Prospects for whole-genome linkage disequilibrium mapping of common disease genes

Effective population size in field pea

BACKGROUND

Effective population size (Ne) is a pivotal parameter in population genetics as it can provide information on the rate of inbreeding and the contemporary status of genetic diversity in breeding populations. The population with smaller Ne can lead to faster inbreeding, with little potential for genetic gain making selections ineffective. The importance of Ne has become increasingly recognized in plant breeding, which can help breeders monitor and enhance the genetic variability or redesign their selection protocols. Here, we present the first Ne estimates based on linkage disequilibrium (LD) in the pea genome.

RESULTS

We calculated and compared Ne using SNP markers from North Dakota State University (NDSU) modern breeding lines and United States Department of Agriculture (USDA) diversity panel. The extent of LD was highly variable not only between populations but also among different regions and chromosomes of the genome. Overall, NDSU had a higher and longer-range LD than the USDA that could extend up to 500 Kb, with a genome-wide average r2 of 0.57 (vs 0.34), likely due to its lower recombination rates and the selection background. The estimated Ne for the USDA was nearly three-fold higher (Ne = 174) than NDSU (Ne = 64), which can be confounded by a high degree of population structure due to the selfing nature of pea.

CONCLUSIONS

Our results provided insights into the genetic diversity of the germplasm studied, which can guide plant breeders to actively monitor Ne in successive cycles of breeding to sustain viability of the breeding efforts in the long term.

Genetic insights into crossbred dairy cattle of Pakistan: exploring allele frequency, linkage disequilibrium, and effective population size at a genome-wide scale

Linkage disequilibrium (LD) affects genomic studies accuracy. High-density genotyping platforms identify SNPs across animal genomes, increasing LD evaluation resolution for accurate analysis. This study aimed to evaluate the decay and magnitude of LD in a cohort of 81 crossbred dairy cattle using the GGP_HDv3_C Bead Chip. After quality control, 116,710 Single Nucleotide Polymorphisms (SNPs) across 2520.241 Mb of autosomes were retained. LD extent was assessed between autosomal SNPs within a 10 Mb range using the r2 statistics. LD value declined as inter-marker distance increased. The average r2 value was 0.24 for SNP pairs < 10 kb apart, decreasing to 0.13 for 50-100 kb distances. Minor allele frequency (MAF) and sample size significantly impact LD. Lower MAF thresholds result in smaller r2 values, while higher thresholds show increased r2 values. Additionally, smaller sample sizes exhibit higher average r2 values, especially for larger physical distance intervals (> 50 kb) between SNP pairs. Effective population size and inbreeding coefficient were 150 and 0.028 for the present generation, indicating a decrease in genetic diversity over time. These findings imply that the utilization of high-density SNP panels and customized/breed-specific SNP panels represent a highly favorable approach for conducting genome-wide association studies (GWAS) and implementing genomic selection (GS) in the Bos indicus cattle breeds, whose genomes are still largely unexplored. Furthermore, it is imperative to devise a meticulous breeding strategy tailored to each herd, aiming to enhance desired traits while simultaneously preserving genetic diversity.

The association of gene polymorphisms with milk production and mastitis resistance phenotypic traits in dairy cattle

The aim of this study was to evaluate the association between gene polymorphisms (SNPs) and mastitis indicators and their relationship with milk production profitability in dairy herd.A functional analysis was also performed of five genes containing the studied SNPs and those located close by. DNA was isolated from the hair bulb of 320 dairy cows kept in three herds and SNP-microarray analysis was performed. The data on 299 cows was subjected to final statistical analysis using AI-REML method with one-trait repeatability test-day animal model and pedigree information using the DMU4 package. Five from 35 SNPs significantly associated with mastitis indicators or production traits and located within a gene or no more than 500,000 nucleotides from the gene were selected for the functional and economic analysis. A questionnaire was also developed to collect associated economic data of 219 cows from three herds, such as the value of milk production and direct costs incurred over three years; this allowed the gross margin, direct profitability index and direct costs incurred to produce one liter of milk to be determined, among others. None of the five studied SNPs were related to protein content. The rs110785912(T/A), found near CXCR4, and rs136813430(T/C), located in the TLR4 gene exon, were associated with lnSCC, while rs110455063(C/G), located near IGFI, was associated with milk yield, fat and total solid contents. rs109421300(T/C), associated with fat/protein content ratio, as well as fat and total solid content, is located in the DGAT1 gene intron. rs41587003(A/C), located in the DLG2 gene intron, was associated with lactose content. The economic analysis revealed differences between the variants of the three tested SNPs. The T/C variant of the rs136813430(T/C) SNP was characterized by the highest gross margin, the highest direct profitability index and the lowest costs incurred to produce 1 liter of milk. The T/A variant of rs110785912(T/A) was related to low lnSCC and was characterized by the highest direct profitability index. In turn, the C/C variant of the rs41587003(T/C) was related to the lowest level of lactose and the highest costs of milk production. It appears that rs136813430(T/C) may be the most promising of the tested SNPs for increasing the profitability of milk production. To our knowledge, it is the first effort to assess directly a correlation between the DNA polymorphism and economic output of a dairy enterprise.

Conventional and new-breeding technologies for improving disease resistance in lentil (Lens culinaris Medik)

Lentil, an important cool season food legume, is a rich source of easily digestible protein, folic acid, bio-available iron, and zinc nutrients. Lentil grows mainly as a sole crop in the winter after harvesting rice in South Asia. However, the annual productivity is low due to its slow growth during the early phase, competitive weed infestation, and disease outbreaks during the crop growth period. Disease resistance breeding has been practiced for a long time to enhance resistance to various diseases. Often the sources of resistance are available in wild crop relatives. Thus, wide hybridization and the ovule rescue technique have helped to introgress the resistance trait into cultivated lentils. Besides hybridization, induced mutagenesis contributed immensely in creating variability for disease tolerance, and several disease-resistant mutant lines have been developed. However, to overcome the limitations of traditional breeding approaches, advancement in molecular marker technologies, and genomics has helped to develop disease-resistant and climate-resilient lentil varieties with more precision and efficiency. This review describes types of diseases, disease screening methods, the role of conventional and new breeding technologies in alleviating disease-incurred damage and progress toward making lentil varieties more resilient to disease outbreaks under the shadow of climate change.

Association analysis of germination level cold stress tolerance and candidate gene identification in Upland cotton (Gossypium hirsutum L.)

Cotton originated from ancestors in the Gossypium genus that grew in semi-desert habitats. As a result, it is adversely affected by low temperatures especially during germination and the first weeks of growth. Despite this, there are relatively few molecular studies on cold stress in cotton. This limitation may present a future breeding handicap, as recent years have witnessed increased low temperature damage to cotton production. Cold tolerance is a sustainable approach to obtain good production in case of extreme cold. In the present study, 110 Upland cotton (Gossypium hirsutum) genotypes were evaluated for cold tolerance at the germination stage. We identified vigorous genotypes with cold-related parameters that outperformed the panel's average performance ( x ¯  = 76.9% CG, 83.9% CSI, 167.5 CWVI). Molecular genetic diversity analysis with 101 simple sequence repeat (SSR) markers yielding 416 loci was used to select tolerant genotypes that could be important materials for breeding this trait. A total of 16 marker-cold tolerance trait associations (p < 0.005) were identified with 10 of them having major effects (PVE > 10%). Based on the positions of these markers, candidate genes for cold tolerance in the G. hirsutum genome were identified. Three of these markers (BNL0569, CIR081 and CIR202) are important candidates for use in marker-assisted breeding for cold tolerance because they mapped to genes previously associated with cold tolerance in other plant species such as Arabidopsis thaliana, rice and tomato.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-022-01184-6.

Cis-epistasis at the LPA locus and risk of cardiovascular diseases

AIMS

Coronary artery disease (CAD) has a strong genetic predisposition. However, despite substantial discoveries made by genome-wide association studies (GWAS), a large proportion of heritability awaits identification. Non-additive genetic effects might be responsible for part of the unaccounted genetic variance. Here, we attempted a proof-of-concept study to identify non-additive genetic effects, namely epistatic interactions, associated with CAD.

METHODS AND RESULTS

We tested for epistatic interactions in 10 CAD case-control studies and UK Biobank with focus on 8068 SNPs at 56 loci with known associations with CAD risk. We identified a SNP pair located in cis at the LPA locus, rs1800769 and rs9458001, to be jointly associated with risk for CAD [odds ratio (OR) = 1.37, P = 1.07 × 10-11], peripheral arterial disease (OR = 1.22, P = 2.32 × 10-4), aortic stenosis (OR = 1.47, P = 6.95 × 10-7), hepatic lipoprotein(a) (Lp(a)) transcript levels (beta = 0.39, P = 1.41 × 10-8), and Lp(a) serum levels (beta = 0.58, P = 8.7 × 10-32), while individual SNPs displayed no association. Further exploration of the LPA locus revealed a strong dependency of these associations on a rare variant, rs140570886, that was previously associated with Lp(a) levels. We confirmed increased CAD risk for heterozygous (relative OR = 1.46, P = 9.97 × 10-32) and individuals homozygous for the minor allele (relative OR = 1.77, P = 0.09) of rs140570886. Using forward model selection, we also show that epistatic interactions between rs140570886, rs9458001, and rs1800769 modulate the effects of the rs140570886 risk allele.

CONCLUSIONS

These results demonstrate the feasibility of a large-scale knowledge-based epistasis scan and provide rare evidence of an epistatic interaction in a complex human disease. We were directed to a variant (rs140570886) influencing risk through additive genetic as well as epistatic effects. In summary, this study provides deeper insights into the genetic architecture of a locus important for cardiovascular diseases.

In Silico Tools for Analysis of Single-Nucleotide Polymorphisms in the Bovine Transferrin Gene

Dairy cattle with a high milk yield are susceptible to many infectious diseases, such as mastitis. Subclinical mastitis (SCM) is the most prevalent form of mastitis that predominantly affects animal health, and causes adverse effects on the quality and quantity of milk. In dairy animals, subclinical mastitis often remains undetected, as no gross changes in udder characteristics are visible. In the present study, 135 Holstein Friesian dairy animals were selected and screened as healthy (n = 25) and mastitic (n = 110) based on diagnostic tests such as the California mastitis test, pH, electrical conductivity, and somatic cell count. In this study, the somatic cell count was used as a gold-standard test in differentiating subclinical mastitis animals from healthy ones. The present study was carried out to study polymorphisms in the bovine transferrin gene in cows (with subclinical mastitis and healthy). For the early detection of resistant/or susceptible animals, a useful marker could be provided by the detection of single-nucleotide polymorphisms (SNPs) in the transferrin gene, which are often associated with mammary innate immune response. The sequencing results revealed three nucleotide substitutions: two transversions (230 A > C, 231 C > A) and one transition (294 A > G) in susceptible cows as compared to disease-free subjects. The nucleotide variations at position 230 (GAC > GCA) and 231 (GAC > GCA) were nonsynonymous, and corresponded to an amino acid change from aspartic acid to alanine; whereas at position 294 (GAA > GAG), the mutation was synonymous. In the present study, many in silico tools were taken into consideration to determine the effect of SNPs on protein structure and function. The PROVEAN tool found the amino acid substitution to be neutral and deleterious. PolyPhen-2 revealed the amino acid variations at positions 320 and 321 to most likely be damaging; and at the 341 position, the variations were benign. The I-Mutant and MUpro tools found that the protein stability decreased for nonsynonymous variations. The SIFT tool revealed the protein function was likely to be affected in nonsynonymous variations, with no change in the case of synonymous ones. Phylogenetic analysis of the bovine transferrin gene revealed a close relation of the CA allele with the Bos taurus transferrin, while the G allele was closely related to a cross of Bos indicus × Bos taurus serotransferrins, followed by the Bison bison transferrin. The least relation was shown by both alleles to Capra hircus, Ovis aries, and Bubalus bubalis.

Further insight into the global variability of the OCA2-HERC2 locus for human pigmentation from multiallelic markers

The OCA2-HERC2 locus is responsible for the greatest proportion of eye color variation in humans. Numerous studies extensively described both functional SNPs and associated patterns of variation over this region. The goal of our study is to examine how these haplotype structures and allelic associations vary when highly variable markers such as microsatellites are used. Eleven microsatellites spanning 357 Kb of OCA2-HERC2 genes are analyzed in 3029 individuals from worldwide populations. We found that several markers display large differences in allele frequency (10% to 35% difference) among Europeans, East Asians and Africans. In Europe, the alleles showing increased frequency can also discriminate individuals with (IrisPlex) predicted blue and brown eyes. Distinct haplotypes are identified around the variants C and T of the functional SNP rs12913832 (associated to blue eyes), with linkage disequilibrium r² values significant up to 237 Kb. The haplotype carrying the allele rs12913832 C has high frequency (76%) in blue eye predicted individuals (30% in brown eye predicted individuals), while the haplotype associated to the allele rs12913832 T is restricted to brown eye predicted individuals. Finally, homozygosity values reach levels of 91% near rs12913832. Odds ratios show values of 4.2, 7.4 and 10.4 for four markers around rs12913832 and 7.1 for their core haplotype. Hence, this study provides an example on the informativeness of multiallelic markers that, despite their current limited potential contribution to forensic eye color prediction, supports the use of microsatellites for identifying causing variants showing similar genetic features and history.

Toward Genome-Based Selection in Asian Seabass: What Can We Learn From Other Food Fishes and Farm Animals?

Due to the steadily increasing need for seafood and the plateauing output of fisheries, more fish need to be produced by aquaculture production. In parallel with the improvement of farming methods, elite food fish lines with superior traits for production must be generated by selection programs that utilize cutting-edge tools of genomics. The purpose of this review is to provide a historical overview and status report of a selection program performed on a catadromous predator, the Asian seabass (Lates calcarifer, Bloch 1790) that can change its sex during its lifetime. We describe the practices of wet lab, farm and lab in detail by focusing onto the foundations and achievements of the program. In addition to the approaches used for selection, our review also provides an inventory of genetic/genomic platforms and technologies developed to (i) provide current and future support for the selection process; and (ii) improve our understanding of the biology of the species. Approaches used for the improvement of terrestrial farm animals are used as examples and references, as those processes are far ahead of the ones used in aquaculture and thus they might help those working on fish to select the best possible options and avoid potential pitfalls.

Diversity analysis, runs of homozygosity and genomic inbreeding reveal recent selection in Blanco Orejinegro cattle

Blanco Orejinegro (BON) cattle have 500 years of adaptation to the Colombian tropic, but little is known about their genetic history. Our aim was to estimate levels of linkage disequilibrium (LD), effective population size (Ne), genomic inbreeding for runs of homozygosity (F_ROH ), genomic relation matrix (F_GRM ), excess of homozygotes (F_HOM ) and pedigree information (F_PEDCOMP ) and to characterize the runs of homozygosity (ROH), searching for selection signatures. A total of 419 BON animals were genotyped, 70 with a 150K chip and 349 with a 50K chip. Next, an imputation to 50K was performed, and, after editing, databases of 40K were obtained. The PLINK v1.90 and R programs were used to estimate LD, ROH, F_ROH and F_HOM . The SNeP v1.1 program was used to obtain Ne, and PreGSf90 was used to elaborate the scaled G matrix. The MTDFNRM program was used to estimate F_PEDCOMP . The LD mean as r² at 1 Mb was 0.21 (r²  > 0.30 at a distance of 96.72kb), and Ne was 123 ± 1. A total of 7,652 homozygous segments were obtained, with a mean of 18.35 ± 0.55 ROH/animal. Most of the genome was covered by long ROHs (ROH_{>8 Mb}  = 4.86%), indicating significant recent inbreeding. The average inbreeding coefficient for F_PEDCOM , F_GRM , F_HOM and F_ROH was 4.41%, 4.18%, 5.58% and 6.78%, respectively. The highest correlation was observed between F_HOM and F_ROH (0.95). ROH hotspots/islands were defined using the extreme values of a box plot that was generated, and correspond to QTLs related to milk yield (55.11%), external appearance (13.47%), production (13.30%), reproduction (8.15%), health (5.24%) and meat carcass (4.74%).

A Journey through Genetic Architecture and Predisposition of Coronary Artery Disease

Introduction

To halt the spread of coronary artery disease (CAD), the number one killer in the world, requires primary prevention. Fifty percent of all Americans are expected to experience a cardiac event; the challenge is identifying those at risk. 40 to 60% of predisposition to CAD is genetic. The first genetic risk variant, 9p21, was discovered in 2007. Genome-Wide Association Studies has since discovered hundreds of genetic risk variants. The genetic burden for CAD can be expressed as a single number, Genetic Risk Score (GRS). Assessment of GRS to risk stratify for CAD was superior to conventional risk factors in several large clinical trials assessing statin therapy, and more recently in a population of nearly 500,000 (UK Biobank). Studies were performed based on prospective genetic risk stratification for CAD. These studies showed that a favorable lifestyle was associated with a 46% reduction in cardiac events and programmed exercise, a 50% reduction in cardiac events. Genetic risk score is superior to conventional risk factors, and is markedly attenuated by lifestyle changes and drug therapy. Genetic risk can be determined at birth or any time thereafter.

Conclusion

Utilizing the GRS to risk stratify young, asymptomatic individuals could provide a paradigm shift in the primary prevention of CAD and significantly halt its spread.

Temporal Variation in Genetic Composition of Migratory Helicoverpa Zea in Peripheral Populations

Migrant populations of Helicoverpa zea (Boddie) captured during 2002, 2005, 2016, and 2018 from Landisville and Rock Springs in Pennsylvania, USA were genotyped using 85 single nucleotide polymorphism (SNP) markers. Samples (n = 702) genotyped were divided into 16 putative populations based on collection time and site. Fixation indices (F-statistics), analysis of molecular variance, and discriminant analysis of principal components were used to examine within and among population genetic variation. The observed and expected heterozygosity in putative populations ranged from 0.317-0.418 and 0.320-0.359, respectively. Broad range of FST (0.0-0.2742) and FIS (0.0-0.2330) values indicated different genotype frequencies between and within the populations, respectively. High genetic diversity within and low genetic differentiation between populations was found in 2002 and 2005. Interestingly, high genetic differentiation between populations from two collection sites observed in 2018 populations was not evident in within-site comparisons of putative populations collected on different dates during the season. The shift of H. zea population genetic makeup in 2018 may be influenced by multiple biotic and abiotic factors including tropical storms. Continued assessment of these peripheral populations of H. zea will be needed to assess the impacts of genetic changes on pest control and resistance management tactics.

Estimation of Linkage Disequilibrium and Effective Population Size in Three Italian Autochthonous Beef Breeds

The objective was to investigate the pattern of linkage disequilibrium (LD) in three local beef breeds, namely, Calvana (n = 174), Mucca Pisana (n = 270), and Pontremolese (n = 44). As a control group, samples of the Italian Limousin breed (n = 100) were used. All cattle were genotyped with the GeneSeek GGP-LDv4 33k SNP chip containing 30,111 SNPs. The genotype quality control for each breed was conducted separately, and SNPs with call rate < 0.95 and minor allele frequency (MAF) > 1% were used for the analysis. LD extent was estimated in PLINK v1.9 using the squared correlation between pairs of loci (r²) across autosomes. Moreover, r² values were used to calculate historical and contemporary effective population size (N_e) in each breed. Average r² was similar in Calvana and Mucca Pisana (~0.14) and higher in Pontremolese (0.17); Limousin presented the lowest LD extent (0.07). LD up to 0.11-0.15 was persistent in the local breeds up to 0.75 Mbp, while in Limousin, it showed a more rapid decay. Variation of different LD levels across autosomes was observed in all the breeds. The results demonstrated a rapid decrease in N_e across generations for local breeds, and the contemporary population size observed in the local breeds, ranging from 41.7 in Calvana to 17 in Pontremolese, underlined the demographic alarming situation.

MaRe: Processing Big Data with application containers on Apache Spark

BACKGROUND

Life science is increasingly driven by Big Data analytics, and the MapReduce programming model has been proven successful for data-intensive analyses. However, current MapReduce frameworks offer poor support for reusing existing processing tools in bioinformatics pipelines. Furthermore, these frameworks do not have native support for application containers, which are becoming popular in scientific data processing.

RESULTS

Here we present MaRe, an open source programming library that introduces support for Docker containers in Apache Spark. Apache Spark and Docker are the MapReduce framework and container engine that have collected the largest open source community; thus, MaRe provides interoperability with the cutting-edge software ecosystem. We demonstrate MaRe on 2 data-intensive applications in life science, showing ease of use and scalability.

CONCLUSIONS

MaRe enables scalable data-intensive processing in life science with Apache Spark and application containers. When compared with current best practices, which involve the use of workflow systems, MaRe has the advantage of providing data locality, ingestion from heterogeneous storage systems, and interactive processing. MaRe is generally applicable and available as open source software.

The nonlinear structure of linkage disequilibrium

The allele frequency dependence of the ranges of all measures of linkage disequilibrium is well-known. The maximum values of commonly used parameters such as r² and D vary depending on the allele frequencies at each locus. However, though this phenomenon is recognized and accounted for in many studies, the comprehensive mathematical framework underlying the limits of linkage disequilibrium measures at various frequency combinations is often heuristic or empirical. Here, it is demonstrated that underlying this behavior is the fundamental shift between linear and nonlinear dependence in the linkage disequilibrium structure between loci. The proportion of linear and nonlinear dependence can be estimated and it demonstrates how even the same values of r² can have different implications for the nature of the overall dependence. One result of this is the value of D^', when defined as only a positive number, has a minimum value of |r|. Understanding this dependence is crucial to making correct inferences about the relationships between two loci in linkage disequilibrium.

On the Extent of Linkage Disequilibrium in the Genome of Farm Animals

Given the importance of linkage disequilibrium (LD) in gene mapping and evolutionary inferences, I characterize in this review the pattern of LD and discuss the influence of human intervention during domestication, breed establishment, and subsequent genetic improvement on shaping the genome of livestock species. To this end, I summarize data on the profile of LD based on array genotypes vs. sequencing data in cattle and chicken, two major livestock species, and compare to the human case. This comparison provides insights into the real dimension of the pairwise allelic correlation and haplo-block structuring. The dependency of LD on allelic frequency is pictured and a recently introduced metric for moderating it is outlined. In the context of the contact farm animals had with human, the impact of genetic forces including admixture, mutation, recombination rate, selection, and effective population size on LD is discussed. The review further highlights the interplay of LD with runs of homozygosity and concludes with the operational implications of the widely used association and selection mapping studies in relation to LD.

Do Genomic Factors Play a Role in Diabetic Retinopathy?

Although there is strong clinical evidence that the control of blood glucose, blood pressure, and lipid level can prevent and slow down the progression of diabetic retinopathy (DR) as shown by landmark clinical trials, it has been shown that these factors only account for 10% of the risk for developing this disease. This suggests that other factors, such as genetics, may play a role in the development and progression of DR. Clinical evidence shows that some diabetics, despite the long duration of their diabetes (25 years or more) do not show any sign of DR or show minimal non-proliferative diabetic retinopathy (NPDR). Similarly, not all diabetics develop proliferative diabetic retinopathy (PDR). So far, linkage analysis, candidate gene studies, and genome-wide association studies (GWAS) have not produced any statistically significant results. We recently initiated a genomics study, the Diabetic Retinopathy Genetics (DRGen) Study, to examine the contribution of rare and common variants in the development of different phenotypes of DR, as well as their responsiveness to anti-VEGF treatment in diabetic macular edema (DME). Our preliminary findings reveal a novel set of genetic variants involved in the angiogenesis and inflammatory pathways that contribute to DR progression or protection. Further investigation of variants can help to develop novel biomarkers and lead to new therapeutic targets in DR.

Application of Indirect Linkage Analysis for Carrier Detection of Hemophilia A in Kurdistan Region of Iraq: Usefulness of Intron 18 BclI T>A, Intron 19 HindIII C>T, and IVS7 nt27 G>A Markers

Hemophilia A (HA) is the most common congenital X-linked coagulopathy caused by mutations in the factor VIII gene. One in 5000 to 10 000 male persons worldwide suffer from HA. It is the archetype of high-cost, low-volume disease. Therefore, identification of carriers is crucial to avoid the birth of affected males. Tracking of the defective X chromosome through indirect linkage analysis represents the most practical method for screening for carriers in developing countries. In this study, 227 individuals from 41 families with HA and 100 normal participants were recruited from the Kurdistan region of Iraq and evaluated for intron 18 BclI, intron 19 HindIII, and IVS7 nt 27 markers by polymerase chain reaction restriction fragment length polymorphism and direct sequencing. Among the studied women, 49%, 42%, and 14% were discovered to be heterozygous for BclI, HindIII, and IVS7 markers, respectively. Using BclI, HindIII, and IVS7 markers, 56%, 46%, and 17% of the families were informative, respectively. The combined informativity of these polymorphic sites reaches 66%. The current study illustrates the effectiveness of the BclI and HindIII markers for the diagnosis of HA carriers among the Iraqi Kurdish population.

Epidermal growth factor receptor rs17337023 polymorphism in hypertensive gestational diabetic women: A pilot study

BACKGROUND

Women with gestational diabetes mellitus have an increased risk of developing gestational hypertension, which can increase fetal and neonatal morbidity and mortality. In the past decade, single nucleotide polymorphisms in several genes have been identified as risk factors for development of gestational hypertension. The epidermal growth factor receptor activates tyrosine kinase mediated blood vessels contractility; and inflammatory cascades. Abnormalities in these mechanism are known to contribute towards hypertension. It is thus plausible that polymorphisms in the epidermal growth factor receptor gene would be associated with the development of hypertension in women with gestational diabetes.

AIM

To determine whether the epidermal growth factor receptor rs17337023 SNP is associated with the occurrence of hypertension in gestational diabetic women.

METHODS

This pilot case-control study was conducted at two tertiary care hospitals in Karachi, from January 2017-August 2018. Two hundred and two women at 28 week of gestation with gestational diabetes were recruited and classified into normotensive (n = 80) and hypertensive (n = 122) groups. Their blood samples were genotyped for epidermal growth factor receptor polymorphism rs17337023 using tetra-ARMS polymerase chain reaction. Descriptive analysis was applied on baseline data. Polymorphism data was analyzed for genotype and allele frequency determination using chi-squared statistics. In all cases, a P value of < 0.05 was considered significant.

RESULTS

Subjects were age-matched and thus no difference was observed in relation to age of the study subjects (P >0.05). Body fat percentage was significantly higher in hypertensive females as compared to normotensive subjects (35.138 ± 4.29 Case vs 25.01 ± 8.28 Control; P < 0.05). Similarly, systolic and diastolic blood pressures among groups were significantly higher in hypertensive group than the normotensive group (P < 0.05). Overall epidermal growth factor receptor rs17337023 polymorphism genotype frequency was similar in both groups, with the heterozygous AT genotype (56 in Case vs 48 in Control; P = 0. 079) showing predominance in both groups. Furthermore, the odds ratio for A allele was 1.282 (P = 0.219) and for T allele was 0.780 (P = 0.221) in this study.

CONCLUSION

This pilot study indicates that polymorphisms in rs17337023 may not be involved in the pathophysiology of gestational hypertension in gestational diabetes via inflammatory cascade mechanism. Further large-scale studies should explore polymorphism in epidermal growth factor receptor and other genes in this regard.

A local score approach improves GWAS resolution and detects minor QTL: application to Medicago truncatula quantitative disease resistance to multiple Aphanomyces euteiches isolates

Quantitative trait loci (QTL) with small effects, which are pervasive in quantitative phenotypic variation, are difficult to detect in genome-wide association studies (GWAS). To improve their detection, we propose to use a local score approach that accounts for the surrounding signal due to linkage disequilibrium, by accumulating association signals from contiguous single markers. Simulations revealed that, in a GWAS context with high marker density, the local score approach outperforms single SNP p-value-based tests for detecting minor QTL (heritability of 5-10%) and is competitive with regard to alternative methods, which also aggregate p-values. Using more than five million SNPs, this approach was applied to identify loci involved in Quantitative Disease Resistance (QDR) to different isolates of the plant root rot pathogen Aphanomyces euteiches, from a GWAS performed on a collection of 174 accessions of the model legume Medicago truncatula. We refined the position of a previously reported major locus, underlying MYB/NB-ARC/tyrosine kinase candidate genes conferring resistance to two closely related A. euteiches isolates belonging to pea pathotype I. We also discovered a diversity of minor resistance QTL, not detected using p-value-based tests, some of which being putatively shared in response to pea (pathotype I and III) and/or alfalfa (race 1 and 2) isolates. Candidate genes underlying these QTL suggest pathogen effector recognition and plant proteasome as key functions associated with M. truncatula resistance to A. euteiches. GWAS on any organism can benefit from the local score approach to uncover many weak-effect QTL.

Cross-Cancer Pleiotropic Associations with Lung Cancer Risk in African Americans

BACKGROUND

Identifying genetic variants with pleiotropic associations across multiple cancers can reveal shared biologic pathways. Prior pleiotropic studies have primarily focused on European-descent individuals. Yet population-specific genetic variation can occur, and potential pleiotropic associations among diverse racial/ethnic populations could be missed. We examined cross-cancer pleiotropic associations with lung cancer risk in African Americans.

METHODS

We conducted a pleiotropic analysis among 1,410 African American lung cancer cases and 2,843 controls. We examined 36,958 variants previously associated (or in linkage disequilibrium) with cancer in prior genome-wide association studies. Logistic regression analyses were conducted, adjusting for age, sex, global ancestry, study site, and smoking status.

RESULTS

We identified three novel genomic regions significantly associated (FDR-corrected P <0.10) with lung cancer risk (rs336958 on 5q14.3, rs7186207 on 16q22.2, and rs11658063 on 17q12). On chromosome16q22.2, rs7186207 was significantly associated with reduced risk [OR = 0.43; 95% confidence interval (CI), 0.73-0.89], and functional annotation using GTEx showed rs7186207 modifies DHODH gene expression. The minor allele at rs336958 on 5q14.3 was associated with increased lung cancer risk (OR = 1.47; 95% CI, 1.22-1.78), whereas the minor allele at rs11658063 on 17q12 was associated with reduced risk (OR = 0.80; 95% CI, 0.72-0.90).

CONCLUSIONS

We identified novel associations on chromosomes 5q14.3, 16q22.2, and 17q12, which contain HNF1B, DHODH, and HAPLN1 genes, respectively. SNPs within these regions have been previously associated with multiple cancers. This is the first study to examine cross-cancer pleiotropic associations for lung cancer in African Americans.

IMPACT

Our findings demonstrate novel cross-cancer pleiotropic associations with lung cancer risk in African Americans.

Parallel adaptation of rabbit populations to myxoma virus

In the 1950s the myxoma virus was released into European rabbit populations in Australia and Europe, decimating populations and resulting in the rapid evolution of resistance. We investigated the genetic basis of resistance by comparing the exomes of rabbits collected before and after the pandemic. We found a strong pattern of parallel evolution, with selection on standing genetic variation favoring the same alleles in Australia, France, and the United Kingdom. Many of these changes occurred in immunity-related genes, supporting a polygenic basis of resistance. We experimentally validated the role of several genes in viral replication and showed that selection acting on an interferon protein has increased the protein's antiviral effect.

Identification of Specific Nuclear Genetic Loci and Genes That Interact With the Mitochondrial Genome and Contribute to Fecundity in Caenorhabditis elegans

Previous studies have found that fecundity is a multigenic trait regulated, in part, by mitochondrial-nuclear (mit-n) genetic interactions. However, the identification of specific nuclear genetic loci or genes interacting with the mitochondrial genome and contributing to the quantitative trait fecundity is an unsolved issue. Here, a panel of recombinant inbred advanced intercrossed lines (RIAILs), established from a cross between the N2 and CB4856 strains of C. elegans, were used to characterize the underlying genetic basis of mit-n genetic interactions related to fecundity. Sixty-seven single nucleotide polymorphisms (SNPs) were identified by association mapping to be linked with fecundity among 115 SNPs linked to mitotype. This indicated significant epistatic effects between nuclear and mitochondria genetics on fecundity. In addition, two specific nuclear genetic loci interacting with the mitochondrial genome and contributing to fecundity were identified. A significant reduction in fecundity was observed in the RIAILs that carried CB4856 mitochondria and a N2 genotype at locus 1 or a CB4856 genotype at locus 2 relative to the wild-type strains. Then, a hybrid strain (CNC10) was established, which was bred as homoplasmic for the CB4856 mtDNA genome and N2 genotype at locus 1 in the CB4856 nuclear background. The mean fecundity of CNC10 was half the fecundity of the control strain. Several functional characteristics of the mitochondria in CNC10 were also influenced by mit-n interactions. Overall, experimental evidence was presented that specific nuclear genetic loci or genes have interactions with the mitochondrial genome and are associated with fecundity. In total, 18 genes were identified using integrative approaches to have interactions with the mitochondrial genome and to contribute to fecundity.

Population Genomic Structure and Genome-Wide Linkage Disequilibrium in Farmed Atlantic Salmon (Salmo salar L.) Using Dense SNP Genotypes

Chilean Farmed Atlantic salmon (Salmo salar) populations were established with individuals of both European and North American origins. These populations are expected to be highly genetically differentiated due to evolutionary history and poor gene flow between ancestral populations from different continents. The extent and decay of linkage disequilibrium (LD) among single nucleotide polymorphism (SNP) impacts the implementation of genome-wide association studies and genomic selection and provides relevant information about demographic processes of fish populations. We assessed the population structure and characterized the extent and decay of LD in three Chilean commercial populations of Atlantic salmon with North American (NAM), Scottish (SCO), and Norwegian (NOR) origin. A total of 123 animals were genotyped using a 159 K SNP Axiom® myDesignTM Genotyping Array. A total of 32 K SNP markers, representing the common SNPs along the three populations after quality control were used. The principal component analysis explained 78.9% of the genetic diversity between populations, clearly discriminating between populations of North American and European origin, and also between European populations. NAM had the lowest effective population size, followed by SCO and NOR. Large differences in the LD decay were observed between populations of North American and European origin. An r 2 threshold of 0.2 was estimated for marker pairs separated by 7,800, 64, and 50 kb in the NAM, SCO, and NOR populations, respectively. In this study we show that this SNP panel can be used to detect association between markers and traits of interests and also to capture high-resolution information for genome-enabled predictions. Also, we suggest the feasibility to achieve similar prediction accuracies using a smaller SNP data set for the NAM population, compared with samples with European origin which would need a higher density SNP array.

Variations on a Chip: Technologies of Difference in Human Genetics Research

In this article we examine the history of the production of microarray technologies and their role in constructing and operationalizing views of human genetic difference in contemporary genomics. Rather than the "turn to difference" emerging as a post-Human Genome Project (HGP) phenomenon, interest in individual and group differences was a central, motivating concept in human genetics throughout the twentieth century. This interest was entwined with efforts to develop polymorphic "genetic markers" for studying human traits and diseases. We trace the technological, methodological and conceptual strategies in the late twentieth century that established single nucleotide polymorphisms (SNPs) as key focal points for locating difference in the genome. By embedding SNPs in microarrays, researchers created a technology that they used to catalog and assess human genetic variation. In the process of making genetic markers and array-based technologies to track variation, scientists also made commitments to ways of describing, cataloging and "knowing" human genetic differences that refracted difference through a continental geographic lens. We show how difference came to matter in both senses of the term: difference was made salient to, and inscribed on, genetic matter(s), as a result of the decisions, assessments and choices of collaborative and hybrid research collectives in medical genomics research.

Microhaplotypes in forensic genetics

Microhaplotype loci (microhaps, MHs) are a novel type of molecular marker of less than 300 nucleotides, defined by two or more closely linked SNPs associated in multiple allelic combinations. The value of these markers is enhanced by massively parallel sequencing (MPS), which allows the sequencing of both parental haplotypes at each of the many multiplexed loci. This review describes the features of these multi-SNP markers and documents their value in forensic genetics, focusing on individualization, biogeographic ancestry inference, and mixture deconvolution. Foreseeable applications also include missing person identification, relationship testing, and medical diagnostic applications. The technique is not restricted to humans.

GWAS by GBLUP: Single and Multimarker EMMAX and Bayes Factors, with an Example in Detection of a Major Gene for Horse Gait

Bayesian models for genomic prediction and association mapping are being increasingly used in genetics analysis of quantitative traits. Given a point estimate of variance components, the popular methods SNP-BLUP and GBLUP result in joint estimates of the effect of all markers on the analyzed trait; single and multiple marker frequentist tests (EMMAX) can be constructed from these estimates. Indeed, BLUP methods can be seen simultaneously as Bayesian or frequentist methods. So far there is no formal method to produce Bayesian statistics from GBLUP. Here we show that the Bayes Factor, a commonly admitted statistical procedure, can be computed as the ratio of two normal densities: the first, of the estimate of the marker effect over its posterior standard deviation; the second of the null hypothesis (a value of 0 over the prior standard deviation). We extend the BF to pool evidence from several markers and of several traits. A real data set that we analyze, with ours and existing methods, analyzes 630 horses genotyped for 41711 polymorphic SNPs for the trait “outcome of the qualification test” (which addresses gait, or ambling, of horses) for which a known major gene exists. In the horse data, single marker EMMAX shows a significant effect at the right place at Bonferroni level. The BF points to the same location although with low numerical values. The strength of evidence combining information from several consecutive markers increases using the BF and decreases using EMMAX, which comes from a fundamental difference in the Bayesian and frequentist schools of hypothesis testing. We conclude that our BF method complements frequentist EMMAX analyses because it provides a better pooling of evidence across markers, although its use for primary detection is unclear due to the lack of defined rejection thresholds.

Linkage disequilibrium levels and allele frequency distribution in Blanco Orejinegro and Romosinuano Creole cattle using medium density SNP chip data

The linkage disequilibrium (LD) between molecular markers affects the accuracy of genome-wide association studies and genomic selection application. High-density genotyping platforms allow identifying the genotype of thousands of single nucleotide polymorphisms (SNPs) distributed throughout the animal genomes, which increases the resolution of LD evaluations. This study evaluated the distribution of minor allele frequencies (MAF) and the level of LD in the Colombian Creole cattle breeds Blanco Orejinegro (BON) and Romosinuano (ROMO) using a medium density SNP panel (BovineSNP50K_v2). The LD decay in these breeds was lower than those reported for other taurine breeds, achieving optimal LD values (r² ≥ 0.3) up to a distance of 70 kb in BON and 100 kb in ROMO, which is possibly associated with the conservation status of these cattle populations and their effective population size. The average MAF for both breeds was 0.27 ± 0.14 with a higher SNP proportion having high MAF values (≥ 0.3). The LD levels and distribution of allele frequencies found in this study suggest that it is possible to have adequate coverage throughout the genome of these breeds using the BovineSNP50K_v2, capturing the effect of most QTL related with productive traits, and ensuring an adequate prediction capacity in genomic analysis.

Insights into population genetics and evolution of polyploids and their ancestors

We have developed the first comprehensive simulator for polyploid genomes (PolySim) and demonstrated its value by performing large-scale simulations to examine the effect of different population parameters on the evolution of polyploids. PolySim is unlimited in terms of ploidy, population size or number of simulated loci. Our process considered the evolution of polyploids from diploid ancestors, polysomic inheritance, inbreeding, recombination rate change in polyploids and gene flow from lower to higher ploidies. We compared the number of segregating single nucleotide polymorphisms, minor allele frequency, heterozygosity, R2 and average kinship relatedness between different simulated scenarios, and to real data from polyploid species. As expected, allotetraploid populations showed no difference from their ancestral diploids when population size remained constant and there was no gene flow or multivalent (MV) pairing between subgenomes. Autotetraploid populations showed significant differences from their ancestors for most parameters and diverged from their ancestral populations faster than allotetraploids. Autotetraploids can have significantly higher heterozygosity, relatedness and extended linkage disequilibrium compared with allotetraploids. Interestingly, autotetraploids were more sensitive to increasing selfing rate and decreasing population size. MV formation can homogenize allotetraploid subgenomes, but this homogenization requires a higher MV rate than previously proposed. Our results can be considered as the first building block to understand polyploid population evolutionary dynamics. PolySim can be used to simulate a wide variety of polyploid organisms that mimic empirical populations, which, in combination with quantitative genetics tools, can be used to investigate the power of genomewide association, genomic selection or breeding programme designs in these species.

Association mapping unveils favorable alleles for grain iron and zinc concentrations in lentil (Lens culinaris subsp. culinaris)

Lentil is a major cool-season grain legume grown in South Asia, West Asia, and North Africa. Populations in developing countries of these regions have micronutrient deficiencies; therefore, breeding programs should focus more on improving the micronutrient content of food. In the present study, a set of 96 diverse germplasm lines were evaluated at three different locations in India to examine the variation in iron (Fe) and zinc (Zn) concentration and identify simple sequence repeat (SSR) markers that associate with the genetic variation. The genetic variation among genotypes of the association mapping (AM) panel was characterized using a genetic distance-based and a general model-based clustering method. The model-based analysis identified six subpopulations, which satisfactorily explained the genetic structure of the AM panel. AM analysis identified three SSRs (PBALC 13, PBALC 206, and GLLC 563) associated with grain Fe concentration explaining 9% to 11% of phenotypic variation and four SSRs (PBALC 353, SSR 317-1, PLC 62, and PBALC 217) were associated with grain Zn concentration explaining 14%, to 21% of phenotypic variation. These identified SSRs exhibited consistent performance across locations. These candidate SSRs can be used in marker-assisted genetic improvement for developing Fe and Zn fortified lentil varieties. Favorable alleles and promising genotypes identified in this study can be utilized for lentil biofortification.

The analysis of APOL1 genetic variation and haplotype diversity provided by 1000 Genomes project

Background

The APOL1 gene variants has been shown to be associated with an increased risk of multiple kinds of diseases, particularly in African Americans, but not in Caucasians and Asians. In this study, we explored the single nucleotide polymorphism (SNP) and haplotype diversity of APOL1 gene in different races provided by 1000 Genomes project.

Methods

Variants of APOL1 gene in 1000 Genome Project were obtained and SNPs located in the regulatory region or coding region were selected for genetic variation analysis. Total 2504 individuals from 26 populations were classified as four groups that included Africa, Europe, Asia and Admixed populations. Tag SNPs were selected to evaluate the haplotype diversities in the four populations by HaploStats software.

Results

APOL1 gene was surrounded by some of the most polymorphic genes in the human genome, variation of APOL1 gene was common, with up to 613 SNP (1000 Genome Project reported) and 99 of them (16.2%) with MAF ≥ 1%. There were 79 SNPs in the URR and 92 SNPs in 3’UTR. Total 12 SNPs in URR and 24 SNPs in 3’UTR were considered as common variants with MAF ≥ 1%. It is worth noting that URR-1 was presents lower frequencies in European populations, while other three haplotypes taken an opposite pattern; 3’UTR presents several high-frequency variation sites in a short segment, and the differences of its haplotypes among different population were significant (P < 0.01), UTR-1 and UTR-5 presented much higher frequency in African population, while UTR-2, UTR-3 and UTR-4 were much lower. APOL1 coding region showed that two SNP of G1 with higher frequency are actually pull down the haplotype H-1 frequency when considering all populations pooled together, and the diversity among the four populations be widen by the G1 two mutation (P₁ = 3.33E-4 vs P₂ = 3.61E-30).

Conclusions

The distributions of APOL1 gene variants and haplotypes were significantly different among the different populations, in either regulatory or coding regions. It could provide clues for the future genetic study of APOL1 related diseases.

Electronic supplementary material

The online version of this article (doi:10.1186/s12882-017-0675-6) contains supplementary material, which is available to authorized users.

The Bos taurus-Bos indicus balance in fertility and milk related genes

Numerical approaches to high-density single nucleotide polymorphism (SNP) data are often employed independently to address individual questions. We linked independent approaches in a bioinformatics pipeline for further insight. The pipeline driven by heterozygosity and Hardy-Weinberg equilibrium (HWE) analyses was applied to characterize Bos taurus and Bos indicus ancestry. We infer a gene co-heterozygosity network that regulates bovine fertility, from data on 18,363 cattle with genotypes for 729,068 SNP. Hierarchical clustering separated populations according to Bos taurus and Bos indicus ancestry. The weights of the first principal component were subjected to Normal mixture modelling allowing the estimation of a gene’s contribution to the Bos taurus-Bos indicus axis. We used deviation from HWE, contribution to Bos indicus content and association to fertility traits to select 1,284 genes. With this set, we developed a co-heterozygosity network where the group of genes annotated as fertility-related had significantly higher Bos indicus content compared to other functional classes of genes, while the group of genes associated with milk production had significantly higher Bos taurus content. The network analysis resulted in capturing novel gene associations of relevance to bovine domestication events. We report transcription factors that are likely to regulate genes associated with cattle domestication and tropical adaptation. Our pipeline can be generalized to any scenarios where population structure requires scrutiny at the molecular level, particularly in the presence of a priori set of genes known to impact a phenotype of evolutionary interest such as fertility.

Resampling-based tests for Lasso in genome-wide association studies

Background

Genome-wide association studies involve detecting association between millions of genetic variants and a trait, which typically use univariate regression to test association between each single variant and the phenotype. Alternatively, Lasso penalized regression allows one to jointly model the relationship between all genetic variants and the phenotype. However, it is unclear how to best conduct inference on the individual Lasso coefficients, especially in high-dimensional settings.

Methods

We consider six methods for testing the Lasso coefficients: two permutation (Lasso-Ayers, Lasso-PL) and one analytic approach (Lasso-AL) to select the penalty parameter for type-1-error control, residual bootstrap (Lasso-RB), modified residual bootstrap (Lasso-MRB), and a permutation test (Lasso-PT). Methods are compared via simulations and application to the Minnesota Center for Twins and Family Study.

Results

We show that for finite sample sizes with increasing number of null predictors, Lasso-RB, Lasso-MRB, and Lasso-PT fail to be viable methods of inference. However, Lasso-PL and Lasso-AL remain fast and powerful tools for conducting inference with the Lasso, even in high-dimensions.

Conclusion

Our results suggest that the proposed permutation selection procedure (Lasso-PL) and the analytic selection method (Lasso-AL) are fast and powerful alternatives to the standard univariate analysis in genome-wide association studies.

Linkage disequilibrium, persistence of phase, and effective population size in Spanish local beef cattle breeds assessed through a high-density single nucleotide polymorphism chip

Linkage disequilibrium (LD) and persistence of phase are fundamental approaches for exploring the genetic basis of economically important traits in cattle, including the identification of QTL for genomic selection and the estimation of effective population size () to determine the size of the training populations. In this study, we have used the Illumina BovineHD chip in 168 trios of 7 Spanish beef cattle breeds to obtain an overview of the magnitude of LD and the persistence of LD phase through the physical distance between markers. Also, we estimated the time of divergence based on the persistence of the LD phase and calculated past from LD estimates using different alternatives to define the recombination rate. Estimates of average (as a measure of LD) for adjacent markers were close to 0.52 in the 7 breeds and decreased with the distance between markers, although in long distances, some LD still remained (0.07 and 0.05 for markers 200 kb and 1 Mb apart, respectively). A panel with a lower boundary of 38,000 SNP would be necessary to launch a successful within-breed genomic selection program. Persistence of phase, measured as the pairwise correlations between estimates of in 2 breeds at short distances (10 kb), was in the 0.89 to 0.94 range and decreased from 0.33 to 0.52 to a range of 0.01 to 0.08 when marker distance increased from 200 kb to 1 Mb, respectively. The magnitude of the persistence of phase between the Spanish beef breeds was similar to those found in dairy breeds. For across-breed genomic selection, the size of the SNP panels must be in the range of 50,000 to 83,000 SNP. Estimates of past showed values ranging from 26 to 31 for 1 generation ago in all breeds. The divergence among breeds occurred between 129 and 207 generations ago. The results of this study are relevant for the future implementation of within- and across-breed genomic selection programs in the Spanish beef cattle populations. Our results suggest that a reduced subset of the SNP panel would be enough to achieve an adequate precision of the genomic predictions.

Association of CD147 genetic polymorphisms with carotid atherosclerotic plaques in a Han Chinese population with cerebral infarction

INTRODUCTION

Given the important role of CD147 in the development of atherosclerosis, we speculated that CD147 genetic polymorphisms might influence the formation of carotid atherosclerotic plaques. The study was to investigate the association between CD147 gene polymorphisms and susceptibility to carotid atherosclerotic plaques in individuals with cerebral infarction (CI).

METHODS

Eight SNPs in the regulatory and coding regions of the CD147 gene were examined using polymerase chain reaction-ligase detection reaction (PCR-LDR) in DNA samples from 732 Chinese patients with CI, divided into a carotid plaque group (n=475) and a non-carotid plaque group (n=257).

RESULTS

Significant differences were found in the genotypes and allele frequencies of the rs4919862 SNP between the carotid plaque and non-carotid plaque groups of CI patients (P<0.05), while the frequencies of the C allele and the CC genotype in the non-carotid plaque group were significantly lower than those in the carotid plaque group, and the frequencies of the T allele in the non-carotid plaque group were significantly higher than those in the carotid plaque group (P<0.05). In addition, there was strong linkage disequilibrium among the rs4919862, rs8637 and rs8259 sites. In a haplotype analysis, the occurrence rate of the haplotype GATGCAGC was 2.095 times higher in the carotid plaque group than in the non-carotid plaque group (P<0.05).

CONCLUSION

These results showed that the rs4919862 SNP of CD147 was closely associated with carotid atherosclerotic plaques formation. Thus, polymorphisms of the CD147 gene may be related to the tendency for carotid atherosclerotic plaques.

The Decay of Disease Association with Declining Linkage Disequilibrium: A Fine Mapping Theorem

Several important and fundamental aspects of disease genetics models have yet to be described. One such property is the relationship of disease association statistics at a marker site closely linked to a disease causing site. A complete description of this two-locus system is of particular importance to experimental efforts to fine map association signals for complex diseases. Here, we present a simple relationship between disease association statistics and the decline of linkage disequilibrium from a causal site. Specifically, the ratio of Chi-square disease association statistics at a marker site and causal site is equivalent to the standard measure of pairwise linkage disequilibrium, r². A complete derivation of this relationship from a general disease model is shown. Quite interestingly, this relationship holds across all modes of inheritance. Extensive Monte Carlo simulations using a disease genetics model applied to chromosomes subjected to a standard model of recombination are employed to better understand the variation around this fine mapping theorem due to sampling effects. We also use this relationship to provide a framework for estimating properties of a non-interrogated causal site using data at closely linked markers. Lastly, we apply this way of examining association data from high-density genotyping in a large, publicly-available data set investigating extreme BMI. We anticipate that understanding the patterns of disease association decay with declining linkage disequilibrium from a causal site will enable more powerful fine mapping methods and provide new avenues for identifying causal sites/genes from fine-mapping studies.

Colocalization of GWAS and eQTL Signals Detects Target Genes

The vast majority of genome-wide association study (GWAS) risk loci fall in non-coding regions of the genome. One possible hypothesis is that these GWAS risk loci alter the individual's disease risk through their effect on gene expression in different tissues. In order to understand the mechanisms driving a GWAS risk locus, it is helpful to determine which gene is affected in specific tissue types. For example, the relevant gene and tissue could play a role in the disease mechanism if the same variant responsible for a GWAS locus also affects gene expression. Identifying whether or not the same variant is causal in both GWASs and expression quantitative trail locus (eQTL) studies is challenging because of the uncertainty induced by linkage disequilibrium and the fact that some loci harbor multiple causal variants. However, current methods that address this problem assume that each locus contains a single causal variant. In this paper, we present eCAVIAR, a probabilistic method that has several key advantages over existing methods. First, our method can account for more than one causal variant in any given locus. Second, it can leverage summary statistics without accessing the individual genotype data. We use both simulated and real datasets to demonstrate the utility of our method. Using publicly available eQTL data on 45 different tissues, we demonstrate that eCAVIAR can prioritize likely relevant tissues and target genes for a set of glucose- and insulin-related trait loci.

Combined effects of multiple linked loci on pairwise sibling tests

The advanced multiplex STR system, PowerPlex Fusion, includes four linked locus pairs. The conventional Identifiler system has one pair of linked loci. Therefore, sibling tests conducted using the advanced system might be more affected by linkage than those conducted using the conventional system. This study simulated single and combined effects of the four linked locus pairs on pairwise sibling tests. Simulated genotypes of 100,000 pairs of full siblings and nonrelatives were constructed according to allele frequencies of the Japanese population. The single linkage effect was evaluated for simulated genotype data by calculating both the likelihood ratio accounting for the linkage between two loci and the likelihood ratio ignoring the linkage. The combined effect was obtained by multiplication of the respective single effects. Furthermore, we investigated the possibility that ignoring the linkage affects subject classification by introducing a scale of the likelihood ratio into sibling tests. The single effect in the Identifiler analysis was 0.645-1.746 times if the linkage was ignored. Overestimations and underestimations were predictable from the identical-by-state status at two linked loci. The combined effect in the PowerPlex Fusion analysis was 0.217-7.390 times. Ignoring the linkage rarely caused a false conclusive or inconclusive result, even from PowerPlex Fusion analysis. Application of the advanced system improved sibling tests considerably. The additional examined loci were more beneficial than the adverse effect of the linkage derived from the four linked locus pairs.

Relevance Study on Cerebral Infarction and Resistin Gene Polymorphism in Chinese Han Population

Recent research on genome-wide associations has implicated that the serum resistin level and its gene polymorphism are associated with cerebral infarction (CI) morbidity and prognosis, and could thereby regulate CI. This study aimed to investigate the association between the resistin single nucleotide polymorphism (SNP) and the susceptibility to CI in the Chinese Han population. A total of 550 CI patients and 313 healthy controls were genotyped. Nine SNPs of the resistin gene previously shown were sequenced and assessed for an association with CI. The numbers of GG genotype carriers of rs3219175 and rs3486119 in the CI group were significantly higher than those in the control group among the middle-aged group (aged 45-65), at 76% vs 67.9% (P=0.025) and 75.5% vs 67.9% (P=0.031). rs3219175 and rs34861192 were associated with CI in the dominant and superdominant models according to the genetic model analysis (P<0.05). Meanwhile, there was strong linkage disequilibrium among the rs34124816, rs3219175, rs34861192, rs1862513, rs3745367, 180C/G and rs3745369 sites. In a haplotype analysis, the occurrence rate of the haplotype AGGCAGC was 1.97 times (P<0.05) higher in the patient group than in the control group. In addition, the numbers of GG genotype carriers of rs3219175 and rs3486119 in the middle-aged male CI patients and the middle-aged small artery occlusion (SAO) CI patients were higher than those in the control group (P<0.05). In the Chinese Han middle-aged population, the GG gene type carriers of the resistin gene sites rs3219175 and rs34861192 had a high risk for CI onset, especially in middle-aged male patients and SAO CI in all middle-aged patients.

Resolving the etiology of atopic disorders by using genetic analysis of racial ancestry

Atopic dermatitis (AD), food allergy, allergic rhinitis, and asthma are common atopic disorders of complex etiology. The frequently observed atopic march from early AD to asthma, allergic rhinitis, or both later in life and the extensive comorbidity of atopic disorders suggest common causal mechanisms in addition to distinct ones. Indeed, both disease-specific and shared genomic regions exist for atopic disorders. Their prevalence also varies among races; for example, AD and asthma have a higher prevalence in African Americans when compared with European Americans. Whether this disparity stems from true genetic or race-specific environmental risk factors or both is unknown. Thus far, the majority of the genetic studies on atopic diseases have used populations of European ancestry, limiting their generalizability. Large-cohort initiatives and new analytic methods, such as admixture mapping, are currently being used to address this knowledge gap. Here we discuss the unique and shared genetic risk factors for atopic disorders in the context of ancestry variations and the promise of high-throughput "-omics"-based systems biology approach in providing greater insight to deconstruct their genetic and nongenetic etiologies. Future research will also focus on deep phenotyping and genotyping of diverse racial ancestry, gene-environment, and gene-gene interactions.

Imputing Phenotypes for Genome-wide Association Studies

Genome-wide association studies (GWASs) have been successful in detecting variants correlated with phenotypes of clinical interest. However, the power to detect these variants depends on the number of individuals whose phenotypes are collected, and for phenotypes that are difficult to collect, the sample size might be insufficient to achieve the desired statistical power. The phenotype of interest is often difficult to collect, whereas surrogate phenotypes or related phenotypes are easier to collect and have already been collected in very large samples. This paper demonstrates how we take advantage of these additional related phenotypes to impute the phenotype of interest or target phenotype and then perform association analysis. Our approach leverages the correlation structure between phenotypes to perform the imputation. The correlation structure can be estimated from a smaller complete dataset for which both the target and related phenotypes have been collected. Under some assumptions, the statistical power can be computed analytically given the correlation structure of the phenotypes used in imputation. In addition, our method can impute the summary statistic of the target phenotype as a weighted linear combination of the summary statistics of related phenotypes. Thus, our method is applicable to datasets for which we have access only to summary statistics and not to the raw genotypes. We illustrate our approach by analyzing associated loci to triglycerides (TGs), body mass index (BMI), and systolic blood pressure (SBP) in the Northern Finland Birth Cohort dataset.

Reflections on the Field of Human Genetics: A Call for Increased Disease Genetics Theory

Development of human genetics theoretical models and the integration of those models with experiment and statistical evaluation are critical for scientific progress. This perspective argues that increased effort in disease genetics theory, complementing experimental, and statistical efforts, will escalate the unraveling of molecular etiologies of complex diseases. In particular, the development of new, realistic disease genetics models will help elucidate complex disease pathogenesis, and the predicted patterns in genetic data made by these models will enable the concurrent, more comprehensive statistical testing of multiple aspects of disease genetics predictions, thereby better identifying disease loci. By theoretical human genetics, I intend to encompass all investigations devoted to modeling the heritable architecture underlying disease traits and studies of the resulting principles and dynamics of such models. Hence, the scope of theoretical disease genetics work includes construction and analysis of models describing how disease-predisposing alleles (1) arise, (2) are transmitted across families and populations, and (3) interact with other risk and protective alleles across both the genome and environmental factors to produce disease states. Theoretical work improves insight into viable genetic models of diseases consistent with empirical results from linkage, transmission, and association studies as well as population genetics. Furthermore, understanding the patterns of genetic data expected under realistic disease models will enable more powerful approaches to discover disease-predisposing alleles and additional heritable factors important in common diseases. In spite of the pivotal role of disease genetics theory, such investigation is not particularly vibrant.

Genetics of Type 2 Diabetes: the Power of Isolated Populations

Type 2 diabetes (T2D) affects millions of people worldwide. Improving the understanding of the underlying mechanisms and ultimately improving the treatment strategies are, thus, of great interest. To achieve this, identification of genetic variation predisposing to T2D is important. A large number of variants have been identified in large outbred populations, mainly from Europe and Asia. However, to elucidate additional variation, isolated populations have a number of advantageous properties, including increased amounts of linkage disequilibrium, and increased probability for presence of high frequency disease-associated variants due to genetic drift. Collectively, this increases the statistical power to detect association signals in isolated populations compared to large outbred populations. In this review, we elaborate on why isolated populations are a powerful resource for the identification of complex disease variants and describe their contributions to the understanding of the genetics of T2D.

Sequence-based Association Analysis Reveals an MGST1 eQTL with Pleiotropic Effects on Bovine Milk Composition

The mammary gland is a prolific lipogenic organ, synthesising copious amounts of triglycerides for secretion into milk. The fat content of milk varies widely both between and within species, and recent independent genome-wide association studies have highlighted a milk fat percentage quantitative trait locus (QTL) of large effect on bovine chromosome 5. Although both EPS8 and MGST1 have been proposed to underlie these signals, the causative status of these genes has not been functionally confirmed. To investigate this QTL in detail, we report genome sequence-based imputation and association mapping in a population of 64,244 taurine cattle. This analysis reveals a cluster of 17 non-coding variants spanning MGST1 that are highly associated with milk fat percentage, and a range of other milk composition traits. Further, we exploit a high-depth mammary RNA sequence dataset to conduct expression QTL (eQTL) mapping in 375 lactating cows, revealing a strong MGST1 eQTL underpinning these effects. These data demonstrate the utility of DNA and RNA sequence-based association mapping, and implicate MGST1, a gene with no obvious mechanistic relationship to milk composition regulation, as causally involved in these processes.