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Nonsyndromic orofacial clefts (OFCs) are among the most common craniofacial birth defects worldwide, and known to exhibit phenotypic and genetic heterogeneity. Cleft lip plus cleft palate (CLP) and cleft lip only (CL) are commonly combined together as one phenotype (CL/P), separately from cleft palate alone. In comparison, our study analyzes CL and CLP separately. A sample of 2218 CL and CLP cases, 4537 unaffected relatives of cases, and 2673 pure controls with no family history of OFC were selected from the Pittsburgh Orofacial Cleft (Pitt-OFC) multiethnic study.genome-wide association studies were run for seven specific phenotypes created based on the cleft type(s) observed within these families, as well as the combined CL/P phenotype. Five novel genome-wide significant associations, 3q29 (rs62284390), 5p13.2 (rs609659), 7q22.1 (rs6465810), 19p13.3 (rs628271), and 20q13.33 (rs2427238), and nine associations (p ≤ 1.0E−05) within previously confirmed OFC loci—PAX7, IRF6, FAM49A, DCAF4L2, 8q24.21, ARID3B, NTN1, TANC2 and the WNT9B:WNT3 gene cluster—were observed. We also found that single nucleotide polymorphisms within a subset of the associated loci, both previously known and novel, differ substantially in terms of their effects across cleft- or family-specific phenotypes, indicating not only etiologic differences between CL and CLP, but also genetic heterogeneity within each of the two OFC subtypes.

The purpose of this study was to examine genetic variability and knee pain in persons with osteoarthritis (OA). Seventy-five participants with medial compartment knee OA were recruited from a large Midwestern tertiary care center. Participants exhibited a mean age of 56.3 years; females comprised 61% of the sample. Measures of pain included subjective pain intensity at rest and with movement, cutaneous mechanical sensation and pain testing, heat pain threshold, and pressure pain threshold. Seventy-four participants were genotyped for 25 genetic variants across 15 candidate genes for central or peripheral pain pathways. Analysis suggests a role for four genes ( EDNRA , COMT , BDRKB1 , and IL1B ) in several components of pain in persons with knee OA. The results from this study will help guide the development and evaluation of tailored strategies to decrease pain, improve function, and prevent the development of new chronic pain syndromes in older adults experiencing OA. [ Research in Gerontological Nursing, 13 (4), 191–202.]

Background Previous studies have found that children born with a non-syndromic orofacial cleft have lower-than-average educational attainment. Differences could be due to a genetic predisposition to low intelligence and academic performance, factors arising due to the cleft phenotype (such as social stigmatization, impaired speech/language development) or confounding by the prenatal environment. A clearer understanding of this mechanism will inform interventions to improve educational attainment in individuals born with a cleft, which could substantially improve their quality of life. We assessed evidence for the hypothesis that common variant genetic liability to non-syndromic cleft lip with or without cleft palate (nsCL/P) influences educational attainment. Methods We performed a genome-wide association study (GWAS) meta-analysis of nsCL/P with 1692 nsCL/P cases and 4259 parental and unrelated controls. Using GWAS summary statistics, we performed Linkage Disequilibrium (LD)-score regression to estimate the genetic correlation between nsCL/P, educational attainment (GWAS n = 766 345) and intelligence (GWAS n = 257 828). We used two-sample Mendelian randomization to evaluate the causal effects of genetic liability to nsCL/P on educational attainment and intelligence. Results There was limited evidence for shared genetic aetiology or causal relationships between nsCL/P and educational attainment [genetic correlation (rg) −0.05, 95% confidence interval (CI) −0.12 to 0.01, P 0.13; MR estimate (βMR) −0.002, 95% CI −0.009 to 0.006, P 0.679) or intelligence (rg −0.04, 95% CI −0.13 to 0.04, P 0.34; βMR −0.009, 95% CI −0.02 to 0.002, P 0.11). Conclusions Common variants are unlikely to predispose individuals born with nsCL/P to low educational attainment or intelligence. This is an important first step towards understanding the aetiology of low educational attainment in this group.

Although genetics affects early childhood caries (ECC) risk, few studies have focused on finding its specific genetic determinants. Here, we performed genome-wide association studies (GWAS) in five cohorts of children (aged up to 5 years, total N = 2974, cohorts: Center for Oral Health Research in Appalachia cohorts one and two [COHRA1, COHRA2], Iowa Fluoride Study, Iowa Head Start, Avon Longitudinal Study of Parents and Children [ALSPAC]) aiming to identify genes with potential roles in ECC biology. We meta-analyzed the GWASs testing ~3.9 million genetic variants and found suggestive evidence for association at genetic regions previously associated with caries in primary and permanent dentition, including the β-defensin anti-microbial proteins. We then integrated the meta-analysis results with gene expression data in a transcriptome-wide association study (TWAS). This approach identified four genes whose genetically predicted expression was associated with ECC (p-values < 3.09 × 10−6; CDH17, TAS2R43, SMIM10L1, TAS2R14). Some of the strongest associations were with genes encoding members of the bitter taste receptor family (TAS2R); other members of this family have previously been associated with caries. Of note, we identified the receptor encoded by TAS2R14, which stimulates innate immunity and anti-microbial defense in response to molecules released by the cariogenic bacteria, Streptococcus mutans and Staphylococcus aureus. These findings provide insight into ECC genetic architecture, underscore the importance of host-microbial interaction in caries risk, and identify novel risk genes.

Orofacial clefts (OFCs) are among the most common craniofacial birth defects and constitute a high public health burden around the world. OFCs are phenotypically heterogeneous, affecting only the lip, only the palate, or involving both the lip and palate. Cleft palate alone is demonstrably a genetically distinct abnormality from OFCs that involve the lip, therefore, it is common to study cleft lip (CL) in combination with cleft lip plus cleft palate (CLP) as a phenotypic group (i.e. cleft lip with or without cleft palate, CL/P), usually considering CLP to be a clinically more severe form of CL. However, even within CL/P, important genetic differences among subtypes may be present. The Pittsburgh Orofacial Cleft (Pitt-OFC) multiethnic study is a rich resource for the study of non-syndromic OFC, comprising a large number of families (∼12,000 individuals) from multiple populations worldwide: US and Europe (whites), Central and South America (mixed Native American, European and African), Asia, and Africa. In this study we focused on the CL/P families from this resource grouped into three non-overlapping family types: those with only CL affected members, only CLP affected members, or both CL and CLP. In all, seven total subtypes besides the combined CL/P phenotype, were defined based on the cleft type(s) that were present within pedigree members. The full sample for these analyses includes 2,218 CL and CLP cases along with 4,537 unaffected relatives, as well as 2,673 pure controls with no family history of OFC. Genome-wide association analyses were conducted within each subset, as well as the combined sample. Five novel genome-wide significant associations were observed: 3q29 (rs62284390, p=2.70E-08), 5p13.2 (rs609659, p= 4.57E-08), 7q22.1 (rs6465810, p= 1.25E-08), 19p13.3 (rs628271, p=1.90E-08) and 20q13.33 (rs2427238, p=1.51E-09). In addition, five significant and four suggestive associations confirmed regions previously published as OFC risk loci - PAX7, IRF6, FAM49A, DCAF4L2, 8q24.21, ARID3B, NTN1, TANC2 and the WNT9B:WNT3gene cluster. At each of these loci, we compared effect sizes of associated SNPs observed across subtypes and the full sample, and found that certain loci were associated with a specific cleft type, and/or specific family types. Our findings indicate that risk factors differ between cleft and family types, but each cleft type also exhibits a certain degree of genetic heterogeneity.AUTHOR SUMMARYOrofacial clefts are common birth defects. Clefts often run in families, but their genetic basis is still an active area of investigation. In this study, we use an innovative approach to identify shared and unique genetic risk factors between two types of orofacial clefts - cleft lip and cleft lip plus cleft palate, by taking the patterns of different cleft types reported in families into account. Our study provides new insights into previously known genetic risk factors, but also identifies novel genetic regions that differentially impact the risk of developing cleft lip versus cleft lip plus cleft palate. This study contributes to the growing evidence that different sets of genes impact different forms of clefting and highlights the importance of incorporating information about familial affection patterns into analyses.

Orofacial clefts (OFCs) are among the most prevalent craniofacial birth defects worldwide and create a significant public health burden. The majority of OFCs are non-syndromic and vary in prevalence by ethnicity. Africans have the lowest prevalence of OFCs (~ 1/2,500), Asians have the highest prevalence (~1/500), Europeans and Latin Americans lie somewhere in the middle (~1/800 and 1/900, respectively). Thus, ethnicity appears to be a major determinant of the risk of developing OFC. The Pittsburgh Orofacial Clefts Multiethnic study was designed to explore this ethnic variance, comprising a large number of families and individuals (~12,000 individuals) from multiple populations worldwide: US and Europe, Asians, mixed Native American/Caucasians, and Africans. In this current study, we analyzed 2,915 OFC cases, 6,044 unaffected individuals related to the OFC cases, and 2,685 controls with no personal or family history of OFC. Participants were grouped by their ancestry into African, Asian, European, and Central and South American subsets, and genome-wide association run on the combined sample as well as the four ancestry-based groups. We observed 22 associations to cleft lip with or without cleft palate at 18 distinct loci withp-values < 1e-06, including 10 with genome-wide significance (p= 6.27e-07), 10q22.2 (rs150952246,p= 3.14e-07), and 10q24.32 (rs118107597,p= 8.21e-07) are novel. Nine were in or near known OFC loci -PAX7, IRF6, FAM49A, DCAF4L2, 8q24.21,NTN1, WNT3-WNT9B, TANC2, andRHPN2. The majority of the associations were observed only in the combined sample, European, and Central and South American groups. We investigated whether the observed differences in association strength were (a) purely due to sample sizes, (b) due to systematic allele frequency difference at the population level, or (c) due to the fact certain OFC-causing variants confer different amounts of risk depending on ancestral origin, by comparing effect sizes to observed allele frequencies of the effect allele in our ancestry-based groups. While some of the associations differ due to systematic differences in allele frequencies between groups, others show variation in effect size despite similar frequencies across ancestry groups.

Background Dental caries is one of the most common chronic diseases and is influenced by a complex interplay of genetic and environmental factors. Most previous genetic studies of caries have focused on identifying genes that contribute to dental caries in specific ethnic groups, usually of European descent. Methods The aim of this study is to conduct a genome-wide association study (GWAS) to identify associations affecting susceptibility to caries in a large multiethnic population from Argentina, the Philippines, Guatemala, Hungary, and the USA, originally recruited for studies of orofacial clefts (POFC, N = 3686). Ages of the participants ranged from 2 to 12 years for analysis of the primary dentition, and 18–60 years for analysis of the permanent dentition. For each participant, dental caries was assessed by counts of decayed and filled teeth (dft/DFT) and genetic variants (single nucleotide polymorphisms, SNPs) were genotyped or imputed across the entire genome. Caries was analyzed separately for the primary and permanent dentitions, with age, gender, and presence/absence of any type of OFC treated as covariates. Efficient Mixed-Model Association eXpedited (EMMAX) was used to test genetic association, while simultaneously accounting for relatedness and stratification. Results We identified several suggestive loci (5 × 10−8 P −6) within or near genes with plausible biological roles for dental caries, including a cluster of taste receptor genes (TAS2R38, TAS2R3, TAS2R4, TASR25) on chromosome 7 for the permanent dentition analysis, and DLX3 and DLX4 on chromosome 17 for the primary dentition analysis. Genome-wide significant results were seen with SNPs in the primary dentition only; however, none of the identified genes near these variants have known roles in cariogenesis. Conclusion The results of this study warrant further investigation and may lead to a better understanding of cariogenesis in diverse populations, and help to improve dental caries prediction, prevention, and/or treatment in future.

Orofacial clefts (OFCs) are among the most prevalent craniofacial birth defects worldwide and create a significant public health burden. The majority of OFCs are non-syndromic and vary in prevalence by ethnicity. Africans have the lowest prevalence of OFCs (∼ 1/2,500), Asians have the highest prevalence (∼1/500), European and Latin Americans lie somewhere in the middle (∼1/800 and 1/900 respectively). Thus, ethnicity appears to be a major determinant of the risk of developing OFC. The Pittsburgh Orofacial Clefts Multiethnic study was designed to explore this ethnic variance, comprising a large number of families and individuals (∼12,000 individuals) from multiple populations worldwide: US and Europe, Asians, mixed Native American/Caucasians, and Africans. In this current study, we analyzed 2,915 OFC cases, 6,044 unaffected individuals related to the OFC cases, and 2,685 controls with no personal or family history of OFC. Participants were grouped by their ancestry into African, Asian, European, and Central and South American subsets, and genome-wide association run on the combined sample as well as the four ancestry-based groups. We observed 22 associations to cleft lip with or without cleft palate at 18 distinct loci with p-values < 1e-06, including 10 with genome-wide significance (< 5e-08), in the combined sample and within ancestry groups. Three loci - 2p12 (rs62164740, p=6.27e-07), 10q22.2 (rs150952246, p=3.14e-07), and 10q24.32 (rs118107597, p=8.21e-07) are novel. Nine were in or near known OFC loci - PAX7, IRF6, FAM49A, DCAF4L2, 8q24.21, NTN1, WNT3-WNT9B, TANC2, and RHPN2. The majority of the associations were observed only in the combined sample, European, and Central and South American groups. We investigated whether the observed differences in association strength were a) purely due to sample sizes, b) due to systematic allele frequency difference at the population level, or (c) due to the fact certain OFC-causing variants confer different amounts of risk depending on ancestral origin, by comparing effect sizes to observed allele frequencies of the effect allele in our ancestry-based groups. While some of the associations differ due to systematic differences in allele frequencies between groups, others show variation in effect size despite similar frequencies across ancestry groups.

While de novo mutations (DNMs) are known to increase risk of congenital defects, DNMs have not been fully explored regarding orofacial clefts (OFCs), one of the most common human birth defects. Therefore, whole-genome sequencing of 756 case-parent trios of European, Colombian, and Taiwanese ancestry was performed to determine the contributions of coding DNMs to OFC risk. Overall, we identified a significant excess of loss-of-function DNMs in genes highly expressed in craniofacial tissues, as well as genes associated with known autosomal dominant OFC syndromes. This analysis also revealed roles for zinc-finger homeobox domain and SOX2-interacting genes in OFC etiology.

Although de novo mutations (DNMs) are known to increase an individual's risk of congenital defects, DNMs have not been fully explored regarding orofacial clefts (OFCs), one of the most common human birth defects. Therefore, whole-genome sequencing of 756 child-parent trios of European, Colombian, and Taiwanese ancestry was performed to determine the contributions of coding DNMs to an individual's OFC risk. Overall, we identified a significant excess of loss-of-function DNMs in genes highly expressed in craniofacial tissues, as well as genes associated with known autosomal dominant OFC syndromes. This analysis also revealed roles for zinc-finger homeobox domain and SOX2-interacting genes in OFC etiology.

Orofacial clefts (OFCs) are among the most prevalent craniofacial birth defects worldwide and create a significant public health burden. The majority of OFCs are non-syndromic, and the genetic etiology of non-syndromic OFCs is only partially determined. Here, we analyze whole genome sequence (WGS) data for association with risk of OFCs in European and Colombian families selected from a multicenter family-based OFC study. This is the first large-scale WGS study of OFC in parent–offspring trios, and a part of the Gabriella Miller Kids First Pediatric Research Program created for the study of childhood cancers and structural birth defects. WGS provides deeper and more specific genetic data than using imputation on present-day single nucleotide polymorphic (SNP) marker panels. Genotypes of case–parent trios at single nucleotide variants (SNV) and short insertions and deletions (indels) spanning the entire genome were called from their sequences using human GRCh38 genome assembly, and analyzed for association using the transmission disequilibrium test. Among genome-wide significant associations, we identified a new locus on chromosome 21 in Colombian families, not previously observed in other larger OFC samples of Latin American ancestry. This locus is situated within a region known to be expressed during craniofacial development. Based on deeper investigation of this locus, we concluded that it contributed risk for OFCs exclusively in the Colombians. This study reinforces the ancestry differences seen in the genetic etiology of OFCs, and underscores the need for larger samples when studying for OFCs and other birth defects in populations with diverse ancestry. Electronic supplementary material The online version of this article (10.1007/s00439-019-02099-1) contains supplementary material, which is available to authorized users.

BACKGROUND This study examined the extent to which genetic variability modifies Transcutaneous Electrical Nerve Stimulation (TENS) effectiveness in osteoarthritic knee pain. METHODS Seventy-five participants with knee osteoarthritis were randomly assigned to either: (a) High-frequency TENS, (b) Low-frequency TENS or (c) Transient Placebo TENS. Pain measures were collected pre- and post-treatment. Participants were genotyped on genes implicated in central or peripheral pain pathways: NGFB, NTRK1, EDNRA, EDNRB, EDN1, OPRM1, TAC1, TACR1, BDNF, BDKRB1, 5HTT, COMT, ESR2, IL6 and IL1B. Genetic association using linear regression modelling was performed separately for the transient placebo TENS subjects, and within the High-frequency TENS + Low-frequency TENS participants, including TENS level as a covariate. RESULTS In the placebo group, SNPs rs165599 (COMT) was significantly associated with an increased heat pain threshold (β = -1.87; p = .003) and rs6827096 (EDNRA) with an increased resting pain (β = 2.68; p = .001). Within the treatment groups, TENS effectiveness was reduced by the SNP rs6537485 (EDNRA) minor allele in relationship to mechanical sensation (β = 184.13; p = 5.5E-9). Individuals with the COMT rs4680 minor allele reported lowered pain at rest after TENS (β = -42.30; p = .001), with a higher magnitude of pain reduction (28 unit difference) in the low-frequency TENS group compared to the high-frequency TENS group (β = 28.37; p = .0004). CONCLUSIONS EDNRA and COMT are implicated in osteoarthritic knee pain and provide a basis for tailoring TENS interventions according to individual characteristics. SIGNIFICANCE Findings from this study demonstrate that genetic variation within the COMT and EDNRA genes influences the effectiveness of TENS, a non-pharmacologic pain-reduction intervention, in the context of osteoarthritic knee pain. Evidence such as this may contribute to risk models that provide a clinically useful tool for personalizing TENS interventions according to individual characteristics in order to best control pain and maximize functional status.

Dermatoglyphic patterns on the fingers often differ in syndromes and other conditions with a developmental component, compared to the general population. Previous literature on the relationship between orofacial clefts-the most common craniofacial birth defect in humans-and dermatoglyphics is inconsistent, with some studies reporting altered pattern frequencies and/or increased asymmetry and others failing to find differences. To investigate dermatoglyphics in orofacial clefting, we obtained dermatoglyphic patterns in a large multiethnic cohort of orofacial cleft cases (N = 367), their unaffected family members (N = 836), and controls (N = 299). We categorized fingerprint pattern types from males and females who participated at five sites of the Pittsburgh Orofacial Cleft study (Hungary, United States of America (Pennsylvania, Texas), Spain, and Argentina). We also calculated a pattern dissimilarity score for each individual as a measure of left-right asymmetry. We tested for group differences in the number of arches, ulnar and radial loops, and whorls on each individual's hands, and in the pattern dissimilarity scores using ANOVA. After taking sex and site differences into account, we did not find any significant pattern count differences between cleft and non-cleft individuals. Notably, we did observe increased pattern dissimilarity in individuals with clefts, compared to both their unaffected relatives and controls. Increased dermatoglyphic pattern dissimilarity in individuals with nonsyndromic orofacial clefts may reflect a generalized developmental instability.

When genome-wide association studies (GWAS) or sequencing studies are performed on family-based datasets, the genotype data can be used to check the structure of putative pedigrees. Even in datasets of putatively unrelated people, close relationships can often be detected using dense single-nucleotide polymorphism/variant (SNP/SNV) data. A number of methods for finding relationships using dense genetic data exist, but they all have certain limitations, including that they typically use average genetic sharing, which is only a subset of the available information. Here, we present a set of approaches for classifying relationships in GWAS datasets or large-scale sequencing datasets. We first propose an empirical method for detecting identity by descent segments in close relative pairs using un-phased dense SNP data and demonstrate how that information can assist in building a relationship classifier. We then develop a strategy to take advantage of putative pedigree information to enhance classification accuracy. Our methods are tested and illustrated with two datasets from two distinct populations. Finally, we propose classification pipelines for checking and identifying relationships in datasets containing a large number of small pedigrees.

Background Dental caries is a common chronic disease among children and adults alike, posing a substantial health burden. Caries is affected by multiple genetic and environmental factors, and prior studies have found that a substantial proportion of caries susceptibility is genetically inherited. Methods To identify such genetic factors, we conducted a genome-wide linkage scan in 464 extended families with 2616 individuals from Iowa, Pennsylvania and West Virginia for three dental caries phenotypes: (1) PRIM: dichotomized as zero versus one or more affected primary teeth, (2) QTOT1: age-adjusted quantitative caries measure for both primary and permanent dentitions including pre-cavitated lesions, and (3) QTOT2: age-adjusted quantitative caries excluding pre-cavitated lesions. Genotyping was conducted for approximately 600,000 SNPs on an Illumina platform, pruned to 127,511 uncorrelated SNPs for the analyses reported here. Results Multipoint non-parametric linkage analyses generated peak LOD scores exceeding 2.0 for eight genomic regions, but no LOD scores above 3.0 were observed. The maximum LOD score for each of the three traits was 2.90 at 1q25.3 for PRIM, 2.38 at 6q25.3 for QTOT1, and 2.76 at 5q23.3 for QTOT2. Some overlap in linkage regions was observed among the phenotypes. Genes with a potential role in dental caries in the eight chromosomal regions include CACNA1E, LAMC2, ALMS1, STAMBP, GXYLT2, SLC12A2, MEGF10, TMEM181, ARID1B, and, as well as genes in several immune gene families. Our results are also concordant with previous findings from association analyses on chromosomes 11 and 19. Conclusions These multipoint linkage results provide evidence in favor of novel chromosomal regions, while also supporting earlier association findings for these data. Understanding the genetic etiology of dental caries will allow designing personalized treatment plans based on an individual’s genetic risk of disease. Electronic supplementary material The online version of this article (10.1186/s12903-018-0559-6) contains supplementary material, which is available to authorized users.

There is a growing body of evidence suggesting that type 1 diabetes (T1D) is a genetically heterogeneous disease. However, the extent of this heterogeneity, and what observations may distinguish different forms, is unclear. One indicator may be T1D-related microvascular complications (MVCs), which are familial, but occur in some families, and not others. We tested the hypothesis that T1D plus MVC is genetically distinct from T1D without MCV. We studied 415 families (2,462 individuals, 896 with T1D) using genome-wide linkage analysis, comparing families with and without MVC. We also tested for interaction between identified loci and alleles at the HLA-DRB1 locus. We found significant linkage scores at 1p36.12, 1q32.1, 8q21.3, 12p11.21 and 22q11.21. In all regions except 1p36.12, linkage scores differed between MVC-based phenotype groups, suggesting that families with MVCs express different genetic influences than those without. Our linkage results also suggested gene-gene interaction between the above putative loci and the HLA region; HLA-based strata produced significantly increased linkage scores in some strata, but not others within a phenotype group. We conclude that families with type 1 diabetes plus MVCs are genetically different from those with diabetes alone.

The traditional variance components approach for quantitative trait locus (QTL) linkage analysis is sensitive to violations of normality and fails for selected sampling schemes. Recently, a number of new methods have been developed for QTL mapping in humans. Most of the new methods are based on score statistics or regression-based statistics and are expected to be relatively robust to non-normality of the trait distribution and also to selected sampling, at least in terms of type I error. Whereas the theoretical development of these statistics is more or less complete, some practical issues concerning their implementation still need to be addressed. Here we study some of these issues such as the choice of denominator variance estimates, weighting of pedigrees, effect of parameter misspecification, effect of non-normality of the trait distribution, and effect of incorporating dominance. We present a comprehensive discussion of the theoretical properties of various denominator variance estimates and of the weighting issue and then perform simulation studies for nuclear families to compare the methods in terms of power and robustness. Based on our analytical and simulation results, we provide general guidelines regarding the choice of appropriate QTL mapping statistics in practical situations.

Interest in mapping susceptibility alleles for complex diseases, which do not follow a classic single-gene segregation pattern, has driven interest in methods that account for, or use information from one locus when mapping another. Our discussion group examined methods related to epistasis or gene x gene interaction. The goal of modeling gene x gene interaction varied across groups; some papers tried to detect gene x gene interaction while others tried to exploit it to map genes. Most of the 10 papers summarized here applied newly created or newly modified statistical methods related to gene x gene interaction, while two groups primarily examined computational issues. As is often the case, comparisons are complicated by little overlap in the data used across the papers, and further complicated by the fact that the available data may not have been ideal for some gene x gene interaction methods. However, the main difficulty in comparing and contrasting methods across the papers is the lack of a consistent statistical definition of gene x gene interaction. But despite these issues, two clear trends emerged across the analyses: First, the methods for quantitative trait gene x gene interaction appeared to perform very well, even in families initially ascertained as affected sib pairs; and second, dichotomous trait gene x gene interaction methods failed to produce consistent results. The difficulty of using (primarily) affected sib pair data in a gene x gene interaction analysis is explored.

When genome-wide association studies (GWAS) or sequencing studies are performed on family-based datasets, the genotype data can be used to check the structure of putative pedigrees. Even in datasets of putatively unrelated people, close relationships can often be detected using dense single-nucleotide polymorphism/variant (SNP/SNV) data. A number of methods for finding relationships using dense genetic data exist, but they all have certain limitations, including that they typically use average genetic sharing, which is only a subset of the available information. Here, we present a set of approaches for classifying relationships in GWAS datasets or large-scale sequencing datasets. We first propose an empirical method for detecting identity by descent segments in close relative pairs using un-phased dense SNP data and demonstrate how that information can assist in building a relationship classifier. We then develop a strategy to take advantage of putative pedigree information to enhance classification accuracy. Our methods are tested and illustrated with two datasets from two distinct populations. Finally, we propose classification pipelines for checking and identifying relationships in datasets containing a large number of small pedigrees.

Background In a typical study of the genetics of a complex human disease, many different analysis programs are used, to test for linkage and association. This requires extensive and careful data reformatting, as many of these analysis programs use differing input formats. Writing scripts to facilitate this can be tedious, time-consuming, and error-prone. To address these issues, the open source Mega2 data reformatting program provides validated and tested data conversions from several commonly-used input formats to many output formats. Results Mega2, the Manipulation Environment for Genetic Analysis, facilitates the creation of analysis-ready datasets from data gathered as part of a genetic study. It transparently allows users to process genetic data for family-based or case/control studies accurately and efficiently. In addition to data validation checks, Mega2 provides analysis setup capabilities for a broad choice of commonly-used genetic analysis programs. First released in 2000, Mega2 has recently been significantly improved in a number of ways. We have rewritten it in C++ and have reduced its memory requirements. Mega2 now can read input files in LINKAGE, PLINK, and VCF/BCF formats, as well as its own specialized annotated format. It supports conversion to many commonly-used formats including SOLAR, PLINK, Merlin, Mendel, SimWalk2, Cranefoot, IQLS, FBAT, MORGAN, BEAGLE, Eigenstrat, Structure, and PLINK/SEQ. When controlled by a batch file, Mega2 can be used non-interactively in data reformatting pipelines. Support for genetic data from several other species besides humans has been added. Conclusions By providing tested and validated data reformatting, Mega2 facilitates more accurate and extensive analyses of genetic data, avoiding the need to write, debug, and maintain one’s own custom data reformatting scripts. Mega2 is freely available at https://watson.hgen.pitt.edu/register/. Electronic supplementary material The online version of this article (doi:10.1186/s13029-014-0026-y) contains supplementary material, which is available to authorized users.

In oocytes with nondisjoined chromosomes 21 due to a meiosis I (MI) error, recombination is significantly reduced along chromosome 21; several lines of evidence indicate that this contributes to the nondisjunction event. A pilot study found evidence that these oocytes also have reduced recombination genome-wide when compared with controls. This suggests that factors that act globally may be contributing to the reduced recombination on chromosome 21, and hence, the nondisjunction event. To identify the source of these factors, we examined two levels of recombination count regulation in oocytes: (i) regulation at the maternal level that leads to correlation in genome-wide recombination across her oocytes and (ii) regulation at the oocyte level that leads to correlation in recombination count among the chromosomes of an oocyte. We sought to determine whether either of these properties was altered in oocytes with an MI error. As it relates to maternal regulation, we found that both oocytes with an MI error (N = 94) and their siblings (N = 64) had reduced recombination when compared with controls (N = 2723). At the oocyte level, we found that the correlation in recombination count among the chromosomes of an oocyte is reduced in oocytes with MI errors compared with that of their siblings or controls. These results suggest that regulation at the maternal level predisposes MI error oocytes to reduced levels of recombination, but additional oocyte-specific dysregulation contributes to the nondisjunction event.

Linkage analysis methods that incorporate etiological heterogeneity of complex diseases are likely to demonstrate greater power than traditional linkage analysis methods. Several such methods use covariates to discriminate between linked and unlinked pedigrees with respect to a certain disease locus. Here we apply several such methods including two mixture models, ordered subset analysis, and a conditional logistic model to genome scan data on the DSM-IV alcohol dependence phenotype on the Collaborative Studies on Genetics of Alcoholism families, and compare the results to traditional nonparametric linkage analysis. In general, there was little agreement among the various covariate-based linkage statistics. Linkage signals with empirical p-values less than 0.001 were detected on chromosomes 3, 4, 7, 10, and 12, with the highest peak occurring at the GABRB1 gene using the ecb21 covariate.

Several programs are currently available for the detection of genotyping error that may or may not be Mendelianly inconsistent. However, no systematic study exists that evaluates their performance under varying pedigree structures and sizes, marker spacing, and allele frequencies. Our simulation study compares four multipoint methods: Merlin, Mendel4, SimWalk2, and Sibmed. We look at empirical thresholds, power, and false-positive rates on 7 small pedigree structures that included sibships with and without genotyped parents, and a three-generation pedigree, using 11 microsatellite markers with 3 different map spacings. Simulated data includes 5,000 replicates of each pedigree structure and marker map, with random genotyping errors in about 4% of the middle marker’s genotypes. We found that the default thresholds used by these programs provide low power (47–72%). Power is improved more by adding genotyped siblings than by using more closely spaced markers. Some mistyping methods are sensitive to the frequencies of the observed alleles. Siblings of mistyped individuals have elevated false-positive rates, as do markers close to the mistyped marker. We conclude that thresholds should be decided based on the pedigree and marker data and that greater focus should be placed on modeling genotyping error when computing likelihoods, rather than on detecting and eliminating genotyping errors.

Summary: Mega2, the manipulation environment for genetic analysis, transparently allows users to process genetic data for family-based or case/control studies accurately and efficiently. In addition to data validation checks, Mega2 provides analysis setup capabilities for a broad choice of commonly used genetic analysis programs, including SimWalk2, ASPEX, GeneHunter, SLINK, SIMULATE, S.A.G.E., SOLAR, Vitesse, Allegro, PREST, PAP, Loki, Merlin and MENDEL. Availability: http://watson.hgen.pitt.edu/register/ Contact: dweeks@watson.hgen.pitt.edu Supplementary information: http://watson.hgen.pitt.edu/docs/mega2_html/mega2.html

Objective: To map loci influencing normal adult height in 335 families from the Framingham Heart Study. Methods: We analyzed data consisting of 1,702 genotyped individuals who have been followed over time. The first height measurement for individuals between the ages 20–55 years was analyzed in a genome-wide scan using variance component linkage analysis. Sex, age, and cohort effects were removed before analysis. Results: Two regions (18pter-p11, 22q11.2) with multipoint LOD scores >1.0 (–log p values >2.0) were detected: we obtained LOD scores of 1.38 at D18S1364, and of 1.10 at D22S345. Analysis of height as a sex-limited phenotype revealed a peak in the 9p21 region near D9S319 with a maximum LOD score of 1.65 (–log p value >3.0) when only male height phenotypes were used. When only female phenotypes were used, a peak with a maximum LOD score of 1.85 (–log p value of 2.70) was observed in the 11q25-qter region near D11S2359. Conclusions: Our region of interest on chromosome 9 has been implicated by two prior studies. Variance components analysis appeared to be sensitive to pedigree structures as well as the method of IBD computation used.

Current linkage analysis methods for quantitative traits do not usually incorporate imprinting effects. Here, we carried out genome-wide linkage analysis for loci influencing adult height in the Framingham Heart Study subjects using variance components while allowing for imprinting effects. We used a sex-averaged map for the 22 autosomes, while chromosomes 6, 14, 18, and 19 were also analyzed using sex-specific maps. We compared results from these four analyses: 1) non-imprinted with sex-averaged maps, 2) imprinted with sex-averaged maps, 3) non-imprinted with sex-specific maps, and 4) imprinted with sex-specific maps. We found four regions on three chromosomes (14q32, 18p11-q21, 18q21-22, and 19q13) with LOD scores above 2.0, with a maximum LOD score of 3.12, allowing for imprinting and sex-specific maps, at D18S1364 on 18q21. While we obtained significant evidence of imprinting effects in both the 18p11-q21 and 19q13 regions when using sex-averaged maps, there were no significant differences between the imprinted and non-imprinted LOD scores when we used sex-specific maps. Our results illustrate the importance of allowing for gender-specific effects in linkage analyses, whether these are in the form of gender-specific recombination frequencies, or in the form of imprinting effects.

Rheumatoid arthritis (RA) is a multifactorial disease with complex genetic etiology, about which little is known. Here, we apply a two-stage procedure in which a quick first-stage analysis was used to narrow down targets for a more thorough and detailed testing for gene × gene interaction. Potentially interesting regions were first identified by testing for major gene effects using non-parametric linkage methods. To select regions of interest, we first tested for linkage to three different RA-related traits one at a time: RA affection status and the quantitative phenotypes rheumatoid factor IgM and anti-cyclic citrullinated peptide levels. These linkage analyses identified regions on chromosomes 3, 5, 6, 8, 16, 18, 19, and 20. We subsequently analyzed the selected regions in a pairwise manner to detect gene × gene interactions influencing RA using a recently developed two-dimensional linkage method. We found evidence of interacting loci on chromosomes 5, 6, and 18.