Jeffrey R. O'Connell

Although genome-wide association and genomic selection studies have primarily focused on additive effects, dominance and imprinting effects play an important role in mammalian biology and development. The degree to which these non-additive genetic effects contribute to phenotypic variation and whether QTL acting in a non-additive manner can be detected in genetic association studies remain controversial. To empirically answer these questions, we analyzed a large cattle dataset that consisted of 42,701 genotyped Holstein cows with genotyped parents and phenotypic records for eight production and reproduction traits. SNP genotypes were phased in pedigree to determine the parent-of-origin of alleles, and a three-component GREML was applied to obtain variance decomposition for additive, dominance, and imprinting effects. The results showed a significant non-zero contribution from dominance to production traits but not to reproduction traits. Imprinting effects significantly contributed to both production and reproduction traits. Interestingly, imprinting effects contributed more to reproduction traits than to production traits. Using GWAS and imputation-based fine-mapping analyses, we identified and validated a dominance association signal with milk yield near RUNX2, a candidate gene that has been associated with milk production in mice. When adding non-additive effects into the prediction models, however, we observed little or no increase in prediction accuracy for the eight traits analyzed. Collectively, our results suggested that non-additive effects contributed a non-negligible amount (more for reproduction traits) to the total genetic variance of complex traits in cattle, and detection of QTLs with non-additive effect is possible in GWAS using a large dataset.

Millions of genetic variants have been identified by population-scale sequencing projects, but subsets of these variants are needed for routine genomic predictions or genotyping arrays. Methods for selecting sequence variants were compared using simulated sequence genotypes and real July 2015 data from the 1000 Bull Genomes Project. Candidate sequence variants for 444 Holstein animals were combined with high-density (HD) imputed genotypes for 26,970 progeny-tested Holstein bulls. Test 1 included single nucleotide polymorphisms (SNPs) for 481,904 candidate sequence variants. Test 2 also included 249,966 insertions-deletions (InDels). After merging sequence variants with 312,614 HD SNPs and editing steps, Tests 1 and 2 included 762,588 and 1,003,453 variants, respectively. Imputation quality from findhap software was assessed with 404 of the sequenced animals in the reference population and 40 randomly chosen animals for validation. Their sequence genotypes were reduced to the subset of genotypes that were in common with HD genotypes and then imputed back to sequence. Predictions were tested for 33 traits using 2015 data of 3983 US validation bulls with daughters that were first phenotyped after August 2011. The average percentage of correctly imputed variants across all chromosomes was 97.2 for Test 1 and 97.0 for Test 2. Total time required to prepare, edit, impute, and estimate the effects of sequence variants for 27,235 bulls was about 1 week using less than 33 threads. Many sequence variants had larger estimated effects than nearby HD SNPs, but prediction reliability improved only by 0.6 percentage points in Test 1 when sequence SNPs were added to HD SNPs and by 0.4 percentage points in Test 2 when sequence SNPs and InDels were included. However, selecting the 16,648 candidate SNPs with the largest estimated effects and adding them to the 60,671 SNPs used in routine evaluations improved reliabilities by 2.7 percentage points. Reliabilities for genomic predictions improved when selected sequence variants were added; gains were similar for simulated and real data for the same population, and larger than previous gains obtained by adding HD SNPs. With many genotyped animals, many data sources, and millions of variants, computing strategies must efficiently balance costs of imputation, selection, and prediction to obtain subsets of markers that provide the highest accuracy.

There is great interindividual variation in systolic blood pressure (SBP) as a result of the influences of several factors, including sex, ancestry, smoking status, medication use, and, especially, age. The majority of genetic studies have examined SBP measured cross-sectionally; however, SBP changes over time, and not necessarily in a linear fashion. Therefore, this study conducted a genome-wide association (GWA) study of SBP change trajectories using data available through the Genetic Analysis Workshop 19 (GAW19) of 959 individuals from 20 extended Mexican American families from the San Antonio Family Studies with up to 4 measures of SBP. We performed structural equation modeling (SEM) while taking into account potential genetic effects to identify how, if at all, to include covariates in estimating the SBP change trajectories using a mixture model based latent class growth modeling (LCGM) approach for use in the GWA analyses. The semiparametric LCGM approach identified 5 trajectory classes that captured SBP changes across age. Each LCGM identified trajectory group was ranked based on the average number of cumulative years as hypertensive. Using a pairwise comparison of these classes the heritability estimates range from 12 to 94 % (SE = 17 to 40 %). These identified trajectories are significantly heritable, and we identified a total of 8 promising loci that influence one’s trajectory in SBP change across age. Our results demonstrate the potential utility of capitalizing on extant genetic data and longitudinal SBP assessments available through GAW19 to explore novel analytical methods with promising results.

Nearly half of adults in the United States who are diagnosed with hypertension use blood-pressure-lowering medications. Yet there is a large interindividual variability in the response to these medications. Two complementary gene–environment interaction methods have been published and incorporated into publicly available software packages to examine interaction effects, including whether genetic variants modify the association between medication use and blood pressure. The first approach uses a gene–environment interaction term to measure the change in outcome when both the genetic marker and medication are present (the “interaction model”). The second approach tests for effect-size differences between strata of an environmental exposure (the “med-diff” approach). However, no studies have quantitatively compared how these methods perform with respect to 1 or 2 degree of freedom (DF) tests or in family-based data sets. We evaluated these 2 approaches using simulated genotype–medication response interactions at 3 single nucleotide polymorphisms (SNPs) across a range of minor allele frequencies (MAFs 0.1–5.4 %) using the Genetic Analysis Workshop 19 family sample. The estimated interaction effect sizes were on average larger in the interaction model approach compared to the med-diff approach. The true positive proportion was higher for the med-diff approach for SNPs less than 1 % MAF, but higher for the interaction model when common variants were evaluated (MAF >5 %). The interaction model produced lower false-positive proportions than expected (5 %) across a range of MAFs for both the 1DF and 2DF tests. In contrast, the med-diff approach produced higher but stable false-positive proportions around 5 % across MAFs for both tests. Although the 1DF tests both performed similarly for common variants, the interaction model estimated true interaction effects with less bias and higher true positive proportions than the med-diff approach. However, if rare variation (MAF <5 %) is of interest, our findings suggest that when convergence is achieved, the med-diff approach may estimate true interaction effects more conservatively and with less variability.

Using genomic applications to glean insights into human biology, we systematically searched for nonsense, single nucleotide variants (SNVs) that are rare in the general population but enriched in the Old Order Amish (Amish) due to founder effect. We identified two non-linked, nonsense SNVs (R12X and W48X) in SLC13A1 (allele frequencies 0.29% and 0.74% in the Amish; enriched 1.2-fold and 3.7-fold, compared to the outbred Caucasian population, respectively). SLC13A1 encodes the apical, sodium-sulfate cotransporter (NaS1) responsible for sulfate (re)absorption in the kidneys and intestine. Both SLC13A1 R12X and W48X were independently associated with a 27.6% (P=2.7x10^-8) and 27.3% (P=6.9x10^-14) decrease in serum sulfate, respectively (P=8.8x10^-20 for carriers of either SLC13A1 nonsense SNV). We further performed the first exome- and genome-wide association study (ExWAS/GWAS) of serum sulfate and identified a missense variant (L348P) in SLC26A1, which encodes the basolateral sulfate-anion transporter (Sat1), that was associated with decreased serum sulfate (P=4.4x10^-12). Consistent with sulfate’s role in xenobiotic detoxification and protection against acetaminophen-induced hepatotoxicity, SLC13A1 nonsense SNV carriers had higher aminotransferase levels compared to non-carriers. Furthermore, SLC26A1 L348P was associated with lower whole-body bone mineral density (BMD) and higher serum calcium, consistent with the osteochondrodysplasia exhibited by dogs and sheep with naturally-occurring, homozygous, loss-of-function mutations in Slc13a1. This study demonstrates the power and translational potential of systematic identification and characterization of rare, loss-of-function variants and warrants additional studies to better understand the importance of sulfate in human physiology, disease, and drug toxicity.

Accurate genotype imputation can greatly reduce costs and increase benefits by combining whole-genome sequence data of varying read depth and array genotypes of varying densities. For large populations, an efficient strategy chooses the two haplotypes most likely to form each genotype and updates posterior allele probabilities from prior probabilities within those two haplotypes as each individual’s sequence is processed. Directly using allele read counts can improve imputation accuracy and reduce computation compared with calling or computing genotype probabilities first and then imputing. A new algorithm was implemented in findhap (version 4) software and tested using simulated bovine and actual human sequence data with different combinations of reference population size, sequence read depth and error rate. Read depths of ≥8× may be desired for direct investigation of sequenced individuals, but for a given total cost, sequencing more individuals at read depths of 2× to 4× gave more accurate imputation from array genotypes. Imputation accuracy improved further if reference individuals had both low-coverage sequence and high-density (HD) microarray data, and remained high even with a read error rate of 16 %. With read depths of ≤4×, findhap (version 4) had higher accuracy than Beagle (version 4); computing time was up to 400 times faster with findhap than with Beagle. For 10,000 sequenced individuals plus 250 with HD array genotypes to test imputation, findhap used 7 hours, 10 processors and 50 GB of memory for 1 million loci on one chromosome. Computing times increased in proportion to population size but less than proportional to number of variants. Simultaneous genotype calling from low-coverage sequence data and imputation from array genotypes of various densities is done very efficiently within findhap by updating allele probabilities within the two haplotypes for each individual. Accuracy of genotype calling and imputation were high with both simulated bovine and actual human genomes reduced to low-coverage sequence and HD microarray data. More efficient imputation allows geneticists to locate and test effects of more DNA variants from more individuals and to include those in future prediction and selection.

Background: SOCS7 is a member of the suppressor of cytokine signaling family of proteins and is expressed in skeletal muscle and islets. SOCS7 deficient mice develop islet hyperplasia in the setting of increased insulin sensitivity and normal glucose tolerance. The objective of this study was to determine if variants in SOCS7 play a role in variation of glucose and insulin levels and the development of type 2 diabetes (T2DM). Results: Five SOCS7 tagging SNPs were genotyped in diabetic and nondiabetic Old Order Amish. A case–control study was performed in T2DM (n = 145) and normal glucose tolerant (n = 358) subjects. Nominal associations were observed with T2DM and the minor alleles for rs8068600 (P = 0.01) and rs8074124 (P = 0.04); however, only rs8068600 remained significant after Bonferroni adjustment for multiple comparisons (P = 0.01). Among nondiabetic Amish (n = 765), no significant associations with glucose or insulin traits including fasting or 2 hour glucose and insulin from the oral glucose tolerance test, insulin or glucose area under the curve, Matsuda Index or HOMA-IR were found for any of the SNPs. Conclusion: In conclusion, genetic variants in the SOCS7 gene do not impact variation in glucose homeostasis traits and only minimally impact risk of T2DM in the Old Order Amish. Our study was not able to address whether rare variants that potentially impact gene function might influence T2DM risk.

In a recent meta-analysis migraine was associated with a two-fold increase in stroke risk. While the mechanism driving this association is unknown, one intriguing hypothesis is that migraineurs are genetically predisposed to developing ischemic stroke. Mutations in theATP1A2gene are implicated in familial hemiplegic migraine type II and increase the severity of ischemic brain injury in animal models. To further explore these observations, we assessed the association betweenATP1A2polymorphisms, migraine, and the risk of ischemic stroke in participants of the Genetics of Early-Onset Stroke Study, a population-based case–control study of ischemic stroke among men and women aged 15–49. Using responses to a headache symptoms questionnaire, subjects were classified as having no migraine, or migraine with or without visual aura. Evaluating a total of 134ATP1A2polymorphisms genotyped using a combination of Illumina platforms (Cardiovascular Gene-centric 50 K SNP Array and HumanOmni1-Quad_v1-0_B Bead Chip), only one polymorphism (rs2070704) demonstrated a nominally significant association with stroke in an age-, gender-, ethnicity-adjusted model (OR = 0.83, 95% CI = 0.71-0.98, p = 0.025) and in a vascular risk factor model adjusting for age, gender, ethnicity, hypertension, diabetes, smoking, and myocardial infarction (OR = 0.74, 95% CI = 0.63-0.89, p = 0.001). Ethnicity-stratified analyses demonstrated a significant association forrs2070704among African-Americans (OR = 0.68, 95% CI = 0.53-0.90, p = 0.005) but not Caucasians (OR = 0.82, 95% CI = 0.64-1.04, p = 0.107). These associations were unchanged when migraine subtypes were included as co-variates. We did not observe an association betweenATP1A2polymorphisms and migraine. While our results do not demonstrate a strong relationship betweenATP1A2polymorphisms and migraine associated stroke risk, the results are hypothesis generating and indicate that an association betweenATP1A2polymorphisms and stroke risk may exist. Additional studies are required.

EIF2AK3 is a type I transmembrane protein that functions as an endoplasmic reticulum (ER) stress sensor to regulate global protein synthesis. Rare mutations in EIF2AK3 cause Wolcott‐Rallison syndrome (OMIM 226980), an autosomal recessive disorder characterized by diabetes, epiphyseal dysplasia, osteoporosis, and growth retardation. To investigate the role of common genetic variation in EIF2AK3 as a determinant of bone mineral density (BMD) and osteoporosis, we sequenced all exons and flanking regions, then genotyped six potentially functional single nucleotide polymorphisms (SNPs) in this gene in 997 Amish subjects for association analysis, and attempted replication in 887 Mexican Americans. We found that the minor allele of a nonsynonymous SNP rs13045 had borderline associations with decreased forearm BMD in both discovery and replication cohorts (unadjusted p = 0.036 and β = − 0.007 for the Amish; unadjusted p = 0.031 and β = − 0.008 for Mexican Americans). A meta‐analysis indicated this association achieved statistical significance in the combined sample (unadjusted p = 0.003; Bonferroni corrected p = 0.009). Rs13045 and three other potentially functional SNPs, a promoter SNP (rs6547787) and two nonsynonymous SNPs (rs867529 and rs1805165), formed two haplotypes: a low‐BMD associated haplotype, denoted haplotype B [minor allele frequency (MAF) = 0.311] and a common haplotype A (MAF = 0.676). There were no differences in mRNA expression in lymphoblastoid cell lines between the two haplotypes. However, after treating lymphoblastoid cell lines with thapsigargin to induce ER stress, cell lines with haplotype B showed increased sensitivity to ER stress (p = 0.014) compared with cell lines with haplotype A. Taken together, our results suggest that common nonsynonymous sequence variants in EIF2AK3 have a modest effect on ER stress response and may contribute to the risk for low BMD through this mechanism. © 2012 American Society for Bone and Mineral Research