Major quantitative trait loci influencing milk production and conformation traits in Guernsey dairy cattle detected on Bos taurus autosome 19.

The goal of this study was to identify potential quantitative trait loci (QTL) for 27 production, fitness, and conformation traits of Guernsey cattle through genome-wide association (GWA) analyses, with extra emphasis on BTA19, where major QTL were observed for several traits. Animals' de-regressed predicted transmitting abilities (PTA) from the December 2018 traditional US evaluation were used as phenotypes. All of the Guernsey cattle included in the QTL analyses were predictor animals in the reference population, ranging from 1,077 to 1,685 animals for different traits. Single-trait GWA analyses were carried out by a mixed-model approach for all 27 traits using imputed high-density genotypes. A major QTL was detected on BTA19, influencing several milk production traits, conformation traits, and livability of Guernsey cattle, and the most significant SNP lie in the region of 26.2 to 28.3 Mb. The myosin heavy chain 10 (MYH10) gene residing within this region was found to be highly associated with milk production and body conformation traits of dairy cattle. After the initial GWA analyses, which suggested that many significant SNP are in linkage with one another, conditional analyses were used for fine mapping. The top significant SNP on BTA19 were fixed as covariables in the model, one at a time, until no more significant SNP were detected on BTA19. After this fine-mapping approach was applied, only 1 significant SNP was detected on BTA19 for most traits, but multiple, independent significant SNP were found for protein yield, dairy form, and stature. In addition, the haplotype that hosts the major QTL on BTA19 was traced to a US Guernsey born in 1954. The haplotype is common in the breed, indicating a long-term influence of this QTL on the US Guernsey population.

Genomic prediction of residual feed intake in US Holstein dairy cattle.

Genomic selection is an important tool to introduce feed efficiency into dairy cattle breeding. The goals of the current research are to estimate genomic breeding values of residual feed intake (RFI) and to assess the prediction reliability for RFI in the US Holstein population. The RFI data were collected from 4,823 lactations of 3,947 Holstein cows in 9 research herds in the United States, and were pre-adjusted to remove phenotypic correlations with milk energy, metabolic body weight, body weight change, and for several environmental effects. In the current analyses, genomic predicted transmitting abilities of milk energy and of body weight composite were included into the RFI model to further remove the genetic correlations that remained between RFI and these energy sinks. In the first part of the analyses, a national genomic evaluation for RFI was conducted for all the Holsteins in the national database using a standard multi-step genomic evaluation method and 60,671 SNP list. In the second part of the study, a single-step genomic prediction method was applied to estimate genomic breeding values of RFI for all cows with phenotypes, 5,252 elite young bulls, 4,029 young heifers, as well as their ancestors in the pedigree, using a high-density genotype chip. Theoretical prediction reliabilities were calculated for all the studied animals in the single-step genomic prediction by direct inversion of the mixed model equations. In the results, breeding values were estimated for 1.6 million genotyped Holsteins and 60 million ungenotyped Holsteins, The genomic predicted transmitting ability correlations between RFI and other traits in the index (e.g., fertility) are generally low, indicating minor correlated responses on other index traits when selecting for RFI. Genomic prediction reliabilities for RFI averaged 34% for all phenotyped animals and 13% for all 1.6 million genotyped animals. Including genomic information increased the prediction reliabilities for RFI compared with using only pedigree information. All bulls had low reliabilities, and averaged to only 16% for the top 100 net merit progeny-tested bulls. Analyses using single-step genomic prediction and high-density genotypes gave similar results to those obtained from the national evaluation. The average theoretical reliability for RFI was 18% among the elite young bulls under 5 yr old, being lower in the younger generations of elite bulls compared with older bulls. To conclude, the size of the reference population and its relationship to the predicted population remain as the limiting factors in the genomic prediction for RFI. Continued collection of feed intake data is necessary so that reliabilities can be maintained due to close relationships of phenotyped animals with breeding stock. Considering the currently low prediction reliability and high cost of data collection, focusing RFI data collection on relatives of elite bulls that will have the greatest genetic contribution to the next generation will give more gains and profit.

Calling known variants and identifying new variants while rapidly aligning sequence data.

Whole-genome sequencing studies can identify causative mutations for subsequent use in genomic evaluations. Speed and accuracy of sequence alignment can be improved by accounting for known variant locations during alignment instead of calling the variants after alignment as in previous programs. The new programs Findmap and Findvar were compared with alignment using Burrows-Wheeler alignment (BWA) or SNAP and variant identification using Genome Analysis ToolKit (GATK) or SAMtools. Findmap stores the reference map and any known variant locations while aligning reads and counting reference and alternate alleles for each DNA source. Findmap also outputs potential new single nucleotide variant, insertion, and deletion alleles. Findvar separates likely true variants from read errors and outputs genotype probabilities. Strategies were tested using cattle, human, and a completely random reference map and simulated or actual data. Most tests simulated 10 bulls, each with 10× simulated sequence reads containing 39 million variants from the 1000 Bull Genomes Project. With 10 processors, clock times for processing 100× data were 105 h for BWA, 25 h for GATK, and 11 h for SAMtools but only about 4 h for SNAP, 3 h for Findmap, and 1 h for Findvar. Alignment programs required about the same total memory; BWA used 46 GB (4.6 GB/processor), whereas >10 processors can share the same memory in SNAP and Findmap, which used 40 and 46 GB, respectively. Findmap correctly mapped 92.9% of reads (compared with 92.6% from SNAP and 90.5% from BWA) and had high accuracy of calling alleles for known variants. For new variants, Findvar found 99.8% of single nucleotide variants, 79% of insertions, and 67% of deletions; GATK found 99.4, 95, and 90%, respectively; and SAMtools found 99.8, 12, and 16%, respectively. False positives (as percentages of true variants) were 11% of single nucleotide variants, 0.4% of insertions, and 0.3% of deletions from Findvar; 12, 8.4, and 2.9%, respectively, from GATK; and 37, 1.3, and 0.4%, respectively, from SAMtools. Advantages of Findmap and Findvar are fast processing, precise alignment, more useful data summaries, more compact output, and fewer steps. Calling known variants during alignment allows more efficient and accurate sequence-based genotyping.

Determination of quantitative trait nucleotides by concordance analysis between quantitative trait loci and marker genotypes of US Holsteins.

Experimental designs that exploit family information can provide substantial predictive power in quantitative trait nucleotide discovery projects. Concordance between quantitative trait locus genotype as determined by the a posteriori granddaughter design and marker genotype was determined for 30 trait-by-chromosomal segment effects segregating in the US Holstein population with probabilities of <10-20 to accept the null hypotheses of no segregating gene affecting the trait within the chromosomal segment. Genotypes for 83 grandsires and 17,217 sons were determined by either complete sequence or imputation for 3,148,506 polymorphisms across the entire genome; 471 Holstein bulls had a complete genome sequence, including 64 of the grandsires. Complete concordance was obtained only for stature on chromosome 14 and daughter pregnancy rate on chromosome 18. For each quantitative trait locus, effects of the 30 polymorphisms with highest concordance scores for the analyzed trait were computed by stepwise regression for predicted transmitting abilities of 26,750 bulls with progeny test and imputed genotypes. Effects for stature on chromosome 11, daughter pregnancy rate on chromosome 18, and protein percentage on chromosome 20 met 3 criteria: complete or almost complete concordance, nominal significance of the polymorphism effect after correction for all other polymorphisms, and marker coefficient of determination >40% of total multiple-regression coefficient of determination for the 30 polymorphisms with highest concordance. An intronic variant marker on chromosome 5 at 93,945,738 bp explained 7% of variance for fat percentage and 74% of total multiple-marker regression variance but was concordant for only 24 of 30 families. The missense polymorphism Phe279Tyr in GHR at 31,909,478 bp on chromosome 20 was confirmed as the causative mutation for fat and protein concentration. For effect on fat percentage on chromosome 14, 12 additional missense polymorphisms were found that had almost complete concordance with the suggested causative polymorphism (missense mutation Ala232Glu in DGAT1). The only polymorphism found likely to improve predictive power for genomic evaluation of dairy cattle was on chromosome 15; that polymorphism had a frequency of 0.45 for the allele with economically positive effects on all production traits.

Prospective Evaluation of Genetic Variation in Platelet Endothelial Aggregation Receptor 1 Reveals Aspirin-Dependent Effects on Platelet Aggregation Pathways.

Genetic variation in the platelet endothelial aggregation receptor 1 (PEAR1) gene, most notably rs12041331, is implicated in altered on-aspirin platelet aggregation and increased cardiovascular event risk. We prospectively tested the effects of aspirin administration at commonly prescribed doses (81, 162, and 324 mg/day) on agonist-induced platelet aggregation by rs12041331 genotype in 67 healthy individuals. Prior to aspirin administration, rs12041331 minor allele carriers had significantly reduced adenosine diphosphate (ADP)-induced platelet aggregation compared with noncarriers (P = 0.03) but was not associated with other platelet pathways. In contrast, rs12041331 was significantly associated with on-aspirin platelet aggregation when collagen and epinephrine were used to stimulate platelet aggregation (P < 0.05 for all associations), but not ADP. The influence of PEAR1 rs12041331 on platelet aggregation is pathway-specific and is altered by aspirin at therapeutic doses, but not in a dose-dependent manner. Additional studies are needed to determine the impact of PEAR1 on cardiovascular events in aspirin-treated patients.

Heritability of young- and old-onset ischaemic stroke.

BACKGROUND AND PURPOSE: Although the genetic contribution to stroke risk is well known, it remains unclear if young-onset stroke has a stronger genetic contribution than old-onset stroke. This study aims to compare the heritability of ischaemic stroke risk between young and old, using common genetic variants from whole-genome array data in population-based samples. METHODS: This analysis included 4050 ischaemic stroke cases and 5765 controls from six study populations of European ancestry; 47% of cases were young-onset stroke (age < 55 years). To quantify the heritability for stroke risk in these unrelated individuals, the pairwise genetic relatedness was estimated between individuals based on their whole-genome array data using a mixed linear model. Heritability was estimated separately for young-onset stroke and old-onset stroke (age ≥ 55 years). RESULTS: Heritabilities for young-onset stroke and old-onset stroke were estimated at 42% (±8%, P < 0.001) and 34% (±10%, P < 0.001), respectively. CONCLUSIONS: Our data suggest that the genetic contribution to the risk of stroke may be higher in young-onset ischaemic stroke, although the difference was not statistically significant.

Mating programs including genomic relationships and dominance effects.

Computerized mating programs using genomic information are needed by breed associations, artificial-insemination organizations, and on-farm software providers, but such software is already challenged by the size of the relationship matrix. As of October 2012, over 230,000 Holsteins obtained genomic predictions in North America. Efficient methods of storing, computing, and transferring genomic relationships from a central database to customers via a web query were developed for approximately 165,000 genotyped cows and the subset of 1,518 bulls whose semen was available for purchase at that time. This study, utilizing 3 breeds, investigated differences in sire selection, methods of assigning mates, the use of genomic or pedigree relationships, and the effect of including dominance effects in a mating program. For both Jerseys and Holsteins, selection and mating programs were tested using the top 50 marketed bulls for genomic and traditional lifetime net merit as well as 50 randomly selected bulls. The 500 youngest genotyped cows in the largest herd in each breed were assigned mates of the same breed with limits of 10 cows per bull and 1 bull per cow (only 79 cows and 8 bulls for Brown Swiss). A dominance variance of 4.1 and 3.7% was estimated for Holsteins and Jerseys using 45,187 markers and management group deviation for milk yield. Sire selection was identified as the most important component of improving expected progeny value, followed by managing inbreeding and then inclusion of dominance. The respective percentage gains for milk yield in this study were 64, 27, and 9, for Holsteins and 73, 20, and 7 for Jerseys. The linear programming method of assigning a mate outperformed sequential selection by reducing genomic or pedigree inbreeding by 0.86 to 1.06 and 0.93 to 1.41, respectively. Use of genomic over pedigree relationship information provided a larger decrease in expected progeny inbreeding and thus greater expected progeny value. Based on lifetime net merit, the economic value of using genomic relationships was >$3 million per year for Holsteins when applied to all genotyped females, assuming that each will provide 1 replacement. Previous mating programs required transferring only a pedigree file to customers, but better service is possible by incorporating genomic relationships, more precise mate allocation, and dominance effects. Economic benefits will continue to grow as more females are genotyped.

The CYP2C19*17 variant is not independently associated with clopidogrel response.

BACKGROUND: Cytochrome P450 2C19 (CYP2C19) is the principal enzyme responsible for converting clopidogrel into its active metabolite, and common genetic variants have been identified, most notably CYP2C19*2 and CYP2C19*17, that are believed to alter its activity and expression, respectively. OBJECTIVE: We evaluated whether the consequences of the CYP2C19*2 and CYP2C19*17 variants on clopidogrel response were independent of each other or genetically linked through linkage disequilibrium (LD). PATIENTS/METHODS: We genotyped the CYP2C19*2 and CYP2C19*17 variants in 621 members of the Pharmacogenomics of Anti-Platelet Intervention (PAPI) Study and evaluated the effects of these polymorphisms singly and then jointly, taking into account LD, on clopidogrel prodrug level, clopidogrel active metabolite level, and adenosine 5'-diphosphate (ADP)-stimulated platelet aggregation before and after clopidogrel exposure. RESULTS: The CYP2C19*2 and CYP2C19*17 variants were in LD (|D'| = 1.0; r(2)  = 0.07). In association analyses that did and did not account for the effects of CYP2C19*17, CYP2C19*2 was strongly associated with levels of clopidogrel active metabolite (ß = -5.24, P = 3.0 × 10(-9) and ß = -5.36, P = 3.3 × 10(-14) , respectively) and posttreatment ADP-stimulated platelet aggregation (ß = 7.55, P = 2.9 × 10(-16) and ß = 7.51, P = 7.0 × 10(-15) , respectively). In contrast, CYP2C19*17 was marginally associated with clopidogrel active metabolite levels and ADP-stimulated platelet aggregation before (ß = 1.57, P = 0.04 and ß = -1.98, P = 0.01, respectively) but not after (ß = 0.40, P = 0.59 and ß = -0.13, P = 0.69, respectively) adjustment for the CYP2C19*2 variant. Stratified analyses of CYP2C19*2/CYP2C19*17 genotype combinations revealed that CYP2C19*2, and not CYP2C19*17, was the primary determinant in altering clopidogrel response. CONCLUSIONS: Our results suggest that CYP2C19*17 has a small (if any) effect on clopidogrel-related traits and that the observed effect of this variant is due to LD with the CYP2C19*2 loss-of-function variant.

Confirmation and discovery of maternal grandsires and great-grandsires in dairy cattle.

Selection, mating, and improvement of dairy animals have required accurate pedigrees. Genomic tools allow paternal ancestors to be easily confirmed or discovered because most sires are genotyped for many markers, but maternal ancestors are more difficult to discover because most female ancestors are not genotyped. Three methods to discover maternal grandsires (MGS) were developed and compared. Conflicts were counted one single nucleotide polymorphism (SNP) at a time between genotypes of the animal and potential MGS (duo method) or also using the sire's genotype (trio method). Alternatively, haplotypes of a potential MGS were matched to the animal's maternal haplotype, obtained by using linkage across loci (HAP method). The duo and trio methods can be performed as soon as a genotype is received because no imputation is required. The HAP method improved accuracy because genotypes with 2,683 (3 K) SNP were imputed to the 45,187 (50K) SNP used for genomic evaluation. The HAP method was tested using modified pedigrees with 5% of true MGS replaced by a random genotyped bull from the same birth year and 5% of MGS set to missing for 4,134 Holsteins, 552 Jerseys, and 142 Brown Swiss that had confirmed, genotyped sires. Those same animals were used to test the duo and trio methods, except that some animals had multiple genotypes and imputed dams were excluded. Accuracy measured how often the correct MGS was selected from among 12,152 genotyped Holstein, 2,265 Jersey, and 1,605 Brown Swiss potential MGS. Accuracies were 61, 60, and 65%, respectively, with the duo method; 95, 91, and 94% with the trio method; and 97, 95, and 97% with the HAP method. Accuracy of the duo method was poor (only 52% for animals genotyped with 3 K and 65% with 50K) because additional information from the paternal genotype is not used. Accuracy of the trio method was 97% with 50K but only 78% with 3K because the missing SNP were not imputed. Accuracy of the HAP method was 94% with 3 K genotypes, 98% with 50K, and 92% with nongenotyped, imputed dams. When the HAP method was extended to great-grandsires, the accuracy of maternal great-grandsire discovery was 92% for 652 Holsteins, 95% for 33 Jerseys, and 85% for 20 Brown Swiss. Accuracy was even higher using simulated genotypes. Because most dairy bulls over several generations have been genotyped, percentages of haplotypes shared with candidate males can accurately confirm, correct, or discover the sires, MGS, and even more distant ancestors of most animals.

Paraoxonase 1 (PON1) gene variants are not associated with clopidogrel response.

A common functional variant in paraoxonase 1 (PON1), Q192R, was recently reported to be a major determinant of clopidogrel response. This variant was genotyped in 566 participants of the Amish Pharmacogenomics of Anti-Platelet Intervention (PAPI) study and in 227 percutaneous coronary intervention (PCI) patients. Serum paraoxonase activity was measured in a subset of 79 PAPI participants. PON1 Q192R was not associated with pre- or post-clopidogrel platelet aggregation in the PAPI study (P = 0.16 and P = 0.21, respectively) or the PCI cohort (P = 0.47 and P = 0.91, respectively). The Q192 allele was not associated with cardiovascular events (hazard ratio (HR) 0.46, 95% confidence interval (CI) 0.20-1.06; P = 0.07). No correlation was observed between paraoxonase activity and post-clopidogrel platelet aggregation (r(2) < 0.01, P = 0.78). None of 49 additional PON1 variants evaluated was associated with post-clopidogrel platelet aggregation. These findings do not support a role for PON1 as a determinant of clopidogrel response.

Prediction of unobserved single nucleotide polymorphism genotypes of Jersey cattle using reference panels and population-based imputation algorithms.

The availability of dense single nucleotide polymorphism (SNP) genotypes for dairy cattle has created exciting research opportunities and revolutionized practical breeding programs. Broader application of this technology will lead to situations in which genotypes from different low-, medium-, or high-density platforms must be combined. In this case, missing SNP genotypes can be imputed using family- or population-based algorithms. Our objective was to evaluate the accuracy of imputation in Jersey cattle, using reference panels comprising 2,542 animals with 43,385 SNP genotypes and study samples of 604 animals for which genotypes were available for 1, 2, 5, 10, 20, 40, or 80% of loci. Two population-based algorithms, fastPHASE 1.2 (P. Scheet and M. Stevens; University of Washington TechTransfer Digital Ventures Program, Seattle, WA) and IMPUTE 2.0 (B. Howie and J. Marchini; Department of Statistics, University of Oxford, UK), were used to impute genotypes on Bos taurus autosomes 1, 15, and 28. The mean proportion of genotypes imputed correctly ranged from 0.659 to 0.801 when 1 to 2% of genotypes were available in the study samples, from 0.733 to 0.964 when 5 to 20% of genotypes were available, and from 0.896 to 0.995 when 40 to 80% of genotypes were available. In the absence of pedigrees or genotypes of close relatives, the accuracy of imputation may be modest (generally <0.80) when low-density platforms with fewer than 1,000 SNP are used, but population-based algorithms can provide reasonably good accuracy (0.80 to 0.95) when medium-density platforms of 2,000 to 4,000 SNP are used in conjunction with high-density genotypes (e.g., >40,000 SNP) from a reference population. Accurate imputation of high-density genotypes from inexpensive low- or medium-density platforms could greatly enhance the efficiency of whole-genome selection programs in dairy cattle.

Distribution and location of genetic effects for dairy traits.

Genetic effects for many dairy traits and for total economic merit are evenly distributed across all chromosomes. A high-density scan using 38,416 single nucleotide polymorphism markers for 5,285 bulls confirmed 2 previously known major genes on Bos taurus autosomes (BTA) 6 and 14 but revealed few other large effects. Markers on BTA18 had the largest effects on calving ease, several conformation traits, longevity, and total merit. Prediction accuracy was highest using a heavy-tailed prior assuming that each marker had an effect on each trait, rather than assuming a normal distribution of effects as in a linear model, or that only some loci have nonzero effects. A prior model combining heavy tails with finite alleles produced results that were intermediate compared with the individual models. Differences between models were small (1 to 2%) for traits with no major genes and larger for heavy tails with traits having known quantitative trait loci (QTL; 6 to 8%). Analysis of bull recessive codes suggested that marker effects from genomic selection may be used to identify regions of chromosomes to search in detail for candidate genes, but individual single nucleotide polymorphisms were not tracking causative mutations with the exception of diacylglycerol O-acyltransferase 1. Additive genetic merits were constructed for each chromosome, and the distribution of BTA14-specific estimated breeding value (EBV) showed that selection primarily for milk yield has not changed the distribution of EBV for fat percentage even in the presence of a known QTL. Such chromosomal EBV also may be useful for identifying complementary mates in breeding programs. The QTL affecting dystocia, conformation, and economic merit on BTA18 appear to be related to calf size or birth weight and may be the result of longer gestation lengths. Results validate quantitative genetic assumptions that most traits are due to the contributions of a large number of genes of small additive effect, rather than support the finite locus model.

Autosome-wide linkage analysis of hip structural phenotypes in the Old Order Amish.

INTRODUCTION: Fracture risk is associated with bone mineral density (BMD) and with other indices of bone strength, including hip geometry. While the heritability and associated fracture risk of BMD are well described, less is known about genetic influences of bone geometry. We derived hip structural phenotypes using the Hip Structural Analysis program (HSA) and performed autosome-wide linkage analysis of hip geometric structural phenotypes. MATERIALS AND METHODS: The Amish Family Osteoporosis Study was designed to identify genes affecting bone health. BMD was measured at the hip using dual X-ray absorptiometry (DXA) in 879 participants (mean age+/-SD=49.8+/-16.1 years, range 18-91 years) from large multigenerational families. From DXA scans, we computed structural measures of hip geometry at the femoral neck (NN) and shaft (S) by HSA, including cross-sectional area (CSA), endocortical or inner diameter (ID), outer diameter (OD) buckling ratio (BR) and section modulus (Z). Genotyping of 731 highly polymorphic microsatellite markers (average spacing of 5.4 cM) and autosome-wide multipoint linkage analysis was performed. RESULTS: The heritability of HSA-derived hip phenotypes ranged from 40 to 84%. In the group as a whole, autosome-wide linkage analysis suggested evidence of linkage for QTLs related to NN_Z on chromosome 1p36 (LOD=2.36). In subgroup analysis, ten additional suggestive regions of linkage were found on chromosomes 1, 2, 5, 6, 11, 12, 14, 15 and 17, all with LOD>2.3 except for our linkage at 17q11.2-13 for men and women age 50 and under for NN_CSA, which had a lower LOD of 2.16, but confirmed a previous linkage report. CONCLUSIONS: We found HSA-derived measures of hip structure to be highly heritable independent of BMD. No strong evidence of linkage was found for any phenotype. Confirmatory evidence of linkage was found on chromosome 17q11.2-12 for NN_CSA. Modest evidence was found for genes affecting hip structural phenotypes at ten other chromosomal locations.

Accounting for relatedness in family based genetic association studies.

OBJECTIVE: Assess the differences in point estimates, power and type 1 error rates when accounting for and ignoring family structure in genetic tests of association. METHODS: We compare by simulation the performance of analytic models using variance components to account for family structure and regression models that ignore relatedness for a range of possible family based study designs (i.e., sib pairs vs. large sibships vs. nuclear families vs. extended families). RESULTS: Our analyses indicate that effect size estimates and power are not significantly affected by ignoring family structure. Type 1 error rates increase when family structure is ignored, as density of family structures increases, and as trait heritability increases. For discrete traits with moderate levels of heritability and across many common sampling designs, type 1 error rates rise from a nominal 0.05 to 0.11. CONCLUSION: Ignoring family structure may be useful in screening although it comes at a cost of a increased type 1 error rate, the magnitude of which depends on trait heritability and pedigree configuration.

The value of molecular haplotypes in a family-based linkage study.

Novel methods that could improve the power of conventional methods of gene discovery for complex diseases should be investigated. In a simulation study, we aimed to investigate the value of molecular haplotypes in the context of a family-based linkage study. The term "haplotype" (or "haploid genotype") refers to syntenic alleles inherited on a single chromosome, and we use the term "molecular haplotype" to refer to haplotypes that have been determined directly by use of a molecular technique such as long-range allele-specific polymerase chain reaction. In our study, we simulated genotype and phenotype data and then compared the powers of analyzing these data under the assumptions that various levels of information from molecular haplotypes were available. (This information was available because of the simulation procedure.) Several conclusions can be drawn. First, as expected, when genetic homogeneity is expected or when marker data are complete, it is not efficient to generate molecular haplotyping information. However, with levels of heterogeneity and missing data patterns typical of complex diseases, we observed a 23%-77% relative increase in the power to detect linkage in the presence of heterogeneity with heterogeneity LOD scores >3.0 when all individuals are molecularly haplotyped (compared with the power when only standard genotypes are used). Furthermore, our simulations indicate that most of the increase in power can be achieved by molecularly haplotyping a single individual in each family, thereby making molecular haplotyping a valuable strategy for increasing the power of gene mapping studies of complex diseases. Maximization of power, given an existing family set, can be particularly important for late-onset, often-fatal diseases such as cancer, for which informative families are difficult to collect.

Rapid multipoint linkage analysis via inheritance vectors in the Elston-Stewart algorithm.

The calculation of multipoint likelihoods of pedigree data is crucial for extracting the full available information needed for both parametric and nonparametric linkage analysis. Recent mathematical advances in both the Elston-Stewart and Lander-Green algorithms for computing exact multipoint likelihoods of pedigree data have enabled researchers to analyze data sets containing more markers and more individuals both faster and more efficiently. This paper presents novel algorithms that further extend the computational boundary of the Elston-Stewart algorithm. They have been implemented into the software package VITESSE v. 2 and are shown to be several orders of magnitude faster than the original implementation of the Elston-Stewart algorithm in VITESSE v. 1 on a variety of real pedigree data. VITESSE v. 2 was faster by a factor ranging from 168 to over 1,700 on these data sets, thus making a qualitative difference in the analysis. The main algorithm is based on the faster computation of the conditional probability of a component nuclear family within the pedigree by summing over the joint genotypes of the children instead of the parents as done in the VITESSE v. 1. This change in summation allows the parent-child transmission part of the calculation to be not only computed for each parent separately, but also for each locus separately by using inheritance vectors as is done in the Lander-Green algorithm. Computing both of these separately can lead to substantial computational savings. The use of inheritance vectors in the nuclear family calculation represents a partial synthesis of the techniques of the Lander-Green algorithm into the Elston-Stewart algorithm. In addition, the technique of local set recoding is introduced to further reduce the complexity of the nuclear family computation. These new algorithms, however, are not universally faster on all types of pedigree data compared to the method implemented in VITESSE v. 1 of summing over the parents. Therefore, a hybrid algorithm is introduced which combines the strength of both summation methods by using a numerical heuristic to decide which of the two to use for a given nuclear family within the pedigree and is shown to be faster than either method on its own. Finally, this paper discusses various complexity issues regarding both the Elston-Stewart and Lander-Green algorithms and possible future directions of further synthesis.

Model-free linkage analysis: performance under real-world conditions.

The simulated data of the Genetic Analysis Workshop 12 problem set affords an ideal environment for testing real-world performance of model-free linkage analysis methods. To this end, we applied three different methods of model-free linkage analysis: mod scores [Hodge and Elston, Genet Epidemiol 11:329-42, 1994], maximized maximum lod score (MMLS) [Greenberg et al., Am J Hum Genet 63:870-9, 1998], and nonparametric linkage (NPL) scores [Whittemore and Halpern, Biometrics 50:118-27, 1994], as well as standard parametric linkage analysis to the detection of major gene 6 (MG6) using only the qualitative disease status data. Our results indicate that both mod scores and NPL scores perform well, even in the presence of an extremely complicated disease model. MMLS analysis did not perform well, except at the disease locus itself.

Comparison of likelihood approaches for combined segregation and linkage analysis of a complex disease and a candidate gene marker under different ascertainment schemes.

We compared two joint likelihood approaches, with complete (L1) or without (L2) linkage disequilibrium, under different ascertainment schemes, for the genetic analysis of the disease trait and marker gene 1 in replicate 42. Joint likelihoods were computed without a correction for the selection scheme. For the different sampling schemes we have explored, our results suggest that L1 is a more powerful approach than L2 to detect major gene and covariate effects as well as to identify accurately gene x covariate interaction effects in a common and complex disease such as the Genetic Analysis Workshop 12 MG6 simulated trait.

Evidence of genetic heterogeneity for hereditary gingival fibromatosis.

Hereditary Gingival Fibromatosis (HGF) is the most common genetic form of gingival fibromatosis. The condition is most frequently reported to be transmitted as an autosomal-dominant trait, but autosomal-recessive inheritance has also been reported. The clinical presentation of HGF is variable, both in the distribution (number of teeth involved) and in the degree (severity) of expression. It is unknown if the variable clinical expression of HGF in different families is due to variable expression of a common gene mutation, allelic mutations, or non-allelic mutations. The apparently different modes of Mendelian inheritance of HGF suggest genetic heterogeneity. A gene locus for HGF has been localized to a 37-cM genetic interval on chromosome 2p21-p22 (D2S1352, Zmax = 5.10, theta = 0.00) flanked by D2S1788 and D2S441. To evaluate the generality of this linkage, we tested linkage with 9 markers from this candidate region in another large family, segregating for an autosomal-dominant form of generalized HGF, and found no support for linkage with any of these markers. Furthermore, statistical tests of this apparent heterogeneity were highly significant. Analysis of these data provides direct evidence that at least two genetically distinct loci are responsible for autosomal-dominant hereditary gingival fibromatosis.

Zero-recombinant haplotyping: applications to fine mapping using SNPs.

As the number of single nucleotide polymorphisms (SNPs) available for genetic analysis increases, researchers will be saturating smaller and smaller regions of the genome with these biallelic markers in an effort to fine map complex diseases. An important tool in this fine-mapping effort is haplotyping. Algorithms are presented that find all possible haplotype configurations of the pedigree data under the assumption that there are no recombinants between the markers. These configurations can be used to estimate the haplotype frequencies, and identify the most common haplotypes in the data. These algorithms have been implemented into a software program (ZAPLO), and were tested on a published data set.