kind for stripe rust resistance in tetraploid wheat and provides an overview of resistance in elite germplasm and reports new loci that can be used in breeding resistant cultivars.

Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders

Personality is influenced by genetic and environmental factors and associated with mental health. However, the underlying genetic determinants are largely unknown. We identified six genetic loci, including five novel loci, significantly associated with personality traits in a meta-analysis of genome-wide association studies (N = 123,132-260,861). Of these genome-wide significant loci, extraversion was associated with variants in WSCD2 and near PCDH15, and neuroticism with variants on chromosome 8p23.1 and in L3MBTL2. We performed a principal component analysis to extract major dimensions underlying genetic variations among five personality traits and six psychiatric disorders (N = 5,422-18,759). The first genetic dimension separated personality traits and psychiatric disorders, except that neuroticism and openness to experience were clustered with the disorders. High genetic correlations were found between extraversion and attention-deficit-hyperactivity disorder (ADHD) and between openness and schizophrenia and bipolar disorder. The second genetic dimension was closely aligned with extraversion-introversion and grouped neuroticism with internalizing psychopathology (e.g., depression or anxiety).

Associations of Toll-Like Receptor and β-Defensin Polymorphisms with Measures of Periodontal Disease (PD) in HIV+ North American Adults: An Exploratory Study

Polymorphisms in toll-like receptor (TLR) and β-defensin (DEFB) genes have been recognized as potential genetic factors that can influence susceptibility to and severity of periodontal diseases (PD). However, data regarding associations between these polymorphisms and PD are still scarce in North American populations, and are not available in HIV+ North American populations. In this exploratory study, we analyzed samples from HIV+ adults (n = 115), who received primary HIV care at 3 local outpatient HIV clinics and were monitored for PD status. We genotyped a total of 41 single nucleotide polymorphisms (SNPs) in 8 TLR genes and copy number variation (CNV) in DEFB4/103A. We performed regression analyses for levels of 3 periodontopathogens in subgingival dental plaques (Porphyromonas gingivalis [Pg], Treponema denticola [Td], and Tannerella forsythia [Tf]) and 3 clinical measures of PD (periodontal probing depth [PPD], gingival recession [REC], and bleeding on probing [BOP]). In all subjects combined, 2 SNPs in TLR1 were significantly associated with Td, and one SNP in TLR2 was significantly associated with BOP. One of the 2 SNPs in TLR1 was significantly associated with Td in Caucasians. In addition, another SNP in TLR1 and a SNP in TLR6 were also significantly associated with Td and Pg, respectively, in Caucasians. All 3 periodontopathogen levels were significantly associated with PPD and BOP, but none was associated with REC. Instrumental variable analysis showed that 8 SNPs in 6 TLR genes were significantly associated with the 3 periodontopathogen levels. However, associations between the 3 periodontopathogen levels and PPD or BOP were not driven by associations with these identified SNPs. No association was found between DEFB4/103A CNV and any periodontopathogen level or clinical measure in all samples, Caucasians, or African Americans. Our exploratory study suggests a role of TLR polymorphisms, particularly TLR1 and TLR6 polymorphisms, in PD in HIV+ North Americans.

Genome-wide association analysis identifies genetic loci associated with resistance to multiple antimalarials in Plasmodium falciparum from China-Myanmar border

Drug resistance has emerged as one of the greatest challenges facing malaria control. The recent emergence of resistance to artemisinin (ART) and its partner drugs in ART-based combination therapies (ACT) is threatening the efficacy of this front-line regimen for treating Plasmodium falciparum parasites. Thus, an understanding of the molecular mechanisms that underlie the resistance to ART and the partner drugs has become a high priority for resistance containment and malaria management. Using genome-wide association studies, we investigated the associations of genome-wide single nucleotide polymorphisms with in vitro sensitivities to 10 commonly used antimalarial drugs in 94 P. falciparum isolates from the China-Myanmar border area, a region with the longest history of ART usage. We identified several loci associated with various drugs, including those containing pfcrt and pfdhfr. Of particular interest is a locus on chromosome 10 containing the autophagy-related protein 18 (ATG18) associated with decreased sensitivities to dihydroartemisinin, artemether and piperaquine – an ACT partner drug in this area. ATG18 is a phosphatidylinositol-3-phosphate binding protein essential for autophagy and recently identified as a potential ART target. Further investigations on the ATG18 and genes at the chromosome 10 locus may provide an important lead for a connection between ART resistance and autophagy.

The effect of phenotypic outliers and non-normality on rare-variant association testing

Rare-variant association studies (RVAS) have made important contributions to human complex trait genetics. These studies rely on specialized statistical methods for analyzing rare-variant associations, both individually and in aggregate. We investigated the impact that phenotypic outliers and non-normality have on the performance of rare-variant association testing procedures. Ignoring outliers or non-normality can significantly inflate Type I error rates. We found that rank-based inverse normal transformation (INT) and trait winsorisation were both effective at maintaining Type I error control without sacrificing power in the presence of outliers. INT was the optimal method for non-normally distributed traits. For RVAS of quantitative traits with outliers or non-normality, we recommend using INT to transform phenotypic values before association testing.

Discovering main genetic interactions with LABNet LAsso-based network inference

Genome-wide association studies can potentially unravel the mechanisms behind complex traits and common genetic diseases. Despite the valuable results produced thus far, many questions remain unanswered. For instance, which specific genetic compounds are linked to the risk of the disease under investigation; what biological mechanism do they act through; or how do they interact with environmental and other external factors? The driving force of computational biology is the constantly growing amount of big data generated by high-throughput technologies. A practical framework that can deal with this abundance of information and that consent to discovering genetic associations and interactions is provided by means of networks. Unfortunately, high dimensionality, the presence of noise and the geometry of data can make the aforementioned problem extremely challenging. We propose a penalised linear regression approach that can deal with the aforementioned issues that affect genetic data. We analyse the gene expression profiles of individuals with a common trait to infer the network structure of interactions among genes. The permutation-based approach leads to more stable and reliable networks inferred from synthetic microarray data. We show that a higher number of permutations determines the number of predicted edges, improves the overall sensitivity and controls the number of false positives.